Comprehensive Pharmacology 0128204729, 9780128204726

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COMPREHENSIVE PHARMACOLOGY

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COMPREHENSIVE PHARMACOLOGY EDITOR IN CHIEF

Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States ASSOCIATE EDITOR IN CHIEF

Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany

VOLUME 1

Pharmacodynamics EDITED BY

Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States

Pharmacokinetics EDITED BY

David S. Riddick Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge MA 02139, United States Copyright Ó 2022 Elsevier Inc. All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers may always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-820472-6 For information on all publications visit our website at http://store.elsevier.com

Publisher: Oliver Walter Acquisition Editors: Blerina Osmanaj and Kelsey Connors Content Project Manager: Michael Nicholls Associate Content Project Manager: Ramalakshmi Boobalan Designer: Vicky Pearson-Esser

EDITOR IN CHIEF

Terry Kenakin received his BS in Chemistry and PhD in Pharmacology from the University of Alberta, Canada. After a postdoctoral fellowship at University College London, UK, he moved to the United States to take a position as a Research Scientist at Burroughs Wellcome in Research Triangle Park, NC. After 7 years, he moved to Glaxo (now GlaxoSmithKline) where he worked for 25 years in drug discovery. His research is on drug receptors, allosteric protein function, and the application of pharmacology to drug discovery. He is the Editor-in-Chief of the Journal of Receptors and Signal Transduction and is on numerous editorial boards. He is a Fellow of the British Pharmacological Society and has received a number of distinctions including the Goodman and Gilman award for receptor pharmacology from ASPET, the Gaddum Memorial award from the British Pharmacological Society, and awards from the Dutch and Norwegian pharmacology societies. He currently is a Professor of Pharmacology in the University of North Carolina School of Medicine in Chapel Hill, NC.

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ASSOCIATE EDITOR IN CHIEF

Martin C. Michel is a physician trained in experimental and clinical pharmacology in Essen (Germany) and San Diego (California). He headed the Nephrology and Hypertension Research Laboratory at the University of Essen (Germany; 1993–2002), the Department of Pharmacology & Pharmacotherapy at the University of Amsterdam (The Netherlands; 2003–2011) and was Global Head of Product and Pipeline Scientific Support at Boehringer Ingelheim (Germany; 2011–2016). His current affiliations include being a Professor of Pharmacology at the Johannes Gutenberg University in Mainz (Germany; since 2012) and being a Senior Partner at the Partnership for the Assessment and Accreditation of Science (PAASP, Heidelberg, Germany; since 2016). His research focuses on urogenital and cardiovascular pharmacology, where he has published more than 500 peer-reviewed articles cited >30,000 times. He is editor or serves on the board of many pharmacological journals including Mol Pharmacol and Pharmacol Rev.

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EDITORIAL BOARD Editor In Chief Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States

Associate Editor In Chief Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany

Section Editors Hamid Akbarali Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, United States Abhijit Bal Department of Microbiology, Queen Elizabeth University Hospital, Glasgow, & Honorary Clinical Associate Professor, University of Glasgow, United Kingdom Kelly A. Berg University of Texas Health San Antonio, Department of Pharmacology, San Antonio, TX, United States Gavin Bewick Kings College London, Diabetes Research Group, 2.21N Hodgkin Building, Guys Campus, London, United Kingdom William P. Clarke University of Texas Health San Antonio, Department of Pharmacology, San Antonio, TX, United States Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany Karnam S. Murthy Department of Physiology and Biophysics, Virginia Commonwealth University, Richmond, VA, United States David S. Riddick Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada Katerina Tiligada Department of Pharmacology, Medical School, National & Kapodistrian, University of Athens, Athens, Greece

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Editorial Board

Andrew Tobin University of Glasgow, Glasgow, United Kingdom Elizabeth Yeh Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN, United States

CONTRIBUTORS TO VOLUME 1 Stephen PH Alexander Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, University of Nottingham Medical School, Nottingham, United Kingdom Mariamena Arbitrio Institute of Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Secondary site of Catanzaro, Italy Samuel LM Arnold Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, United States; and Division of Allergy and Infectious Diseases, School of Medicine, University of Washington, Seattle, WA, United States Erin F Barreto Department of Pharmacy, Mayo Clinic, Rochester, MN, United States W Matthijs Blankesteijn Department of Pharmacology&Toxicology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands Gianluca Catucci Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy Sergio C Chai Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, United States Thomas KH Chang Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada Taosheng Chen Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, United States

Lisa Cheng Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada Nikki J Clauss Departments of Cellular & Integrative Physiology and Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States David Dahlgren Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden James Dalton Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; and Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain Evangelos P Daskalopoulos Pôle de Recherche Cardiovasculaire (CARD), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium Lynette C Daws Departments of Cellular & Integrative Physiology and Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States Maria Teresa Di Martino Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy Dario Doller Alcyoneus ScienceWorks, LLC, Sparta, NJ, United States

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Contributors to Volume 1

Robert S Foti Preclinical Development (ADME & Discovery Toxicology), Merck & Co., Inc, Boston, MA, United States

Margaret O James Department of Medicinal Chemistry, University of Florida Academic Health Center, Gainesville, FL, United States

Paul K Fyfe Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom

Kelly Karl Program in Molecular Biophysics and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States

Carles Gil Laboratory of Molecular Neuropharmacology and Bioinformatics, Institute of Neurosciences and Department of Biochemistry & Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Spain

Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States

Gianfranco Gilardi Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy Jesús Giraldo Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; and Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain F Peter Guengerich Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States Bruno Hagenbuch Department of Pharmacology, Toxicology and Therapeutics, The University of Kansas Medical Center, Kansas City, KS, United States Sam RJ Hoare Pharmechanics LLC, Owego, NY, United States Kalina Hristova Department of Materials Science and Engineering, Program in Molecular Biophysics and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States Nina Isoherranen Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, United States Shinya Ito Division of Clinical Pharmacology and Toxicology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada

Emily J Koubek Department of Oncology Research, Mayo Clinic, Rochester, MN, United States Isaias Lans Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; and Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia Thomas R Larson Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States Hans Lennernäs Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden Taylor P Light Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States Eliza R McColl Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada Alison McFarlane Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom Thomas D Meek Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, United States Ignacio Moraga Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom

Contributors to Volume 1

Jordi Ortiz Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain; and Laboratory of Molecular Neuropharmacology and Bioinformatics, Institute of Neurosciences and Department of Biochemistry & Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Spain Licia Pensabene Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy Micheline Piquette-Miller Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada Pedro Renault Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; and Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain David S Riddick Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada David Roche Department of Economic and Social Sciences, International University of Catalonia, Barcelona, Spain Sheila J Sadeghi Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy

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Philip Sandoval Global Drug Metabolism and Pharmacokinetics, Takeda Pharmaceutical Company Limited, Cambridge, MA, United States Francesca Scionti Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy Pierosandro Tagliaferri Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy Pierfrancesco Tassone Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy Rommel G Tirona Departments of Physiology & Pharmacology and Medicine, University of Western Ontario, London, ON, Canada Claire Townsend GlaxoSmithKline R&D, Collegeville, PA, United States Jonathan D Tyzack EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom Vessela Vassileva Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom Harvey Wong Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada Bin Zhou Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; and Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain

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FOREWORD Unlike “pure” sciences such as chemistry, biochemistry, and genetics, contemporary pharmacology draws on all of these and many other disciplines and applies them in service of understanding and controlling physiologic processes with drugs. This amalgamation of different approaches generates a self-reinforcing cycle in which pharmacological knowledge leads to better understanding of physiology, which in turn enables the discovery of new drug targets and medicines to prevent, diagnose, and treat illnesses. The development of new technologies continuously accelerates these cycles. Thus, pharmacology is tightly linked to the technological advances applied to the study of physiology leading to an ever-changing scientific environment. One consequence of the heterogeneity and diversity of scientific disciplines leveraged by modern pharmacologists is that it has become almost impossible to find authoritative comprehensive collections of information on this variegated science. It is to meet this need that Co-Editors-in-Chief Terry Kenakin and Martin Michel and a superlative cast of Section Editors and authors have labored to create this remarkable all-encompassing compendium, Comprehensive Pharmacology. This work covers the myriad of drugs and techniques applied to the treatment of disease. It ranges from detailed discussions of pharmacological mechanisms of drugs at the molecular and cellular levels to the clinical application of those drugs. Encompassing 219 articles, it is arranged in volumes by various pharmacologic disciplines (Pharmacodynamics, Pharmacokinetics, Pharmacogenomics, Drug Discovery) as well as therapeutic areas (cardiovascular, central nervous system, cancer, gastrointestinal, immunology, endocrinology, anti-infectives) written by leading experts in each field. The encyclopedic and detailed coverage will make this work the first stop for anyone seeking up-to-date definitive information about essentially any topic in basic or clinical pharmacology. Robert J. Lefkowitz, MD, Nobel laureate Duke University School of Medicine James B. Duke Distinguished Professor of Medicine

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PREFACE Pharmacology draws from many other disciplines including chemistry, biochemistry, anatomy, and physiology. In contrast to those, pharmacology directly intends to better human life by improving the prevention, diagnosis, and treatment of human disease, and sometimes even providing cure, for instance, in infectious diseases. The past two decades have seen major achievements in science such as the sequencing of the human genome in general and its variations between populations and individuals. Crystal structures have been resolved at high resolution for many proteins that serve as drug targets, often even in multiple conformations. Concomitantly, we have seen the evolvement of key novel technologies including those for analyzing and manipulating DNA and creating novel drug candidates by combinatorial chemistry. The speed of progress has become revolutionary. Accordingly, a major reference book in pharmacology today must be considerably different from those written 15 years ago. Defining pharmacology as the chemical control of physiology, it is a science that touches a wide realm of other disciplines from medicine, therapeutics, physiology and just about any research endeavor that concerns living tissue. This being the case, this present book has relevance to practitioners of medicine, bench scientists and students at all levels from graduate to undergraduate. The generation of useful new drugs requires not only an interaction between pharmacologists and other scientists but also among various subspecialties within pharmacology. This includes vertical interactions, e.g., between medicinal chemists and clinical pharmacologists, but also horizontal interactions, e.g., between those specializing in the pharmacology of the central nervous and the respiratory system. To enable such interactions, scientists need sources of information that provide authoritative reviews of various techniques and fields that allow to quickly grasp the essence of fields in which one is not an expert, but needs a critical understanding for good inter-disciplinary work. Comprehensive Pharmacology is an attempt to meet this need with a compendium devoted to the study and application of drug therapy. This work reflects the myriad of drugs and techniques applied to the treatment of disease. It ranges from detailed discussions of pharmacological mechanisms of drugs to their clinical application. It is arranged in volumes on various pharmacologic disciplines (Pharmacodynamics, Pharmacokinetics, Pharmacogenomics, Drug Discovery) and therapeutic areas (cardiovascular, central nervous system, cancer, gastrointestinal, immunology, endocrinology, anti-infectives). These topics are presented by experts in each field, and this gives the volumes much more specific discussion of the topics than any single author work. Thus, this work provides a concise and detailed treatment of a diverse discipline. Terry Kenakin, Editor in Chief Martin C. Michel, Associate Editor in Chief

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CONTENTS OF VOLUME 1 Editor in Chief

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Associate Editor in Chief

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Editorial Board

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Contributors to Volume 1

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Foreword

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Preface

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PHARMACODYNAMICS 1.01

Pharmacodynamics: Overview Terry Kenakin

1.02

The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology: Achieving Consensus in Nomenclature and Championing Reproducible Pharmacology Stephen PH Alexander

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1.03

Receptor Tyrosine Kinases Kelly Karl, Taylor P Light, and Kalina Hristova

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1.04

Cytokine Receptors Alison McFarlane, Paul K Fyfe, and Ignacio Moraga

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1.05

An Overview of Steady-State Enzyme Kinetics Thomas D Meek

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1.06

Ion Channels Claire Townsend

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1.07

Nuclear Receptors Sergio C Chai and Taosheng Chen

151

1.08

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs Nikki J Clauss and Lynette C Daws

1.09

Pharmacological Receptor Theory Terry Kenakin

165 205

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Contents of Volume 1

1.10

Kinetics of Drug-Target Binding: A Guide for Drug Discovery Sam RJ Hoare

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1.11

Orthosteric Receptor Antagonism Terry Kenakin

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1.12

Allosteric Modulation Dario Doller

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1.13

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism Jesús Giraldo, Bin Zhou, David Roche, Carles Gil, Jordi Ortiz, Isaias Lans, James Dalton, and Pedro Renault

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1.14

Agonism and Biased Signaling Terry Kenakin

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1.15

The Pharmacology of WNT Signaling Evangelos P Daskalopoulos and W Matthijs Blankesteijn

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PHARMACOKINETICS 1.16

Pharmacokinetics: Overview David S Riddick

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1.17

Oral Drug Delivery, Absorption and Bioavailability David Dahlgren and Hans Lennernäs

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1.18

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations Shinya Ito

1.19

Drug Metabolism: Cytochrome P450 F Peter Guengerich

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1.20

Drug Metabolism: Other Phase I Enzymes Gianluca Catucci, Gianfranco Gilardi, and Sheila J Sadeghi

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1.21

Drug Metabolism: Phase II Enzymes Margaret O James

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1.22

Drug TransportdUptake Philip Sandoval and Bruno Hagenbuch

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1.23

Drug Transporters: Efflux Eliza R McColl, Vessela Vassileva, and Micheline Piquette-Miller

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1.24

Drug Excretion Erin F Barreto, Thomas R Larson, and Emily J Koubek

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1.25

Mathematical Aspects of Clinical Pharmacokinetics Rommel G Tirona

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1.26

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters Mariamena Arbitrio, Francesca Scionti, Maria Teresa Di Martino, Licia Pensabene, Pierfrancesco Tassone, and Pierosandro Tagliaferri

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1.27

Drug-Drug Interactions With a Pharmacokinetic Basis Lisa Cheng, Thomas KH Chang, and Harvey Wong

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Contents of Volume 1

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1.28

ADME of Biologicals and New Therapeutic Modalities Robert S Foti

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1.29

Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development Samuel LM Arnold and Nina Isoherranen

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1.30

Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools Jonathan D Tyzack

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1.01

Pharmacodynamics: Overview

Terry Kenakin, Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States © 2022 Elsevier Inc. All rights reserved.

Reference

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Pharmacodynamics is the study of drug-target interaction. Pharmacology is the chemical control of physiology and there are many elements in physiological systems that qualify as drug targets; in many cases the kinetics, techniques and descriptors of these interactions are unique to the type of target. For instance, membrane bound receptors are studied biochemically in soluble protein systems, in cell membrane systems, whole cells and even structured organs. Enzymes have their own unique set of kinetic equations that are used to characterize the interaction of both substrates and inhibitors etc. This volume discusses the various aspects of pharmacodynamics as they pertain to different drugs and their response systems. The main aim of pharmacodynamics is to characterize drug properties in system independent terms such that the activity of a drug can be quantified and compared to other drugs used in therapy. Such independent scales then are used to gage drug activity by medicinal chemists in efforts to optimize drug activity and thus improve therapy. It is important to have system-independent scales of drug activity because drugs are rarely if ever directly first tested in the final therapeutic system. Rather, drugs are evaluated in test systems and the data extrapolated to the therapeutic system. This volume is divided into two principle sets of topics: (1) Drug targets and the systems utilized to quantify drug activity in on these targets and (2) generalized techniques and concepts common to the pharmacodynamic analysis of drug activity. The volume begins with a chapter by Stephen Alexander on The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology: Achieving Consensus in Nomenclature and Championing Reproducible Pharmacology (Chapter 1.02). This chapter discusses a fundamental thesis in pharmacology namely the taxonomy of drugs, targets and cellular components utilized in assessing drug activity. Traditionally pharmacologists and clinicians aimed at classifying drugs in order to simplify their use in therapy, i.e. a ‘b-blocker’ was characterized on the basis of its affinity for the b-adrenoceptor and thus became part of a family of other b-blockers that could be used in a variety of diseases such as hypertension and heart failure. However, as the array of functional assays has increased through advancing technology, such simplistic classifications have become obsolete and actually obstructive to the effective use of drugs. Technology has been the key to the re-classification of drugs as new assays (new eyes to see) reveal new activities that differentiate what appeared to be uniform classes of drugs. One of the first b-blockers to be discovered is propranolol yet when tested in a new functional pharmacological assay 39 years after its discovery propranolol was found to be an activator of ERK pathways through b-arrestin (Azzi et al., 2003). The expansion of pharmacological assays over the years has altered pre-existing simple drug taxonomy to the point where original classifications have become blurred and much more textured than they have been. Recognition of this changing situation is important in terms of assigning correct molecules for various pathological states. Traditionally, the ‘low hanging fruit’ of drug targets has been G protein coupled receptors (GPCRs) which are membrane bound receptors receiving signals from extracellular molecules and transmitting them to the cellular signaling systems in the cell cytosol. GPCRs respond to a wide variety of stimuli from small molecules, proteins, peptides, ions and even light to control a myriad of cell signaling cascades. GPCRs account for a large number of targets for marketed drugs and continue to be a rich source of potential therapeutics. Pharmacological theory began with the description of drug-receptor interactions with GPCRs and the resulting so called ‘drug receptor’ theory forms the basis of GPCR discovery programs; this is described in Pharmacological Receptor Theory (Chapter 1.09, Terry Kenakin). This chapter describes the equations used to characterize drug receptor interactions based on the mass action equation; these were derived utilizing the ‘receptor’ as a theoretical entity controlling drug response. Subsequent biochemical definitions of receptors are consistent with these equations. The binding of agonists and antagonists to receptors are described (orthosteric interactions) as well as the alteration of receptor protein conformation (allosteric interactions) to yield fundamental molecule patterns that describe drug activity in terms of affinity (strength of binding to the target) and efficacy (alteration of receptor state) to yield measures of drug activity that transcend the systems where they are measured. Traditionally GPCRs were considered to be ‘switches’ activated by hormones and neurotransmitters to give cells uniform stimuli of varying strength. This idea emanated from the limited nature of the functional assay systems available at the time to report cellular response due to GPCR activation. However, as the number of different assays to measure various sub-classes of activation emerged with technology and with these advancements it became clear that GPCR activation does not uniformly activate all cellular signaling pathways but rather different drugs produced different patterns of activation to cells, so called ‘biased signaling.’ This is described in the chapter on Agonism and Biased Signaling (Chapter 1.14, Terry Kenakin). While early treatments of receptor-based agonism considered receptors as switches yielding homogeneous signals to cells varying only in strength, the availability of multiple assays to measure the many effects activated receptors can have on cells clearly indicate that the total signals emerging from agonist-activated receptors are heterogeneous and varying composition. For instance, agonists that activate multiple pathways such as G proteins and b-arrestin show that different agonists provide different levels

Comprehensive Pharmacology, Volume 1

https://doi.org/10.1016/B978-0-12-820472-6.00194-8

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Pharmacodynamics: Overview

of relative activation of these pathways. i.e. one receptor may emphasize a single pathway at the expense of another; this effect, referred to ‘biased signaling’ has opened up new vistas for receptor signaling whereby different agonists may have efficacies that vary in quality as well as quantity. A discussion of an important signaling pathway in cells that can be exploited therapeutically is given for The Pharmacology of WNT Signaling (Chapter 1.15, W. Matthijs Blankesteijn). Receptors control a large number of signaling systems in the body which support normal physiology. However, sometimes these systems produce inappropriate signals or signals of inappropriate strength requiring modulation with antagonist drugs. It is often important to negate such receptor-mediated activity and this is done with antagonist drugs. A large classification of these are orthosteric antagonists which bind to the natural agonist binding site to preclude agonist binding and thus reduce agonist effect. Such agents can be orthosteric antagonists which bind to the same binding site as the endogenous hormone or neurotransmitter but do not produce a response; these are described in the chapter Orthosteric Receptor Antagonism (Chapter 1.11, Terry Kenakin). This is often referred to as ‘steric hindrance’ and it can produce a range of outcomes that depend on the relative kinetics of agonist and antagonist binding. This chapter considers the impact of these kinetics to yield either competitive, non-competitive or hemiequilibrium antagonism for therapeutic applications. Another mechanism whereby drugs can eliminate natural signaling is through allostery whereby the antagonist binds to its own site on the receptor to reduce endogenous responses. In addition, such allosteric modulators can alternatively increase failing physiological responses; this is a rapidly expanding therapeutic application of CGPCR ligands. Allosteric mechanisms are described in the chapter Allosteric Modulation (Chapter 1.12, Dario Doller). Part of the realization of the range of behaviors practiced by GPCRs included the fact that within the cell membrane they can form multimeric units (usually dimers) that confer a new set of activities for drugs; this is a way cells can fine tune GPCR responses in a cell dependent manner to invoke unique physiology when needed. GPCRs are also known to form homodimers and heterodimers in the cell membrane and these new entities have different profiles of activity for drugs and physiological processes. As such, dimers can be a unique cell-specific drug target for therapy. The process of receptor dimerization and the methods and models used to study this mechanism and quantify the effects of drugs on the process of dimerization is given in the chapter Analysis of the Function of Receptor Oligomers by Operational Models of Agonism (Chapter 1.13, Jesús Giraldo et al). This chapter discusses mathematical models of dimer formation and function in terms of the current most common model for receptor function, the Black/Leff operational model. The models described include constitutive receptor activity and incorporate the allosteric parameters known to control the interactions of receptors both with each other and with ligands. In addition, the in vitro study of GPCR effects (an indeed drug effects on all targets) is not complete without the consideration of real time kinetics. While most in vitro assays yield snapshots of drug activity at a prescribed time, these processes have a rate of initiation and a rate of termination that controls the magnitude of effects over a period of time. Since drugs are used in in vivo systems, drug concentration is never constant (increasing with drug absorption and waning with drug clearance) therefore real time is critical to the magnitude of in vivo drug effect. The consideration of kinetics in drug receptor interactions and the impact of this all-important topic on pharmacodynamics is described in. Kinetics of Drug-Target Binding: A Guide for Drug Discovery (Chapter 1.10, Samuel Hoare). Measurement of drug activity are often made as snapshots in time of a particular drug response. While this is useful to predict pharmacology, the therapeutic use of drugs in vivo involves real time in that concentration is never constant and the time that the drug associates with the target depends on kinetic parameters. Thus binding kinetics should be measured in special experiments to yield the rate of formation of the drug-target complex (the association rate constant) and the stability of the complex (the residence time, defined by the dissociation rate constant). This chapter discusses the models and techniques used to measure kinetics of drug interaction and shows how kinetics impacts the value of drugs in therapeutic systems. There are many kinds of receptors yielding a wealth of therapeutic signals to cells. One such target system is controlled by Cytokine receptors, a unique family of receptors mediating pathological processes; these receptors are described in Cytokine Receptors (Chapter 1.04, Ignacio Moraga), Much of the theory and modeling for pharmacology has been pioneered with membrane bound receptors but there is a wealth of other targets available for drug therapy that currently are being exploited. These targets have their pharmacodynamic models and systems. Thus enzymes are a rich source of drug therapeutics; this target class and the kinetic models used to quantify their effects is given in An Overview of Steady-State Enzyme Kinetics (Chapter 1.05, Thomas Meek). A special type of enzyme that is ubiquitous in cell signaling systems are kinases; these are described in Receptor Tyrosine Kinases (Chapter 1.03, Kalina Hristova). Another type of unique receptor, namely nuclear receptors, control transcription of genes in the cell nucleus and form an extremely important therapeutic target class; these are described in Nuclear Receptors (Chapter 1.07, Taosheng Chen, Sergio Chai). Another form of signaling prevalent in cells involves the transport of ions into and out of cells; ion channels provide this function and their role in drug therapeutics is given in Ion Channels (1.06, Claire Townsend). Ion channels are discussed as a group of ion-transporting proteins diversified by their structures, the variety of ions they transport, their pharmacology and the multiple modes of channel activation. While automation has allowed a broader application of electrophysiology, this technology still has had a limited impact so far on the development of new therapeutics. This chapter considers Ion channel structure and function (voltage-gated, ligand-gated, mechanosensor, other channel types) in terms of functional properties (permeation and gating), intracellular and extracellular control, and ion channel pharmacology of various ligands (antagonists, pore blockers, gating modifiers, orthosteric, negative allosteric, inverse agonists, antibodies) and activators (agonists, channel openers, gating modifiers). The methods of studying and defining these mechanisms is considered especially in terms of recent progress in molecular biology, genetics, structural biology, and computational methods. Finally, in addition to ions many other important chemicals are also transported into and out of cells and these processes can be modulated by drugs; the application of therapeutics to transporters is described in Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs (Chapter 1.08, Lynette Daws).

Pharmacodynamics: Overview

3

Reference Azzi, M., Charest, P.G., Angers, S., Rousseau, G., Kohout, T., Bouvier, M., Piñeyro, G., 2003. Beta-arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors. Proceedings of the National Academy of Sciences of the United States of America 100, 11406–11411.This page

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1.02 The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology: Achieving Consensus in Nomenclature and Championing Reproducible Pharmacology Stephen P.H. Alexander, Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, University of Nottingham Medical School, Nottingham, United Kingdom © 2022 Elsevier Inc. All rights reserved.

1.02.1 1.02.2 1.02.3 1.02.3.1 1.02.3.2 1.02.3.3 1.02.3.4 1.02.4 1.02.5 Acknowledgments References

An introduction to NC-IUPHAR Membership, aims and objectives of NC-IUPHAR Outputs from NC-IUPHAR Pharmacological reviews British Journal of Pharmacology The Concise Guide to Pharmacology (previously Guide to Receptors and Channels) GuidetoPharmacology.org (Twitter @GuidetoPHARM) Reflections on nomenclature successes and “failures” Future challenges

4 5 5 6 6 6 7 7 8 8 9

Glossary GtoPdb The Guide to Pharmacology database at www.GuidetoPharmacology.org. IUPHAR The International Union of Basic and Clinical Pharmacology. NC-IUPHAR The Nomenclature and Standards Committee of IUPHAR.

1.02.1

An introduction to NC-IUPHAR

NC-IUPHAR is the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (IUPHAR). IUPHAR is an umbrella organization for national and regional societies of pharmacology, with 58 affiliated societies worldwide. It is a Scientific Union Member of the International Science Council (ICS), with an established goal to support pharmacology research and education, and their application to improve global health. NC-IUPHAR was established in 1987 at the Xth International Congress of Pharmacology, by the president, Sir Colin Dollery, with the goal of issuing guidelines for the nomenclature and classification of receptors and ion channels. This has been extended to include all the targets of current/future prescription medicines. NC-IUPHAR has addressed many important questions, issues and controversies in pharmacology, and has overseen the development and expert-driven annotation of an authoritative and open access, global online resource, the IUPHAR/BPS Guide to PHARMACOLOGY database (GtoPdb, see below). Since its inception, NC-IUPHAR has had four Chairs (Table 1), who are proposed by NC-IUPHAR and validated by the Executive Committee of IUPHAR. To quote from our website, “NC-IUPHAR has the objectives of: 1. Issuing guidelines for the nomenclature and classification of all the (human) biological targets, including all the targets of current and future prescription medicines; 2. Facilitating the interface between the discovery of new sequences from the Human Genome Project and the designation of the derived entities as functional biological targets and potential drug targets;

Table 1

4

Chairs of NC-IUPHAR past and present.

Term

Chair

Location

1989–1998 1998–2002 2002–2014 2015–present

Paul Vanhoutte Robert Ruffolo Michael Spedding Stephen Alexander

Paris, France Collegeville, Pennsylvania, USA Paris, France Nottingham, UK

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3. Designating polymorphisms and variants which are functionally important; 4. Developing an authoritative and freely available, global online resource, the IUPHAR database, accessible via the Guide to PHARMACOLOGY portal (https://www.guidetopharmacology.org), with a remit to: • provide access to data on all known biological targets; • enable students and scientists in academia and industry, working in areas related to pharmacology and drug/target research, to exploit the full potential of the considerable amount of information on drug action available in the published literature; • provide an entry point into the pharmacological literature for basic and clinical scientists from other disciplines; • provide an integrated educational resource with access to high quality training in the principles of basic and clinical pharmacology and techniques; • foster innovative drug discovery.” NC-IUPHAR holds 1-day scientific meetings twice a year, which have been supported by unrestricted grants from Servier Laboratories and also, previously, the Wellcome Trust (Fig. 1). These would normally take place in either Paris or Edinburgh, where there is high level discussion of developments across the discipline of pharmacology and updates to the IUPHAR/BPS Guide to Pharmacology online database, GtoPdb. In general, these meetings have a focused group of international attendees (typically 20–30) with an over-arching theme for two sessions on the day, combined with a more general “current awareness” session. In November 2020, a focus for the scientific symposium was fibrosis and its pharmacological/therapeutic targetting. Due to the electronic format of the meeting, some of these talks have been made freely available for wider viewingdhttps://www.guidetopharmacology.org/ fibrosisSymposium20.jsp.

1.02.2

Membership, aims and objectives of NC-IUPHAR

Within the organization of IUPHAR, there are multiple examples of cross-linking, to which NC-IUPHAR adds considerable weight, for example with representation from ImmuPhardthe immunopharmacology section within IUPHAR. The core of NC-IUPHAR includes liaisons with responsibility for drug target groups (G protein-coupled receptors, nuclear hormone receptors, ligandgated ion channels), as well as geographical and topic constituencies (e.g. industry links and therapeutic antibodies). Ex officio members of NC-IUPHAR provide links to the IUPHAR executive (President and Secretary-General) and other organizations, such as the Human Genome Nomenclature Committee and IUPHAR’s Pharmacology Education Project. A fundamental aim of NC-IUPHAR is to save pharmacologists time and energy. If we all use a logical, systematic nomenclature for drug targets, we don’t waste time on confusion or trivia. Additionally, the involvement of human researchers and curators ensures that selection of the “Gold Standard” compounds (see below) is critical and informed. A high value is put on evidence triangulated from multiple sources to ensure the reproducibility of data. The role of the Chair and core members of NC-iUPHAR is to coordinate the
95 subcommittees and the database curatorial team of www.GuidetoPharmacology.org based at the University of Edinburgh (Fig. 1). As a major objective of NC-IUPHAR is to communicate its observations, there is an editorial process linked to publications in Pharmacological Reviews and the British Journal of Pharmacology (see below and Fig. 1).

1.02.3

Outputs from NC-IUPHAR

There are four major outputs from NC-IUPHAR, which are coordinated by weekly meetings of the Chair with the curatorial team in Edinburgh, quarterly meetings of the NC-IUPHAR Executive group and the biannual meetings of NC-IUPHAR: discussions of target nomenclature and standards in Pharmacological Reviews; updates on pharmacological issues in the British Journal of Pharmacology, the GtoPdb website and the Concise Guide to Pharmacology (Fig. 1).

Fig. 1

The NC-IUPHAR network.

6

The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology

1.02.3.1

Pharmacological reviews

From 1992 to date (mid-2021), 111 reviews have been published in Pharmacological Reviews, following an agreement with ASPET (Fig. 2A). These IUPHAR-badged reviews are free to access and have > 43,500 citations (according to Web of Science), although inevitably the distribution is skewed to the older publications (Fig. 2B). Generally, Pharmacol Rev reviews focus on issues of nomenclature of drug targets, with significant “compendium” issues on voltage-gated ion channels (lead by Bill Catterall) (Catterall et al., 2003a) and nuclear hormone receptors (lead by Vincent Laudet) (Germain et al., 2006) which have revolutionized nomenclature in the respective fields, as did landmark reviews on chemokine receptors (lead by Phil Murphy) (Murphy et al., 2000), and of course, 5-HT receptors (Barnes et al., 2021). Other publications have more generalized applications. An early review in 1996 (IUPHAR 10) focused on establishing guidelines for the nomenclature of new receptor subtypes (Vanhoutte et al., 1996), which aimed to present logical unifying principles for receptor nomenclature and avoid the confusion observed when different groups applied personal preferences for names. Later, in 2003, IUPHAR 38 updated the terms and symbols in quantitative pharmacology (Neubig et al., 2003) and in 2014, IUPHAR 90 provided recommendations for the nomenclature of receptor allosterism (Christopoulos et al., 2014).

1.02.3.2

British Journal of Pharmacology

Since 2012, NC-IUPHAR has published 31 reviews in BJP, with over 1990 citations (Fig. 2), following an agreement with the British Pharmacological Society. The focus of reviews have been updates of the pharmacology of molecular targets with established nomenclature, or more diverse topics ranging from splice variants (IUPHAR 4, Bonner, 2014) to translational pharmacology (IUPHAR 6, Dollery, 2014) to immunopharmacology (IUPHAR 16, Tiligada et al., 2015).

1.02.3.3

The Concise Guide to Pharmacology (previously Guide to Receptors and Channels)

The Concise Guide to Pharmacology is produced biennially as a special issue of the British Journal of Pharmacology. Currently, it is the only hard copy printed by BJP. Together with a precursor, the Guide to Receptors and Channels, 10 editions (þ two online revisions) have been published (Figure 4), with the most recent edition published in 2021. These have accumulated over 9250 citations, with a mean rate in recent years of close to 2 000 citations/year. The Concise Guide is a point-in-time snapshot of
1900 molecular

Publications

12 10 8 6 4 2 0

Pharmacol Rev

8000

BJP

Citations

6000 4000 2000

20 20

15 20

10 20

05 20

00 20

95 19

19

90

0

Year of publication Fig. 2 Publications in Pharmacological Reviews and the British Journal of Pharmacology. Illustrated in panel A are the 111 and 31 reviews arranged by year of publication, while panel B indicates the number of citations each review accrued up until mid-2021.

The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology

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targets of drugs presented in less detail than in the online database. The intention is to highlight the pharmacology in tabular comparisons of families of receptors, ion channels, enzymes and transporters (as well as non-classical drug targets), where the “best” agents are listed. In this situation, “best” not only refers to the most selective agents, but a requirement for inclusion is that these agents must be commonly available, either commercially or as a gift. There is extensive connection to original data sources to identify the evidence base for selecting the pharmacological tools described. The Concise Guide is freely available to download, and as a collection of PDF files on USB memory devices for distribution to areas of the world with poor internet access.

1.02.3.4

GuidetoPharmacology.org (Twitter @GuidetoPHARM)

IUPHAR, particularly NC-IUPHAR, together with the British Pharmacological Society (BPS), have for many years run an open access online database (IUPHAR/BPS Guide to PHARMACOLOGY database; www.guidetopharmacology.org, GtoPdb) of pharmacological agents and their targets, together with more modest records on disease and cell types. GtoPdb is actively promulgated by the British, American, Japanese, Indian and Chinese Pharmacological societies. Google Analytics reports over 21,000 current users across the world (developed and developing) who between them access GtoPdb an average of 32,000 times per month. GtoPdb provides access to expert-curated pharmacological, chemical, genetic, functional and pathophysiological data on all the known human targets of approved and experimental drugs. The data in GtoPdb are largely derived from the primary literature, utilizing an international network of 500 þ researchers arranged into 90 þ NC-IUPHAR subcommittees. This level of curation together with the considered, careful advice of the expert subcommittees ensure that it has a much higher quality, in terms of accuracy, than resources built by algorithmic data-mining. The most recent release of the database in September 2021 included 2995 targets (approximately half of which are human proteins, but also include targets in the malaria parasites and SARS-CoV-2 virus). We have 11,025 ligands, with 18,624 curated binding constants and 41,041 references. It also provides an integrated educational resource with access to high quality training in the principles of basic and clinical pharmacology and techniques through IUPHAR’s Pharmacology Education Project. In October 2016, the IUPHAR Guide to IMMUNOPHARMACOLOGY database project (GtoImmuPdb), supported by the Wellcome Trust, was launched to address the expanding are of the pharmacology of immunity, inflammation and infection. The data in GtoImmunPdb are fully integrated into the core GuidetoPharmacology.org database but are presented through an “immunologistfriendly” web portal. In a similar vein, the Medicines for Malaria Venture (MMV) funded a custom portal to provide information about antimalarials (MMV Guide to MALARIA PHARMACOLOGY).

1.02.4

Reflections on nomenclature successes and “failures”

I’d like to reflect on two areas over the last two decades, which have simplified and clarified nomenclature. First is the story of chemokines and their receptors. Chemokines are a series of small proteins (typically < 10 kDa) associated with the immune system, often as chemoattractants for different populations of white blood cells. Following extensive discussion with numerous interested parties, Phil Murphy and colleagues presented an initial consensus document in 2000, which grouped chemokines based on the number and position of conserved cysteine residues (Murphy et al., 2000). For example, CC chemokines, such as CCL5 (alternatively known as RANTES), have two adjacent cysteine residues close to the N-terminus, while CXC chemokines also have two cysteine residues close to the N-terminus, however, these are separated by a single amino acid (hence the “X”). These chemokines activate a group of 20 þ G protein-coupled receptors, which had names which were uncoordinated. So just over 20 years ago, a systematic nomenclature was described linking families of receptors with the families of ligands. The nomenclature also defined the ligands with an appropriately located “L” and receptors with an appropriately located “R.” Thus, CCR chemokine receptors, such as CCR5, are predominantly activated by CC chemokines, such as CCL4 (less systematically known as MIP1b or macrophage inflammatory protein 1b). CXCL8 (alternatively known as interleukin-8), on the other hand, activates primarily CXCR1 and CXCR2 receptor. Given that there are over 20 chemokine receptors in the human genome, the systematic nomenclature was a major task, which has reduced uncertainty in the area considerably. The second example of a nomenclature success is of the voltage-gated ion channels. Bill Catterall and colleagues identified commonalities of structure of mammalian and non-mammalian channels, sufficient to describe a “chanome.” A feature of the families of voltage-gated ion channels was a selectivity for a single ionic species, with some variation in the mechanisms of endogenous regulation. In a series of publications (Catterall et al., 2003a, b, c; Gutman et al., 2003; Hofmann et al., 2003), they proposed that individual voltage-gated ion channels should be identified using the chemical symbol of the principal permeating ion (Na, K, or Ca) with the principal physiological regulator indicated as a subscript. Thus voltage-gated sodium channels have the general description of NaV, while calcium-activated potassium channels are described as KCa. Gene families of ion channels are identified by a numerical suffix, while a number following a decimal point defines an individual channel isoform (e.g. NaV1.1), where the numbering is organized in chronology from first identification. While these two examples show the value of systematic nomenclature prompting widespread adoption, there are infrequent examples where there remains inconsistent usage.

8

The Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology

One of the most studied families of G protein-coupled receptors responds to catecholamines. Very early in the history of NCIUPHAR, the late Paul Vanhoutte together with very eminent colleagues provided a suggestion for systematic receptor nomenclature: “The receptor should be named after the endogenous agonist, or the appropriate collective term when a family of related substances may interact with the receptor” (Vanhoutte et al., 1994). Logically, therefore, those receptors which respond to the hormone adrenaline (epinephrine) or neurotransmitter noradrenaline (norepinephrine) should be called “adrenaline receptors,” “noradrenaline receptors” or perhaps “catecholamine receptors” (McGrath, 2015). As Ian McGrath noted, “adrenaline receptor” was employed by many distinguished pharmacologists up until the early 1960s. However, the current literature reflects a mixture of usage of adrenoceptor, adrenoreceptor and adrenergic receptor (with historical examples of epinephrinergic and norepinephrinergic receptors Aronson, 2000), each of which could be argued to be illogical (McGrath, 2015). NC-IUPHAR prefers the use of adrenoceptors (Bylund et al., 1994), since “adrenergic” is more appropriately used to described the nerves that release adrenaline. There has, however, been some division along national lines, with “adrenoceptors” being more commonly applied in the UK, while “adrenergic receptors” is more frequent in the US. Another long-established pharmacological target involves the actions of opiates. Over 20 years ago, a review, based on the guidelines from NC-IUPHAR, recommended replacement of the terms, m, d, and k for the three G protein-coupled receptors that respond to opioids (opiate-like drugs) with the terms OP3, OP1, and OP2, respectively (Dhawan et al., 1996). However, in the three years subsequent to the publication of this recommendation, almost all papers referring to opioid receptors continued to use the wellestablished Greek symbol nomenclature, while many in the field voiced their concerns that the use of the Greek characters was so well-established that introduction of an alternative nomenclature was both inappropriate and confusing. Indeed, it was argued that elimination of the terminology based on the Greek symbols would lead to impaired access to, and reduced citation of, the large body of research literature already published on the structure and properties of opioid receptors. NC-IUPHAR reconvened its opioid receptor subcommittee in late 1999 and charged it with developing revised recommendations for the nomenclature for opioid receptors consistent with the overall guidelines of NC-IUPHAR (Table 2) (Cox et al., 2015). One might reflect that the pervasive influence of mobile phone communication, where (outside of the eastern Mediterranean) use of the Greek character set would be difficult, that many pharmacologists would prefer to use mu or MOP in place of m, for example.

Table 2

Nomenclature of opioid and opioid-related receptors (Cox et al., 2015).

Current NC-IUPHAR approved nomenclature

Other (non-approved) nomenclature

Examples of endogenous ligand/s

m, mu, or MOP

MOR, OP3

d, delta, or DOP

DOR, OP1

k, kappa, or KOP

KOR, OP2

NOP

ORL1, OP4

b-Endorphin Enkephalins Enkephalins b-Endorphin Dynorphin A Dynorphin B Nociceptin/orphanin FQ (N/OFQ)

1.02.5

Future challenges

While a substantial number of G protein-coupled receptors are established as partners of endogenous activators, about 100 members of these very successfully exploited drug targets have no established endogenous agonist or where candidates fail to meet NC-IUPHAR’s stringent criteria for pairing (Davenport et al., 2013). While nuclear hormone receptors are well-defined from sequence analysis of the genome, there exists a grey area for transcription factors which are regulated by mediators which may be either endogenous or environmentally derived. These may have attributes of receptors and are sometimes named as such, including the aryl hydrocarbon receptor, but is there a better way to classify them? There are examples of complex pharmacology which is suggestive of a combination of receptors, often referred to as heterodimers. While NC-IUPHAR has defined recommendations for nomenclature of these entities (Spedding et al., 2002), it is apparent that receptors are often part of signaling complexes, which may involve mediator proteins, ion channels, enzymes and anchoring proteins.

Acknowledgments I would like to thank all the members of NC-IUPHAR past and present, as well as all the subcommittee members. In particular, I would like to thank Michael Spedding, Jamie Davies, Arthur Christopoulos, Doriano Fabbro and Anthony Davenport as core members of NC-IUPHAR, as well as the curatorial team from the University of Edinburgh, Elena Faccenda, Simon Harding, Chris Southan, and Adam Pawson.

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References Aronson, J.K., 2000. “Where name and image meet”dThe argument for “adrenaline”. BMJ 320, 506–509. https://www.ncbi.nlm.nih.gov/pubmed/10678871. Barnes, N.M., Ahern, G.P., Becamel, C., Bockaert, J., Camilleri, M., et al., 2021. International union of basic and clinical pharmacology. CX. Classification of receptors for 5hydroxytryptamine; pharmacology and function. Pharmacological Reviews 73, 310–520. https://www.ncbi.nlm.nih.gov/pubmed/33370241. Bonner, T.I., 2014. Should pharmacologists care about alternative splicing? IUPHAR Review 4. British Journal of Pharmacology 171, 1231–1240. https://www.ncbi.nlm.nih.gov/ pubmed/24670145. Bylund, D.B., Eikenberg, D.C., Hieble, J.P., Langer, S.Z., Lefkowitz, R.J., et al., 1994. IV. International Union of Pharmacology nomenclature of adrenoceptors. Pharmacological Reviews 46, 121–136. https://www.ncbi.nlm.nih.gov/pubmed/7938162. Catterall, W.A., Chandy, K.G., Clapham, D.E., Gutman, G.A., Hofmann, F., et al., 2003a. International Union of Pharmacology: Approaches to the nomenclature of voltage-gated ion channels. Pharmacological Reviews 55, 573–574. Catterall, W.A., Goldin, A.L., Waxman, S.G., 2003b. International Union of Pharmacology. XXXIX. Compendium of voltage-gated ion channels: sodium channels. Pharmacological Reviews 55, 575–578. https://www.ncbi.nlm.nih.gov/pubmed/14657413. Catterall, W.A., Striessnig, J., Snutch, T.P., Perez-Reyes, E., 2003c. International Union of Pharmacology. XL. Compendium of voltage-gated ion channels: calcium channels. Pharmacological Reviews 55, 579–581. https://www.ncbi.nlm.nih.gov/pubmed/14657414. Christopoulos, A., Changeux, J.P., Catterall, W.A., Fabbro, D., Burris, T.P., et al., 2014. International Union of Basic and Clinical Pharmacology. XC. Multisite pharmacology: Recommendations for the nomenclature of receptor allosterism and allosteric ligands. Pharmacological Reviews 66, 918–947. https://www.ncbi.nlm.nih.gov/pubmed/ 25026896. Cox, B.M., Christie, M.J., Devi, L., Toll, L., Traynor, J.R., 2015. Challenges for opioid receptor nomenclature: IUPHAR Review 9. British Journal of Pharmacology 172, 317–323. https://www.ncbi.nlm.nih.gov/pubmed/24528283. Davenport, A.P., Alexander, S.P.H., Sharman, J.L., Pawson, A.J., Benson, H.E., et al., 2013. International Union of Basic and Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings with cognate ligands. Pharmacological Reviews 65, 967–986. https://www.ncbi.nlm.nih.gov/pubmed/23686350. Dhawan, B.N., Cesselin, F., Raghubir, R., Reisine, T., Bradley, P.B., et al., 1996. International Union of Pharmacology. XII. Classification of opioid receptors. Pharmacological Reviews 48, 567–592. https://www.ncbi.nlm.nih.gov/pubmed/8981566. Dollery, C.T., 2014. Lost in Translation (LiT): IUPHAR Review 6. British Journal of Pharmacology 171, 2269–2290. https://www.ncbi.nlm.nih.gov/pubmed/24428732. Germain, P., Staels, B., Dacquet, C., Spedding, M., Laudet, V., 2006. Overview of nomenclature of nuclear receptors. Pharmacological Reviews 58, 685–704. https://www.ncbi. nlm.nih.gov/pubmed/17132848. Gutman, G.A., Chandy, K.G., Adelman, J.P., Aiyar, J., Bayliss, D.A., et al., 2003. International Union of Pharmacology. XLI. Compendium of voltage-gated ion channels: Potassium channels. Pharmacological Reviews 55, 583–586. https://www.ncbi.nlm.nih.gov/pubmed/14657415. Hofmann, F., Biel, M., Kaupp, U.B., 2003. International Union of Pharmacology. XLII. Compendium of voltage-gated ion channels: Cyclic nucleotide-modulated channels. Pharmacological Reviews 55, 587–589. https://www.ncbi.nlm.nih.gov/pubmed/14657416. McGrath, J.C., 2015. Localization of a-adrenoceptors: JR Vane Medal Lecture. British Journal of Pharmacology 172, 1179–1194. https://www.ncbi.nlm.nih.gov/pubmed/ 25377869. Murphy, P.M., Baggiolini, M., Charo, I.F., Hebert, C.A., Horuk, R., et al., 2000. International Union of Pharmacology. XXII. Nomenclature for chemokine receptors. Pharmacological Reviews 52, 145–176. https://www.ncbi.nlm.nih.gov/pubmed/10699158. Neubig, R.R., Spedding, M., Kenakin, T., Christopoulos, A., 2003. International Union of Pharmacology Committee on Receptor Nomenclature and Drug Classification. XXXVIII. Update on terms and symbols in quantitative pharmacology. Pharmacological Reviews 55, 597–606. https://www.ncbi.nlm.nih.gov/pubmed/14657418. Spedding, M., Bonner, T.I., Watson, S.P., 2002. International Union of Pharmacology. XXXI. Recommendations for the nomenclature of multimeric G protein-coupled receptors. Pharmacological Reviews 54, 231–232. https://www.ncbi.nlm.nih.gov/pubmed/12037139. Tiligada, E., Ishii, M., Riccardi, C., Spedding, M., Simon, H.U., et al., 2015. The expanding role of immunopharmacology: IUPHAR Review 16. British Journal of Pharmacology 172, 4217–4227. https://www.ncbi.nlm.nih.gov/pubmed/26173913. Vanhoutte, P.M., Barnard, E.A., Cosmides, G.J., Humphrey, P.P., Spedding, M., et al., 1994. I. International Union of Pharmacology Committee on RECEPTOR NOMENCLATURE AND DRUG CLAssification. Pharmacological Reviews 46, 111–116. https://www.ncbi.nlm.nih.gov/pubmed/7938161. Vanhoutte, P.M., Humphrey, P.P., Spedding, M., 1996. X. International Union of Pharmacology recommendations for nomenclature of new receptor subtypes. Pharmacological Reviews 48, 1–2. https://www.ncbi.nlm.nih.gov/pubmed/8685244.

1.03

Receptor Tyrosine Kinases

Kelly Karl , Taylor P. Lightb, and Kalina Hristovac, a Program in Molecular Biophysics and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States; b Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States; and c Department of Materials Science and Engineering, Program in Molecular Biophysics and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD, United States a

© 2022 Elsevier Inc. All rights reserved.

1.03.1 1.03.2 1.03.3 1.03.4 1.03.4.1 1.03.4.2 1.03.5 1.03.5.1 1.03.5.2 1.03.5.3 1.03.6 1.03.7 1.03.7.1 1.03.7.1.1 1.03.7.1.2 1.03.7.1.3 1.03.7.1.4 1.03.7.2 1.03.7.2.1 1.03.7.2.2 1.03.7.2.3 1.03.7.2.4 1.03.7.3 1.03.7.3.1 1.03.7.3.2 1.03.7.3.3 1.03.7.3.4 1.03.7.4 1.03.7.4.1 1.03.7.4.2 1.03.7.4.3 1.03.7.4.4 1.03.7.5 1.03.7.5.1 1.03.7.5.2 1.03.7.5.3 1.03.7.5.4 1.03.7.6 1.03.7.6.1 1.03.7.6.2 1.03.7.6.3 1.03.7.6.4 1.03.8 1.03.8.1 1.03.8.2 1.03.8.3 1.03.8.4

10

Introduction Architecture of RTK extracellular domains Overview of RTK kinase anatomy and function ATP binding and the act of phosphorylation Post-Kinase domains Overview of ligands Types of Inhibitors and how they work TKIs Antibodies Aptamers Ligand traps RTK subfamilies and their inhibitors FGF receptors Function and dysfunction Structure Ligands Inhibitors ErbB (EGF) receptors Function and dysfunction Structure Ligands Inhibitors VEGF receptors Function and dysfunction Structure Ligands Inhibitors Eph receptors Function and dysfunction Structure Ligands Inhibitors Trk receptors Function and dysfunction Structure Ligands Inhibitors Insulin receptors Function and dysfunction Structure Ligands Inhibitors Challenges, alternative strategies, and outlook Enhancing specificity Overcoming resistance Biased inhibitors Inhibitor cocktails

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Receptor Tyrosine Kinases 1.03.9 References Relevant Websites

Outlook

11 31 31 36

Glossary Activation site A tyrosine in the activation loop that must be phosphorylated for kinase activity to occur. Active site A region in the kinase where the phosphorylation of the substrate takes place. Allostery A process by which biological molecules regulate their activity by transmitting the effects of binding at one site to another site. Angiogenesis Growth of blood vessels from pre-existing ones. Domain A stable folded portion of a polypeptide. Kinase An enzyme that mediates phosphorylation. Ligand A small polypeptide that binds to the extracellular region of an RTK and activates downstream signaling. Phosphorylation The act of adding a phosphoryl group to a substrate such as tyrosine.

1.03.1

Introduction

Receptor tyrosine kinases (RTKs) are a large family of membrane receptors that play diverse roles in human health and development (Fantl et al., 1993; Lemmon and Schlessinger, 2010; Paul and Hristova, 2019a; Schlessinger, 2000a,b). These receptors are expressed on the cell surface where they sense extracellular signals or “ligands” (Kufareva et al., 2017; Lemmon and Schlessinger, 2010; Murai and Pasquale, 2003). For RTKs, ligands are generally small proteins (between 50 and a few hundred amino acids long), which are secreted by cells. The ligands bind to RTKs, promoting RTK dimerization and in some cases, oligomerization, which brings the two kinase domains in close proximity. As a result, the kinases phosphorylate each other to activate each other. The activated kinases bind and phosphorylate cytoplasmic effector proteins, triggering cascades of phosphorylation events that control cell growth, differentiation, migration, metabolism, and survival (Lemmon and Schlessinger, 2010; Schlessinger, 2000a,b). RTKs share a similar general architecture as they all consist of an extracellular (EC) region, a single-pass transmembrane (TM) helix, and an intracellular (IC) region (Fig. 1) (Lemmon and Schlessinger, 2010; Schlessinger, 2000a,b; Trenker and Jura, 2020). Members of the RTK family are subdivided into 20 subfamilies according to their sequence and structural properties along with their ligand binding partners. RTK extracellular regions are different between subfamilies and consist of a wide variety of structural domains (see Fig. 1) (Lemmon and Schlessinger, 2010; Schlessinger, 2000a,b). RTKs within the same subfamily tend to have similar extracellular domains and thus bind similar ligands. Therefore, each RTK subfamily has a family of ligand binding partners that are known to bind some or all the members of that subfamily. These ligands bind to the receptors with varying affinities. Interestingly, there are some RTKs that do not bind any of the currently known ligands. The identities of the ligand and the RTK in the complex determine the nature of the signals that are transmitted into the cell (Karl et al., 2020; Trenker and Jura, 2020). RTKs are classified by the presence of a catalytic kinase domain located on the IC region that is vital for activating downstream signaling cascades that control cellular processes (Lemmon and Schlessinger, 2010; Nishimura et al., 2014; Schlessinger, 2000a,b). Kinase activity requires ATP binding to a conserved active site of the kinase domain. Subsequently, ATP hydrolysis occurs resulting in the transfer of a phosphate group from ATP to specific tyrosine residues. The first tyrosine that is phosphorylated is the one located on the activation loop proximal to the active site. Then, through a process that is still not completely understood, other tyrosine residues located on various intracellular domain sites also become phosphorylated. Many of the phosphorylated tyrosines serve as docking sites for site-specific adaptor and effector proteins which initiate specific downstream signaling cascades (Lemmon and Schlessinger, 2010; Del Piccolo and Hristova, 2017; Schlessinger, 2014). Within the same RTK subfamily, these phosphorylation sites tend to be highly conserved. In addition, RTKs in different subfamilies have different regulatory domains such as the juxtamembrane (JM) segment, post-kinase tail, and/or sterile alpha motif (SAM) domain which can harbor additional phosphorylation sites (Lemmon and Schlessinger, 2010; Schlessinger, 2000a,b). As a result, the manner in which RTKs achieve complete activation can be different for each RTK subfamily, and for each RTK as well. RTK activity is controlled by the lateral interaction between RTKs on the cell surface, as it controls the proximity of the kinase domains and thus the first step in the RTK activation process (Chen et al., 2020; Kavran et al., 2014; Paul and Hristova, 2019b; Sarabipour and Hristova, 2016a,b; Singh et al., 2016). Therefore, RTK dimerization and oligomerization is vital for RTK signaling. It has long been known that ligand binding stabilizes RTK dimers and thus it was considered that ligands are required for RTK dimerization and oligomerization. However, now it is known that RTKs generally exist in monomer-dimer equilibrium in the absence of ligand binding (Paul and Hristova, 2019b). Therefore, unliganded RTK dimers can exhibit basal levels of activity and

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Receptor Tyrosine Kinases

Fig. 1

Architecture of the 20 RTK subfamilies.

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13

can induce downstream signaling. In addition, most RTKs have been found to interact with other RTKs, either in the same family or between subfamilies, and with other membrane proteins such as cadherins, integrins, and G-protein Coupled Receptors (GPCRs) (Paul and Hristova, 2019a). Each RTK and its activating ligands plays a specialized role in cellular signaling and thus all RTKs are critical for human development and health. RTK signaling activates downstream signaling cascades including the Ras/MAPK, PLCg1/PKC, PI3K/Akt, and STAT pathways, which are involved in a wide variety of cellular processes such as cell proliferation, differentiation, survival, and migration (Chao et al., 2006; Lemmon and Schlessinger, 2010; Li and Hristova, 2006; Neben et al., 2019; Ornitz, 2000; Pasquale, 2010; Schlessinger and Lemmon, 2003; Schlessinger, 2000a,b, 2014; Smith et al., 2018; Sun and Bernards, 2014; Timsah et al., 2014; Trenker and Jura, 2020). These signaling pathways are activated by binding and subsequent phosphorylation of specific cytosolic proteins such as Grb2, PLCg, NCK, FRS2, Shc, Jak, Gab1, SHP2, STAT, and the p85 subunit of PI3K, to name some notable examples. Aberrant RTK activity and signaling has been implicated in many diseases and disorders including various cancers, neurodegenerative diseases, cardiovascular diseases, and developmental disorders (Boye et al., 2009; Browne et al., 2009; Cunningham et al., 2007; Foldynova-Trantirkova et al., 2012; Nessa et al., 2009; Phay and Shah, 2010; Robertson et al., 2000; Vail et al., 2014; Webster and Donoghue, 1997). RTK signaling abnormalities can arise in several ways: (i) overactivation or inhibited activation as a result of mutations or deletions (including alternative splicing), (ii) overexpression due to gene amplification and (iii) the generation of oncogenic fusion proteins resulting from chromosomal translocation. Just about every RTK has been linked to disease. Thus, in efforts to prevent or treat RTK-associated diseases, RTKs have become increasingly popular and promising drug targets (Saraon et al., 2021). In this review, we discuss RTKs in pharmacological context, highlighting some of the RTK subfamilies and the therapeutics that have been developed to target these receptors to combat disease. RTK inhibitors target either the extracellular domain or the kinase domain, as the transmembrane domain is protected by the cell membrane, and its role in RTK activation is still debated (Bocharov et al., 2017). Here we will begin by describing common architectural motifs of RTKs, as well as their function. Special emphasis will be placed on the structure of the kinase domain. This detailed understanding of the domains will help with the understanding of how inhibitors work.

1.03.2

Architecture of RTK extracellular domains

The extracellular domains of the RTKs vary significantly between families as shown in Figs. 1 and 2, giving scientists opportunities to design inhibitors that target specific RTK extracellular domains. Within a family, the extracellular domains share similar structures. Some of the more common domains that are used as building blocks in many RTKs include immunoglobulin (Ig) domains, fibronectin (FN) III domains, cysteine rich domains, and leucine rich domains. A common feature of RTKs is the significant degree of conformational flexibility in the extracellular domains. The manner in which this occurs and the amount of conformational change varies across the families, but in general RTKs must undergo some form of conformational change to bind the ligand and to engage stabilizing inter-molecular contacts between the two EC domains in the RTK dimers. In fact, for some RTKs it has been observed that different ligands can induce unique extracellular dimer conformations based solely on ligand identity (Freed et al., 2017; Plotnikov et al., 2000). Immunoglobulin (Ig) domains are one of the most prevalent domains in the human genome and are typically associated with immune response (Travers et al., 2001). They are found in many RTKs, including FGFRs and VEGFRs. The immunoglobulin fold is characterized by a pair of beta sheets linked by a single disulfide bond (Berg et al., 2002). The beta sheets are made up of antiparallel beta strands, and the fold is often described as a “sandwich-like” structure with the beta sheets on the outside and the hydrophobic residues in the interior. The Ig domains are capable of being strung together like “beads on a string” as their N and C termini are at opposite ends of the structure (Bork et al., 1994). Many RTKs have a series of Ig domains, up to as many as seven in a row as in the VEGFR subfamily. The Ig domain incorporates a series of loops close to the N-terminus that compose the highly variable binding section. This can account for some of the variability in ligand binding across RTKs with Ig domains (Encyclopedia of Immunology, 1999; Plotnikov et al., 2000). For example, FGFR1 and FGFR2 do not bind the same ligands with equal affinities, even though they both possess three very similar Ig-like domains (Ornitz and Itoh, 2015). In addition, many proteins have ligand binding domains that are in a cleft between two consecutive Ig domains. Immunoglobulin domains are also helpful in stabilizing dimerization interactions as other structural features of the domain, such as the beta strands, can form favorable contacts with each other (Berg et al., 2002). Fibronectin III (FN3) domains are most commonly found in fibronectin, a protein important for wound healing (Campbell and Spitzfaden, 1994; Leahy et al., 1992). FN3 domains are found in the Eph receptor family, the insulin receptor family, and the ROS1 family to name a few (Bencharit et al., 2007). They are similar to the Ig domains discussed above but sequence homology studies suggest that they evolved independently (Buchanan and Revell, 2015). In fact, FN3 domains have such low sequence homology amongst themselves that it is postulated that many families evolved this fold independently due to its versatility. FN3 domains consist of two beta sheets that sandwich a hydrophobic core, but the sheets are not held together by a disulfide bridge as in the Ig domains. The two beta sheets are made up of 4 and 3 strands respectively (Ward and Garrett, 2001). The main function of the FN3 domain seems to be that it acts as a “spacer” to position other domains, and as a mediator of protein-protein interactions. As in the Ig domains, the N and C-termini of the domain are at opposite ends, and thus FN3 domains can be strung one after the other (Campbell and Spitzfaden, 1994).

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Fig. 2 Ligand-bound RTK extracellular region structures. Shown are structures of the extracellular domains of EGFR, VEGFR1, FGFR1, EphA2, TrkA, and IR. The gray rectangles indicate the cell membrane. EGFR: The complete structure of EGFR bound to EGF was assembled using PDB 1IVO (Ogiso et al., 2002) and PDB 1YY9 (Li et al., 2005). Domains 1–3 (D1,D2,D3) and part of D4 are from 1IVO. D4 from 1YY9 was aligned to the partial D4 from 1IVO. VEGFR1: The structure of VEGFR1 bound to VEGF-A was assembled using PDB 5T89 (Markovic-Mueller et al., 2017) which consists of domains 1–6 (Ig1–6) and VEGF-A. The Ig7 domain was obtained from a structure of the VEGFR2 domain 7 dimer (PDB 3KVQ (Yang et al., 2010)) and was aligned to PDB 5T89. FGFR1: The structure of FGFR1 bound to FGF2 was assembled from PDB 1FQ9 (Schlessinger, 2000a,b) which includes domains Ig2–3 and the ligand FGF2 and PDB 2CKN (Kiselyov et al., 2006) which includes domain Ig1. The Ig1 domains were connected to the Ig2 domains by an arbitrary linker. EphA2: The full structure of the EphA2 ectodomain bound to ephrin-A5 was assembled by aligning PDB 3MX0 (Himanen et al., 2010) with PDB 3FL7 (Himanen et al., 2010). 3MX0 consists of ephrin-A5 and all EphA2 domains except FN-III(2) while 3FL7 consists of all EphA2 domains but not the ligand. TrkA: The structure of TrkA bound to NGF (PDB 2IFG (Wehrman et al., 2007)). IR: The structure of the insulin receptor bound to insulin (PDB 6SOF (Gutmann et al., 2020)).

Cysteine rich domains are found in a variety of proteins and are often associated with recognition and binding of Wnt, a protein important for body axis patterning in embryogenesis (Abreu et al., 2002). They are found in the Trk, EGFR, and insulin receptor subfamilies. Across the different proteins, cysteine rich domains perform many different functions (O’Leary et al., 2004). In the case of EGFR, it is known that the cysteine rich domains are responsible for regulating the dimerization of EGFR via the creation of an auto-inhibited extracellular conformation where the two domains are tethered together (Aertgeerts et al., 2011; Ferguson, 2008; Sihto et al., 2005). One of the cysteine rich domains in EGFR also contains the dimerization arm which is critical for inter-protein interactions and determines the extracellular conformation of the dimer based on the identity of the bound ligand (Freed et al., 2017). In the insulin receptor family, the cysteine rich domains interact with the ligand and help determine ligand specificity via electrostatic interactions (Bork and Doolittle, 1992; Kavran et al., 2014; Scapin et al., 2017). Leucine rich domains are found in EGFR, the insulin receptor and Trk family members. The 3D structure and consensus motif of these RTK domains are remarkably similar to those found in leucine rich repeat proteins such as ankyrin, a family of proteins that anchors membrane proteins to the cytoskeleton (Bork, 1993; Ng and Xavier, 2011; Zampieri and Chao, 2006). Leucine rich repeats are known to form single-stranded right-handed b-helices. Leucine rich domains in RTKs have slightly more variable sequences as compared to other leucine rich domains, and they sometimes have inserts in the consensus sequence which manifest themselves as loops that do not affect the supercoiling of the chain (Ng and Xavier, 2011). In RTKs they are generally flanked on at least one side by cysteine rich domains and they serve to mediate ligand binding as well as inter-protein interactions (Ward and Garrett, 2001).

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Here, we covered the most common extracellular domain motifs in the RTK sub-families that we discuss below, but this is by no means an exhaustive list. Some other extracellular domains that appear in RTKs include the EGF-like domain, kringle domains, and sushi domains (see Fig. 1). Some of these domains are well studied, and their function in RTKs is relatively clear, while others are still waiting to be fully understood.

1.03.3

Overview of RTK kinase anatomy and function

On the intracellular side, RTKs contain a highly conserved kinase domain which is vital for RTK signaling. Here we discuss the general architecture and function of the RTK kinase domains using FGFR1 as a model system. The biophysical adage states that “function follows structure” and therefore it stands to reason that understanding the structure of a kinase and how it relates to its function is critical for understanding and designing novel inhibitors. RTK kinases are made up of two lobes, the N-lobe and the C-lobe connected via a hinge region (see Fig. 3). The N-lobe is predominantly made up of beta sheets, with 1 alpha helix, known as the C-helix (shown in purple). The C-lobe is primarily made up of alpha helices and turns. The C-lobe contains a hydrophobic alpha helix, termed the F-helix (shown in yellow in Fig. 3), which is buried and is responsible for providing the kinase with much of its structure. Researchers have used local spatial patterning alignment to determine that certain physical locations in the kinase have conserved residues, even though there is no sequence homology of note near these regions (Kornev and Taylor, 2010). These conserved, non-contiguous residues make up two (relatively) linear structures in the interior of the kinase that are called “hydrophobic spines” (Fig. 3). The F-helix contributes the bases of both the regulatory (R) spine (shown in red in Fig. 3) and the catalytic (C) spine (shown in orange). These spines provide stabilizing hydrophobic contacts between the N and C lobe, helping the kinase form its canonical “kidney bean” shape (Kornev and Taylor, 2010; McClendon et al., 2014). The attachment of the spines to the Fhelix keeps all elements anchored together and ensures that the kinase adopts its active structure (Fig. 3). These spines assemble and disassemble based on the local biochemistry of the kinase, meaning that these spines can form and then disassemble as the kinase lapses into an inactive state. The regulatory spine is essential for catalytic activity, as it has been shown that manipulation of residues in the regulatory spine from hydrophobic to hydrophilic would decrease or completely abolish catalytic activity (Meharena et al., 2013; Robinson, 2013). The catalytic spine is critical for the positioning of the ATP and the substrates, and also provides key catalytic machinery for kinase function (Kornev and Taylor, 2010). The adenine in ATP completes the catalytic spine (see the break in the C-spine in Fig. 3) and therefore the catalytic spine is unable to form in the absence of ATP or an ATP analog.

Fig. 3 RTK kinase domain structures. Shown are structures for the kinase domains of FGFR1, EGFR, VEGFR2, EphA2, TrkB, and IR. The FGFR1 kinase domain (top) is used as an example to highlight the various motifs. All elements are colored similarly: gatekeeper residue (magenta), KEN triad (gray), hinge (light green), F-helix (yellow), C-helix (purple), DFG motif (salmon), HRD motif (dark green), activation loop tyrosine residues (cyan), APE motif (black), C-spine (orange), R-spine (red). Structures are from (Bae et al., 2010; Bertrand et al., 2012; Heinzlmeir et al., 2016; Hubbard et al., 1994; McTigue et al., 1999; Zhang et al., 2006).

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The activation segment is a section of the kinase that includes the activation loop and the DFG motif discussed below. This segment provides stability to the C-lobe and varies the most between kinases in terms of length and sequence. The activation loop is relatively unstructured when inactive and can block substrates from binding to the kinase. Upon activation, the activation loop assumes its open conformation and allows substrates to bind to the C-lobe (McClendon et al., 2014; Taylor and Kornev, 2011). The activation loop contains a tyrosine residue (sometimes more than one), known as the activation site(s) that needs to be phosphorylated for the kinase to become active. The main activation site on FGFR1 is residues Y653/Y654. Upon phosphorylation, the phospho-tyrosine forms hydrogen bonds with both the N and C-lobe, thus forming the final critical contacts to stabilize the active kinase conformation. Once the kinase is active and both spines are fully formed, the conformation can “breathe” to allow ATP and substrates into the active site. The catalytic loop is a loop in the C-lobe that contains the HRD motif. The HRD motif, residues 621–623 in FGFR1, contains the catalytic base, D623 in FGFR1, which is a key catalytic residue (Farrell and Breeze, 2018). The catalytic base is often a conserved aspartic acid that is critical for preparing the tyrosine being phosphorylated for the phosphate transfer. The histidine in the HRD motif helps make up the regulatory spine, while the backbone of the arginine anchors the HRD to the F helix to help stabilize the active conformation of the kinase (McClendon et al., 2014; La Sala et al., 2016). The DFG motif in the activation segment is positioned at the N-terminus of the activation loop. Depending on the phosphorylation state of the activation site, the DFG motif can adopt either an “in” conformation in active kinases, which allows for the regulatory spine to form, or an “out” conformation, classically associated with an inactive kinase. The difference between these two conformations is a rotation of the phenylalanine of almost 180 degrees (Fig. 4) (Treiber and Shah, 2013). The DFG motif is critical for positioning of magnesium ions (Mg2þ) present in the active site, needed to overcome the net negative charge of the phosphates in the ATP tail. When the DFG motif adopts the “in” conformation, the phenylalanine binds to the histidine side chain from the HRD motif, stabilizing the active conformation (McClendon et al., 2014). Another critical feature of the kinase is the gatekeeper residue, in FGFR1 this is V561. The gatekeeper residue is generally a small hydrophobic residue that helps determine the size of the hydrophobic pocket that forms when the kinase is inactive and thus the DFG motif is in the “out” conformation. This pocket is often targeted by inhibitors as a way to stabilize the DFG-out motif and keep the kinase inactive (Treiber and Shah, 2013). Mutations in this gatekeeper residue can confer drug resistance via steric hindrance, by either disrupting hydrogen bonding or by stabilizing the DFG-in conformation. As the gatekeeper residue is often small, a mutation to a large and/or charged residue can prevent inhibitors from entering the binding pocket. A common mutation is from valine to methionine, which not only confers steric inhibition, but alters the hydrophobicity of the pocket as well (Sohl et al., 2015). The glycine rich loop is a flexible linker in the N-lobe. This loop has the motif of GxGxVG where the Gs are conserved glycines, the x’s are random residues, and the V is a hydrophobic residue. In FGFR1, residues 485–490 represent the glycine rich loop which is important for aligning the phosphate tail of the ATP molecule in the active site (Matte et al., 1998).

Fig. 4 FGFR1 kinase domain structures in the DFG-in or DFG-out conformation. (A) The FGFR1 kinase domain in complex with the ATP analog AMP-PCP shows the DFG motif in the “in” conformation. On the left is the entire kinase domain structure and on the right is a zoomed-in view of the binding site. (B) Inhibitors such as ponatinib cause a structural rearrangement of the DFG motif to the “out” conformation. (C) Inhibitors such as AZD4547 stabilize the “DFG-in” conformation. Structures are from (Bae et al., 2009; Tucker et al., 2014).

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The C-helix in the N-lobe is critical for activation. It can swing outwards from the active site, disrupting contacts between the N and C-lobes and disabling the catalytic domain from performing nucleophilic attack and thus attaching a phosphate group to the tyrosine. A KEN triad of residues, N546, E562, and K638 in FGFR1, forms at least 10 observable polar bonds in the inactive kinase and makes up what is known as the molecular brake, which stabilizes the inactive conformation (Klein et al., 2015). Upon activation, the glutamate undergoes significant conformational change, and the lysine moves slightly, reducing the number of polar bonds formed to three and allowing the C-helix to swing in towards the active site. Notably, not all RTK sub-families have this KEN triad (Farrell and Breeze, 2018; McClendon et al., 2014).

1.03.4

ATP binding and the act of phosphorylation

The ATP is positioned very precisely within the kinase domain (see Fig. 4) and many of the kinase motifs discussed above play a role in the alignment, binding, and cleavage of the ATP. First, the adenine base fits into a hydrophobic pocket between the N and C-lobes of the kinase and completes the catalytic spine. The adenine base then forms multiple hydrogen bonds with residues in the hinge region (Beenstock et al., 2016). The phosphate tail of the ATP is the most critical component for positioning and catalysis as the proper alignment is what will allow the enzyme to transfer the g phosphate (the phosphate on the end) to the substrate tyrosine. Next in the alignment ballet, the DFG motif adopts an “in” conformation where the asparagine can bind a Mg2þ ion, while a second asparagine in the catalytic loop binds to a second Mg2þ ion. The charge of the magnesium ions serves to stabilize the negatively charged tail of the ATP. In addition, the magnesium ions recruit water molecules into the pocket, which help to anchor the ATP to the kinase and also create a “coordination sphere” to accommodate the phosphoryl transfer onto the tyrosine residue (Matte et al., 1998; McClendon et al., 2014). At least one water molecule is necessary for the hydrolysis of ATP. The glycine rich loop interacts with the b and g phosphates, further stabilizing the tail. At last, a conserved lysine in the C-helix (K514) further serves to coordinate the locations of the b and g phosphates. To recap, the ATP is now forming stabilizing contacts with the hinge region, the C-helix, the glycine rich loop in the N-lobe, and the catalytic loop in the C-lobe (Matte et al., 1998). The catalytic base binds to the P þ 1 site of the substrate (or the amino acid immediately following the tyrosine to be phosphorylated) and the substrate is further stabilized via additional contacts with the C-lobe as well as through coordination with the magnesium ions (McClendon et al., 2014; Taylor and Kornev, 2011). The act of phosphorylation is not completely understood but involves the magnesium ions increasing the modulation of the charges of the b and g phosphates of ATP to increase their mutual repulsion. Then, the catalytic base abstracts a proton from the tyrosine substrate, which then allows the tyrosine to perform nucleophilic attack on the g phosphate from the ATP (Matte et al., 1998). This causes cleavage of the g phosphate of ATP and the phosphoryl group is transferred to the tyrosine to create a phospho-tyrosine (Fig. 5).

1.03.4.1

Post-Kinase domains

While most RTKs have a short stretch of amino acids immediately following the C-terminus of the kinase, some RTKs have additional domains following the kinase that may play a role in kinase function. Examples include sterile alpha motif (SAM) domains, PDZ binding motifs, and long post-kinase tails.

Fig. 5 Schematic of ATP hydrolysis and tyrosine phosphorylation. The tyrosine performs nucleophilic attack on the g phosphate of ATP (highlighted in green) which results in the transfer of a phosphoryl group onto the tyrosine to create phospho-tyrosine and ADP as a byproduct.

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SAM domains are found in many different proteins and are often associated with mediating protein-protein interactions, but actually have a large array of functions (Kim and Bowie, 2003). They can bind to both other SAM domains and non-SAM domains (Singh et al., 2017). They are predominantly alpha helical in structure and are capable of forming higher order oligomers in solution (Stapleton et al., 1999). SAM domains are found in the Eph receptor subfamily. Also found in the Eph receptor subfamily are PDZ binding motifs which are located on the C-terminal ends of these receptors. PDZ binding motifs are three amino acids in length and bind PDZ domains of proteins. These domains are named after the three proteins that were first discovered to share the domain: PSD95, Dlg1, and zo-1 (Lee and Zheng, 2010). Finally, the most common “domain” following the kinase domain is the long post-kinase tail. These tails perform a number of functions: they may possess additional phosphorylation sites, they can bind to downstream signaling partners, or they can act as regulatory sequences. While the kinase domains are homologous across the entire RTK family, and the extracellular domains are homologous across sub-families, there is little sequence homology between the post-kinase tails (Crudden and Girnita, 2020). These tails tend to be unstructured and are not well understood.

1.03.4.2

Overview of ligands

RTK ligands, often called growth factors, tend to be small in size, they range from  5 kDa to 50 kDa (Ogiso et al., 2002; Toth et al., 2001; Weiss et al., 2000; Wlesmann et al., 1999). Structures of some ligands that bind RTKs are shown in Fig. 6 and the ligand binding sites are shown in Fig. 2. There are paracrine ligands, which are secreted and activate receptors in a small, localized area, as well as endocrine ligands that are expressed in one tissue but travel through the bloodstream to activate cells elsewhere in the body (Beenken and Mohammadi, 2009). In some special cases such as the ephrins, the ligands are produced in a neighboring cell and remain tethered to that cell via anchors in the plasma membrane (Darling and Lamb, 2019; Toth et al., 2001). Some ligands are actually trafficked in vesicles and endocytosed into the cells for use (Chao, 2003; Maness et al., 1994; Wilding et al., 2020). Until recently, it was believed that each RTK sub-family has its own specific set of growth factors that bind and activate the receptors. Now it is known that some ligands are very specific and only bind to one RTK while others are more promiscuous and bind to multiple RTKs. Sometimes, cross-family activation has been seen which means that ligands bind to more than one RTK subfamily (Paul and Hristova, 2019a). Ligands are either monomeric or dimeric (although some in vitro assays have shown evidence of higher order oligomers), which affects how they can bind to and interact with the RTK dimer (Lu et al., 2001; Weiss et al., 2000). A monomeric ligand can bind to the dimer in a 2:2 ratio, meaning 2 ligands bind to a dimer containing two receptors. A dimeric ligand can bind in a 1:2 ratio meaning that 1 ligand dimer binds to the two receptors in a dimer. This may seem like a small difference, but simulations show significant differences in RTK dimerization and behavior in response to monomeric and dimeric ligands (Paul and Hristova, 2019b). In summary, some ligands across subfamilies are similar and potentially evolutionarily linked, while some like the ephrins are unique. Ultimately, the purpose of each of the ligands is the same, to bind to the extracellular domain of the RTKs, to stabilize the dimers, and to induce allosteric conformational changes that will ensure full activity of the kinases. The close proximity of the kinases, and their correct orientation with respect to each other in the dimers allows them to auto-phosphorylate each other and become catalytically active (Li and Hristova, 2006; Paul and Hristova, 2019b).

Fig. 6 Structures of ligands that bind the EGFR, VEGFR, FGFR, Eph, Trk, and IR families. EGF, FGF2, ephrin-A5, and insulin are monomeric ligands while VEGF-A and NGF are dimeric ligands. Structures are from (Gutmann et al., 2020; Himanen et al., 2010; Markovic-Mueller et al., 2017; Ogiso et al., 2002; Schlessinger, 2000a,b; Wehrman et al., 2007).

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Fig. 7 Illustration of some common RTK inhibition strategies. Ligand traps (top) are inhibitors that bind to the ligand and prevent its binding to the receptor. RTK inhibitors that target the receptor to block ligand binding (left) can be one of two types: (i) allosteric inhibitors that cause structural changes to prevent ligand binding, or (ii) orthosteric inhibitors that directly block ligand binding. Other inhibitors do not interfere with ligand binding but inhibit receptor dimerization (bottom). Tyrosine kinase inhibitors (TKIs, right) are small molecules that bind specifically to the kinase domain and alter the kinase activity.

There have been great successes in the large-scale production of synthetic RTK ligands. Some of them are being used as drugs. One of the most common examples is porcine insulin to treat diabetes. Porcine insulin is very similar to human insulin, and could be expressed and purified, until recently, much more easily than human insulin (Weiss et al., 2000). Synthetic ligands are also used in research; one example is a soluble ephrin variant that allows for easier bio-activity studies in vitro (Darling and Lamb, 2019).

1.03.5

Types of Inhibitors and how they work

Fig. 7 show some common inhibition strategies that we will discuss here. They include (i) inhibition of ligand binding (using either ligand traps (top) or allosteric/orthosteric binding inhibitors (left)), (ii) inhibition of RTK dimerization (bottom), or (iii) inhibition of kinase function (right). To prevent ligand binding and thus disable the ligand, a signaling incompetent molecule can be used to bind to the receptor and either allosterically prevent ligand binding or outcompete the ligand for the binding site. Alternatively, the ligand can be disabled by using drugs to sequester the ligand, and thus prevent it from binding to the receptor (Katoh, 2016, 2019; Tanner and Grose, 2016). To prevent dimerization, antibodies or other large molecules are used to bind the receptor and sterically hinder dimerization (Katoh, 2016; Tanner and Grose, 2016). The most common method to inhibit kinase function is to use a small molecule called tyrosine kinase inhibitor (TKI), which binds to the kinase and prevents it from functioning properly.

1.03.5.1

TKIs

Small molecule inhibitors generally refer to the class of inhibitors known as tyrosine kinase inhibitors or TKIs. TKIs work by binding to the kinase domain and either blocking the ATP binding site, or allosterically preventing ATP hydrolysis (Katoh, 2016). To date, the TKIs that bind to the active site contain a pyrimidine or pyrimidine analog at their core (Liu and Gray, 2006; Milik et al., 2017). Small molecule inhibitors come in a wide array of specificities. Some TKIs bind to the kinase domains of many families of RTKs, some bind only to RTKs within the same family, some bind to a subset of RTKs within a family, and some only bind to a specific RTK or RTK mutant. TKIs are the most commonly used inhibitors in cancer treatment that target the RTKs (Katoh, 2016; Tanner and Grose, 2016). Inhibitors that target mutant pathogenic variants of RTKs are widely studied due to their potential utility as anticancer drugs (Ko et al., 2021; Tanner and Grose, 2016; Touat et al., 2015; Yue et al., 2021).

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Receptor Tyrosine Kinases

Fig. 8 Structures of tyrosine kinase inhibitors (TKIs) from different classes. Class 1: gefitinib, Class 2: ponatinib, Class 1.5: AZD4547, Class 3: trametinib, Class 4: SSR128129E, Class 5: neratinib, Class 6: futibatinib (TAS-120). The pyrimidine or pyrimidine analog groups are highlighted in red, the hydrophilic, solubilizing groups are highlighted in blue, and the hydrophobic groups are highlighted in yellow.

There are seven classes of tyrosine kinase inhibitors (Engelhardt et al., 2019; Gower et al., 2014; Hartmann et al., 2009; Liu and Gray, 2006; Park et al., 2012; Roskoski, 2020; Yosaatmadja et al., 2015). Class 1 TKIs bind to the active, DFG-in, C-helix in conformation. Class 2 bind to the inactive, DFG-out, C-helix out conformation. Class 1.5 bind the conformation mid-way between active and inactive, where the DFG is in but the C-helix is out (Park et al., 2012). Classes 3 and 4 are non-ATP competitive inhibitors that prevent catalytic activity through allosteric effects. Class 5 pertains to bivalent inhibition in which the inhibitor targets two distinct binding surfaces of the kinase domain (Gower et al., 2014) and class 6 refers to irreversible inhibition, often through the formation of covalent bonds (Roskoski, 2020; Yosaatmadja et al., 2015). Examples of inhibitors in each class is shown in Fig. 8. The scaffold region in Fig. 8 is highlighted in red; notice how all the inhibitors contain a pyrimidine or pyrimidine analog. The hydrophilic region which is responsible for modulating the solubility of the TKI is shown in blue. The hydrophobic region which modulates selectivity and potency is shown in yellow (Engelhardt et al., 2019; Liu and Gray, 2006).

Receptor Tyrosine Kinases

21

Inhibitors of classes 1, 2, and 1.5 are the most common. These inhibitors tend to include a core scaffold (red in Fig. 8) that serves as an adenine mimetic and binds to the hinge region (Yun et al., 2007). Class 2 TKIs are increasingly becoming more common due to the ability to fine tune their selectivity and potency. This can be done by taking advantage of the DFG-out motif and utilizing the hydrophobic pocket that forms when the phenylalanine flips out. The selectivity of a class 2 TKI can be adjusted by changing the group that extends into this pocket (known as the “back pocket”). In fact, in order to balance the potency of such inhibitors, pharmacologists generally add a solubilizing group to the end of the inhibitor (yellow in Fig. 8) that extends into the solvent (Ishikawa et al., 2011; Milik et al., 2017). This adds an interesting push/pull effect as the hydrophobic group wants to nest into the back pocket and stay there, and the solubilizing group is hydrophilic and wants to be in solvent so it works to pull the inhibitor back into solution. Finally, we note that there are some small molecule inhibitors that do not bind to the catalytic domain, but rather bind to the extracellular domain and inhibit signaling by preventing ligand binding and thus dimerization and kinase activation in much the same way as some of the antibodies discussed below (Aertgeerts et al., 2011; Noberini et al., 2008).

1.03.5.2

Antibodies

Antibodies are large proteins ( 150 kDa) that can bind to a variety of substrates with high specificity. Antibodies generally work by binding to the extracellular domain of the RTK and either by preventing dimerization via steric hindrance or by preventing the ligand from binding (Katoh, 2016). Once the antibodies identify a target, they bind strongly to it (dissociation constants in the sub-nM and nM range). The general structure of an antibody looks a bit like a Y as shown in Fig. 9. This Y is composed of two major regions, the variable region and the constant region (Chiu et al., 2019). The variable region is the region that has low sequence homology and adapts to identify and bind to different substrates (or antigens). As seen in Fig. 9, the variable antigen binding region resembles a notched groove, which optimizes the exposed surface area that the antibody can use to bind to its substrate. The variable region is often called the Fragment antigen binding domain, or Fab (Chiu et al., 2019). The specificity of antibodies is often modulated via electrostatic, rather than hydrophobic, interactions, such that the selectivity of the antibody for the antigen can be tuned (Travers et al., 2001). The antibody-substrate interaction can take on many forms, but it is generally accepted that upon binding both the antibody and the substrate undergo localized conformational changes. These changes can be sorted into three different categories: Lock and key, induced fit, and conformational selection. Lock and key allows the two proteins to fit together without requiring large conformational changes, much like a key fitting into a lock. Induced fit is the most dynamic method of binding, and is sometimes attributed to the antibodies with the highest antigen diversity, as both the antibody and the antigen undergo major conformational changes to fit together. This conformational flexibility allows antibodies to vary in their ability to recognize both the charges and the shape of the antigen. Finally, the conformational selection method allows the variable region of the antibody to sample many different conformations in solution so it can bind when it finds an antigen that it fits, both sterically and electrostatically. This is quite useful as it allows the antibody to bind to an antigen that may have undergone minor modifications due to its local microenvironment (Chiu et al., 2019). The constant region has low sequence variability and is historically known to crystallize easily. It is thus called the Fc domain for “fragment crystallizable” (Chiu et al., 2019). This region is used to classify antibodies into different classes of immunoglobulins: IgM, IgD, IgG, IgA, and IgF based on their constant regions. IgG is the most common type of antibody found in the blood. It is generally used as the starting point for antibody design and is the type referred to in this article.

Fig. 9 Structure of a monoclonal immunoglobulin antibody (PDB 1IGT). The heavy chains are labeled and colored red and blue, the light chains are labeled and colored orange and cyan, and the disulfide bonds are shown as pairs of spheres and are circled. Structure is from (Harris et al., 1997).

22

Receptor Tyrosine Kinases

Fig. 10

Structure of an RNA aptamer bound to IgG domains (PDB 3AGV). Structure is from (Nomura et al., 2010).

The really great thing about anti-RTK antibodies is their diversity in mechanisms of action and targets. Some antibodies have been designed to target the RTK by binding to the extracellular domain. This can either target the cell for destruction, as it may initiate an immune response, or it can prevent ligand binding or dimerization via steric occlusion (Bennasroune et al., 2004; Leopold and Verkhusha, 2020; Linardic and Crose, 2011; Yamaoka et al., 2018). The other way that antibodies have been used as RTK inhibitors is by designing antibodies that target the ligand. By binding to the ligand, the antibodies mark it for degradation and prevent it from binding to the RTK, thus preventing the perpetuation of signaling (Bennasroune et al., 2004; Leopold and Verkhusha, 2020; Linardic and Crose, 2011; Yamaoka et al., 2018).

1.03.5.3

Aptamers

RNA and DNA aptamers, as shown in Fig. 10, are short nucleotide sequences that behave very much like antibodies. They are single stranded but can fold into complex geometries. They are generally selected from a large random sequence pool and can display high selectivity and potency (Blind and Blank, 2015; Lakhin et al., 2013). Aptamers can be advantageous over antibodies because they can be produced synthetically, often at a much lower cost than an antibody, and can have more conducive storage requirements. Aptamers also have the advantage of being modifiable to prevent degradation in vivo and during storage. Thus, they may be effective in lower quantities than antibodies. Unmodified aptamers are cleared from the bloodstream in a matter of minutes or hours, while modified aptamers can last for days or weeks (Lakhin et al., 2013). DNA aptamers seem to have a slightly higher intrinsic stability than RNA aptamers, but there has yet been no clear advantage identified for one type over another (Lakhin et al., 2013). Aptamers often need to be conjugated with polyethylene glycol to increase their mass. These short nucleotide sequences are generally less than 15 kDa and can be cleared by the kidneys. Increasing the mass to 20–40 kDa can help solve this problem (Lakhin et al., 2013). Probably the biggest drawback to the use of aptamers is the time and labor required to design, select, and test the aptamers. In addition, the quality of the aptamer is often dependent on the selection protocol used, and therefore a suboptimal protocol can result in aptamers with low affinity or selectivity (Blind and Blank, 2015). Finally, even the most selective aptamer can exhibit some cross-reactivity and can interact with similar receptors. This means that, for example, an aptamer that targets FGF2, a ligand for the FGFR family, may also bind to EGF, a ligand for the ErbB family, and thus result in off-target effects (Blind and Blank, 2015).

1.03.6

Ligand traps

Ligand traps work by binding to the ligand and preventing it from binding to the receptor or they can bind both the ligand and the extracellular domain of the signaling-capable receptor. This serves double duty, because not only does it sequester the ligand, it also prevents the receptor from dimerizing and thus becoming active (Chiu et al., 2019; Wang et al., 2012). Ligand traps can take on many forms such as RNA/DNA aptamers or truncated versions of the extracellular domain of the receptors. Truncated extracellular domains are soluble versions of the receptor that bind the ligand and essentially compete with the fulllength receptor for the ligands on the cell surface. Truncated extracellular domains resemble some version of the ligand’s cognate receptor or a receptor from the same family. This often includes just a short truncation of the extracellular domain and will include the ligand binding domain and any domains necessary for binding. These soluble receptors have no signaling capabilities, so they render the ligand ineffective (Adams et al., 2009). RNA and DNA aptamers can work in a similar manner, by binding the ligand and preventing it from binding to the receptor (Esposito et al., 2011). Finally, the last kind of ligand trap is an anti-ligand antibody. These antibodies are raised against specific ligands and sequester them to prevent them from activating the receptor. An antibody functioning as a ligand trap has the same structure as the antibodies discussed above, with a variable chain that targets the ligand instead of the receptor.

Receptor Tyrosine Kinases

1.03.7

23

RTK subfamilies and their inhibitors

The RTK superfamily is quite extensive with 58 members in the human genome. Therefore, in effort to provide a detailed and comprehensive review of RTK inhibitors, we will discuss only some of the most prominent RTK subfamilies that are commonly targeted by inhibitors. We do not present a comprehensive list of all the RTK inhibitors available, but a general overview of progress in the field.

1.03.7.1

FGF receptors

The four fibroblast growth factor receptors FGFR(1–4), along with their 18 activating FGF ligands, play pivotal roles in organogenesis, the formation of organs from the germ tissue layers, as well as osteogenesis, the formation of the skeletal system, during development. They are also important for injury response in adults (Katoh, 2016; Ornitz and Marie, 2015; Tanner and Grose, 2016; Touat et al., 2015; Webster and Donoghue, 1997).

1.03.7.1.1

Function and dysfunction

Over-activity or under-activity of FGFRs is linked to a number of skeletal syndromes such as achondroplasia, as well as developmental syndromes such as Kallmann syndrome and LADD syndrome (Foldynova-Trantirkova et al., 2012; Goetz and Mohammadi, 2013; Mohammadi et al., 1996; Vajo et al., 2000). In addition, FGFRs are often found overexpressed in cancers and the presence of FGFR in tumors is associated with a higher likelihood of ineffective chemotherapy (Babina and Turner, 2017). This is because FGFRs play a role in drug resistance as well as in tumor angiogenesis.

1.03.7.1.2

Structure

FGFRs have an extracellular domain that consists of three immunoglobulin-like domains, a transmembrane alpha helix, and an intracellular kinase domain (Figs. 1 and 2). Between the first and second immunoglobulin domains is a region referred to as the “acid box”, which is an acidic residue-rich section of the protein that is presumed to be involved in auto-inhibition. The ligand-binding region of the FGFRs is between the second and third immunoglobulin domains (Plotnikov et al., 1999; Vajo et al., 2000; Wilkie, 2005).

1.03.7.1.3

Ligands

FGFRs interact with a family of 18 different fibroblast growth factors (FGFs). FGFs are monomeric ligands that bind to the FGFRs in a 1:1 ratio (Plotnikov et al., 1999). The FGFs require the presence of heparan sulfate or Klotho as binding cofactors (Dai et al., 2019). The FGFs exhibit a beta-trefoil fold which creates a beta barrel with a hairpin “cap.” Each beta barrel is made up of a series of beta hairpins and beta strands (Fig. 6). This shape is well conserved across the FGF family, even as the charge and the function of the different FGFs are different. FGFs bind in the cleft between FGFR immunoglobulin domains 2 and 3. Ligand binding involves both hydrophobic and electrostatic effects (Plotnikov et al., 1999, 2000). The FGF ligands can be either paracrine or endocrine. FGFRs require different co-factors for the binding of paracrine and endocrine ligand. The paracrine ligands require heparan sulfate, which binds to the receptor and the ligand. As heparan sulfate is commonly found on cell surfaces, the high affinity of FGFs keeps them localized on the cellular membrane near the secretion site (Eswarakumar et al., 2005; Ibrahimi et al., 2004; Ornitz, 2000). The endocrine ligands have lower heparan sulfate affinity, which allows the ligands to diffuse away from the secretion site. They need the Klotho co-receptor to bind and activate the FGF receptors (Beenken and Mohammadi, 2009).

1.03.7.1.4

Inhibitors

Small molecule inhibitors for the FGFR family includes AZD4547, Dovitinib, Erdafitinib, and Ponatinib (Pottier et al., 2020). AZD4547 demonstrates excellent selectivity against FGFR1–3, and potent inhibition of FGFR phosphorylation (Gavine et al., 2012; Gudernova et al., 2016; Roskoski, 2020; Sohl et al., 2015; Yosaatmadja et al., 2015). AZD4547 has also been shown to be successful at preventing cell proliferation in cancer lines expressing FGFR1, 2, or 3, and prevents kinase activation and downstream signaling cascades in cancer lines (Touat et al., 2015). It inhibits direct binding partners such as PLCg and FRS2, which in turn inhibits the ERK and MAPK pathways (Babina and Turner, 2017; Katoh, 2019). Ponatinib (see Fig. 8) is composed of several motifs designated as the imidazopyridazine template (shown in red), the methylphenyl A ring, and the trifluoromethylphenyl B ring (both shown in yellow) as well as two linkers. Ponatinib binds to the DFGout motif of FGFR1 and FGFR4 in the hinge region where ATP would usually bind (Fig. 4B). The pyridazine template forms a hydrogen bond with the backbone nitrogen atom located in the hinge, and the A ring occupies the hydrophobic pocket located behind the gatekeeper residue. Critically important, the DFG motif at the end of the activation loop acquires a “DFG-out” conformation which creates a hydrophobic pocket that the B ring can target. This confers specificity to the inhibitor. The linker connecting the A and B rings is an amide which forms hydrogen bond interactions with the aspartic acid in the DFG motif and a conserved glutamate in the C-helix (Tucker et al., 2014). AZD4547 (see Fig. 8) is an inhibitor of FGFR1, 2, and 3, as well as VEGFR1, 2, and 3. It is currently in clinical trials for a variety of FGFR1-driven tumors (Gavine et al., 2012; Roskoski, 2020; Sohl et al., 2015). AZD4547 is composed of different moieties such as the pyrazole amide template, the dimethoxyphenyl A ring, and the dimethylpiperazinylphenyl solubilizing group. AZD4547

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Receptor Tyrosine Kinases

binds in the ATP cleft as do most pyrazole series inhibitors. In this case, the activation loop exhibits a DFG-in conformation (Fig. 4C). The amide template (red in Fig. 8) forms hydrogen bonds with the hinge region, while the A ring occupies the hydrophobic pocket behind the gatekeeper residue. One of the methoxy oxygen atoms in the A ring forms a hydrogen bond with the aspartate of the DFG motif. The dimethylpiperazinylphenyl (yellow in Fig. 8) extends away from the hinge region and does not form any specific interactions with the FGFR1 kinase. The activation loop is well ordered and adopts a closed conformation which shields the A ring from the surrounding solvent. The phenylalanine at the tip of the activation loop stacks against the A ring and occupies a constricted indentation at the base of the ATP-binding pocket sometimes referred to as the “pit” (Dai et al., 2019; Roskoski, 2020). Some antibodies have been raised against different FGFRs to prevent ligand-mediated dimerization. To date, some well-known ones are BAY1179470, FPA144, and MFGR1877S (Katoh, 2016, 2019; Touat et al., 2015). The first two target FGFR2, while the last one targets FGFR3. An anti-FGFR1 antibody, IMC-A1, failed in clinical trials but shows strong binding to FGFR1 and prevents the binding of FGF1 and FGF2 to the receptor (Farrell and Breeze, 2018; Sun et al., 2007). Some ligand traps designed for FGFRs include the APT-F2 aptamer, the 3H3 antibody, and the anti-FGF2 antibody (Jin et al., 2016; Wang et al., 2012). APT-F2 and anti-FGF2 both bind FGF2 to prevent receptor activation, so they work for all FGFRs that bind FGF2. APT-F2 is very specific and has been shown to be fairly effective. Anti-FGF2 has been shown to have higher efficacy but lower potency, as compared to APT-F2. The 3H3 antibody is known to be less effective than either anti-FGF2 or APT-F2 (Wang et al., 2012). Treatment of cells exposed to FGF2 with APT-F2P inhibited the activation of the ERK pathway and inhibited FGF2-induced proliferation, viability, and differentiation (Jin et al., 2016). This success was comparable to that of the neutralizing 3H3 and anti-FGF2 antibodies (Jin et al., 2016; Wang et al., 2012). Mouse trials have shown that APT-F2P is successful at preventing FGF2-mediated joint and bone deterioration (Jin et al., 2016). Gal-F2 is a unique anti-FGF2 antibody in the sense that it recognizes the shape of FGF2, rather than a particular sequence (Wang et al., 2012). Gal-F2 was shown to inhibit binding of FGF2 to all members of the FGFR family equally well if not better than the 3H3 antibody. Gal-F2 does not compete with 3H3 or bFM-1, both FGF2 antibodies, as they recognize different regions of FGF2 (Jin et al., 2016; Wang et al., 2012). In mouse trials, Gal-F2 has proven capable of inhibiting tumor growth in mice infected with human hepatocellular carcinoma (HCC) (Wang et al., 2012). FP-1039 and sFGFR3 are ligand traps that are used against FGFRs. These are soluble proteins that contain the extracellular regions of either FGFR1 or FGFR3. These proteins can bind to the FGF ligands and compete with the full-length receptors for ligand. FP-1039 was designed by conjugating the extracellular domain of FGFR1 to the Fc domain of the immunoglobulin G1 (IgG1) on the C-terminus (Harding et al., 2013; Tolcher et al., 2016). sFGFR3 is able to rescue certain chondrodysplasia phenotypes in mouse trials (Harding et al., 2013; Tolcher et al., 2016). Importantly, FP-1039 fails to bind hormonal FGFs due to the absence of Klotho or b-Klotho, which decreases its toxicity. However, it is able to bind many mitogenic FGFR1 ligands and has been shown to block tumor angiogenesis in mice and FGF-induced proliferation in HUVECs (Harding et al., 2013). FP-1039 includes the entire extracellular domain of FGFR1 (Harding et al., 2013). A variant called FGF trap was developed by removing domain D1 and the D1-D2 linker. FGF trap, which includes residues 145–374 of FGFR1, was shown to bind FGF2 in the presence of heparan sulfate. FGF trap was further shown to inhibit proliferation and migration of HUVECs and the proliferation of tumor cells (Katoh, 2016; Li et al., 2014). HUVEC samples treated with both FGF2 and FGF trap showed significant decrease in phosphorylation of ERK and AKT, relative to samples treated only with FGF2. FGF trap was also shown to decrease upregulation of Cyclin D1 and Cyclin E, as compared to cells treated with FGF2 only. FGF trap was shown to significantly decrease tumor size in Caki-1 and A549-generated tumors in mouse experiments, without having any negative effect on total body weight. Ligand binding experiments have shown that heparan sulfate is essential for the FGF2-FGFR1 complex formation. Deletion of the heparan binding site of the FGF trap prevented binding to FGF2, even in the presence of heparan sulfate, further supporting the important role of heparan sulfate in the binding process (Li et al., 2014).

1.03.7.2

ErbB (EGF) receptors

The epidermal growth factor (EGF) receptor subfamily (also called ErbB or Her receptors) of RTKs has four members: ErbB1/Her1, also commonly known as EGFR, ErbB2/Her2, ErbB3/Her3, and ErbB4/Her4. EGFR is one of the most extensively studied RTKs and the first discovered RTK. EGFR plays a prominent role in human development and disease (Harrison et al., 2020; Rajaram et al., 2017; Rayego-Mateos et al., 2018). The ErbB receptors, except for ErbB2 which does not bind any known ligand, can bind 10 different activating ligands: EGF, transforming growth factor-a (TGF-a), epiregulin, amphiregulin (AR), betacellulin, heparanbinding EGF-like growth factor (HB-EGF), and four neuregulins (NRG-(1–4)) (Ferguson, 2008; Zhang et al., 2006). HER2 has a unique extracellular domain and does not bind to any known ligands (Jin et al., 2009; Milik et al., 2017). HER2 also does not homo-dimerize due to repulsive forces between the dimerization arms in the extracellular domain (Kumar et al., 2020). This is very interesting because HER2 overexpression is one of the most common indicators of poor prognosis for cancer patients (Jang et al., 2018; Jin et al., 2009; Milik et al., 2017). HER2 instead tends to hetero-dimerize with EGFR and other ErbB receptors which can then amplify or diversify their signaling (Aertgeerts et al., 2011; Kumar et al., 2020).

Receptor Tyrosine Kinases 1.03.7.2.1

25

Function and dysfunction

All ErbB receptors are fundamentally important for the development of the epithelium and for tissue maintenance in adults. Overexpression of EGFR and other ErbB receptors is found in a number of cancers including prostate, lung, colon, breast, and brain cancer which makes them an enticing target for inhibition (Kumar et al., 2020).

1.03.7.2.2

Structure

The ErbB receptors have an extracellular domain comprised of two leucine rich domains alternating with two cysteine rich domains (Figs. 1 and 2). Both leucine domains are involved in the binding of ligand, while the cysteine domains are associated with dimerization and auto-inhibition (Sihto et al., 2005) (Aertgeerts et al., 2011).

1.03.7.2.3

Ligands

Epidermal Growth Factor (EGF) is, as the name suggests, a small growth stimulating protein that interacts with EGFR and other ErbB receptors (Fig. 6). EGF is very small, relative to some of the other ligands in the RTK family, with human EGF only 53 amino acids in length. Despite its small size, EGF is still able to induce a large conformational change in EGFR allowing dimerization and initiation of downstream signaling cascades. As EGF is monomeric, it forms 2:2 ligand:receptor complexes. In some crystal structures the ligands are shown to be as far as 79 Å apart (Ogiso et al., 2002)! EGF ligands are encased independently in their binding cleft and do not interact with one another even upon dimerization of the protein. Most notably, recent crystal structures of different EGF ligands in complex with EGFR have shown that the ligand identity influences the extracellular conformation of the receptor upon dimerization (Freed et al., 2017). The general structure of EGF can be described as three loops, each of which is a series of amino acids bordered by a disulfide bond. These loops are known as the A-loop, B-loop, and C-loop (Lu et al., 2001). The A-loop is slightly alpha helical, while the B and C-loops are both antiparallel beta sheets. EGF interacts with the leucine rich domains 1 and 3 in EGFR through both electrostatic and van der Waals interactions, but not with the cysteine rich domains 2 and 4. The A and C-loops both form favorable interactions with domain 3, while the B-loop reaches across the binding cleft and attaches to domain 1, linking domains 1–3 into a C shape (Ogiso et al., 2002).

1.03.7.2.4

Inhibitors

The EGFR kinase has an “active” conformation that is fairly stable, whether the kinase is phosphorylated or not. This means that most EGFR inhibitors need to target the DFG-in, C helix in conformations and so TKIs for EGFR tend to be class I. Common inhibitors for EGFR include erlotinib and gefitinib (see Fig. 8). The kinase domain of HER2, however, has a much less stable active conformation and so HER2 TKIs tend to be in class 2. Common HER2 inhibitors include SYR127063, which is very potent against HER2 but binds to EGFR with low affinity (Ishikawa et al., 2011). Because HER2 and EGFR can hetero-dimerize, in many cases there is a need for dual-inhibition (Ishikawa et al., 2011; Stamos et al., 2002; Yosaatmadja et al., 2015). These dual inhibitors tend to be in class 1.5, 2, or 6 so that they are able to bind both to HER2 and EGFR. Lapatinib is a common class 1.5 inhibitor that is used to target both EGFR and HER2. Another common approach to dual inhibition is to use multiple drugs to target multiple kinases, i.e., a class 1 EGFR specific inhibitor in concert with a class 2 highly specific HER2 inhibitor (Planken et al., 2017; Yun et al., 2007). Because EGFR is one of the best understood RTKs, many different antibodies with different mechanisms have been designed and tested. Some common ones include Nimotuzumab, Cetuximab, Matuzumab, mAb 806, mAb 175, and mAb 7A7. Uniquely, mAb 7A7 is one of the rare anti-murine antibodies as most studied antibodies recognize only the human isotope of EGFR (Talavera et al., 2011). Nimotuzumab, Cetuximab, and Matuzumab all bind to domain 3 of EGFR, thus preventing ligand binding (Esposito et al., 2011; Martini et al., 2019; Talavera et al., 2011). Some ligand traps that target ErbB ligands include 1D11, which is an anti-TGF antibody, and the soluble extracellular domains Ad.STbII-Fc and sEGFR501.Fc. The soluble extracellular domains are either full length or truncated ErbB extracellular domains that are conjugated to the Fc domain of an antibody (Adams et al., 2009; Connolly et al., 2012; Park et al., 2012).There have also been aptamers developed against the EGF receptor including CL4, an RNA aptamer that binds specifically to the extracellular domain of EGFR. CL4 has been shown to prevent formation of both EGFR homodimers and heterodimers and can successfully prevent EGFR activation and signaling (Park et al., 2015).

1.03.7.3

VEGF receptors

Vascular endothelial growth factor receptors VEGFR(1–3) are primarily involved in the development and regulation of the vasculature (Inai et al., 2004; Lacal and Graziani, 2018; Modi and Kulkarni, 2019). They are activated by six different ligands, VEGF(A-E) and placental growth factor (PlGF) (Ceci et al., 2020; Koch and Claesson-Welsh, 2012; Lacal and Graziani, 2018; Olsson et al., 2006). In humans, VEGF-VEGFR signaling plays a fundamental role in both vasculogenesis, the development of blood vessels from endothelial precursor cells during embryogenesis, and angiogenesis, the formation of new blood vessels from pre-existing ones (Lacal and Graziani, 2018; Modi and Kulkarni, 2019).

1.03.7.3.1

Function and dysfunction

VEGFR2 is known as the key receptor that regulates angiogenesis, mainly in response to VEGF-A (Jeltsch et al., 2013; Li et al., 2014; Vempati et al., 2014). VEGFR1, just like VEGFR2, is expressed in endothelial cells, but its main role is to sequester the VEGFA ligands

26

Receptor Tyrosine Kinases

and modulate VEGFR2 signaling. VEGFR3 is expressed mainly in lymphatic vessels and is best known for its involvement in lymph angiogenesis and maintenance of the lymphatic endothelium. While angiogenesis is critical for functions such as fetal development, wound healing and tissue repair, an overabundance of vasculature can prove to be detrimental (Lacal and Graziani, 2018; Pandey et al., 2018). For example, overexpression of VEGFR2 can result in over-vascularization of the eye which is associated with loss of eyesight due to diabetic retinopathy (Ceci et al., 2020; Lacal and Graziani, 2018; Muller et al., 1997). Perhaps the largest risk presented by VEGFR2 over-activity is its role in cancer (Ceci et al., 2020; Tvorogov et al., 2010; Wang et al., 2020b). In order to increase in size and to prevent necrosis, tumors must be able to induce and maintain their own vascular system, which is regulated by VEGFR2. Tumor vascularization often correlates with the aggressiveness of the cancer (Ceci et al., 2020; Lacal and Graziani, 2018; Muller et al., 1997; Waxman, 2007).

1.03.7.3.2

Structure

VEGFR extracellular region includes seven immunoglobulin-like domains (see Figs. 1 and 2). It is followed by the transmembrane helix and kinase domain (Simons et al., 2016).

1.03.7.3.3

Ligands

VEGFs are glycoproteins that belong to the cysteine knot motif superfamily (Iyer et al., 2006; Muller et al., 1997; Shibuya, 2011). They contain seven beta strands which are linked together through a series of three disulfide bonds (see Fig. 6). The structure also contains two alpha helices. Alternative splicing can result in many different isoforms of VEGF, with different binding characteristics. Notably, VEGFs can act in both paracrine and endocrine manner (Villegas et al., 2005). The paracrine VEGFs show an affinity for heparan sulfate, much like the paracrine FGFs, which means that they are unable to diffuse large distances from where they are expressed. The endocrine VEGFs contain the same N terminal and C terminal residues as the paracrine VEGFs, but are spliced to remove their heparin binding domain. This leaves the endocrine VEGFs able to diffuse throughout the bloodstream. Interestingly, the binding of heparan sulfate is not necessary for the activation or dimerization of VEGFR2, but instead serves to keep the ligands close by. This may be a way of tightly controlling the amount of VEGFs in the system (Lacal and Graziani, 2018; Muller et al., 1997). One of the interesting things about VEGF is that it forms a constitutive homodimer (Muller et al., 1997). There are two disulfide bonds that link each monomer together in an anti-parallel side by side dimer. There are no stabilizing hydrogen bonds formed between the monomers. Instead, disulfide bridges are used for stable linkage. The homodimer binds to the immunoglobulin domains 2 and 3 on the VEGF receptors via 5 residues on the external face of the ligand dimer (Muller et al., 1997). The ligand is able to bind to VEGFR2 in a 2:1 or 1:1 ratio, where 1 VEGF homodimer is capable of binding a single VEGFR monomer or 1 VEGF homodimer can bind to 2 VEGFR monomers simultaneously, which creates the active dimer of VEGFR2. It has been suggested that dimeric ligands can prevent homodimerization of VEGFR2 if expressed in large quantities (Paul and Hristova, 2019b), but this is yet to be demonstrated experimentally.

1.03.7.3.4

Inhibitors

The development of VEGF-A/VEGFR2 inhibitors is considered a priority in cancer research due to the importance of angiogenesis in human physiology and pathology. This interest has led to the first ever aptamer approved by the FDA, targeting VEGF-A and helping to treat macular degeneration (Lacal and Graziani, 2018; Parashar, 2016). Other VEGFR2 inhibitors include bevacizumab, an antibody that targets VEGF-A and a novel ligand trap, Aflibercept, sometimes known as VEGF-Trap (Lacal and Graziani, 2018). VEGF-Trap is a recombinant protein that contains the extracellular ligand binding domains of VEGFR1 and VEGFR2 attached to the Fc domain of immunoglobulin. This ligand trap is effective against the different VEGFR ligands due to the presence of both VEGFR1 and VEGFR2 ligand binding domains (Inai et al., 2004; Pandey et al., 2018). D16F7 is an antibody that specifically binds to VEGFR2 and prevents the receptor from dimerizing, therefore preventing signaling. Other anti-VEGFR2 antibodies include ramucirumab, KM1730, and KM1732, all of which prevent the binding of VEGF-A to VEGFR2 (Estrada et al., 2019; Modi and Kulkarni, 2019; Simons et al., 2016). The tyrosine kinase inhibitors developed for the VEGFRs tend to be non-specific and are inhibitory across many members of the RTK family. Examples of different classes of inhibitors include sunitinib in class 1 and sorafenib in class 2. These inhibitors can bind to other RTKs such as FGFR and platelet-derived growth factor receptor (PDGFR) (Estrada et al., 2019; Pandey et al., 2018). Thus far, some of these VEGFR inhibitors have been approved for use by the FDA. However, the side effects often seen with many of the VEGFR inhibitors do present cause for concern. The most common side effect seen is hypertension, but thromboembolism and nephrotoxicity have also been observed (Modi and Kulkarni, 2019).

1.03.7.4

Eph receptors

The largest subfamily of RTKs is the Eph receptor family with 14 known members in the human genome that are divided into two subtypes, type-A and type-B. There are nine EphA receptors which generally bind five ephrin-A ligands, and five EphB receptors which generally bind three ephrin-B ligands (Janes et al., 2014; Pasquale, 2010). The Eph receptors in the plasma membrane associate with ephrin ligands on the surface of neighboring cells. These binding events trigger bidirectional signaling-forward signaling propagated into the receptor-expressing cell and reverse signaling propagated into the ligand-expressing cell. Ephrin ligands also exist as soluble proteins released from the cell surface, which bind Eph receptors and activate forward signaling (Leung, 2004).

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The Eph receptors are unique, as they are known to form large oligomers, not just dimers (Himanen et al., 2010; Ojosnegros et al., 2017; Pasquale, 2010; Seiradake et al., 2010). It has been suggested that the degree of oligomerization controls function (Singh et al., 2018), but the exact mechanism is still under investigation. Oligomerization has been proposed to work as an “amplifier” to increase the sensitivity of the biological response to low ligand concentrations (Bray et al., 1998). Furthermore, it has been demonstrated that Eph receptor oligomerization plays a role in the remodeling of the cytoskeleton and in cell invasiveness (Salaita et al., 2010).

1.03.7.4.1

Function and dysfunction

These receptors are expressed in different cell types and are involved in many physiological processes including regulation of axon guidance, synaptic plasticity, and tissue homeostasis (Grandi et al., 2019; Saha et al., 2018). They are also associated with immune response such as immune cell activation, migration, proliferation, and adhesion. Because of this, upregulation of Eph receptors in cancer is often associated with a poor prognosis and malignancy. However, under some conditions Eph receptor kinase signaling has been shown to have anti-tumorigenic effects (Miao et al., 2009). The Eph receptors have also been found to play major roles in leukemia, Alzheimer’s disease, and have been found at lesions associated with multiple sclerosis (Martini et al., 2019; Miao et al., 2009).

1.03.7.4.2

Structure

The N-terminal domain of the Eph receptors is the ephrin-binding domain (see Figs. 1 and 2). Next, a cysteine-rich sushi domain and an EGF-like domain are followed by two fibronectin III domains. A sushi domain is a 5-strand beta sandwich that resembles a piece of nigiri sushi and is commonly found in proteins associated with adhesion. An EGF-like domain is a small domain that bears high sequence and structural homology to the EGF ligand. As with all RTKs, this is followed by the transmembrane alpha helix, and the intracellular kinase domain. After the kinase domain, the Eph receptors have a sterile alpha motif (SAM) domain. A three residue PDZ binding motif caps the C-terminus (Dravis, 2010; Saha et al., 2018).

1.03.7.4.3

Ligands

The most notable difference in Ephrins, as compared to other RTK ligands, is that they are anchored in the plasma membrane, and can transmit cell to cell signals (Darling and Lamb, 2019). Ephrins, just like the Eph receptors, can be divided into two classes, class A and class B, and generally interact with the associated class of Eph receptor (Dravis, 2010; Palmer et al., 2002). Ligand classification is based on how the ligand is anchored in the plasma membrane. Class A ephrins have a glycosylphosphatidylinositol linkage which anchors them into the plasma membrane, while class B ephrins have a hydrophobic transmembrane domain and a highly conserved cytoplasmic tail. Ephrins are responsible for transmitting the “reverse signals” introduced above. Class B ephrins, upon binding to an Eph receptor, get phosphorylated on the cytoplasmic tail which can trigger downstream signaling cascades (Dravis, 2010; Palmer et al., 2002). The extracellular domains of both classes of ephrins are fairly similar, both are globular proteins with a single glycosylation site. This domain is made up of an eight stranded open faced beta sandwich (sometimes called a beta-taco) that forms around a hydrophobic core, along with 2 alpha helices and a 3–10 helix. The beta taco contains a disulfide bond which holds two of the beta strands together, and an additional disulfide bond anchors the alpha helices (see Fig. 6). The extracellular domains are linked to the membrane via a 37 amino acid flexible linker (Toth et al., 2001). The ephrins are capable of homodimerizing with themselves. Crystal structures of ephrins have shown they can form higher order oligomers, and tetrameric ephrins have been proven to transmit signals in vitro (Darling and Lamb, 2019; Dravis, 2010; Toth et al., 2001).

1.03.7.4.4

Inhibitors

The Eph receptors and their ligands have been targeted via antibodies, small molecule inhibitors, small peptides, ligand traps, and kinase inhibitors (Darling and Lamb, 2019; Dravis, 2010; Gomez-Soler et al., 2019; Grandi et al., 2019; Leung, 2004). What is interesting is that Eph receptors are implicated in both tumorigenesis and tumor growth inhibition, depending on the receptor/ligand and the cancer type. This means that the Eph receptors have been targeted by both agonists and antagonists, depending on the desired outcome. In addition, because the Eph receptors engage in both forward and reverse signaling, inhibitors can be designed to also work directionally (Dravis, 2010; Murai and Pasquale, 2003). Such compounds may inhibit signaling in one direction while stimulating signaling in the other. For example, the proteins ephrin-B1-Fc and EphB1-Fc can work this way. In the case of ephrin-B1-Fc, an ephrin domain is conjugated to the Fc portion of an antibody. This protein can bind to EphB1, allowing Eph receptors to oligomerize and signal normally. The binding, however, prevents the binding of ephrin to EphB1 and therefore prevents the reverse signaling discussed above, because the membrane-bound ligand is unable to oligomerize and activate. The opposite effect can be achieved by conjugating the Fc domain to the solubilized extracellular domain of the Eph receptor, which can then bind to the membrane bound ligand and propagate reverse signaling while preventing forward signaling (Grandi et al., 2019). Signaling in both directions can be inhibited by using a solubilized Eph receptor extracellular domain such as sEphB4. sEphB4 is able to bind to the ligand and the EphB4 receptor, which keeps them from oligomerizing and signaling. sEphB4 is in clinical trials for solid tumors, and another soluble protein, sEphA7, is also in development (Burns et al., 2020; Saha et al., 2018).

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Inhibitors that we have not yet discussed but are commonly used for Eph receptors are short peptides. These peptides can function similarly to the aptamers designed for other receptors, but are made of amino acids instead of nucleic acids. These peptides can work in many ways. For example, the peptides SNEW peptide and TNYL peptide both inhibit angiogenesis by binding to the Eph receptor and rendering it signaling incompetent and unable to bind the ligand (Koolpe et al., 2005; Riedl and Pasquale, 2015; Saha et al., 2018). The KYL peptide binds to the receptor and acts as an agonist, allowing the receptor to become signaling competent in a bid to prevent Alzheimer’s disease progression (Saha et al., 2018). The peptide YSA and its derivatives bind to the ephrin binding pocket of EphA2 and sterically prevent ephrin from binding (Gomez-Soler et al., 2019). However, they activate EphA2 themselves, sometimes in a very potent manner. Despite being activating ligands, these peptides can be developed into anti-cancer therapeutics, as in some cases EphA2 kinase activity has been shown to suppress oncogenic processes (Miao et al., 2009). Antibodies specifically targeting the Eph receptors have also been developed (Janes et al., 2020; Leung, 2004; Saha et al., 2018; Vail et al., 2014). An anti-EphA2 antibody has shown promising results in the treatment of ovarian and breast cancer, while an antiEphB4 antibody has been shown to prevent tumor growth and angiogenesis (Janes et al., 2020; Leung, 2004; Saha et al., 2018). So far, the antibodies targeting the receptor are in various stages of clinical trials. Antibodies targeting the ligand are rare. One example is an antibody targeting ephrin-B2, which has been shown to prevent cell migration and tube formation in certain types of cancer (Saha et al., 2018). Finally, TKIs are also used to inhibit the Eph receptors (Charmsaz et al., 2017; Grandi et al., 2019; Martini et al., 2019). TKIs can only target forward signaling since the ligands do not possess kinase domains. ALW-II-41-27 is a type 2 tyrosine kinase inhibitor that shows good affinity and specificity for EphA2. ALW-II-41-27 was able to reduce activation of AKT and EphA2 in HCT15 and SW48CR cells and was shown to reduce tumor growth in mice. ALW-II-41-27 is being evaluated together with the co-therapeutic Cetuximab, which is an antibody that can target many of the RTKs (Saha et al., 2018).

1.03.7.5

Trk receptors

The three tropomyosin receptor kinase (Trk) receptors, designated A, B and C, are vital for the development of the central and peripheral nervous systems. The Trks have four activating ligands: nerve growth factor (NGF), brain-derived neurotrophin factor (BDNF), neurotrophin 3 (NT-3), and neurotrophin 4 (NT-4). The Trk receptors are often associated with “higher-order” neural functioning as they are not found in Drosophila or C. elegans (Mele and Johnson, 2020).

1.03.7.5.1

Function and dysfunction

The Trk receptors guide the development of the nervous system. They play a role in neural plasticity in both development and adulthood (Ascaño et al., 2009; Kuruvilla et al., 2004). Trks are involved in neurodegenerative diseases and cancer (Bothwell, 2016; Chao et al., 2006; Zampieri and Chao, 2006). TrkA specifically is known to play a role in pain transmission as well as behavioral aspects such as aggression and depression (Chao, 2003).

1.03.7.5.2

Structure

The Trk extracellular domain is composed of two cysteine rich regions that bookend a series of three leucine rich repeat domains. These regions are followed by two immunoglobulin domains and then the transmembrane helix and the intracellular kinase domain (Jiang et al., 2021; Wehrman et al., 2007). (see Figs. 1 and 2).

1.03.7.5.3

Ligands

Like VEGF, the neurotrophins contain a cysteine knot motif and three intra-molecular disulfide bonds (see Fig. 6). Also like VEGF, the monomers of neurotrophins dimerize in solution and interact with their cognate receptors as dimers (Rodríguez-Tébar et al., 1991). Unlike VEGF, the dimer interface between these monomers involves only electrostatic and hydrophobic interactions; there are no disulfide bonds in the interface. This allows the neurotrophins to not only homodimerize, but also occasionally form heterodimers with other neurotrophins (Treanor et al., 1995). The core of the neurotrophin monomer is made up of 2 two-stranded twisted beta sheets with a cysteine knot at one end and 3 beta hairpin loops at the other. The monomers form a parallel dimer along the beta sheets (Maness et al., 1994; Wehrman et al., 2007). The neurotrophin ligands bind to domain 5 of the Trk receptors, which is an immunoglobulin like domain (Huang and Reichardt, 2001). This family of ligands is highly selective as to what Trk receptor each will bind. This is likely due to an N terminal sequence that exhibits low sequence conservation within the family. This drives specificity because it has been shown that upon binding, the N-terminus takes on an alpha helical structure which forms important contacts with the receptor (Wlesmann et al., 1999). Neurotrophins are produced and excreted by target cells. Some of them are internalized into vesicles and trafficked to the cell body, along with its receptor (Meakin and Shooter, 1992; Zampieri and Chao, 2006).

1.03.7.5.4

Inhibitors

Trk receptors are important inhibitor targets for anti-cancer treatments as well as in the treatment of chronic pain (Patel et al., 2018; Stack et al., 2020; Ugolini et al., 2007). The most common method of targeting the Trk receptors for pain management is via an antibody that sequesters the ligands. To this end, both the B30 antibody and tanezumab have made it into clinical trials, but severe

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side effects such as osteonecrosis and the increased need for joint replacements has deterred their use (Merkouris et al., 2018; Schnitzer et al., 2019). Recently an anti-Trk receptor antibody called MNAC13 has been investigated. In both in vitro and in vivo studies, it exhibited powerful analgesic effects (Patel et al., 2018; Stack et al., 2020; Ugolini et al., 2007). There are few antibodies or aptamers developed against the Trk receptors for use in cancer treatments, as many cancer pathologies are due to Trk receptor chimeras that lack the Trk extracellular domain (Jiang et al., 2021; Merkouris et al., 2018; Ugolini et al., 2007). In such cases, an extracellular antagonist would do little good. TKIs including larotrectinib, entrectinib, cabozantinib and VMD-928 have been developed against the different Trk receptors (Wilding et al., 2020). Larotrectinib and entrectinib are both type 1 inhibitors that are effective across the Trk family, but also have off target effects and can bind to many RTK kinases (Wilding et al., 2020). So far, these inhibitors have proven effective against tumor activity. A second generation of Trk class 1 inhibitors is being designed to focus on overcoming drug resistant mutations (Wilding et al., 2020). The type 2 TKIs for Trks exhibit a higher selectivity and may inhibit only the Trk kinases. Cabozantinib is currently in clinical trials, while altiratinib has been shown to have a high affinity for TrkA, TrkB, and TrkC (Jiang et al., 2021; Wilding et al., 2020). Altiratinib created a lot of excitement when it was first discovered as it was found to cross the blood brain barrier. However, the phase 1 study has been terminated (Jiang et al., 2021). There are some allosteric tyrosine kinase inhibitors such as VMD-928, AK1830, and VM-902A (Jiang et al., 2021; Wilding et al., 2020). They bind the kinase in a location other than the catalytic domain and induce a conformational change in the ATP binding pocket through allosteric effects, resulting in kinase inhibition (Jiang et al., 2021; Wilding et al., 2020).

1.03.7.6

Insulin receptors

The insulin receptor (IR) and the insulin-like growth factor receptor (IGF–1R) are well known due to the central role IR plays in diabetes progression. These receptors are activated by three ligands, including insulin and two insulin-like growth factor (IGF) ligands. Different from most RTKs which exist in monomer-dimer equilibrium, the insulin receptors exist as disulfide-linked dimers on the cell surface (Kavran et al., 2014).

1.03.7.6.1

Function and dysfunction

Interestingly, insulin receptors are more commonly targeted for activation rather than inhibition as in diabetes they are often linked to insulin resistance. Insulin is generally responsible for glucose metabolism and cell growth (Malaguarnera and Belfiore, 2011). Furthermore, recent work that has shown that overabundance and over-activation of IR and IGF-1R can lead to the formation of many solid tumors (Kavran et al., 2014; Lapolla and Dalfrà, 2020; Malaguarnera and Belfiore, 2011).

1.03.7.6.2

Structure

IR and IGF-1R have dimeric extracellular domains composed of a and b subunits. The two a subunits are connected by disulfide bonds and the two b subunits are linked to each a subunit via a disulfide bond. The a subunit is entirely extracellular and contains the ligand binding domain, while the b subunit contains a small portion of the extracellular domain followed by the transmembrane and kinase domain (see Figs. 1 and 2) (Lou et al., 2006; Stamos et al., 2002; Ward and Garrett, 2001). The EC domains include two leucine-rich repeat domains, a cysteine-rich region, and three fibronectin-like domains. One of the fibronectin domains resides on the b subunit, while the rest of the extracellular domain is contained on the alpha subunit (Srinivas and Grunberger, 1994).

1.03.7.6.3

Ligands

Insulin is known to control metabolic homeostasis, while IGFs control growth, development and differentiation (Dupont and LeRoith, 2001). Like EGFs, insulin is a small protein,  6 kDa in size (see Fig. 6). The monomer is made up of two distinct chains, chain A and chain B that are excreted as part of a long continuous peptide, pro-insulin, and then cleaved into two parts (Weiss et al., 2000). Chain A is mostly alpha helical, while chain B is made up of an alpha helix flanked by a beta turn. The chains are held together via disulfide bonds, with contributions from the hydrophobic effect (Scapin et al., 2017; Weiss et al., 2000). Also like EGF, insulin can incite a large conformational change of the receptor upon ligand binding. Insulin binds to multiple sites on the extracellular domain of the receptor. Unlike EGF, insulin actually contacts the leucine rich domain of one IR chain in the dimer, and then crosses to the second IR chain to make contact with its fibronectin domain. IR contains an additional ligand binding site on the fibronectin domain that can bind to a second insulin (Buchanan and Revell, 2015; Lapolla and Dalfrà, 2020; Mukherjee et al., 2018; Uchikawa et al., 2019; Weiss et al., 2000). This results in a 4:2 ligand to receptor ratio. Both the A and B chains of insulin contact the leucine rich and fibronectin domains of IR, and the binding interactions are mediated by hydrophobic and electrostatic effects of the side chains (Lou et al., 2006; Scapin et al., 2017). Insulin is capable of forming dimers at concentrations above 10 6 M in solution, and often forms hexamers in the presence of zinc (Lapolla and Dalfrà, 2020; Mukherjee et al., 2018; Weiss et al., 2000). Insulin is a peptidyl hormone, i.e., it diffuses throughout the body via the bloodstream. The monomeric and dimeric forms of insulin are both easily capable of diffusing into the bloodstream, but the hexameric form diffuses poorly due to its large size. Only the monomer appears to be signaling-competent and the insulin monomers make no contact with one another in the active insulin receptor (Buchanan and Revell, 2015; Kavran et al., 2014; Uchikawa et al., 2019; Weiss et al., 2000).

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1.03.7.6.4

Inhibitors

IR and IGF-1R make for interesting targets due to their role in diabetes. While it has been shown that overexpression and overstimulation of these receptors can result in cancer, much of the existing effort has been to up-regulate insulin and increase IR stimulation in diabetics via synthetic insulin injections (Weiss et al., 2000). Insulin resistance is associated with worse cancer outcomes (Crudden and Girnita, 2020; Malaguarnera and Belfiore, 2011). There are some drugs that prevent insulin binding, either by binding to IR directly or via indirect effects. These include B11B4 which is an antibody that binds competitively to the receptor binding site, as well as B11B5 which is an antibody that binds to the IR extracellular domain and prevents ligand binding via allosteric effects. The peptide s961 binds to the IR ligand-binding domain and directly competes with insulin for the binding site (Cieniewicz et al., 2017; Dong et al., 2010; Malaguarnera and Belfiore, 2011). BMS-754807 is an ATP competitive TKI that can inhibit both IR and IGF–1R, but its binding is somewhat non-specific as it binds to the kinase domains of many other RTKs. BMS-754807 has proven effective in inhibiting cancer growth (Buchanan and Revell, 2015; Malaguarnera and Belfiore, 2011; Srinivas and Grunberger, 1994). A more specific kinase inhibitor, OSI-906, has been shown to bind to the active site of IGF-1R and to lead to tumor repression (Leiphrakpam et al., 2014; Puzanov et al., 2015; Srinivas and Grunberger, 1994). Unfortunately, due to their role in diabetes, many of the IR and IGF-1R inhibitors have somewhat severe side effects such as hyperglycemia and hyperinsulinemia. In many patients, diabetes and cancer can be co-occurring so these side effects can prove serious to fatal.

1.03.8

Challenges, alternative strategies, and outlook

1.03.8.1

Enhancing specificity

Since RTKs are ubiquitous and involved in just about all cellular functions, it is not surprising that inhibitors that are meant to target a particular RTK can have many side effects. One way to increase inhibitor specificity is to develop inhibitors that act only on pathogenic RTK mutants, but not on wild-type RTKs. It is well known, for example, that gefitinib and erlotinib are much more effective against the L858R EGFR mutant linked to non-small-cell lung cancer than against wild-type EGFR (Carey et al., 2006; Yun et al., 2007). Curiously, the mAb 806 and mAb 175 antibodies against the extracellular domain of EGFR have been found to bind to wildtype EGFR when overexpressed in cancer cells, but not to wildtype EGFR in normal cells. To explain this observation, it has been suggested that the extracellular domain of EGFR undergoes conformational change in the cancerous environment (Kumar et al., 2020). New inhibitors could be developed to target conformational changes in other receptors as well. A possible way to increase specificity towards pathogenic signaling events is to use inhibitors against cytoplasmic signaling partners, not the RTKs themselves. Such inhibitors can target particular specific downstream signaling cascades, without interfering with others. For instance, an inhibitor of the oncogenic phosphatase SHP2, which exerts control over the RAS-ERK pathway triggered by ErbB receptors, has been demonstrated to suppress oncogenic processes (Chen et al., 2016; Dance et al., 2008).

1.03.8.2

Overcoming resistance

Many tumors that overexpress RTKs eventually acquire resistance, as they are able to develop mutations that limit the efficacy of the drug. For instance, mutations in the gatekeeping residues of the kinases are known to lead to resistance. Indeed, the clinical efficacy of inhibitors in L858R EGFR non-small-cell lung cancer is severely limited due to the gatekeeper T790M mutation (Kobayashi et al., 2005). Useful inhibitors for the T790M EGFR mutant have been identified by screening an irreversible kinase inhibitor library specifically against this mutant (Zhou et al., 2009). Irreversible inhibitors can have increased potency without requiring binding in the hydrophobic pocket which is often blocked by gatekeeper mutations (Lelais et al., 2016; Michalczyk et al., 2008; Planken et al., 2017; Sohl et al., 2015). Screens for inhibitors that are specific against pathogenic RTK mutants have the potential to identify other useful therapeutics that may overcome drug resistance. Furthermore, it has been reported that conformational flexibility in the drug can allow it to bind to kinases with gatekeeper mutations (Sohl et al., 2015). An approach to overcome resistance is to use inhibitors that do not bind directly to the target RTK, but work to inhibit RTKs via different mechanisms. The examples of inhibitors discussed below either target RTK processing and trafficking, or promote RTK degradation, ultimately inhibiting RTK signaling (Burslem et al., 2018; Contessa et al., 2008; Duchesne et al., 2006). One non-traditional way of RTK inhibition is to target RTK production in cells. For instance, the use of glycosylation inhibitors leads to decreased RTK expressions and activation, as well as enhancement of radiosensitivity and chemotherapy effects (Contessa et al., 2008, 2010). Inhibition of chaperones, which are required for the correct folding of RTKs is an alternative approach that is being explored. Such inhibitors have been shown to impair RTK activity and to have anti-cancer effects (Citri et al., 2004; Wang et al., 2020a; Zsebik et al., 2006). Another viable strategy for inhibition is to promote RTK degradation (Burslem et al., 2018; Von Kleist et al., 2011; Ménard et al., 2018; Robertson et al., 2014). Typically, RTKs are uptaken into endosomes via a clathrin mediated mechanism, and then are either

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downregulated or recycled back to the plasma membrane. Endocytosis inhibitors such as Pitstop2 have been shown to promote increased degradation, and thus inhibit oncogenic RTK activity (Dutta et al., 2012; Liashkovich et al., 2015; Willox et al., 2014). Inhibitors of vesicular trafficking within the Golgi network have also been shown to decrease surface levels of various RTKs (Ohashi et al., 2016, 2018; Orcl et al., 1991).

1.03.8.3

Biased inhibitors

The development of biased RTK ligands and inhibitors is an exciting new development (Karl et al., 2020). Biased ligands can be small molecules or proteins that bind similarly to the natural ligand, but can activate preferentially only certain beneficial pathways. Alternatively, they can preferentially inhibit the activation of pathogenic pathways (Kenakin, 2011, 2019). In one example, a derivative of the c-Kit ligand, stem cell factor, was engineered to have impaired ability to promote dimerization of the c-Kit receptor (Ho et al., 2017). This ligand exhibited bias towards cell proliferation and against mast cells IL-6 secretion (Ho et al., 2017), as compared to the natural ligand. In another example, the small molecule SSR128129 binds to FGFR extracellular domains and preferentially inhibits pathways mediated by the adaptor FRS2, without affecting pathways downstream of PLCg (Bono et al., 2013; Herbert et al., 2013). Such ligands could compete with the natural ligand and alter the biological response. As biased ligands are similar to natural ligands, they are likely to have good bio-availability and no toxicity.

1.03.8.4

Inhibitor cocktails

Mixtures of RTK inhibitors with different functionalities can be used simultaneously to treat some cancers. These treatments can target several mutants of an RTK, multiple RTKs in the same family, or multiple RTK families (Janjigian et al., 2014; Kumar et al., 2020; Le et al., 2021). These cocktails can be combinations of TKIs targeting different kinases, of different antibodies targeting different RTKs, or of an antibody against one RTK and a TKI targeting another RTK (Janjigian et al., 2014; Kumar et al., 2020; Larbouret et al., 2012). As an example, the antibody cetuximab and the kinase inhibitor afatinib have been used in combination and have shown significant promise for the regression of tumors expressing EGFR with gatekeeper mutations (Janjigian et al., 2014). As discussed above, a cocktail of the antibodies cetuximab and trastuzumab has been used to target simultaneously EGFR and HER2 (Larbouret et al., 2012). Furthermore, the simultaneous targeting of VEGFR and EGFR has been shown to be beneficial for the treatment of some types of lung cancer (Le et al., 2021; Naumov et al., 2009).

1.03.9

Outlook

We expect that the utility of RTK inhibitors in the clinics will continue to grow. There is no doubt that new types of inhibitors will be developed in the years to come, as our understanding of RTK signaling mechanisms becomes more profound. Cocktails of inhibitors that have different mechanism of action, as well as biased inhibitors, will likely prove most advantageous. We are excited about the future, and we are confident that new RTK inhibitors will lead to improved quality of life for patients around the globe.

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Relevant Websites https://clinicaltrials.gov/dClinical Trials. https://www.drugs.com/dNIH Drugs. https://www.rcsb.org/dRCSB Protein Data Bank.

1.04

Cytokine Receptors

Alison McFarlane, Paul K. Fyfe, and Ignacio Moraga, Division of Cell Signalling and Immunology, School of Life Sciences, University of Dundee, Dundee, United Kingdom © 2022 Elsevier Inc. All rights reserved.

1.04.1 1.04.2 1.04.2.1 1.04.2.2 1.04.2.2.1 1.04.2.2.2 1.04.2.2.3 1.04.2.2.4 1.04.3 1.04.3.1 1.04.3.2 1.04.3.3 1.04.3.3.1 References

Introduction The class I cytokine receptor family The homodimeric class I cytokine receptors The heterodimeric class I cytokine receptors The gp130 family The IL-12 receptor family The common Beta chain receptor family The common gamma chain receptor family The class II receptor family The type I IFN receptors The type II IFN receptors The IL-10Rb and related family receptors (IFN class III - IL-28R): The IL-20 receptor cytokine sub-family

37 38 41 43 43 45 46 47 51 51 52 53 53 54

Glossary ECD Extracellular Domain GP130 Glycoprotein 130 JAK Janus Kinase kDa Kilo Dalton STAT Signal Transducer and Activator of Transcription TYK2 Tyrosine Kinase 2 bc Common beta chain gC Common Gamma Chain

1.04.1

Introduction

Cytokines are a large family of soluble ligands that control all aspects of mammalian physiology (Haan et al., 2006; Murray, 2007; Schindler et al., 2007). A major role of cytokines is to allow immune cells to communicate to each other, critically contributing to orchestrate a coordinated immune response (Haan et al., 2006; Murray, 2007; Schindler et al., 2007). Dysregulation of cytokines or cytokine-signalling pathways often leads to disease making the study of this family highly relevant for human health (Murray, 2007; Schindler et al., 2007; Foster et al., 2002; Kovanen and Leonard, 2004). Cytokines bind a cell surface receptor comprised of at least two subunits to trigger the activation of the associated Janus Kinases (JAK) (Haan et al., 2006; Yamaoka et al., 2004). JAKs in turn phosphorylate Tyrosine (Tyr) residues in the cytokine receptor intracellular domains, which then act as docking sites for Signal Transducers and Activators of Transcription (STAT) proteins (Murray, 2007; Schindler et al., 2007; Heim, 1999). Upon docking, Tyr residues on the STATs are phosphorylated by the JAKs, leading to their homo- or hetero-dimerization and translocation to the nucleus, where they bind specific sequences in the promoter regions of responsive genes and induce unique gene expression programs (Ramana et al., 2000; Ihle, 2001; Levy and Darnell, 2002). Given the limited number of JAKs (JAK1/JAK2/JAK3 plus TYK2) and STATs (STAT1/2/3/4/5/6), many cytokines share signalling components yet elicit unique responses, raising the question of how functional specificity is achieved by these ligands. Recent studies from our lab and others have highlighted that cytokine-induced signalling is more complex than initially thought and less linear in nature (Kalie et al., 2008; Piehler et al., 2012; You et al., 2016; Schreiber, 2017; Martinez-Fabregas et al., 2019, 2020; Wilmes et al., 2021). Data from different cytokine systems have started to draw a picture where intracellular trafficking dynamics of cytokine-receptor complexes and STAT binding affinities for phospho-Tyr on cytokine receptor intracellular domains (ICD) act synergistically to define signalling potency and identity. This means that the concentration of cytokine receptors and STAT proteins ultimately define the identity of the signalling responses induced by cytokines. Since receptor and STAT concentrations vary

Comprehensive Pharmacology, Volume 1

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Cytokine Receptors

across different cell types and in health vs disease conditions (Heim, 1999; Levy and Darnell, 2002), understanding how these factors ultimately regulate cytokine responses will be crucial to improve our ability to predict and manipulate cytokine behavior. Exactly how cytokines engage their receptors to initiate signalling remains controversial, with two main models still present in the literature. One model postulates that cytokine receptors exist as pre-assembled dimers on the cell surface, and that cytokine binding triggers a series of conformational changes in the dimer that initiates signalling (Livnah et al., 1999; Remy et al., 1999; Matthews et al., 2011; Brooks et al., 2014). A second competing model suggests that cytokine receptors freely diffuse as monomers in the plasma membrane, and upon cytokine stimulation they form dimers or oligomers to initiate signalling (Piehler et al., 2012; Moraga et al., 2014; Spangler et al., 2015a; Gorby et al., 2018). Many reviews already describe these two models in detail, and therefore we refer the readers to them to obtain a more detailed explanation (Spangler et al., 2015a; Gorby et al., 2018; Atanasova and Whitty, 2012). Both models however agree in that cytokine binding is the initial step for cytokine receptor activation and signalling (Haan et al., 2006). This review will focus on how cytokine receptors trigger signalling and provide flashcard-like annotations for each receptor subunit comprising the cytokine receptor family where we will highlight specific structural and biological aspects that define each receptor and contribute to their unique biological activities. To keep this review focused and contained, we will describe only cytokine receptors activating the JAK/STAT signalling pathway.

1.04.2

The class I cytokine receptor family

The class I cytokine receptor family is the largest among the cytokine receptor families (Wang et al., 2009). Receptors in this family are single-pass transmembrane proteins with an N-terminal extracellular and a C-terminal intracellular structure. The availability of multiple crystal structures for cytokine-receptor complexes has provided us with a clear understanding on the structural organization of the cytokine receptor extracellular domain (ECD), and how it interacts with cytokines (de Vos et al., 1992; Dagil et al., 2012; Chow et al., 2001; Boulanger et al., 2003a, b; Huyton et al., 2007; Wang et al., 2005; LaPorte et al., 2008; Hansen et al., 2008). Much less information is available regarding the structure and function of the transmembrane and intracellular domains of cytokine receptors. A limited number of NMR structures of the transmembrane domains (TMDs) of cytokine receptors have been solved and show an a-helical configuration (Bugge et al., 2016; Kim et al., 2007; Li et al., 2014, 2015). Studies describing the structural organization of the cytokine receptor intracellular domain (ICD) suggest that these domains are intrinsically disordered throughout their entire length (Haxholm et al., 2015). However, how these domains convey cytokine binding information into active signalling is currently not known. To be able to answer this critical question, high-resolution structures of full-length cytokine-receptor complexes are needed. An initial study started to break this technological barrier with the report of a low-resolution EM structure of the fulllength IL-6 receptor with bound JAK1 (Lupardus et al., 2011). However, no recent advances have been reported. The extracellular domain of a class I cytokine receptor is characterized by an approximately 200-amino acid residue-long modular region known as a cytokine receptor homology (CHR) domain. The CHR is comprised of two fibronectin type-III domains connected by a linker that function to recognize cytokines (Wang et al., 2009). Each FNIII domain of the CHR possesses seven b-strands that are sequentially named A, B, C, C0 , E, F, and G, where these strands form a sandwich of two antiparallel b-sheets, one with three strands (A, B, and E) and the other with four strands C, C0 , F, and G. They carry four conserved cysteine residues that are buried deep in the core of the N-terminal FNIII domain of a CHR and form two disulfide bonds that connect strands A to B and C0 to E (de Vos et al., 1992; Bagley et al., 1997). The linker between the two domains is short, rather inflexible, and performs a short turn, generating an angle between the domains. This arrangement presents two tryptophans, one from each FNIII domain, toward the incoming ligand where they form key components of the binding site. All type I cytokine receptors have four conserved cysteine residues and a Trp-Ser-X-Trp-Ser (WSxWS) motif in their CHR domains that define the family, with the exception of the growth hormone receptor, which has a homologous sequence YGeFS (Waters and Brooks, 2011). The membrane distal FNIII domain of the CHR carries the four conserved cysteines, which form two disulfide bonds (de Vos et al., 1992; Waters and Brooks, 2011). The membrane proximal FNIII domain in the CHR contains the WSxWS motif. The WSxWS motif is not required for cytokine binding; but plays a crucial role for maintaining the tertiary structure of the cytokine receptor (Dagil et al., 2012; Hilton et al., 1995; Olsen and Kragelund, 2014; Bazan, 1990a; Liongue and Ward, 2007). Upon cytokine binding this domain undergoes conformational changes required for signal activation. The class I cytokine receptor family can be subdivided into further subclasses depending on the nature and stoichiometry of the ligand-receptor complex engaged. While some members of this family can form homodimers on the surface of responsive cells (EPOR, TPOR, GHR, PRLR), the majority of receptors from this family form hetero- dimers or trimers. Additionally, members of this family can form larger oligomeric complexes - hexamers (human IL-6/IL-6Ra/gp130) (Chow et al., 2001; Stauber et al., 2006) and dodecamers (GM-CSF/GM-CSFRa/bc) (Hansen et al., 2008). In this latter subclass of hetero-dimeric or oligomeric complexes, we can find three receptor subunits that are shared by many different cytokines and create their own sub-families: gp130, common beta chain and common gamma chain. A detailed list of cytokine receptors comprising this family and the subclasses where they belong is provided in Table 1. In the next pages, we will describe specific details of each sub-class and provide annotations for each receptor subfamily, including mutations described in the literature linked to disease and current available treatments.

Table 1

The above table provides a detailed description of each cytokine receptor family and the specific details that are associated to each one, ranging from the JAKS/STATs associated to the structural information. Cytokine

JAK Identified

STAT identified

Relevant PDB Codes (Structures containing Family receptor only in Italics)

1 1 1 1 1 1 1 1 1 1 1

gc “ “ “ “ “ “ bc “ “ “

IL2-Ra IL2-Rb IL-4Ra IL-7R IL-9R IL-15Ra IL-21Ra – IL-3Ra IL-5Ra GM-CSFRa

JAK1/JAK3 JAK1/JAK3 JAK1/JAK3 JAK1/JAK3 JAK1/JAK3 JAK1/JAK3 JAK1 JAK2 JAK2 JAK2 JAK2

STAT1/STAT3/STAT5 STAT1/STAT3/STAT5 STAT1/STAT6 STAT1/STAT3/STAT5/STAT6 STAT1/STAT3/STAT5/STAT6 STAT1/STAT3/STAT5 STAT1/STAT3/STAT5/STAT6 – STAT5 STAT5 STAT3/STAT5

1Z92, 2B5I, 2ERJ, 2IU3, 3NFP, 6VWU 2B5I, 2ERJ, 3QAZ, 4GS7, 5M5E, 6E8K 1IAR, 1IRS, 3BPL, 3BPN, 3BPO, 5E4E, 6OEL, 3QB7 3DI2, 3DI3, 3UP1, 5J11, 6P50, 6P67 – 2ERS, 2Z3Q, 2Z3R, 4GS7 3TGX, 4NZD, 6PLH 1C8P, 1EGJ, 1GH7, 2GYS, 2NA8, 2NA9, 5DWU 4JZJ, 5UV8, 5UWC, 6NMY 1OBX, 1OBZ, 3QT2, 3VA2, 6H41 4NKQ, 4RS1

1 1 1 1 1 1

gp130 “ “ “ “ “

– IL-6Ra IL-11Ra IL-27Ra LIFR LIFR

JAK1/JAK2 JAK1 JAK1 JAK1/JAK2 JAK1 JAK1

– STAT2/STAT3 STAT2/STAT3 STAT1/STAT3 STAT1/STAT3 STAT1/STAT3

1BJ8, 1BQU, 3L5H, 3L5I, 3L5J 1N26, 1P9M, 2ARW, 5FUC, 7DC8, 1I1R 6O4P – 3E0G, 1PVH 3E0G

1

“

OSMR

JAK1

STAT3

–

1

“

LIFR

JAK1

STAT1/STAT3

3E0G

1 1 1 1

“ “ IL-12b1 IL-12b2

CNTFRa CNTFRa IL-12b2 IL-23R

IL-2 1L-2, IL-15 IL-4 IL-7 IL-9 IL-15 IL-21 – IL-3 IL-5 GM-CSF (granulocyte-macrophage colony stimulating factor) – IL-6 IL-11 IL-27 LIF OSM (Oncostatin M) OSM (Oncostatin M) CT-1 (Cardiotrophin) CNTF CLC IL-12 IL-23

JAK1 JAK1 JAK1/JAK2/TYK2 JAK1/JAK2/TYK2

STAT3 STAT3 STAT4 STAT3/STAT4

1UC6 1UC6 – 5MZV (Continued)

Cytokine Receptors

Class Receptor Family Cytokine Receptor

39

40

The above table provides a detailed description of each cytokine receptor family and the specific details that are associated to each one, ranging from the JAKS/STATs associated to the structural information.dcont'd

Class Receptor Family Cytokine Receptor

Cytokine

JAK Identified

STAT identified

1

EPO-R

EPO-R (homodimer) Erythropoeitin

JAK2

STAT5

1

GHR

GH

JAK2

STAT5

1

PRLR

GHR (homodimer) PRLR (homodimer)

Prolactin

JAK2

STAT5

1 1 2

TPOR LEPR IL-10Rb

TPOR (homodimer) LEPR (homodimer) IL-10a

Thrombopoietin Leptin IL-10

JAK2 JAK2 JAK1/TYK2

STAT5 STAT3/STAT5 STAT1/STAT3

2 2 2 2 2

IL-20Rb IL-10Rb “ IFNAR1

IL-20Ra IL-20Ra IL-22Ra1 IL-20Ra IFNAR2

IL-19 IL-20 IL-22 IL-26 Interferon - (13 isoforms), a, b, 3, d, k, s, 2, u, z

JAK1 JAK1 JAK1/TYK2 JAK1/TYK2 JAK1/TYK2

STAT3 STAT3 STAT3(STAT1/STAT5) STAT1/STAT3 STAT1/STAT2

2

IFNgR1

IFNgR2

Interferon-g

JAK1

STAT1

2

IL-10Rb

IFNLR (IL-28R)

Interferon-l

JAK1/TYK2

STAT1/STAT2(STAT3–5)

Relevant PDB Codes (Structures containing Family receptor only in Italics) 1CN4, 1EBA, 1EBP, 1EER, 1ERN, 2JIX, 2MV6, 4Y5V, 4Y5X, 4Y5Y, 6E2Q, 6MOE, 6MOV, 6MOH, 6MOI, 6MOJ, 6MOK, 6MOL 1A22, 1AXI, 1HWG, 1HWH, 1KF9, 2AEW, 3HHR, 5OEK, 5OHD 1BP3, 2LFG, 2N7I, 3D48, 3MZG, 3N06, 3N0P, 3NCB, 3NCC, 3NCE, 3NCF, 4I18 – 3V6O, 6E2P 3LQM, 5T5W, 1Y6K, 5IXI, 1Y6N, 1LQS, 1Y6M, 1J7V, 6X93 4DOH, 6DF3 4DOH, 6DF3 3LQM, 5T5W, 3DLQ, 6DF3,3DGC 3LQM, 5T5W 3S98, 4PO6, 3SE3, 3SE4, 1N6U, 1N6V, 2HYM, 2KZ1, 2LAG, 3S8W, 3S9D 1FYH, 1JRH, 1FG9, 6E3K, 6E3L, 5EH1 3LQM, 5T5W, 5L04, 3OG6, 3OG4, 5IXI, 5IXD

Cytokine Receptors

Table 1

Cytokine Receptors 1.04.2.1

41

The homodimeric class I cytokine receptors

The homodimeric class I cytokine receptor family is comprised of the growth hormone receptor (GHR) (de Vos et al., 1992), erythropoietin receptor (EPOR) (Krantz, 1991; Bazan, 1990b), prolactin receptor (PRLR) (Freeman et al., 2000), thrombopoietin receptor (TPOR) (de Sauvage et al., 1994; Bartley et al., 1994) and leptin receptor (LEPR) (Fong et al., 1998), which bind growth hormone (GH), erythropoietin (EPO), prolactin (PRL), thrombopoietin (TPO) and leptin (LEP) respectively. This family of cytokine receptors exerts a variety of hormone-like functions which are mainly related with growth, differentiation and metabolism of cells. GHR stimulates growth, EPOR stimulates the production and maintenance of red blood cells, PRLR drives breast growth and milk production, TPOR regulates the production of platelets and LEPR regulates energy balance and hunger (de Vos et al., 1992; Krantz, 1991; Bazan, 1990b; Freeman et al., 2000; de Sauvage et al., 1994; Bartley et al., 1994; Fong et al., 1998). All these activities are achieved by activating a shared JAK2/STAT5 signalling pathway. While GHR, EPOR and PRLR use a single CHR to bind their ligands, TPOR and LEPR use two CHRs. To initiate signalling, two copies of a given receptor are assembled by its cognate cytokine in a stepwise manner. The ligand interacts first with a receptor through a high affinity site-1 binding interface and subsequently with a second receptor via a low affinity site-2 binding interface. This unique receptor assembly kinetics results in bell-shaped signalling profiles by receptors in this family. At low concentrations, cytokines in this family can bind to site-1 on empty receptors and will then quickly locate free receptors to form the signalling active dimer. At high concentrations, however, all receptors will have ligands bound via their high affinity site-1 binding site, and therefore there will be no available receptors to then form the active dimer (Wells, 1996). GHR – GHR is abundantly expressed across a wide range of cell subsets (Derfalvi et al., 1998). The mature human GHR (71.5 kDa) contains 638 amino acids and consists of an extracellular domain of 246 amino acids, a membrane-spanning region of 24 amino acids, and a cytoplasmic domain of 350 amino acids. Two copies of this receptor are bound by one molecule of growth hormone in a canonical site-1/site-2 binding process to drive activation of the JAK2 and STAT5 signalling pathway (de Vos et al., 1992; Brooks and Waters, 2010). Additional pathways including the PI3K and MAPK pathways are also activated by this receptor and contribute to shape its activity (Brooks and Waters, 2010). Mutations in the GHR gene cause Laron syndrome. This syndrome is characterized by short stature, obesity and other symptoms affecting multiple systems within the body (Amselem et al., 1989). More than 70 mutations in the GHR gene have been described. Most mutations localize in the extracellular domain of the receptor and prevent ligand binding and hence preclude the activation of signalling. Few mutations have been found in the intracellular domain but those identified also diminish the ability of this receptor to transmit signalling responses (Savage et al., 1999; Dos Santos et al., 2004; Jorge and Arnhold, 2009). The inability of cells to respond to growth hormone disrupts the normal growth of many different tissues leading to disease. Antagonists: Peguisomant – This growth hormone antagonist is used to treat clinical indications presenting with excess growth hormone production such as acromegaly. This antagonist was engineered by mutating Gly120 to Lys in the growth hormone protein. It binds GHR with high affinity on its site-1 interface but cannot recruit a second molecule of GHR via its site-2 interface, thus blocking signalling by endogenous growth hormone (Narasimhan, 1978). Agonists: Somatropin – Recombinant growth hormone used to treat growth failure in children and adults who lack natural growth hormone in a daily dose (Reh and Geffner, 2010). More recently, a long-lived growth hormone called Somatrogon has shown promising effects in phase 3 clinical trials in a one dose a week regime (Cara et al., 2020). However, none of these agonists can rescue lack of growth hormone signalling resulting from loss-of-function mutations in GHR. New drugs targeting truncated or mutated receptors are needed to treat these disorders. EPOR – EPOR is normally expressed on erythrocytic progenitors and precursors in bone marrow (Winter et al., 1996). The mature human EPOR (55 kDa) contains 508 amino acids and consists of an extracellular domain of 226 amino acids, a membrane-spanning region of 23 amino acids, and a cytoplasmic domain of 235 amino acids. Two copies of EPOR are bound by one molecule of EPO in a canonical site-1/site-2 manner (Livnah et al., 1999). This drives activation of the JAK2/STAT5 signalling pathway. Other STAT and non-STAT pathways have been described to be engaged by EPO and contribute to fine-tune its responses (Constantinescu et al., 2003). Mutations in the EPOR gene cause familial erythrocytosis. This is an inherited condition that leads to increased number of red blood cells and risk of blood clots. More than 16 different mutations in the EPOR gene have been characterized (Al-Sheikh et al., 2008; Constantinescu et al., 1999). The majority of these mutations localize in the intracellular domain and result in a truncated EPOR protein (Al-Sheikh et al., 2008; de la Chapelle et al., 1993; Watowich, 2011). This truncated EPOR evades regulation by negative feedback mechanisms leading to strong and long-lasting signalling. Due to the critical role that EPOR plays, loss of function mutations are not compatible with life and therefore are not found (Al-Sheikh et al., 2008; de la Chapelle et al., 1993; Watowich, 2011). Antagonists: To date there is not a clinical approved antagonist of EPOR signalling. However, the literature has reported different protein scaffolds designed to block binding of EPO to its receptor. These include peptides, antibodies and EPO site-2 mutants (Johnson et al., 1998; Yasuda et al., 2015; Livnah et al., 1998; Elliott et al., 1996; Zhang et al., 2009). Agonists: Epogen - Recombinant erythropoietin (Brinks et al., 2011); Darbepoetin – Engineered erythropoietin with increased glycosylation and longer half-life (Powell and Gurk-Turner, 2002); Mircera – Pegylated erythropoietin with long half-life (Walrafen et al., 2005); Peginesatide – a synthetic dimeric peptide able to induce EPOR dimerization and signalling (Schmid, 2013). All these agonists are used to treat anemia resulting from chronic renal failure and chemotherapy. Non-clinical approved EPOR agonists include small molecules and antibodies (Liu et al., 2007; Lacy et al., 2008; Wrighton et al., 1996; Bugelski et al., 2008; PérezRuixo et al., 2009; Sathyanarayana et al., 2009).

42

Cytokine Receptors

PRLR – PRLR is expressed in a wide range of cell subsets (Sakai et al., 1975; Nagano and Kelly, 1994; Brown et al., 2010; Kokay et al., 2018). The mature human PRLR (69.5 kDa) contains 622 amino acids and consists of an extracellular domain of 210 amino acids, a membrane-spanning region of 24 amino acids, and a cytoplasmic domain of 364 amino acids. Two copies of PRLR are recruited by PRL via a site-1/site-2 binding mechanism to drive JAK2/STAT5 activation (Clevenger, 2003; Clevenger et al., 2009). Alternative pathways are engaged by this receptor to fine tune its activities (Clevenger, 2003; Clevenger et al., 2009). Mutations in the PRLR gene have been associated with fibroadenomas of the breast and hyperprolactinemia (Gorvin et al., 2019). The I146L mutation in the extracellular domain of PRLR was found in patients with multiple fibroadenomas and resulted in a constitutively active receptor and increased signalling in breast tissue (Bogorad et al., 2008). This mutation has also been described as a SNP and occurs in almost 2.5% of the European/American population. Loss-of-function mutations in PRLR cause hyperprolactinemia (Newey et al., 2013). These mutations localize in the extracellular domain of the receptor and disrupt ligand binding, resulting in blocked activation of JAK2/STAT5. Some of these mutations are: H212R, R171X, P269L (Newey et al., 2013; Kobayashi et al., 2018). Antagonists: Currently, there are no clinically approved PRLR antagonists. However, several antagonists have been reported in the literature for treatment of breast and prostate cancers, including LFA102, a monoclonal antibody targeting PRLR and blocking PRL binding (Agarwal et al., 2016), and the delta1-9-G129R-PRL mutant, which binds PRLR with high affinity in its site-1 interface but cannot recruit a second PRLR molecule via its site-2 interface (Rouet et al., 2010). Agonists: There are no clinically approved PRL agonists. However, several clinical trials are testing recombinant PRL to induce lactation in adoptive mothers and as a contraceptive method (Powe et al., 2010). TPOR – TPOR is expressed in megakaryocytic progenitors and precursor cells as well as in hematopoietic stem cells (Vigon et al., 1992). The mature human TPOR (71.2 kDa) contains 635 amino acids and consists of an extracellular domain of 466 amino acids, a membrane-spanning region of 22 amino acids, and a cytoplasmic domain of 122 amino acids. The extracellular portion of TPOR is comprised of two CHR domains, although only the membrane distal CHR is used for TPO binding (Morris et al., 2018). Two copies of TPOR are recruited by one molecule of TPO via a site-1/site-2 interface to trigger activation of the JAK2/STAT5 signalling pathway (Varghese et al., 2017). Other STAT and non-STAT pathways are activated by this receptor, which contribute to fine-tune its activity (Varghese et al., 2017). Three main disorders result from mutations altering the activity of TPOR - essential thrombocythemia, primary myelofibrosis and congenital amegakaryocytic thrombocytopenia (Chaligne et al., 2008; Ding et al., 2009; Germeshausen et al., 2006; Pikman et al., 2006; Tefferi, 2010). While the first two syndromes result from gain-of-function mutations, the latter is caused by loss-of-function mutations in the TPOR gene. Essential thrombocythemia is characterized by an increased number of platelets in the blood, causing increased clotting (Ding et al., 2009; Germeshausen et al., 2006). This syndrome is caused by the TPOR W515X mutations, with X usually being a leucine (Chaligne et al., 2008). Familial essential thrombocythemia on the other hand is caused by a TPOR S505N mutation (Ding et al., 2009). In both instances, TPOR is constitutively active leading to abnormal signalling. Primary myelofibrosis is characterized by scar tissue (fibrosis) in the bone marrow, leading to abnormal blood cell production (Pikman et al., 2006; Tefferi, 2010). Here again, the most common mutation site associated with this disorder is W515. Currently, it is not known how similar mutations in TPOR lead to completely different disorders. Congenital amegakaryocytic thrombocytopenia is a rare condition characterized by low numbers of megakaryocytes and platelets (Germeshausen et al., 2006). Antagonists: There are no current TPOR antagonist therapies available. However, several small molecules, peptides and antibodies antagonists are being developed to treat conditions with increase TPOR signalling (Wang et al., 2016). However, in these instances the most accepted treatment is blocking JAK2 activity with small molecule inhibitors (Wang et al., 2016). Agonists: Several TPOR agonists are clinically approved to increase the number of platelets in chronic idiopathic thrombocytopenia, severe aplastic anemia and hepatitis C patients undergoing treatment with interferon-ribavirin (Rodeghiero and Carli, 2017). They include eTPO recombinant TPO; Romiplostim – a FC-peptide fusion protein that binds and activates TPOR; Eltrombopag, Lusutrombopag and Avatrombopag – small molecules that drive TPOR dimerization and signalling (Rodeghiero and Carli, 2017). LEPR– LEPR is expressed in a wide range of cell subsets (Elmquist et al., 1998; Mercer et al., 1996; Schwartz et al., 1996; Burguera et al., 2000). The mature human LEPR (132.5 kDa) contains 1165 amino acids and consists of an extracellular domain of 818 amino acids, a membrane-spanning region of 23 amino acids, and a cytoplasmic domain of 303 amino acids. As in TPOR, LEPR has two CHR domains. However, LEPR has an additional N-terminal Ig domain (NTD), connecting the two CHR domains, and two further membrane proximal FNIII domains (Morris et al., 2018). Homology-based structural analysis and mutagenesis studies have highlighted three distinct binding sites on Leptin that contribute to recruit LEPR and form a signalling active complex (Peelman et al., 2004). Site-2 interacts with the CHR2 of LEPR with high affinity, while site-3 binds the Ig-like domain connecting the CHR1 and CHR2 with low affinity (Peelman et al., 2004). However, the role that site-1 plays in receptor dimerization and signalling remains controversial due to the lack of robust mutagenesis and functional studies that describe its importance (Peelman et al., 2004). Upon complex formation, LEPR triggers the activation of JAK2/STAT5/STAT3 pathway (Morris et al., 2018). Additional pathways are also engaged by LEPR and contribute to modulate its responses (Bjorbaek et al., 1997; Chua et al., 1996; Ghilardi et al., 1996; Vaisse et al., 1996). More than 18 different mutations have been found in the LEPR gene and result in leptin receptor deficiency (Clement et al., 1998; Kimber et al., 2008; Lee, 2009; Wasim et al., 2016). These are associated with disorders such as excessive hunger, weight gain and the reduced production of hormones that direct sexual development. Most of the mutations found in this gene result in impaired membrane traffic of the receptor leading to decreased signalling (Clement et al., 1998; Kimber et al., 2008; Lee, 2009; Wasim et al., 2016). Antagonists: Currently, there are no clinically approved LEPR antagonists. However, different antagonists using Leptin mutants, antibodies and peptides targeting LEPR are being developed (Fiedor and Gregoraszczuk, 2017). Agonists: Recombinant leptin – used to treat patients with congenital defect in Leptin production (Heymsfield et al., 1999). REGN4461 – Agonistic LEPR antibody currently in phase 2 trials.

Cytokine Receptors 1.04.2.2

43

The heterodimeric class I cytokine receptors

The heterodimeric class I cytokine receptors family is the largest family among cytokine receptors activating the JAK/STAT pathway and controls a vast array of cellular responses (Bazan, 1990a; Cosman, 1993). This family can be further subdivided into three subfamilies on the basis of the ability of family members to form complexes with one of three different types of common receptor components - gp130, common beta chain (bc) or common gamma chain (gc). Here, we will describe specific features of each subclass within this large family and provide summaries for each family member.

1.04.2.2.1

The gp130 family

The gp130 family of receptors is comprised of gp130, OSMR, LIFR, IL-6Ra, IL-11Ra, and CNTFRa subunits (Hibi et al., 1990; Heinrich et al., 1998; Layton et al., 1992; Yamasaki et al., 1988; Van Leuven et al., 1996; Man et al., 2003). These receptors are engaged in different heterodimeric complexes by the cytokines IL-6, IL-11, LIF, CNTF, OSM, CT-1 and CLF to activate signalling. All these receptor complexes share gp130 as a component critical for signal transduction and activation of the JAK1/STAT3 signalling pathway (Murakami et al., 1993; Lütticken et al., 1994; Boulton et al., 1994; Stahl et al., 1995; Narazaki et al., 1994; Zhong et al., 1994). In addition to JAK1/STAT3, the gp130 family of cytokines can also activate MAPK and PI3K pathways to fine tune their responses (Daeipour et al., 1993; Al-Khalili et al., 2006; Nicolas et al., 2012). Uncontrolled activation of the JAK/STAT, MAPK and PI3K/Akt pathways can lead to serious pathologies such as cancer, multiple sclerosis, rheumatoid arthritis, diabetes, anemia, inflammatory bowel disease and Chron’s disease (Nicolas et al., 2012; Gao et al., 2012; Fleischmann et al., 2012; Timofeeva et al., 2007; Johnson et al., 1996; Khorami et al., 2015; Kotelnikova et al., 2019). Signalling is initiated by the stepwise recruitment of the receptor components by the specific cytokine. However, the stoichiometry of the complexes formed by the different cytokines of this family differ significantly ranging from trimers to tetramers and hexamers (Boulanger et al., 2003b; Ward et al., 1994; Paonessa et al., 1995). In general terms, an alpha chain, with specificity for their respective cytokine, binds in a first step the ligand in a site-1 interface with high affinity and presents it to a second receptor chain, which binds the cytokine via a site-2 interface with low affinity (Wang et al., 2009). Since the alpha chains in this trimeric complex lack intracellular domains capable of activating signalling, a third receptor component is recruited to the complex via a site-3 interface to activate signalling. This general mechanism applies to IL-6, IL-11, CNTF, CLF and CT-1, but not for LIF and OSM, which form complexes with LIFR/gp130 and OSMR/gp130 respectively to initiate signalling (Wang et al., 2009; Grotzinger et al., 1997). gp130 – gp130 is the founding member of this family. It is ubiquitously expressed and regulates a broad spectrum of cellular responses (Xu et al., 2010). The mature human gp130 (103 kDa) contains 896 amino acids and consists of an extracellular domain of 597 amino acids, a membrane-spanning region of 22 amino acids, and a cytoplasmic domain of 277 amino acids (Xu et al., 2010; Kamimura et al., 2017). The gp130 extracellular domain comprises a N-terminal Ig-like domain, one CHR domain and three membrane proximal FNIII domains all required for signal transduction (Wang et al., 2009; Xu et al., 2010). gp130 possesses two cytokine binding sites localized to its CHR and Ig-like domains that allows it to form complexes with different stoichiometries ranging from trimeric complexes (LIF, OSM) to tetrameric complexes (CNTF, CT-1, CLC) and hexameric complexes (IL-6, IL-11) (Wang et al., 2009; Xu et al., 2010). Upon complex formation, gp130 triggers the activation of the JAK1/STAT3/STAT1 pathway (Heinrich et al., 1998). Additional pathways are also engaged by gp130 and contribute to modulate its responses (Heinrich et al., 1998). Several mutations have been found in IL6ST altering its activities and causing disease. Loss-of-function mutations localize in the gp130 extracellular domain (R281Q, N404Y, P498L) and cause severe immune defects (Schwerd et al., 2017; Tala et al., 2019). Rather than blocking ligand binding, these mutations tend to impair the transmission of the binding event to the intracellular domain of gp130 by a mechanism not yet described. This results in preferential inhibition of subsets of cytokines within the family by each mutation and heterogenous phenotypes. Gain-of-function mutations result from in frame somatic deletions in the extracellular domain of gp130 and have been described to cause benign hepatocellular adenomas (Rebouissou et al., 2009). These deletions trap gp130 in intracellular compartments where it triggers signalling in a ligand-independent manner. Antagonists: There are no current clinically approved gp130 antagonists. However, Olamkicept, a soluble gp130Fc construct, is currently in clinical trials to block IL-6 activities in inflammatory disorders (Schreiber et al., 2021). Agonists: Signalling downstream of gp130 is associated with inflammation, so currently there are no attempts to translate gp130 agonists to the clinic. However, agonistic gp130 antibodies and engineered IL-6 variants have been described and could be used to fine tune gp130 signalling and activities in the future (Martinez-Fabregas et al., 2019; Frasca et al., 1999; Gu et al., 2000; Kim et al., 2020). LIFR – LIFR is expressed at low levels by various epithelial, hematopoietic and mesenchymal cell types in adult tissues and plays key roles in embryogenesis (Fletcher et al., 1990; Leary et al., 1990). The mature human LIFR (123.7 kDa) contains 1097 amino acids and consists of an extracellular domain of 833 amino acids, a membrane-spanning region of 24 amino acids, and a cytoplasmic domain of 238 amino acids (Gearing et al., 1991). The LIFR extracellular domain is further subdivided into two NTerminal CHR domains connected by an Ig-like domain, followed by three membrane-proximal FNIII domains (Gearing et al., 1991; Davis and Pennypacker, 2018). LIFR binds LIF via a high affinity site-I interface localized in its Ig-like and most membrane-proximal CHR domains (Gearing et al., 1991). Upon recruitment of gp130 to the complex via a low affinity site-2 interface, LIF triggers activation of the JAK1/STAT3/STAT1 pathway. Additional pathways are also engaged by this complex and contribute to modulate LIF responses (Boulton et al., 1994; Oh et al., 1998). At least 27 mutations in the LIFR gene have been described and cause Stuve-Wiedemann syndrome (Dagoneau et al., 2004; Mikelonis et al., 2014; Tomida, 2000). This condition is characterized by abnormally curved (bowed) legs, breathing problems, and episodes of dangerously high body temperature (hyperthermia). Most of the mutations result in loss of protein, while other mutations produce truncated proteins that are unable

44

Cytokine Receptors

to function normally (Dagoneau et al., 2004; Mikelonis et al., 2014; Tomida, 2000). Antagonists: There are no current clinically approved LIFR antagonists. However, there are several reports in the literature describing neutralizing antibodies (e.g. MSC-1) (Hyman et al., 2018; Wang et al., 2019) or engineered LIF dominant-negative variants, that could fill this void if the right application is found (Hyman et al., 2018). Agonists: Currently there are no LIFR agonists approved for clinical use. However recombinant LIF has been used to treat neurodegenerative diseases, such as MS (Slaets et al., 2010), and it is currently in phase 2 clinical trials to assist in embryo implantation (Salleh and Giribabu, 2014). OSMR – OSMR is highly expressed by various non-hematopoietic mesenchymal cells as well as hepatocytes, mesothelial cells, glial cells and epithelial cells from several organs (Dillon et al., 2004). The mature human OSMR (110.5 kDa) contains 979 amino acids and consists of an extracellular domain of 740 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 217 amino acids (Mosley et al., 1996). The OSMR extracellular domain is further subdivided into one N-terminal fibronectin domain and one CHR domain followed by three membrane-proximal FNIII domains (Mosley et al., 1996). Experimental evidence suggests that OSMR binds OSM via a high affinity site-1 interface localized in CHR domains, although no crystal structure of the OSM-receptor complex is available to confirm this (Mosley et al., 1996). In a second step, gp130 is recruited to the complex via a low affinity site-2 interface, to trigger activation of the JAK1/STAT3/STAT1 pathway (Jones and Jenkins, 2018). Additional pathways are also engaged by this complex and contribute to modulate OSM responses. OSM can also form a complex comprising gp130 and LIFR subunits to drive signalling (Walker et al., 2010). Whether the two complexes engaged by OSM drive differential responses is not clear at the moment. At least 13 mutations in the OSMR gene have been described (Arita et al., 2008). These mutations cause primary localized cutaneous amyloidosis (PLCA) (Arita et al., 2008), characterized by an accumulation of amyloids clumps in the skin. The observed mutations (e.g. G618A and I691T) preferentially map to the two most membrane-proximal FNIII domains, which are required to form the receptor heterodimer and drive signalling (Arita et al., 2008). These mutations cause a decrease in signalling by the OSMR receptor complex, which it is believed to lead to apoptosis of skin cells. Antagonists: There are no clinically approved OSMR antagonists. However, there are several neutralizing antibodies or engineered OSM variants described in the literature (Nguyen et al., 2019; Kucia-Tran et al., 2018). Agonists: Currently, there are no OSMR agonists approved for clinical use. Recombinant OSM has been shown to confer neuroprotection (Guo et al., 2015). IL-6Ra – IL-6Ra is expressed in a limited number of cell subsets, including macrophages, neutrophils, T-cells, podocytes, and hepatocytes (Yamasaki et al., 1988). The mature human IL-6Ra (51.5 kDa) contains 468 amino acids and consists of an extracellular domain of 365 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 81 amino acids (Yamasaki et al., 1988). IL-6Ra extracellular domain is comprised of one N-terminal Ig-like domain, one CHR and a long stalk region (22 amino acids), lacking secondary structure, that connects the CHR domain with the transmembrane region is required for signalling (Boulanger et al., 2003b; Baran et al., 2013). IL-6Ra binds IL-6 via a high affinity site-1 interface and presents it to gp130 which then binds to form a complex via a site-2 interface (Boulanger et al., 2003b). The IL-6Ra intracellular domain does not interact with JAKs and thus requires two molecules of gp130 to initiate signalling (Boulanger et al., 2003b). Two hetero dimeric IL-6Ra/gp130 complexes come together forming a site-3 interaction on gp130 to form an active hexameric IL-6 receptor complex (2 IL-6/ 2 IL-6Ra/2  gp130). This hexameric complex can then drive activation of the JAK1/STAT1/STAT3 signalling pathway (Boulanger et al., 2003b; Spencer et al., 2019). This is known as IL-6 classical signalling. Proteases of the ADAM family can recognize and cleave the IL-6Ra stalk region, releasing a soluble form of IL-6Ra from the plasma membrane (Baran et al., 2013; Jones et al., 2001). Soluble IL-6Ra can bind IL-6 to form a heterodimeric complex that can engage gp130 on the surface of responsive cells. This complex is capable of signalling and amplifies pro-inflammatory signals. This mechanism of signalling is known as IL-6 trans-signalling (Rose-John and Heinrich, 1994; Schumacher et al., 2015). Several mutations have been found in the IL-6RA gene that contribute to abnormal inflammation, immunodeficiencies and atypical dermatitis. A group of these mutations (I279N, H280P) localize in the membrane proximal FNIII domain and prevent gp130 binding to the complex disrupting signalling. A second group localize in the stalk region, increasing the soluble form of IL-6Ra and leading to excess trans-signalling (Spencer et al., 2019). Antagonists: Tocilizumab and Sarilumab are antibodies that bind IL-6Ra and block binding of IL-6 (Lescure et al., 2021; Guaraldi et al., 2020). Tocilizumab is indicated for the treatment of rheumatoid arthritis, giant cell arthritis, interstitial lung disease, cytokine release syndrome and COVID-19 (Guaraldi et al., 2020). Sarilumab is indicated for rheumatoid arthritis and COVID-19 (Lescure et al., 2021). ALX-0061 is a bispecific Nanobody with a high affinity and potency for IL-6Ra, combined with an extended half-life by targeting human serum albumin (Van Roy et al., 2015). Currently ALX-0061 is in phase 2 clinical studies. Agonists: Currently there are no IL-6Ra agonists approved for clinical use. IL-11Ra – IL-11Ra is expressed on a variety of different cell types and tissues, including intestine, spleen, heart, and lung (Cherel et al., 1996). The mature human IL-11Ra (45.2 kDa) contains 422 amino acids and consists of an extracellular domain of 370 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 30 amino acids (Cherel et al., 1996). The IL-11Ra extracellular region consists of an N-terminal Ig-like domain, a CHR domain and a membrane proximal stalk domain (55 amino acids), with no secondary structure (Lokau and Garbers, 2018; Metcalfe et al., 2020). IL-11Ra binds IL-11 with high affinity via a site-1 interface and presents it to gp130 to form a heterodimeric complex via a site-2 interface (Metcalfe et al., 2020). Since IL-11Ra lacks a signalling active intracellular domain, two IL-11Ra/gp130 heterodimeric complexes need to be formed via a site-3 interface in gp130 to activate the JAK1/STAT1/STAT3 signalling pathway (Metcalfe et al., 2020). Similar to the IL-6 system, this is known as IL-11 classical signalling. Following the parallelism with IL-6Ra, IL-11Ra can also be cleaved by ADAM proteases, generating a soluble form that binds IL-11 and amplifies responses to this cytokine (Lokau et al., 2016). This is known as IL-11 trans-signalling. Several mutations have been described in the IL-11Ra gene, which cause Crouzon-like craniosynostosis syndrome (Nieminen et al., 2011). The mutations are distributed over the entire extracellular part of IL-11Ra, but they are

Cytokine Receptors

45

concentrated within the CHR domain, and prevent the transport of the receptor to the cell surface. Antagonists: There are no clinically approved IL-11Ra antagonists. However, there are several neutralizing antibodies described in the literature (Winship et al., 2016; Underhill-Day et al., 2003; Barton et al., 2000). Agonists: Currently, there are no IL-11Ra agonists approved for clinical use. CNTFRa – CNTFRa is principally expressed within the nervous system and skeletal muscle (Davis et al., 1991; Ip et al., 1992). The mature human CNTFRa (40.6 kDa) contains 372 amino acids and consists of an extracellular domain of 342 amino acids and a pro-peptide that it is removed in its mature form of 29 amino acids (Pennica et al., 1996). The CNTFRa extracellular region consists of an N-terminal Ig-like domain and a CHR domain. CNTFRa is anchored to the cell membrane through a glycosylphosphatidylinositol linkage (Man et al., 2003). CNTFRa binds with high affinity CNTF and CLC via a site-1 interface and presents it to gp130 and LIFR to form a heterotrimeric complex via a site-2 and a site-3 interface (Man et al., 2003). This leads to the activation of the JAK1/STAT3 signalling pathway (Barnabe-Heider et al., 2005). CNTFRa is also expressed in a soluble form, where interacts with its ligands and amplifies their activities (Dembic, 2015). No mutations of the CNTFR gene have been reported as being associated with disease. Antagonists: There are no clinically approved CNTFRa antagonists. Agonists: Currently there are no CNTFRa agonists approved for clinical use. However, CNTF agonists and antagonists have been described in the literature and used to understand CNTFR biology (Kim et al., 2020; Di Marco et al., 1996).

1.04.2.2.2

The IL-12 receptor family

The IL-12 receptor family is related to the gp130 receptor family, and in some instances is considered as a subfamily of the latter. The IL-12 and gp130 receptor family are also referred to as the tall receptor cytokine family due to the large extracellular domains of the different receptors comprising these families (Wang et al., 2009). The IL-12 receptor family is comprised of IL-12Rb1, IL-12Rb2, IL23R and IL-27R, which are used by IL-12, IL-23, IL-27 and IL-35 to drive a vast array of immuno-modulatory activities. A unique feature of this family is that all its cytokines are heterodimers (Vignali and Kuchroo, 2012). IL-27 and IL-35 both use gp130 as part of their signalling complex, which makes the differentiation of these two families complicated (Tait Wojno et al., 2019). This family exhibits both anti-inflammatory and pro-inflammatory responses via the activation of the JAK1/JAK2/TYK2/STAT1/STAT3/STAT4 signalling pathways. Signalling is initiated by the binding of the heterodimeric cytokine to a receptor subunit via a high affinity site-2 binding interface, followed by recruitment of a second receptor subunit via a site-3 interface with low affinity (Tait Wojno et al., 2019). IL-12Rb1 – IL-12Rb1 is expressed by multiple leukocyte lineages (Robinson et al., 2010). The mature human IL-12Rb1 (73.1 kDa) contains 662 amino acids and consists of an extracellular domain of 545 amino acids, a membrane-spanning region of 24 amino acids, and a cytoplasmic domain of 91 amino acids (Chua et al., 1994; van de Vosse et al., 2013). The IL-12Rb1 extracellular domain consists of N-terminal CHR domain and three membrane proximal FNIII domains. IL-12Rb1 binds with low affinity the p40 subunit in IL-12 and IL-23 to drive activation of STAT4 by IL-12 and STAT3/STAT4 by IL-23 (Presky et al., 1998; Glassman et al., 2021). Several mutations have been described in the IL-12RB1 gene and cause immunodeficiency and susceptibility to mycobacterial disease (de Jong et al., 1998). The majority of these mutations result in lack of IL-12Rb1 expression. Antagonists: There are no clinically approved IL-12Rb1 antagonists. However, there are several clinically approved antibodies that block signalling by IL-23 and IL-12 by binding the p40 subunit (Dasgupta et al., 2008). Agonists: Currently there are no IL-12Rb1 agonists approved for clinical use. IL-12 was used in the past to boost anti-tumor responses, but its usage was discontinued due to severe associated toxicity (Weiss et al., 2007). IL-12Rb2 – While the expression of IL-12Rb1 is constitutive in leukocytes, IL-12Rb2 is upregulated by IFN-gamma (IFNg), thus limiting its availability (Wu et al., 2000). The mature human IL-12Rb2 (97.1 kDa) contains 862 amino acids and consists of an extracellular domain of 622 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 218 amino acids (Presky et al., 1996). The IL-12Rb2 extracellular domain consists of a N-terminal Ig-like domain, a CHR domain and three membrane proximal FNIII domains (Presky et al., 1996). To initiate signalling, IL-12Rb2 binds IL-12 with high affinity in the first step of complex formation via a site-3 interface. In the second step, IL-12Rb1 is recruited to the complex enabling STAT4 activation (Presky et al., 1996). As with IL-12Rb1, mutations in the IL-12RB2 gene that disrupt its expression have been associated with immunodeficiency (Martínez-Barricarte et al., 2018). However, mutations in IL-12Rb1 are more common. Antagonists: There are no clinically approved IL-12Rb2 antagonists. Agonists: Currently there are no IL-12Rb2 agonists approved for clinical use. IL-23R – The expression of IL-23R is restricted to specific T cell subsets, a small number of B cells and innate lymphoid cells (Chognard et al., 2014). The mature human IL-23R (71.7 kDa) contains 629 amino acids and consists of an extracellular domain of 355 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 252 amino acids (Parham et al., 2002). The IL-23R extracellular domain is comprised of a N-terminal Ig-like domain, followed by a CHR domain and connected to the transmembrane domain by a long non-structured stalk domain (37 amino acids) (Parham et al., 2002). IL-23R binds in a first step and with high affinity to IL-23 via a site-2 binding interface. In a second step, this complex recruits IL-12Rb1, to activate the STAT3/STAT4 signalling pathway (Parham et al., 2002). Mutations in the IL-23R gene can have a positive or negative association with ankylosing spondylitis, Crohn’s disease, psoriatic arthritis, ulcerative colitis and psoriasis, all resulting from dysregulation in the inflammatory response (Duerr et al., 2006; Tonel et al., 2010; Fisher et al., 2008). One particular mutation, R381Q, appears to have a protective effect (Duerr et al., 2006). Antagonists: There are no clinically approved IL-23R antagonists. However, there are several antibodies and peptides being developed (Kuchar et al., 2014; Quiniou et al., 2014). In addition, there are clinically approved antibodies targeting the p19 subunit of IL-23 and blocking its binding to IL-23R (Fotiadou et al., 2018). Agonists: Currently there are no IL-23R agonists approved for clinical use.

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Cytokine Receptors

IL-27Ra – IL-27Ra is widely expressed in the immune system and drives signalling in T cells, B cells and myeloid cells, and mainly exhibits immune-suppressive activities (Pflanz et al., 2002; Dibra et al., 2009; Bosmann and Ward, 2013; Jones et al., 2015). The mature human IL-27Ra (69.5 kDa) contains 636 amino acids and consists of an extracellular domain of 516 amino acids, a membrane-spanning region of 20 amino acids, and a cytoplasmic domain of 98 amino acids (Chen et al., 2000). The IL-27Ra extracellular domain is comprised of a N-terminal CHR domain followed by three membrane proximal FNIII domains (Chen et al., 2000). To initiate signalling, IL-27Ra binds in a first step IL-27 (p28/EBI3) with high affinity, via a site-2 interface, and then in a second step, recruits to the complex gp130 via a site-3 interface (Bosmann and Ward, 2013) to trigger the activation of the JAK1/JAK2/STAT1/STAT3 signalling pathway. Although mutations on IL-27Ra have been found in cancer patients, there are no reports describing causality between IL-27Ra mutations and disease (Pradhan et al., 2007). Antagonists: There are no clinically approved IL-27Ra antagonists. However, there are neutralizing antibodies against IL-27 being developed for cancer immunotherapy (Bosmann et al., 2014). Agonists: Currently there are no IL-27Ra agonists approved for clinical use. However, due to its immunoregulatory activities, modulating IL-27Ra responses could be beneficial in autoimmune disorders and inflammation.

1.04.2.2.3

The common Beta chain receptor family

The beta common (bc) cytokine receptor family includes the common bc, IL-3Ra, IL-5Ra and GM-CSFRa. These receptors are bound by IL-3, granulocyte-macrophage colony-stimulating factor (GM-CSF) and IL-5. Cytokines in this family have crucial roles in the regulation of growth and differentiation of hematopoietic cells and the activation of neutrophils and macrophages (Dougan et al., 2019). Dysregulation of cytokines or receptors in this family can lead to pathological conditions such as chronic inflammatory diseases and myeloid leukemia (Hercus et al., 2018). Receptors from the bc family are expressed at very low levels, less than 1500 copies per cell, on hematopoietic cells (Broughton et al., 2012). In this family, the signalling complexes are formed from heterodimers comprised of an alpha chain and the bc chain, which are shared by all cytokines. In a first step, the cytokines bind with high affinity to their specific a receptor via a site-1 interface, and in a second step this heterodimeric complex recruits a molecule of bc with intermediate affinity. From this moment on, there is an intertwined series of binding events involving six binding interfaces, causing this family to form the most complex cytokine-receptor complexes in the entire cytokine family. IL-3 and GM-CSF form dodecameric complexes with six binding interfaces that drive activation of the JAK2/STAT5 pathway (Hansen et al., 2008; Broughton et al., 2012). IL-5, which is a dimeric cytokine, forms hexameric complexes which activate the same pathway (Kusano et al., 2012). This family also activates the MAPK and PI3K pathways, which contributes to fine tune their activities (Adachi and Alam, 1998; Reddy et al., 2000). bc – bc is the shared signalling subunit for the receptors of IL-3, IL-5, and GM-CSF. bc is expressed by hematopoietic progenitor cells, monocytes, neutrophils, eosinophils, basophils, endothelial cells, fibroblasts, and Langerhans cells (Hercus et al., 2018; Shearer et al., 2003). The mature human bc (97.3 kDa) contains 897 amino acids and consists of an extracellular domain of 443 amino acids, a membrane-spanning region of 16 amino acids, and a cytoplasmic domain of 436 amino acids. The bc extracellular domain is comprised of four FNIII domains, forming two contiguous CHR modules. Interestingly, the structure of unliganded bc revealed an intertwined, strand-swapped, antiparallel homodimer (Carr et al., 2001). This is possible due to long connecting amino acid linkers (> 10 amino acids) between FN1-2 and FN3-4. The solution of a complete GM-CSF/bc crystal structure revealed the bc dimer interacting with two cytokine-a chain heterodimers via two composite interfaces formed by the FN1 domain of one bc chain and the FN4 of the other bc chain in the dimer, leading to the formation of a hexameric complex (Hansen et al., 2008). Since the a chains in this family do not engage JAK kinases, bc, which engages JAK2, is solely responsible for the initiation of signalling by this family (Hansen et al., 2008; Quelle et al., 1994; Lopez et al., 2010). The hexameric complex places the two bc subunits  120 Å apart from each other and therefore unable to activate their associated JAK2 kinases (Carr et al., 2001). Crystallographic contacts between bc D4 domains of two separate bc/GMRa/GM-CSF hexamers have been described, suggesting that bc signalling may be mediated by two hexamers dimerized into a dodecameric structure (Hansen et al., 2008). Mutagenesis studies confirmed this model and suggested a similar outcome for IL-3 signalling complex (Murphy et al., 2003). IL-5 signalling complex however appears to depend only in a hexameric complex formation (Patino et al., 2011). Mutations of the bc cytokine receptor gene are only infrequently observed and have been associated with T-cell acute lymphoblastic leukemia (gain-of-function mutations) (Hercus et al., 2018; Watanabe-Smith et al., 2016) and hereditary pulmonary alveolar proteinosis (loss-of-function mutations) (Hercus et al., 2018; Suzuki et al., 2011). Antagonists: There are no clinically approved bc antagonists. However, there are neutralizing antibodies against bc being developed for inflammatory airway diseases (Panousis et al., 2016). Agonists: Currently, there are no specific bc agonists approved for clinical use. However, GM-CSF (Sargramostim) is used for several disorders including COVID-19 (Korzenik et al., 2005; Bosteels et al., 2020; Skowron et al., 1999). GM-CSF enhances host responses to certain cancers by recruitment and activation of antigen-presenting cells (Soiffer et al., 1998). Thus, GM-CSF, as well as oncolytic viruses secreting GM-CSF have been developed for cancer treatment (Malhotra et al., 2007). In addition, GM-CSF has been added to regimens for the mobilization of hematopoietic progenitor cells (Cashen et al., 2004; Lonial et al., 2004). GM-CSFRa – GM-CSFRa is expressed on myeloid cells, epithelial cells, endothelial cells and neurons (Vogel et al., 2015; Shiomi et al., 2016). A soluble form of GM-CSFRa is also formed through alternative splicing or through cleavage from the cell surface by proteases of the ADAM family (Raines et al., 1991; Prevost et al., 2002). The function of this soluble form is not clear at the moment, but it may act as a decoy receptor to block GM-CSF signalling (Brown et al., 1995). The mature human GM-CSFRa (46.2 kDa) contains 400 amino acids and consists of an extracellular domain of 320 amino acids, a membrane-spanning region of 25 amino acids, and a cytoplasmic domain of 53 amino acids. The GM-CSFRa extracellular domain is comprised of a N-terminal Ig-like domain and a membrane proximal CHR module. GM-CSFRa binds in a first step GM-CSF with high affinity via a site-1 interface,

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then presents it to the bc chain to form the dodecameric signalling active complex (Hansen et al., 2008). Although the intracellular domain of GM-CSFRa does not bind JAK2, it is required for signal activation. In addition, it may contribute with activation of nonSTAT signalling that could fine-tune GM-CSF responses (Papp et al., 2019). As with mutations found in the bc chain, loss-of-function mutations in the GM-CSFRa (CSF2RA) gene results in hereditary pulmonary alveolar proteinosis (Hadchouel et al., 2020). Antagonists: Several antibodies targeting GM-CSF and GM-CSFRa are being currently developed to neutralize GM-CSF signalling. The GM-CSF blocking antibody GSK3196165/MOR103 has shown efficacy in patients with RA and was well tolerated in a phase Ib clinical trials in patients with MS (Behrens et al., 2015). GM-CSF blocking antibodies lenzilumab/KB003, MORAb-022, and namilumab are currently in clinical trials in patients with RA, plaque psoriasis CMML, or asthma (Molfino et al., 2016; Taylor et al., 2019; Papp et al., 2019; Lee et al., 2020). Mavrilimumab/CAM-3001, a GM-CSFRa blocking antibody, showed significant responses in patients with RA in phase II clinical trials, with moderate toxicity and no signs of lung toxicity (Crotti et al., 2019). Agonists: Currently, there are no specific GM-CSFRa agonists approved for clinical use. See above, in the bc description, for clinical applications of GM-CSF. IL-3Ra – IL-3Ra is expressed in hematopoietic progenitor cells and in a wide range of mature immune cell populations, including B cells and monocytes (Morikawa et al., 1996; Gorman et al., 1992). IL-3Ra is also overexpressed on progenitor cells of patients with acute myeloid leukemia (AML), and its expression levels correlate with reduced patient survival rate (Testa et al., 2002). The mature human IL-3Ra (43.3 kDa) contains 378 amino acids and consists of an extracellular domain of 305 amino acids, a membrane-spanning region of 19 amino acids, and a cytoplasmic domain of 52 amino acids. (Kitamura et al., 1991). The IL-3Ra extracellular domain is comprised of a N-terminal Ig-like domain and a membrane proximal CHR module (Kitamura et al., 1991). To initiate signalling, IL-3Ra binds with moderate affinity IL-3 in a first step via a site-1 interface. After the IL-3Ra-IL-3 complex is formed, the bc dimer is recruited to the complex through sites-2 and 3 interfaces forming a highly stable hexameric complex (Kitamura et al., 1991). As for GM-CSF, it has been proposed that IL-3 also forms a dodecameric complex using site-4, 5 and 6 interfaces to trigger activation of the JAK2/STAT5 pathway, although no formal proof of this has been reported yet (Hansen et al., 2008; Broughton et al., 2014). No disease-causing mutations have been described for IL-3Ra to date. However, high expression levels of the receptor subunits are associated with different types of hematological cancers (Muñoz et al., 2001). Antagonists: Several antibodies targeting IL-3Ra are being developed to treat patients with leukemia and myelodysplastic syndromes. CSL362, an anti-IL-3Ra MAb that blocks IL-3 binding and signalling has recently progressed to phase II clinical trials in patients with AML in combination with decitabine (Montesinos et al., 2021). This MAb is a potential therapy for SLE as well, due to its ability to deplete pDCs and inhibit IFN production (Oon et al., 2016). The anti-IL-3Ra MAb, KHK2823 and the bispecific antibody MGD006 (IL-3Ra and CD3) are currently in phase I clinical trials in patients with AML or MDS (Akiyama et al., 2015; Uy et al., 2014). In addition to neutralizing antibodies, other molecules have been engineered to target the IL-3Ra receptor in hematological malignancies. An example is SL-401/DT388IL3, a molecule that fuses the catalytic and translocation domains of diphtheria toxin (DT) to IL-3 (Alkharabsheh and Frankel, 2019). Phase I clinical trials with DT388IL3 in patients with chemorefractory AML or myelodysplasia showed good tolerance and some degree of efficacy (Testa et al., 2005). Agonists: Currently, there are no specific IL-3Ra agonists approved for clinical use. However, IL-3Ra agonists are being tested in the ex vivo expansion of stem cells and dendritic cells to reverse chemotherapy-induced myelosuppression (Hodi and Soiffer, 2002). IL-5Ra – IL-5Ra is expressed in eosinophils and basophils (Barretto et al., 2020; Wright et al., 2017; Smith et al., 2016). The mature human IL-5Ra (47.7 kDa) contains 420 amino acids and consists of an extracellular domain of 342 amino acids, a membrane-spanning region of 19 amino acids, and a cytoplasmic domain of 57 amino acids (Kusano et al., 2012). The IL5Ra extracellular domain is comprised of a N-terminal Ig-like domain and a membrane proximal CHR module (Kusano et al., 2012). To initiate signalling, IL-5Ra first binds with high affinity to IL-5, which is a dimer, via a site-1 interface (Kusano et al., 2012). This complex then recruits in a second step the bc dimer through sites-2 and 3 interfaces forming a highly stable octameric complex (Kusano et al., 2012). The hexadecameric assembly of the IL-5:IL-5Ra complex has been proposed by analogy with the dodecameric receptor complex of GM–CSF, but formal proof of this model is yet to be reported (Kusano et al., 2012). No disease-causing mutations have been described for IL-5Ra to date. However, polymorphisms in IL-5Ra are associated with allergic disorders (Losol et al., 2013). Antagonists: IL-5 plays a crucial role in activating eosinophils in asthma (Webb et al., 2000; Mould et al., 2000). As a result of this, a number of antibodies blocking IL-5Ra activities have been developed (Bagnasco et al., 2017). However, most of these antibodies have shown disappointing efficacy treating asthma in the clinic (Leckie et al., 2000). Nonetheless some antibodies have shown efficacy, for instance benralizumab, an anti-IL-5Ra MAb, which has shown reduced annual exacerbation rates and safety for uncontrolled severe asthma patients with elevated eosinophils in a recent phase III trial (Busse et al., 2019). Agonists: Currently, there are no specific IL-5Ra agonists approved for clinical use.

1.04.2.2.4

The common gamma chain receptor family

The common gamma chain (gc) family of receptors is comprised of gc, IL-2Ra, IL-2Rb, IL-4Ra, IL-7Ra, IL-9Ra, IL-15Ra and IL21Ra. These receptors are engaged in different hetero -dimeric and -trimeric complexes by the cytokines IL-2, IL-4, IL-7, IL-9, IL-15 and IL-21 to activate signalling (Morgan et al., 1976; Howard et al., 1982; Namen et al., 1988; Uyttenhove et al., 1988; Lodolce et al., 1998; Ozaki et al., 2000; Parrish-Novak et al., 2000). All these complexes share gc as a component critical for signal transduction and activate the JAK1/JAK3/STATs signalling pathway (Miyazaki et al., 1994; Russell et al., 1994; Witthuhn et al., 1994; Chen et al., 1997). In addition to the JAK/STAT pathway, members of this family engage non-STAT pathways such as the MAPK and PI3K pathways to fine tune their responses (Graves et al., 1992; Dadi et al., 1994; Venkitaraman and Cowling, 1994). Given the crucial role that members of this family play in the development and coordination of the immune response,

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modulating the actions of this family of cytokines and receptors has become an important strategy for managing a broad range of human pathologies, including immunodeficiency, allergy, infection and cancer (Witthuhn et al., 1994; Chen et al., 1997; Leonard et al., 2019). In general terms, cytokines comprising this family bind in a first step and with high affinity, an alpha chain, before then recruiting in a second step with low affinity the gc to trigger signalling (LaPorte et al., 2008). This is the case for IL-4, IL-7, IL-9 and IL-21 (LaPorte et al., 2008; McElroy et al., 2009; Hamming et al., 2012). IL-2 and IL-15, however, bind with high affinity in a first step an alpha chain which lacks intracellular domains capable of activating downstream signalling and it is specific for each cytokine (IL-2Ra and IL-15Ra respectively) (Wang et al., 2005). The alpha chains present the cytokines to IL-2Rb in a second step, to create an intermediate affinity complex, and then recruit gc in order to trigger downstream signalling in a high affinity hetero-tetrameric complex (Wang et al., 2005). A more detailed description on the gc family of receptors can be found in Table 1. Common Gamma Chain Receptor (gc) - The gc receptor is expressed on lymphoid cells (Noguchi et al., 1993). The mature human gc (42 kDa) contains 347 amino acids and consists of an extracellular domain of 240 amino acids, a membranespanning region of 21 amino acids, and a cytoplasmic domain of 86 amino acids (Kobayashi et al., 1993). The gc extracellular domain consists of a CHR module. The gc possess two sites (site-2a and site-2b) that allows it to interact with other members of the gc receptor family (a and b chains) with varying affinities depending on the cytokine receptors engaged (Walsh, 2012). These interactions range from heterodimeric complexes (IL-4, IL-7, IL-9) to heterotrimeric complexes (IL-2, IL-15). Upon complex formation, gc triggers the activation of JAK3 (associated to gc)/JAK1/STAT1/STAT3/STAT5/STAT6 pathways (Miyazaki et al., 1994; Russell et al., 1994; Boussiotis et al., 1994). Additional pathways are also engaged by complexes formed by this receptor subunit and contribute to fine-tune the responses of the cytokines comprising this family (Merida et al., 1991; Karnitz et al., 1995; Burns et al., 1993; Ravichandran and Burakoff, 1994). The gc is the only cytokine receptor encoded in the X chromosome and is subject to X chromosome inactivation. The IL-2RG gene encodes the gc and is mutated in humans with X-linked severe combined immunodeficiency (XSCID) (Noguchi et al., 1993; Schmalstieg and Goldman, 2002; Lebet et al., 2008; Buckley, 2004). XSCID is a disease characterized by a strong reduction of T and NK cells and a normal level of B cells which are nonfunctional, this leads to patients developing severe immunodeficiency (Noguchi et al., 1993; Schmalstieg and Goldman, 2002; Lebet et al., 2008; Buckley, 2004). More than 300 mutations in the IL-2RG gene have been identified in people with XSCID (Lebet et al., 2008) and result in lack of expression or in the production of a non-functional version of the gc (Schmalstieg and Goldman, 2002; Lebet et al., 2008; Buckley, 2004). Antagonist: Blocking the gc with a neutralizing monoclonal antibody attenuated acute chronic graft versus host disease in mouse and human models (Hechinger et al., 2015). Blockade of the gc receptor resulted in less granzyme B levels from CD8 þ T cells and a reduction in JAK3 levels (Hechinger et al., 2015). Agonists: Currently, there are no agonists that target the gc receptor. IL-2Ra - IL-2Ra is basally expressed by T-regulatory cells and potently induced upon T cell activation (Nelson and Willerford, 1998). The mature human IL-2Ra (30.8 kDa) contains 272 amino acids and consists of an extracellular domain of 219 amino acids, a membrane-spanning region of 19 amino acids, and a cytoplasmic domain of 13 amino acids (Nikaido et al., 1984). The extracellular domain of IL-2Ra differs from the majority of the hematopoietic growth factor receptor family as it does not contain the signature CHR domain but instead possesses two sushi domains ( 65 amino acids) which allows IL-2Ra to use a composite surface to dock onto a groove of IL-2 with intermediate affinity via a site-3 interaction and then presents it to IL-2Rb and gc to form a high affinity complex via site-1 and site-2 interfaces (Rickert et al., 2005). The IL-2Ra does not possess the intracellular components needed to activate signalling on its own and relies on IL-2Rb and gc to trigger signalling. A truncation mutation in the IL-2Ra chain whereby 4 bp are deleted (bp 60–64) results in a translational frameshift (Caudy et al., 2007). This mutation causes the IL2Ra to be defective or not produced at all leading to IL-2Ra deficiency which manifests as severe immunodeficiency in humans. This immunodeficiency is characterized by decreased numbers of peripheral T cells that display abnormal proliferation patterns, but B cell and B cell development remains normal (Caudy et al., 2007). Additionally, there are 5 small nucleotide polymorphisms (SNPs) in the IL2RA gene that are associated with a higher susceptibility of type 1 diabetes and potentially multiple sclerosis and rheumatoid arthritis (rs11594656, rs2104286, rs41295061, rs3118470, rs706778 (Lowe et al., 2007; D’Netto et al., 2009; Maier et al., 2009). Lastly, patients with severe COVID-19 have significantly higher levels of IL-2Ra in their plasma (Hou et al., 2020). Antagonists: Daclizumab; a humanized antihuman CD25 antibody specific for the IL-2Ra chain was approved for clinical use in 1997 for patients with relapsing-remitting multiple sclerosis (RRMS) (Wuest et al., 2011). Daclizumab exerts its effects by blocking the binding of IL-2 to IL-2Ra thereby interfering with the activation and expansion of T cells. However, in 2018 the European Medicines Agency (EMA) recommended the immediate suspension of daclizumab. The suspension announcement follows reports of serious inflammatory brain disorders in a significant number of patients (Gold et al., 2020). Basilliximab is a recombinant chimeric monoclonal antibody that is mainly used as an immunosuppressive agent. It works by binding to the IL-2Ra chain and prevents IL-2 from binding. This stops the activation of IL-2 mediated T cell activation, a critical response involved in allograft rejection and is therefore important for use in transplant patients (Chapman and Keating, 2003). Agonists: There are currently no approved agonists that are used in the clinic that target the IL-2Ra, but new approaches have been devised that show high promise for clinical trials. Two monoclonal antibodies against mouse IL-2 can elicit bias activity toward specific T cell subsets when administered along with mouse IL-2 (Spangler et al., 2015a; Boyman et al., 2006). The first antibody immunocomplex mIL-2:JES6-1 induces the proliferation of IL-2Rahi cells which expands T-regulatory cells over other effector cell types. The JES6-1 immunocomplexes promote graft tolerance and shows promising results in preclinical models of diabetes (Boyman et al., 2006; Spangler et al., 2015b). The second antibody immunocomplex mIL-2:S4B6 stimulates the proliferation of all immune cell subsets but favors effector cells which exhibits potent anti-tumor activity without resulting in toxicity (Boyman et al., 2006; Spangler et al., 2015b). Recently, this antibody

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approach has been explored further by identifying human antibodies against human IL-2 (F5111.2) that favors T-regulatory cell expansion and was effective in autoimmune models including type 1 diabetes, experimental autoimmune encephalomyelitis (EAE) and graft versus host disease (Trotta et al., 2018). IL-2Ra agonists show high promise for the future clinical treatment of proinflammatory pathologies. IL-2Rb – IL-2Rb binds IL-2 and IL-15 to trigger signalling and is constitutively expressed by T cells, NK cells, B cells, macrophages, monocytes, and DCs (Grabstein et al., 1994). The mature human IL-2Rb (61.1 kDa) contains 551 amino acids and consists of an extracellular cytoplasmic domain of 214 amino acids, a membrane-spanning region of 25 amino acids, and an intracellular domain of 286 amino acids. IL-2Rb is comprised of a CHR module that bind the cytokines. IL-2Rb binds to IL-2Ra-IL-2 or IL-15Ra-IL-15 hetero complexes with intermediate affinity via a site-1 interaction before gc is recruited to trigger activation of the JAK1/JAK3/ STAT5 pathway (Johnston et al., 1994; Russell et al., 1994; Witthuhn et al., 1994; Nakamura et al., 1994; Nelson et al., 1994; Levin et al., 2012). Additional pathways are also engaged by IL-2Rb which all contribute to the modulation of its response (Graves et al., 1992; Ray et al., 2015; Witthuhn et al., 1994; Finlay et al., 2012). Mutations within the IL2RB gene have been reported and lead to autoimmunity and immunodeficiency pathologies. One of these mutations is a homozygous 9-bp in frame deletion in the extracellular motif of IL-2Rb which leads to diminished IL-2Rb surface expression resulting in a reduction of T cells and an inconsistent NK cell proliferation pattern (Fernandez et al., 2019; Suzuki et al., 2000). Additionally, a homozygous mutation in exon 4 of the IL2RB gene (L77P) has been found to occur in the extracellular D1 domain of IL-2Rb. This substitution mutation causes decreased surface expression of IL-2Rb but increases cytosolic levels, suggesting that the mutation causes misfolding and improper trafficking of IL-2Rb to the cell surface. This mutation causes a significant decrease in T cell levels in patients. Loss of function mutations also occur on IL-2Rb (Q96X) but these mutations result in a prenatal phenotype and death soon after birth (Zhang et al., 2019). Antagonists and Agonists: There are no clinically approved agonists or antagonists that target IL-2Rb specifically. However, a partial CD122 agonist BEMPEG is currently in phase 3 clinical trials and has been shown to be effective in increasing CD8 þ T cells in circulation and in tumor tissue (Ray et al., 2015; Finlay et al., 2012; Diab et al., 2020; Sharma et al., 2020; Parisi et al., 2020; Bentebibel et al., 2019; Charych et al., 2016). BEMPEG provides a sustained activation of the IL-2 pathway via IL-2Rb chain signalling and favors the stimulation and proliferation of CD8 þ T cells over the CD4 þ cells. Clinical studies have shown that it is safe and effective in treating melanoma, renal, lung, bladder and breast cancer by decreasing the T-regulatory levels in tumor tissue and increasing the effector T cell population (Diab et al., 2020; Sharma et al., 2020; Parisi et al., 2020; Bentebibel et al., 2019; Charych et al., 2016). Following on from this, THOR-707 is a PEGylated version of IL-2 that prevents the engagement of IL2Ra which contributes to toxicity and instead retains binding to the IL2Rb and gc receptors (Milla et al., 2019; Joseph et al., 2019). This biased binding selectively expands effector T cells and NK cells while decreasing the proliferation of T-regulatory cells and is now entering phase 2 clinical trials (Milla et al., 2019; Joseph et al., 2019). Numerous forms of engineered IL-2, e.g. Super-2 and Bempegaldesleukin, have been developed and moved onto clinical trials against various forms of cancer (Levin et al., 2012; Charych et al., 2016); recently reviewed by Mullard, (Mullard, 2021). Meanwhile, NL-201 from Neoleukin Therapeutics, is a computationally designed protein that is a potent stimulator of IL-2 signalling in the absence of IL2Ra, interacting exclusively with IL-2Rb and the common gc (Silva et al., 2019). IL-15Ra – IL-15Ra is expressed on a wide range of cells, including immune cells (T cells, B cells, macrophages and stromal cell lines) and non-immune cells (keratinocytes) (Grabstein et al., 1994). The mature human IL-15Ra (28.2 kDa) contains 267 amino acids and consists of an extracellular domain of 175 amino acids, a membrane-spanning region of 23 amino acids, a cytoplasmic domain of 39 amino acids. The extracellular domain of IL-15Ra is comprised of a single sushi domain ( 65 amino acids) which is essential for IL-15 binding (Wei et al., 2001). The IL-15Ra receptor binds with high affinity IL-15 via a site-3 interaction and then presents it to IL-2Rb and gc to form a heterotrimeric complex via site-1 and site-2 interfaces. Parallel to IL-2Ra, IL-15Ra cannot activate signalling and relies on IL-2Rb and gc. IL-2 and IL-15 thus activate the same downstream signalling, with their effects being differentiated by the differential expressions of their alpha subunits. There are no mutations reported that severely affect IL-15Ra activity or expression. However, there are reports in the literature of single nucleotide polymorphisms (SNPs) that increase susceptibility to pathologies such as ossification and carcinoma (Guo et al., 2014; Kim et al., 2011). There are no clinically approved IL15Ra agonists or antagonists, but preclinical studies have begun to explore the therapeutic benefit of targeting IL-15Ra (Zhao et al., 2016; Rubinstein et al., 2006; Han et al., 2011; Wrangle et al., 2018). Antagonist: A natural occurring isoform of IL-15 which lacks exon 6 (IL-15DE6) was found to inhibit IL-15 mediated T cell proliferation. Studies showed that IL-15DE6 could bind to IL-15Ra and competes with IL-15 for binding. In mouse models of EAE it was shown to significantly reduce severity of the disease and is hoped to be a potential therapeutic agent for treating autoimmune pathologies (Zhao et al., 2016). Agonist: A soluble IL15:IL15Ra superagonist (15N72D) was produced and used along with a fusion protein (IL-15RaSu/Fc) and this complex was shown to have a long serum half-life which results in significantly increased NK and CD8þ T cell levels both in vivo and in vitro compared to treating with WT IL-15 alone. This superagonistic approach shows promise for using IL-15 as a future immunotherapy for treating proinflammatory conditions (Rubinstein et al., 2006; Han et al., 2011; Wrangle et al., 2018). IL-4Ra - IL-4Ra is ubiquitously expressed on hematopoietic cells and is important in the proper development of the immune response (Ohara and Paul, 1987). The mature human IL-4Ra (89.6 kDa) contains 825 amino acids and consists of an extracellular domain of 207 amino acids, a membrane-spanning region of 24 amino acids, and a cytoplasmic domain of 569 amino acids. The N-terminal domain of IL-4Ra consists of a CHR binding module. IL-4Ra binds IL-4 with high affinity via a site-1 interaction, for then recruiting in a second step gc to the complex to trigger the activation of JAK1/JAK3/STAT6 pathway (Ohara and Paul, 1987; Park et al., 1987; Russell et al., 1993; Wang et al., 1992; Keegan et al., 1994; Kawakami et al., 2000). The IL-4Ra-IL-4 complex can also recruit IL-13Ra1 to trigger signalling forming the IL-4 Type II receptor complex. IL-4Ra also activates non-STAT pathways that contribute to fine-tune its activities (Wang et al., 1993; Yin et al., 1995). Allelic variations in the IL4R gene have been shown to

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contribute to atopy, a condition that results in allergic rhinitis, sinusitis, asthma, or eczema (I50V, C3223T), however further investigations are needed to confirm these observations (Danielewicz et al., 2021). Furthermore, a gain of function mutation in the IL4R gene with the single nucleotide polymorphism F709 results in an upregulation of IL-4Ra and leads to a failure in producing antigen specific T-regulatory cells (Tachdjian et al., 2010). Antagonists: Airway inflammation is driven by Th2 related cytokines such as IL-4 in asthma. Pitrakinra (aka AER-001/BAY-16-9996) is an IL-4 mutein which binds to IL-4Ra and prevents the inflammatory phenotype induced by IL-4. Pitrakinra reduces allergen induced inflammation in animal models of asthma. In clinical trials, Pitrakinra reduced airway inflammation in asthma patients and also showed modest efficacy in atopic eczema (Antoniu, 2010). Pitrakinra is currently in phase 2 trials. Dupilimab is a human monoclonal antibody that targets IL-4Ra and inhibits IL-4 binding and signalling (Wenzel et al., 2013). Agonists: There are currently no approved IL-4Ra agonists, however protein engineering studies are providing more insights into the biology of IL-4 that could rescue its use in the clinic. For example, IL-4 variants which possess high specificity for IL4 type I and type II receptor complexes have been engineered. These variants revealed that T cell responses were dependent upon the type-1 IL4 receptor complex whereas dendritic cell maturation was more dependent on type-2 IL-4 receptor complexes (Junttila et al., 2012; Spangler et al., 2019). IL-7Ra - IL-7Ra is expressed on lymphoid cells (Sudo et al., 1993; Akashi et al., 1998). The mature human IL-7Ra (51.6 kDa) contains 459 amino acids and consists of an extracellular domain of 219 amino acids, a membrane-spanning region of 25 amino acids, and a cytoplasmic domain of 195 amino acids. The extracellular domain consists of a CHR binding module that binds IL-7. IL-7Ra binds to IL-7 with high affinity via a site-1 interaction, for then recruiting to the complex gc in a second step with low affinity to triggers the activation of JAK1/JAK3/STAT5 pathway (McElroy et al., 2009; Foxwell et al., 1995). Variations in the IL-7R gene (e.g Thr244Iso) can lead to an increased risk of developing multiple sclerosis (MS) (Gregory et al., 2007; Lundmark et al., 2007). This mutation causes the retention of IL-7Ra chain inside the cell, which ultimately contributes to characteristic immune symptoms found in MS (Gregory et al., 2007; Lundmark et al., 2007). Additionally, loss-of-function mutations in the IL7R gene can cause severe combined immunodeficiency (SCID), where the patients have few or no T cells but normal functional B cells and NK cells (Puel et al., 1998). Gain-of-function mutations in the IL7R gene contribute to cancers of the blood, specifically B cell acute lymphoblastic leukemia (ALL). Constitutive IL-7 signalling increases the proliferation of B cells and T cells which leads to ALL (Shochat et al., 2011). Antagonists: OSE-127 is an anti-CD127 monoclonal antibody targeting IL-7Ra. Binding of OSE-127 to IL-7Ra inhibits downstream signalling and blocks migration of T cells while maintaining T regulatory cells. This strategy could be useful to treat a range of autoimmune diseases including MS, type-1 diabetes, rheumatoid arthritis, systemic lupus erythematous and even Sjögren’s (Belarif et al., 2018). Agonists: IL-7 is crucial for T cell development and for controlling the immune response. A long-acting human IL-7 cytokine (NT-I7) has been shown to increase absolute lymphocyte counts and CD4þ and CD8 þ T cell counts (Campian et al., 2019). NT-I7 is undergoing multiple clinical trials in solid tumors, with the hypothesis that increased T cell counts can further synergize the immune response along with other cancer treatments (Campian et al., 2019). Lymphopenia is common in patients with COVID-19 and is associated with worse clinical outcomes, NT-I7 is currently in a clinical trial in COVID-19 patients which is due to end in 2022 (Clinical Trials 2021: [Identifier– NCT04501796]). The increased T cell response induced by NT-I7 is hypothesized to aid in the controlled response of the immune system and the clearance of COVID-19. IL-9Ra - IL-9Ra is expressed on a wide range of cells such as T cells, mast cells and macrophages and is essential for immune system development (Druez et al., 1990). The mature human IL-9Ra (57.1 kDa) contains 522 amino acids and consists of an extracellular domain of 230 amino acids, a membrane-spanning region of 19 amino acids, and a cytoplasmic domain of 229 amino acids. The IL-9Ra extracellular domain consists of a CHR module that binds IL-9. IL-9Ra binds to IL-9 with high affinity via a site-1 interaction, and then in a second step recruits gc with low affinity to trigger the activation of JAK1/JAK3/STAT5 pathway (Kermouni et al., 1995; Knoops and Renauld, 2004; Zhu et al., 1997; Demoulin et al., 1996, 2000; Ihle and Kerr, 1995). Additional pathways are also engaged by IL-9Ra which contribute to the modulation of its responses (Yin et al., 1995; Demoulin et al., 2000, 2003). There are few reported mutations that target the IL-9R gene. However, a study found that the sDF2*10 allele of the IL-9R gene was more frequently transmitted to asthmatic offspring, but these observations need further validation (Kauppi et al., 2000). Antagonists: MEDI-528/Enokizumab, a humanized monoclonal antibody, binds to IL-9 and blocks its binding to IL-9Ra (Oh et al., 2013). Results from this antibody in clinical trials have been modest for moderate to severe asthma patients and it is currently in phase 1 clinical trials (Oh et al., 2013). Agonist: There are currently no approved agonists that target the IL-9Ra. IL-21Ra - IL-21Ra is expressed on lymphohematopoietic cells and contributes to T cell and B cell proliferation and activation along with promoting NK cell cytotoxicity (Ozaki et al., 2000; Parrish-Novak et al., 2000; Jin et al., 2004; Brandt et al., 2003; Distler et al., 2005; Caruso et al., 2007). The mature human IL-21Ra (59.1 kDa) contains 538 amino acids and consists of an extracellular domain of 213 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 285 amino acids. The IL-21Ra extracellular domain consists of a CHR module that binds IL-21 (Hamming et al., 2012). IL-21Ra binds to IL-21 with high affinity via a site-1 interaction, for then in a second step recruits gc with low affinity to trigger the activation of JAK1/STAT1/STAT3 pathway (Hamming et al., 2012; Habib et al., 2002; Asao et al., 2001). Additional pathways are also engaged by IL-21Ra which all contribute to the modulation of its responses (Zeng et al., 2007). Loss of function mutations have been found in the IL-21R gene (e.g. Arg201Leu), which cause aberrant trafficking of the IL-21Ra to the plasma membrane, preventing IL-21 binding, and ultimately result in poor STAT1, STAT3 phosphorylation (Kotlarz et al., 2013). These mutations are thought to contribute to immunodeficiency due to defects in proliferation of B cells, T cells and NK cells. There are currently no agonist or antagonists that are approved for clinical use with regard to IL-21/IL-21Ra. However, there are promising studies that show the therapeutic potential of targeting IL-21Ra. Antagonists: A humanized anti-IL-21Ra antibody was used in a humanized skin transplantation model and demonstrated that blockade of IL-21 signalling can delay allograft rejection by blocking STAT3 activation (de Leur et al., 2019).

Cytokine Receptors

1.04.3

51

The class II receptor family

The class II receptor family binds and signals in response to Interferons type 1–3, and the Interleukin-10 family of cytokines. In common with the class I receptors, they are composed of a single transmembrane span, with extensive N-terminal extracellular and C-terminal intracellular structures connected by a transmembrane single-pass alpha-helix. The extracellular domains, like the class I receptors, are composed of modular fibronectin type III (FNIII) domains. However, the two classes differ in that the class II receptors do not carry the WSxWS motif and carry only a single disulfide linked cysteine pair per FNIII domain. Two such domains are found in all class II members except in the IFNAR1 receptor, where a gene duplication event is believed to have been responsible for its unique 4 x FNIII domain structure (Gaboriaud et al., 1990). It is this extracellular part of the receptor that forms the CHR, with the cytokine recognition site lying around the elbow of the two FNIII domains in all but IFNAR1 where domains 1–3 have been implicated (Lamken et al., 2005; Thomas et al., 2011). Cytokine binding to IFNAR1 causes a conformational change in the membrane proximal domain 4, required for signal transduction (Strunk et al., 2008). Class II cytokine receptors can be subclassified depending upon the nature and stoichiometry of the ligand-receptor complex created. There are no homo-complexes, as found within the class I families, all class II receptors form hetero complexes. A detailed list of each of the receptors contained within this class, their sub-class and further pertinent information is given in Table 1. A brief discussion of each follows below, laid out as for the class I receptors above.

1.04.3.1

The type I IFN receptors

The Type 1 interferons are all bound by a single common type II cytokine receptor formed by the binary complex of IFNAR1 and IFNAR2. Both receptors are ubiquitously expressed, but at levels that vary according to cell type (de Weerd and Nguyen, 2012). Complex formation occurs through IFN binding to the high-affinity site-1 on IFNAR2, followed by binding to the low affinity site-2 of an IFNAR1 monomer to form the binary complex. IFNAR1 also forms a site-3 interaction through domain 1 of its ECD. The binary IFNAR1:IFNAR2 complex then signals via the JAK/STAT pathway, with IFNAR1 associated with TYK2 and IFNAR2c interacting with JAK1. Signalling proceeds through the formation of the IFN-stimulated gene factor 3 (ISGF3) complex, which comprises phosphorylated STAT1 and STAT2 plus IFN-regulated factor 9 (IRF9) (Paul et al., 2018). There are 20 type 1 IFNs causing their own unique and specific signalling responses. The specificity of cytokine binding and signalling cannot be determined through the formation of unique receptor combination as is commonly found in other families, instead this must proceed via a shared receptor complex. Further, the evidence demonstrates that there is significant conservation in the binding mode of these IFN molecules to the receptor (Thomas et al., 2011). The specificity comes from an additional layer of complexity whereby differences in the cytokine:receptor affinity and subtle alterations in the interacting interfaces appear to tune the stability of the particular IFN/IFNAR complexes (Lamken et al., 2005; Thomas et al., 2011; Roisman et al., 2005) alongside differing levels of expression of the receptors on the cell surface (Levin et al., 2011; Moraga et al., 2009). IFNAR1 - The low affinity IFNAR1 receptor (63 kDa) is found as a single isoform and consists of an extracellular domain of 409 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 100 amino acids. The IFNAR1 extracellular domain is composed of four FN-III domains with IFN binding the preserve of domains 1–3, with domain 4 being proximal to the membrane (Lamken et al., 2005). The IFN ligand primarily binds in the site-2 hinge region of domains 2 and 3, with the binding site crowned by domain 1 forming a site-3 interaction. A deletion mutation in the IFNAR1 gene responsible for mistermination and the addition of an additional C-terminal 15 amino acids has been described (Hoyos-Bachiloglu et al., 2017), resulting in the patient having increased susceptibility to Mycobacterial, Streptococcus and Cytomegalovirus infection. Complete IFNAR1 deficiencies were reported in 2 patients, previously regarded as healthy individuals, that developed severe illness upon exposure to either the MMR or Yellow Fever vaccine (Hernandez et al., 2019). Antagonists: There are currently no available clinically approved antagonists. However, anifrolumab, an anti-IFNAR1 human monoclonal antibody is under investigation as a treatment for systemic lupus erythematosus (Riggs et al., 2018). Several mutant forms of human IFNa as IFNAR1 antagonists have been developed (Pan et al., 2008; Sandler et al., 2014; Urin et al., 2015). The variants showed similar binding affinity to IFNAR2, but lower binding affinities to IFNAR1. Antibodies active against IFNa have also been developed, for example sifalimumab and anifrolumab, and have been utilized in the treatment of systemic lupus erythmatosus. Agonists: Various recombinant forms of IFN have been developed and shown to have clinical applications. Numerous forms of IFNa have been approved for treatment of conditions such as refractory or recurring external condyloma acuminata (Yang et al., 2009), chronic Hepatitis B (Woo et al., 2017) and C (Combination treatment with ribavirin; (Chung et al., 2008), hairy cell leukemia (Lauria et al., 1988), AIDS-related Kaposi’s sarcoma (Lane et al., 1988), chronic myelogenous leukemia (Talpaz et al., 2013) and malignant melanoma (Tarhini et al., 2012). Meanwhile, recombinant IFNb forms have been used for treatment of relapsing/remitting multiple sclerosis (Jakimovski et al., 2018), condyloma acuminatum (Yang et al., 2009), and is currently being investigated with respect to treatment of coronavirus infections (Monk et al., 2021). IFNAR2 - IFNAR2 exists in three isoforms formed through alternative splicing, exon skipping, and multiple polyadenylation sites (Lutfalla et al., 1995). The mature human IFNAR2c (57 kDa) contains 515 amino acids and consists of an extracellular domain of 217 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 251 amino acids. The IFNAR2 extracellular domain is composed of two FN-III domains, IFNAR2b is a shorter form (331 amino acids, 34 kDa), still carrying the transmembrane region but no intracellular domain. Finally, IFNAR2a is a highly truncated soluble form (239 amino acids, 24 kDa) of the receptor, truncated prior to the transmembrane domain but carrying a unique addition of

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11C-terminal hydrophobic amino acid residues. All of these forms can interact with type 1 IFNs and with IFNAR1 but the two truncated forms are unable to activate signalling. IFNAR2b circulates in the bloodstream and can act as both an agonist and antagonist (Hardy et al., 2001). Genetic variations in IFNAR2 have been demonstrated to alter the susceptibility to hepatitis B virus (Frodsham et al., 2006; Romporn et al., 2013). Complete IFNAR2 deficiency was reported following fatal encephalitis following MMR vaccine, where subsequent investigations revealed dermal fibroblasts showed no response to IFNa/IFNb (Duncan et al., 2015, 2021). Antagonists: There are currently no available clinically approved antagonists. Agonist: Nylidrin has been indicated as an agonist for IFNAR2 (Drugbank Accession Number: [DB06152]). The mode of action remains undescribed. Also, recombinant forms IFNa and IFNb as per IFNAR1.

1.04.3.2

The type II IFN receptors

The Type II interferon, IFN-g, is bound by a single common type II cytokine receptor formed by the binary complex of IFNgR1 and IFNgR2. Both receptors are ubiquitously expressed on the surface of most cell types (Bach et al., 1997), however the expression of IFNgR2 tends to be more tightly regulated (Bach et al., 1995). The IFNgR forms through IFNg binding to one copy of IFNgR1 which causes the recruitment of a second IFNgR1 to form a homodimer. This homodimer then recruits two monomers of IFNgR2 to form the completed tetrameric receptor. As such the complete tetrameric receptor:IFNg structure is composed of two site-1 interfaces between IFNg and IFNgR1, two site-2 interfaces between IFNg and IFNgR2 and two site-3 interfaces between IFNgR1 and IFNgR2 (Thiel et al., 2000; Chill et al., 2003; Mikulecky et al., 2016; Mendoza et al., 2017). Upon formation of the complete IFNgR:IFN complex, signalling progresses via JAK2 (associated with IFNgR2) and JAK1 (associated with IFNgR1) activation. Phosphorylation of tyrosines on both IFNgR1 subunits is then performed by JAKs, leading to the formation of binding sites for STAT1. The subsequent phosphorylation of the STAT1 molecules causes the formation of a STAT1 homodimer which is released from the receptor forming the so called IFNg activation factor (Briscoe et al., 1996). IFNgR1 - The mature human IFNgR1 (54.5 kDa) contains 489 amino acids and consists of an extracellular domain of 228 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 223 amino acids. The IFNgR1 extracellular domain is composed of 2 FNIII domains. Patients with dysfunctional IFNgR1 lack in the ability to respond to mycobacterial and intramacrophagic microorganisms such as Helicobacter pylori (Gutierrez et al., 2016; Thye et al., 2003). Numerous mutations have been reported within IFNGR1 identified as causing medical disorders, including missense/deletions causing complete abolition of cell surface expression (Newport et al., 1996; van de Vosse and van Dissel, 2017). For example, the combination of the mutation Val61 to Glu and the deletion of Glu218 causes severe susceptibility to mycobacterial infections (Jouanguy et al., 2000). These mutations do not affect the expression of the receptor, but instead prevent binding of the IFNg ligand and the progression of the signalling cascade. There are two forms of IFNgR1 deficiencies that can occur in humans and are caused by mutations in IFNGR1. The first type of deficiency is caused by a single substitution mutation (I87T) which decreases IFNgR1 expression on the cell surface and impairs IFNg responses (Jouanguy et al., 1997; Remiszewski et al., 2006). The second type of deficiency is caused by a dominant IFNgR1 mutation which occurs at exon 6 (818del4) (Jouanguy et al., 2000; Lee et al., 2009; Glosli et al., 2008; Prando et al., 2010). This mutation gives rise to a premature stop codon in the proximal intracellular domain resulting in a truncated receptor lacking the intracellular components needed to trigger signalling. These truncated proteins accumulate on the cell surface and impede signal transduction by exerting a dominant-negative effect on normal INFgR1 molecules (Okada et al., 2007; Storgaard et al., 2006; Villella et al., 2001). Patients with these IFNgR1 deficiencies are more susceptible to mycobacterial disease. In addition, a mutation in the initiation codon of IFNgR1 (M1K) has also been found to occur (Kong et al., 2010). This mutation impairs IFNgR1 expression leading to an IFNgR1 deficiency despite the protein being of a normal molecular weight and function. Patients with this mutation have a severe clinical phenotype almost as severe as patients with a complete IFNgR1 deficiency (Kong et al., 2010). Antagonist: There are currently no available clinically approved antagonists. Agonist: Recombinant Interferon gamma 1b is used to treat chronic granulomatous disease (Errante et al., 2008) and osteopetrosis (Key et al., 1995). It binds to the IFNgR1 receptor stimulating the formation of the active signalling complex with IFNgR2 and leads to activation of signalling. However, IFNg therapies result in extremely toxic side effects such as disorientation, anorexia, rigors and in the most severe cases death. Therefore, using IFNg to treat pro-inflammatory/cancerous pathologies will require an alternative approach. IFNgR2 - The mature human IFNgR2 contains 337 amino acids (38 kDa) and consists of an extracellular domain of 220 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 69 amino acids. The IFNgR2 extracellular domain is composed of 2 FNIII domains. IFNgR2 mutations are numerous and confer similar properties as those for IFNgR1. An IFNgR2 substitution mutation (G227R) causes complete loss of expressed IFNgR2 on the cell surface leading to recurrent mycobacterial infection (Kilic et al., 2012). A rare case of altered glycosylation through an inherited missense mutation that created a new glycosylation site is described in (Vogt et al., 2005). The additional glycosylation site caused assembly of a non-functional receptor on the cell surface. Misfolding caused by removal of glycosylation sites through 4 mutations (R114C, S124F, G141R and G227R) has also been described for patients that were found to have lowered levels of IFNgR2 expressed on the cell surface and impaired response to IFN-g (Moncada-Vélez et al., 2013). In this case the mis-folding caused the majority of expressed receptors to be retained within the endoplasmic reticulum. Antagonist: There are currently no available clinically approved antagonists. Agonist: Interferon gamma 1b as described for IFNgR1.

Cytokine Receptors 1.04.3.3

53

The IL-10Rb and related family receptors (IFN class III - IL-28R):

The IL-10Rb receptor family, is responsible for the binding of IL-10, the IL-20 sub-family (IL-19, IL-20, IL-22, IL-24 and IL-26), and the type III IFN-ls. Several receptors are utilized by these cytokines, with two low affinity beta receptors and four high affinity alpha receptors. The stoichiometry of the signalling active receptors for the family diverges with a tetrameric receptor in the case of IL10, but heterodimeric receptors for the remainder of the family. In all cases, signalling proceeds via the JAK/STAT pathway. IL-10Rb - The mature human IL-10Rb contains 325 amino acids (37 kDa) and consists of an extracellular domain of 201 amino acids, a membrane-spanning region of 22 amino acids, and a cytoplasmic domain of 83 amino acids. It is a shared cell surface receptor for IL-10, IL-22, IL-26, IL-28 and the type III IFN-ls (Moore et al., 2001). The IL-10Rb extracellular domain is composed of 2 FNIII domains. It is constitutively expressed in most cell types and provides the low affinity half of the receptor complex utilized by all members of the IL-10Rb family. Cytokine binding and receptor formation is a two-step process, with initial binding to site-1 on the high affinity receptor a-chain producing a dimer complex that then has sufficient affinity to bind the b-chain (site-2) to form the signalling active complex (Josephson et al., 2001). Signalling involves the JAK/STAT pathway, with the cytokine specific signalling patterns determined by the a-partner within the receptor complex (Moore et al., 2001; Walter, 2014; Bedke et al., 2019). A large number of mutations have been identified in IL-10Rb and have been linked to disease. These have recently been summarized by (Mandola et al., 2017) with mutations, deletions and splicing defects all highlighted. Typical disease states caused by mutations in this family are Very Early Onset Inflammatory Bowel Syndrome (VEI-IBD), dermatitis, arthritis, folliculitis, B-cell lymphoma and immune hepatitis (Beser et al., 2015; Moran et al., 2013; Galatola et al., 2013; Glocker et al., 2009; Kotlarz et al., 2012; Engelhardt et al., 2013). Antagonists: There are currently no available clinically approved antagonists. Agonists: The IL-10 receptor family does not as yet have an approved drug available in the clinic. However, recombinant IFNl has been investigated in a number of clinical trials against Hepatitis C (Donnelly et al., 2011). IL-10Ra - The mature human IL-10Ra contains 578 amino acids (63 kDa) and consists of an extracellular domain of 214 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 322 amino acids. It participates in IL10mediated anti-inflammatory functions, acting to limit excessive damage to tissues caused by inflammation. The IL-10Ra extracellular domain is composed of 2 FNIII domains. It is the high affinity a receptor subunit utilized by IL-10. It is expressed on most hematopoietic cells at a basal level but is upregulated by various cells upon activation, suggesting its importance in inhibitory pathways. The 2IL-10Rb:2IL-10Ra receptor is formed upon IL-10 binding and activates JAK1 (IL10Ra) : TYK2(IL10Rb) and leads to the recruitment, phosphorylation and activation of STAT3, and to lesser extent STAT1. As is the case with IL-10Rb, numerous mutations have been identified and are located widely throughout the protein. In addition to individual mutations, mis-splicing events and deletions have also been detected (Mandola et al., 2017; Zhu et al., 2017; Khoshnevisan et al., 2019). In terms of the clinical presentation of the genetic dysregulation various disease states have been linked to IL-10Ra many unexpectedly similar to that of IL-10Rb: VEO-IBD, eczema, food intolerance, pyoderma gangrenosum, sepsis, folliculitis, arthritis, atopic dermatitis, B cell lymphoma, immune thrombocytopenic purpura & juvenile myelomonocytic leukemia (Galatola et al., 2013; Glocker et al., 2009; Lee et al., 2014; Kim et al., 2014; Neven et al., 2013; Shim et al., 2013; Shim and Seo, 2014; Yanagi et al., 2016). Antagonists: There are currently no available clinically approved antagonists. Agonists: There are currently no available clinically approved agonists. Due to IL-10s potent anti-inflammatory properties, recombinant IL-10 was thought to be a hopeful approach into treating autoimmune disease. However, despite significant efficacy in mouse studies, IL-10 therapy failed to render any significant results in the clinic (Cardoso et al., 2018; Colombel et al., 2001; Buruiana et al., 2010). More recently, a pegylated version of IL-10 (Pegilodecakin) was shown to induce systemic CD8 þ T cell activation in cancer patients (Naing et al., 2018). Moreover, new engineered IL-10 variants with enhanced activities have been described, holding the potential to rescue failed IL-10 therapies (Gorby et al., 2020; Saxton et al., 2021).

1.04.3.3.1

The IL-20 receptor cytokine sub-family

Interleukins 19, 20, 22, 24 and 26 form a subset of the IL-10 family, sometimes referred to as the IL-20 receptor cytokines. There are 4 receptors utilized by this sub-family to initiate signalling, IL-20Ra, IL-22Ra1 along with IL-10Rb. These receptors are all highly expressed in epithelial cells, osteoclasts and in respiratory and intestinal epithelial cells. A naturally occurring bloodstream form of the IL-22Ra receptor, IL-22Ra2 (263 amino acids, 30.5 kDa), is expressed by dendritic cells (Xu et al., 2001; Huber et al., 2012), and binds to IL-22 at a higher affinity than the membrane bound for and acts to suppress IL-22 activity (Jones et al., 2008). Signalling progresses through JAK1/TYK2 and STAT1, STAT3 and STAT5. IL-20Rb - The mature human IL-20Rb contains 311 amino acids (35 kDa) and consists of an extracellular domain of 204 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 57 amino acids. IL-20Rb extracellular domain contains 2 FNIII domains. IL-20Rb is the alternative low affinity receptor found within the IL-10 family. It interacts with IL-19, IL-20 and IL-24 forming a dimeric receptor in each case with either IL-20Ra (IL-19, IL-20 & IL-24) or alternatively with IL-22Ra1 (IL-20 & IL-24). Expression is highest in skin keratinocytes, osteoclasts and in intestinal and airway epithelial cells, although IL-20Rb has also been detected in hematopoietic cells (Nagalakshmi et al., 2004). Cytokine binding and receptor formation is a two-step process, with initial binding to site-1 on the high affinity receptor alpha chain producing a complex that then has sufficient affinity to bind to site-2 on IL-20Rb and form the signalling active complex. Signalling involves the JAK/STAT pathway, with IL-20Rb associating with JAK1. The cytokine specific signalling patterns determined by the a-partner within the receptor complex. T104M mutation in IL-20Rb renders the receptor incapable of launching the appropriate signalling response post IL-20 family stimulation and

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Cytokine Receptors

has implications for Glaucoma (Keller et al., 2014; Wirtz and Keller, 2016). Antagonists: There are currently no available clinically approved antagonists. Agonists: There are currently no available clinically approved agonists. IL-20Ra - IL-20Ra is a high affinity receptor for IL-19, IL-20 and IL-24 forming a heterodimeric receptor with IL-20Rb. IL-20 and IL-24 can also signal via IL-20Ra1:IL-20Rb. Meanwhile, IL-26 can also signal through IL-20Ra but in this case it does so in conjunction with the low affinity receptor IL-10Rb. The mature human IL-20Ra contains 553 amino acids and consists of an extracellular domain of 221 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 282 amino acids. The IL-20Ra extracellular domain is composed of 2-FNIII domains. The IL-20Ra:IL20Rb receptor complex progresses through site-1 capture of IL-19, IL-20 or IL-24 by IL-20Ra followed by recruitment through site-2 binding of an IL-20Rb monomer to form the active signalling complex. The IL-26 receptor (IL-20Ra:IL10Rb) signals via IL-26 binding to IL-20Ra first and the recruiting IL10Rb to the complex. Phosphorylation of JAK1 and TYK2 is followed by the binding and phosphorylation of STAT1 or STAT3. Limited information is available on mutations within IL-20Ra, however several SNPs that show an association of IL-20Ra (and IL-20Rb) polymorphisms with chronic disease, and in particular with psoriasis have been described (Kingo et al., 2008). Antagonists: There are currently no available clinically approved antagonists. Agonists: There are currently no available clinically approved agonists. IL-22Ra1 - The mature human IL-22Ra1 contains 574 amino acids and consists of an extracellular domain of 213 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 325 amino acids. The IL-20Ra1 extracellular domain is composed of 2 FNIII domains. IL-22Ra1 forms an active signalling complex with IL-22, recruiting a monomer of IL10Rb, triggering the phosphorylation and activation of the JAK1/TYK2/STAT3 pathway (Lejeune et al., 2002). This sub-family elicits both pro- and anti-inflammatory responses during autoimmune diseases and chronic inflammation and have been implicated in inflammatory bowel disease (Niess et al., 2018), psoriasis (Kingo et al., 2008) and arthritis (Kragstrup et al., 2018). IL22Ra1 K58A mutation strongly reduces responses to IL-22 and IL-22Ra1. A Y60A mutation results in complete loss of response to IL-22 (Bleicher et al., 2008). Antagonists: There are currently no available clinically approved antagonists. Agonists: While no drugs are currently available in the clinic, several are progressing through clinical trials. Promenakin (F-652), an IL-22-Fc dimer (IgG2Fc) is in phase 2 trials against alcoholic hepatitis (Arab et al., 2020), graft versus host disease (Ponce et al., 2020) and COVID-19 (Drugbank Accession Number: [DB16366]). RG7880 (Efmarodocokin-a) an IL-22Fc IgG4 fusion is in phase 2 trials against ulcerative colitis, Crohn’s disease and COVID-19 (Drugbank Accession Number: [DB16398]), (Stefanich et al., 2018). IL-28Ra (IFNLR) - The mature human IL28Ra (57.6 kDa) contains 520 amino acids and consists of an extracellular domain of 208 amino acids, a membrane-spanning region of 21 amino acids, and a cytoplasmic domain of 271 amino acids, forms a heterodimeric receptor with its low affinity partner IL-10Rb (Mendoza et al., 2017; Miknis et al., 2010). The IL-28Ra extracellular domain is composed of 2 FNIII domains. It is responsible for the binding of the IFNl cytokines IFNl 1-3, also known as IL-29, IL-28a and IL28b, plus IFNl-4. Expression of this receptor is largely in keratinocytes, but significant expression is also found in the colon, pancreas, thyroid, skeletal muscle, heart, prostate & testis (Sheppard et al., 2003; Sommereyns et al., 2008). The signalling complex is a heterodimer composed of a single molecule each of IL-28Ra and IL-10Rb. Signalling proceeds through site-1 binding of the IFNl with IL28Ra, followed by site-2 binding of a monomer of IL-10Rb. The complete complex then causes the phosphorylation of the JAK1/TYK2/STAT1/STAT2 pathway. Some phosphorylation of STATS3/STAT5 have also been described with activation by IFNl’s (Li et al., 2009). The specific signalling response to IFNl is believed to be a direct relation to the level of IL-28Ra receptor on the surface of the cell and hence tissue specific (Sommereyns et al., 2008), however there is also significant variation in the strength of the binding affinity of the different IFN-l species (Mendoza et al., 2017; Syedbasha et al., 2016). Dysfunction of this receptor has been implicated in psoriasis and lupus (Goel et al., 2020), interestingly an IFNLR1 allele that is protective for psoriasis was found to be associated with exacerbated systemic lupus erythematosus (Li et al., 2013). Antagonists: There are currently no available clinically approved antagonists. Agonists: Pegylated IFN-l investigated for use against Hepatitis (Phillips et al., 2017) and COVID-19 infections (Feld et al., 2021).

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An Overview of Steady-State Enzyme Kineticsq

1.05

Thomas D. Meek, Department of Biochemistry & Biophysics, Texas A&M University, College Station, TX, United States © 2022 Elsevier Inc. All rights reserved.

1.05.1 1.05.2 1.05.3 1.05.3.1 1.05.3.2 1.05.4 1.05.4.1 1.05.5 1.05.6 1.05.6.1 1.05.6.2 1.05.6.3 1.05.6.4 1.05.6.5 1.05.7 1.05.7.1 1.05.7.2 1.05.8 A A.1 A.2 References

1.05.1

Introduction Nomenclature Enzyme kinetics in the steady-state Steady-state vs. rapid equilibrium kinetic models Kinetic rate law under steady-state conditions Kinetic mechanism Use of initial velocity, product, and dead-end inhibition studies to determine kinetic mechanisms Literature examples of kinetic mechanisms Rate-limiting steps Use of net rate constants to calculate kinetic parameters The use of net-rate constants and the determination of rate-limiting steps in enzymatic catalysis Kinetic isotope effects and rate-limiting steps Examples The effects of solution viscosity on enzyme-catalyzed reactions Determination of the chemical mechanisms of enzyme catalysis using steady-state kinetics Examples from the literature Formate dehydrogenase Concluding remarks Appendix Rapid-Equilibrium Random Bi Bi Kinetic mechanism. Full derivation of the initial velocity expression Expressions for pH-rate profiles for kcat/Km and kcat for

65 65 66 67 68 72 78 82 90 91 93 94 97 98 99 107 107 109 109 113 114 116

Introduction

Enzymes are a large sub-class of proteins that catalyze the chemical reactions that comprise the essential processes of cellular metabolism. Put simply, the study of enzymes involves both form and function: form includes characterization by proteomics, nuclear magnetic resonance, and particularly, structural solution by X-ray crystallography, while function is exemplified in large part by enzyme kinetics, the analysis of the rates of the chemical reactions catalyzed by enzymes, which provides much of the data necessary to fully elucidate the chemical mechanism. Enzyme kinetics as a field of study has a long and venerable history that over the past 120 years has paralleled the advances made in molecular biology and biochemistry. Of the plethora of excellent textbooks, chapters, and review articles devoted to this subject since the early 1960s, the works of Cleland (1963a,b,c, 1970, 1977), along with Cook (1991), and the textbooks of Segel (1975), Cook and Cleland (2007), and Johnson (2019), while sometimes diverse in their areas of emphasis, each provide essential treatises. We will make no attempt here to review these works, but rather select areas of focus from them, with an inclination toward the conventions employed by Cleland (1963a,b,c, 1970, 1977) and Cook and Cleland (2007), and the preponderance of nomenclature, notation, mathematics, and methodologies from these references in this overview are such that they are not exhaustively referenced below. We will also address the interplay between enzyme kinetics, 50 years hence, in a scientific environment in which kinetic results are often contemporaneous with detailed structural data for enzyme-ligand complexes. A distinction of this overview is the provision of significant algebraic detail in the derivation of equations, for which the minority of expert readers may ignore, with the hoped-for gratitude of the presumed non-expert majority.

1.05.2

Nomenclature

Enzyme Substrates, Products, Reaction Intermediates, and Inhibitors. For this chapter, we will notate enzyme substrates and co-enzymes as A, B, C, etc., enzyme products as P, Q, R, S, etc., enzyme inhibitors as I, J, etc., and enzyme-bound reaction intermediates as X, Y,

q

This chapter is dedicated to Professors W.W. Cleland and John S. Blanchard. The author also wishes to acknowledge his more than 40-year association and friendship with Grad Student #13 from the Cleland lab, Professor Frank Raushel, and also the mentorship he has received from Professors Raushel, Paul Cook, Vern Schramm, and Ken Johnsondfine kineticists all, though all but one has managed to maintain the steady-state. The author thanks Kayla Glockzin for critical reading of the manuscript.

Comprehensive Pharmacology, Volume 1

https://doi.org/10.1016/B978-0-12-820472-6.00124-9

65

66

An Overview of Steady-State Enzyme Kinetics

etc. (Cleland, 1963a,b,c, 1970, 1977; Johnson, 2019). This alphabetical nomenclature replaces the use of S to designate a substrate in order to accommodate enzymes that bind multiple substrates. Where metal ions are bound, they will be designated in kind. Binary, ternary, or quaternary complexes are defined as, respectively, an enzyme bound to 1, 2, or 3 ligands. Examples of this are: EA, EAB, EAI, or EPQR complexes. In ordered kinetic mechanisms, as described below, A and B are the respective first and second substrates to bind to enzyme, P and Q are the first and second products to be released from the enzyme, while for a random mechanism, A and B are and P and Q are merely different substrates and products, respectively. Enzyme Forms. As with all catalysts, an enzyme is restored to its original chemical form at the end of each catalytic cycle; that is, a free enzyme form (with no ligands bound except prosthetic co-factors and metal ions) designated as E, will be E before and after catalysis. Enzyme forms are of two types: transitory and stable. Enzyme complexes such as EA, EB, EQ, EPQ, and EI are transitory complexes; they exist solely in the presence of the added ligand, and cannot be isolated. The designation of E in transitory complexes indicates that the free enzyme form has not be chemically (covalently) modified. Stable enzyme forms are designated as E, F, G, etc., for which F and G are forms of E that have been modified into stable, or metastable, covalent, reaction intermediates during substrate turnover, such as in double-displacement (ping-pong) mechanisms. E becomes F, and if modified in a second covalent reaction, then G. This does not include covalent post-translation modifications, but rather the stable, modified enzyme forms of F, G, or beyond, which arise as intermediates in the chemical mechanism of the enzymatic catalysis. Examples of stable enzyme forms include the pyridoxamine-enzyme form in transaminase catalysis, the thiohemiacetal formed between glyceraldehyde 3phosphate and glyceraldehyde 3-phosphate dehydrogenase, and the phospho-enzyme intermediate in the reaction catalyzed by ATP-citrate lyase (Fan et al., 2012). Stable enzyme complexes are often isolable. Those enzyme forms that undergo reaction, such as EAB 0 EPQ or EA 0 FP are referred to as central complexes; here enzyme catalysis converts substrates A and B to products P and Q, (EAB ¼> EPQ), and EA leads to a modified enzyme form FP, which then releases P after an initial half-reaction in a doubledisplacement mechanism, as elaborated below. Rate and Equilibrium Constants. The rate at which an enzyme form proceeds through a specific mechanistic step is described by a microscopic rate constant, kn. Forward and reverse rate constants are notated here by respective odd- and even-numbered subscripts, so that kn þ 1 (n  1) is the reverse rate constant of a step described by the forward rate constant, kn (Mechanism I). Microscopic rate constants are either uni- or bi-molecular: the addition of substrate A to free enzyme E to produce binary complex EA is described by a bi-molecular rate constant (k1, typical units of M 1 s 1), while the release of A from EA is described by the unimolecular rate constant k2 (typical units of s 1). Michaelis and equilibrium constants (such as inhibition or dissociation constants) are respectively notated by Kn, Ki, or Kin where n designates the germane substrate or product. For example, a Michaelis constant for a given substrate contains a subscript designating each substrate: Ka, Kb, Kc. When one studies the reverse reaction, these substrates become product inhibitors defined by the inhibition constants: Kia, Kib, Kic, wherein Kia ¼ k2/k1, as defined above, which is the dissociation constant for the binding of substrate A to E, as given by Kia ¼ [E][A]/[EA] ¼ k2/k1. Likewise, when enzyme products are added to a studied reaction, their inhibition constants name the product, such as Kip, Kiq, Kir, where for example, Kiq ¼ [E] [Q]/[EQ]. Inhibition constants are therefore, generally, a subset of dissociation constants. Inhibition of E by binding of inhibitors I or J is defined by inhibition constants Ki and Kj, which equal the ratio of the rate constants for desorption/binding (koff/kon; Ki ¼ [E] [I]/[EI] ¼ k8/k7 in Mechanism I) for either inhibitor.

1.05.3

Enzyme kinetics in the steady-state

In general, the use of steady-state kinetic methods to characterize enzyme mechanisms involves three phases: (1) the determination of an enzyme’s kinetic mechanism, (2) the determination of the rate-limiting step(s) of an enzyme mechanism, and (3) the elucidation of the chemical, or catalytic, mechanism of the enzyme including the identification of reaction intermediates therein, as well as identification of the amino acid residues involved and their catalytic roles. A fourth phase is the characterization of the structure of the transition state(s) of an enzymatic mechanism, which almost always employs the use of kinetic isotope effects, as practiced in particular in the laboratories of Cleland (1977), Cook and Cleland (2007), Cook (1991), Cook and Cleland (2007) and Schramm (2018), and many others. We will make mention of this fourth phase below. The kinetic mechanism of a multi-substrate/multiproduct enzyme is the order, or lack thereof, of substrate binding and product release. The rate-limiting (or rate-determining) step(s) of an enzyme mechanism describes which chemical, binding, or desorption events comprise the slowest rates in enzymatic catalysis. Rate-limiting steps for enzymatic catalysis may include chemical steps, as is the case for non-enzymatic catalysis, but may also arise from non-chemical steps, such as (a) the slow release of reaction products, (b) an enzyme isomerization event, or (c) even the binding of substrates. When an enzyme is rate-limited by substrate binding, that is, the slowest step in catalysis is the collision of an enzyme form with its substrate(s), the enzyme is thought to be at its ultimate state of evolution, as the slowest step in a multi-step mechanism is the velocity at which a substrate may diffuse through solution to adsorb to the enzyme. Such enzymes are referred to as being under “diffusion-control,” and the bi-molecular rate constants of diffusion-controlled enzymes, such as the glycolytic enzyme, triose phosphate isomerase, are on the order of limiting values of 108–109 M 1 s 1. An enzyme-catalyzed reaction may not exceed this rate (Blacklow et al., 1988). Methods for ascertaining which reaction steps of an enzyme mechanism are ratelimiting are discussed below. The chemical, or catalytic, mechanism of an enzyme is defined by the chemistry that occurs in enzyme-catalyzed reactions, the structures of enzyme-bound reaction intermediates, some of which are stable and even isolable, and the catalytic involvement of enzymatic amino acids, including lysine, arginine, histidine, aspartate, glutamate, tyrosine, serine, threonine, proline and histidine. In general, enzymatic residues act as nucleophiles, forming covalent bonds with a fragment of

An Overview of Steady-State Enzyme Kinetics

67

a substrate, or provide general-acid or general-base catalysis, or both, by proton-transfer (prototropic) processes. Electrophilic catalysis in enzymatic catalytic mechanisms generally involves co-enzymes and metal-ion co-factors. Methods for elucidating the chemical mechanisms of enzymes are discussed below.

1.05.3.1

Steady-state vs. rapid equilibrium kinetic models

Mechanism I describes a simple uni-reactant enzyme which catalyzes the formation of P through a single chemical step k3, followed by its release described by the k5 step. The dissociation and product inhibition constants for this mechanism are: Kia ¼ k2/k1 and Kip ¼ k6/k5, respectively, while the Michaelis constant, Ka, will be defined below. (I)

The relative concentrations of [E], [EA], or [EP] will be completely dependent on the fixed concentration of A, how much total enzyme, [Et], is added to the reaction mixture, and the values of k1  k5. The maximal velocity, Vmax, will be observed only when [A] 0 N, under which conditions [E] ¼ 0, [Et] ¼ [EA] þ [EP], and the initial velocity will be determined solely by the rate constants k3, k4, and k5. Shown in Fig. 1 is a simulated model of the concentrations of substrate A, product P, and enzyme species E, EA, and EP as established within milliseconds of addition of enzyme to a reaction mixture containing substrate A. For the time-course model in Fig. 1A and B, the values of k1  k6 as shown in the figure legend are in a setting in which [A]0 ¼ 10[Et], k3 < k5, and importantly, k2 ¼ k3. In this pre-steady-state phase (t < 20 ms), free enzyme E binds substrate A, leading to the rapid diminution of [A] and [E], with a commensurate accumulation of [EA], the Michaelis complex. Substrate A desorbs from the EA complex as rapidly as it progresses to the EP complex (k2 ¼ k3 ¼ 10 s 1). A substrate with these kinetic properties is referred to as a “sticky” substrate, wherein k3/ k2  1.0. The chemical reaction step (k3 ¼ 10 s 1) is far slower than the release of product P (k5 ¼ 50 s 1), exemplified by the

(B)

(A)

[A]

[E]

[EA]

[EP]

[P]

[P]

Concentration (µM)

Concent r at i on (µM )

[A]

[E]

Time (0-200 ms)

[EA]

[EP]

Time (0-1000 ms)

(C)

(D)

Concent r at i on ( µM)

Concentration (µM)

[A]

[E]

[EP] [EA]

Time (0-200 ms)

[P]

[E]

[EA]

[EP]

[P]

Time (0-1000 ms)

Fig. 1 Time courses of concentrations of species for Mechanism I at 0–200 ms and 0–1 s. (A) and (B): 8 1 1 1 1 1 1 [A ] 0 = 0.5 M, [ E ] 0 = 50 nM, [ EA ] 0 = [ EP ] 0 = [P ] 0 = 0, [E ] 0 = 50 nM, k1 ¼ 5  10 M s , k2 ¼ 10 s , k3 ¼ 10 s , k4 ¼ 1 s , k5 ¼ 50 s , k6 ¼ 0 s 1; (C) and (D): [A ] 0 = 0.5 M, [ E ] 0 = 50 nM, [EA ] 0 = [ EP ] 0 = [P ] 0 0, [ E ] 0 = 50 nM, k1 ¼ 5  108 M 1 s 1, k2 ¼ 1000 s 1, k3 ¼ 10 s 1, k4 ¼ 1 s 1, k5 ¼ 50 s 1, k6 ¼ 0 s 1. Simulations were generated using Kintek Explorer® (Johnson et al., 2009).

68

An Overview of Steady-State Enzyme Kinetics

accumulation of [EA] compared to [EP]. After 20 ms, the concentrations of the transitory complexes E, EA and EP achieve steadystate levels, that is, they remain unchanged until P accumulates and A is depleted (Fig. 1B). The kinetic rate law of this steady-state model is derived below. For the simulation in Fig. 1C and D, all rate constants remain the same as in Fig. 1A and B, except for k2 ¼ 1000 s 1. Substrate A rapidly desorbs from the EA complex, k3/k2 ¼ 0.01, and [E] [ [EA], that is, a steady-state concentration of EA is not present. The accumulation of an EA complex is driven by mass-action, that is, the equilibrium constant of the EA complex and [A] drives its formation. This case is known as the rapid-equilibrium model, and is derived below.

1.05.3.2

Kinetic rate law under steady-state conditions

We may derive rate laws for the Mechanism I based on these two models: the steady-state and rapid equilibrium models. The rate of the reaction, v, is determined by measuring the diminution of the concentration of A or the increase in the concentration of P over time, exactly as in the case of chemical kinetics. These two rates will be equal yet opposite, and are defined by Eq. (1), and are as exemplified in Fig. 1. Enzyme kinetics are performed under initial velocity (initial rate) conditions, in which reaction proceeds to an extent of < 10%. In Fig. 1, this is true at t < 200 ms, but for lower concentrations of enzyme (1–10 nM) and much higher concentrations of substrate A (0.1–10 mM), this initial rate phase can last for many minutes (The “extreme” concentrations of A and Et in Fig. 1 were designed to allow visualization of substrate, product, and enzyme complexes in a single figure). Under initial velocity conditions [At]  [A0] and [Pt]  0, so that we may set k6 ¼ 0 in Mechanism I. Accordingly, the [A] is effectively unchanged, and there is no re-binding of the product, which would lead to inhibition due to formation of the EP complex, because the accumulation of P during the determination of v is negligible, as evident by observation of a linear reaction rate. The initial velocity is given by Eq. (1): v ¼  d½A =dt ¼ d½P=dt

(1)

For Mechanism I, one may write the following rate laws: d½E=dt ¼  k1 ½E½A  þ k2 ½EA  þ k5 ½EP

(2)

d½EA=dt ¼ k1 ½E½A   ðk2 þ k3 Þ½EA  þ k4 ½EP

(3)

d½EP=dt ¼ k3 ½EA  ðk4 þ k5 Þ½EP

(4)

and where the total enzyme and substrate concentrations are given by, respectively, [Et] and [At]: ½Et  ¼ ½E þ ½EA þ ½EP

(5)

½A t  ¼ ½A 0  ¼ ½A 

(6)

In general, ½A t : >> ½Et  [P] ¼ 0, but [EP] > 0 Under these steady-state conditions as in Fig. 1A and B, the rate of change of these enzyme forms equals zero, so that Eq. (2) to Eq. (4) all equal zero, and one may solve for the concentrations of all three enzyme forms in terms of [A], [Et], and the rate constants. From the equality of Eq. (2) to Eq. (4), we may write: ½EA  ¼ fðk4 þ k5 Þ=k3 g ½EP

(7)

k1 ½E½A  ¼ fðk2 þ k3 Þðk4 þ k5 Þ=k3 Þg½EPÞ  k4 ½EP

(8)

and substituting Eq. (5) to express [E], we obtain: k1 f½Et   ½EA  ½EPg½A  ¼ ððk2 þ k3 Þðk4 þ k5 Þ=k3 ½EPÞ  k4 ½EP k1 f½Et   ððk4 þ k5 Þ=k3 Þ½EP  ½EP½A  ¼ ððk2 þ k3 Þðk4 þ k5 Þ=k3 Þ½EPÞ  k4 ½EP k1 f½Et   ððk4 þ k5 Þ=k3 Þ½EP  ðk3 =k3 ÞEPg½A  ¼ ððk2 þ k3 Þðk4 þ k5 Þ=k3 Þ½EPÞ  k4 ½EP k1 k3 ½A ½Et  ¼ fðk4 þ k5 Þ½EP þ k3 EP½A  þ ðk2 k4 þ k2 k5 þ k3 k4 þ k3 k5 Þ½EPÞ  k3 k4 ½EP k1 k3 ½A ½Et  ¼ ½EPðk3 þ k4 þ k5 Þ½A  þ ðk2 k4 þ k2 k5 þ k3 k5 Þ k1 k3 ½A ½Et =ðk3 þ k4 þ k5 Þ ¼ ½EPf½A þ ðk2 k4 þ k2 k5 þ k3 k5 Þ=ðk3 þ k4 þ k5 Þg k3 ½A ½Et =ðk3 þ k4 þ k5 Þ ¼ ½EPf½A  þ ðk2 k4 þ k2 k5 þ k3 k5 Þ=k1 ðk3 þ k4 þ k5 Þg

69

An Overview of Steady-State Enzyme Kinetics and, ½EP ¼ fk3 ½A ½Et =ðk3 þ k4 þ k5 Þgf½A  þ ðk2 k4 þ k2 k5 þ k3 k5 Þ=k1 ðk3 þ k4 þ k5 Þg The initial velocity for this mechanism is given by any of the steps, and here we choose: v ¼ k5 ½EP ¼ fk3 ½A ½Et =ðk3 þ k4 þ k5 Þgf½A  þ ðk2 k4 þ k2 k5 þ k3 k5 Þ=ðk1 ðk3 þ k4 þ k5 ÞÞg

(9)

For which we can define the collections of rate constants as: Vmax ¼ k3 k5 ½Et =ðk3 þ k4 þ k5 Þ; Ka ¼ ðk2 k4 þ k2 k5 þ k3 k5 Þ=ðk1 ðk3 þ k4 þ k5 ÞÞ So that Eq. (9) becomes ¼ Vmax ½A=ðKa þ ½A Þ

(10)

Eq. (10) is the Michaelis-Menten Eq. (13), (Michaelis and Menten, 1913) in which Vmax is the maximal velocity and Ka is the Michaelis constant, for which microscopic rate constants k1  k5 are specific for Mechanism I, that is: Vmax ¼ k3 k5 ½Et =ðk3 þ k4 þ k5 Þ

(11)

kcat ¼ k3 k5 =ðk3 þ k4 þ k5 Þ

(12)

the turnover number for the enzyme and the specificity constant for the enzyme and substrate A is: kcat =ðKa ½Et Þ ¼ k1 k3 k5 =ðk2 k4 þ k2 k5 þ k3 k5 Þ

(13)

At ½A :  Ka ; ½Et  ¼ ½E and at ½A :[Ka ; ½Et  ¼ ½EA þ ½EP What this means is that when [A] [ Ka, Eq. (10) reduces to: v ¼ Vmax ¼ kcat Et

(14)

v ¼ ðVmax =Ka Þ½A  ¼ ðkcat Et =Ka Et Þ ½A  ¼ ðkcat =Ka Þ½A 

(15)

and when [A]  Ka, Eq. (10) reduces to: The initial rate of Eq. (14) reports on the condition where [Et] ¼ [EA] þ [EP], and for Eq. (15), [Et] ¼ [E]. Note that Eq. (14) is a kinetic rate law that has zero-order dependence on [A] with a rate constant ¼ Vmax ¼ kcatEt which is comprised of rate constants for steps following the binding or desorption of A. For Eq. (15), the kinetic rate law has a first-order dependence on [A], with a rate constant of Vmax/Ka (kcatEt/Ka), which is comprised of kinetic steps defined by substrate binding and desorption (k1 and k2), and subsequent kinetic steps which include the chemical steps (k3 and k4), and the first irreversible step in the mechanism, namely, release of product P (k5). When the chemical step is slower than product release k3  k5, and k4 ¼ 0, kcat  k3, and when product release is slower than chemistry, k5  k3 and k4 ¼ 0, kcat  k5. For these two respective cases, Ka ¼ (k2 þ k3)/k1 and Ka ¼ (k2 þ k3)k5/k3k1. The former case is more familiar in which Ka ¼ (k2 þ k3)/k1 which describes a substrate partitioning between progression through catalysis, k3, or desorption from the EA complex, k2. Accordingly, two of the three kinetic parameters, Vmax and Vmax/Ka describe different aspects of the overall mechanism. Note also that when k4 ¼ 0, the first irreversible step in Mechanism I is now k3, and the expression for kcat/Ka becomes k1k3/(k2 þ k3), demonstrating again that the parameter kcat/Ka reports on all kinetic steps beginning with substrate binding up to the first irreversible step in the mechanism. Also, when k4 ¼ 0, kcat ¼ k3k5/(k3 þ k5) ¼ 1/[1/k3 þ 1/k5]. Rapid-Equilibrium Conditions. For Mechanism I, when chemistry (k3) is wholly the rate-limiting step, as shown in Fig. 1, [EA] and [EP] are at very low concentrations compared to [E], and never achieve steady-state concentrations. One may view this as the inability of the chemical step to “sweep” the bound substrate through to product before it falls off the enzyme. The rate law for the rapid-equilibrium case assumes that the chemical step is rate-limiting, that is: v ¼ k3[EA], and for which [Et] ¼ [E] þ [EA] þ [EP] and the germane equilibrium constants are: Kia ¼ ½E½A =½EA ¼ k2 =k1 and Kip ¼ ½E½P=½EP ¼ k6 =k5 To express [EA] in terms of the measurable concentration of [Et], ½EA  ¼ ½Et ½EA =ð½E þ ½EA þ ½EPÞ ¼ ½Et½EA =ð½EA ðKia=AÞ þ ½EA  þ ½EA  ½EAðKia=AÞðKip=½P ¼ ½Et=ð1 þ Kia=Að1 þ Kip=½PÞÞ So,


 
v ¼ k3 ½EA ¼ k3 ½Et =ð1 þ Kia =A 1 þ Kip =½P ¼ k3 ½Et ½A =ð½A  þ Kia 1 þ Kip =½P

70

An Overview of Steady-State Enzyme Kinetics

which equals v ¼ k3[Et][A]/(Kia þ [A]) when k6 ¼ 0, and which may be written as: v ¼ kcat ½Et ½A =ðKia þ ½A Þ

(16)

The rapid-equilibrium rate law (Eq. 16) has the same form as its steady-state counterpart, Eq. (10), and the two equations only differ by the rate constants found in the kinetic parameters kcat, Kia, and Ka. Under rapid-equilibrium conditions, k2 [ k3, k4, k5 [ k3, and the “steady-state” expression for Ka ¼ (k2k4 þ k2k5 þ k3k5)/k1(k3 þ k4 þ k5) devolves to Ka ¼ k2/k1, which is the substrate dissociation constant, Kia, while kcat ¼ k3. Plotting initial velocity data (v) measured at multiple concentrations of variable substrate A for either a steady-state or rapid equilibrium mechanism will yield a rectangular hyperbola, emblematic of Michaelis-Menten kinetic data (Fig. 2A). At low [A], the plot of v vs. [A] has an apparent first-order dependence on [A] in which [Et]  [E], and v ¼ Vmax[A]/Ka. At high [A], the initial rate is no longer dependent on [A], as the enzyme becomes saturated with A, [Et]  [EA] þ [EP]. Here, v approaches its asymptotic maximal rate, v  Vmax. The concentration of variable substrate A that leads to v ¼ 0.5Vmax is the Michaelis constant, Ka. Lineweaver and Burk converted the Michaelis-Menten equation to its linear, double-reciprocal form Eq. (17), which allowed data-fitting to that of a straight line in which the slope equals Vmax/Ka, and the y-intercept and x-intercepts respectively equal 1/Vmax and  1/Ka (Lineweaver and Burk, 1934). 1=v ¼ ðKa =Vmax Þð1=½A Þ þ 1=Vmax

(17)

The use of Lineweaver-Burk plots (Figs. 2 and 3) allows several advantages for the interpretation of kinetic data, especially when reaction mixtures contain, in addition to the variable substrate A, multiple lines containing “changing-fixed” concentrations of another substrate, a co-factor, or an added inhibitor. (1) The y-intercept at which 1/[A] ¼ 0 is the value of 1/v when [A] 0 N. The reciprocal of the y-intercept therefore provides a true value for Vmax, as well as conditions in which no free enzyme exists. Conversely, the slope of a Lineweaver-Burk plot, as the reciprocal of the parameter Vmax/Ka, reflects conditions in which all enzyme is free enzyme. Consequently, for the binding of variable substrate A to E, variable substrate B to EA, or variable substrate A to EI, one or more fixed concentrations of an additive (B, P, I, metal ion, etc.) will allow assessment by examination of the slope and intercept effects of double-reciprocal plots to which enzyme forms the variable substrate and “changing-fixed” additively bind. This provides a diagnostic tool for elucidating the kinetic mechanisms of enzymes, as described below. For example, in a bi-reactant mechanism in which substrate B binds only to the EA complex (an ordered sequential kinetic mechanism), one expects that the addition of increasing changing-fixed concentrations of substrate B (a different [B] for each line of a plot of 1/v vs. 1/[A]) will convert more and more [EA] to [EAB], which will in turn convert [E] to [EA], thereby changing the ratios of [EA]/[E] and [EAB]/[E], by the reversibly-connected reaction steps: E þ A 5 EA þ B 5 EAB (Fig. 3). For a plot of 1/v vs. 1/[A] at changing-fixed concentrations of B (also called an “initial velocity” plot of B vs. A), a family of intersecting lines will be observed in which increasing fixed concentrations of B will lower the apparent slopes (apparent Ka/Vmax) and y-intercepts (apparent 1/Vmax) of each line in a plot of 1/v vs. 1/[A]. The slopes, which account for the reaction rates when A 0 0, and thereby free enzyme E, are lowered because increasing levels of B lower the ratio [E]/[EA]. Likewise, increasing changing-fixed concentrations of A would elevate the concentrations of EA, and lower the slopes of lines in a plot of 1/v vs. 1/[B] because the ratio [E]/[EA] would be lowered, as there is more EA to which B binds. Binding of B and A are therefore said to have a “reversible connection” as steady-state levels of E, EA, and EAB are interconnected (Figs. 3 and 4). For the y-intercepts, which account for the reaction rate when A 0 N, and thereby when reaction rates are where [EA]>>[E], increasing changing-fixed concentrations of B will decrease the y-intercepts, commensurate with increasing levels of EAB. An initial velocity plot of B vs. A for a sequential bi-reactant enzyme is shown as a double-reciprocal plot in Fig. 3. Each line is a Lineweaver-Burk plot of 1/v vs. 1/[A] at five changing-fixed concentrations of the second substrate B, in which slopes and

(A)

(B)

Fig. 2 (A) Plot of initial velocity v vs. substrate concentration [A] for Mechanism I. The kinetic parameters that generated the data are included in the figure. (B) Double-reciprocal (Lineweaver and Burk, 1934) plot of kinetic data in (A).

An Overview of Steady-State Enzyme Kinetics

(A) 200

[B], (mM)

a

0.3

150

1/v (s / mM)

0.7

100

2.0 7.0 / 14.0

50

0 0.0

(B)

71

0.5

1.0

1.5

2.0

2.5

1/[A] (1/mM)

(C)

1 / [B]

(1 / mM)

Fig. 3 Theoretical initial velocity data shown as double-reciprocal plots. (A) A plot of substrate B vs. substrate A for a bireactant sequential mechanism (here for an ordered mechanism, but in general, random or ordered). Concentrations of variable substrate are 0.5–10 mM, wherein Kia ¼ 2 mM, Ka ¼ 5 mM, Kb ¼ 0.3 mM, kcat ¼ 20 s 1, and [Et] ¼ 20 nM. Note that as [B] exceeds Kb, the lines compress to indicate the approach to saturation by the changing-fixed substrate B. (B) a plot of B vs. A for a Ping Pong Uni Uni Uni Uni mechanism in which concentrations of variable substrate are 10–100 mM, wherein Ka ¼ 7 mM, Kb ¼ 9 mM, kcat ¼ 650 s 1, and [Et] ¼ 20 nM. (C) a plot of A vs. B for an Equilibrium-Ordered Bi Bi mechanism in which concentrations of variable substrate B are 3.3–33 mM, wherein Kia ¼ 0.05 mM, Kb ¼ 2.5 mM, kcat ¼ 5 s 1, and [Et] ¼ 20 nM.

72

An Overview of Steady-State Enzyme Kinetics

Fig. 4 Graphical notation for enzyme kinetic mechanisms. (A) Ordered Bi Bi mechanism, a sequential mechanism in which the compulsory binding of substrate A, followed by substrate B, results in a ternary complex EAB, that is followed by catalysis to produce products P and Q that are released from enzyme in order. (B) Random Bi Bi mechanism is a sequential mechanism in which A, B, P and Q may bind to free E or a binary complex. (C) Ping Pong Uni Uni Uni Uni double-displacement mechanism in which A binds to E, the resulting EA central complex undergoes catalysis to produce new enzyme form FP, which releases product P, and then binds the second substrate B. Transfer of a substituent from F to B through a second set of central complexes leads to a bound product Q that is then released from EQ. (D) The less common equilibrium-ordered mechanism. A binds to E but a steady-state level of EA does not form, followed by binding of B to form the ternary complex EAB which is converted to EPQ, followed by ordered release of products P and then Q. (E) Ordered Ter Ter mechanism in which all three substrates, A, B, and C, bind in order, followed by catalysis to form EPQR, after which products are released in order.

y-intercepts both decrease as the level of [B] increases. The fitted lines intersect to the left of the 1/v axis, indicating that increasing changing-fixed concentrations of B lower both the slopes and y-intercepts of plots of 1/v vs. 1/[A]. The effect on the slopes means that the binding of B and A to enzyme has a reversible connection, as in the case of either random binding of the substrates, or the compulsory ordered binding of both substrates as E þ A 5 EA þ B 5 EAB, and the lowering of the y-intercept indicates that B and A bind to different enzyme forms, and that the apparent value of 1/Vmax for each line decreases because increased levels of B convert more of the EA enzyme form to products (Fig. 3). As explained below, the analysis of slope and y-intercept effects assist in the elucidation of the kinetic mechanism of any enzyme, with the assistance from inhibition studies. While double-reciprocal plotting of enzyme kinetic data has receded in the past decades due to improvements in linear regression analysis of non-linear kinetic data, we will here continue to employ them to describe the effects of two additives on the slope (apparent Ka/Vmax) and yintercepts effects (apparent 1/Vmax) in the analysis of enzyme kinetics as the reader will be able to more easily understand which enzyme forms are affected by the components in reaction mixtures.

1.05.4

Kinetic mechanism

Enzymes employ two general types of kinetic mechanism: sequential or ping-pong (Fig. 4). A sequential kinetic mechanism is one in which all substrates bind to the active site before chemistry ensues, followed by product release (as in the case of Fig. 4). A pingpong mechanism is a double-displacement mechanism, in which a substrate A binds to free enzyme E, transfers one of its substituents to a prosthetic group on the enzyme to form a stable enzyme intermediate F, followed by release of product P (Fig. 4C). A second substrate B then binds, accepts the enzyme-bound substituent from F, and then product Q is formed which then desorbs. Sequential mechanisms are of two general types, ordered and random (Fig. 4A and B), although there are many instances of kinetic mechanisms that are partially-ordered. In the case of an ordered sequential mechanism, one substrate must bind before the other substrate(s) are able to bind, and this is generally true for the release of products (Fig. 4A). For random sequential kinetic mechanisms of bi-substrate enzymes, either substrate may bind to free enzyme or either of the enzyme-substrate binary complexes (EAB), and either product may desorb first or last from the ternary enzyme-products complex (EPQ). Some enzymes exhibit both ordered and random steps. Before discussion of how one ascertains the kinetic mechanism of an enzyme, we will first address notation. Cleland first introduced the “line notation” of kinetic mechanisms as shown in Fig. 5 (Cleland, 1963a,b,c, 1970). The Ordered Bi mechanism is

An Overview of Steady-State Enzyme Kinetics

(A)

(C)

73

(B)

(D)

Fig. 5 Top. Ordered Bi Bi mechanism in which reversible connections between substrates A and B, and inhibitors P and Q and either substrate are indicated by double-pointed arrows. (A) Reversible connections under conditions in which either [A] 0 0 (Vmax/Ka) or [B] 0 0 (Vmax/Kb), and the other substrate is at  Km concentrations, in which color-coding denotes the major enzyme form under these conditions. (B). Effects of saturating levels of A or B on the respective predominant enzyme forms EA EAB, and the loss of reversible connections arising from saturating levels of either substrate. Note that substrate B may be identified in two plots of P vs. A in which [B] is at low and high concentrations, respectively, the product inhibition plots will be, respectively non-competitive and un-competitive. Bottom. (C) Rapid-Equilibrium Random Bi Bi mechanism at [A] 0 0 (top) or [B] 0 0 (bottom) under which reversible connections (not shown) exist for both substrates and products as all may bind to binary and ternary enzyme forms. (D) Mechanism at saturating concentrations of either A (top) or B (bottom) reflect apparent Vmax conditions, indicating that the respective major enzyme forms are the binary EA and EB forms to which the other substrate binds. Accordingly, saturation with one substrate effectively converts a rapid-equilibrium random bi bi mechanism to a uni-reactant mechanism in terms of initial velocity. Addition of either product Q or P to either the case of (C) or (D), is likely to produce the ternary dead-end substrate-product complexes EBQ or EAP, particularly when, respectively, either B or A is at saturation (D). The complexes EBQ or EAP will only form if the enzyme may sterically accommodate both ligands.

a sequential mechanism in which substrate A binds to free enzyme E, with the bimolecular rate constant k1, and with a rate of substrate desorption as described by the unimolecular rate constant k2. Substrate B in kind binds to transitory complex EA to afford EAB with the bimolecular rate constant k3, and with a rate of substrate desorption as described by the unimolecular rate constant k4. With both substrates bound, catalysis proceeds through the ternary EAB complex to form EPQ, from which products P and Q desorb in order with rates based on the respective microscopic rate constants of k5 and k7. The designation Bi Bi refers to: two substrates bind, chemistry occurs, and two products are released. In Fig. 5B is shown a Random Bi Bi sequential mechanism. The differences between ordered and random kinetic mechanisms can be very subtle, and often depend upon how tightly a substrate is bound compared to the velocity of the subsequent chemical reaction. For example, a substrate that desorbs from its enzyme-substrate complex very rapidly may “fall off” many times prior to a successful catalytic event, and this may militate a random mechanism because an otherwise ordered process that led to an EAB complex, suffers the release of A from the EAB complex. Another important aspect of ordered vs. random mechanisms is that they are condition-dependent. A poor substratedone which is slowly catalyzeddmay exhibit random binding while for the same enzyme, a better substrate may bind in an ordered fashion. How are kinetic mechanisms elucidated? In general, initial velocity data, where possible acquired in the forward and reverse directions of enzyme catalysis, coupled with inhibition data by added reaction products (product inhibition) and substrate or product analogs (dead-end inhibitors) are sufficient to establish the kinetic mechanism of an enzyme. Initial velocity studies. We use the nomenclature A-C and P-R here because while we know the identities of the substrates and products (for example, NADH, ketone, NADþ and alcohol with alcohol dehydrogenase), we do not know which binds first, middle, or

74

An Overview of Steady-State Enzyme Kinetics

last, and therefore do not know the enzyme mechanism depicted in Fig. 4. For a bi-reactant enzyme, an initial velocity study comprises the measurement of initial rates at multiple concentrations of a variable substrate at changing-fixed concentrations of the other, that is B vs. A as shown in Fig. 3, or A vs. B wherein the double-reciprocal plot would be 1/v vs. 1/[B]. For a terreactant enzyme, one would construct at least three sets of data: B vs. A at fixed C, A vs. C at fixed B, and B vs. C at fixed A. Data are then analyzed by “global” data-fitting to equations specific to the kinetic mechanisms specified in Fig. 4 to ascertain which mechanism is operative. Inspection of double-reciprocal plots of initial velocity data in general will present as: a family of lines that intersect (A) to the left of, or (B), on the 1/v-axis, or (C) a family of parallel lines. These three patterns respectively describe both (A) slope and intercept effects, (B) slope-effect only, and (C) intercept-effect only. The generic mechanisms in Figs. 4 and 5 allow simple diagnosis of which initial-velocity patterns to expect. For either the Ordered or Random Bi Bi mechanisms, as the concentration of variable substrate equals infinity at the 1/v intercept, increasing levels of the changing-fixed substrate may still bind to enzyme as the two substrates bind to different enzyme forms as in all mechanisms in Figs. 4 and 5. While, strictly speaking, in a random sequential mechanism substrates A and B may both bind the free enzyme E (the same enzyme form), each substrate has its own binding sub-site thereby allowing one substrate to bind when the other is saturating. Accordingly, either substrate will exert an “intercept effect” on the other with the exception of the Equilibrium-Ordered Bi Bi mechanism (Fig. 4D). In this uncommon mechanism, substrate A is in rapid-equilibrium with free enzyme, a steady-state level of EA does not occur, and so at an “infinite” concentration of B, all of free enzyme E will by mass action be converted to the EAB complex, and accordingly, there will be no intercept effect in the plot of 1/v vs. 1/[B] at all changing-fixed concentrations of A. This is an asymmetrical mechanism in that the plot of B vs. A exhibits both slope and intercept effects, while A vs. B does not. Consequently, examination of both initial velocity plots (B vs. A and A vs. B) will ascertain not only an equilibriumordered mechanism, but also identify which substrate is A and which is B. In terms of slope effects in these three types of sequential mechanisms, increasing levels of B will lower the slope of the plot 1/v vs. 1/[A] because increasing levels of B “pull” the minute level of the EA complex into the EAB complex even though the slope of the plot measures the initial velocity where [Et]  [E]. An effect on the [E]/[EA] ratio for any plot of 1/v vs. 1/[A] by a second substrate B or an inhibitor I which may affect this ratio (that is, it is reversibly connected to the binding of A) will change this slope; lowering slopes by a second substrate, raising slopes by an inhibitor. Likewise, for the plot of 1/v vs. 1/[B], increasing changing-fixed levels of A will also lower the slopes even at low concentrations of B because the ratio of [EA]/[E] will be increased. As stated above, the reversible connection between the binding of A and B in both the Ordered and Random Bi Bi mechanisms will produce slope effects in initial velocity plots. The same would be true for all initial-velocity patterns of the Ordered Ter Ter mechanism (Fig. 4E) except in the pattern of C vs. A (or A vs. C) in which the fixed concentration of B is near saturation. This is because the reversible connection between the binding of A to EA and C to EAB is eliminated when [EA] goes to zero when B is effectively saturating. This is a useful method to identify substrate B in an Ordered Ter Ter mechanism, that is, the observation that an initial-velocity pattern of the other two substrates C and A are converted from an intersecting pattern to a parallel one as [B] goes from low to very high fixed levels. This is graphically depicted in Fig. 6. To summarize, intersecting initial-velocity patterns are the hallmark of the four sequential mechanisms shown in Fig. 4, regardless of whether rapid-equilibrium or steady-state conditions are in effect. For the Ping Pong Uni Uni Uni Uni mechanism (Fig. 4), as substrates A and B bind to different enzyme forms, indeed, one enzyme form is E and the other F, such that an infinite concentration of one substrate cannot prevent the binding of the other, and so double-reciprocal plots of B vs. A or A vs. B will exhibit intersect effects like those of the sequential mechanisms. However, since the binding of A to E and the binding of B to the altered enzyme form F is “bracketed” on either side by the desorption (into infinite dilution in the reaction mixture) of products P and Q, the ability of A to affect the [F]/[FB] ratio and B to affect the [E]/[EA] ratio is eliminated, and there will be no slope effect (Fig. 7). Consequently, the hallmark of initial-velocity plots for ping pong mechanisms will be a family of intersecting lines. If one is able to determine the initial-velocity pattern(s) for the reverse reactions of the generic mechanisms in Fig. 4, these results would be useful to solve the kinetic mechanism, perhaps without the need for product and dead-end inhibition studies.

Fig. 6 Ordered Ter Ter mechanism showing schemes for an initial velocity study of A vs. C at fixed [B] ¼ Kb (left) in which a reversible connection (double arrow) between the binding of A and C exists, (right) the same plot with which [B] > 10Kb, little to no [EA] remains, and this reversible connection between A and C is ablated.

An Overview of Steady-State Enzyme Kinetics

Fig. 7

75

Graphical view of initial velocity patterns of a Ping Pong Uni Uni Uni Uni mechanism.

What are the equations for fitting initial velocity data for enzyme kinetic mechanisms and how are they derived? To do this, we could use the same approach we applied above to the steady-state model as in Eqs. (1)–(15) for the simple Uni Uni mechanism of Mechanism I for multi-reactant enzymes, but this can become exceptionally complicated. For an Ordered Bi Bi mechanism (Fig. 4A), we can use the graphical method of King and Altman (1956) to derive a steady-state equation for initial velocity and the effects of added reaction products as inhibitors. The full derivation of the steady-state Ordered Bi Bi kinetic mechanism using the King-Altman method, and expressions for product and dead-end inhibition of this mechanism, is available in the Appendix. From this and other derivations, the initial velocity patterns for the Ordered Bi Bi, the Ping Pong Uni Uni Uni Uni, and the Equilibrium-Ordered Bi Bi mechanisms are given by Eq. (18) to Eq. (20), respectively. v¼

VAB Kia Kb þ Kb A þ Ka B þ AB

(18/A12)

VAB Kb A þ Ka B þ AB

(19/A15)

VAB Kia Kb þ Kb A þ AB

(20/A16)

v¼ v¼

Product and Dead-End Inhibition Studies. The addition of changing-fixed concentrations of either a product or dead-end inhibitor to reaction mixtures containing a variable substrate (along with fixed levels of other substrates) will provide a means to determine the enzyme forms to which these inhibitors bind, from which a full kinetic mechanism may be ascertained. Like initial-velocity studies, the effect(s) of adding an inhibitor to reaction mixtures with a variable substrate, with the other substrates at fixed (invariant) concentrations, will be exemplified as whether the inhibitor raises the slopes of the plots, the intercepts, or both. Inhibition patterns of these types are referred to, respectively, as competitive, uncompetitive, or mixed inhibition (also known as noncompetitive inhibition) (Fig. 8). What is meant by a product inhibitor as opposed to a dead-end inhibitor? In initial-velocity studies, products do not accumulate and so do not attain sufficient concentrations in the course of the studies to bind to enzyme. However, addition of changing-fixed levels of an enzyme product (P, Q, R, etc.) will exert inhibition as the products binds to their “assigned” enzyme forms, which then either retards or even reverses the forward catalysis because it can participate in catalysis, albeit in the reverse reaction direction. A dead-end inhibitor, on the other hand, cannot enjoin a catalytic pathway, but rather, binds to free enzyme, enzyme-substrate(s) or enzyme-product(s) complexes, and siphons off the afflicted enzyme complex to a catalytic dead end (Fig. 8). One of the pioneers of the use of dead-end inhibitors was Fromm (1977,1995). Oft-used examples of dead-end inhibitors include the MgATP analoginhibitor MgAMP-PNP, which beguiles many a MgATP-utilizing enzyme, but is incapable of phosphoryl transfer. Likewise, 3-acetyl-pyridine-ribosyl-ADP (Ac-NAD(H)), a NADþ or NADH analog in which a 3-acetyl group replaces the 3-carboxamide group of the nicotinamide ring of the co-factor, is not subject to robust hydride-transfer catalysis, thereby providing a dead-end inhibitor for NADþ (or NADH), such that an E-Ac-NAD binary complex must reverse in order to return the oxidoreductase E to a productive catalytic cycle. Nitrate ion is an analog used for enzymes that bind bicarbonate ion. An advantage of the use of product inhibition is that the investigator knows that they, most likely, bind to a predictable enzyme form present in the catalytic pathway of the enzyme of study. Also, product inhibitors are normally readily available. A disadvantage of the use of two or more product inhibitors is that more than one assay will need to be devised if the assay consists of the measurement of a product formation, rather than the diminution of a substrate. In contrast, this development of multiple assays is not required when one utilizes dead-end inhibition. A dead-end inhibitor may bind to a single substrate sub-site, that is, it is an un-reactive analog of that substrate, or it may possess structural features of two substrates. The latter examples are referred to as bi-substrate analogs, and notable examples include N-phosphonacetyl-L-aspartate (PALA) (Collins and Stark, 1971) and di-adenosyl-pentaphosphate (Ap5A) (Leinhard and Secemski, 1973). PALA binds to both the binding sub-sites for carbamoylphosphate and aspartate in the active site of aspartyl trans-carbamoylase, while Ap5A spans the AMP and ATP binding sub-sites of adenylate kinase (myokinase). For the Ordered Bi Bi mechanism (Fig. 4A), product P binds to the EQ complex, which exists at steady-state concentrations despite the absence of added Q, and the resulting ternary EPQ complex is able to revert to the EAB central complex. This means

(A)

[Q] ( M)

8

120 6

1/v (s / mM)

80 4

40 2

0

0

0.00

0.05

0.10

0.15

0.20

1 / [A] (1 / mM ) (B)

25

[P], 7.5

20

15

1/v (s / mM)

M

5.0

10

2.5 5

0 0 0.00

0.02

0.04

0.06

1/[B] (1 /

0.08

0.10

0.12

M)

(C)

[I], ( M)

7

0.8 0.6 0.4 0.2

6

0.0

1/ v (s / mM)

5

4

3

2 0

1

2

3

4

5

6

1 / [A] (1 / mM) Fig. 8 Theoretical dead-end and product inhibition data shown as double-reciprocal plots. (A) A plot of product inhibitor Q vs. substrate A for an Ordered Bi Bi mechanism which demonstrates competitive inhibition. Concentrations of variable substrate are 2.0–200 mM, changing-fixed levels of product inhibitor Q are 0–120 mM wherein Ka ¼ 17 mM, Kb ¼ 0.3 mM, Kis ¼ 3.0 mM, and Vmax ¼ 50 mM/s. (B) A plot showing the non-competitive inhibition afforded by product inhibitor P vs. B for an Ordered Bi Bi mechanism in which concentrations of variable substrate B are 10–100 mM, changing-fixed levels of product inhibitor P are 0–7.5 mM, the fixed concentration of B ¼ 2 mM, wherein Ka ¼ 2.0 mM, Kb ¼ 20 mM, Kis ¼ 3.0 mM, Kii ¼ 0.5 mM, and Vmax ¼ 2 mM/s. (C) A plot showing the uncompetitive inhibition afforded by dead-end inhibitor I vs. A for an Ordered Bi Bi mechanism in which concentrations of variable substrate A are 0.2–2 mM, the dead-end inhibitor is 0.2–2 mM, the fixed concentration of B ¼ 2 mM, wherein Kia ¼ 0.01 mM, Ka ¼ 0.3 mM, Kb ¼ 0.5 mM, Kii ¼ 0.4 mM, and Vmax ¼ 0.5 mM/s.

An Overview of Steady-State Enzyme Kinetics

77

that in a reversible catalytic mechanism, the addition of P can change the ratios of [EAB]/[EA] and [EA]/[E] as the flux of EPQ 0 EAB 0 EA 0 E inhibits catalysis. Therefore the plots of P vs. B and P vs. A will exhibit a slope effect (Fig. 5A and 7A). Also, since P binds to a different enzyme form that either A or B, its inhibition will include an intercept effect, so that for the Ordered Bi Bi and Equilibrium-Ordered Bi Bi mechanisms of (Fig. 4A and 4D), the first product released, P, will exert non-competitive (mixed) inhibition vs. either variable A or B, However, if the fixed level of B is saturating in this product inhibition plot, the reversible connection of EPQ to EA will be eliminated, and the slope effect will be absent, that is, uncompetitive inhibition will be observed (Fig. 5B and 7D). Consequently, high and low levels of fixed [B] will allow the identification of which substrate is A and which is B. For the Random Bi Bi mechanism, P will bind to either E or EQ, and since P normally binds to the same sub-site as substrate B, the y-intercept of a double-reciprocal plot of P vs. B will not be affected by changing-fixed levels of [B] unless a dead-end EBQ complex forms, in which case, P vs. B will be seen as mixed or non-competitive inhibition (Fig. 5C and D, and 7B). This EBQ complex will form so long as substrate B and product Q may be sterically accommodated in the enzyme active site. Q vs. A will be seen as competitive inhibition unless an EBQ complex may form, in which case non-competitive (mixed) inhibition is observed. Again this is dependent upon the ability of product Q to bind to the EB complex without steric interference from the binding of substrate B. Otherwise, all product inhibition data for this random mechanism will be observed as competitive inhibition. We return to the final form of the initial velocity equation of the Ordered Bi Bi mechanism to explore the mathematical basis of product inhibition. When product P is added at changing-fixed levels, Eq. (18) becomes Eq. (21) (see Appendix): v¼

VAB Kia Kb þ Kb A þ Ka B þ AB þ Kq AP=Kia þ ABP=Kip; þ Kq P

(21)

Which may be re-arranged to Eq. (22). v¼

VAB


   Kia Kb 1 þ Kq P=Kia Kb þ Kb A 1 þ Kq P=Kia Kb þ Ka B þ AB 1 þ P=Kip0

(22)

In double reciprocal form we have:


  1=v ¼ Kia Kb 1 þ Kq P=Kia Kb =VAB þ Kb 1 þ Kq P=Kia Kb =VB þ Ka =VA þ 1 þ P=Kip0 =V From which it is evident that there will be effects on the slopes and y-intercepts effects in both plots of 1/v vs. either 1/[A] and 1/[B] at changing-fixed levels of inhibitor P, therefore giving rise to apparent non-competitive inhibition. The apparent slope and intercept inhibition constants obtained from fitting, Kis and Kii, respectively, are composed of different collections of kinetic parameters, for example, Kis ¼ KiaKbKq/KiaKbVB when A is the variable substrate, and so the slope effect here is dependent upon the fixed concentration of B. Indeed, this provides a useful means to discover the identities of substrates A and B. In two plots of 1/v vs. 1/[A] in which [B] ¼ Kb in the first plot, and [B] [ Kb in the second plot, P vs. A will exert, respectively non-competitive inhibition (slope and intercept effects) and un-competitive inhibition (intercept effect only). This is because when B is saturating the terms KiaKb(1 þ KqP/KiaKb)/V1AB þ Kb(1 þ KqP/KiaKb)/V1B become zero, but the intercept effect remains due to the residual (1 þ P/ Kip’)/V term. When B is the variable substrate, a saturating fixed concentration of A in kind eliminates the KiaKb(1 þ KqP/KiaKb)/ V1AB term, but a slope effect remains due to the Kb(1 þ KqP/KiaKb)/VB term which is unaffected by the concentration of A. If one inspects the line notation for the Ordered Bi Bi mechanism in Fig. 4A, the established “reversible” connection between the binding of A and P (EA 5 EAB 5 EPQ 5 EQ) is lost when B is saturating (Fig. 5A and B), but the reversible connection of binding of B and P is not lost when fixed concentrations of A are saturating. These results will also assist in distinguishing the kinetic mechanism from that of a Random Bi Bi mechanism, as the saturation of a fixed substrate will not break this reversible connection of EB 5 EAB 5 EPQ (Fig. 5C and D). Likewise, an expression for product inhibition by Q can be derived from the full initial velocity expression found in the Appendix. VAB Kia Kb þ Kb A þ Ka B þ AB þ Ka BQ=Kiq þ Kp Q

(23)

VAB
 Kia Kb þ Kb A þ Ka B 1 þ Q=Kiq Ka þ AB þ Kp Q

(24)

v¼ Which may be re-arranged to: v¼

A double-reciprocal plot of 1/v vs. 1/[A] is then

  1=v ¼ Ka 1 þ Q=Kiq Ka þ Kia Kb =VB þ Kp Q=VB ð1=½A Þ þ Kb =VB þ 1=VA; and in this case, there is a slope effect on Q vs. A, but not an effect on the y-intercept. Hence, Q is competitive vs. A at any fixed level of B. This is expected since A and Q both bind only to E. In the plot of 1/v vs. 1/[B], the expression becomes:

 1=v ¼ Kia Kb =VA þ Kb A=VA þ Kp Q=VA ð1=½BÞ þ ðKa =VAÞ 1 þ Q=Kiq Ka þ 1=V;

78

An Overview of Steady-State Enzyme Kinetics

and for this equation, in the plot of Q vs. B will exhibit both slope and intercept effects. Notice that every term containing [Q] has [A] in its denominator, such that, as expected for a competitive inhibitor, a saturating fixed concentration of A will eliminate all terms affected by the concentration of Q. Finally, we examine the case in which inhibitor I is a dead-end inhibitor. When I binds to E to form EI, we can modify the initial velocity expression as follows: v¼

VAB ðKia Kb þ Ka BÞð1 þ I=Ki Þ þ Kb A þ AB

(25)

Which in double-reciprocal form is: 1=v ¼ ðKia Kb =VB þ Ka =VÞð1 þ I=Ki Þð1=½A Þ þ ð1 þ Kb =VBÞ=V 1 in which the dead-end inhibitor exerts only a slope effect even at high fixed levels of B as expected for a compound which competes with A for E. The plot of 1/v vs. 1/[B] is: 1=v ¼ ½ðKia Kb Þð1 þ I=Ki Þ=VA þ Kb =VÞ:ð1=½BÞ þ ðKa =VAÞð1 þ I=Ki Þ þ 1=V in which the dead-end inhibitor exerts both a slope and intercept effect even at high fixed levels of A as expected for a compound which competes with A for E. When the dead-end inhibitor I binds to EA, the initial velocity equation becomes: v¼

VAB ðKia Kb þ Ka BÞ þ Kb Að1 þ I=Ki Þ þ AB

(26)

The double-reciprocal plot of 1/v vs. 1/[B] is: 1=v ¼ ðKia Kb =VA þ Kb ð1 þ I=Ki Þ=VÞð1=½BÞ þ ðKa =A þ 1Þ=V and so the dead-end inhibitor will exert a slope effect on this plot but not an intercept effect, as expected for an inhibitor which is competitive vs. the variable substrate. For the plot of I vs. A, the outcome is different. 1=v ¼ ðKia Kb =V 1 B þ Ka =VÞð1=½A Þ þ ðKb =VBÞ ð1 þ I=Ki Þ þ 1=V There is no slope effect in the plot of I vs. A, but an intercept effect will be observed, resulting in uncompetitive inhibition. This would be the case, generally, when Q is the product MgADP, A is substrate MgATP, and I is a dead-end inhibitor analog of MgATP, such as MgAMP-PNP, which binds to enzyme, but is incapable of phosphoryl transfer. This is also true when A ¼ NADþ, Q ¼ NADH, and dead-end inhibitor I is, for example, 3-acetyl-pyridine-ribosyl-ADP (Ac-NAD), an NADþ analog in which a 3-acetyl group replaces the 3-carboxamide group on the nicotinamide ring of the co-factor, and is not subject to robust hydride-transfer catalysis. Accordingly the E-Ac-NAD into a is a dead-end binary complex that must reverse in order to return the enzyme to a productive catalytic cycle. This is true for all kinetic mechanisms in Fig. 4. If increasing fixed levels of inhibitor I result in the elevation of the slope of a plot of 1/v vs. 1/[A], with no effect on the y-intercept of this plot, that is, there is no inhibition when [A] 0 N, then I and A bind to the same enzyme form. As is apparent in Fig. 4A, changing-fixed levels of product Q will reduce the levels of free enzyme E in all of the mechanisms, but when [A] 0 N, Q cannot bind, and there is no intercept effect. This will induce a slope effect in the Q vs. A or I vs. A plots because either I or Q reduce the [E]/[EA] ratio (apparent values of Ka/Vmax are increased as I and Q are increased). This is competitive inhibition; A and Q (or I) both compete for binding to E. An obvious consequence of this is that I, Q, and A are likely structurally-similar ligands, and bind to the same enzyme sub-sites.

1.05.4.1

Use of initial velocity, product, and dead-end inhibition studies to determine kinetic mechanisms

In Table 1 is a non-comprehensive collection of initial velocity, product inhibition and dead-end inhibition patterns expected for six common kinetic mechanisms of bi-reactant enzymes. A full set of studies here comprises 18 experiments, though all may not be required. Conduct of all of these studies should provide an unambiguous kinetic mechanism for the germane enzyme. The reader is encouraged to “fill in” the table of the predicted initial velocity and inhibition patterns in the table and use the generic “line” mechanisms in Figs. 4–7 to predict the outcomes of each experiment, prior to further reading (a quiz!!!). Examples of all six of these mechanisms are listed in Table 2 and described thereafter. (1) Ordered Bi Bi Mechanism. As shown from derived expressions above, initial velocity patterns for this mechanism will always be intersecting, except in cases in which Kia  Ka, for which the pattern may appear to be parallel. Product inhibition patterns obtained at Km levels of the fixed substrate provide a single competitive pattern for Q vs. A, as both bind to the same enzyme form E. All other product inhibition patters are non-competitive, as P and Q bind to different enzyme forms than B, and P binds to a different enzyme form than A. Saturation of the fixed substrate provides further evidence of the mechanism. Saturation with fixed B in a plot of P vs. A reverts to un-competitive inhibition because the reversible connection between the binding of P and A is disrupted. Saturation with A will prevent binding of Q to E, and so a plot of Q vs. B will show no inhibition. A dead-end

Table 1

Elucidation of Kinetic Mechanism of Bi-substrate Enzymes Using Initial Velocity, Product Inhibition and Dead-End Inhibition Studiesa

Kinetic Mechanism

Ordered Bi Bi Equilibrium-Ordered Bi Bi (EA and EQ  0)

Rapid-Equilibrium Random Bi Bi (EBQ complex forms)

Rapid-Equilibrium Random Bi Bi (EAP and EBQ complexes form)

Ping Pong Uni Uni Uni Uni

a

v =

v =

v =

VAB K iaK b + K aB + K bA + AB VAB K ia K b + K b A + AB

VAB K ia K b + K aB + K b A + AB

v =

VAB K ia K b + K aB + K b A + AB

Exceedingly complex expression

v

=

VAB K b A + K aB + AB

Initial Velocity Study

B vs. A or A vs. B intersecting pattern B vs. A intersecting pattern A vs. B intersecting pattern lines intersect on 1/v-axis B vs. A or A vs. B intersecting pattern

B vs. A or A vs. B intersecting pattern

B vs. A or A vs. B curvilinear intersecting patterns

B vs. A or A vs B parallel pattern

Product Inhibition Studies

Dead-End Inhibition Studies

P vs. A

P vs. B

Q vs. A

Q vs. B

IA vs. A

IA vs. B

IB vs. A

IB vs. B

Km

NC

NC

C

NC

C

NC

UC

C

100Km

UC

NC

C

NI

C

NI

NI

C

Km

NI

NI

C

C

C

C

UC

C

100Km

NI

NI

NI

NI

NI

NI

NI

C

Km

C

C

C or NC

NC

C

NC

NC

C

100Km

NI

NI

NC

NI

C NC, (EIB)

NI

NI

C NC, (EAI)

Km

NC

C

C

NC

C

NI

NI

NC

100Km

NI

NC

NC

NI

C NC, (EIB)

C

C

C NC, (EAI)

Km

NC

C

C

NC

NC

NC

NC

NC

100Km

NI

C NC with (EAP)

C NC with (EBQ)

NI

C

NC

NC

C

Km

NC

C

C

NC

C

UC

C

UC

100Km

NI

C

C

NI

C

NI

C

NI

C, competitive inhibition; NC, non-competitive (mixed) inhibition; UC, n-competitive inhibition; NI, no inhibition; IA,IB, dead-end inhibitor competitive with substrate A, B, respectively; Color-coding of enzyme forms in the equations: E. EA, EB, EA, EAB..

An Overview of Steady-State Enzyme Kinetics

Steady-State Random Bi Bi

Equation

Fixed [A] or [B]

79

Experimentally-determined kinetic mechanisms of all enzyme major classes.

80

Table 2

Enzyme

Kinetic Mechanism

References

Ordered Uni Bi

M. tuberculosis isocitrate lyase

Isocitrate Y (E-isocitrate 4 E-succinate-glyoxylate) succinate [ glyoxylate [

Ping Pong Uni Bi

Cruzain

Ping Pong Uni Uni Uni Bi

Yeast glutathione reductase

Ping Pong Uni Uni Uni Uni

Pig heart aspartate aminotransferase

Ordered Bi Bi

Yeast formate dehydrogenase M. tuberculosis malate synthase

Equilibrium-Ordered Bi Bi

Human p38 MAP kinase

Rapid-Equilibrium Random Bi Bi

Beef heart creatine kinase

Cbz-Phe-Arg-C(O)NH-AMC Y (E-peptide-AMC 4 E-S-C(O)-peptide-AMC) AMC [ Cbz-Phe-Arg-COOH [ NADPH Y (E-FAD-NADPH 4 F-FADH-NADPþ) NADPþ [ GSSG Y (F-FADHGSSG 4 E-FAD-2 GSH) GSH [ GSH [ Asp Y (E-PLP-Asp 4 F-PAP-oxalate) Ox[ a-KG Y (E-PAP-a-KG 4 E-PLP-Glu) Glu [ NADþ Y formate Y (E-NADþ-formate) 4 E-(NADH-CO2) CO2 [ NADH [ glyoxylate Y AcCoA Y (E-glyoxylate-AcCoA 4 E-malate-CoA-SH) malate [ CoA-SH [ GST-ATF h MgATP Y(E-GST-ATF-MgATP 4 E-GST-ATF-P-MgADP) MgADP [ GSTATF-P [ (creatine or MgATP) YY(E-creatine-MgATP 4 E-creatine-P-MgADP) (creatine-P or MgADP) [[ (pH 8.0)

Sharma et al. (2000), Quartararo et al. (2013) and Moynihan and Murkin (2014) Zhai and Meek (2018)

Beef heart creatine kinase

Steady-State Random Bi Bi

Human protein arginine methyltransferase I Bovine liver fructokinase E. coli dihydrofolate reductase Human focal adhesion kinase

Ordered Bi Ter Partially Ordered Bi Ter Ping Pong Bi Uni Uni Bi Ordered Ter Ter Partially Ordered Ter Ter Ordered Quad Quint Partially-Ordered, Equilibrium- Ordered Quad Quint

S. pneumoniae keto-ACP reductase Pigeon liver malic enzyme Bovine liver glutamate dehydrogenase V. cholerae asparagine synthetase E. coli glutamine synthetase Bovine liver arginino-succinate synthetase E. coli carbamoyl-phosphate synthetase Hamster (CAD) carbamoyl-phosphate synthetase

MgATP Y creatine Y (E-creatine-MgATP 4 E-creatine-P-MgADP) (creatine-P or MgADP) [[ (pH 7.0) (Arg-peptide or SAM) YY (E-R-peptide-SAM 4 E-RMe-peptide-SAH) (RMe-peptide or SAH) [[ (MgATP or 1-deoxy-D-fructose) YY (E-MgATP-dFruc 4 E-MgADP-dFruc-P) (Dfructose 1-P or MgADP)[[ (NADPH or dihydrofolate) YY (E-NADPH-DHF 4 E-NADPþ-THF) (tetrahydrofolate or NADPþ) [[ (FAK-tide or MgATP) YY (E-MgATP-FAK-tide 4 E-P-FAKtide-MgADP) (MgADP or phospho-F AK-tide) [[ (NADPH or AcAcCoA) YY (E-NADPH-AcAcCoA 4 E-NADPþ-3-hydroxy-butyrylCoA) (NADPþ MgADP or 3-hydroxy-butyrylCoA) [[ NADþ Y malate Y (E-NADþ-malate 4 E-CO2-pyruvate-NADH) CO2 [ pyruvate [ NADH [ (NADPþ or glutamate) YY (E-NADPþ- Glu 4 E-NH3-a-KG-NADPH ) NH3 [ a-ketoglutarate [ NADPH [ MgATP Y Asp Y (E-MgATP-Asp 4 E-MgADP-Asp-AMP-PPi) PPi [ NH3 Y Asn [ MgADP [ Gln Y (E-Gln 4 E-Glu-NH3) Glu [ NH3 [ MgATP Y Glu Y NH3 Y (E-MgATP-Glu-NH3 4 Pi -Gln-MgADP) Pi [ Gln [ MgADP [ MgATP Y citrulline Y aspartate Y (E-MgATP-Cit-Asp 4 E-Arg-Succ-MgPPi- AMP) Arg-Succ [ MgPPi, [ AMP [ MgATP Y HCO3- Y NH3 Y (E-MgATP-HCO3--NH3) 4 (E-MgADP-carbamate-Pi) Pi [ MgATP Y MgADP [ (E-carbamate-MgATP 4 E-carbamoyl-P-MgATP) [ MgADP [ Gln Y (E-Gln 4 E-Glu-NH3) Glu [ NH3 [ MgATP Y HCO3– ↨ Gln Y (E-MgATP-HCO3–-Gln) 4 (E-MgADP-carbamate-Pi) Pi [ MgATP Y MgADP [ (E-carbamate-MgATP 4 E-carbamoyl-P-MgATP) (MgADP or carbamoyl-P MgADP) [[

Wong et al. (1988) and Williams Jr (1976) Velick and Vavra (1962) and Kirsch et al. (1984) Blanchard and Cleland (1980) Quartararo and Blanchard (2011) LoGrasso et al. (1997) Morrison and Cleland (1966), Morrison and James (1965) and Schimerlik and Cleland (1973) Cook et al. (1981) Obianyo et al. (2008) Raushel and Cleland (1977a,b) Stone and Morrison (1982, 1988) and Morrison and Stone (1988) Schneck et al. (2010) Patel et al. (2005) Hsu et al. (1967) Rife and Cleland (1980) Fresquet et al. (2004) Meek and Villafranca (1980) Raushel and Seiglie (1983) Raushel et al. (1978) DeBrosse et al. (1987)

Experimentally-derived kinetic mechanisms notated as: Y, follows step where substrates binds in order; ↨, follows step where substrate binds in rapid equilibrium in order ; 4, designates chemical steps in (central complexes); [, follows step where product is released in order; [[ or YY, designates random-binding of substrates or release products.

An Overview of Steady-State Enzyme Kinetics

Kinetic Mechanism Class

An Overview of Steady-State Enzyme Kinetics

(2)

(3)

(4)

(5)

81

inhibitor which is an analog of A (IA) will exert non-competitive inhibition in a plot of IA vs. B, as these two components bind to different enzyme forms (intercept effect). As IA binding to form EAIA siphons off this enzyme form, the ratio of EA/EAB will be decreased, and a slope effect will ensure, leading to the observed non-competitive inhibition. IB vs. B will be competitive inhibition, as both ligands compete for EA. Since IB binds to EA, and EA does not exist when A 0 0 (the slope of this plot) there is not slope effect in the plot of IB vs. A, leading to un-competitive inhibition. IB vs. B, each of which bind to the same enzyme form EA, will result in competitive inhibition. These results demonstrate the power of dead-end inhibition to determine kinetic mechanism. Equilibrium-Ordered Bi Bi Mechanism. As discussed above, when substrate A binds to free enzyme E in rapid equilibrium, there is no steady-state level of EA. B vs. A provides an intersecting pattern that is indistinguishable from that of an Ordered Bi Bi mechanism, while the plot of A vs. B, while intersecting, all lines meet on the 1/v-axis. This is because an infinite concentration of B “pulls” (even at equal nanomolar concentrations of E and A) all of free E to the EAB complex. Product Q is likely to also be in rapid-equilibrium with E. Accordingly, there is insufficient [EQ] to which P may bind, and there is no inhibition from added P under all conditions. The pattern of Q vs. A is competitive as is intuitive, but Q vs. B is also competitive despite the fact that the two ligands bind to different enzyme forms. This is because at the 1/v-intercept of the plot of Q vs. B, no free enzyme exists. Dead-end inhibition patterns of IA vs. either A or B at Km levels of the fixed substrate differ from that of the Ordered Bi Bi mechanism in that both patterns display competitive inhibition, as IA is an analog of product inhibitor Q. The dead-end analog of substrate B, IB, also exerts un-competitive inhibition vs. A, and is competitive vs. B. Unlike the Ordered Bi Bi case, saturating levels of either fixed substrate eliminates inhibition by either dead-end inhibitor except when B is the variable substrate, because saturating B eliminates free enzyme in all other cases. Rapid-Equilibrium Random Bi Bi mechanism with formation of an EBQ dead-end complex. Plots of 1/v vs. 1/A or 1/B yield intersecting patterns, similar to that of the Ordered Bi Bi mechanism. Considering a case in which product Q is sterically smaller than product P (because substrate A transferred a substituent to product P), it is likely that added changing-fixed concentrations of Q in a product inhibition study will elaborate a ternary EBQ complex, that is a substrate-product dead-end complex. The inhibition patterns of P vs. A or P vs. B at Km levels of the fixed substrate should both be competitive as no EAP complex may form, and P and B occupy the same enzyme sub-site. Saturation of the fixed substrate in either pattern will render no inhibition from P. In this mechanism Q will also bind to E, which will be ablated when A 0 N. Without the formation of an EBQ dead-end complex, all product inhibition patterns are competitive. And inhibition will be eliminated when either fixed substrate is saturating, as E is bound to either A or B. The formation of the EBQ complex in product inhibition data of Q vs. A introduces a complex that saturating concentrations of A cannot preclude, which will be enhanced at saturating fixed levels of B, and so an intercept effect is introduced. Consequently, this product inhibition pattern will be non-competitive; the value of Kis will provide the inhibition constant for the formation of EQ, while the value of Kii characterizes the formation of the EBQ complex, and this value will decrease at increasing fixed levels of B. Dead-end inhibitors IA and IB will exert competitive inhibition vs. A and B, respectively, unless the inhibitor encroaches on the binding sub-site of the substrate of which it is not an analog (such as a bi-substrate analog). IA and IB will display non-competitive inhibition vs. substrate B and A, respectively, because saturation levels of either B or A cannot prevent the binding of either IA and IB which may still form EIAB and EAIB ternary complexes when, respectively, (the 1/v intercept), especially when respective concentrations of B and A are saturating. When A is saturating in the plot of IA, or B is saturating in the plot of IB vs. A no inhibition will be observed. Rapid-Equilibrium Random Bi Bi mechanism with formation of EBQ and EAP dead-end complexes. Plots of 1/v vs. 1/A or 1/B yield intersecting patterns, similar to that of the Ordered Bi Bi mechanism. The formation of the EAP complex is where overlapping substituents of the “larger” substrate/product pair of A and P is accommodated by the enzyme, despite a predicted steric exclusion by the common substituents of P and A (for example, both ligands contain a phosphate group that is transferred in a kinase reaction). The EAP complex will form which adds an intercept effect to a plot of P vs. A. Plots of P vs. B and Q vs. A will of course be competitive. Q vs. B will be non-competitive as an EBQ complex may form. At saturating fixed levels of B and A, respectively, plots of P vs. A and Q vs. B will exhibit no inhibition (the sub-sites are blocked for the product inhibitors), and plots of P vs. B and Q vs. A will be non-competitive due to the formation of the EAP and EBQ dead-end complexes. Plots of IA vs. A and IB vs. B will both be competitive (the dead-end inhibitors cannot bind when their cognate substrates are saturating). Plots of IA vs. B at Km levels of A and IB vs. A at Km levels of B exhibit non-competitive inhibition due to the formation of EIAB and EIBA complexes (if indeed they do form). The formation of these EIAB and EIBA are more likely to form when the respective fixed substrate is saturating, leading to non-competitive inhibition in both cases. Steady-State Random Bi Bi. The initial velocity equation for a Steady-State Random Bi Bi mechanism contains squared terms in the numerator and denominator, and is exceptionally complex. Plots of 1/v vs. 1/A or 1/B as a result are often curvilinear either concave-upward or concave-downward. These patterns are rarely observed or reported. Simplistically, such non-linear doublereciprocal plots arise due to effectively two values of Michaelis constants and maximal velocity for either substrate, as the E 0 EA 0 EAB 0 EPQ “arm” of the mechanism may have entirely different microscopic rate constants to that of E 0 EB 0 EAB 0 EQP. Unlike the rapid-equilibrium random mechanisms, one expects steady-state levels of EA, EB, EAB, EPQ, EP, and EQ complexes. This means that one expects the formation of EP, EAP, EQ, and EBQ complexes, of which the ternary complexes are expected to increase when A or B are saturating. Plots of P vs. A and Q vs. B will be non-competitive, as both EAP and EBQ complexes readily form. Plots of P vs. B and Q vs. A will be competitive at low and high levels of the fixed substrate. At high fixed concentrations of B or A, respectively, Plots of IA vs. A and IB vs. B will be competitive unless EIAB or EAIB form, in which case non-competitive patterns will observed.

82

An Overview of Steady-State Enzyme Kinetics

(6) Ping Pong Uni Uni Uni Uni. Plots of B vs. A and A vs. B will be parallel. For product inhibition plots, Q vs. A, in which both ligands bind to free enzyme E, will afford a competitive plot. The same is true for the plot of P vs. B, as both ligands compete for the enzyme form F. Q binds to E, while B binds to F, and Q will exert non-competitive inhibition vs. B as the binding of Q can affect the ratio of E/EA, unless A is saturating, where no inhibition will be observed. Likewise, P vs. A will be non-competitive (P is reversibly connected to the binding of A) unless B is saturating, wherein no inhibition will be observed. A dead-end inhibitor mimicking A (IA) will be competitive vs. A for any concentration of B, and the same is true for a plot of IB vs. B. As IA binds only to free enzyme E, and B binds after the irreversible release of product P, IA vs. B will display un-competitive inhibition. Likewise, a plot of IB vs. A, in which IB binds to F while A binds to E, will also be un-competitive, as there are no reversible connections between the binding of the substrate and the inhibitor. Two un-competitive inhibition plots will therefore be highly diagnostic of a ping-pong mechanism.

1.05.5

Literature examples of kinetic mechanisms

Kinetic mechanisms are usually ascertained without any chemical evidence of the nature of chemical catalysis, yet, the kinetic mechanism may inform the investigator as to which chemical mechanisms are the most likely. In other words, the order of substrate binding and product release can put tight constraints on what active-site chemistry is afoot. We show below the kinetic mechanisms of a variety of enzymes (Table 2), and in schemes below that exemplify the kinetic mechanisms in line notation. In most of the examples below, we have indicated the ultimate chemical mechanisms and reaction intermediates of these enzymes, which have been determined by additional studies from which either putative chemical intermediates were identified by isolation or some other form of characterization. The enzyme isocitrate lyase (ICL) catalyzes the retro-aldol scission of the C2–C3 of D-isocitrate to make the aldehyde glyoxylate and the aci-acid form of succinate, which is the protonated by active-site Cys191 (Fig. 9). (Sharma et al., 2000; Quartararo et al., 2013; Moynihan and Murkin, 2014). The kinetic mechanism of isocitrate lyase from numerous organisms is Ordered Uni Bi. Recently, the Andrew Murkin laboratory has used initial velocity data of the reverse reaction and product inhibition studies to demonstrate that succinate desorption precedes that of glyoxylate in the direction of lysis (Fig. 9) (Moynihan and Murkin, 2014). We found that maleate is an uncompetitive dead-end inhibitor of M. tuberculosis ICL vs. isocitrate, which is also consistent with the ordered release of succinate and glyoxylate. When maleate is evaluated in the reverse direction (isocitrate synthesis), it is competitive vs. succinate. In such a case in which uncompetitive and competitive inhibition is observed for the same inhibitor, the ratio of the two inhibition constants allows a calculation of the ratio of the off-rate of succinate/kcat in the forward direction (Pham et al., 2021). Cruzain is the major cysteine protease of the parasitic protozoan, Trypanosoma cruzi, and is a drug target for Chagas disease. The cysteine proteases operate via double-displacement mechanisms, which are in effect Ping Pong Uni Bi mechanisms wherein water is likely already bound (Fig. 10). In one study for cruzain, (Zhai and Meek, 2018) the dipeptide substrate used was Cbz-Phe-Arg-AMC (7-amino-4-methyl-coumarin), which upon peptidolysis releases the fluorescent product AMC (P) after the formation of a stable thio-ester intermediate (F) (Fig. 10). Water (B) is de-protonated by an active-site histidine from which the resulting hydroxide ion attacks and cleaves the covalent thioester-enzyme to afford the carboxylic acid product (Q) (Fig. 10). The kinetic mechanism of yeast glutathione reductase is Ping Pong Uni Uni Uni Bi, in which A and P are NADPH and NADPþ, respectively, the former of which provides reduction of the prosthetically-bound flavin co-factor (Wong et al., 1988; Williams Jr, 1976) (Fig. 11). The F enzyme form is now “charged” with the equivalent of a hydride ion. The reduced flavin reduces a proximal cystine, leading to addition of the resulting thiolate to the flavin (E_cFAD-S-Cysc_ GSSG_c S-Cys) followed by attack of the Cys-S on GSSG, to form a new mixed di-sulfide adduct (Cys-SS-G) and the first molecule of GSH (Q), which desorbs. The FAD-bound cysteine made then attacks Cys-SS-G, to produce the second molecule of GSH with restoration of the initial cystine. The aminotransferases comprise perhaps the best-known class of double-displacement enzyme mechanisms. These enzymes provide a means to catalyze the formation of one amino acid, from its a-keto acid cognate (B), by transfer of an amino group

Fig. 9

Kinetic mechanism of M. tuberculosis isocitrate lyase 1 (Sharma et al., 2000; Quartararo et al., 2013; Moynihan and Murkin, 2014).

An Overview of Steady-State Enzyme Kinetics

Fig. 10

Kinetic mechanism of cruzain.

Fig. 11

Kinetic mechanism of yeast glutathione reductase.

83

from a second amino acid (A) to the a-keto group, which is subsequently reduced to the product amino acid (Q) and a new a-keto acid (P). The example below is aspartate aminotransferase (Fig. 12). The amino group of aspartate displaces an imine formed between the prosthetically-bound pyridoxal co-factor (PLP) and Lys258 to form an aldimine (FA_cPLP-aldimine; Fig. 12) (Velick and Vavra, 1962; Kirsch et al., 1984). Loss of the a-proton from the resulting aldimine results in a quinonoid adduct (not shown) of the aspartate and pyridoxal. This leads to a ketimine intermediate, which upon hydrolysis, produces oxaloacetate (P) and enzyme-bound pyridoxamine-phosphate (PMP); enzyme form (FP_cPMP). The first half-reaction is completed when oxaloacetate desorbs from the enzyme. Binding of a-keto-glutarate (B) to F_cPMP initiates a second set of central complexes (FB 4 EQ) in which the keto group forms an imine with E-PMP, and the catalytic “reverse” reaction of EA 4 FB leads to formation of E-PLP (E) and a new amino acid, glutamate. Resultingly, the E-PLP 4 F-PMP enzyme forms provide, in essence, a covalent sequestration of ammonia as a “substrate” to allow the transamination reaction to proceed, while the pyridoxal co-factor, through electrontransfer steps, completes reduction of the final imine to afford an amino-acid product. Both yeast format dehydrogenase (FDH) and M. tuberculosis malate synthase employ steady-state Ordered Bi Bi mechanisms (Blanchard and Cleland, 1980; Quartararo and Blanchard, 2011). For product inhibition studies of FDH, NADH vs. NADþ yielded competitive inhibition, while all other product inhibition patterns were non-competitive (Blanchard and Cleland, 1980). Dead-end inhibition with the format analogs nitrate and azide yielded competitive inhibition vs. formate, but were un-competitive vs. NADþ. These data are all consistent with the Ordered Bi Bi shown in Fig. 13 below. In the central complexes a hydride is transferred from formate to NADþ (EAB 4 EPQ) resulting in the products CO2 (P) and NADH (Q), which then dissociate in that order. This kinetic mechanism is observed for many dehydrogenases, in which binding of the nicotinamide co-enzymes occur on free enzyme, as if to prepare the substrate for binding thereafter. The sequential mechanism observed here does not allow for hydride transfer to a recipient on enzyme, such as FAD, but rather indicates a direct hydride transfer from formate to bound NADþ. The kinetic mechanism thereby limits the mechanisms by which the chemistry must occur.

Fig. 12

Kinetic mechanism of aspartate aminotransferase.

84

Fig. 13

An Overview of Steady-State Enzyme Kinetics

Kinetic and chemical mechanisms of yeast formate dehydrogenase with competitive inhibitors azide and nitrate.

For mycobacterial malate synthase, the sequential transfer mechanism is more complex. Malate vs. glyoxylate exhibited competitive inhibition, while CoA-SH vs. glyoxylate was non-competitive. The dead-end inhibitor dethio-CoA was competitive vs. Ac-CoA, while dethio-CoA vs. glyoxylate was un-competitive (Quartararo and Blanchard, 2011). The kinetic mechanism is therefore ordered, in which glyoxylate first binds to free enzyme, followed by acetyl-S-CoA, while CoA-SH desorbs from central complexes before the release of malate (Fig. 14). Much occurs in the central complexes. The enzyme de-protonates the acetyl group of acetyl-S-CoA, and the resulting enolate undergoes an aldol reaction with the aldehyde group of glyoxylate to form the intermediate EX. Attack of a magnesium-bound water molecule on EX produces CoA-SH and malate (EPQ). Elimination of the thiolate of CoA-SH then produces the product malate, which is released from the ternary central complex before CoA-SH. Unlike the Ordered Bi Bi mechanism of FDH, the large co-enzyme Ac-S-CoA binds second, but is the first product to be released. This is likely due to the fact that the enzyme catalysis is assisted by a bound, divalent magnesium ion, which binds to glyoxylate in the EA complex, and from which malate “de-coordinates,” and is desorbed following the release of CoA-SH. Here again, knowledge of the role of the co-factor Mg2þ and the elucidated kinetic mechanism provided the investigators with important clues as to how the chemical mechanism proceeds. Human p38 MAP (mitogen-activated protein) kinase is an important drug target for the development of non-steroidal antiinflammatory drugs (NSAIDS). Indeed, it was discovered by using an NSAID tool compound (SB203580) to covalently label protein targets. Among other substrates, the kinase is a key point in signal transduction arising from stress to a cell, and catalyzes phosphorylation, and thereby activation, of alcohol-containing residues on MAPKAP kinase 2 and transcription factors such as ATF2 (GST-ATF). For the substrate ATF-2, p38 MAP kinase employs an equilibrium-ordered kinetic mechanism in which ATF-2 (A) binds in rapid-equilibrium, followed by the binding of MgATP (B) (LoGrasso et al., 1997). Dead-end inhibition with SB203580 was competitive vs. MgATP, and un-competitive vs. ATF-2, also in accord with an equilibrium-ordered mechanism (Fig. 15). The non-cleavable ATP analog, AMP-PCP also exerted competitive inhibition vs. MgATP, and un-competitive inhibition vs. ATF-2. The resulting E-ATF-2-MgATP ternary central complex then undergoes transphosphorylation to produce P-ATF-2 (Q) and MgADP (P), and the phosphorylated product is released after MgADP. Does the rapid-equilibrium binding of ATF-2 convey a message? Speculatively, the kinetic mechanism may signal to an investigator that this kinase, which occupies a key “decision

Fig. 14

Kinetic and chemical mechanisms of M. tuberculosis malate synthase.

An Overview of Steady-State Enzyme Kinetics

Fig. 15

85

Kinetic mechanism of human p38 MAP kinase.

point” in cellular signal transduction, needs to be punctilious about the selection of its substrate. That is, the binding of other proteins not involved in the signaling pathway, bearing a Ser, Thr, or Tyr sidechain, must be vetted until the proper signaling component is bound, in this case ATF-2. Does this kinetic mechanism matter? If a pharmaceutical company planned on conducting a vast high-throughput screening campaign for p38 MAP kinase without first elucidating the kinetic mechanism of this enzyme, and added saturating levels of MgATP to millions of assay plate-wells, even at low concentrations of ATF-2, their p38 MAP kinase would be completely ensconced in an E-ATF-2-MgATP complex arising from the equilibrium-ordered mechanism, and no free enzyme would be available for the sampling of inhibitors in an HTS campaign. The classic Rapid-Equilibrium Random Bi Bi mechanism is provided by mammalian (beef heart) creatine kinase. The enzyme catalyzes reversible phosphorylation of creatine, from which accumulated creatine-phosphate provides a buffer of creatinephosphate, which serves as a means to rapidly produce MgATP from MgADP in cells under energy demand. The RapidEquilibrium Random Bi Bi kinetic mechanism was elucidated from initial velocity and isotope exchange studies at pH 8.0 (Fig. 16). Theoretically, all four product inhibition patterns should be competitive, but two substrate-product dead-end complexes form, namely E-MgADP-creatine (EBQ), and E-MgATP-creatine-phosphate (EAP), which lead to non-competitive inhibition patterns for MgADP vs. creatine and creatine-phosphate vs. MgATP (Morrison and Cleland, 1966; Morrison and James, 1965). Eight years afterward, the Cleland group showed that at pH 7.0, creatine kinase utilized rapid-equilibrium-ordered binding of MgATP, followed by binding of creatine, and following phosphoryl transfer, both MgADP and creatine-phosphate products were released randomly (Schimerlik and Cleland, 1973; Cook et al., 1981). This suggests that creatine and/or creatine-phosphate has become more sticky at neutral pH, as was demonstrated for the binding of creatine-phosphate. The kinetic mechanism of human protein arginine methyltransferase 1 (PRMT1) is also rapid-equilibrium random. PRMT1catalyzed methyl transfer from S-adenosyl-methionine (SAM) to the arginine residue in the peptide AcH4-21 (AcSGRGKGGKGLGGAKRHRKV) provided an intersecting initial velocity pattern, either non-competitive or competitive patterns from the product inhibitors RMe-AcH4-21 peptide and S-adenosyl-homocysteine (SAH) respectively, while the dead-end inhibitors AcH4-21R3K (in which the highlighted arginine in AcH4-21 is replaced with a lysine), and the SAM analog sinefungin, afforded competitive and non-competitive inhibition patterns as expected for a random mechanism (Obianyo et al., 2008). There are numerous examples of Random Bi Bi kinetic mechanisms that achieve steady-state enzyme transitory complexes. Bovine liver fructose is steady-state random, in which D-fructose or MgATP bind randomly with a degree of sticky binding (Raushel and Cleland, 1977a). While product inhibition plots of MgADP vs. MgATP and fructose-1-phosphate vs. fructose were both competitive, all other product inhibition plots were non-competitive; emblematic of a random mechanism. Dead-end inhibition by 1deoxy-fructose, CrATP, and CrADP exhibited competitive patterns vs. D-fructose and MgATP, respectively, and non-competitive patterns vs. MgATP and the sugar. The steady-state nature of this random kinetic mechanism was ascertained in latter studies (Raushel and Cleland, 1977b). The dihydrofolate reductase of E. coli displayed curvilinear, downward concave initial-velocity plots of 1/v vs. 1/NADPH and 1/v vs. 1/DHF, which are expected for steady-state random mechanisms (Stone and Morrison, 1982). The investigators also utilized product inhibition studies, and dead-end inhibition studies using the NADPH analog, reduced acetyl-pyridine-adenine-dinucleotide

Fig. 16

Kinetic mechanism of pig heart creatine kinase.

86

An Overview of Steady-State Enzyme Kinetics

phosphate (APADPH), which provided either non-competitive or competitive inhibition vs. DHF and NADPH, respectively, indicative of a random mechanism. The authors also determined that both substrates were sticky in the E-DHF-NADPH complex, which led to the formation of steady-state transitory complexes (Stone and Morrison, 1988; Morrison and Stone, 1988). Focal adhesion kinase (FAK) catalyzes phosphorylation of a tyrosine residue on a peptide of sequence (AcRRRRRRSETDDYAEIID-NH2, hereafter known as FAK-tide). The initial velocity plot was intersecting, with concave-downward curvilinear double-reciprocal plots (Schneck et al., 2010). Product inhibition data were consistent with a random mechanism. Dead-end inhibition studies with the non-cleavable ATP analog AMP-PNP, and a FAK-tide analog in which the acceptor tyrosine was replaced with a phenylalanine, provided competitive inhibition vs. MgATP and FAK-tide, respectively, and non-competitive patterns of FAK(Phe/Tyr)-tide vs. MgATP and AMP-PNP vs. FAK-tide; again consistent with a random mechanism. b-Ketoacyl-ACP reductase from Staphylococcus pneumoniae, the product of the FabG gene, catalyzes reduction of b-acetoacetyl-ACP and b-acetoacetyl-CoA (AcAcCoA) in which the acetoacetyl (AcAc) fragment is appended to the acyl-carrier protein (ACP) or CoA via a thio-ester. Enzyme-catalyzed reduction of AcAcCoA with NAPDH as the reductant, produced (3S)-hydroxybutyryl-CoA and NADPþ. Initial velocity data were intersecting, and the product inhibition pattern of NADPþ vs. AcAcCoA was non-competitive suggesting the formation of a E-NADPþ-AcAcCoA substrate-product dead-end complex like that observed for creatine kinase (Patel et al., 2005). Product inhibition patterns of NADPþ vs. NADPH, and D,L-hydroxybutyryl-CoA vs. AcAcCoA were both competitive, as expected for a random mechanism (Fig. 17), while an unusual competitive inhibition pattern was observed for the D,Lhydroxybutyryl-CoA vs. NADPH, for which the two ligands are expected to have discrete binding sub-sites. This competitive pattern is classic for a true Rapid-Equilibrium Random Bi Bi mechanism in which binary transitory complexes such as EA and EQ are negligible. Malic enzyme from pigeon liver is a Bi Ter enzyme that binds NADPþ and L-malate. Transfer of a hydride group from carbon-2 from L-malate to NADPþ, and the presumed enzyme-bound oxalate intermediate undergoes metal ion-assisted decarboxylation to produce carbon dioxide and pyruvate (Fig. 18). This chemistry was suggested by initial velocity data which exhibited an intersecting double-reciprocal plot, and product inhibition data (Hsu et al., 1967). Bicarbonate was a non-competitive inhibitor vs. both NADPþ and malate; pyruvate vs. both substrates demonstrated un-competitive inhibition, and NADPH vs. NADPþ and vs. malate rendered, respectively, competitive and non-competitive inhibitor. Concluded from these data was the Ordered Bi Ter mechanism shown below. Bovine liver glutamate dehydrogenase is a sequential Ter Bi mechanism in the catalytic direction of a-keto-glutarate synthesis. The initial velocity pattern is intersecting (Rife and Cleland, 1980). Much of this kinetic mechanism was ascertained by the use of dead-end a-ketoglutarate analogs, such as norvaline and oxalylglycine, for which these analogs exerted non-competitive inhibition vs. ammonium ion, competitive inhibition vs. a-ketoglutarate, and un-competitive inhibition vs. NADPH (Fig. 19). Other studies revealed that hydride transfer from the a-hydrogen of glutamate to NADPþ resulted in the expected imine intermediate, which, after addition of water, the subsequent carbinolamine eliminates ammonia to produce a-ketoglutarate. The asparagine synthetase (in the current case, from Vibrio cholerae) catalysis the biosynthesis of asparagine from MgATP, aspartate, and ammonia. The enzyme may use either exogenously-added ammonia, or ammonia obtained from a second subunit with a glutaminase activity. Initial velocity data of MgATP vs. aspartate were intersecting, while patterns of Gln vs. MgATP or aspartate and ammonia vs. MgATP or aspartate were parallel, suggesting a mixed sequential, ping-pong mechanism (Fig. 20) (Fresquet et al., 2004). The parallel patterns likely indicated a product release step prior to the binding of ammonia. Pyrophosphate vs. MgATP, aspartate, ammonia, and glutamine exhibited, respectively, competitive, non-competitive, un-competitive, and un-competitive product inhibition patterns. Does this indicate that PPi binds to free enzyme like MgATP, and is the last product to be released from enzyme? Asn vs. Gln was competitive. Dead-end inhibition patterns of AMP-P-CH2-P vs. MgATP, aspartate, glutamine, and ammonia were respectively, competitive, non-competitive, un-competitive, and un-competitive. Dead-end inhibition patterns of the aspartate analog, L-cysteine sulfinic acid vs. MgATP, aspartate, glutamine, and ammonia exhibited un-competitive, competitive,

Fig. 17

Kinetic mechanism of S. pneumoniae b-Keto-ACP reductase.

An Overview of Steady-State Enzyme Kinetics

Fig. 18

Kinetic mechanism of pigeon liver malic enzyme.

Fig. 19

Kinetic mechanism of bovine liver glutamate dehydrogenase.

Fig. 20

Kinetic mechanism of V. cholerae asparagine synthetase.

87

non-competitive, and un-competitive inhibition, respectively. The dead-end inhibition patterns provided the most straightforward path to elucidation of the kinetic mechanism. The respective C and UC inhibition patterns of AMP-P-CH2-P vs. MgATP and aspartate, respectively indicated an ordered binding of MgATP followed by aspartate. This is consistent with the NC and C inhibition patterns of L-cysteine sulfinic acid vs. MgATP and aspartate, indicating that MgATP binds before aspartate. Dead-end inhibition patterns of AMP-P-CH2-P vs. either glutamine or ammonia were un-competitive, which suggested downstream binding of

88

An Overview of Steady-State Enzyme Kinetics

ammonia, regardless of its source. The UC pattern of AMP-P-CH2-P vs. Gln is consistent with Gln binding to a different subunit (E0 ) compared to that of MgATP. It was resolved that PPi bound to free enzyme (in the MgATP site) as a dead-end complex, and the same is true with product inhibitor asparagine binding to the glutamine site. The kinetic mechanism most consistent with the data is that shown below (Fig. 20). MgATP and aspartate bind in that order, with glutamine binding to subunit E0 . Following the apparent activation of the b-carboxyl group of aspartate by the formation of an acyl-phosphate intermediate, aspartyl-AMP, pyrophosphate is released. Ammonia then binds to the E-AMP form, with the release of glutamate from the E’ subunit. Displacement of AMP produces asparagine, released from enzyme prior to AMP. Bovine liver argininosuccinate synthetase is a urea-cycle enzyme that catalyzes the conversion of MgATP, citrulline, and aspartate to argininosuccinate, pyrophosphate, and AMP. The next enzyme in the urea cycle, argininosuccinate lyase catalyzes the E2-type elimination of fumarate to afford arginine. Initial velocity studies of argininosuccinate synthetase of all three substrates gave intersecting patterns, including MgATP vs. aspartate at a low fixed concentration of citrulline, but this pattern became parallel at a 10-fold higher concentration of citrulline (Raushel and Seiglie, 1983). This identified citrulline as substrate B. AMP vs. MgATP was competitive, but vs. citrulline and aspartate, AMP was non-competitive; again, consistent with AMP and MgATP both binding to free enzyme (Fig. 21). Argininosuccinate vs. all three substrates gave non-competitive inhibition, indicating that this was likely the first product released from central complexes. Pyrophosphate was non-competitive vs. MgATP, but un-competitive vs. the two aminoacid substrates. This suggested that pyrophosphate was the second product released, and the non-competitive inhibition vs. MgATP was due to the formation of an E-PPi complex at low concentrations of MgATP (the slope effect). Arginine, as a dead-end analog of citrulline, displayed respective UC, C, and NC inhibition patterns vs. MgATP, citrulline, and aspartate, indicative of substrate binding order of MgATP, citrulline, and aspartate. In kind, the aspartate analog a-methyl-aspartate exerted un-competitive inhibition patterns vs. MgATP and citrulline, and was competitive vs. aspartate. These findings provide an abundance of data indicating an Order Ter Ter mechanism for argininosuccinate synthetase, via a mechanism as shown below in Fig. 21. Complexity escalates with the number of substrates and products. Glutamine synthetase (GS) is a ter ter enzyme that catalyzes the formation of glutamine (Gln) from glutamate (Glu), ammonia, and MgATP. A detailed analysis of the kinetic mechanism of the Escherichia coli GS in which both the forward and reverse reactions were analyzed resulted in the following data (Meek and Villafranca, 1980): Initial velocity data of the forward direction produced intersecting patterns, including NH3 vs. MgATP at a low fixed concentration of glutamate (Glu) (Fig. 22). However, when the fixed concentration of glutamate was at near saturating concentrations, this pattern became parallel like in Fig. 6. This suggested that glutamate bound between the binding of MgATP and NH3. All initial velocity patterns performed for the reverse reaction were intersecting, regardless of the concentration of the fixed substrate. Glutamine at a near-saturating, fixed concentration did not the convert the initial velocity pattern of phosphate vs. MgADP to a parallel one, which might be expected if the substrate-binding order was like that of the forward reaction. From product inhibition studies, MgADP vs. MgATP was competitive, while MgADP exerted non-competitive inhibition vs. glutamate and ammonia. This was consistent with MgATP being the first substrate to bind, and MgADP being the last product to dissociate. Phosphate was a non-competitive inhibitor vs. all three substrates. The same was true for glutamine, which suggested random release of these two products, as one expects un-competitive patterns for an Ordered Ter Ter mechanism for one of the three products. The dead-end inhibitor D,L-3-amino-3-carboxy-propanesulfonamide (ACPS), a potential transition-state analog of the GS reaction, was a competitive inhibitor vs. Glu for the forward reaction, and was, respectively, un-competitive and non-competitive vs. MgATP and ammonia. This affirmed the kinetic order of the forward reaction as MgATP, Glu, and then, NH3. It also suggested that either before or after the binding of NH3, the E-MgATP-Glu complex undergoes phosphoryl transfer from the g-phosphate of MgATP to the g-carboxylate of Glu to form an acyl-phosphate intermediate, g-glutamyl-phosphate (intermediate in Fig. 22). The attack of ammonia on this putative intermediate affords phosphate and glutamine, along with MgADP. Analysis of the reverse reaction using ACPS as a glutamine analog and arsenate as a phosphate analog led to the assignment of the reverse reaction as fully ordered: MgADP binds first, followed by glutamine and then phosphate, as shown in the scheme above.

Fig. 21

Kinetic mechanism of bovine liver argininosuccinate synthetase.

An Overview of Steady-State Enzyme Kinetics

Fig. 22

89

Kinetic mechanism and inhibition of E. coli glutamine synthetase.

The lack of un-competitive inhibition exhibited by glutamine, which is sandwiched between the release of phosphate and MgADP, was attributed the formation of a dead-end MgATP-Gln complex in the product inhibition studies. Carbamoyl-phosphate synthetase (CPS) comprises one of the most complex enzymes in terms of kinetic mechanism. There are four substrates (2 MgATPs, bicarbonate, glutamine or ammonium ion) and four or five products (2 MgADPs, phosphate, glutamate, and carbamoyl-phosphate), whether or not the source of ammonia is from its exogenous addition to reaction mixtures, or like asparagine synthetase, from the action of a glutaminase subunit (E’) (Fig. 23). For either synthetase, this glutaminase subunit contains a cysteine residue which attacks glutamine to form a g-glutamyl thio-ester intermediate, concomitantly releasing a free ammonia molecule to seek out the chemistry on the adjacent, larger subunit or domain of either synthetase. Data for the determination of the kinetic mechanism of E. coli CPS provided the following data (Raushel et al., 1978): (1) High, fixed concentrations of bicarbonate converted an intersecting initial velocity pattern of MgATP vs. NH4þ to a parallel one, indicating that bicarbonate binds between the binding of MgATP and NH4þ. This is analogous to the binding of glutamate to GS. (2) Plots of MgATP or bicarbonate vs. Gln were both parallel, indicating that the binding of Gln is not reversibly connected to the binding of these two substrates. (3) The double-reciprocal plot of 1/v vs. 1/MgATP was linear, and not parabolic, which means that the two binding events are not reversible connected, indicating a product release step in between their bindings. (4) Phosphate was a competitive inhibitor vs. variable MgATP, indicating that they both bind to the same enzyme form, while it was non-competitive vs. bicarbonate, unless Gln was saturating, whereupon the plot was un-competitive. (5) Saturation with Gln therefore abrogates the reversible connection between binding of phosphate and bicarbonate. (6) Carbamoyl-phosphate exhibited un-competitive inhibition vs. MgATP, bicarbonate, and NH4þ, indicating that its release from enzyme likely occurs between the release of two other product inhibitors. (7) Inhibition by MgADP was non-competitive vs. MgATP, HCO3, and NH4þ, except for the un-competitive plot of MgADP vs. NH4þ in which the fixed concentration of HCO3 was saturating. This pattern conversion means that NH4þ binds after HCO3 binds and before the release of MgADP. These extensive data are most consistent with the Ordered Quad Bi Uni Ter Quint Quint kinetic mechanism shown above. MgATP binding is followed by binding of HCO3 and then NH4þ, and where NH4þ evolves from the glutaminase subunit, Gln may bind unaffected by the other subunit. Release of product Glu (P) attends progression of NH4þ to the “biosynthetic” subunit in which phosphoryl transfer from the g-phosphate of MgATP putatively leads to the formation of carboxy-phosphate (X) as an initial intermediate, which is then attacked by NH4þ to afford enzyme-bound carbamate (Y) as the second intermediate in the EYQT transitory complex. Phosphate, a non-competitive inhibitor vs. both HCO3 and NH4þ, likely is the first product (Q) released from the major central complexes, and its competitive inhibition vs. MgATP indicates that both bind to the same enzyme form (EYT). The MgATP (substrate E) that binds to this enzyme form provides phosphoryl transfer to carbamate to afford the biosynthetic product, carbamoyl-phosphate, which is released from enzyme after the “second” MgADP and before the “first” MgADP, accounting for the un-competitive inhibition patterns with this inhibitor.

Fig. 23

Kinetic mechanism of E. coli carbamoyl-phosphate synthetase.

90

Fig. 24

An Overview of Steady-State Enzyme Kinetics

Kinetic mechanism of mammalian carbamoyl-phosphate synthetase.

Mammalian carbamoyl-phosphate synthetase II, including its glutaminase domain, is found in a tri-functional protein along with aspartate trans-carbamoylase and dihydro-orotase activities house in a single 243-kDa polypeptide chain. The role of mammalian CPS II is solely in the pathway of de novo UMP biosynthesis; indeed, half of the anabolic steps in this pathway are housed within this single protein. A full analysis of the kinetic mechanism of Syrian hamster CPS II, in which the ammonia source was invariantly glutamine, exhibited some similarity in the initial velocity and product inhibition patterns with the bacterial CPS, with some key differences (Fig. 24) (DeBrosse et al., 1987). The results of the analysis is a partially-ordered Ter Bi Uni Bi Quint Quint mechanism as shown in Fig. 24. MgATP, bicarbonate, and glutamine bind in that order, with the binding of bicarbonate in rapid-equilibrium. In the resulting quaternary transitory complex, carboxy-phosphate is formed, and the ammonia generated from Gln “quenches” this unstable intermediate to afford enzyme-bound carbamate, as with the bacterial enzyme. Glu and the phosphate are released in that order, the binding of a second MgATP occurs, leading to phosphorylation of carbamate, followed by ordered release of MgADP, and then the random release of carbamoyl-phosphate and MgADP.

1.05.6

Rate-limiting steps

By now, gentle reader, you have formulated the following question: How do I use the King-Altman method for the derivation of equations to analyze what happens after substrates bind and chemistry begins? What can I do? One needs methods that can “crack open” the chemical machinations that occur in the mysterious central complexes, where the sausage is made. An examination of the 20 kinetic mechanisms detailed above in which numerous studies tailored to the chemistry of each enzyme were required to characterize chemical intermediates that have been shown above. Accordingly, such enzyme studies require a customized set of experiments to not only unequivocally identify the chemical nature of reaction intermediates, but, just as importantly, demonstrate that these intermediates are formed and utilized by the enzyme in a kinetically-competent fashion. Said another way, one must not only characterize a reaction intermediate chemically, but also prove that that the kinetics of its formation and dissipation on the reaction pathway occur at reaction rates that are either equal to or faster than the slowest step(s) in the full catalytic pathway. The employment of pre-steady-state kinetic analysis, including stopped-flow spectro/fluoroscopy or rapid-quench, single-turnover kinetic methods, is an exceptionally useful toolbox for these studies, but, alas, beyond our page limitations here. However, if we dare to confine ourselves here to steady-state kinetics, much may still be learned or inferred. To do this, we must first expand on our kinetic models, and develop new algebraic expressions beyond the King-Altman algorithm to characterize them in terms of steady-state kinetics. We first consider an Ordered Bi Bi enzyme mechanism in which a reaction intermediate X occurs on the catalytic pathway: k3 k5 B k1 A k7 k9 k13 k11 E % EA % EA 0 %ðE0AB / EXQ /EPQÞ / EQ / E k2

k4

k6

k70 k30 k50 k90 k130 k110 k10 E / EA / EA0 /ðE0AB / EXQ /EPQÞ / EQ / E

(II)

In Mechanism II, A binds to free enzyme E, and the resulting binary complex EA undergoes a conformational isomerization to form the E0 A species, for which the active site is now pliant for the binding of the second substrate B, which then proceeds to central complexes. The first (irreversible) chemical step affords EXQ, which proceeds via a second chemical step to the ternary complex EPQ. Products P and then Q are released (irreversibly) in order. The mechanism in Mechanism II could easily be a reductase. A is NADPH, which induces a conformational change upon binding. B is a ketone or an imine (as in the case of dihydrofolate). Hydride transfer provides reduction of the double bond in EXQ, Q is NADPþ, and a proton-transfer step k9 leads to the final product P. Both product release steps are irreversible under initial velocity conditions. The King-Altman method can be employed for this complicated, albeit realistic, mechanism, but the complexities of the resulting algebra will win few adherents. How, then, to develop mathematical expression of the catalytic steps that do not involve substrate addition or product release?

An Overview of Steady-State Enzyme Kinetics

91

Net Rate Constants. Such a kinetic tool was introduced by Cleland (1975); the method of net-rate constants. It is the opinion of this author that this paper comprises one of the most important ever published in the field of enzyme kinetics. Here, we will summarize key aspects of this work, and add more detail and algebraic transformations to relate this method to the derivations we have conducted above. The bottom mechanism in Mechanism II is now simplified in terms of total rate constants, as the net rate constants shown provide the flux through each reaction step, in which flux is a partitioning ratio of the forward step divided by the sum of the forward and reverse steps, as described below. The initial velocity for this mechanism, similar to the King-Altman model, may be written for any kinetic step, and will be equal to the net-rate constant multiplied by the concentration of the germane enzyme form, e.g., v ¼ kn0 [En]. If kn0 has a small value compared to other net-rate constants, [En] will be the enzyme form with the highest concentration and thereby the largest ratio of [En]/[Et]. The distribution of any enzyme form in Mechanism II may be obtained from: [En]/[Et] ¼ (1/kn0 )/[S(1/km0 )], in which km’ is all net-rate constants including kn’. To make this less general and abstract, we return to Mechanism I, in which we derived the following: kcat ¼ k3 k5 =ðk3 þ k4 þ k5 Þ ¼ 1=½ðk4 þ k5 Þ=k3 k5 þ 1=k5 

(27)

for which (1/k3)Et/[1/k3 þ 1/k5] þ (1/k5)Et/[1/k3 þ 1/k5] ¼ [EA] þ [EP] This means that k30 ¼ k3k5/(k3 þ k4 þ k5) and k50 ¼ k5. In Mechanism I, the only enzyme forms represented by the expression for kcat are EA and EP. We may now write the two distribution equations for these two enzyme forms: ½EA =½Et  ¼ ð1=k30 Þ=½1=k30 þ 1=k50  ¼ ððk4 þ k5 Þ=k3 k5 Þ=½1=ðk4 þ k5 Þ=k3 k5 þ 1=k5 : and ½EP=½Et  ¼ ð1=k50 Þ=½1=k30 þ 1=k50  ¼ ð1=k5 Þ=½ðk4 þ k5 Þ=k3 k5 þ 1=k5 : This means that, generally as stated above, any enzyme distribution expression may be calculated from: [En]/[Et] ¼ (1/kn0 )/[S(1/km0 )], which comprises an important simplification. Importantly, one now has the means to write out the enzyme distribution equations for EA0 and the intermediate-bearing enzyme form, EXQ, from which the ratios of these enzyme forms inform the investigator as to their contributions to ratelimitation on the overall catalytic pathway, if not the kinetic competence of these enzyme forms. The maximal rate, Vmax ¼ kcat[Et] may be thought of as the rate through either the k3 or k5 step, which, by our convention above would be: Vmax ¼ k3[EA] ¼ k5[EP], wherein [Et] ¼ [EA] or [EP], respectively, when the k3 or k5 step is rate-limiting. [EA]/[Et] ¼ (1/k30 )/[1/k30 þ 1/k50 ] and [EP]/[Et] ¼ (1/k50 ])/[1/k30 þ 1/k50 ] And by substituting from above: Vmax ¼ k3 ½Et  ð1=k30 Þ=½1=k30 þ 1=k50  ¼ k5 ½Et  ð1=k50 Þ=½1=k30 þ 1=k50  ¼ k3 k5 ½Et =½k3 þ k4 þ k5 : Accordingly, the initial rate may be defined by any kinetic step as: v ¼ kn ð½En =½Et Þ  ½Et  for which ½En =½Et  ¼ ð1=kn’ Þ=½Sð1=km’ Þ; or v ¼ ½Et =½Sð1=km’ Þ For the case of Vmax, the reciprocal values of all net rate constants that contain a substrate concentration term for all substrates will equal zero as the concentrations of these substrates are now infinite, as we will see below. In the case of Mechanism II, we may write the initial rate equation as Eq. (28): v ¼ ½Et =½Sð1=km0 Þ ¼ ½Et =½1=k10 þ 1=k30 þ 1=k50 þ 1=k70 þ 1=k90 þ 1=k110 þ 1=k130 

(28)

Note, that if a single net constant is significantly slower than all others, here we exemplify the chemical step k9’, this initial rate equation becomes: v ¼ ½Et =ð1=k90 Þ ¼ k90 ½Et ; such that ½Et   ½EXQ

1.05.6.1

Use of net rate constants to calculate kinetic parameters

For Mechanism II, one may develop expressions for V/Ka, V/Kb, and Vmax (V), based on the kinetic flux through each catalytic step in which the forward and reverse reactions are replaced by a single “net-rate” constant (kn’). The kinetic parameters V/Ka and V/Kb are the initial rates when [A] and [B] approach zero, respectively. From Eq. (28), we have: v ¼ ½Et =½1=k10 þ 1=k30 þ 1=k50 þ 1=k70 þ 1=k90 þ 1=k110 þ 1=k130 

92

An Overview of Steady-State Enzyme Kinetics For which as [A] 0 zero, the initial rate becomes: v ¼ k10 [E] ¼ (V/Ka)/[A], and as [B] 0 0, v ¼ k30 [EA] ¼ (V/Kb)/[B], and: V=Ka ¼ k10 ½E=½A  and V=Kb ¼ k30 ½EA=½B

To calculate the values of k10 and k30 , we consider that after the binding of substrate A to E, the EA complex may either proceed forward through k30 , or substrate is released by step k2. So the forward flux for EA is defined by (k30 /(k2 þ k30 )). From this, k10 ¼ (k1k30 A)/(k2 þ k30 ). Likewise, k30 ¼ ðk3 k50 BÞ=ðk4 þ k50 Þ The solutions of these two V/K expressions demonstrate the following generalization: The flux through any kinetic step is given by the microscopic rate constant for that step, multiplied by the net rate constant for all subsequent steps. Consider this as follows: a substrate binds, and then to what extent does it desorb from its transitory complex vs. progression through all subsequent kinetic steps? For substrate A, one designates the net rate constant for the progression to the EA complex as k1A(k30 /(k2 þ k30 )), in which k2 is the rate of desorption of A from EA, while k3’ is a collection of rate constants that describe the flux of EA to form products and free E. A net rate constant for any step n may be defined generically as: (knk(n þ 2)0 )/(k(n þ 1) þ k(n þ 2)0 ), wherein, k’(n þ 2) is derived from the net rate constants of subsequent steps. By the formula above, this is equal to k10 ¼ (k1k30 A)/(k2 þ k30 ). What then is k30 ? By the mechanism, k30 ¼ (k3k50 B/(k4 þ k50 )), in which k50 , by the same rules is, k50 ¼ (k5k70 B)/(k6 þ k70 ”)).” In kind, k70 ¼ (k7k9)/(k8 þ k9) and for the first irreversible step, k90 ¼ k9. Knowing that for an irreversible step, kn0 ¼ kn, we can work backwards from the irreversible product release steps to obtain all of the net rate constants for Mechanism II: k130 ¼ k13 ; k110 ¼ k11 ; k90 ¼ k9 ; k70 ¼ k7 k50 ¼ ðk5 Bk70 Þ=ðk6 þ k70 Þ ¼ ðk5 k7 B=ðk6 þ k7 ÞÞ k30 ¼ ðk3 k5 k7 BÞ=½k4 ðk6 þ k7 Þ þ k5 k7 BÞ k10 ¼ ðk1 k3 k5 k7 ABÞ=½k2 ðk4 ðk6 þ k7 ÞÞ þ k5 k7 BÞ þ k3 k5 k7 B From this, we can fully define V/Ka and V/Kb: V=Ka ¼ k10 =½A  ¼ ðk1 k3 k5 k7 BÞðEÞ=½k2 ðk4 ðk6 þ k7 ÞÞ þ k5 k7 BÞ þ k3 k5 k7 B V=Kb ¼ k30 =½B ¼ ðk3 k5 k7 BÞðEA Þ=½k4 ðk6 þ k7 ÞÞ þ k5 k7 B Generically then, V/Kn ¼ (kn0 )[En]/[N], where [En] is the concentration of the enzyme form to which substrate N binds. One then wishes to express the enzyme concentration [En] in terms of [Et]. In the case of V/Ka, [E] ¼ [Et] where [A] 0 0, so: V=Ka ¼ ðk1 k3 k5 k7 BÞðEt Þ=½k2 ðk4 ðk6 þ k7 Þ þ k5 k7 BÞ þ k3 k5 k7 B Note that when [B] is saturating, this becomes: V=Ka ¼ ðk1 k3 k5 k7 ÞðEt Þ=ðk2 k5 k7 þ k3 k5 k7 Þ ¼ ðk1 k3 Et Þ=ðk2 þ k3 Þ In the case of V/Kb, [EA0 ] depends upon the fixed concentration of A, which is defined by the dissociation constant of A binding to E, that is Kia ¼ k2/k1, as well as the ratio [EA]/[EA0 ] which will equal Keq3 ¼ k4/k3.
 Kia ¼ ½E½A =½EA ; and ½Et  ¼ ½E þ ½EA  þ ½EA0 ¼ ½EA0 1 þ Keq3 ð1 þ Kia =½A  
½EA0 ¼ ½Et = 1 þ Keq3 ð1 þ Kia =½A : We may therefore write: V=Kb ¼ k50 =½B ¼ ðk5 k7 ÞðEA0Þ=½k4 ðk6 þ k7 Þ þ k5 k7  ¼   
¼ ðk5 k7 Þ ½Et ½A = ½A ð1 þ Keq3 ð1 þ Kia = A =½k4 ðk6 þ k7 Þ þ k5 k7  For Vmax, we return to the initial rate as expressed in Eq. (28): v ¼ ½Et =½1=k10 þ 1=k30 þ 1=k50 þ 1=k70 þ 1=k90 þ 1=k110 þ 1=k130  Note that: 1=k10 ¼ 1=½k2 ðk4 ðk6 þ k7 ÞÞ þ k5 k7 BÞ þ k3 k5 k7 B=ðk1 k3 k5 k7 ABÞ 1=k30 ¼ ½k4 ðk6 þ k7 Þ þ k5 k7 BÞ=ðk3 k5 k7 BÞ 1=k50 ¼ ½k6 þ k7 =ðk5 k7 BÞ

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93

As [A] ¼ [B] ¼>N, for Vmax, both 1/k10 , 1/k50 ¼ 0, and 1/k30 ¼ 1/k3,. So: Vmax ¼ ½Et =½ð1=k3 þ 1=k7 þ 1=k9 þ 1=k11 þ 1=k13  which may also be written as: Vmax ¼ k3 k5 k7 ½Et =½k5 k7 þ k3 k5 ð1 þ k7 =k9 þ k7 =k11 þ k7 =k13 Þ The example above provides the advantage of the use of net rate constants to define the three essential kinetic parameters for Mechanism II (Michaelis constants may be calculated from these results, but may be less useful than corresponding values of Vmax/Km), and given the complexity of this mechanism, this method should be easily transferable to any kinetic mechanism.

1.05.6.2

The use of net-rate constants and the determination of rate-limiting steps in enzymatic catalysis

We start with a Mechanism III, an Ordered Bi Bi reaction mechanism with an intermediate X, the formation of which is reversible. k3 B k5 k1 A k7 k9 k11 E % EA %ðEAB % EXQ /EPQÞ / EQ / E k2

k4

k6

k70 k30 k50 k90 k110 k10 E / EA /ðEAB / EXQ /EPQÞ / EQ / E Mechanism 3

(III)

Net rate constants: k110 ¼ k11 ; k90 ¼ k9 ; k70 ¼ k7 ; k50 ¼ k5 k7 =ðk6 þ k7 Þ; k30 ¼ k3 k5 k7 B=ðk4 ðk6 þ k7 Þ þ k5 k7 Þ; k10 ¼ k1 k3 k5 k7 AB=ðk2 ðk4 ðk6 þ k7 Þ þ k5 k7 Þ þ k3 k5 k7 BÞ; V=Ka ¼ k10 =A ¼ k1 k3 k5 k7 BEt =ðk2 ðk4 ðk6 þ k7 Þ þ k5 k7 Þ þ k3 k5 k7 BÞ and when [B] 0 N, the limiting value of V=Ka ð½B0infinityÞ ¼ k1 Et V=Kb ¼ k30 =B ¼ k3 k5 k7 ½EA=ðk4 ðk6 þ k7 Þ þ k5 k7 Þ ¼ k3 k5 k7 ½Et f½A =ð½A  þ Kia Þg=ðk4 ðk6 þ k7 Þ þ k5 k7 Þ and when [A] 0 N, the limiting value of V/Kb ([A]

0 infinity)

becomes equal to k3k5k7[Et]/(k4(k6 þ k7) þ k5k7).

V ¼ Et =½1=k50 þ 1=k70 þ 1=k90 þ 1=k110  ¼ Et =½ðk6 þ k7 Þ=k5 k7 þ 1=k7 þ 1=k9 þ 1=k11  which may be re-arranged to V ¼ k5 Et =½1 þ k5 =k7 þ k5 =k9 þ k5 =k11 þ k6 =k7 : One or more of the catalytic steps in this mechanism may be slower than others, and so becomes part of the steps which determine the ultimate value of kcat. A key slow step could be one of the two chemical steps (k5 and/or k7), or a product-release step (k9 and/or k11). In the realm of steady-state kinetics, what methods are available to ascertain which steps are the slowest? Why is this important? It needs to be recognized that the chemistry catalyzed by enzymes does not mean that the chemistry involved is the slowest step. Frequently, the rates of the chemical steps are rapid, and rate-limitation arises from enzyme isomerization steps, including conformational changes, and product release steps. If one wishes to discover or design an inhibitor of an enzyme target, the exercise of attention to what is/are the rate-limiting step(s) may assist the informed enzymologist as to how to develop an appropriate assay for the discovery of inhibitors. For example, if the desorption of product Q is the slowest overall reaction step, perhaps an assay for high-throughput screening should be devised to enhance the concentration of the EQ complex as a target for the applied compound library. The toolkit available to determine the rate-limiting step(s) of enzyme catalysis is unfortunately not universally applicable to all enzymes, because it relies on the quantification of enzyme transitory complexes containing chemical intermediates to compare with other transitory complexes, which are specific to the enzyme of interest. With that, there are two techniques that may be applied to many enzymes to determine the rate-limiting step(s) of any enzyme-catalyzed reaction. These include (1) kinetic isotope effects, and (2) the use of viscosogens to slow the absorption and desorption rates of, respectively, substrates and products. We will discuss each below. First we turn our attention to the partitioning of the two substrates in an Ordered Bi Bi reaction. If the mechanism is ordered as in Mechanism III, we can use the net rate constant method to write expressions for the kinetic parameters V/Ka, V/Kb and Vmax (V) (Northrop, 1981; Cook and Cleland, 1981a,b,c; Cleland, 2003): V=Ka ¼ ðk1 k3 k5 BEt =k2 k4 Þ=ð1 þ ðk5 =k4 Þð1 þ k3 B=k2 Þ þ k6 =k7 Þ

(29)

94

An Overview of Steady-State Enzyme Kinetics V/Kb (A

0 infinity)

may be written as: ðk3 k5 Et =k4 Þ=ð1 þ k5 =k4 þ k6 =k7 Þ

(30)

The terms (k5/k4)(1 þ k3B/k2) and k5/k4 in the respective denominators report on the extent to which bound A or B proceed toward the first chemical step, k5, rather than desorb from their respective transitory complexes, E and EA. These expressions are known as forward commitments to catalysis, cf (Northrop, 1981; Cook and Cleland, 1981a). Substrate A is considered “sticky” when k3B/k2  1, and clearly, high [B] will commit EA into the first catalytic step, k5. Substrate B is considered “sticky” when k5/ k4  1. The term k6/k7 reports on the extent that the catalytic step described by k5 is reversible by the k6 step, as opposed to proceeding through the second catalytic step, k7, and is referred to as the reverse commitment to catalysis, cr, for this specific mechanism. As above, the expression for Vmax may be written as, V ¼ k5 Et =½1 þ k5 =k7 þ k5 =k9 þ k5 =k11 þ k6 =k7 ;

(31)

an algebraic form which places emphasis on the first chemical step described by k5. It is noteworthy that the expressions of all three kinetic parameters in Eq. (29) to Eq. (31) are all dependent on the rate constant of the first chemical step, k5, but how this rate constant has impact on the these expressions clearly are likely to be different; the ratio of k5/k4 affects the two expressions of V/K, while k5/k7, k5/k9, and/or k5/k11, in which the k5 step is compared to all subsequent steps, determines the impact of k5 on the value of V. For the denominator of the expression for V, the common reverse commitment factor cr is also seen, along with the collective forward commitment factor for Vmax known as cVf, which for this mechanism is equal to: k5/ k7 þ k5/k9 þ k5/k11, from which the value of the denominator will depend on the value of k5 vs. the second chemical step (k7), and the rate constants of the two product release steps, k9 and k11.

1.05.6.3

Kinetic isotope effects and rate-limiting steps

When a heavy isotope is substituted for a light isotope at a position involved in bond-breaking and bond-making, the rate of the chemical step is reduced due to the stronger bond of the heavy isotope vs. that of the light isotope, in the simplest terms. This is true for either an enzyme-catalyzed or non-enzyme-catalyzed chemical reaction. Comparison of the two rates, such as (V/Klight)/(V/ Kheavy) and (Vlight)/(Vheavy) establishes the extent to which a kinetic isotope effect on that chemical step is expressed on the kinetic parameters, V/Kx and V, in which x is any of the substrates in the kinetic mechanism. It is important to note here that the “chemical” isotope effect on the bond-making/bond-breaking steps, known as the intrinsic isotope effect, will be invariant within the individual expressions of V/Kx and V, but other rate constants will determine the extent to which the intrinsic kinetic isotope effect (KIE) is expressed on these kinetic parameters. In the absence of an enzyme catalyst, the measurement of the ratio of the overall reaction rate constants of heavy isotope/light isotope is given by klight/kheavy This comprises the intrinsic kinetic isotope effect of this chemical step, which gives an experimenter a view as to the nature of bond-breaking and bond-making in the transition state of the reaction, if not the actual structure of the transition state. If bond-breaking/bond-making in the transition state is symmetrical, that is, the bond being broken is equal in length to the bond being made, one observes the highest effect on klight/kheavy, while if the transition state is early (little breakage of the bond with the heavy/light atom) or late (nearly complete breakage of the bond with the heavy/light atom), the value of klight/kheavy will be less, and for a “late” transition state, the value of klight/kheavy will approach the isotope effect on the equilibrium constant of the reaction (Keq-light/Keq-heavy). When klight/kheavy > 1 or klight/kheavy < 1, one obtains, respectively, a normal or inverse KIE. Since these KIEs comprise bond-breaking/bond-making steps, they are primary KIEs, while secondary KIEs arise where the isotopic substitution occurs on atoms that are attached to the transferred atom (a-secondary KIEs) or are found next to the transferred atom (b-secondary KIEs). Inherently, secondary KIEs are lower in value than primary KIEs, but provide important information about the nature of the bond order of that atom in the transition state. The theoretical maximal values for primary, intrinsic KIEs commonly encountered in enzymology are: 1H/3H ¼ 15, 1H/2H ¼ 6, 12C/14C ¼ 1.04–1.09, 12C/13C ¼ 1.02–1.04, 14 N/15N ¼ 1.04, 16O/18O ¼ 1.07, and many studies have involved the measurement of more than one of these KIEs on a single chemical step to provide ultimately a true structure of the enzymatic transition state, as has been widely practiced in the laboratory of Schramm (2018). Indeed, characterization of an enzymatic transition state constitutes the apotheosis of the understanding of enzymatic catalysis. Kinetic isotope effects are measured as (V/Klight)/(V/Kheavy) and (Vlight)/(Vheavy), and are designated as DV/K, DV, a-TV/K, 13V/K, and 15V/K for, respectively, a primary deuterium KIE on V/K and V, an a-secondary tritium effect, and primary KIEs for 13C/12C and 15 N/14N. We will confine our discussion here to primary deuterium KIEs, in which the rate of transfer of a deuterium atom from a substrate, compared to that of protium, is compared kinetically. In enzyme-catalyzed reactions in which the isotope-sensitive, chemical step is also the rate-limiting step, the experimental values of xV/K ¼ xV, and these experimental KIEs are equal to the value of the intrinsic KIE for this germane chemical step. However, an intrinsic KIE, which would unveil the nature of the transition state of the studied enzyme-catalyzed reaction, is often attenuated by other non-chemical steps. For example, with a substrate in which one wishes to differentiate the rates of CeH vs. CeD bondbreaking, if a “committed” substrate “sticks” tightly to the enzyme upon binding (cf  1), then the enzyme is hampered in terms of expelling the less desirable CeD-containing substrate in favor of the more pliant CeH-containing substrate. Accordingly, “sticky”

An Overview of Steady-State Enzyme Kinetics

95

substrates blunt the expression of the intrinsic KIEs, and this is evident in values of V/K as we will see below. In the case of observable KIEs on V, the stickiness of the substrates has no effect (they are saturating), and in cases in which post-chemical steps, such as conformational changes or product-release steps are slower than those chemical steps, the intrinsic KIE is also attenuated. The reaction product, regardless of whether it bears a light or heavy atom, piles up in one or more enzyme-product(s) complexes, and there is again a blunting of the expression of the intrinsic KIE on the value of lightV/heavyV. Accordingly, a case in which [lightV/K]/[heavyV/ K] > 1.0 while lightV/heavyV ¼ 1.0, indicates that a post-catalytic step, such as product release, is involved in rate-limitation for the full enzymatic reaction. In Fig. 25 are double-reciprocal plots of initial velocity data for an Ordered Bi Bi enzyme in which either protium (black) or deuterium (red) is transferred in a chemical step. Comparison of the plots of 1/vH vs. 1/[A] and 1/vD vs. 1/[A] demonstrated nearly identical slopes, but a significant intercept effect. The ratios of (V/Ka)H/(V/Ka)D and VH/VD provide ready calculation of the KIEs: D (V/Ka) ¼ 1.2 and DV ¼ 2.8. The intrinsic isotope effect is poorly expressed in the value of D(V/Ka) because [B] ¼ 100 Kb, as discussed below. Likewise, comparison of the plots of 1/vH vs. 1/[B] and 1/vD vs. 1/[B] demonstrate significant changes in both the slopes and intercepts, and from this, isotope effects of D(V/Kb) ¼ 2.5 and DV ¼ 2.8 may be readily calculated. Consider a hypothetical oxidoreductase in which the transfer of a hydride anion from NAD(P)H to the b-carbon of an a,b-unsaturated ketone (Fig. 26) comprises a reversible chemical step, followed by an irreversible proton-transfer step on the a-carbon to afford a ketone product. The free-energy diagram in Fig. 26 depicts the transfer of either protium or deuterium from the A-side of C-4 of the nicotinamide co-factor to the double bond via a transition state in which bond-breaking and bond-making is symmetrical. When deuterium is transferred, the free-energy barrier of the transition of EAB to EXQ is increased due to the intrinsic KIE (Dk5), to form the enolate reaction intermediate X. In a second chemical step, X is protonated by the enzymatic base BeH resulting in the ketone product P, followed by ordered release of this product and NAD(P)þ (Q). The free-energy barriers of EAB going to EXQ with either isotope are far greater than the “return” to free enzyme, such that neither substrate A or B is “sticky,” and the hydride-transfer chemical step (k5), while reversible, is evidently rate-limiting in this mechanism. Protonation of X by BeH also constitutes a significant free-energy barrier (k7), albeit, one that is lower than the k5 step, but unlike the latter, it is essentially irreversible considering the free energy of the resulting EPQ complex. The free-energy barriers for the product-release steps (k9 and k11) are smaller than chemical steps ensuring that the release of the two products do not contribute to rate-limitation of the overall enzymatic reaction. The intrinsic KIEs for the forward and reverse reactions are therefore Dk5 and Dk6, respectively, and as this step is reversible, we need to define an equilibrium isotope effect (EIE) on this step as DKeq5 ¼ Dk5/Dk6 ¼ (k5Hk6D/k5Dk6H). From this, k6D ¼ k6H(DKeq5/Dk5). What are the expressions for this KIE on V/Ka, V/Kb, and V? For DV/Kb ¼ (V/Kb)H/(V/Kb)D, we have: ½ðk3 k5H Et =k4 Þ=ð1 þ k5H =k4 þ k6H =k7 Þ=½ðk3 k5D Et =k4 Þ=ð1 þ k5D =k4 þ k6D =k7 Þ ¼ ðk5H =k5D Þ½ðk3 Et =k4 Þ=ðk3 Et =k4 Þ½ð1 þ k5D =k4 þ k6D =k7 Þ=ð1 þ k5H =k4 þ k6H =k7 Þ;

Fig. 25 Double-reciprocal analysis of an Ordered Bi Bi mechanism in which a protium (black) or deuterium (red) is transferred in the chemical step. Plots of 1/vH vs. 1/[A] (black circles) and 1/vD vs. 1/[A] (red circles) at [B] ¼ 200 mM demonstrate a small effect on the slopes D(V/Ka) ¼ 1.2, but a significant intercept effect DV ¼ 2.8. The plots of 1/vH vs. 1/[B] (black squares) and 1/vD vs. 1/[B] (red squares) demonstrate a larger effect on the slopes D(V/Kb) ¼ 2.5, and the same intercept effect DV ¼ 2.8.

96

An Overview of Steady-State Enzyme Kinetics

Fig. 26 Free-energy profile for a hypothetical a,b-unsaturated ketone reductase. Formation of the transition state for hydride transfer from NAD(P)H constitutes the highest free-energy barrier for the reaction, and is considerably higher when deuterium is substituted at C-4 of the nicotinamide substrate (red). The resulting intrinsic KIE on the forward reaction, Dk5 is indicated. The enzyme-bound enolate X is next protonated by an enzymatic base (B-H), followed by release of the two products. Free energy barriers depicted with dotted lines, from left to right, indicate large values of cfa, cr, and k5/k11, respectively.

or: D

V=k5 ¼D k5 ½ð1 þ k5D =k4 þ k6D =k7 Þ=ð1 þ k5H =k4 þ k6H =k7 Þ:

Multiplying through by Dk5 gives (Dk5 þ k5/k4 þ Dk5k6D/k7)/(1 þ k5H/k4 þ k6H/k7), and setting values of kxH to kx; we have ( k5 þ k5/k4 þ Dk5k6D/k7)/(1 þ k5/k4 þ k6/k7). Since k6D ¼ k6H(DKeq5/Dk5), the expression becomes:  ðV=Kb ÞH=ðV=Kb ÞD¼D V=Kb ¼ D k5 þ k5 =k4 þ ðk6 =k7 ÞD Keq5 =ð1 þ k5 =k4 þ k6 =k7 Þ; (32) D

which may be expressed as: D

V=Kb ¼


D


 k5 þ cfb þ cr D Keq5 = 1 þ cfb þ cr ;

wherein

cfb ¼ k5 =k4 and cr ¼ k6 =k7

(33)

D

For V/Ka(B  Kb), we have ½k1 k3 k5H k7 BEt =ðk2 ðk4 ðk6H þ k7 Þ þ k5H k7 Þ þ k3 k5H k7 BÞ=½k1 k3 k5D k7 BEt =ðk2 ðk4 ðk6D þ k7 Þ þ k5D k7 Þ þ k3 k5D k7 BÞ D  k5 ½ðk2 ðk4 ðk6D þ k7 Þ þ k5D k7 Þ þ k3 k5D k7 BÞ=½k1 k3 k5H k7 BEt =ðk2 ðk4 ðk6H þ k7 Þ þ k5H k7 Þ þ k3 k5H k7 BÞ .D i D h   k2 k4 k6H D Keq5 k5 þ k7 þ k5D k7 þ k3 k5D k7 B =½k1 k3 k5H k7 BEt =ðk2 ðk4 ðk6H þ k7 Þ þ k5H k7 Þ þ k3 k5H k7 BÞ ¼ k5 h .D i  ¼ ðk6 =k7 ÞD Keq5 k5 þ D k5 þ k5 k7 þ k3 k5 k7 BÞ ½k1 k3 k5 k7 BEt =ðk2 ðk4 ðk6 þ k7 Þ þ k5 k7 Þ þ k3 k5 k7 BÞ ¼

¼

D

D

V=Ka ¼

V=KaðBKbÞ ¼

h

D

D

i k5 þ ðk5 =k4 Þð1 þ k3 B=k2 Þ þ ðk6 =k7 ÞD Keq5 =½1 þ ðk5 =k4 Þð1 þ k3 B=k2 Þ þ k6 =k7 

   k5 þ cfa þ cr D Keq5 = 1 þ cfa þ cr wherein

cfa ¼ ðk5 =k4 Þð1 þ k3 B=k2 Þ and cr ¼ k6 =k7

(34) (35)

Note that cfa is dependent on the fixed concentration of B, and becomes infinite when [B] ¼> infinity, and so no KIE on DV/Ka would be observed as [B] 0 N, as cfa ¼ N. Such a finding would confirm that Mechanism III is indeed Ordered Bi Bi. For an Random Bi Bi mechanism one expects that DV/Ka or DV/Kb are > 1.0 at any concentration of the fixed substrate. Accordingly, the determination of KIEs for enzymatic reactions not only leads to a characterization of the chemical nature of the transition state (if the intrinsic KIE may be ascertained), but also provides an additional tool to differentiate ordered and random kinetic mechanisms (Cook and Cleland, 1981a; Cleland, 2003).

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97

The expression for DV is derived from the following ratio; VH =VD ¼D V ¼ k5H Et =½1 þ k5H =k7 þ k5H =k9 þ k5H =k11 þ k6H =k7 =fk5D Et =½1 þ k5D =k7 þ k5D =k9 þ k5D =k11 þ k6D =k7 g ¼ D k5 ½1 þ k5D =k7 þ k5D =k9 þ k5D =k11 þ k6D =k7 =½1 þ k5 =k7 þ k5 =k9 þ k5 =k11 þ k6 =k7  ¼  D k5 þ k5 =k7 þ k5 =k9 þ k5 =k11 þ ðk6 =k7 ÞD Keq5 =½1 þ k5 =k7 þ k5 =k9 þ k5 =k11 þ k6 =k7  D

V¼

D

   k5 þ cVf þ cr D Keq5 = 1 þ cVf þ cr

(36)

(37)

wherein cVf ¼ k5/k7 þ k5/k9 þ k5/k11 and cr ¼ k6/k7. The commitment factor cVf (Cook and Cleland, 1981a; Cleland, 2003) gives the sum of the ratios of the rate constant for the first chemical step k5 to that of all subsequent forward steps (k5/k7, k5/k9, and k5/k11), and its value will be zero when k5 1, or [B] [ Kb; dotted line in Fig. 26), substrate B is not “sticky” k5/k4  0, and the hydride transfer step is essentially irreversible (cr ¼ k6/ k7  0). DV/Ka < DV/Kb ¼ DV ¼ Dk5. Case 3: At a value of pH above the pKa of the enzymatic acid BeH, much of BeH is unprotonated, and the energy barrier for the proton-transfer step, k7, becomes equal to or higher than the barrier for the k5 step (dotted line in Fig. 26). Under these conditions, the hydride transfer step, with either H or D, comes to equilibrium because cr ¼ k6/k7 [ 1, so that one now measures the equilibrium isotope effect: DV/Ka  DV/Kb ¼ DKeq5, and DV ¼ [Dk5 þ k5/k7 þ (k6/k7)DKeq5]/[1 þ k5/k7 þ k6/k7]. By measuring these KIEs at several values of pH (Cook and Cleland, 1981b,c; Cleland, 2003), the KIE results above indicate a mechanism in which the hydride and proton transfer steps occur on different chemical steps. Otherwise, KIEs obtained at a pH at which BeH is partly or fully de-protonated, and occurs on the same step as hydride transfer, would lower the value of k5/k4 and enhance expression of the intrinsic KIE Dk5 on DV/Ka and DV/Kb. A similar result would be observed if the KIEs are determined in a solvent of deuterium oxide, such that bond-breaking of BeD is slower than BeH, which would also raise the value of k6/k7, because the value of the de-protonation step k7 will be decreased in D2O. Case 4: The rate of the chemical step k5 is slow, and the nicotinamide product is “sticky” in the EQ complex (k5/k11 > 1; dotted line in Fig. 26), substrate B is not “sticky” k5/k4  0, and the hydride transfer step is essentially irreversible (cr ¼ k6/k7  0). DV/ Ka  DV/Kb ¼ Dk5, and DV ¼ [Dk5 þ k5/k11]/[1 þ k5/k11]. The finding that DV < DV/Ka or DV/Kb strongly indicates that a productrelease step is rate-limiting for the enzyme.

1.05.6.4

Examples

As described above (Fig. 14), the study of the kinetics of yeast format dehydrogenase (FDH) by John Blanchard and W.W. Cleland demonstrated that it has an Ordered Bi Bi mechanism in which the nicotinamide co-enzymes bind to free enzyme. The reaction catalyzed by FDH is the transfer of hydride from formic acid to NADþ, in which this forward reaction direction is essentially irreversible, due in part to the evolution of the gaseous product, carbon dioxide. The isotope effects obtained for FDH were: D V ¼ 2.3 0.2, DV/KNAD ¼ 1.0 (at high formate), DV/Kformate ¼ 3.4 0.5, and 13V/Kformate ¼ 1.043. The values of DV/KNAD ¼ 1 and DV/Kformate ¼ 3.4 indicate a steady-state ordered mechanism as described above (Blanchard and Cleland, 1980; Cook and Cleland, 1981a). The large isotope effects indicate low values for commitment factors, suggesting that these values are equal to the intrinsic isotope effects. That DV < DV/Kformate indicates that product release is partly rate-limiting. The authors characterized the transition state from the apparently intrinsic values of Dk and 13k as depicted in Fig. 27. The transition is late, as indicated by the somewhat low value of DV/Kformate ¼ 3.4 0.5, but a near-maximal value of 13V/Kformate ¼ 1.043, for which the trigonal geometry of formate has become nearly linear as in the CO2 product. Azide, an exceptionally potent inhibitor of FDH in which Ki ¼ 90 nM, is likely an excellent structural mimic of the transition-state structure of formate, which accounts for its potency. For the dihydrofolate reductase from E. coli, the laboratory of John Morrison measured deuterium isotope effects at multiple values of pH, and found that at respective values of pH ¼ 7.8, 8.6, and 10.2, the KIEs obtained were: DV/KNADPH ¼ 1.1, 1.9, and 3.00, DV/KDHF ¼ 2.8, 2.8, and 3.00, and DV ¼ 1.9, 2.4, and 2.8 (Morrison and Stone, 1988) While the kinetic mechanism of E. coli DHFR is steady-state random (Stone and Morrison, 1982), it becomes ordered at pH 7.8 in which the first substrate to bind, NADPH, becomes “sticky” at this lower value of pH. At pH 8.6 this DHFR is steady-state random, and is fully rapidequilibrium random at pH 10.2 at which all three kinetic parameters are equal, indicating a loss of commitment factors at high pH (Morrison and Stone, 1988; Cook and Cleland, 1981b,c). The laboratory of Paul Cook conducted a full study of the malic enzyme from Ascaris suum, and found that the KIEs on the three kinetic parameters were pH-independent, except that DV falls to 1.0 at low pH (Kiick et al., 1986). At pH 7.5, DV/KNAD ¼ DV/

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An Overview of Steady-State Enzyme Kinetics

Fig. 27

Transition-state structure of formate oxidation as catalyzed by yeast formate dehydrogenase (Blanchard and Cleland, 1980).

Kmalate ¼ DV ¼ 1.45–1.49. This malic enzyme has a steady-state ordered mechanism from which limits on the values of the commitment factors could be elucidated.

1.05.6.5

The effects of solution viscosity on enzyme-catalyzed reactions

During the 1980s research in the laboratory of Jack Kirsch included the analysis of the effects of increasing solution viscosity on the initial rates of enzyme-catalyzed reactions (Brouwer and Kirsch, 1982). The binding and desorption of substrates and products involves their diffusion through solutions to encounter the macromolecular enzyme. This diffusion should be slowed down with increasing solution viscosity (generally in a linear fashion), while enzyme isomerization steps or chemical steps would not be affected by an increase in solution viscosity. Commonly-used, small-molecule viscosogens are sucrose and glycerol. These are referred to as microviscosogens as they are small enough in size to affect the entrance and exit of substrates and products from enzyme active sites. On the other hand, macroviscosogens, polymeric compounds such as Ficoll, only affect the macroviscosity, and have little effect on small-molecule diffusion. The effects of the macroviscosogen on initial rates provide a control experiment for the effects of the microviscosogen. Sucrose solutions of 0–40% (w/v) provide relative viscosities (hrel) of 1.0–3.8, and glycerol solutions of 0–12% (w/v) provide relative viscosities of (hrel) of 1.0–1.28. For Mechanism III, one expects the values of k1, k2, k3, k4,



k9, and k11, will be diminished by division by the value of the relative viscosity, hrel, specifically, k1 ¼ k1 /hrel, k2 ¼ k2 /hrel, and so

on, in which kx is the microscopic rate constant in the absence of microviscosogen. The expressions for the kinetic parameters of Ka/ Vmax, Kb/Vmax, and 1/Vmax in the presence of viscosogen are: ðKa =Vmax Þh¼1=ðKa =Vmax Þhrel ¼ ½ðk2 o =hrel Þ½ðk4 o=hrel Þðk6 þ k7 Þþ k5 k7  þ ðk3 o =hrel Þk5 k7 B=½ðk1 o =hrel Þðk3 o =hrel Þk5 k7 B ðKa =Vmax Þh¼1=ðKa =Vmax Þhrel ¼ ½k2 o k4 o ðk6 þ k7 Þ þ k5 k7 Bhrel =½k1 ok3 ok5 k7 B ðKa =Vmax Þh¼1=ðKa =Vmax Þhrel ¼ ½k2 o ðk4 o ðk6 þ k7 Þ=½k1 o k3 o k5 k7 B þ ½k2 o þ k3 o Bhrel =½k1 o k3 o B

(38)

From this, the plot of (Ka/Vmax) h¼1 / (Ka/Vmax)hrel ¼ [k2o(k4o(k6 þ k7)] / [k1ok3ok5k7B] þ [k2o þ k3oB]hrel] / [k1ok3oB], and its slope will provide the value of [k2oþ k3oB]/ k1ok3oB. 
o      ðKb =Vmax Þh¼1=ðKb =Vmax Þhrel ¼ k4 =h rel ðk6 þ k7 Þ þ k5 k7 Þ = k3 o =h rel k5 k7 ¼  o  ðKb =Vmax Þh¼1=ðKb =Vmax Þhrel ¼ ðk4 ðk6 þ k7 Þ þ k5 k7hrel =½k3 o k5 k7  ¼ k4 o ðk6 þ k7 Þ=k3 o k5 k7 þ hrel =k3 o

(39)

From this, the plot of (Kb/Vmax)h ¼ 1/(Kb/Vmax)hrel will provide from the slope, the value of k3, and from the intercept, k4(k6 þ k7)/k3k5k7, which provides the value of (k5/k4)(1 þ k6/k7). This allows the calculation of not only k3, but also cfb ¼ (k5/ k4)(1 þ k6/k7). ðVmax Þh¼1=ðVmax Þhrel ¼ ½1 þ k5 =k7 þ k6 =k7 þ k5 ðhrel Þ=k9 o þ k5 ðhrel Þ=k11 o  ¼ k5 ðhrel Þð1=k9 o þ 1=k11 o Þ þ 1 þ k5 =k7 þ k6 =k7

(40)

from which one may calculate k5/k7 þ k6/k7 and k5/k9 þ k5/k11, which allows the determination of the ratios of rate constants for the chemical rate constants k5 and k7 as well as the rate constants for the product-release steps, k9 and k11. An interesting example of the use of viscosity effects on initial velocity kinetics is seen with the human focal adhesion kinase (FAK) (Schneck et al., 2010). As described above, initial velocity, product inhibition and dead-end inhibition data is consistent with a Random Bi Bi mechanism (Fig. 4, Table 2). Increasing solvent viscosity using 0–40% (w/v) (hrel ¼ 1.0–3.8) had no effect on V/KFAK-tide, the peptide substrate, but did diminish Vmax. This indicated that a product release step(s) contribute(s) to overall ratelimitation. Surprisingly, for the other substrate, V/KMgATP also decreased linearly at increasing hrel. How can viscosity affect the binding of this smaller substrate, and not the larger one in a Random Bi Bi mechanism? As the binding of MgATP to protein kinases involves a large enzymatic conformational change, that accompanies the movement of the “activation loop” found in protein

An Overview of Steady-State Enzyme Kinetics

99

kinases, the observed diminution of V/KMgATP vs. hrel was attributed to this conformational change which was apparently sensitive to solution viscosity.

1.05.7

Determination of the chemical mechanisms of enzyme catalysis using steady-state kinetics

The last section of this chapter deals with the use of steady-state kinetics to determine the identities of the nucleophilic amino acids and the general acids and bases involved in the catalytic mechanism of an enzyme, also known as the chemical mechanism. This section draws in large part from the discussion of this topic found in references (Cleland, 1977; Cook and Cleland, 2007). In these studies, one ascertains the changes in the kinetic parameters V/K (for all substrates), Ki (for a competitive inhibitor), and V over a broad range of pH values. Such data, referred to as pH-rate profiles, are generally evaluated as plots of log (V/K, V, or pKi) vs. pH, and, where pH-dependence is observed, are characterized by, among other plot types, (1) a linear decrease with a slope ¼ 1 or 2 of the kinetic parameter as pH decreases, (2) a linear decrease with a slope ¼  1 or  2 of the kinetic parameter as pH increases, or (3) both, as described below. Observed decreases in the apparent values of V/K indicate the protonation or de-protonation of an enzymatic residue on the enzyme form to which that substrate binds, or a prototropic group on the substrate. Observed decreases in the apparent value of V indicate the protonation or de-protonation of an essential general acid or base on the enzyme in the Esubstrate complex, and since the variable substrate is at saturation, the protonation/de-protonation of a group on the substrate does not contribute to the profile of log V vs. pH. An investigator first determines the stability of the enzyme over a broad range of pH values (often pH 5–9) by pre-incubation at those pH values, followed by restoration of the pH to 7.0–7.5, and determination of the residual activity of the enzyme to establish the range of pH that will provide reliable data in these studies. One then obtains initial rate data at each value of pH in either a set of buffers that overlap in their buffering range, or, a single mixture of buffers that provides effective buffering over the targeted range of pH values, as well as invariant ionic strength (Ellis and Morrison, 1983). From these pH-rate profiles one obtains the apparent pKa or pKb values of the general acid or base involved in catalysis. As described below, it is often difficult to relate the observed values of pKa or pKb of enzymatic groups involved in proton-transfer steps in catalysis to specific amino acids for two reasons: (1) The actual pKa or pKb of the enzymatic group(s) are significantly altered by hydrogen-bonding with other active-site residues or by the hydrophobic environment found in some active sites. Here, a pKa value of a carboxylic group (normally 3.5–5 may be as high as 7, the pKa of sulfhydryl group on a cysteine (normally 8), may be as low as 4, and the amino group of a lysine (normally 9) could be as low as 7. Some of these apparently aberrant values of pKa result from “reverse protonation,” in which the proximity of a basic group to an acidic group (B-H-A) drastically lowers the value of pKa of H-A while raising the value of pKb of B), as described for a specific case below. (2) The measured values of pKa or pKb are perturbed due to the stickiness of the substrate (the proton to be removed or added to the amino acid is also “stuck”). For the case of substrate “stickiness,” the determination of the actual values of pKa or pKb by use of a competitive inhibitor of one of the substrates is very useful since competitive inhibitors of micro-molar or lesser potency are not inherently sticky, and the value of Kis (the slope inhibition constant) describes changes to the value of V/K for the substrate with which it is competitive. A plot of pKis vs. pH will then give the true value of pKa or pKb for an enzymatic residue, and its comparison with values of pKa or pKb obtained from the plot of log (V/K) vs. pH will indicate whether or not the substrate is “sticky.” In addition, an investigator may also wish to determine the pH-dependence of time-dependent inactivation by affinity agents, such as diethylpyrocarbonate (which acylates histidine groups) or N-ethyl-maleimide (which alkylates cysteine groups), to ascertain the true pKa or pKb values of these enzymatic groups. Historically, pH-rate profiles, along with affinity labelling, were the only means to identify the enzymatic groups responsible for catalysis. However, the arrival of genomic platforms, the discovery of enzyme super-families, the use of molecular biology to mutagenize specific amino acids, and the panoply of structural data of enzymes or members of their families, provide excellent guidance as to which enzymatic residues are likely to comprise those residues that are involved in covalent or prototropic mechanisms. Are kinetic methods still required to elucidate the roles of general acids and bases involved in catalysis, even when the residues proximal to bound substrates are readily evident from viewing crystal structures of the enzyme of interest or its homologues or paralogues? To answer this question, we return to the isocitrate lyase of M. tuberculosis. The x-ray structure of this enzyme was first reported by the laboratory of James Sacchettini 21 years ago, along with a reasonable chemical mechanism based on the structure and the affinity-labelling of active-site Cys191 by 3-bromopyruvate (Sharma et al., 2000). Over a decade later, the laboratories of Andrew Murkin and John Blanchard separately published a set of kinetics studies, including pH-rate data and kinetic isotope effects, from which it was concluded that the thiolate of Cys191 acts as a general base to de-protonate C-2 of succinate in the reverse reaction (Quartararo et al., 2013; Moynihan and Murkin, 2014). Despite the availability of structures, the nature of the general base (B in Fig. 9) that removes the proton from the 2-hydroxy group of isocitrate remains unknown. Recent data from our laboratory suggests that Lys191 abstracts a proton from a magnesium-bound water molecule, the resultant hydroxide of which becomes a specific base for this de-protonation (Pham, 2018). An ancillary method for the identification of the actual amino-acid residues which correlate to the measured values of pKs is to ascertain the temperature dependence of these pKs. pH-Rate profiles for log (V/K) and log (V) vs. pH are obtained at four or more temperatures (for example, at 15, 20, 25, 30, and 37 C), and the resulting data are plotted as pK vs. 1/T (in Kelvin), and upon fitting to Eq. (42), one obtains

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An Overview of Steady-State Enzyme Kinetics

pK ¼

OHion OSion RT



R

(41)

values for the enthalpy and entropy of ionization of the enzymatic group associated with this value of pK. Values of OHion are highly characteristic of the amino-acid residues involved in general-acid, general-base catalysis: carboxylic acids ( 1.5 kcal/mol), histidine and cysteine (6–8 kcal/mol), and metal-coordinated water and lysine (10–13 kcal/mol), and values of OHion of 15 or greater are generally associated with protein conformational changes during catalysis (Cleland, 1977; Cook and Cleland, 2007). Below are numerous schemes for the protonation or de-protonation of enzymatic groups that lead to the sort of pH-rate profiles one encounters for enzymes. Concentrations of proton ([Hþ]) and enzyme species (e.g., [E], [EH], [HEH]) will hereafter be notated as H, E, EH, and HEH, respectively. In general, protonation or de-protonation of one, two, or more enzymatic residues will prevent catalysis either for the substrate-free enzyme form E, as reported by the V/K or kcat/Km value, or the EA or downstream forms (EX, EP, E’, etc.) as reported by the Vmax (V) or kcat value. One obtains full Michaelis-Menten plots of a given substrate at appropriate values of pH, that is, those values of pH in which the enzyme remains stable and active. For an inhibitor that is competitive vs. substrate A, use of Dixon plots may be employed to ascertain changes in the apparent values of Ki over a range of pH values. These rate profiles are generally plotted as: log (V/K), log (V), and  log(Ki) (pKi) vs. pH over a range of pH values in which the enzyme remains stable (such as pH ¼ 5–9). A protonable group on the substrate may contribute an observed change in the pH-rate profile for log (V/K) vs. pH, but not for log (V) vs. pH, as at true substrate saturation it does not matter if the substrate is appropriately protonated or not. One may identify a pK value contributed by a substrate by determining the pK values of the substrate itself independently. For multi-substrate enzymes, the non-variable substrate(s) are generally held at saturating fixed concentrations, particularly for random enzymes. Case 1. Protonation of an essential basic group on the enzyme leads to loss of activity at low pH the pKa is represented by K1.

For Case 1, the protonation of a basic group on the free enzyme E prevents binding of substrate A, and the apparent values of V/ Ka will be invariant at pH values one or more units higher than the acid dissociation constant pK1, but will fall at pH values below pK1. Accordingly, in a plot of log V/Ka vs. pH (or log (kcat/Ka) as in Fig. 28), the apparent value of kcat/Ka will decrease as pH becomes

Fig. 28 Plots of log (kcat/Ka) and log (kcat) (blue) vs. pH for Case 1 (red) and Case 2 (black). Case 1: The pH-independent values are kcat/ Ka ¼ 104 M 1 s 1 and kcat ¼ 130 s 1, and the protonation of an enzymatic group of pK1 ¼ 7.5 leads to the diminution of kcat/Ka. Case 2: The pHindependent values are kcat/Ka ¼ 104 M 1 s 1 and kcat ¼ 130 s 1, and the protonation of an enzymatic group of pK1 ¼ 6.0 and de-protonation of an enzymatic group of pK2 ¼ 7.5 leads to diminution of log (kcat/Ka).

An Overview of Steady-State Enzyme Kinetics

101

less than pK1, and the slope of this decrease will equal 1 (Fig. 28). The same profile would be observed when the protonation of substrate A prevents the binding of AH to E. Prior knowledge of the acid dissociation constant(s) of a given substrate will allow discrimination between these two options. For V (or kcat), an infinite concentration of A will convert all free enzyme to EA, regardless of the pH, and a plot of log (kcat) vs. pH will be a horizontal line as in Fig. 28. Case 2. Protonation of an essential basic group on the enzyme leads to loss of activity at low pH (the pKa is represented by K1), and

de-protonation of an acidic group leads to loss of activity at high pH (the pKb is represented by K2).

In Case 2, the protonation of a basic group on the free enzyme EH, and the de-protonation of an acidic group on EH both prevent binding of substrate A, and the apparent value of V/Ka will decrease at pH < pK1 with a slope of 1, and will decrease at pH > pK2 with a slope of  1 to generate the “bell-shaped” curve shown in Fig. 28. Assuming rapid-equilibrium binding of protons to E and EH in Cases 1 and 2, we may write out expressions for the enzyme forms with respect to pH: For Cases 1 and 2, respectively, we have [Et] ¼ [E] þ [EH] and [Et] ¼ [E] þ [EH] þ [HEH], and since, K1 ¼ [H][E]/[EH] for Case 1, and K1 ¼ [H][EH]/[HEH] and K2 ¼ [H][E]/[EH] for Case 2, and we may write for Case 1, [EH] ¼ [E] [H]/K1, and for Case 2, [HEH] ¼ [EH][H]/K1 and [E] ¼ [EH]K2/[H]. For Case 1, [Et] ¼ [E] þ [EH] ¼ [E](1 þ H/K1), and the active enzyme form E may be written as [Et](1 þ H/K1), and with derivations of V/Ka and V easily at hand by the method of net rate constants, we may write: V=Ka ¼ k1 k3 ½Et =½ðk2 þ k3 Þð1 þ H=K1 Þ (42) For Case 2, [Et] ¼ [E] þ [EH] þ [HEH] ¼ [EH](1 þ H/K1 þ K2/H) and the active enzyme form EH may be written as [Et](1 þ H/ K1 þ K2/H), such that: (43) V=Ka ¼ k1 k3 ½Et =ðk2 þ k3 Þð1 þ H=K1 þ K2 =HÞ Typically, data for the pH-rate profiles of Cases 1 and 2, respectively, are fitted to: i h 
 log V=Kapp ¼ log½c=ð1 þ H=K1 Þ ¼ log c= 1 þ 10ðpK1pHÞ ; in which c ¼ k1 k3 Et =ðk2 þ k3 Þ i h 
 log V=Kapp ¼ logðc=ð1 þ H=K1 þ K2 =HÞ ¼ log c= 1 þ 10ðpK1pHÞ þ 10ðpHpK2Þ

(44) (45)

in which c ¼ k1 k3 Et =ðk2 þ k3 Þ

Case 3. Protonation of an acidic and a basic group on the substrate or enzymatic group in enzyme forms EH and EAH eliminates activity (activity lost at both high and low pH).

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An Overview of Steady-State Enzyme Kinetics

For Case 3, both the free enzyme and enzyme-substrate forms are subject to activity loss at low and high values of pH. Protonation and de-protonation of two enzymatic residues (or the substrate) with respective acidic and basic dissociation constants of K1 and K2 leads to diminution of V/K, while for V, protonation and de-protonation, respectively, of enzymatic residues with acidic and basic dissociation constants of K3 and K4 lead to diminution of maximum velocity V. Plots of both log (V/Ka) and log (V) vs. pH will be bell-shaped as that of Fig. 28. The enzymatic residues which represent dissociation constants K1 and K2 for the V/Ka expression may or may not be the same as those of the expression for log V vs. pH, that is, K1 does not necessarily equal K3, K2 does not necessarily equal K4. Experimentally, these values may not be equal even if they represent the same enzymatic groups, due to the fact that substrate saturation may perturb the true pK values found on free enzyme E (it may be more difficult to protonate or de-protonate an enzymatic group when substrate “sits” on top of it). The expression for log (V/Ka) vs. pH is the same as Eq. (43). For V, the fraction of Et that proceeds through reaction is EAH/ (HEAH þ EAH þ EA) for which K3 ¼ (H)(EAH)/HEAH and K4 ¼ (H)(EA)/EAH, so HEAH ¼ (EAH)(H)/K3, EAH ¼ (EA)(H)/K4, leading to EAH ¼ Et/(1 þ H/K3 þ K4/H), and for which c ¼ k3k9EAH/(k3 þ k9). Vapparent ¼ k3 k9 EAH=ðk3 þ k9 Þ ¼ ½k3 k9 Et =ðk3 þ k9 Þ=ð1 þ H=K3 þ K4 =HÞ ¼ c=ð1 þ H=K3 þ K4 =HÞ

(46)

i h  ¼ log Vapp ¼ log c= 1 þ 10ðpK1–pHÞ þ 10ðpH–pK2Þ ; in which c ¼ k3 k9 EAH=ðk3 þ k9 Þ:

(47)

Eq. (47) is convenient for fitting of data by the method of least squares. Even in cases in which the enzymatic groups represented by acid and base dissociation constants, K1 and K2, are the same as those of, respectively, K3 and K4, as mentioned above, perturbation of the values of K1 and K2 upon substrate saturation may lead to different values of K3 and K4, often exhibited as: K3 < K1 and K4 > K2. This has the effect of a flattening of the bellshaped curve of log V vs. pH compared to that of the plot of log (V/K) vs. pH, which consequently “pushes out” the apparent values of pK3 and pK4. Case 4. Protonation of 2 acidic groups and 1 basic group on the substrate or an enzymatic group in enzyme form EH eliminates activity (activity lost at both high and low pH).

Case 4 is the same as Case 2 except that the protonation of two basic groups on the enzyme (or substrate) prevents the binding of A to the enzyme form, EH, that proceeds through catalysis. A plot log (V/Ka) vs. pH for Case 4 may be found in Fig. 29, which is an asymmetrical bell-shaped curve in which there are slopes of 2 and  1, respectively, on the acidic and basic sides of the profile. The fraction of the active enzyme EH is given by ðEHÞ=ðH2 EH þ HEH þ EH þ EÞ K1 ¼ ðHÞðEHÞ=ðHEHÞ; K5 ¼ ðHÞðHEHÞ=ðH2 EHÞ and K3 ¼ ðHÞðEÞ=ðEHÞ; so ðHEHÞ ¼ ðEHÞðHÞ=K1 ;
 ðH2 EHÞ ¼ ðHÞðHEHÞ=K5 ¼ H2 ðEHÞ=K1 K5 ; and ðEHÞ ¼ ðEÞðHÞ=K3 ; leading to Et ¼ (EH)(1 þ (H/K1)(1 þ H/K5) þ K3/H), so that this fraction becomes: (EH)/[(EH)(1 þ (H/K1)(1 þ H/K5) þ K3/ H] ¼ 1/(1 þ (H/K1)(1 þ H/K5) þ K3/H). So, V=Kapp ¼ k1 k3 EH=½ðk2 þ k3 Þ ¼ ½k1 k3 Et =ðk2 þ k3 Þ=½1 þ ðH=K1 Þð1 þ H=K5 Þ þ K3 =HÞ ¼ c=½1 þ ðH=K1 Þð1 þ H=K5 Þ þ K3 =HÞ; in which c ¼ k1 k3 Et =ðk2 þ k3 Þ

(48)

An Overview of Steady-State Enzyme Kinetics

103

Fig. 29 Plot of log (kcat/Ka) vs. pH for the mechanism in Case 4. The pH-independent value of kcat/Ka ¼ 104 M 1 s 1, and the protonation of two enzymatic groups of pK1 ¼ 7.5 and pK5 ¼ 5.5 leads to a sharp decrease in this kinetic parameter to give an apparent slope of 2, and the protonation of an acidic group of pK3 ¼ 9.0 leads to diminution of kcat/Ka with an apparent slope of 1.

This may be plotted as:


 log V=Kapp ¼ log½c=½1 þ ðH=K1 Þð1 þ H=K5 Þ þ K3 =HÞ h  i ¼ log c= 1 þ 10ðpK1þpK5–2pHÞ þ 10ðpK1–pHÞ þ 10ðpH–pK3Þ

(49)

Case 5. A competitive inhibitor I binds to E and EH.

The binding of a competitive inhibitor to (free) enzyme is likely to be dependent on solution pH, whether that is because deprotonation/protonation of a group on either the inhibitor, enzyme, or both eliminates the ability of the inhibitor to bind. If protonation of an enzymatic group prevents the binding of a competitive inhibitor, then one will observe a plot like that of the “red curve” in Fig. 28. What is often observed is a modification of Case 1 in which I, an inhibitor that competes with substrate A for free enzyme E, may also bind to EH to form EHI. Such a situation can provide accurate values of pK1 and pK3, and normally, KiH > Ki. For purposes of this discussion, the binding of I to either enzyme form does not result in potent, time-dependent inhibition, that is, the inhibitor is not “sticky.” Because I vs. A affords competitive inhibition, the binding of I to E lowers the apparent value of V/ Ka, and provides true values for pK1 and pK3, even when the binding of A is “sticky,” and does not afford an accurate value for pK1. In Fig. 30 is the expected plot for pKi vs. pH wherein KiH > Ki. As I binds less potently to the EH complex compared to that of E, the plot may be described as a “wave,” in which the inflection point between the upper and lowers plateaus, respectively defined by EI and EHI, provide pK1, characterizing the enzymatic group that once protonated, binds I more weakly and does not bind A at all.

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An Overview of Steady-State Enzyme Kinetics

Fig. 30

Plot of pKi vs. pH based on the mechanism in Case 5 in which Ki ¼ 10 4 M, KiH ¼ 0.01 M, pK1 ¼ 8.0 and pK3 ¼ 6.0.

Expressions to describe the pH-rate profile of Case 5 may be written out as below: K1 ¼ ½E½H=½EH; K3 ¼ ½EI½H=½EHI; Ki ¼ ½E½I=½EI; KiH ¼ ½EH½I=½EHI KiH ¼ ½EH½I=½EHI ¼ ½EH½I K3 =½EI½H ¼ ½E½I K3 =½EI K1 ¼ Ki K3 =K1 The apparent value of Kiapp ¼ fð½E þ ½EHÞ½Ig=ð½EHI þ ½EIÞ ¼ ½Et ½Ið1 þ ½H=K1 Þ=½EIt ð1 þ ½H=K3 Þ ¼ Ki ð1 þ ½H=K1 Þ=ð1 þ ½H=K3 Þ;

(50)

from which, at low and high pH, respectively, Kiapp ¼ KiH ¼ Ki ðK3 =K1 Þ and Kiapp ¼ Ki

(51)

pKiapp ¼ log ½ðKi ð1 þ ½H=K1 ÞÞ=ð1 þ ½H=K3 Þ

(52)

Data may plotted as

Case 6. Protonation of an acidic group on a “sticky” substrate or enzyme eliminates binding.

For Case 6, the “neutral” E complex binds substrate A to form the Michaelis complex EA which progresses through the chemical step k3 to form the enzyme-product complex EP followed by product release to provide E. Protonation of the free-enzyme complex E yields the protonated free enzyme form EH, which, while catalytically incompetent, may bind and release a “sticky” substrate A to afford EAH. EAH can progress to the competent complex EA by expulsion of this proton, which may also be “sticky.” At low pH, E is absent, but the predominant free-enzyme form EH, unlike EH in earlier cases, retains a path to the catalytically-competent EA complex. The overall effect on the log(V/Ka) vs. pH profile is a “flattening” of the curve of a slope ¼ 1 in Fig. 31 to produce a “hollow” on the acidic side (Fig. 31A), and particularly for the plot in (Fig. 31B), on the basic side of the profile. In effect, the

An Overview of Steady-State Enzyme Kinetics

105

(A)

(B)

(C)

Fig. 31 Plot of log (V/Ka) vs. pH (A) based on the model in Case 6 in which c ¼ 10,000 M 1 s 1 and pK1 ¼ 6.0 which conforms to Eq. (54) in which the coefficients a and b that generated these profiles are in the figure. (B) From the model in Case 7, of a bell-shaped profile in which substrate and/or proton “stickiness” affects the basic side of the profile, and in which c ¼ 10,000 M 1 s 1, pK1 ¼ 5.5, and pK1 ¼ 9.0, and in which the coefficients a and b that generated these profiles are in the figure. (C) Plot of log (V) vs. pH demonstrating the changes in a “sticky” substrate and/or proton binding according to Case 6. The values for kcat ¼ 40 s 1, pK1 ¼ 6.0; and for the blue curve: pK2 ¼ 5 (blue) and 6 (red), k3 ¼ 50 s 1, k8 ¼ 56 (blue) and 5.6 (red) s 1, and k9 ¼ 200 (blue) and 2000 (red) s 1.

106

An Overview of Steady-State Enzyme Kinetics

“stickiness” of the substrate allows it to remain bound at values of pH for which a less sticky substrate would have desorbed. As we will see below, one cannot obtain accurate values of the acid dissociation constant K1 as they are perturbed to lower values (to higher values for base dissociation constants). It is for this reason that kineticists employ “poor” (non-sticky) substrates for pH rate profiles, which are also useful in the evaluation of kinetic isotope effects as their commitment factors are low or nonexistent. Accurate acid/base dissociation constants may also be ascertained from the pH profiles of inhibition by a competitive inhibitor of substrate A, as described above. The derivation for the expression of V/Ka for Case 6 is found in the Appendix, and is shown in Eqs. (53) and (54). ½k1 k3 Et =ðk2 þ k3 Þ½1 þ ððk7 =k1 HÞ=ðK1 ð1 þ k8 =k5 Þ (53/A21) V=Ka ¼ ½1 þ H=K1 ½1 þ Hðk7 =k1 Þ=ðK1 ð1 þ k3 =k2 Þð1 þ k8 =k5 Þ Which may be written as: V=Ka ¼

½k1 k3 Et =ðk2 þ k3 Þ½1 þ aH=K1  ½1 þ H=K1 ½1 þ aH=bK 1 

(54/A22)

For which a ¼ (k7/k1)(1 þ k8/k5) and b ¼ (1 þ k3/k2). The ratio k3/k2 is the stickiness factor (or cf) for the substrate, which measures the partitioning of the EA complex through the chemistry step (k3) vs. desorption from EA (k2). Note that when the substrate is not sticky, b ¼ (1 þ k3/k2)  1.0, and: k1 k3 Et =ðk2 þ k3 Þ (42) V=Ka ¼ ½1 þ H=K1  as in Case 1. The coefficients a and b in Eq. (54) describe, respectively, the extent to which the species at low pH (EH and EAH) contribute to product formation (a), and how the forward commitment factor (b) perturbs the expression of the true value of K1 which is observed in the black curve of Fig. 31A where b ¼ 1. When a ¼ 1, b ¼ 10, the acidic arm of the plot is greatly shifted to the left from which the apparent value of pK1 will be considerably lower than the true value, if the data are fitted to Eq. (54). When a ¼ 5, b ¼ 2, and a ¼ 10, b ¼ 10, this sinistral shift also occurs to varying extents, and a “hump” is introduced as the low-pH pathway involving step k7 occurs more readily than that of k1 at higher pH. The pH-dependent expression of V for Case 6 is given by Eq. (55). V¼

k3 k9 Et ½1þðk5 k8 HÞ ½ðk3 þk9 Þðk2 ðk5 þk8 ÞK2 Þ

½1þ½ðk2 k8 þk2 k5 þk3 k8 þk5 k8 Þk9 þk3 k5 k8 ðH=K2 Þþðk5 k8 k9 ÞðH=K2 Þ2 

(55/A23)

½k2 ðk5 þk8 Þðk3 þk9 Þ

This equation may be fitted to:

  log ðV Þ ¼ log ½cð1 þ H=Kc Þ= 1 þ aðH=K2 Þ þ bðH=K2 Þ2

(56/A24)

where c ¼ k3 k9 =ðk3 þ k9 Þ; K2 ¼ k5 =k6 ; Kc ¼ ðk5 k8 Þ=ðk2 ðk5 þ k8 ÞK2 ¼ ðk8 =k2 Þ=ð1 þ ðk8 =k5 ÞK2 a ¼ ðk3 k5 k8 þ k5 k8 k9 þ k3 k8 k9 þ k2 k8 k9 þ k2 k5 k9 Þ=ðk2 ðk5 þ k8 Þðk3 þ k9 ÞÞ and b ¼ k 5 k 8 k 9 =ðk 2 ðk5 þ k8 Þðk 3 þ k 9 ÞÞ The plot of log (V) vs. pH in Fig. 31C shows the changing shapes of these curves when coefficients K2, Kc, a, and b are varied (in which a “hump” is emerging in the red curve), indicating again that for the mechanism in Case 6 it is difficult to obtain accurate values of pKs from these plots. Case 7. For a pH-rate profile that is bell-shaped, in which proton and/or substrate “stickiness” occurs on the basic arm of the plot, the

mechanism of Case 7 will be as shown in Fig. 31, and the equation for V/Ka is Eq. (57).

An Overview of Steady-State Enzyme Kinetics

V=Ka ¼

½k1 k3 Et =ðk2 þ k3 Þ½1 þ H=Ka  ½1 þ H=K1 þ K2 =H½1 þ H=bK a 

107

(57)

in which Ka ¼ K1(k1/k7)/(1 þ k2/k6); and b ¼ (1 þ k3/k2)/(1 þ k3/(k2 þ k6)). A pH-rate profile for log (V/Ka) vs. pH for the mechanism in Case 7 is found in Fig. 31B for the case of a non-sticky (blue curve) and sticky substrate (red curve). The sticky substrate (the red curve) in which a ¼ 20 and b ¼ 10, shows both a “hump” and a “hollow.”

1.05.7.1

Examples from the literature

Below, we summarize pH-rate data for four enzymes, which have been discussed above, and which represent four of the seven classes of enzymes. These are discussed from earliest to latest, to underscore the value of pH-rate data prior to the relatively recent wide access to x-ray crystal structures of the studied enzymes.

1.05.7.2

Formate dehydrogenase

pH-Rate profiles were obtained for Vmax, V/KNADþ and V/Kformate over a range of pH ¼ 5–10. All three profiles exhibited “unperturbed” bell-shaped curves of apparent slopes of þ 1 and  1 on the respective acidic and basic arms of the profiles, as that shown in Fig. 28. For the profile of log Vmax vs. pH, the downward turns of the curve were observed at lower and higher pH values than those seen for V/KNADþ and V/Kformate. For the V/K profiles, the fixed substrate was held at saturating concentrations. Fitting of these data to Eq. (43) or Eq. 46 resulted in values of: log (Vmax) vs. pH; pK1 ¼ 5.89 0.02 and pK2 ¼ 9.82 0.04; log (V/KNADþ) vs. pH: pK1 ¼ 6.42 0.07 and pK2 ¼ 8.42 0.06; log (V/Kformate) vs. pH: pK1 ¼ 6.42 0.07 and pK2 ¼ 8.42 0.06; and for the inhibitor azide, pKi vs. pH: pK1 ¼ 6.43 0.06 and pK2 ¼ 8.27 0.04. The strong agreement between the pK values of log (V/Kformate) vs. pH and that of its competitive inhibitor, azide, suggests that formate is not a sticky substrate. The further agreement of values of pK1 ¼ 6.4 and pK2 ¼ 8.3/8.4 also suggests that neither substrate is sticky, and these pK values reflect the true values of the respective base and acid dissociation constants in this catalytic mechanism. The temperature dependence of pK1 ¼ 6.4 and pK2 ¼ 8.4 from the V/K profiles exhibited values of DHion ¼  2.5 and 20 kcal/mol, respectively, while for the values of pK1 ¼ 5.8 and pK2 ¼ 9.8 from the V profile, DHion ¼ 5 and 29 kcal/mol, respectively. From these results, the authors tentatively assigned an un-protonated carboxylic acid for the residue of pK1 ¼ 6.4, and the basic group which needs to remain protonated below pK2 ¼ 8.3/8.4 as lysine or histidine, as this protonated base may serve as a counter ion to formate. The large values of DHion ¼ 20–29 kcal/mol were attributed to a large conformational change during catalysis. Nearly 30 years later, a crystal structure of a bacterial formate dehydrogenase obtained at atomic resolution revealed that the binding of the carboxamide group of NADþ apparently involves a hydrogenbonding interaction with the sidechain of Asp308, while the bound azide ligand in this structure clearly interacts with His332 (Shabalin et al., 2009). Creatine Kinase. For rabbit muscle creatine kinase, pH-rate profiles of log (V/Kcreatine) and log (Vmax) vs. pH (forward reaction) decrease below neutral pH, the latter with a slope of 2 (as in the left arm of the plot of Fig. 28), with respective values of pK1 ¼ 7.01 0.05 (log (Vcreatine)) and pK1 ¼ 7.4 0.04 and pK3 ¼ 5.57 0.04 (log (V/Kcreatine)) (Cook et al., 1981). In the reverse reaction, bell-shaped curves were observed for both log (VPcreatine) vs. pH and log (V/KP-creatine) vs. pH, with respective values of pK1 ¼ 6.16 0.02 and pK2 ¼ 7.87 0.02 (log (Vmax)) and pK1 ¼ 5.57 0.04, and exhibit three pK2 values ranging from 7.2– 8.3. Interestingly, the temperature-dependence of the plots of log (V/KP-creatine) vs. pH exhibit an increasing “hollow,” indicating increasing substrate “stickiness,” as the assay temperature is lowered from 35 to 12 C. Analysis of the temperature dependence of the enzymatic group of pK ¼ 5.6–6 which must be de-protonated for catalysis provided a value of DHion ¼ 2 kcal/mol, suggestive of a carboxylate ion. The authors tentatively assigned the pK ¼ 7.4 0.04 to a cationic histidine, which would act as a general acid (Cook et al., 1981). Years later, the use of site-directed mutagenesis and structural analysis of muscle creatine kinase revealed that the putative carboxylic acid group is a glutamate, which likely assists with the de-protonation of the guanidinium group of creatine in order to promote its nucleophilic attack on MgATP (Cantwell et al., 2001). Malate synthase. pH-Rate profiles of the C619S mutant of the malate synthase of M. tuberculosis were determined in the laboratory of Prof. John Blanchard (Quartararo and Blanchard (2011)). The plots of log (V/Kglyoxylate) and log (V) vs. pH both exhibited bellshaped curves in which fitting to Eqs. (43 and 46) provided values of log (V/Kglyoxylate) vs. pH: pK1 ¼ 5.3 0.1 and pK2 ¼ 9.2 0.1; and for log V vs. pH, the pK values, as often observed, were “pushed outwardly”; pK1 ¼ 4.6 0.2 and pK2 ¼ 9.1 0.3. Assisted by an available crystal structure of this enzyme, the enzymatic “base” which causes loss of activity upon protonation, pK1 ¼ 5.3, was identified as Asp633, and the enzymatic “acid” which causes loss of activity upon de-protonation, pK2 ¼ 9.2, was identified as Arg339. In the proposed chemical mechanism, Asp633 de-protonates the methyl group of substrate AcCoA, the anion of which then attacks the aldehyde of the magnesium-bound glyoxylate to form an aldol conjugate. A proton is subsequently provided by Arg339 to protonate the thiol group of CoA-SH upon its elimination from this conjugate, resulting in the formation of CoA-SH and malate. Cruzain and Cathepsin C. Cruzain (Zhai and Meek, 2018; McGrath et al., 1995) and human cathepsin C (Schneck et al., 2008; Rubach et al., 2012; Mølgaard et al., 2007) are both members of Clade CA/Family C1 of cysteine proteases, and are therefore “relatives” of its archetypal member, papain. Cathepsin C is an amino-dipeptidase, which prefers substrates of the type H2N-Ser-Tyr-X, while cruzain is an endopeptidase which recognizes a wide variety of peptide substrates, many of which contain a Phe or Leu in their P2 peptide positions. Like other cysteine proteases, the active site is composed of a catalytic dyad of a cysteine and histidine which conducts peptidolysis by a double-displacement mechanism involving two half-reactions: (1) acylation of the active-site cysteine

108

An Overview of Steady-State Enzyme Kinetics

Fig. 32 Proposed chemical mechanism of human cathepsin C with H2N-Ser-Try-AMC as substrate (top) which apparently binds to the thiolateimidazolium form of the free enzyme (Eþ/; top) for which the pK values of the thiolate group of Cys and imidazolium group of His are 4.3 and 6.5, respectively. This represents a case of reverse protonation, as the imidazole of His appears to be more basic that the thiolate of Cys. For cruzain (bottom), the two substrates Cbz-Phe-Arg-AMC and Cbz-Arg-Arg-AMC bind to the neutral thiol-imidazole form of free enzyme E for which the pK values of the thiol group of Cys and imidazole group of His are 9.6 and 6.6, respectively.

which results in peptide bond scission, followed by (2) its de-acylation by histidine-catalyzed attack of water on the intermediate enzyme-substrate thio-ester (Fig. 32). In studies of cathepsin L and cruzain, it has been shown that the rates of enzyme acylation for peptide substrates with values of V/K of 105 M 1 s 1 to 107 M 1 s 1 are much greater than that of de-acylation, and so in these cases, values of kcat are determined by the de-acylation half-reaction, while values of V/K “report” on the kinetic steps comprising enzyme acylation (Zhai and Meek, 2018; Schneck et al., 2008; Rubach et al., 2012). The pH-rate profile for V/KH-Ser-Tyr-AMC for human cathepsin C is a bell-shaped curve with an apparent slope of þ 1 on the acidic arm, and an apparent slope of  2 on the basic arm (Fig. 33). Fitting of the data provided values of pKa ¼ 4.3 0.1, pKb1 ¼ 6.5 0.1, and pKb2 ¼ 7.7 0.7, indicating a single basic group of pKa ¼ 4.3, which upon protonation, activity is lost, but two acidic groups of pKb1 ¼ 6.5, and pKb1 ¼ 7.7, which when de-protonated, activity is lost. The plot of log V vs. pH or this substrate is characterized by a decrease in V upon the protonation of a enzymatic group of pK ¼ 3.5. This pK was assigned to the neutral His which de-protonates the lytic water in the de-acylation half-reaction. In contrast, for cruzain, the plot of log (V/K) vs. pH for the substrate Cbz-Phe-Arg-AMC is a perturbed bell-shaped curve which when fitted to Eq. (54), values of pKa ¼ 6.4 0.7 and pKb  10 were obtained, with a stickiness factor of  70 (Zhai and Meek,

Fig. 33 Fitted plots of log (V/K) for human cathepsin C using H2N-Ser-Tyr-AMC as substrate (black) and cruzain using Cbz-Phe-Arg-AMC (red) and Cbz-Arg-Arg-AMC (blue) as substrates. Values of (V/K) were normalized to that of H2N-Ser-Tyr-AMC (V/K ¼ 350,000 M 1 s 1).

An Overview of Steady-State Enzyme Kinetics

109

2018). The plot of log V for this substrate is characterized by a decrease in V upon the protonation of a enzymatic group of pK ¼ 5.3. A second substrate of cruzain, Cbz-Arg-Arg-AMC had a value of V/K which was 100-fold lower than that of Cbz-Phe-Arg-AMC. Unlike the former substrate, the plot of log (V/K)Cbz-Arg-Arg-AMC vs. pH is an unperturbed bell-shaped curve for which fitting to Eq. (43) gave values of pKa ¼ 6.4 0.1, and pKb ¼ 9.6 0.1. The pH-dependence of cruzain inactivation by the respective sulfhydryl- and imidazole-specific affinity labelling agents iodoacetamide and diethylpyrocarbonate indicated that, unlike, cathepsin C, the pKa ¼ 6.4 0.1 was that of His, and the pKb ¼ 9.6 0.1 was that of Cys. The plot of log V vs. pH for this substrate was characterized by a decrease in log V upon the protonation of a enzymatic group of pK ¼ 5.6, which again, was assigned to the neutral His in the de-acylation half-reaction. The temperature dependence of the pKa ¼ 6.4 0.1 for plots of log (V/K) and log (V), respectively, resulted in values DHion ¼ 8.4 0.2 and 7.1 0.1 kcal/mol which are consistent with that of a histidine residue. The pH-rate profiles of these two cysteine proteases are very revealing despite the prior availability of highly-resolved crystal structures of either protease (Mølgaard et al., 2007; McGrath et al., 1995). One feature of the study conducted with cruzain demonstrated that the value of the use of a poor substrate to overcome the obscuring nature of a sticky substrate. While the two enzymes have highly similar chemical mechanisms, the nature of the protonation states of the free enzyme forms are different. The thiolimidazolium form of the catalytic dyad for cathepsin C is the form to which substrates bind, while substrates bind to the neutral thiol-imidazole form for cruzain. The neutral thiol of the cysteine in the latter enzyme would presumably be far less nucleophilic than the thiolate form in the former enzyme, perhaps indicating that cathepsin C exerts less substrate selectivity than cruzain, because for cruzain, only an “appropriate” substrate upon binding may trigger the required proton transfer leading to catalysis. This subtle differentiation may prove important for the design of inhibitors and inactivators which covalently engage the catalytic cysteine of these enzymes but are sensitive to their initial protonation states.

1.05.8

Concluding remarks

Fifty years ago structural biology was in its early adolescence while investigators dispatched their graduate students to the local abattoir in order to begin the tedious process of purifying enzymes from animal tissues, and the only means to alter and thereby explore the function of a catalytic amino-acid was to subject it to either chemical modification or to an untoward value of pH. This period, beginning about 1970, nonetheless comprised a, if not the, golden age of the study of enzyme mechanisms, and steady-state kinetics was its singular, indispensable tool. For this timeframe as well as today, steady-state kinetics was also highly economical in terms of the lower costs of the required instrumentation (spectrophotometers and pH-meters vs. X-ray diffractometers) to conduct its practice, as well as the nanomole amounts of active enzyme needed to acquire kinetic data. In the ensuing half-century, the use of molecular biology has not only placed many milligrams of protein into the eager hands of crystallographers, but its “table-scraps” has provided sufficient, highly available and highly purified enzymes to enable enzymologists to spend less time preparing enzyme, and more time investigating their mechanisms. Today, crystal structures for enzymes of interest become extant often before or during the time needed for their mechanistic evaluations. Enzyme sequences are known from genomic databases prior to initiating studies, and, coupled with structural data, the facile use of site-directed mutagenesis allows the elucidation of the functional roles of enzyme amino-acids, whether conserved or not, but not without kinetic analysis. As Igor Stravinsky once remarked (and he would know!): “Everything new does harm to something old.” In the land of plenty that is now modern enzymology, with its bright and shiny toys and scads of pure materials afforded by molecular cloning, the great composer’s aphorism may seem to apply to the dogged, outdated practice of steady-state kinetics, with its silly data plots and cumbersome algebra. Not so. When one proclaims the solution of an enzymatic mechanism solely from inspection of a crystal structure or association with its membership in some gene super-family, one may as well teach ballroom dancing by using only photographs. Said less flippantly, the structure or generic class of a catalyst, be it large or small, does not provide the full picture of how it functions without the study of its kinetics. Steady-state enzyme kinetics has held its place in the ever-enlarging toolkit for the study of how enzymes perform chemistry, as the author hopes, gentle reader, he has conveyed through the plethora of text above.

A

Appendix

Derivation of the full initial velocity expression for the Ordered Bi Bi mechanism using the King-Altman method (Segel, 1975; Cook and Cleland, 2007; King and Altman, 1956). A scheme for the Ordered Bi Bi mechanism is in Fig. A1 below. There is/are no rate constant(s) for the catalytic events after both substrates A and B have bound to enzyme. In the King-Altman method, one constructs all of the possible patterns which do not have a closed loop (Fig. A1), 1–4, that can lead to formation of all four enzyme forms involved in the progression of free enzyme E, through EA, EAB 5 EPQ (the central complexes), and EQ to evolve E and the products P and Q. The initial velocity will be the forward and reverse rate constants, through any kinetic step. For example, v ¼ k1EA/ Et  k2EA/Et. The fraction of each of the four enzyme forms with respect to total enzyme (i.e., EA/Et), known as enzyme distribution expressions, will be a function of the concentrations of the two substrates and two products, and the value of the rate constants that comprise the path to their formation. For example, the path to formation of EQ from pattern 1 above is the product k2k4k8Q. In kind, the ratio of EQ/Et will be the sum of all four of such product terms divided by the concentration of total enzyme, and for

110

An Overview of Steady-State Enzyme Kinetics

Fig. A1 King-Altman patterns for an Ordered Bi Bi kinetic mechanism. The enzyme distribution forms below are constructed from the collections of rate constants and additives in the color-coded patterns c through f, with color-coding carried through to Eq. (A5).

which Et will be the sum of all sets of rate constants in the numerators of each equation below Eq. (A5).

E/Et = [ c k2k4k7 + d k3k5k7B + e k2k4k6P + f k2k5k7)]/E t

(A1)

EA/E t = [c k1k4k7A + d k4k6k8PQ + e k1k4k6AP + f k1k5k7A]/E t

(A2)

(EAB -EPQ)/E t = [c k1k 3 k 7AB + d k3k6k8BPQ + e k1k3 k6 ABP + f k2k6k8PQ]/E t

(A3)

(EQ)/Et = [c k2k4k8Q +d k3k5k8BQ + e k1k3k5AB + f k2k5k8Q]/E t

(A4)

Re-arranging and summing the terms of the numerators above, we get:

E t = k2(k4 + k5)k7 + k1(k4 + k5)k7A + k3 k 5 k 7B + k1k3(k5 + k 7)AB + k2k4k6P + k2 (k4 + k5)k8Q + (k2 + k4) k6 k8 PQ

k1k4k6 AP + k3 k5 k8BQ + k1k3 k6 ABP + k3 k6 k8 BPQ

(A5)

The initial velocity, for both forward and reverse reactions, is given by the kinetic rate law applied to any step; here we use the first step in the reaction: v ¼ k1 ½E½A =Et  k2 ½EA =Et

(A6)

v = k1A(k2k4k7 + k3k5k7B + k2k4k6P + k2k5k7) /E t - k2(k1k4k7A + k4k6k8PQ + k1k4k6AP + k1k5k7A) /E t which may be re-arranged to: v ¼ ðk1 k2 k4 k7 A þ k1 k3 k5 k7 AB þ k1 k2 k4 k6 AP þ k1 k2 k5 k7 AÞ=Et  ðk1 k2 k4 k7 A þ k2 k4 k6 k8 PQ þ k1 k2 k4 k6 AP þ k1 k2 k5 k7 AÞ=Et Upon doing the subtractions; v ¼ ðk1k2k4k7A þ k1 k3 k5 k7 AB þ k1k2k4k6AP þ k1k2k5k7AÞ=Et  ðk1k2k4k7A þ k2 k4 k6 k8 PQ þ k1k2k4k6AP þ k1k2k5k7AÞ=Et v ¼ k1 k3 k5 k7 AB=Et  k2 k4 k6 k8 PQ=Et and v¼

k1 k3 k5 k7 AB  k2 k4 k6 k8 PQ k2 ðk4 þ k5 Þk7 þ k1 ðk4 þ k5 Þk7 A þ k3 k5 k7 B þ k1 k3 ðk5 þ k7 ÞAB þ k2 k4 k6 P þ k2 ðk4 þ k5 Þk 8 Q þðk2 þ k4 Þk6 k8 PQ þ k1 k4 k6 AP þ k3 k5 k8 BQ þ k1 k3 k6 ABP þ k3 k6 k8 BPQ

(A7)

We may now convert the microscopic rate constants to convenient constant terms including coefficient terms for each additive:

An Overview of Steady-State Enzyme Kinetics

v¼

111

num1 AB  num2 PQ const þ coef A A þ coef B B þ coef AB AB þ coef P P þ coef Q Q þ coef PQ PQ þ coef AP AP þ coef BQ BQ þ coef ABP ABP þ coef BPQ BPQ (A8)

In which; num1 ¼ k1 k3 k5 k7 coef B ¼ k3 k5 k7

num2 ¼ k2 k4 k6 k8

const ¼ k2 ðk4 þ k 5 Þk7

coef AB ¼ k1 k3 ðk5 þ k7 Þ

coef PQ ¼ ðk2 þ k4 Þk6 k8

coef P ¼ k2 k4 k6

coef AP ¼ k1 k4 k6

coef A ¼ k1 ðk4 þ k5 Þk7 coef Q ¼ k2 ðk4 þ k5 Þk 8

coef BQ ¼ k3 k5 k8

coef ABP ¼ k1 k3 k6

coef BPQ ¼ k3 k6 k8 The maximal velocities for the forward and reverse reaction directions, V1 and V2, respectively, for any kinetic mechanism is given by the respective numerator term (“num”) divided by the coefficients for all substrates for the forward reaction, and all products for the reverse direction. The equilibrium constant for any mechanism will be the ratio of the numerator terms. In our case: V 1 ¼ num1 =coef AB ¼ k5 k7 =ðk5 þ k7 Þ V 2 ¼ num2 =coef PQ ¼ k2 k4 =ðk2 þ k4 Þ Keq ¼ num1 =num2 ¼ k1 k3 k5 k7 =k2 k4 k6 k8 It is now useful to multiply the numerator and denominator of Eq. (A8) by num2/(coefABcoefPQ): v¼

num2 num2 PQ num1 num2 AB coef AB coef PQ  coef AB coef PQ

const num2 =coef AB coef PQ þ coef A num2 A=coef AB coef PQ þ coef B num2 B=coef AB coef PQ þ coef AB num2 AB=coef AB coef PQ þ coef P num2 P=coef AB coef PQ þ coef Q num2 Q=coef AB coef PQ þ coef PQ num2 PQ=coef AB coef PQ þ coef AP num2 AP=coef AB coef PQ þ coef BQ num2 BQ=coef AB coef PQ þ coef ABP num2 ABP=coef AB coef PQ þ coef BPQ num2 BPQ=coef AB coef PQ

(A9)

Substitutions using the expressions above for V1, V2, and Keq results in: v¼

V 1 V 2 AB  V 1 V 2 PQ=Keq V 2 const=coef AB þ V 2 coef A A=coef AB þ V 2 coef B B=coef AB þ V 2 AB þ V 2 coef AP AP=coef AB þ  V 2 coef BQ BQ=coef AB þ V 2 coef ABP ABP=coef AB þ ðcoef P num2 num1 PÞ= num1 coef AB coef PQ þ     coef Q num2 num1 Q = num1 coef AB coef PQ þ coef PQ num2 num1 PQ = num1 coef AB coef PQ þ   coef BPQ num2 num1 BPQ = num1 coef AB coef PQ

(A10)

In general, Michaelis constants for substrates in both reaction directions are given by the coefficient for another substrate divided by the coefficient for all substrates: Ka ¼ coef B =coef AB ¼ k3 k5 k7 =k1 k3 ðk5 þ k7 Þ ¼ k5 k7 =k1 ðk5 þ k7 Þ Kb ¼ coef A =coef AB ¼ k1 ðk4 þ k5 Þk7 =k1 k3 ðk5 þ k7 Þ ¼ ðk4 þ k5 Þk7 =k3 ðk5 þ k7 Þ Kp ¼ coef Q =coef PQ ¼ k2 ðk4 þ k5 Þk8 =ðk2 þ k4 Þk6 k8 ¼ k2 ðk4 þ k5 Þ=ðk2 þ k4 Þk6 Kq ¼ coef P =coef PQ ¼ k2 k4 k6 =ðk2 þ k4 Þk6 k8 ¼ k2 k4 =ðk2 þ k4 Þk8 Dissociation constants for all substrates and ligands may be obtained from other ratios of coefficients: const=coef A ¼ k2 ðk4 þ k 5 Þk7 =k1 k2 ðk4 þ k5 Þ ¼ k2 =k1 ¼ Kia coef P =coef AP ¼ k2 k4 k6 =k1 k4 k6 ¼ k2 =k1 ¼ Kia coef AP =coef ABP ¼ k1 k4 k6 =k1 k3 k6 ¼ k4 =k3 ¼ Kib coef BQ =coef ABP ¼ k1 k4 k6 =k1 k3 k6 ¼ k4 =k3 ¼ Kib coef Q =const ¼ k2 ðk4 þ k 5 Þk8 =k2 ðk4 þ k 5 Þk7 ¼ k8 =k7 ¼ Kiq

112

An Overview of Steady-State Enzyme Kinetics coef BQ =coef B ¼ k3 k5 k8 =k3 k5 k7 ¼ k8 =k7 ¼ Kiq num1 ¼ k1 k3 k5 k7 coef B ¼ k3 k5 k7

num2 ¼ k2 k4 k6 k8

const ¼ k2 ðk4 þ k 5 Þk7

coef AB ¼ k1 k3 ðk5 þ k7 Þ

coef PQ ¼ ðk2 þ k4 Þk6 k8

coef P ¼ k2 k4 k6

coef AP ¼ k1 k4 k6

coef A ¼ k1 ðk4 þ k5 Þk7 coef Q ¼ k2 ðk4 þ k5 Þk 8

coef BQ ¼ k3 k5 k8

coef ABP ¼ k1 k3 k6

coef BPQ ¼ k3 k6 k8 and from these expressions:

  coef AP =coef AB ¼ ðcoef AP =coef P Þ coef P =coef PQ coef PQ num1 =num2 coef AB ðnum2 =num1 Þ ¼ V 1 Kq =V 2 Kia Keq  coef BQ =coef AB ¼ coef BQ =coef B ðcoef B =coef AB Þ ¼ Ka =Kiq coef ABP =coef AB ¼ Kip0 coef BPQ =coef AB ¼ Kib0  ðcoef P num2 num1 Þ= num1 coef AB coef PQ ¼ V 1 Kq =Keq 

 coef Q num2 num1 = num1 coef AB coef PQ ¼ V 1 Kp =Keq



 coef PQ num2 num1 = num1 coef AB coef PQ ¼ V 1 =Keq

When all of these substitutions are made into Eq. (A10), it becomes: v¼

V 1 V 2 AB  V 1 V 2 PQ=Keq V 2 Kia Kb þ V 2 Kb A þ V 2 Ka B þ V 2 AB þ V 1 Kq AP=Kia Keq þ V 2 Ka BQ=Kiq þ V 2 ABP=Kip0 þ V 1 Kq P=Keq þ V 1 Kp Q=Keq þ V 1 PQ=Keq þ V 1 BPQ=Kib0 Keq (A11)

Note that Keq ¼ num1/num2 can be constructed from these coefficients, and equals at least one expression, which is: Keq ¼ V1KiqKp/V2KiaKb, an expression known as a Haldane. For Eq. (A11), when [P] ¼ [Q] ¼ 0, this equation becomes Eq. (A12): V 1 AB (A12) v¼ Kia Kb þ Kb A þ Ka B þ AB For this Ordered Bi Bi mechanism, the origin of the four terms in the denominator in terms of the enzyme distribution equations indicates which enzyme form they represent. For example, the constant term elaborates the KiaKb form, and comes from the E/Et expression, as does KaB. KbA comes from the k3k5k7B term in the EA/Et, while the AB term comes from the k1k3k7AB term in the (EAB-EPQ)/Et expression. So, Eq. (A12) could be designated as below to represent the E, EA, and EAB enzyme forms. These distribution forms are useful, because, if for example, an inhibitor I binds to the EA form in an Ordered Bi Bi mechanism, then an expression for its inhibition would be Eq. (A14)

v =

v =

V 1AB K ia Kb + KaB + KbA + AB

(A13)

V 1AB KiaKb + KaB + KbA(1 + I/Ki) + AB

(A14)

Use of the distribution equations simplifies the derivation of expressions for fitting data from dead-end inhibitors for an Ordered Bi Bi mechanism. One simply multiplies the terms in the denominator which reflects the enzyme form (or forms) to which the inhibitor binds by (1 þ I/Ki), keeping in mind that by binding to more than one enzyme form will likely require more than one value of Ki (and therefore more than one inhibition constant). An inhibitor which is competitive vs. substrate A binds to free enzyme E, leading to the expression in Eq. (A15):

v =

V 1AB (KiaKb + KaB )(1 + I/Ki) + KbA+ AB

(A15)

An Overview of Steady-State Enzyme Kinetics

113

The initial velocity expression for the Ordered Bi Bi mechanism can be viewed another way: when [B] [ [A], it becomes: v ¼ V1A/(Ka þ A), which is the same as the uni-reactant initial velocity expression. Likewise when [B] [ [A], v ¼ V1B/(Kb þ B). The double-reciprocal form of Eq. (A12) is also illustrative: 1=v ¼ ðKia Kb =V 1 B þ Ka =V 1 Þð1=AÞ þ ðKb =B þ 1Þð1=V 1 Þ which describes a family of straight lines when changing-fixed [B] are applied. Increasing [B] will lower the apparent slopes and intercepts, leading to the intersecting patterns of sequential mechanisms. It is the KiaKb/V1AB term that leads to the slope effect in plots of B vs. A or A vs. B. Application of the King-Altman method to the Ping Pong Uni Uni Uni Uni and Equilibrium-Ordered Bi Bi mechanisms yields Eqs. (A16) and (A17), respectively. Again the color-coded terms in the denominator reflect the E, EA, and EAB and F enzyme forms.

v =

V 1AB KbA + KaB + AB

(A16)

v =

V 1AB KiaKb + KbA + AB

(A17)

For the ping pong mechanism, the KiaKb term is absent in the denominator because none of the terms in the enzyme distribution expressions, unlike that of E/Et, contain a collection of rate constants that is not multiplied by A, B, P or Q, and the absence of the KiaKb term in the denominator is the mathematical reason that the double-reciprocal plot of 1/v vs. either 1/[A] or 1/[B] at changingfixed concentrations of the second substrate produces a family of parallel lines. For the equilibrium-ordered mechanism, the KaB terms does not appear in the denominator because there is no appreciable EA form and thereby, no expression for the EA/Et distribution term. Double-reciprocal expressions for this mechanism are then: 1/ v ¼ (KiaKb/V1B)(1/A) þ (Kb/B þ 1)(1/V1) and 1/v ¼ (KiaKb/V1A þ Kb/V1)(1/[B]) þ 1/V1. Both double-reciprocal plots will exhibit slope effects from the changing fixed levels of the other substrate, and the former plot will exhibit an intercept effect at changing-fixed levels of B. However, changing-fixed levels of A in the plot of 1/v ¼ 1/[B] will not exhibit an intercept effect, and so analysis of both plots will confirm an equilibrium-ordered mechanism.

A.1 Rapid-Equilibrium Random Bi Bi Kinetic mechanism. Full derivation of the initial velocity expression An advantage of the rapid-equilibrium model is the ease at which one may derive expressions for initial velocity and inhibition for multi-substrate enzymes. What below is partly over-simplified, but the initial velocity expression for a rapid-equilibrium, random, bi-substrate, bi-product enzyme can be readily solved for Eq. (A18): v ¼ kcat ð½EAB=½Et 

¼ kcat ð½EAB=ð½E þ ½EA  þ ½EB þ ½EABÞ

(A18)

meaning that the initial rate is simply the turnover number multiplied by the fraction of total enzyme that is in central complexes ([EAB]), and the initial velocity as in Eq. (A18) is this numerator divided by a denominator that is the sum of the enzyme forms. One may convert Eq. (A18) to the same form as Eq. (A12) by substitution using the expressions of the dissociation constants: The first simplification is that there is no distinction between [EBA] and [EAB]; thermodynamically speaking, that is: “work is independent of path.” Kia ¼ ½E½A =½EA Kib ¼ ½E½B=½EB Kb ¼ ½EA ½B=½EAB Ka ¼ ½EB½A =½EBA  and

[E t] = [E] + [EA] + [EB] + [EAB]. One may then write:

[E t] = [E](1 + [A]/Kia + [B]/Kib + [A][B]/KiaKb), And since: [E], [EA], and [EB] are respectively equal to KiaKb[EAB]/[A][B], Kb[EAB]/[B], and Ka[EAB]/[A], then; ½Et  ¼ ½EABðKia Kb =½A ½B þ Kb ½B þ Ka ½A  þ 1Þ ½EAB ¼ ½A ½B½Et =ðKia Kb þ Kb =½A  þ Ka =½B þ ½A ½BÞ; ½EAB ¼ ½A ½B½Et =ðKia Kb þ Kb ½A  þ Ka ½B þ ½A ½BÞ

(A19)

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An Overview of Steady-State Enzyme Kinetics

The initial velocity equation becomes: v ¼ kcat ½EAB ¼ kcat ½A ½B½Et =ðKia Kb þ Kb ½A  þ Ka ½B þ ½A ½BÞ

(A20)

Note that where [A] [ [B] or [B] [ [A], Eq. (A20) becomes respectively, v ¼ k3[A][B][Et]/(KiaKb þ Kb[A] þ Ka[B] þ [A][B]) and v ¼ k3[A][B][Et]/(KiaKb þ Kb[A] þ Ka[B] þ [A][B]), becoming respectively, v ¼ k3[B][Et]/(Kb þ [B]) and v ¼ k3[A][Et]/(Ka þ [A]), which express two equations which describe a rectangular hyperbola like that of the Michaelis-Menten equation.

A.2 Expressions for pH-rate profiles for kcat/Km and kcat for Cases 6 and 7 Expressions for the pH dependence of kcat/Km and kcat for the mechanisms in Case 6 and 7 may be obtained using the King-Altman approach. We assume that the protonation of EH to form HEH occurs in rapid-equilibrium, while all other steps are in the steadystate. Using the approximation method of Sungman Cha for rapid-equilibrium steps (Cha, 1968) we may treat EH and HEH as fractional amounts of these two free-enzyme forms: ½EH=f½EH þ ½HEHg ¼ f 1 ¼ f 10 ¼ 1=f1 þ ½H=K1 g; K 1 ¼ ½H½EH=½HEH; ½HEH ¼ ½H½EH=K1 ; ½EH=f½EH þ ½HEHg ¼ f 1 ¼ f 10 ¼ 1=f1 þ ½H=K1 g; ½HEH=f½EH þ ½HEHg ¼ f 7 ¼ ð½H=K1 Þ=f1 þ ½H=K 1 g; f 7 =f 1 ¼ ½H=K1 ¼ H=K 1 So that microscopic rate constants for k1, k7, and k10 become k1f1, f7k7, and f10k10. The mechanistic scheme reduces to four enzyme forms from which eight unclosed King-Altman patterns are possible. From the scheme we may derive a second acid dissociation constant, K1 ¼ k2k5k7/k1k6k8. The distribution equations for the four enzyme forms are: X=Et ¼ ðk2 k5 k9 þ k2 k4 k8 þ k2 k8 k9 þ k2 k4 k5 þ k6 k8 k9 H þ k4 k6 k8 H þ k3 k5 k9 þ k3 k8 k9 Þ=Et
 HEAH=Et ¼ k1 f 1 k6 k9 AH þ k2 k4 k7 f 7 A þ k2 k7 f 7 k9 A þ k1 f 1 k4 k6 AH þ k6 k7 f 7 k9 AH þ k4 k6 k7 f 7 AH þ k4 k6 k10 f 10 HP þ k3 k7 f 7 k9 A =Et
 EAH=Et ¼ k1 f 1 k5 k9 A þ k1 f 1 k4 k8 A þ k1 f 1 k8 k9 A þ k1 f 1 k4 k5 A þ k5 k7 f 7 k9 A þ k4 k5 k7 f 7 A þ k4 k5 k10 f 10 P þ k4 k8 k10 f 10 P =Et
 EPH=Et ¼ k2 k5 k10 f 10 P þ k1 f 1 k3 k8 A þ k2 k8 k10 f 10 P þ k1 f 1 k3 k5 A þ k6 k8 k10 f 10 P þ k3 k5 k7 f 7 A þ k3 k5 k10 f 10 P þ k3 k8 k10 f 10 P =Et Et is the sum of numerators. The steady-state rate will be the forward and reverse reaction rates for any step, here we select the following: v=Et ¼ k3 ðEAH=Et Þ  k4 ðEPH=Et Þ 
¼ k3 k1 f 1 k5 k9 A þ k1f 1k4k8A þ k1 f 1 k8 k9 A þ k1f 1k4k5A þ k5 k7 f 7 k9 A þ k4k5k7f 7A þ k4k5k10f 10P þ k4k8k10f 10P =Et  k4 ðk2 k5 k10 f 10 P þ k1f 1k3k8A þ k2 k8 k10 f 10 P þ k1f1k3k5A þ k6 k8 k10 f 10 P þ k3k5k7f 7A þ k3k5k10f 10P þ k3k8k10f 10PÞ=Et
 v=Et ¼ k1 f 1 k3 k5 k9 A þ k1 f 1 k3 k8 k9 A þ k3 k5 k7 f 7 k9 A =Et  ðk2 k4 k5 k10 f 10 P þ k2 k4 k8 k10 f 10 P þ k4 k6 k8 k10 f 10 PÞ=Et Under initial velocity conditions, P ¼ 0, so the expression for initial velocity becomes:
 v=Et ¼ k1 f 1 k3 k5 k9 A þ k1 f 1 k3 k8 k9 A þ k3 k5 k7 f 7 k9 A =Et ; v=Et ¼ k1 f 1 AðX=Et Þ  k2 ðEAH=Et Þ ¼ k1 f 1 ðk2k5k9A þ k2k4k8A þ k2k8k9A þ k2k4k5A þ k5 k8 k9 A þ k3 k6 k8 AH þ
k4 k6 k10 f 10 AHP þ k3 k8 k9 A=Et Þ  k2 ð k1f 1k5k9A þ k1f1k4k8A þ k1f 1k8k9A þ k1f1k4k5A þ k5 k7 f 7 k9 A þ k4 k5 k7 f 7 A  þ k4 k5 k10 f 10 P þ k4 k8 k10 f 10 P =Et for which Et ¼ ðk2 k5 k9 þ k2 k4 k8 þ k2 k8 k9 þ k2 k4 k5 þ k6 k8 k9 H þ k4k6k8H þ k3 k5 k9 þ k3 k8 k9 þ k1 f 1 k6 k9 AH þ k2k4k7f 7A þ k2 k7 f 7 k9 A þ k1f 1k4k6AH þ k6 k7 f 7 k9 AH þ k4k6k7f 7AH þ k3 k7 f 7 k9 A þ k1 f 1 k5 k9 A þ k1f 1k4k8A þ k1 f 1 k8 k9 A þ k1f 1k4k5A þ k5 k7 f 7 k9 A þ k4k5k7f7A þ k1 f 1 k3 k8 A þ k1 f 1 k3 k5 A þ k3 k5 k7 f 7 AÞ; because k4 ¼ P ¼ 0. So, Et ¼ [k2k5k9 þ k2k8k9 þ k3k5k9 þ k3k8k9 þ k6k8k9H þ (k2k7f7k9 þ k3k7f7k9 þ k1f1k5k9 þ k1f1k8k9 þ k5k7f k9 þ k1f1k3k8 þ k1f1k3k5 þ k3k5k7f7 þ k1f1k6k9H þ k6k7f7k9H)(A)], So,

An Overview of Steady-State Enzyme Kinetics

115


 v=Et ¼ k1 f 1 k3 k5 k9 A þ k1 f 1 k3 k8 k9 A þ k3 k5 k7 f 7 k9 A =    ðk2 þ k3 Þðk5 þ k8 Þk9 þ k6 k8 k9 H þ k7 f 7 ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A þ k1 f 1 ðk3 þ k9 Þðk5 þ k8 ÞA þ k1 f 1 k6 k9 AH þ k6 k7 f 7 k9 AH
 


  v=Et ¼ k1 f 1 k3 k5 1 þ k7 f 7 =k1 f 1 þ k8 k9 A = k2 ðk5 þ k8 Þk9 þ k3 k8 k9 þ k3 k5 k9 þ k6 k8 k9 H þ k7 f 7 ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A  þ k1 f 1 ðk3 þ k9 Þðk5 þ k8 ÞA The initial velocity at v/A at A 0 0 is the expression for V/K:



   V=KEt ¼ k1 f1 k3 k5 1 þ k7 f7 =k1 f1 þ k8 k9 =fk2 ðk5 þ k8 Þk9 þ ðk3 þ k5 Þk8 k9 g



  V=KEt ¼ k1 f1 k3 k5 1 þ k7 f7 =k1 f1 þ k8 =fðk2 ðk5 þ k8 Þ þ k3 k8 þ k3 k5 þ k6 k8 Hg 
  V=KEt ¼ k1 f1 k3 þ k5 k7 f7 =ðk5 þ k8 Þk1 f1 =fðk2 þ k3 Þ þ k6 k8 H=ðk5 þ k8 Þg  
 V=KEt ¼ ½k1 f1 k3 =ðk2 þ k3 Þ 1 þ k5 k7 f7 =ðk5 þ k8 Þk1 f1 =ðk2 þ k3 Þðk5 þ k8 Þ =f1 þ k6 k8 H=ðk2 þ k3 Þðk5 þ k8 Þg V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk5 k7 H=K1 ðk5 þ k8 Þk1 Þg=f½1 þ H=K1 ½1 þ k6 k8 H=ðk2 þ k3 Þðk5 þ k8 Þg

(A21)

V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk5 k7 H=K1 ðk5 þ k8 Þk1 Þg=f½1 þ H=K1 ½1 þ k6 k8 H=ðk2 þ k3 Þðk5 þ k8 Þg V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk7 =k1 Þ½H=K1 ð1 þ k8 =k5 Þg=f½1 þ H=K1 ½1 þ k6 k8 H=ðk2 þ k3 Þðk5 þ k8 Þg Note that: K1 ¼ k2k5k7/k1k6k8, so, k6k8 ¼ k2k5k7/K1k1 V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk7 =k1 ÞðH=K1 Þð1 þ k8 =k5 Þg=f½1 þ H=K1 ½1 þ k2 k5 k7 H=½K1 k1 ðk2 þ k3 Þðk5 þ k8 Þg V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk7 =k1 ÞðH=K1 Þð1 þ k8 =k5 Þg=f½1 þ H=K1 ½1 þ k5 k7 H=½K1 k1 ð1 þ k3 =k2 Þðk5 þ k8 Þg V=KEt ¼ f½k1 k3 =ðk2 þ k3 Þ½1 þ ðk7 =k1 ÞðH=K1 Þð1 þ k8 =k5 Þg=f½1 þ H=K1 ½1 þ Hðk7 =k1 Þ=½K1 ð1 þ k3 =k2 Þð1 þ k8 =k5 Þg V=Ka ¼

½k1 k3 Et =ðk2 þ k3 Þ½1 þ ðk7 H=k1 ÞH=ðK1 ð1 þ k8 =k5 Þ ½1 þ H=K1 ½1 þ Hðk7 =k1 Þ=ðK1 ð1 þ k3 =k2 Þð1 þ k8 =k5 Þ

(A22)

k1 k3 Et =ðk2 þ k3 Þ½1 þ aH=K1  ½1 þ H=K1 ½1 þ aH=bK 1 

(A23)

Which we may write as: V=Ka ¼

For which, a ¼ (k7/k1)(1 þ k8/k5) and b ¼ (1 þ k3/k2). The ratio k3/k2 is the stickiness factor for the substrate, which measures the partitioning of the EA complex through the chemistry step (k3) vs. desorption from EA (k2). Note that when the substrate is not sticky, b ¼ (1 þ k3/k2)  1.0, Eq. (A23) becomes Eq. (42). Other conditions that may occur are: ðk7 =k1 Þ ¼ ð1 þ k8 =k5 Þ; and k1 ¼ k7 and k5 > k8 The initial velocity at v/A at A 0 infinity is the expression for V/Et:  


  V=Et ¼ k1 f 1 k3 k5 1 þ k7 f 7 =k1 f 1 þ k8 k9 A =    k1 f 1 ðk3 þ k9 Þðk5 þ k8 ÞA þ k7 f 7 ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A þ k1 f 1 k6 k9 AH þ k6 k7 f 7 k9 AH 


  V=Et ¼ k1f 1k3 k5 1 þ k7 f 7 =k1 f 1 þ k8 k9 A =

     k1f 1 ðk3 þ k9 Þðk5 þ k8 ÞA þ k7 f 7 =k1 f 1 ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A þ k6 k9 AH þ k6 k9 AH k7 f 7 =k1 f 1 And since K 1 ¼ k2 k5 k7 =k1 k6 k8 ; f 7 =f 1 ¼ H=K 1 ; k7 =k1 ¼ K 1 k6 k8 =k2 k5 ; k7 f 7 =k1 f 1 ¼ k6 k8 H=k2 k5 ; and K 2 ¼ k5 =k6 V=Et ¼

½k3 ðk5 ð1 þ ðk6 k8 H=k2 k5 ÞÞ þ k8 Þk9 A fðk3 þ k9 Þðk5 þ k8 ÞA þ ðk6 k8 H=k2 k5 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A þ k6 k9 AH þ k6 k9 AHðk6 k8 H=k2 k5 Þg

And since K2 ¼ k5/k6: V=Et ¼ ½k3 ðk5 ð1 þ ðk8 H=k2 K 2 ÞÞ þ k8 Þk9 A=    ðk3 þ k9 Þðk5 þ k8 ÞA þ ðk8 H=k2 K 2 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 A þ k5 k9 AH=K 2 þ ðk5 k8 k9 =k2 ÞðAH=K 2 Þ2 V=Et ¼½k3 k9 Aðk5 þ k8 Þ þ ðk5 k8 H=k2 K 2 :=ðk3 þ k9 Þ:½ðk5 þ k8 ÞA þ ðk8 H=k2 K 2 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 =ðk3 þ k9 Þ   þ k5 k9 AH=ðk3 þ k9 ÞK 2 þ ðk5 k8 k9 A=k2 ðk3 þ k9 ÞðH=K 2 Þ2 V=Et ¼ k3 k9 A=ðk3 þ k9 Þ:½1 þ ðk5 k8 H=k2 ðk5 þ k8 ÞK 2 ::=A1 þ ðk8 H=k2 K 2 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 =½ðk3 þ k9 Þðk5 þ k8 Þ

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þ k2 k5 k9 AH=½k2 ðk3 þ k9 Þðk5 þ k8 ÞK 2 þ ðk5 k8 k9 =k2 ðk5 þ k8 Þðk3 þ k9 ÞðH=K 2 Þ2 : V=Et ¼ k3 k9 A=ðk3 þ k9 Þ:½1 þ ðk5 k8 H=k2 ðk5 þ k8 ÞK 2 ::=A1 þ ðk8 H=k2 K 2 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5 =½ðk3 þ k9 Þðk5 þ k8 Þ þk2 k5 k9 H=½k2 ðk3 þ k9 Þðk5 þ k8 ÞK 2 þ ðk5 k8 k9 =k2 ðk5 þ k8 Þðk3 þ k9 ÞðH=K 2 Þ2 :  V=Et ¼ k3 k9 =ðk3 þ k9 Þ:½1 þ ðk5 k8 H=k2 ðk5 þ k8 ÞK 2 ::= 1 þ ðk8 H=K 2 Þ½ðk2 þ k3 þ k5 Þk9 þ k3 k5  þ k2 k5 k9 H=K 2  þ ðk5 k8 k9 ÞðH=K 2 Þ2 =ðk2 ðk5 þ k8 Þðk3 þ k9 ÞgÞ  V=Et ¼ k3 k9 =ðk3 þ k9 Þ:½1 þ ðk5 k8 H=k2 ðk5 þ k8 ÞK 2 ::= 1 þ ½ðk8 Þðk2 þ k3 þ k5 Þk9 þ k3 k5 þ k2 k5 k9 ðH=K 2 Þ  þ ðk5 k8 k9 ÞðH=K 2 Þ2 =ðk2 ðk5 þ k8 Þðk3 þ k9 Þ  V ¼ k3 k9 Et =ðk3 þ k9 Þ:½1 þ ðk5 k8 H=k2 ðk5 þ k8 ÞK 2 ::= 1 þ ½ðk2 k8 þ k2 k5 þ k3 k8 þ k5 k8 Þk9 þ k3 k5 k8 ðH=K 2 Þ  þ ðk5 k8 k9 ÞðH=K 2 Þ2 =ðk2 ðk5 þ k8 Þðk3 þ k9 Þ V¼

k3 k9 Et ð1þðk5 k8 HÞÞ ðk3 þk9 Þ ðk2 ðk5 þk8 ÞK2

½1þ½ðk2 k8 þk2 k5 þk3 k8 þk5 k8 Þk9 þk3 k5 k8 ðH=K2 Þþðk5 k8 k9 ÞðH=K2 Þ2 

(A24)

ðk2 ðk5 þk8 Þðk3 þk9 Þ

This equation may be fitted to:

  log ðV Þ ¼ log ½cð1 þ H=Kc Þ= 1 þ aðH=K2 Þ þ bðH=K2 Þ2

(A25)

where c ¼ k3 k9 =ðk3 þ k9 Þ; K2 ¼ k5 =k6 ; Kc ¼ ðk5 k8 Þðk2 ðk5 þ k8 ÞK2 ¼ ðk2 =k8 Þð1 þ ðk8 =k5 ÞK2 a ¼ ðk3 k5 k8 þ k5 k8 k9 þ k3 k8 k9 þ k2 k8 k9 þ k2 k5 k9 Þ=ðk2 ðk5 þ k8 Þðk3 þ k9 ÞÞ and b ¼ k5 k8 k9 =ðk2 ðk5 þ k8 Þðk3 þ k9 Þ

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1.06

Ion Channels

Claire Townsend, GlaxoSmithKline R&D, Collegeville, PA, United States © 2022 Elsevier Inc. All rights reserved.

1.06.1 1.06.2 1.06.3 1.06.3.1 1.06.3.2 1.06.3.2.1 1.06.3.2.2 1.06.3.2.3 1.06.3.3 1.06.3.3.1 1.06.3.3.2 1.06.3.3.3 1.06.3.4 1.06.4 1.06.4.1 1.06.4.2 1.06.4.2.1 1.06.4.2.2 1.06.4.2.3 1.06.5 1.06.5.1 1.06.5.1.1 1.06.5.1.2 1.06.5.1.3 1.06.5.1.4 1.06.5.1.5 1.06.5.1.6 1.06.5.1.7 1.06.5.2 1.06.5.2.1 1.06.5.2.2 1.06.5.2.3 1.06.5.2.4 1.06.5.2.5 1.06.5.3 1.06.6 1.06.6.1 1.06.6.2 1.06.6.3 1.06.6.3.1 1.06.6.3.2 1.06.6.4 1.06.6.5 1.06.7 1.06.7.1 1.06.7.2 1.06.7.3 1.06.8 References Relevant Websites

118

Introduction Ion channels: A diverse family of ion-transporting proteins Ion channel structure Voltage-gated ion channels Ligand-gated ion channels Neurotransmitter-gated channels Epithelial sodium channels and ATP-gated channels Calcium release channels Other channels Chloride channels Cation channels Mechanosensitive channels Auxiliary subunits Functional properties of ion channels: Permeation and gating Ion permeation Gating mechanisms Physical factors: Voltage, temperature, and stretch Chemical or ligand-gating: Ions, neurotransmitters, and intracellular signaling molecules Biological pathways Ion channel pharmacology Ion channel inhibitors Pore blockers Pore modulators Gating modifiers Competitive antagonists Inverse agonists Negative allosteric modulators Inhibitory antibodies Ion channel agonists Ion channel openers Orthosteric agonists Gating modifiers Positive allosteric modulators Agonist-like antibodies Ion channel trafficking modulators Characterization of ion channel-ligand interactions: From whole-tissue to single molecules Native versus recombinant systems Binding Electrophysiology: Single-channel and macroscopic recordings Electrophysiological methods Electrophysiology mechanistic studies of channel inhibition and activation Ion flux measurements Indirect measurements of channel activity: Fluorescent indicators and genetically-encoded sensors Emerging technologies and ion channel pharmacology Gene editing and genetics Structural biology Computational methods: Virtual screening, artificial intelligence, and machine learning Conclusion

Comprehensive Pharmacology, Volume 1

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Glossary Cryogenic electron microscopy (cryo-EM) Electron microscopy applied to samples cooled to cryogenic temperatures (< 150
C). Electrophysiology Study of the production of electrical signals in organisms, method using voltage or current as readout. Gating The opening (by activation) or closing (by deactivation or inactivation) of ion channels. Q10 Temperature coefficient that is a measure of the rate of change of a biological or chemical system as a result of a 10
C rise in the temperature.

1.06.1

Introduction

The fast transduction of many physiological signals is crucial to all living organisms. For instance, the quick withdrawal movements following contact with a very hot surface or a cut from a sharp object are critical to prevent further injury. Survival, in general, requires rapid reflex movements. How are these fast responses possible? The transduction of biological signals occurs through two main mechanisms: chemical and electrical. Chemical signaling involves the secretion and transport through the blood or other tissue of molecules binding to specific receptors that translate this signal to cellular responses. Chemical signaling, however, is relatively slow. For instance, the release of peripheral hormones (e.g. cortisol) following a central signal (e.g. ACTH release from the pituitary gland following a stress-producing stimulus) occurs on a scale of minutes (Becker, 2001). Electrical signals, in contrast, are extremely fast: the average nerve conduction velocity in humans is about 70 m s–1 (Chouhan, 2016) and fast conduction underlies the rapid, sub-second response times triggered by various sensory stimuli (e.g. heat, pain, sight, or sound). The molecular basis for electrical signaling is the rapid movement of ions across biological membranes such as the nerve axonal membrane and the efficient propagation of this electrical signal. The latter is mediated through nodes of Ranvier in axons. The electrical signal or action potential arises from the opening and closing of specific ion-transporting proteins, ion channels. Ion channels are membrane-spanning proteins that mediate the passive transport of ions across biological membranes at rates greater than 106 ions per second and up to 108 ions per second (Hille, 2001). Ion movement through ion channels follows the electrochemical gradient of the specific ion being transported. These basic properties distinguish ion channels from other iontransporting membrane proteins such as transporters and ionic pumps. Ion transport through the latter occurs at significant slower rates (Dubyak, 2004) and most transporters, but no channels, can catalyze the active transport of specific ions against their electrochemical gradients, through a net expenditure of cellular energy. The opening of ion channels can cause fast, transient changes in transmembrane potential. The fast and concerted transport of ions through sodium-, calcium-, and potassium-selective channels underlies the action potentials of excitable cells. Aside from these fundamental properties, ion channels are a diverse family of proteins in terms of structure, ionic selectivity, and activation (or gating) mechanisms. They are found in all living organisms, from bacteria to plants and humans, and play a role in a variety of biological processes, from viral replication and shedding to neurotransmitter release at synapses (Hille, 2001; Sze and Tan, 2015). At the cellular level, they are localized in the plasma membrane as well as in the membrane of various subcellular compartments such as the nucleus, mitochondria, endoplasmic reticulum, and lysosomes. Ionic selectivity, gating mechanism, and cellular localization dictate the effects of an ion channel opening in a given cell. For instance, ion channels play a major role in excitation-contraction coupling in cardiac and skeletal muscle. In cardiomyocytes, plasma membrane calcium-selective and voltage-activated channels open in response to membrane depolarization triggered by the first phase of the cardiac action potential. Opening of these channels causes a transient increase in intracellular calcium that, in turn, activates calcium-activated calcium release channels from the sarcoplasmic reticulum, further increasing calcium concentration and switching on the contractile machinery of these cells through direct calcium binding to troponin C (Bers, 2002). Another major role of ion channels is stimulus-secretion coupling in various hormone-secreting cells, such as pancreatic b cells, pituitary neurons, adrenal chromaffin cells, and atrial cells (Misler, 2009). In pancreatic b cells, membrane depolarization caused by the closure of metabolic state (ATP) sensitive potassium channels activates voltage-activated calcium channels. Calcium influx through these channels raises intracellular calcium which then triggers fusion of insulin-containing intracellular vesicles with the plasma membrane and insulin release (Ashcroft et al., 1994). The essential role of ion channels in physiology is highlighted by the numerous naturally-occurring mutations in the genes encoding these proteins and their link to disease or to specific phenotypes, ranging from excitatory disorders to hormonal imbalances. For instance, loss of function mutations that abolish the expression of the voltage-gated sodium channel Nav1.7 are associated with complete loss of pain sensation or congenital insensitivity to pain. Nav1.7 channels are highly expressed in nociceptive neurons along with other Nav channels but Nav1.7 channels are essential to the sensation of various painful stimuli, including burns, bone fractures, and cuts. Thus, individuals lacking Nav1.7 channels injure themselves continuously (Cox et al., 2006). Nav1.7 gain of function mutations, on another hand, are associated with pain phenotypes and familial erythromelalgia, an autosomal dominant painful neuropathy characterized by redness of the skin and intermittent burning sensation of extremities (Yang et al., 2004). Another well-characterized group of channelopathies is linked to the ATP-sensitive potassium channels (KATP) found

120

Ion Channels

in pancreatic b cells. Loss of function mutations in these channels underlie congenital hyperinsulinism while gain of function mutations cause neonatal diabetes mellitus (Ashcroft, 2005). Not surprisingly, there are many natural products found in prokaryotes and eukaryotes that modulate ion channel function. These ion channel ligands allow these organisms to survive through impairing their predators or their preys. The neuromuscular junction, for instance, is the target of multiple natural toxins, such as the plant alkaloid tubocurarine (curare) and the snake venom peptide toxin alpha bungarotoxin. Tubocurarine is a reversible competitive antagonist of post-junctional nicotinic acetylcholine receptors (cation-selective channels) while alpha bungarotoxin is an irreversible antagonist of these channels. Both cause muscle paralysis (Bowman, 2006). Ion channels are also the targets of many drugs covering a wide range of therapeutic areas, from cardiovascular to neurological and endocrine indications. This article reviews the various ion channel families, their functional properties, and the many ways ligands can alter their function. It also discusses the methods used to characterize ion channel-ligand interactions and novel avenues for exploring and expanding their rich pharmacology. It focuses on main principles with representative examples from the literature.

1.06.2

Ion channels: A diverse family of ion-transporting proteins

Ion channels have been divided into families based on their structure, the primary ion they transport, and the mechanism(s) that trigger their opening or activation. They can been binned into three broad families, voltage-gated ion channels (VGICs), ligandgated ion channels (LGICs), and “other ion channels” (Alexander et al., 2019). Table 1 includes all known human ion channels and their respective families. Proteins that are not strictly transporting ions, such as porins, are not included in this table nor discussed in this article. Voltage-gated ion channels open in response to changes in membrane potential. The VGIC family includes voltage-gated potassium (Kv) channels, voltage-gated sodium (Nav) channels, voltage-gated calcium (Cav) channels, and hyperpolarization-activated (HCN) channels. It also comprises non-voltage gated ion channels that are structurally related to VGICs, such as inwardly-rectifying potassium (Kir) channels, transient receptor potential (TRP) channels, and two-pore potassium (K2P) channels. All these channels are thought to have evolved from a common ancestor and are therefore grouped under the same family (Table 1). In contrast, the ligand-gated channel family includes structurally-distinct sub-families of channels but all are opened by a ligand, such as protons for acid-sensing ion channels (ASICs) or neurotransmitters, such as acetylcholine, glutamate, or GABA for nicotinic, glutamate, and GABA ion channels, respectively. Channels that do not belong to the VGIC family or that are not ligand-gated are grouped under the “other ion channels” family. Various chloride channels, Orai store-operated calcium channels, otopetrin proton channels, mechanosensitive Piezo channels, and the non-selective cation channels NALCN and TACAN are members of this family.

1.06.3

Ion channel structure

Ion channels have two structural elements in common: a membrane-spanning domain and an ion-conducting pore. However, their overall structure varies greatly. Ion channels may be formed by small, one-transmembrane segments assembling into a conducting pore. This is the case for several viral ion channels, where short (around 100 amino acids) polypeptides form homo-tetramers or homo-pentamers (Fischer and Sansom, 2002). The influenza M2 channel, for example, is formed by four single-transmembrane 97amino acid polypeptides (Schnell and Chou, 2008). Ion channels can also be very large, such as the 24-transmembrane Nav and Cav channels where the pore is contained within this large subunit, the mechanosensitive Piezo channels formed by the assembly of three subunits, each with 30–40 predicted transmembrane segments, or the huge ryanodine calcium-release channel complex with a molecular weight exceeding 2 MDa. Furthermore, in addition to the principal pore-forming a subunit, many ion channels have one or more auxiliary, non-pore-forming subunits that modulate their expression, function, or pharmacology. Finally, some ion channel families can form hetero-multimers of different pore-forming subunits, with various stoichiometries reported in situ or in recombinant expression systems (Bartoi et al., 2014; Hofmann et al., 2002; Koch et al., 1997; Strubing et al., 2001). The architecture of the voltage-gated and ligand-gated families of human ion channels is shown in Fig. 1.

1.06.3.1

Voltage-gated ion channels

Voltage-gated ion channels may have evolved from prokaryotic channels consisting of two transmembrane (2TM) segments with a membrane-reentrant pore loop. Many bacteria have 2TM potassium channels resembling inwardly-rectifying potassium channels (Kirs) and some bacteria also have 6TM voltage-gated potassium channels. Kir channels are formed by the assembly of four subunits consisting of 2TM segments (Fig. 1). The Kir channel pore is made up by the four pore loops linking the two membrane segments. The extracellular regions flanking the pore loops and connecting them to transmembrane segments are called “turrets.” These regions play a role in channel inactivation and are binding sites for several channel modulators (Van Der Cruijsen et al., 2013; Xu et al., 2003). Other VGICs have structures derived from this simple 2TM architecture. Two-pore domain potassium channels (K2Ps) consist of two 4TM subunits, each subunit having two pore loops, one between TM1 and TM2 and one between TM3 and TM4, analogous a duplicated 2TM Kir channel. Many voltage-gated channels are formed of 6TM subunits, with the first 4 TMs containing a voltage sensor followed by the simple 2TM architecture of Kir channels. Voltage-gated potassium (Kv) channels,

Ion Channels Table 1

121

Human ion channel families.

Family

Sub family

Ion channels

Voltage-gated channels

Voltage-gated potassium channels

Delayed rectifiers channels, Kv1.1-1.8, Kv2.1-2, Kv3.1-4, Kv4.1-3 Electrically-silent Kvs, Kv5.1, Kv6.1-4, Kv8.1-2, Kv9.1-3 Slow delayed rectifiers, Kv7.1-7.5 (KCNQ1-5) Ether-a-go-go channels, Kv10.1, Kv10.2, Kv11.1, Kv11.2, Kv11.3, Kv12.1, Kv12.2, Kv12.3 K2P1.1 (TWIK1), K2P2.1 (TREK1), K2P3.1 (TASK1), K2P4.1 (TRAAK1), K2P5.1 (TASK2), K2P6.1 (TWIK2), K2P7.1, K2P9.1 (TASK3), K2P10.1 (TREK2), K2P12.1 (THIK2), K2P13.1 (THIK1), K2P15.1 (TASK5), K2P16.1, (TALK1), K2P17.1 (TALK2), K2P18.1 (TRESK) Kir1.1, Kir2.1, Kir2.2, Kir2.3, Kir2.4, Kir3.1, Kir3.2, Kir3.3, Kir3.4, Kir4.1, Kir4.2, Kir5.1, Kir6.1, Kir6.2, Kir7.1 Large-conductance calcium-activated potassium channel subfamily M, BKCa

Two-pore domain potassium channels

Inwardly-rectifying potassium channels Calcium- and sodium-activated potassium channels

Voltage-gated sodium channels Voltage-gated calcium channels Transient Receptor Potential channels

Cyclic nucleotide-regulated channels CatSper and Two-Pore channels Ligand-gated channels

Voltage-gated proton channel Pentameric (Cys-loop) non-selective cation channels Pentameric (Cys-loop) chloride channels Non-selective cation channels Degenerin/epithelial sodium channels Calcium channels

Other channels

Chloride channels

Calcium channels Proton channels Non-selective cation channels

Calcium-activated potassium channel subfamily N, KCa1.1, KCa2.1,KCa2.2, KCa2.3, KCa3.1 Sodium-activated potassium channels subfamily T, KNa1.1, KNa1.2 Calcium-activated potassium channel subfamily U, KCa5.1 Nav1.1-1.9 High-voltage activated, L type: Cav1.1, Cav1.2, Cav1.3, Cav1.4, P/Q type: Cav2.1, N type: Cav2.2, R type: Cav2.3 Low-voltage activated, T type: Cav3.1, Cav3.2, Cav3.3 Transient receptor potential ankyrin 1, TRPA1 Transient receptor potential canonical, TRPC1-7 Transient receptor potential melastatin, TRPM1-8 Transient receptor potential mucolipin, TRPML1-3 Polycystin 2 (PKD2), polycystin 2-like, PKD2L1, PKD2L2 Transient receptor potential vanilloid, TRPV1-6 Cyclic nucleotide-gated channels, CNGA1-4 Hyperpolarization-activated cyclic nucleotide gated potassium channels, HCN1-4 Sperm-specific cation channels, CatSper1-4 Two-pore channels, TPC1-2 Hv1 Nicotinic acetylcholine receptors, a1–7, a9–10, b1–4, g, d, 3 subunits 5-HT3 receptors, 5HT3A-E Zinc activated ion channel, ZAC GABAA receptors, a1–6, b1–3, g1–3, d, 3, q, p, r1–3 subunits Glycine receptors, a1–3, b subunits Ionotropic glutamate receptors, GluA1-4 (AMPA); GluD1-2; GluK1-5 (kainate); GluN1, 2A-2D, 3A-B (NMDA) Purinergic P2X receptors, P2X1-7 Acid-sensing (proton-gated) ion channels, ASIC1-5 Epithelial sodium channel, ENaCa, b, g, d IP3 receptors, IP3R1-3 Ryanodine receptors, RyR1-3 Voltage-gated chloride channels, ClC-1, ClC-2, ClC-Ka, ClC-kb Calcium activated chloride channels CaCC, anoctamin 1-10 Cystic fibrosis transmembrane conductance regulator, CFTR Proton-activated chloride channel, PAC Orai channels, Orai1-3 Otopetrins, OTOP1 Piezo1 and piezo2 mechanosensitive channels NALCN sodium leak channel TACAN mechanosensitive cation channel

Classification and nomenclature based in part on the International Union of Basic and Clinical Pharmacology/British Pharmacological Society; Alexander SPH; Mathie A; Peters JA; Veale EL; Striessnig J; Kelly E; Armstrong JF; Faccenda E; Harding SD; Pawson AJ; Sharman JL; Southan C; Davies JA; and CGTP Collaborators. The concise guide to pharmacology 2019/20: Ion channels. British Journal of Pharmacology 176(Suppl. 1): S142–S228, with the addition of recently identified ion channels, such as PAC, otopetrin 1, and TACAN.

Ion Channels

Voltage-gated channels

122

x4

x4

Kir

K2P

x4

Kv, TRP, KCa, KNa, CNG, HCN, CatSper

TPC

x1

Nav, Cav, NALCN

Ligand-gated channels

x2

x5

nACh, 5-HT3, ZAC, GABA A, Glycine

x4

Glutamate-gated

x2

Hv1

x4

x3

ASIC, ENaC, P2X

IP 3R, RyR

Fig. 1 Membrane topology of the pore-forming subunits of voltage-gated ion channels and ligand-gated channels and stoichiometry for pore formation (1 ¼ monomer, 2 ¼ dimer, etc.). The grayed area represents the membrane.

transient receptor potential (TRP) channels, calcium- and sodium- activated potassium channels (KCa, KNa), cyclic nucleotidegated (CNG) channels, hyperpolarization-gated (HCN) channels, and cation sperm-specific (CatSper) channels are tetramers of such 6TM subunits. Fig. 2A shows the structure of a prototypical voltage-gated Kþ channel, Kv1.2, with a transmembrane region, a large intracellular domain, and the pore formed by the assembly of four subunits (Long et al., 2005). In addition, KCa channels have internal regulator of Kþ conductance (RCK) domains at their C terminus. These domains bind a broad range of biological ligands, including calcium, sodium, and nucleotides, that modulate channel activity (Giraldez and Rothberg, 2017). CNG, HCN, and voltage-gated KCNH channels have cyclic nucleotide binding domains (CNBDs) in their C terminus and a C linker region that connects these CNBDs to the channel pore. In Kir channels, N and C termini interact to form a large cytoplasmic domain that includes binding sites for nucleotides, Gbg proteins, small regulatory molecules, and ions (Baronas and Kurata, 2014). Other VGICs have the same overall 24TM structure but are formed of either dimers of 12TM subunits (TPC channels) or single 24TM subunits in the case of Nav, NALCN, and Cav channels (Yu et al., 2005). The latter consist of four linked 6TM domains (I–IV) that have the basic architecture of 6TM channels (e.g. Kvs), with a voltage sensor region and a pore region. One exception is the voltage-gated proton channel Hv1. This channel does not have pore loops. Instead, the conduction pathway is located within its voltage-sensing domain, which includes the entire 4TM region of Hv1, much like voltage sensing in the 4TM voltage sensing phosphatase ci-VSP (Ramsey et al., 2006). In the latter, membrane depolarization is coupled to enzymatic activity. Hv1 channels form dimers through a coiledcoil interaction in the C terminus. However, each 4TM monomer has its own conduction pathway and Hv1 channels are two-pore channels (Koch et al., 2008; Lee et al., 2008; Tombola et al., 2008). Aside from Hv1, all VGICs have pores formed from four membrane-reentrant loops and a total of 8–24 transmembrane segments surrounding the pore regions.

1.06.3.2 1.06.3.2.1

Ligand-gated ion channels Neurotransmitter-gated channels

Based on topology, neurotransmitter-gated channels can be divided into glutamate-gated channels and pentameric cysteine loop (Cys-loop) channels. Glutamate-gated channels are composed of four subunits that form a central pore. Each subunit has four domains: two extracellular domains (an amino-terminal domain and a ligand binding domain), a transmembrane domain which includes three transmembrane alpha helices (TM1, TM3, and TM4) and a partial membrane-spanning helix (TM2) with a pore loop, and a cytoplasmic carboxyl-terminal domain (Fig. 1). The ligand binding domain of glutamate-gated channels is formed by two extracellular regions: a membrane-proximal region of the extracellular N terminus and the loop between TM3 and TM4 (Traynelis et al., 2010). Pentameric Cys-loop ligand-gated channels include the nicotinic acetylcholine (nACh), serotonin (5HT3), GABAA, and glycine receptor channels as well as the zinc-activated channel, ZAC. This family is named for a 13-amino acid loop within the

Ion Channels

(A)

(B)

Kv1.2

123

(C)

Glycine

Piezo 1

Side view

Top view

Fig. 2 Structures of channels belonging to the three major ion channels families, voltage-gated (Kv1.2), ligand-gated (glycine), and “other ion channels” (stretch-activated Piezo1). The dotted lines delimit the membrane. Structures determined by cryo-electron microscopy. Images from RCSB PDB (rcsb.org), PDB codes for Kv1.2, glycine, and Piezo1 are 2A79, 3JAE, and 3JAC, respectively; Du J, Lü W, Wu S, Cheng Y, and Gouaux E (2005) Glycine receptor mechanism elucidated by electron cryo-microscopy. Nature 526: 224–229; Long SB, Campbell EB and Mackinnon R (2005) Crystal structure of a mammalian voltage-dependent shaker family K þ channel. Science 309: 897–903; Ge J, Li W, Zhao Q, Li N, Chen M, Zhi P, Li R, Gao N, Xiao B and Yang M (2015) Architecture of the mammalian mechanosensitive Piezo1 channel. Nature 527: 64–69.

extracellular domain that is enclosed by a pair of cysteines forming a disulfide bond. Cys-loop channels have a pentameric structure with an N-terminal extracellular ligand binding domain, a pore-forming transmembrane domain with four membrane-spanning alpha helices, and a cytoplasmic domain. Amino acids in TM2 line the pore of Cys-loop channels (Lynch, 2004). The structure of the pentameric glycine receptor channel, as determined by cryo-electron microscopy (cryo-EM), is shown in Fig. 2B (Du et al., 2015).

1.06.3.2.2

Epithelial sodium channels and ATP-gated channels

The epithelial sodium channel family (also known as the degenerin family) comprises epithelial sodium channels (ENaCs) and acid-sensitive ion channels (ASICs). These channels are composed of three subunits, each consisting of two transmembrane segments, intracellular N- and C-termini, and a large extracellular loop connecting TM1 and TM2 (Fig. 1). ENaC channels are formed of three paralog subunits, a (or d), b, and g, arranged in a counterclockwise manner (Noreng et al., 2018). In contrast, ASIC subunits co-assemble in different combinations to form homo- or hetero-trimeric channels. The ionic pore of ENaCs and ASICs is formed by the three second transmembrane segments (Hanukoglu and Hanukoglu, 2016; Kellenberger and Schild, 2002). ATP-gated channels or P2X receptors have the same architecture, with three 2TM subunits forming homo- or heterotrimeric channels, intracellular N- and C-termini, as well as a pore formed by residues in the second transmembrane helices (Migita et al., 2001; Rassendren et al., 1997). Three orthosteric ATP binding sites are located at the interfaces of the three subunits extracellular domains (Hattori and Gouaux, 2012).

1.06.3.2.3

Calcium release channels

Inositol 1,4,5-trisphosphate receptors (IP3Rs) and ryanodine receptors (RyRs) are endo/sarcoplasmic calcium release channels gated by IP3 and calcium, respectively. The IP3R channels consist of homo- or hetero-tetramers. IP3R monomers have the topology of the tetrameric cation channels described earlier, with six transmembrane helices, of which TM5 and TM6 are linked by a membranereentrant loop forming the channel pore and TM1–4 form a peripheral bundle. They also have a large cytoplasmic domain with a ligand binding domain near the N-terminus (Baker et al., 2017). Ryanodine receptors have a similar topology. IP3R and RyRs have about 40% homology in their transmembrane domains. However, RyR subunits have a much larger cytoplasmic domain forming a “foot” structure, making RyRs the largest known ion channels with an overall molecular weight greater than 2 MDa.

1.06.3.3 1.06.3.3.1

Other channels Chloride channels

ClC chloride channel subunits assemble as hetero- or homo-meric dimers, which each subunit containing its own chloride conduction pathway. These channels are therefore “double-barreled.” Each subunit consists of 17 membrane-spanning helices and half

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helices, aB to aR, with connecting loops (Wang et al., 2019a). Amino acid residues from several transmembrane helices form the pores of ClC channels. In ClC1, aN, aF, and aD helices line the narrow region of the pore (Park and Mackinnon, 2018). Calciumactivated chloride channels from the anoctamin/TMEM16 family form homodimers with two independently-activated pores, one in each subunit (Jeng et al., 2016; Lim et al., 2016). Anoctamin subunits consist of 10 transmembrane helices with amino acid residues from TM3 through TM8 lining each pore (Dang et al., 2017). The cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel is the only known transporter-like protein that functions as an ion channel. CFTR belongs to the ATP-binding cassette (ABC) transporter family. Other ABC transporters utilize the chemical energy of ATP hydrolysis to fuel substrate transport against a chemical gradient. In the case of CFTR, ATP-induced conformational changes drive high rates of anion transport down their electrochemical gradients (Gadsby et al., 2006; Liu et al., 2017). CFTR channels are formed from a single polypeptide consisting of an N-terminal lasso motif, two transmembrane domains, each composed of six membrane-spanning a helices, two nucleotide-binding domains (NBD1 and NBD2), and a regulatory R domain. NBD1 and the R domain are in the cytoplasmic linker between the two transmembrane domains. NBD2 is in the cytoplasmic C terminus. CFTR channels have a single pore that is lined by amino acids from multiple transmembrane a helices (Liu et al., 2017; Linsdell, 2017). Finally, the proton-activated chloride channel PAC was recently identified through two independent RNA interference screens. The protein underlying PAC currents is predicted to have two transmembrane segments and intracellular N- and C-termini, with amino acid residues in TM2 lining the channel pore (Ullrich et al., 2019; Yang et al., 2019). The subunit composition of this channel has not been determined.

1.06.3.3.2

Cation channels

The Orai store-operated calcium channels are hexamers of subunits containing four transmembrane helices (TM1–4), with amino acid side chains from each TM1 lining the ionic pore (Cai et al., 2016; Yen et al., 2016). One of the helices, TM4, extends into the cytoplasm and plays a role in channel activation by the endoplasmic reticulum calcium sensor STIM1 (Hou et al., 2012). Otopetrin proton channels have a dimeric architecture with each subunit consisting of 12 transmembrane helices and 2 domains, each with 6TMs (Tu et al., 2018). Finally, NALCN is a voltage-independent, sodium leak channel with a predicted structure similar to Nav and Cav channel pore-forming subunits, consisting of four linked 6TM domains or 24 TMs overall and four membrane-reentrant loops between each TM5–TM6 segment (Lu et al., 2007).

1.06.3.3.3

Mechanosensitive channels

The mechanosensitive Piezo channels are structurally unrelated to any other protein and have a unique architecture. These channels resemble a propeller structure with three blade-like subunits arranged around a central pore (Fig. 2C) (Ge et al., 2015). Each subunit is formed of up to 40 transmembrane domains arranged partly in nine 4TM repeats that confer Piezo channels their blade structure and mechano-sensitivity (Saotome et al., 2018). TACAN (TMEM120A) is a recently identified pore-forming subunit of mechanosensitive channels involved in the transduction of painful mechanical stimuli (Beaulieu-Laroche et al., 2020). Its exact architecture has not been reported yet. But TACAN encodes a protein with 6 predicted transmembrane domains.

1.06.3.4

Auxiliary subunits

In addition to their pore-forming a subunits, some ion channels have auxiliary subunits that are critical to their function, expression, or sensitivity to biological and pharmacological modulators. ATP-sensitive channels (KATP), for instance, are formed by the assembly of four Kir a subunits (Kir6.1 or Kir6.2) and four sulfonylurea receptor subunits (SUR1, SUR2A, or SUR2B), proteins belonging to the ATP-binding cassette family. SUR subunits consist of 12 transmembrane segments and two intracellular nucleotide binding domains (NBDs) that form an NBD dimer. Cryo-EM structures of KATP channels show a central Kþ channel formed by four Kirs surrounded by four SURs arranged in a helical or quatrefoil fashion (Lee et al., 2017; Li et al., 2017b). SUR subunits have binding sites for ADP, an intracellular KATP activator, and several KATP channel openers and inhibitors. The a2d subunits of Cav channels play a major role in the trafficking and expression of these channels (Davies et al., 2007). These subunits consist of a single transmembrane domain, a large extracellular N-terminal domain, and a very small intracellular domain. The a2 and d proteins are products of a single gene that form disulfide-linked a2d subunits. Co-expressing a2d with the pore-forming subunit of Cav1 and Cav2 voltage-activated calcium channels increases whole-cell calcium currents, mainly due to an increase in the number of channels at the cell surface (Dolphin et al., 1999; Gao et al., 2000).

1.06.4

Functional properties of ion channels: Permeation and gating

1.06.4.1

Ion permeation

A key feature of ion channels is their ionic pore. They provide a path for charged ions to cross hydrophobic cellular membranes at transport rates close to the free diffusion through aqueous solutions. A region within the pore, the selectivity filter, defines which ions will be transported. Ionic selectivity can be extremely high or low to none, at least within cation- or anion-selective channels. Ion channel pores also provide binding sites for pore blockers. Selectivity is best understood for potassium channels, where high-resolution X-ray structures have helped model potassium ion permeation (Zhou et al., 2001). In these channels, the selectivity filter is formed by four strands (one per subunit) containing a conserved amino acid sequence (TVGYG) that provides carbonyl oxygens for the coordination of permeating ions (Heginbotham

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125

et al., 1994). Potassium ions permeate through that filter by a knock-on mechanism where two or three Kþ ions separated by one water molecule move in a single file (Morais-Cabral et al., 2001). Multiple occupancy of Kþ ions in the pore creates enough electrostatic repulsion to allow fast ion translocation. Selectivity for Kþ over Naþ ions is thought to arise from multiple factors: ion hydration free energy, channel conformational flexibility, and kinetics (Nimigean and Allen, 2011; Roux, 2017). Permeation of calcium through voltage-gated calcium channels occurs with a similar knock-on mechanism. Extracellular sodium concentration is about 70-fold greater than calcium concentration and both ions have diameters around 2 Å. How can calcium channels be over 1000-fold more selective for calcium over sodium ions and calcium transport rates  500-fold greater than sodium transport rates? Calcium channels have a selectivity filter with a high field strength site containing negatively charged glutamate residues. They also have a ring of negatively charged amino acids in their outer vestibule that is critical for calcium selectivity. The X-ray structure of a Cav channel shows calcium binding sites sequentially occupied by Ca2þ ions as they move through the pore. The high affinity for calcium and the same knock-on electrostatic effect as Kþ channels allows for selectivity and high flow rates (Tang et al., 2014). The nicotinic acetylcholine receptors are non-selective cation channels with three groups of negatively-charged residues in their second transmembrane segments forming anionic rings in the extracellular, membrane, and intracellular regions of the pore. Chloride channels from the ClC family are homodimers with a pore in each monomer. Anion selectivity and permeation also derive from an electrostatically-favorable pore with three anion binding sites (Park and Mackinnon, 2018; Park et al., 2017).

1.06.4.2

Gating mechanisms

A key determinant of the physiological effects of ion channel activity is the length of time a channel conducts ions. Ion channels exist in at least two conformational states, an open (activated) state where the pore of the channel is open and ions can flow down their electrochemical gradient and a closed (deactivated, inactivated, or desensitized) state, where the channel pore is closed, and no ion transport takes place. Transitions from a closed to an open state as well as the time a channel spends in the open state are determined by various intrinsic and extrinsic factors, depending on the channel’s structural features and localization at the cell or wholeorganism level. Ion channel gating refers to the opening and closing of ion channels. The next sections describe three classes of regulatory gating mechanisms, physical, chemical, and biological, and their structural bases. Additionally, channels may be opened by multiple mechanisms. Examples of such “polymodal” channels are TRP channels that may be activated by a combination of changes in temperature, voltage, and small biological ligands. Fig. 3 summarizes the various types of activation mechanisms.

1.06.4.2.1

Physical factors: Voltage, temperature, and stretch

A channel’s physical environment can directly affect its gating behavior, converting physical stimuli into electrical and chemical signals. Voltage-gated ion channels are sensitive to changes in cellular transmembrane voltages. Most VGICs open in response to membrane depolarization, with the threshold for activation and the steepness of the voltage dependence of activation varying

Voltage Temperature Ions

Small molecules Mechanical stress

Receptor pathways Fig. 3 Ion channel gating modulators. Physical (mechanical stress, temperature, voltage), ligand (ions, small molecules), and biological (small molecules, pathways). Ion channel image from RCSB PDB (rcsb.org), PDB code for Kv1.2 is 2A79; Long SB, Campbell EB, Mackinnon R (2005) Crystal structure of a mammalian voltage-dependent shaker family K þ channel. Science 309: 897–903.

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between channel types. For example, Cav3 calcium channels activate at relatively low voltages, –70 mV and higher, compared to Cav1 and Cav2 channels, which activate at –30 mV and above (Hille, 2001). How do channels sense changes in membrane voltage? In VGICs, sensitivity to transmembrane voltage is derived primarily from a single transmembrane domain containing positivelycharged amino acid residues (e.g. arginines and lysines). In Kv channels, it is the fourth transmembrane segment or S4. Similarly, in Cav and Nav channels, it is the fourth segment of each of the four channel domains. Depolarizing the membrane causes an outward movement of these domains that is coupled to the opening of the channel pore (Mannuzzu et al., 1996; Yang and Horn, 1995). Interestingly, hyperpolarization-activated channels only open in responses to hyperpolarization despite having positively-charged voltage-sensing regions (Gauss et al., 1998; Ludwig et al., 1998; Santoro et al., 1998). The recent determination of the HCN1 channel structure by cryo-EM has shed some light into the mechanism of this reverse polarity voltage-dependent gating behavior (Lee and Mackinnon, 2017). HCN1 channels have a long S4 helix that interacts with an intracellular domain controlling pore opening and closing. At resting voltages, the S4 helix is in a relative “depolarized” position and stabilizes the closed pore. Hyperpolarization causes an inward movement of the S4 helix which in turns disrupts stabilizing interactions and causes pore opening. In some VGICs, sustained depolarization causes open channels to inactivate quickly, on a millisecond timescale, effectively stopping ion flow through the channel pore. In Kvs and Navs, depolarizations cause occlusion of the open pore by intracellular regions through a “ball and chain” mechanism, the ball representing a tethered intracellular domain that moves to occlude the inner vestibule of the pore (Hoshi et al., 1990; Stuhmer et al., 1989; Vassilev et al., 1988). Channels recover from inactivation following repolarization of the cell membrane and movement of this intracellular domain away from the pore. In addition to this fast inactivation, VGICs can undergo slow inactivation over prolonged or repeated depolarizations, on a second to minute timescale. Slow-inactivated channels will require more time at negative voltages to recover and effectively represent a memory of previous excitation. The structural basis for slow inactivation is less defined than for fast inactivation. Conformational changes in the outer region of the pore may underlie the loss of ion conduction during slow inactivation (Chatterjee et al., 2018; Silva, 2014). Changes in temperature are another example of physical mechanism for gating. A subset of TRP channels, thermo-TRPs, are activated by changes in temperature. Most ion channels have gating Q10 values of 3–5, but the Q10 of thermo-sensitive TRP channels is usually > 10 (Brauchi et al., 2004; Clapham, 2003). TRPM8 and TRPA1 are activated by cooling, at temperatures  25
C and  17
C, respectively. TRPM8 agonists such as menthol and icilin elicit cold sensations, consistent with a role in cold temperature activation for TRPM8. Furthermore, mice lacking TRPM8 lose most of their sensitivity to cold (Colburn et al., 2007; Dhaka et al., 2007). A role for TRPA1 in cold sensing is less clear (Bautista et al., 2006; Kwan et al., 2006). On another hand, TRPV1–4 and TRPM2–5 channels are activated at warm to noxious hot temperatures, from  15
C for TRPM4 and TRPM5 to  52
C for TRPV2 (Lamas et al., 2019). How do these channel sense changes in temperature? Truncations of the C terminal of TRPV1 showed that the distal half of this domain was essential for temperature activation (Vlachová et al., 2003). In addition, swapping C termini between TRPM8 and TRPV1 conferred cold activation to TRPV1 and heat activation to TRPM8 (Brauchi et al., 2006). Random mutagenesis of TRPV3 identified five mutations in the S6 helix that are required for heat activation (Grandl et al., 2008). Forming TRPV3 chimeras with 28 amino acids of the temperature-insensitive TRPV2 S6 and pore region abolished temperature activation but not ligand activation. Thus, mechanisms for temperature sensing may differ between TRP channel types. In addition, three members of the two-pore potassium channel family are sensitive to warm temperatures, TREK1, TREK2, and TRAAK, with thresholds for activation ranging from 22 to 31
C (Kang et al., 2005; Maingret et al., 2000). Partial deletion of the C terminus of TREK1 or replacing that region with the C terminus of the temperature-insensitive TASK1 channels also greatly reduces channel activation by temperature (Maingret et al., 2000). Recent structural analyses of various temperaturesensitive channels suggest coupling between the C terminal domain and the cytoplasmic gate of the channel (Arrigoni and Minor Jr., 2018; Yin et al., 2018). Finally, some channels are sensitive to mechanical forces in their immediate environment, such as membrane stretch or cell swelling. Various ion channels have been described as mechanically-activated channels, including members of the TRP, K2P, degenerin/epithelial sodium channel, and Piezo families (Ranade et al., 2015). Two fundamental gating mechanisms have been described for mechanosensing: direct “force from lipid” gating and “force from filament” or tethered gating, where accessory proteins, the cytoskeleton, or the extracellular matrix act as force transducers (Ridone et al., 2019). In “force from lipid” gating, channels are directly sensing changes in membrane tension and mechano-sensitive gating can be recapitulated with purified channels reconstituted into liposomes in the absence of all other cellular components. The K2P TRAAK and TREK1 channels are directly sensing changes in lipid tension (Brohawn et al., 2014). In contrast, TRP channels seem to require a tether or second messengers to transduce mechanical stimuli into electrical signals (Nikolaev et al., 2019). At least two studies suggest Piezo1 channels are directly gated by “force from lipid” mechanisms (Cox et al., 2016; Syeda et al., 2016a). Piezo2 channels may require a tether but additional studies are required to confirm lipids cannot directly activate these channels (Hu et al., 2010). The molecular mechanisms underlying mechano-sensitivity are not well defined. The recently published structures of Piezo1 and Piezo2 have shed some light on how these channels transduce mechanical force into electrochemical signals. Piezo1 and Piezo2 form huge protein complexes, homo-trimers of 38 transmembrane subunits for Piezo1 (Wang et al., 2019b; Saotome et al., 2018). Each Piezo1 monomer forms a blade-like structure and the three blades assemble around a central pore (Fig. 2C). Mechano-transduction is proposed to result from a lever-like mechanism where the blades serve as mechanosensors and a beam pivots around two residues in a lever-like movement for coupling to the central pore (Zhao et al., 2019).

Ion Channels 1.06.4.2.2

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Chemical or ligand-gating: Ions, neurotransmitters, and intracellular signaling molecules

Ion channels may be activated by transient changes in ligand concentrations. Physiological ligands for ion channels span a wide range of molecules, from simple ions (e.g. protons, calcium, sodium) to metabolites (e.g. ATP, IP3, cyclic nucleotides) and transmitters (e.g. GABA, glutamate, acetylcholine, glycine, and serotonin). Ion channel ligands may lead to channel opening though binding to intracellular or extracellular sites. Also, they may be cooperative or not. 1.06.4.2.2.1 Transmitter-gated channels Several classes of ligand-gated channels are directly activated by transmitters released at synapses or at the neuromuscular junction. The location of the specific transmitter-gated channel (pre- or post-synaptic) as well as the charge of the ion being transported by that channel (anion or cation) determine the overall excitatory or inhibitory effect of a given neurotransmitter. Acetylcholine (Ach), serotonin (5HT), glutamate, and ATP activate cation-selective channels while GABA and glycine open anion-selective channels. Transmitter-gated channels belong to three classes: pentameric Cys-loop channels (ACh, 5HT3, GABAA, glycine), glutamate-gated channels (AMPA, kainate, and NMDA), and purinergic ATP-gated channels (P2X1–7). In all cases, transmitters bind to extracellular sites on the pore-forming subunits of these channels and cause channel opening though specific conformational changes in their extracellular and transmembrane domains. Pentameric ligand-gated ion channels (pLGICs) have two to five orthosteric sites per channel, depending on their subunit composition, and channel opening kinetics follow an allosteric kinetic model. These channels can open in the absence of ligand, although with a very low open probability. Ligand binding to one site increases the channel open probability and each successive binding of ligand further favors channel opening. The Monod-Wyman-Changeux model of allosterism has been widely applied to study pLGIC gating and remains pertinent in the era of X-ray and cryo-EM structural biology (Einav and Phillips, 2017; Gielen and Corringer, 2018; Steinbach and Akk, 2019). In addition, akin to fast and slow inactivation in VGICs, prolonged exposure to a ligand causes desensitization of LGICs. Desensitized channels do not conduct ions and represent a protective mechanism over excitotoxicity or prolonged depression of synaptic activity (Papke et al., 2011). In pLGICs, transmitters bind to the N terminal extracellular domain (ECD), at the interface between adjacent subunits. Activation of glutamate and P2X channels involves ligand-induced closure of the orthosteric site in a clamshell-like manner and ECD transduction of this conformational change to the transmembrane and pore regions. In trimeric P2X3 channels, ATP binding between two subunits ECDs induces binding cleft closure and, through structural coupling, an outward flexing of the membrane proximal region of the ECDs. This movement then pulls on the transmembrane segments and an iris-like rotation of TM2 opens the channel pore. It also causes the formation of a cytoplasmic cap which anchors the cytoplasmic surfaces of the transmembrane domains and provides cytoplasmic fenestrations through which ions exit the pore. Transition from an open to a desensitized state involves unfolding of the cytoplasmic cap, unwinding of TM2, and pore closure. Finally, cap stability determines the rate of channel desensitization (Hattori and Gouaux, 2012; Mansoor et al., 2016). Glycine activation of glycine channels involves a similar, iris-like anticlockwise rotation of TM segments around the pore axis leading to channel opening (Du et al., 2015). 1.06.4.2.2.2 Ion-gated channels Local acidosis during high metabolic demand associated with inflammation, injury, ischemia, and cancer can activate protonactivated channels such as ASICs and proton-activated chloride channels (PACs). The midpoint of the pH dependence of activation is  6.5 for ASIC1a and ASIC3,  6.1 for ASIC1b,  4.5 for ASIC2a, and  5.0 for PAC (Yang et al., 2019; Kellenberger and Schild, 2015; Wemmie et al., 2013). ASIC channels are activated by protons through multiple protonation sites in their extracellular acidic pocket and “palm” regions, and rapidly desensitize in the continued presence of protons (Vullo et al., 2017). In contrast, intracellular acidosis activates Hv1 proton-gated proton channels for the rapid return to higher intracellular pH (pHi). Proton sensing in these channels may occur through a “counter-charge” mechanism where the protonation of intracellular amino acid residues at low pHi favors channel opening and lower extracellular pH stabilizes channels in a closed state through protonation of externally accessible acidic groups (Decoursey, 2018). Intracellular free calcium concentrations are very low (100 nM in mammalian skeletal muscle) and tightly controlled by multiple cellular mechanisms, including calcium pumps, channels, and transporters. However, local calcium concentrations can temporarily reach high levels and activate signaling proteins such as ion channels. In large-conductance calcium-activated potassium channels (BKCa), calcium binds to “calcium bowls” located in the intracellular C terminus of each of the four pore-forming subunits of the channel. Each bowl consists of five negatively-charged aspartic acid residues (Schreiber and Salkoff, 1997; Yuan et al., 2010). BKCa channels may open in any state of occupancy of the calcium bowls, from zero to four, following depolarization. However with increased calcium bowl occupancy, the Hill coefficient for activation by calcium increases and the apparent affinity for calcium increases, indicating cooperative activation by calcium (Niu and Magleby, 2002). Potassium channels that are activated by intracellular sodium (KNa) were first described in cardiomyocytes (Kameyama et al., 1984). They have since been reported in a variety of other tissues, including the brain, the kidney, and skeletal muscle. Two genes encode KNa channels, KCNT1 and KCNT2, also known as Slack and Slick. Both channels are activated at intracellular sodium concentrations greater than 20 mM and have half-maximal activations of 40 mM (Slack) and 89 mM (Slick). Slack channels require Naþ for activation while Slick channels have some basal activity in the absence of Naþ (Bhattacharjee et al., 2003). Normal intracellular sodium concentration in mammalian skeletal muscle is around 12 mM and local increases in concentration (e.g. due to Naþ channel activation) may activate KNa channels (Hille, 2001). Sodium binding to a C terminal RCK domain mediates KNa channel opening (Zhang et al., 2010). Finally, extracellular zinc ions activate the pentameric Cys-loop ZAC channels at concentrations above

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30 mM and with an EC50 of 540 mM. While normal free zinc concentrations are in the nanomolar range, proximity to the release site of secretory vesicles containing high zinc concentrations (> 1 mM) could activate ZAC channels (Davies et al., 2003; Peralta and Huidobro-Toro, 2016). 1.06.4.2.2.3 Intracellular signaling metabolites gated channels Several ion channels are gated by changes in intracellular signaling molecules, including nucleotides and inositol lipids. KATP and CFTR channels have auxiliary subunits (KATP) or pore forming subunits (CFTR) that have nucleotide binding domains (NBDs) and their gating is controlled by nucleotides. KATP channels, as mentioned earlier, are highly regulated by the metabolic state of cells. Pancreatic b cells respond to rising blood glucose by increasing oxidative metabolism, leading to increased ATP production in the mitochondria and an elevated ATP/ADP ratio in the cytoplasm. ATP inhibits KATP channels while ADP activates them. ATP acts through direct binding to the Kir subunit and non-competitive inhibition of PIP2 binding, a ligand required for channel opening. ADP binds to the SUR subunit NBD dimer and has been proposed to allosterically inhibit ATP binding, therefore relieving inhibition by ATP and opening KATP channels (Lee et al., 2017). The cystic fibrosis transmembrane regulator (CFTR) chloride channel is, like the SUR subunit of KATP channels, a member of the ATP binding cassette family and binds ATP and ADP. Binding of ATP to both NBDs of CFTR triggers NBD dimerization and channel opening. However, unlike ligand-gated channels, CFTR’s ligand must be hydrolyzed during the gating cycle to mediate channel closure. This explains why Mg2þ ATP and not ATP alone is required for CFTR gating, as Mg2þ is an essential co-factor for ATP hydrolysis. In addition, mutating residues essential for ATP hydrolysis or co-application of nonhydrolyzable ATP analogs such as AMP-PNP or pyrophosphate lock CFTR in long open states (Hwang et al., 1994). ADP, on the other hand, inhibits CFTR activity. Binding and mutagenesis studies show that ADP both competes with ATP for NBD binding and is a substrate for NBD2 reverse adenylate kinase activity (ADP þ ADP / ATP þ AMP). Adenylate kinase activity does not activate channels as ATP production will be limited to NBD2, NBD1 is occupied by ADP, and both NBD1 and NBD2 binding are required for channel opening (Randak and Welsh, 2005). Cyclic nucleotide-gated (CNG) channels are another family of channels that is modulated by intracellular metabolites. CNG channels are expressed in photoreceptor cells and olfactory sensory neurons where photon absorption and odorant binding, respectively, produce changes in intracellular nucleotides. CNG channels transduce these signals into fast, electrical signals to the brain. In photoreceptor cells, light activation of rhodopsin leads to activation of phosphodiesterase, a decrease in cGMP levels, CNG channel closure, and cell membrane hyperpolarization. In olfactory neurons, odorant activation of olfactory receptors leads to activation of adenylate cyclase, production of cAMP, and activation of CNG channels. CNG channel opening depolarizes cells, increases intracellular calcium, and activates calcium-activated chloride channels, further depolarizing cells and amplifying the odorant stimulus. CNG channels differ in their sensitivity to cyclic nucleotides. Rod photoreceptor CNGA1 channels can be fully activated by cGMP but cAMP causes little activation of these channels. In contrast, olfactory CNGA2 and CNGA4 channels are fully activated by either saturating cAMP or cGMP concentrations. Cryo-EM structure of the C. elegans TAX-4 CNG channel shows cGMP binding to the C terminal nucleotide binding domain (CNBD) leads to conformational changes in the C linker domain that connects the CNBD to the S6 transmembrane segment and the channel pore. In the liganded state, C linkers from each subunit form a tight gating ring that causes S6 segments to splay wide and the pore to be in the open conformation. Upon unbinding of cGMP, the gating ring loosens or dissociates from the gating regions, S6 segments constricts, and channels close (Li et al., 2017a). Hyperpolarization-gated cation (HCN) channels also have CNBDs and are sensitive to cyclic nucleotides. However, their primary gating mechanism, as described earlier, is voltage and activation by hyperpolarization. Cyclic AMP binding to HCN channels accelerates channel opening, shifts activation to less hyperpolarized voltage, and increases maximal currents (James and Zagotta, 2018). The signaling molecules phosphatidyl inositol 4,5-bisphosphate (PIP2) and inositol 1,4,5-triphosphate (IP3) can also directly gate ion channels. Both are derived from the membrane lipid phosphatidyl inositol (PI) and act as intracellular signaling molecules through modulation of multiple proteins. PIP2 is found primarily in the inner leaflet of the plasma membrane and is formed by successive phosphorylation reactions: (1) PI phosphorylation by PI4 kinase to form PI4P and (2) PI4P phosphorylation by PI4P5 kinase a to form PIP2. Cleavage of the phospholipid at the phosphate group by phospholipase C then forms IP3 and DAG. PIP2, by its cellular localization, is well positioned to modulate membrane proteins such as ion channels. It is required for Kir potassium channel activity and it also modulates the activity of many other ion channels, including some K2P channels and TRP channels (Hille et al., 2015). In Kir channels, PIP2 binds to a conserved site consisting of TM1 and TM2 basic residues proximal to the channel pore and intracellular domain, stabilizing the open conformation. A specific arginine residue in that region (R188 in Kir1.1) is key in conferring PIP2 sensitivity to Kirs (Hansen, 2015; Huang et al., 1998; Martin et al., 2017). The IP3 receptor (IP3R) is an IP3-gated ion channel that releases calcium from the endoplasmic reticulum. Receptor tyrosine kinase or 7TM receptor activation of phospholipase C leads to increases in intracellular IP3 levels and activation of IP3Rs. The IP3R has a very large,  2200 amino-acid cytosolic domain that consists of five subdomains: an N-terminal suppressor domain, an IP3 binding area, and three alpha helical regions. The IP3 binding site is quite distant from the channel area, located more than 70 Å from the pore (Fan et al., 2015). How can binding of a small signaling molecule like IP3 induce opening of a channel from that distance? X-ray structural studies of IP3R wild-type and mutant cytosolic domains show that IP3 binding triggers channel opening through long-range conformational changes involving the first and third N-terminal helical regions and a leaflet region facing both the cytosolic N-terminal domain and the channel area (Hamada et al., 2017). Cryo-EM structures of apo and IP3-bound tetrameric IP3Rs show a network of interactions between neighboring N- and C-termini enables the propagation of conformational changes and channel opening initiated by IP3 binding (Paknejad and Hite, 2018).

Ion Channels 1.06.4.2.3

129

Biological pathways

In addition to the intracellular and extracellular ligand-gating mechanisms described earlier, ion channel opening and closing may be highly regulated by signal transduction pathways through direct protein-channel interactions and post-translational modifications. Those are distinct from a subunit–auxiliary subunit interactions. A subgroup of inwardly-rectifying potassium channels, Kir3, is directly activated by heterotrimeric GTP-binding (G) proteins and are also referred to as GIRKs or G protein-gated inwardly-rectifying potassium channels. This family has four members (Kir3.1–3.4) that can form homo- or hetero-tetramers. Kir3 channels mediate inhibitory signals in the brain and the heart following activation of Gi/o proteins. G protein specificity is mediated through association of the helical domain of Gi/o a subunits with the C terminal helix of Kir3 channels (Kano et al., 2019). Activation of the G protein then induces partial or total dissociation of Gbg subunits that in turn bind at the interface of two channel subunit cytoplasmic domains, causing a rotation of these regions and an opening of the inner helix gate (Whorton and Mackinnon, 2013). Kir channel opening causes potassium influx and repolarization or hyperpolarization of the cells, effectively inhibiting excitation. Another example of direct protein gating is the activation of the cell surface Orai calcium channel by stromal interaction molecule 1 (STIM1), an integral membrane protein of the endoplasmic reticulum (ER), when ER calcium is depleted, allowing extracellular calcium influx and refilling of internal calcium stores. When ER calcium is high, calcium is bound to the luminal EF domain of STIM1 and STIM1 is in a resting state with its cytoplasmic domain folded. Upon ER calcium depletion, calcium dissociation from STIM1 causes an unfolding of the STIM1 cytoplasmic domain and binding of the C-terminal STIM-Orai Activating Region (SOAR) to the Orai channel N termini. Cryo-EM and X-ray studies concur on allosteric activation of Orai through SOAR binding to the TM4 C terminus region of Orai. However, the exact conformational changes involved in STIM1 activation of Orai are not defined (Hou et al., 2018; Zhou et al., 2019). Finally, excitation-contraction (EC) coupling in skeletal muscle occurs through a direct interaction between plasma membrane Cav1.1 channels and sarcoplasmic reticulum RyRs. EC coupling in the heart, in contrast, involves calcium ions flowing through plasma membrane Cav channels and no direct protein activation. In skeletal muscle, Cav1.1 and RyR channels are in a structure called a tetrad, where four Cav1.1 channels associate with the four cytoplasmic domains of a single tetrameric RyR (Rios et al., 2019). Cav1.1 channels a subunits act as the voltage sensors in EC coupling (Hernandez-Ochoa and Schneider, 2018). Interactions with signaling proteins also include various post translational modifications (PTMs) of ion channels. In general, these modifications alter channel gating and do not directly gate channel activity. The main PTMs modulating channel gating include phosphorylation, lipidation, S-acylation, and glycosylation (Laedermann et al., 2015; Levitan, 1994; Shipston, 2011, 2014). An example of key regulatory mechanism by PTM is the phosphorylation of CFTR channels by PKA. However, this modification is necessary but not sufficient for CFTR channel activity. The intracellular regulatory R domain of CFTR is located between the two transmembrane domains and NBDs of CFTR. It is about 150 residues long and has multiple potential phosphorylation sites, five of which have been shown to be phosphorylated in vivo (Hwang and Kirk, 2013). In the resting unphosphorylated state, this domain inhibits channel activity by preventing NBD dimerization. Activation of PKA through stimulatory G protein pathways leads to phosphorylation of the R domain. This stabilizes that domain away from the NBDs and allows ATP-induced NBD dimerization (Zhang et al., 2018).

1.06.5

Ion channel pharmacology

A wide variety of ion channel modulators have been described and drugs targeting ion channels are a major class of therapeutics, representing 18% of drugs (Santos et al., 2017). In addition to small molecule ligands, ion channel modulators include peptides and antibodies (Wulff et al., 2019). Nature is rich in ion channel modulators, plant- or animal-derived peptides or small molecules that affect channel function. Classes of ion channel modulators comprise (1) inhibitors, molecules that directly block the channel pore or that indirectly reduce channel activity, (2) agonists, molecules that enhance ion channel activity through direct activation (pore or ligand binding site) or indirect effects, and (3) trafficking modulators, ligands that increase (molecular chaperones) or reduce channel expression through direct binding.

1.06.5.1

Ion channel inhibitors

Inhibition of channel function may result from direct block of the ion permeation pathway, by occlusion of the pore through binding to the extracellular outer vestibule of the channel or through binding deeper in the pore. It may also be a consequence of effects on a channel’s gating mechanisms. Finally, for LGICs, inhibition may occur by competitive on non-competitive inhibition of native ligand binding.

1.06.5.1.1

Pore blockers

Pore blockers represent a large class of ion channel inhibitors. A subset of pore blockers physically plugs channel pores from the extracellular side, binding to the outer vestibule of the pore. These blockers are typically large molecules, toxins derived from bacteria, algae, or animals such as spiders, cone snails, snakes, and scorpions. These organisms evolved to produce these channel toxins to deter predators or to capture prey. Targeting ion channels is an effective strategy to paralyze, kill, or inflict pain to a victim organism. Examples of outer pore blockers include m-conotoxins found in the venom of the fish-hunting marine snail Conus geographus. These peptides allow cone snails to rapidly immobilize their preys (Green and Olivera, 2016). They bind to the outer

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Ion Channels

pore of voltage-gated sodium channels and totally occlude the permeation pathway with a 1:1 stoichiometry and in an all-or-none fashion. They consist of 22 amino acids with six cysteines forming three disulfide bridges that provide a very rigid structure for these peptides. High affinity block by m-conotoxins results from the sum of numerous relatively weak pore-toxin interactions. Positivelycharged residues in the toxins play a critical role in binding as neutralization or reversal of these charges greatly reduces channel block (Becker et al., 1992; Sato et al., 1991). The same snail species also produces u-conotoxin GVIA, a toxin which selectively binds to and occludes the pore of voltage-gated Cav2.2 calcium channels. GVIA is a 27-amino acid peptide that also contains three disulfide bridges. Like m-conotoxins, positively-charged residues in GVIA play a role in binding to a negatively charged pore (Lew et al., 1997). This blocking mechanism is common to many peptide toxins and several ion channel families. Charybdotoxin and agitoxin are peptide toxins from the venom of the scorpion Leiurus quinquestriatus and target the pore of voltage-gated potassium channels. In these toxins, a lysine amino group functions as a Kþ ion mimic to block the pore and the peptide occludes the pore vestibule (Miller, 1995). Relatively smaller molecules that bind deeper into channel pores and block ion flow represent another group of pore blockers. These molecules are natural products as well as synthetic compounds, including several routinely-used drugs. For instance, the venom of several insects contains polyamine toxins that bind to the pore of glutamate-gated channels and nicotinic acetylcholine receptors, thereby rapidly paralyzing preys. These toxins include wasp philanthotoxins, tarantula spider argiotoxins, and the joro spider toxin JSTX-3 (Kachel et al., 2018). All are fast, slowly-reversible to irreversible blockers that require the channels to be opened by agonists first in order to access the channel pore. So, they act as non-competitive channel blockers. It was proposed that the polyamine chain of these toxins interacts with negatively-charged or neutral amino acid residues lining the pore of cationic LGICs (Mellor et al., 2003). Synthetic, small molecule pore blockers comprise the commonly-used local anesthetics lidocaine, bupivacaine, and tetracaine, that exert their action primarily through the blockade of Nav channels. These small molecules bind to the inner pore, which includes the S6 segments and pore loops. Most local anesthetics are amine compounds with net charges ranging from zero at basic pH to þ 1 at acidic pH, as the amine group becomes protonated. They enter the channel pore only when the channels are open and accumulate with repetitive opening of the channels, acting as use-dependent blockers (Catterall and Swanson, 2015). The antiepileptic drugs diphenylhydantoin, carbamazepine, and lamotrigine and the antiarrhythmic drugs, quinidine, procainamide, and flecainide act of Nav channels in a similar manner. In addition to being frequency-dependent, all these blockers are voltage-dependent and only block sodium channels during depolarizations and fast firing, making those drugs relatively safe and effective. Use- and voltage-dependent pore block of voltage-gated calcium channels is also an effective therapeutic approach. Phenylalkylamine calcium channel blockers such as verapamil and benzothiazepines like diltiazem act at receptor sites in the pore of Cav channels and block them, much like Nav drugs block sodium conductance (Catterall and Swanson, 2015).

1.06.5.1.2

Pore modulators

In addition to direct pore block, ion conduction may be inhibited by ligands binding to the turret region of ion channels, an area peripheral to the pore region. Several toxins bind to the turret region of potassium channels and block ion flow without physically occluding the pore (Xu et al., 2003; Verdier et al., 2005; Zhang et al., 2003). Verdier et al. (Verdier et al., 2005) identified a novel pharmacophore for peptide toxins binding to the turret of Kv channels, consisting of a ring of positive charges. Recent studies have shed some light on how turret binding could translate into channel block. Conkunitzin-S1, a peptide toxin isolated from the venom of the cone snail Conus striatus, was shown to bind to the turrets of Kv1.2 channels and target hydrogen bonds that regulate water permeation into the peripheral cavities that surround the pore. The altered water occupancy was proposed to trigger a physical collapse of the external pore region analogous to slow inactivation (Karbat et al., 2019).

1.06.5.1.3

Gating modifiers

Another major mechanism for inhibition of channel function is to alter gating mechanisms. For voltage-gated channels, this may be achieved by limiting the movement of the voltage sensors or by inhibiting the coupling between voltage sensors and pore opening. Hanatoxin (HaTx), a peptide toxin isolated from the venom of the tarantula spider Grammostola spatulata, was one of the first gating modifiers described (Swartz and Mackinnon, 1997a). HaTx shifts the voltage dependence of activation of Kv2.1 channels towards more positive voltages, effectively increasing the energy barrier for channel opening. It also increases the rate of channel closure or deactivation during depolarizations. HaTx and another gating-modifier from tarantula venom, grammotoxin, bind to the same site on these channels, in the extracellular loop between S3 and S4 (Li-Smerin and Swartz, 1998). Gating-modifying toxins of Nav and Cav channels bind to analogous sites, the S3–S4 loops in domain IV of Nav1.2 channels (a-scorpion toxin and sea anemone toxin receptor sites) and in domain IV of Cav2.1 channels (agatoxin receptor site). S3–S4 gating modifier sites in these channels are composed of two hydrophobic residues (F ¼ I, L, M, or F) and one acidic residue (E) in the sequence FFXXE. The toxins acting at this site are thought to trap the S4 voltage sensors in an inward position, thereby preventing channel opening (Winterfield and Swartz, 2000). Small molecules may also act as gating modifiers. McCormack and colleagues first described a class of small molecule channel inhibitors that interfere with Nav channel gating (Mccormack et al., 2013). ICA-121431 is a potent and selective inhibitor of human Nav1.1 and Nav1.3 channels (IC50s  23 nM vs. 240 nM for Nav1.2 and  10 mM for all other Nav channel tested) and the structurally related arylsulfonamide PF-04856264 is a selective human Nav1.7 channels inhibitor (IC50 ¼ 28 nM vs.  10 mM for Nav1.3 and Nav1.5). Chimeras and single-point mutations showed this class of Nav inhibitors interacts with the voltage sensing domain (S1–S4) of domain IV of Nav channels, with extracellularly-facing S2 and S3 amino acids determining subtype selectivity. These compounds bind to and stabilize the inactivated state of Nav channels, resulting in a reduction of channel open time. Since this

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131

first report, additional Nav1.7-selective arylsulfonamides have been developed such as PF-05089771, a former clinical compound for pain (Alexandrou et al., 2016; Mcdonnell et al., 2018; Theile et al., 2016). Gating modifiers have also been reported for ligand-gated channels. Mambalgins are peptides isolated from the venom of the black mamba snake Dendroaspis polylepis polylepis. They inhibit ASIC channels and have strong analgesic activity in vivo. They bind to the closed and inactivated state of ASIC channels and inhibit their opening by protons, shifting the ASIC1a pH for half-activation from 6.4 to 5.6 and therefore act as gating modifiers (Diochot et al., 2012). Mambalgins bind to an acidic pocket in the extracellular domain of ASIC channels and trap the pH sensor so that protons cannot open ASIC channels (Salinas et al., 2014). Psalmotoxin 1 (PcTx1), a peptide found in the venom of the tarantula Psalmopoeus cambridgei, is another gating modifier inhibitor of ASIC channels. This peptide has analgesic properties and is neuroprotective in rodent models of pain and stroke, respectively. However, its activity is isoform dependent. PcTx1 is an inhibitor of rat ASIC1a homomers and ASIC1a-containing heteromers (ASIC1a/2b and ASIC1a/2a) but potentiates the activity of ASIC1b homomers. Electrophysiology and binding studies have shown that PcTx1 binds to cysteine-rich regions of the extracellular domain of rat ASIC1a channels (Salinas et al., 2006). Structural studies have revealed PcTx1 binds to chicken ASIC1 channel in a bimodal fashion, with hydrophobic residues of the toxin interacting with the palm region and basic residues binding to carboxylic side chains in the acidic ligand pocket (Dawson et al., 2012). The differential activity of this toxin for ASIC1 isoforms is thought to result from differences in state-dependence of binding. PcTx1 binds to ASIC1a in the desensitized and open state, promoting channel desensitization, and it binds to ASIC1b in the open state, stabilizing channel opening (Chen et al., 2006). Finally, modification of channel gating may result from drug binding to an auxiliary subunit. ATP-sensitive potassium channels (KATP) are the targets of the anti-diabetic sulfonylurea drugs glibenclamide and tolbutamide. Both drugs are allosteric inhibitors of KATP channels and bind to the sulfonylurea receptor (SUR) auxiliary subunits to prevent channel opening. Cryo-EM studies show glibenclamide bound to SUR1 stabilizes the channel in a closed conformation through intracellular loops located between TM5 and TM6 of each SUR1 subunit. These loops prevent the movement of Kir6.2 C terminal domains, thereby inhibiting channel opening. When SUR1 subunits are in an active conformation, the glibenclamide binding sites are structurally blocked or disrupted, because glibenclamide and Mg2þ ADP, a channel activator, have negative functional cooperativity (Li et al., 2017b; Hambrock et al., 2002).

1.06.5.1.4

Competitive antagonists

Inhibition of ligand-gated channel activity may result from competitive displacement of the natural ligand from its binding site, preventing ligand-induced channel opening. Competitive antagonists have been reported for all major classes of LGICs. The clinically-used odansetron and other -setron class 5HT3 receptor antagonists bind to the serotonin binding pocket of these channels and inhibit cation influx (Duffy et al., 2012). Bicuculline and gabazine (SR-95531) competitively inhibit GABA binding to GABAA chloride channels, reducing channel open time and opening frequency (Macdonald et al., 1989). Glutamate is a major excitatory amino acid in the brain and the search for glutamate-gated channel inhibitors has resulted in the identification of competitive antagonists such as LY293558 and (–)-LY377770 (AMPA and kainate receptor antagonists), UBP302 and LY382884 (selective kainate receptor antagonists), D-()-2-amino-5-phosphonopentanoic acid (D-AP5) and UBP141 (NMDA receptor antagonists) (Catarzi et al., 2007; Larsen and Bunch, 2011; Vyklicky et al., 2014). Agonist binding to NMDA receptors causes a rapid rearrangement of the agonist binding domain and a clamshell-like closure of that domain. This triggers a series of conformational changes that result in channel opening. D-AP5 and other competitive antagonists stabilize an open cleft conformation of the agonist binding domain and prevent the subsequent conformational changes required for channel opening (Furukawa and Gouaux, 2003; Jespersen et al., 2014). A similar mechanism has been described for AMPA and kainate receptor competitive inhibitors (Karakas et al., 2015; Kumar and Mayer, 2013; Pohlsgaard et al., 2011). The ATP analog TNP-ATP and the non-nucleotide antagonist A-317491 bind to the purinergic P2X3 channel orthosteric sites, at the extracellular interfaces of the three channel monomers. These competitive antagonists bind to the ATP site in the resting state of the channel and preclude ATP-induced closure of the binding cleft. Cleft closure is linked to the outward movement of extracellular beta sheets that are coupled to the first and second transmembrane segments, causing them to expand outwardly and open the channel pore (Mansoor et al., 2016). While some orthosteric site antagonists are safe and effective medicines, a challenge for competition at the native ligand binding site has been the identification of subtype selective antagonists. This has been especially difficult for glutamate-gated receptors. Hence, alternative modes of actions have been pursued for many LGIC-targeted ligand discovery efforts.

1.06.5.1.5

Inverse agonists

Inverse agonists exert the opposite effect of agonists. They bind to the ligand binding site of constitutively activated receptors and stabilize them in an inactive state, effectively exhibiting negative intrinsic activity (Fig. 4). Inverse agonists have been described for several families of 7TM receptors. However, for ion channels, only GABAA channel inverse agonists have been reported. As for 7TM receptors, this may be due to the experimental conditions under which ion channel modulators have been studied. Inverse agonists behave as competitive antagonists under “resting” or “quiescent” states and may be falsely classified as competitive antagonists (Kenakin, 2004). The beta carbolines FG-7142 and DMCM, the heterocyclic annelated 1,4-diazepine Ro19-4603, and the pyrazolo triazine MK-016 are examples of inverse agonists of GABAA chloride channels. These molecules bind to the benzodiazepine site of GABAA channels and stabilize them in a resting, closed state. While GABAA channel agonists like muscimol dampen neuronal excitability and have relaxant effects, inverse agonists are proconvulsive and have anxiogenic effects (Belzung et al., 1990; Chambers et al., 2004; Evans and Lowry, 2007).

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Intrinsic

Modulated

Super agonist Full agonist/activation by saturating endogenous ligand

Response

Partial agonist

Endogenous ligand-gated Partial antagonist Full antagonist Constitutive

Ligand Concentration

Partial inverse agonist Full inverse agonist/ channel blocker

Fig. 4 Modulation of ion channel activity by orthosteric ligands with varying efficacies. The intrinsic tonic activity consists of the ligand-gated part and, in some cases, a constitutive part led by spontaneously open channels (the latter shaded blue). Physiological ligand concentration is normally only sufficient to gate a submaximal tonic activity. The right part illustrates modulation of the intrinsic tonic activity by orthosteric ligands with different efficacies (but equal potencies) as well as by a channel blocker. Original figure modified from Krall J, Balle T, Krogsgaard-Larsen N, Sørensen TE, Krogsgaard-Larsen P, Kristiansen U, and Frølund B (2015) Chapter eightdGABAA receptor partial agonists and antagonists: Structure, binding mode, and pharmacology. In: Rudolph U (ed.), Advances in Pharmacology. Academic Press; pp. 201–227.

1.06.5.1.6

Negative allosteric modulators

Allosteric inhibition of native ligand binding is another means of inhibiting ligand-gated ion channels. The identification of allosteric sites on these channels represents an attractive avenue in the search for non-orthosteric site inhibitors or non-pore blockers. Orthosteric sites present the challenge of finding selective molecules that compete with natural ligands affecting multiple biological pathways and highly homologous binding sites. Those ligands are also sometimes present at high concentrations and therefore may be difficult to competitively displace from their binding sites. Pore blockers too often lack selectivity due to the relatively high homology of the pore region between members of an ion channel family. Negative allosteric modulators (NAMs) of LGICs include AF-353 and AF-219, two related diaminopyridine derivatives that bind to P2X3 channels. X-ray crystallography and mutagenesis studies have revealed that these molecules bind to a site that is distinct from the ATP binding pocket, at the interface of each P2X3 monomer and adjacent to domains that undergo conformational changes during channel opening. Specifically, the occupancy of this allosteric site inhibits its collapse during channel opening and therefore hinders the movement of regions coupling ATP binding to channel opening (Wang et al., 2018b). Two independent studies identified a similar allosteric binding pocket in P2X7 channels, where binding of antagonists prevents the shrinking of this pocket and channel activation. Allsop and colleagues identified this site through a combination of molecular modeling and mutagenesis studies with the selective P2X7 antagonist AZ10606120, while Karasawa et al. solved the crystal structures of P2X7 channels with AZ10606120 and four other antagonists (Allsopp et al., 2017; Karasawa and Kawate, 2016). While the mechanism for negative allosterism is similar in P2X3 and P2X7 channels, the binding pockets for allosteric antagonists are very different and represent an exciting opportunity for the design of subtype-selective P2X channel antagonists. Negative allosteric modulators have been reported for other LGICs, notably for the glutamate-gated NMDA channels. Three distinct NAM sites have been reported for this ion channel family (Burnell et al., 2019; Hansen et al., 2018). The sulfonamide TCN-201 and other GluN2A-selective NAMs bind to the GluN1/GluN2A interface of heteromeric channels near the glycine co-agonist site. Occupancy of this site then stabilizes the GluN1 ligand binding domain in a resting, apo state and negatively modulates binding of glycine (Yi et al., 2016). Ifenprodil and related phenylethanolamine compounds, which specifically inhibit GluN1 and GluN2B channel heterodimers, bind to a pocket formed at the interface of the GluN1 and GluN2B subunits in a region proximal to the N terminus and stabilize the N terminal domain’s clamshell structure in a closed conformation, preventing channel opening (Karakas et al., 2011). Finally, the GluN2C- and Glu2D-selective quinazolin-4-ones (QNZ) and dihydroquinolone-pyrazoline (DQP) analogs such as QNZ-46 and DQP-1105 bind to an allosteric site that is proximal to the transmembrane domains of the channel complex, in the lower lobe of the GluN2 agonist binding domain (Acker et al., 2011; Hansen and Traynelis, 2011). Both inhibitor families depend on glutamate binding and are therefore use-dependent inhibitors. The structural basis for allosteric modulation of channel gating by QNZ and DQP inhibitors has not been determined.

Ion Channels 1.06.5.1.7

133

Inhibitory antibodies

Because of their exquisite specificity, antibodies represent an attractive modality for highly subtype-selective inhibitors of ion channel function. Like small molecules or toxins, antibodies can block the channel pore or alter channel gating. In addition, they may induce channel internalization and degradation, or they may be engineered with Fc-mediated functions such as cytotoxicity to target specific cells. Autoantibodies of ion channels are good examples of the possible mechanisms of functional inhibition by antibodies. Autoimmune diseases are associated with an increased incidence of cardiac arrythmias and autoantibodies from patients have been demonstrated to target Cav, Nav, and Kv cardiac channels (Lazzerini et al., 2017). In these patients, all reported inhibiting autoantibodies target the S5–S6 extracellular pore region. Some antibodies have acute blocking effects and, upon prolonged exposure, can also reduce channel density by increasing channel internalization. Examples of autoantibodies with chronic effects on channel expression include antibodies targeting Kv11.1 (hERG) channels (Nakamura et al., 2007; Szendrey et al., 2019). These effects are fully reversible upon washout of the antibodies. Seventy-five to eighty percent of myasthenia gravis patients have detectable levels of nicotinic acetylcholine receptor autoantibodies in their serum. Three mechanisms of action have been reported for these antibodies. First, some antibodies bind to nicotinic acetylcholine receptors and activate the complement. Second, another type of antibodies induces channel internalization and degradation. Finally, a minority of antibodies binds to the acetylcholine binding site, compete with the natural ligand, and inhibit channel opening (Gilhus et al., 2016). Several strategies have been pursued to obtain novel ion channel antibodies. The simplest approach has been the use of antigenic peptides derived from ion channel extracellular domains. The third extracellular loop (E3) of 6TM channel subunits such as Kv and TRP pore-forming subunits includes the extracellular region flanking the pore and has been widely used to design antigenic peptides for the generation of functional inhibitory antibodies (Xu et al., 2005). This approach has been successful for the generation of both polyclonal and monoclonal (mAb) inhibitory antibodies, such as anti Kv10.1 mAbs for cancer indications. Kv10.1 channels are expressed in many cancerous tissues and may play a role in tumor growth. There are no selective small molecule inhibitors of these channels. Kv10.1 E3-targeted antibodies inhibited Kv10.1 currents in a state-dependent manner, where membrane depolarization and channel opening were required to observe block. They inhibited Kv10.1 currents by 34% after a 10-min incubation with repeated depolarizations and were inactive if cells were not depolarized during that incubation time. These antibodies were highly selective as they did not bind to a closely-related channel, Kv10.2 (Eag2), nor blocked the relatively promiscuous Kv11.1 (hERG) channels. Furthermore, these antibodies had some antitumor activity in vivo (Gomez-Varela et al., 2007). However, immunizations with peptide antigens is not always successful, possibly because these peptides do not represent native ion channel structures. Alternative strategies to generate ion channel antibodies include immunizations with cDNA, recombinant purified proteins, ion channeloverexpressing cells, or ion channel-containing viral particles. In addition, some variations of antibody-like molecules have been pursued, including nanobodies and knotbodies (Wulff et al., 2019). Nanobodies are derived from camelids, who have antibodies composed of only heavy chains and in which the antigen binding domain is composed of one variable domain. Nanobodies are therefore smaller, easier to express, and can bind to sites that conventional antibodies cannot access. They can also be multivalent or multispecific, increasing their efficacy. Stortelers and colleagues, for example, reported Kv1.3 bivalent nanobodies that are about 10 times more potent than monovalent nanobodies. In addition, these Kv1.3 nanobodies were extremely selective for these channels, having no activity at four closely related Kv channels (Stortelers et al., 2018). Knotbodies are fusions of peptide toxins containing a conserved cysteine knot motif and antibody complementarity-determining regions (CDRs), combining the potency of ion channel toxins with the therapeutic advantages of antibodies. One of the first knotbodies reported was a fusion of a potent Kv1.3 toxin, Vm24, and the CDR domain of a humanized anti-lysozyme antibody. This Vm24-CDR knotbody inhibited Kv1.3 activity with an IC50 of 0.6 nM and showed efficacy in a rat model of delayed-type hypersensitivity (Wang et al., 2016). However, in spite of the promise for high specificity, only two ion channel antibodies have reached clinical trials, BIL-010t, a polyclonal antibody targeting non-functional P2X7 receptors (nfP2X7) for the treatment of non-melanoma skin cancers (Gilbert et al., 2017), and DS-2741a, a humanized anti-Orai1 antibody for the treatment of atopic dermatitis (ClinicalTrials.gov Identifier: NCT04211415). A variant of P2X7, nfP2X7, is highly expressed at the surface of many cancer cells, including melanoma cells. NfP2X7 channels are unable to form the large P2X7 pores responsible for cell death and therefore allow cancer cells to survive and grow in a relatively ATP-rich cancer microenvironment. Targeting these channels with inhibitory antibodies is thus a good strategy to limit cancer growth. In addition, nfP2X7 channels present a unique epitope, E200, and an antigenic peptide containing E200 was used to generate anti-nfP2X7 polyclonal antibodies. These antibodies bound specifically to nfP2X7 channels and significantly reduced the growth of B16F10 melanoma cells-derived tumors in mice (Gilbert et al., 2017, 2019). In order to develop Orai1 mAbs, anti-Orai1 antibodies were generated by first immunizing rats with Orai1 cDNA. These antibodies were subsequently cloned and humanized. DS-2741a mAbs antibodies bound specifically to Orai1 channels and inhibited IL2 release from peripheral blood mononuclear cells (Komai et al., 2017).

1.06.5.2

Ion channel agonists

Like ion channel antagonism, agonism by small molecules, peptides, or antibodies can result from several mechanisms. Those include direct channel activation, orthosteric agonism, allosteric agonism, positive allosteric modulation, and gating modification.

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1.06.5.2.1

Ion channel openers

Small molecules and biological ligands can directly open channels through various mechanism. Examples of channel openers with diverse mechanisms of actions include small molecule Kv7 openers, electrophilic TRPA1 activators, and KATP channel openers. Retigabine is a potassium channel opener that has been used in the clinic to reduce neuronal excitability and treat epilepsy. Retigabine activates Kv7.2-5/Kv7.3 hetero-tetrameric channels, the main channels underlying the neuronal M (muscarinic inhibited) current. M channels are partially open at resting membrane potentials and, in the absence of muscarinic receptor agonists, limit neuronal excitability. Retigabine binds to the S5–S6 pore domain of Kv7 channels and causes a hyperpolarizing shift in channel activation, enhancing M currents in neurons. Studies with chimeric and mutant channels have demonstrated that a specific tryptophan residue in the pore region, Trp236 in Kv7.2 and Trp265 in Kv7.3, confers sensitivity to retigabine (Syeda et al., 2016b; CorbinLeftwich et al., 2016; Schenzer et al., 2005). Retigabine preferentially binds to closed and open states and stabilizes the channel pore in an open conformation (Wang et al., 2018a). The transient receptor potential channel TRPA1 is activated by a variety of ligands, some with unique mechanisms of action. For instance, TRPA1 channels are activated by cell-permeant electrophilic compounds through covalent modification of cysteines in their N terminus. Electrophilic TRPA1 agonists include isothiocyanates (compounds found in mustard oil and wasabi), cinnamaldehyde, and acrolein. Cysteine modification may be reversible, through a thiol-Michael adduct formation, or irreversible, through nucleophilic SN2 reaction or thiol-a,b-unsaturated aldehyde reaction (Hinman et al., 2006; Macpherson et al., 2007). Cysteine 621 is the most sensitive to covalent modification and is thought to play a key role in channel activation. Cryo-EM studies suggest covalent modification of cysteines alters the configuration of the N terminus from a closed clamshell to an open conformation, triggering structural changes in downstream regions of the N terminus ultimately leading to channel opening (Suo et al., 2020). Ion channel openers may also act indirectly, binding through non-pore-forming subunits of channel complexes. Like KATP blockers, KATP openers act on the SUR subunit of the channels. KATP openers are structurally diverse, including benzopyrans (levcromakalim, bimakalim), benzothiadiazines (diazoxide), cyanoguanidines (pinacidil, P1075), cyclobutenediones (WAY-151616), nicotinamides (nicorandil), pyrimidines (minoxidil), tertiary carbonoles (ZD-6169), thioformamides (aprikalim), and dihydropyridine-like compounds (ZM-244085) (Jahangir and Terzic, 2005). Some openers are more selective for KATP subtypes. For instance, pinacidil opens SUR2A/Kir6.2 but not SUR1/Kir6.2 channels and diazoxide activates SUR1/Kir6.2 but is only weakly active at SUR2A/Kir6.2 channels. SUR2B/Kir6.2 channels are activated by both pinacidil and diazoxide. Binding and functional studies with chimeric and mutant SUR subunits have identified distinct regions for KATP opener activity. The cytoplasmic loop between TM13 and TM14 as well as two amino acid residues in TM17 of SURs play a role in cyanoguanidines and thioformamides binding and channel activation. TM6–11 and the first nucleotide-binding domain (NBD1) of SUR1 are required for SUR1/Kir6.2 activation by diazoxide. NBD2 is possibly involved in the ADP-dependent activation by diazoxide, and the SUR2A C-terminal tail inhibits it (Babenko et al., 2000; Hambrock et al., 2004; Matsuoka et al., 2000; Uhde et al., 1999).

1.06.5.2.2

Orthosteric agonists

Natural or synthetic molecules may bind to the natural, orthosteric site of ligand-gated ion channels and either potentiate or reduce channel activity, depending on their relative potency and efficacy. For instance, at higher or equal potency with endogenous agonists, super agonists are more efficacious than the natural ligand and may truly enhance channel activity or cause further desensitization, while partial agonists may have a relative inhibitory effect, depending on their efficacy (Fig. 4). Orthosteric agonists have been reported for all classes of LGICs. In the case of GABAA channels, full or super agonists such as muscimol induce rapid desensitization and have limited overall agonistic effects. Antagonists, on another hand, have potential anxiogenic or proconvulsant activities (Krall et al., 2015). Therefore, partial agonism of GABAA receptor, in addition to other mechanisms such allosteric modulation, is an attractive approach for therapeutics targeting that ion channel. Muscimol is a 3-hydroxyisoxazole bioisostere of the carboxylate of GABA from the mushroom Amanita muscaria and has been used as a lead structure in the search for novel GABAA ligands. New GABA derivatives have been synthesized with the goal of finding partial agonists or antagonists at the native ligand binding site. However, subunit composition of GABAA channels can greatly affect agonist pharmacology. THIP (4,5,6,7-tetrahydro-isoxazolo [5,4-c]pyridin-3-ol) is an example of a partial agonist of GABAA channels composed of a1b3g2 or a4b3g2 subunits but it is a super agonist of a4b3d channels (Frolund et al., 2002; Mortensen et al., 2010). Activation of cation-selective nicotinic acetylcholine receptors (nAChRs) by acetylcholine and other agonists, such as succinylcholine and nicotine, increases neuronal excitability and neurotransmitter release. Activation of neuronal nAChRs also underlies the addictive effects of nicotine by eliciting the release of dopamine. Thus, nAChRs have been a major focus for the development of smoking cessation therapies. However, like GABAA, full agonists are not desirable as they could mimic nicotine’s effects. On another hand, antagonists such as mecamylamine compete with nicotine but have excessive withdrawal effects, including irritability, depression, insomnia, fatigue, headaches, constipation, and weight gain. Therefore, major efforts have been focused on the development of partial agonists. Varenicline (ChantixÒ) is an example of selective a4b2 channel partial agonist that is clinically used for smoking cessation (Jordan and Xi, 2018). In a recombinant system, it activates a4b2 with 45% of nicotine’s efficacy and with an EC50 of 3.1 mM, comparable to nicotine’s EC50. This effect translated to a relative reduction in dopamine release from rat striatal neurons (Rollema et al., 2007).

1.06.5.2.3

Gating modifiers

As toxins and small molecules can interfere with channels gating mechanisms to inhibit their activity, some ligands can increase channel activity through the voltage-sensing domains of VGICs. Lipid-soluble gating-modifier toxins targeting Nav channels

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bind to two distinct sites on Nav channels to enhance channel activity. Plant-derived grayanotoxins (Ericaceae family), veratridine (Liliaceae family), and acotinine (Aconitum napellus) as well as the frog skin-derived batrachotoxin (Phyllobates aurotaenia) bind to the S6 segments of domains I and IV of Nav channels, also referred to as “neurotoxin site 2.” At resting membrane potentials, these toxins preferentially bind to the activated state of Nav channels and cause persistent activation, resulting in a shift in the voltage dependence of activation toward negative potentials and an inhibition of channel inactivation (Hille, 2001). Veratridine has been commonly used to open Nav channels in the absence of voltage control, e.g. in fluorescence-based functional assays used to identify channel blockers. Other lipid-soluble toxins, such as brevetoxin and ciguatoxin from the marine dinoflagellates Karenia brevis and Gambierdiscus toxicus, respectively, bind to a site formed by amino acids from the S6 segments of domain I and the S5 segment of domain IV, also known as “neurotoxin site 5” (Lombet et al., 1987; Trainer et al., 1991). They have functional effects comparable to the other lipid-soluble toxins, left-shifting the voltage-dependence of activation and inhibiting inactivation (Hille, 2001; Catterall et al., 2007). Hydrophilic peptide toxins may also enhance Nav channel activity. They act at a different site, through the S3–S4 and S1–S2 extracellular loops. Delta-theraphotoxin-Hm1a and delta-theraphotoxin-Hm1b are peptide toxins isolated from the venom of the tarantula Heteroscodra maculata (Osteen et al., 2016). Both toxins selectively activate Nav1.1 over other Nav1 family channels. Binding to the domain IV S1–S2 loop seems to confer this subtype selectivity. These toxins inhibit voltage-dependent inactivation by shifting it to more depolarized voltages and, as a result, create a window current where activation and steady-state inactivation voltage curves overlap. Hm1 toxins also inhibit channel slow inactivation, further enhancing channel activity (Osteen et al., 2017). Hanatoxin, described earlier as an inhibitory gating modifier of Kv2.1 channels, has an opposite effect on Kv1.2 channels. This toxin shifts the voltage-dependence of channel opening to more negative voltages, making it easier to open Kv1.2 channels. Hanatoxin binds to the S3–S4 area of Kv1.2 channels and stabilizes both an activated conformation of the voltage sensor and the open state of the channel. A related tarantula toxin, GxTx-1E, binds to the same site on Kv1.2 but has opposite effects. These results suggest both binding site and toxin structures are key determinants of gating modulation (Milescu et al., 2013). In addition to retigabine, Kv7 channels are activated by another class of compounds, substituted benzamides such as ICA-27243 and ICA-069673. These compounds act through binding to the voltage-sensing region of Kv7 channels and are more selective than retigabine for Kv7.2/7.3 channels. In addition, these two classes of Kv7 activators bind to different states of the channels. Unlike retigabine, which binds to either open or closed states, ICA-27243 and ICA-069673 can only access their binding sites on the voltage sensor in the activated state and they stabilize that state (Wang et al., 2018a; Padilla et al., 2009). Small molecule agonist-like gating modifiers also include dihydropyridines as well as the cdk inhibitor roscovitine and derived compounds. Dihydropyridines (DHPs) are a class of Cav1, L-type calcium channel, modulators that includes both agonists and antagonists. Stereoisomers of compounds in this family, including Bay K 8644 and SZ-202-791, exert opposite effects on channel activity. DHP agonists increase Cav peak currents, shift the voltage-dependence of activation to more negative voltages, and slow deactivation. DHPs bind to a single site on Cavs comprising amino acids in the S5 and S6 segments of domain III, the S6 of domain IV, and the first pore helix of domain III (Mitterdorfer et al., 1996; Peterson et al., 1996; Zhao et al., 2019a). Cryo-EM studies have highlighted different orientations of the nitro groups of DHP agonists and antagonists when bound to Cav1.1 could contribute to the opposite effects on channel activity. These studies also suggested DHP agonists bind preferably to Cav channels in an activated state, inhibit channel inactivation, and prolong channel opening. Antagonists, on another hand, prefer the inactivated state and prevent transitions to other states. These effects may be mediated through alteration in S6 helix movements associated with pore gating (Zhao et al., 2019a). Roscovitine and derived compounds, such as MF-06, bind to Cav2 channels and have effects comparable to DHP agonists, binding preferentially to open channels and slowing deactivation. However, these compounds do not increase peak currents nor shift the voltage dependence of activation (Wu et al., 2018; Yan et al., 2002). The location of the Cav2 binding site for the roscovitine family of compounds is not known.

1.06.5.2.4

Positive allosteric modulators

When ligand binding lowers the energy barrier for resting to activated state transitions or raises the energy barrier for activated to desensitized states, there is an increased probability the channel will be in an active state in the presence of an agonist. Such a ligand is a called a positive allosteric modulator (PAM) or a potentiator and it increases a channel’s apparent affinity and efficacy for orthosteric agonists. Positive allosteric modulation may be preferable over direct agonism in therapeutic applications because it alters the effects of endogenous ligands, rather than activating the channels per se, hence restricting the drug effects to areas and times where and when the natural ligand is present. PAMs have been reported for all major LGIC families, including GABAA (e.g. diazepam and zolpidem), nicotinic (e.g. ivermectin), glutamate (e.g. aniracetam and cyclothiazide for AMPA channels), glycine (e.g. THC and anandamide), and purinergic channels (e.g. clemastine). These positive allosteric modulators bind to specific allosteric sites on channels, such as the benzodiazepine site on GABAA channels. Benzodiazepines increase the number of channel openings by increasing the average time GABA is bound (Bianchi et al., 2009). Structural studies with wild-type and mutant GABAA channels have confirmed the location of the high-affinity benzodiazepine site at the interface of a and g subunits and the key role of histidine 102 in the human a1 subunit for benzodiazepine sensitivity (Phulera et al., 2018; Zhu et al., 2018). Subsequent GABAA structures obtained from channel complexes reconstituted in nanodiscs support the existence of a second, lower affinity benzodiazepine binding site, in the transmembrane domain and involving TM2 and TM3 of the b subunit as well as TM1 of the a subunit (Masiulis et al., 2019). This site overlaps with the GABAA anesthetic binding site and may underlie the biphasic potentiation of GABA activity observed with diazepam and other benzodiazepines (Walters et al., 2000). Structural data are consistent with GABA opening channels through a “lock and pull” mechanism, where b subunit extracellular domains (ECDs) lock with neighboring a subunit ECDs and pull them into a counterclockwise motion linked to TM segments, resulting in an opening the channel pore. Benzodiazepine

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binding has been proposed to strengthen a-g subunit ECD interfaces and therefore potentiate these GABA-induced conformational changes (Masiulis et al., 2019). PAMs of the homomeric a7 nAChRs potentiate acetylcholine action at these channels either through facilitating channel opening (type I PAMs) or through inhibition of desensitization (type II PAMs). Type I PAMs affect only the peak response without affecting kinetics, while type II PAMs alter agonist response kinetics and desensitization profile. Type I a7 nAChR PAMs include ivermectin and NS-1738. Type II modulators, such as PNU-120596 and TQS (4-naphthalene-1-yl-3a,4,5,9b-tetrahydro-3-H-cyclopenta[c]quinoline-8-sulfonic acid amide), increase ACh response and sensitivity but also profoundly alter desensitization kinetics. Both types enhance cognition in rodent models of learning and memory or schizophrenia (Bertrand and Gopalakrishnan, 2007). Site-directed mutagenesis studies showed a methionine residue in TM2 of the a7 subunit is critical for type II PAM activity in addition to an extracellular site required for type I PAMs. Structural information for PAM binding has been derived from studies with soluble Ach binding proteins and are consistent with an ECD PAM binding site (Papke and Lindstrom, 2020).

1.06.5.2.5

Agonist-like antibodies

While most antibody studies have been focused on the development of inhibitory antibodies, agonist-like antibodies have been reported both from patients (autoantibodies) and from de novo antibody generation efforts. Agonist-like autoantibodies have been described for Cav1.2 and Kv7.1 channels (Lazzerini et al., 2017). These antibodies are associated with ventricular tachycardia. The epitope for the majority of Cav1.2 autoantibodies is in the N terminal intracellular domain and it has been proposed that abnormal membrane permeability or protein turnover in disease could provide access to these epitopes. Cav1.2 autoantibodies increased Cav1.2 currents in Xenopus oocytes three- to four-fold after two to three minutes of exposure and this effect was inhibited by nifedipine or pre-treatment with antigenic peptide (Xiao et al., 2012). Kv1.7 autoantibodies, on another hand, target the extracellular S5–S6 pore region. They also caused direct activation of the channels and did not change Kv7.1 cell surface expression. Exposure of HEK cells expressing Kv7.1 channels to these autoantibodies for  1 hour resulted in two- to threefold increases in Kv7.1 currents (Li et al., 2013). Agonist-like bivalent nanobodies have been generated for P2X7 channels following immunizations of llamas with P2X7 cDNA, phage display selections, and protein engineering. Out of six nanobody families identified by screening, one (14D5) had agonistic activity and five were inhibitory. Dimerization of the agonist nanobody increased its potency (EC50) for P2X7 activation from 5.7 nM to 0.3 and 0.1 nM for HLE dimers and Fc fusion nanobodies, respectively. A 20-min preincubation of Xenopus oocytes expressing P2X7 with Fc-fusion 14D5 nanobodies led to a greater than five-fold increase in P2X7 currents. Further characterization of these antibodies showed they reduced the ATP threshold required for activation. Agonist activity was inhibited by both antagonists nanobodies identified in that study and a small molecule P2X7 inhibitor. In addition, 14D5 bivalent nanobodies were active in a mouse model of glomerulonephritis (Danquah et al., 2016). Determining the binding site and mechanism of action of these bivalent nanobodies could shed light on novel ways to modulate purinergic ion channels.

1.06.5.3

Ion channel trafficking modulators

Ligand binding to an ion channel may not only alter its function but also its cellular trafficking, localization, and expression. For instance, some ligands of the cystic fibrosis transmembrane regulator (CFTR) ion channel act as molecular chaperones or “correctors” and are clinically used to increase cell surface expression of the trafficking-defective CFTR channels underlying CF. Molecular chaperones have also been reported for hERG potassium channels. Chaperone effects are usually observed after prolonged ligand exposure (hours to days) and may be missed in short, acute measurements of channel function. Extended exposure to an ion channel ligand may also have the opposite effect, reducing channel trafficking or expression. Examples include pentamidine inhibition of hERG trafficking and gabapentin inhibition of Cav channel cell surface expression (Balse and Boycott, 2017; Dennis et al., 2007; Kukkar et al., 2013). In the case of cystic fibrosis, where the most common defect lies in reduced expression of functional CFTR channels due to inherited mutations, ligands that increase expression of mutant channels are of clinical interest. The screening of small molecule libraries has resulted in the identification of such molecules and the development of the CFTR correctors lumacaftor, tezacaftor, and elexacaftor (Ghelani and Schneider-Futschik, 2020; Van Goor et al., 2011). These drugs bind to mutant CFTR protein and help it fold properly so that it can traffick to the cell surface. To further enhance channel activity at the cell surface, these correctors are used in combination with potentiators such as ivacaftor to treat CF patients (Egan, 2020). Ivacaftor and other potentiators bind CFTR in the transmembrane region, at the protein-lipid interface, at a site formed by TM4, TM5, and TM8. This site overlaps with a hinge region in TM8 that allows channel opening upon activation by ATP. Potentiator binding may stabilize this region in the activated conformation (Liu et al., 2019). Correctors, on another hand, have been reported to bind to the first membrane spanning domain and to the first nucleotide binding domain (NBD1) of wild-type and F508del-CFTR, the most common CFTR trafficking mutant. F508 is in NBD1 and close to the fourth intracellular loop (CL4), an area affected by correctors. Thus, correctors may cause allosteric changes in NBD1, CL4, and other parts of the channel so it folds properly and trafficks more efficiently to the cell surface (Hudson et al., 2017; Ren et al., 2013). In the heart, hERG potassium channels underlie a key current, IKr, mediating cardiac action potential repolarization and reducing hERG channel activity can lead to QT interval prolongation and fatal arrhythmias. A wide variety of drugs bind hERG and inhibit IKr currents, causing drug-induced QT prolongation or long QT (LQT) syndrome (El-Sherif et al., 2018). Several cardiac ion channel mutations have also been associated with LQT, with hERG mutations categorized as LQT2 mutations (Smith et al., 2016). Some

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hERG ligands (astemizole, cisapride, fexofenadine, and E-4031) can rescue LQT2 trafficking mutant hERG channels. But these drugs also acutely block hERG channels and overall drug effect may be a reduction in channel function. However, in the case of fexofenadine, trafficking rescue occurs at concentrations well below the IC50 for acute channel block and it may be of use in LQT2 patients (Rajamani et al., 2002). On another hand, some drugs such as pentamidine or arsenic trioxide bind wild-type hERG and inhibit its trafficking to the cell surface. Another group of drugs, such as fluoxetine and ketoconazole, has both acute and trafficking effects. Pentamidine’s effects are reversed in the presence of astemizole or dofetilide, suggesting that these trafficking inhibitors compete for the same binding site within the hERG channel, although the precise mechanism of action of pentamidine has not been elucidated. Lastly, ion channel trafficking may be inhibited through ligand binding to auxiliary subunits. For instance, gabapentin and pregabalin affect voltage-gated calcium channel expression through its a2d subunits. These drugs were initially developed as nonhydrolyzable analogs of GABA to treat epilepsy. However, ligand binding studies later revealed that both drugs bind to the a2d subunits of Cav channels (Belliotti et al., 2005; Gee et al., 1996). These subunits increase Cav trafficking to the cell membrane and are overexpressed in the central nervous system under pathological conditions. Some of gabapentin’s effects on neuronal excitability are thought to result from its effects on calcium channel expression. Prolonged administration of gabapentin reversed a2d subunits effects and prevented an upregulation of Cav channel expression in dorsal root ganglions and the spinal dorsal horn (Morimoto et al., 2012).

1.06.6

Characterization of ion channel-ligand interactions: From whole-tissue to single molecules

1.06.6.1

Native versus recombinant systems

Before initiating any ion channel studies, the choice of the ion channel source or preparation is key. Early pharmacological studies of ion channels were performed with native tissue preparations such as the squid (Doryteuthis pealeii) giant axon for voltage-gated sodium and potassium channels or the electric organ of the electric eel (Torpedo californica) for nicotinic acetylcholine receptors. The first ion channel gene was cloned from electric eel in 1982 (Giraudat et al., 1982; Noda et al., 1982). Since then, advances in molecular biology and genetics have led to the cloning of hundreds of ion channel genes and heterologous expression of channels is the method of choice for their study. Site-directed mutagenesis studies followed and, over the past 30þ years, have shed light on numerous ion channel-ligand interactions. One challenge that remains for all heterologous expression studies is the often poor understanding of ion channel composition and relevant physiological complexes, e.g. the exact stoichiometry of the channel and the association with auxiliary subunits or regulatory proteins. For instance, the various subunits forming a hetero-multimeric channel can be transfected into cells but the distribution and stoichiometry of hetero-multimers cannot be controlled. In addition, overexpression may not allow physiological coupling to regulatory proteins such as G proteins or regulatory enzymes. Nevertheless, recombinant expression provides means to alter the sequence of ion channels and therefore better understand the structural basis for gating, permeation, or ligand binding. All common approaches to study protein structure function have been applied to ion channels and include single-point to domain swap (chimera) mutagenesis and variations of these methods such as alanine scanning (Swartz and Mackinnon, 1997b), unnatural amino acid mutagenesis (Duffy et al., 2012; Kim et al., 2015) , substituted cysteine accessibility methods (Akabas, 2015), and mutant cycle analysis (Aiyar et al., 1996, 1995; Yang et al., 2015). The combination of mutagenesis in heterologous expression systems with biochemical and functional approaches described in the next sections of this article has led to major advances in our knowledge of ion channel pharmacology and function.

1.06.6.2

Binding

Fundamental questions in pharmacology are: is a molecule binding directly to the protein of interest? where is the ligand binding site located? and what is the affinity of a ligand for its binding site? Those questions may all be answered with ligand binding studies. Before the cloning of ion channels, all ion channel binding studies were conducted with membranes isolated from native tissues. For instance, membranes from the electric organ of Torpedo electric rays have been widely used for nAChR binding studies and rat brain synaptic membranes for neuronal voltage- and ligand-gated channels (Chak and Karlin, 1992; Enna and Snyder, 1975; Hartshorne et al., 1982; Pedersen et al., 1999; Tamkun et al., 1984; Yoneda and Ogita, 1989). Heterologous expression systems have since enabled large scale binding studies for ligand discovery or broad ligand selectivity profiling. Binding studies may be placed into two categories: ligand-displacement and direct binding studies. The former has been extensively used for ion channels but do require a high affinity labeled ligand (radioactive or fluorescent). The second category of studies can be used to verify direct interactions with channel proteins or specific domains and do not require prior knowledge of binding site location nor a labeled probe. Despite advances in ion channel functional assays, binding displacement assays are still commonly applied to probe ligand binding to channels. For instance, several contract research companies have panels of ion channel binding assays to determine binding to known drug sites such as the benzodiazepine site of GABAA channels or the dihydropyridine site of Cav channels. Most direct binding, label-free methods require large amounts of purified material and properly folded proteins and have therefore been more challenging to apply to the study of ligand-ion channel interactions. However, progress in protein engineering and expression have started to enable some of these studies. Thermal shift methods such as isothermal titration calorimetry (ITC) have been used to study small molecule binding to channel proteins (Karakas et al., 2011; Wohri et al., 2013). Surface plasmon resonance (SPR) is another label-free method for probing ligand binding to ion channels (Dawson et al., 2012; Martin-Eauclaire

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et al., 2015). It can also provide binding kinetic data. For instance, psalmotoxin binding to purified ASIC1a channels had on- and off-rates of binding measured by SPR comparable to those measured by electrophysiology (Dawson et al., 2012). A newer label-free binding technique, cellular thermal shift assay (CETSA), was recently reported to detect ligand binding to three large transmembrane proteins, the calcium pump SERCA2, the 7TM receptor PAR2, and the mitochondrial protein TSPO (Kawatkar et al., 2019). CETSA involves treatment of cells or cell lysates with a compound of interest, heating to denature and precipitate proteins, cell lysis (for whole-cell experiments), and the separation of cell debris and aggregates from the soluble protein fraction. Ligand-free proteins denature and precipitate at elevated temperatures, while ligand-bound proteins are stabilized and remain in solution. Soluble protein can then be quantified by Western blotting or higher throughput systems, including reporter and homogeneous antibody-based methods (Henderson et al., 2020; Jafari et al., 2014; Shaw et al., 2019). This technique, if proven to be widely applicable to membrane proteins, could offer a new avenue for ion channel ligand discovery. While binding studies can help determine the location of a specific binding site, ligand affinity, as well as on- and off-rates of binding in the case of SPR, they have several limitations. They do not distinguish between agonists and antagonists. Also, in the case of displacement binding, they focus on a single binding site and may not detect ligands binding to other sites or allosteric modulators. In addition, voltage- or state-dependent ligand binding may not be captured. Therefore, binding studies must often be complemented by functional ones.

1.06.6.3 1.06.6.3.1

Electrophysiology: Single-channel and macroscopic recordings Electrophysiological methods

The method of choice for studying ion channel function is to directly measure currents going through these channels by electrophysiological methods. This may be done at a macroscopic scale, in whole cells, or at the single-molecule level, on small patches of membrane or in reconstituted systems (planar lipid bilayers or proteoliposomes). Since its development by Neher, Sakmann, and colleagues (Hamill et al., 1981; Neher and Sakmann, 1976), the patch clamp technique has been widely applied to study ion channels in the plasma membrane and intracellular membranes such as the inner and outer mitochondrial (Sorgato et al., 1987), nuclear (Mak et al., 2013), and endolysosomal membranes (Chen et al., 2017). This method is based on tight, gigaohmlevel seals between a small glass (or quartz) pipette and the cell membrane and the earlier development of field-effect transistors with low voltage noise and sub-picoampere input currents, allowing low noise recordings and the study of single-channel currents. Until the early 2000s, all patch clamp electrophysiology measurements were conducted in a manual fashion, where an experimenter recorded ion channel currents from one cell (or one membrane patch or bilayer) at a time. The advent of automated electrophysiology has since enabled large (up to 384) parallel electrophysiology recordings from cells and expanded the application of electrophysiology in pharmacology. Manual electrophysiology methods use patch pipettes with apertures small enough to record from single cells (e.g. 2–3 mM with a resistance between 1 and 5 MU). Ion channel current recordings may be made under different configurations (Fig. 5): (1) Wholecell, where suction is applied to the patch pipette so a > 1 GU seal is formed and the membrane within the pipette tip is then

2. Planar lipid bilayer

1. Conventional patch clamp a.Whole-cell

b.Cell-attached amplifier

cell cis

trans

3. Parallel planar patch clamp c. Perforated Patch

d. Inside-out patch

e. Outside-out

amphotericin B

Internal Solution

Cell

Fig. 5 Electrophysiology recording configurations: (1) Conventional patch clamp recordings from a cell expressing ion channels; (2) Artificial lipid bilayers; and (3) Planar patch clamp principle of automated electrophysiology platforms. Multiple recording sites (holes) may be present per well and a recording plate may have up to 384 wells.

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ruptured to gain electrical access to the entire cell. This method implies diffusion between the intracellular pipette solution and the cytoplasm. (2) Cell-attached, where a tight seal is formed but the cell membrane is not ruptured. This configuration allows recording of channels within that patch of membrane. (3) Perforated patch, where in cell-attached mode, the pipette contains a membrane perforating agent such as the antibiotics nystatin or amphotericin B. These antibiotics form small pores in the membrane patch and allow electrical access (monovalent ion transport) to the cell without dialysis of cellular components that may be critical to channel activity, e.g. ATP (Linley, 2013). (4) Excised inside-out or outside-out patch. Inside-out patches may be formed after seal formation, upon quickly pulling the pipette away from the cell. Outside-out patches are formed from a whole-cell configuration, again pulling the pipette quickly from the cell. These excised patch configurations allow isolated, cell-free recordings of ion channel activity. In addition, at low channel densities, cell-attached and excised patch configurations allow the study of gating transitions at the single channel level (Sakmann and Neher, 1995). (5) Reconstituted systems, where channels or cell membranes are purified and reconstituted into liposomes or fused to planar lipid bilayers (Morera et al., 2007). In contrast, automated electrophysiology methods are based on planar patch clamp recordings, i.e., cell sealing not to a patch pipette but to a hole bored at the bottom of a recording well following the application of suction under that well (Fig. 5). Recordings may be performed in multiple wells at a time with an array of parallel electrodes. The highest throughput automated platforms can record from 384 wells simultaneously (Obergrussberger et al., 2018). In addition, each recording well may contain multiple holes so that currents from multiple cells are added (population patch clamp). The latter reduces cell to cell variations in current amplitude and allows higher throughput recordings (Dale et al., 2007). However, seal resistances achieved with these systems are often significantly lower than those obtained with manual electrophysiology systems (hundreds of MU versus GU seals) and recording quality is lower. Voltage control is less accurate and leak currents limit studies of small currents such as currents in primary cells. Nevertheless, these automated systems can be useful in the characterization of primary cell preparations, if assay throughput and cost are not main concerns (Toh et al., 2020). These automated platforms are broadly used for the screening or characterization of novel ion channel ligands, generally with recombinant expression systems.

1.06.6.3.2

Electrophysiology mechanistic studies of channel inhibition and activation

As described earlier, ion channel pharmacology is very diverse in terms of modulatory effects and types of ion channel modulators. Electrophysiology provides a means to measure activity and conduct classical pharmacology studies, including concentration-effect responses and competition studies such as Schild analyses or kinetic on- and off-rate measurements (Duffy et al., 2012; Yi et al., 2016; Hansen et al., 2012; Jarvis et al., 2002). But electrophysiological studies, where membrane voltage and ionic conditions can be controlled, offer some unique insights into ligand effects. Furthermore, the possibility to record single-channel events provides additional mechanistic information. Pore blockers bind within the ion conduction pathway and therefore can be distinguished from non-pore blockers in several ways, for instance by examining the voltage dependence of block or the effects of permeant ions. Changing transmembrane voltage may affect the binding and unbinding of these ligands, as voltage alters the driving force for permeant ions. In addition, charged blockers in the pore will be affected by changes in the electric field in the open state. Pore blockers may also be displaced by permeant ions, by increasing the flux of ions entering from the intracellular side, an effect called trans-enhanced dissociation. For instance, increasing either voltage or intracellular Kþ concentration increased charybdotoxin’s off-rates for calcium-activated Kþ channel block. The voltage effects on charybdotoxin were attributed to increased Kþ permeation. Ions that were not permeant did not induce trans-enhanced dissociation of charybdotoxin (Mackinnon and Miller, 1988). On another hand, charged Nav channel pore blockers such as the local anesthetics lidocaine and tetracaine are directly affected by changes in transmembrane voltage. Voltage protocols may also be tuned to identify use-dependent blockers of VGICs. To attain significant block, channels must cycle through multiple open and closed state. This is achieved by applying trains of voltage pulses to cells expressing the channel of interest. Pulse duration and frequency will affect the extent of channel block. Such protocols are key to study this type of ligands and can only be applied in electrophysiological recordings. Similarly, state-dependent block may require voltage control. For example, ligand binding to a closed inactivated state can only be achieved if channels can be placed into that state (e.g. through prolonged depolarizations for Nav channels) and measuring the fraction of unblocked channels will require a recovery period followed by another depolarization. For VGICs, the voltage-dependence of activation and inactivation in the presence and absence of ligand can reveal gating modifier effects. For instance, veratridine induces a  90 mV hyperpolarizing shift in the voltage dependence of activation of muscle Navs (Leibowitz et al., 1986). Single-channel studies can further inform mechanisms of ligand-induced inhibition or activation. Effects on ion channel kinetics can be characterized through measurements of several parameters (e.g. open time, closed time, burst duration, number of openings per burst, inter burst duration, and first latency for opening) and derived rate constants and kinetic models. Single-channel conductance can also be measured (Colquhoun and Hawkes, 1995). A reduction in single-channel conductance can result from fast pore block, as fast closing events may be filtered out and appear as a decrease in single-channel currents. It may also result from a screening effect, where charges near the pore are altered by charged molecules. For instance, polyamine-induced reductions in NMDA single-channel currents occur through screening negative charges near the pore (Rock and Macdonald, 1992). Changes in conductance can also indicate ligand effects on pore conformation. For example, Canul-Sánchez and colleagues reported the TRPV1 agonists capsaicin and lysophosphatidic acid (LPA) increased channel opening as well as produced two different conductance states. The conductance of LPA-activated TRV1 channels was 41% larger than capsaicin-induced currents. Moreover, LPA effects were dependent on a lysine residue in the C terminus. The differences in conductance effects between capsaicin and LPA

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suggest these agonists induce distinct conformational changes within the channel pore and are consistent with cryo-EM studies showing agonist-specific pore conformations (Canul-Sanchez et al., 2018; Cao et al., 2013a). High resolution, single-channel studies of nAChR activation by the full agonist acetylcholine show concentration-dependent decreases in channel closed times and an increase in the percentage of time channels are open without large effects on open times. Carbamylcholine, a less potent and partially efficacious agonist, also reduced closed times with increasing concentrations but the percent open time was lower than with acetylcholine and closed times distributions were different, with more abundant short and long closings and less abundant intermediate closings. Kinetic analysis of these recordings showed that nAChR go into a short-lived primed conformation before opening and that the partial agonist carbamylcholine is less efficient in that pre-opening step (Mukhtasimova et al., 2016). In summary, single-channel studies bring unique mechanistic insights into ligand effects. Their use, however, is extremely limited due to the manual nature of these recordings.

1.06.6.4

Ion flux measurements

Cellular flux of ions through channels and other ion-transporting proteins can be directly determined with the use of radioisotopes or atomic absorption spectroscopy (AAS). Frequently used radioisotopes include 45Ca for calcium channels, 22Na for sodium channels, 86Rb for potassium channels, and 125I for chloride channels. Radioactive flux assays were widely used until the development of AAS and fluorescent indicators (see next section). AAS provides a non-radioactive, label-free means to measure ion flux with high throughput (Stankovich et al., 2004). For potassium channels, Rbþ is loaded into cells, cells are washed, and relative Rbþ efflux is measured by AAS analysis of the cells and cell medium following channel activation with either Kþ-induced depolarization or agonists (Terstappen, 1999). This method is also applicable to chloride channels (Cl- flux) and sodium channels (Liþ flux). Major limitations of flux assays are the lack of voltage control and the requirement for wash steps which limit their use and capacity, respectively.

1.06.6.5

Indirect measurements of channel activity: Fluorescent indicators and genetically-encoded sensors

Ionic currents through channels are also measured by indirect means, for instance with the use of ion-sensitive or membrane potential-sensitive fluorescent dyes and proteins. Several ion-sensitive fluorescent dyes have been developed and are widely used for the identification and characterization of ion channel ligands. Those include calcium-, sodium-, and chloride-sensitive dyes. Potassium channels are highly permeable to thallium and BTC-AM, a cell-permeant coumarin benzothiazole-based dye that binds thallium, is commonly used to measure potassium channel activity (Weaver et al., 2004). Activation of ion channels often results in significant changes in membrane potential and their activity may also be measured with membrane potential-sensitive dyes such as the oxonol dye DiBAC4(3), the fluorescent imaging plate reader (FLIPR) membrane potential dye FMP, and fluorescence resonance energy transfer (FRET) dye systems (Wolff et al., 2003). Proteins have also been engineered to detect changes in intracellular ion concentrations or membrane potential. The chemiluminescent calcium sensor aequorin, anion-sensitive yellow fluorescent proteins (YFP), and calcium-sensing green fluorescent proteindcalmodulin fusion proteins or GCaMPs are examples of genetically-encoded ion sensors (Badura et al., 2014; Dupriez et al., 2002; Kruger et al., 2005). Voltage-sensitive protein sensors contain a voltage-sensing domain (VSD) from transmembrane proteins such as ion channels, voltage-sensitive phosphatases, and microbial opsins, fused to a single fluorescent protein or to a FRET fluorescent protein pair (e.g. cyan and yellow fluorescent proteins). Changes in membrane potential induce a conformational change in the VSD that is transduced to the fused fluorescent protein and results in a fluorescent signal. These genetically-encoded sensors are first introduced in cells of interest through transient or stable expression methods. Channel activity can then be measured as in other plate-based assays, following Kþ-induced depolarization for VGICs or agonist application. In addition, unlike fluorescent dye indicators, these probes may be used in situ, by directing expression to either a precise subcellular compartment (e.g. the endoplasmic reticulum, mitochondria, or nucleus) with specific targeting sequences or to a particular cell type with cell-specific promoters, for instance thy1 for neurons (Cao et al., 2013b; Chen et al., 2012; Dreosti et al., 2009; Suzuki et al., 2016). Unlike ion flux measurements, most studies with fluorescent dyes or protein sensors do not require washing steps and are therefore amenable to high-throughput screening or testing of ion channel ligands. But voltage control is also limited, and voltage-dependent or use-dependent ligand effects may be missed.

1.06.7

Emerging technologies and ion channel pharmacology

As described earlier in this article, ion channels are a diverse family of proteins that play critical roles in multiple physiological processes. A wide variety of ligands modulate their activity in many ways and ion channels are therefore considered tractable therapeutic targets. However, the development of novel ion channel medicines has been challenging despite advances in techniques such as automated electrophysiology. A major hurdle is the identification of efficacious and safe subtype-selective ion channel modulators. A prominent example is the quest for selective voltage-gated sodium channel inhibitors. Pain management with non-addictive therapies is a top public health priority. Three sodium channel subtypes, Nav1.7, Nav1.8, and Nav1.9, have been reported to play a major role in central and peripheral pain transduction (Ma et al., 2019). Nav1.5 channels underlie the upstroke of cardiac action potentials and ligand selectivity over these channels is key. Selective Nav blockers such as A-803467 (Jarvis et al., 2007) and PF-05089771 (Theile et al., 2016) have been reported but have failed in clinical trials (Kingwell, 2019). Novel means to

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identify subtype-specific ligands will expand the chemical space for sodium channel inhibitors and hopefully yield novel therapies for the treatment of pain. Advances in human genetics, structural biology, and computational methods offer new avenues for the development of ion channel drugs.

1.06.7.1

Gene editing and genetics

Heterologous expression of ion channels in mammalian cells has been invaluable to the study of ion channel structure and function. However, ion channels may be formed by several subunits and may be regulated by signal transduction pathways that are specific to a cell type and that may not be recapitulated in a heterologous system. Recent advances in genome editing technologies, such as the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein 9 (Cas9) system, has opened the door to novel ways to study ion channel function and pharmacology, such as interrogating the role of a specific ion channel gene in a physiologically-relevant cell. For instance, CRISPR/Cas9 was used by two independent groups to show the TMEM206 gene in HEK293 and HeLa cells forms the pore of the widely expressed acid-sensitive outwardly rectifying anion channels (ASORs), also known as PACs, proton-activated chloride channels (Ullrich et al., 2019; Yang et al., 2019). To examine the specificity of clinical Nav1.7 compounds, McDermott and colleagues knocked out Nav1.7 in human iPSCs. They generated nociceptive neurons from these iPSCs and tested the effects of clinically relevant concentrations of two Nav1.7 blockers, PF-05089771 and BII074, on excitability (rheobase current or smallest injected current generating an action potential and action potential firing). BII074, but not PF-05089771, inhibited neural firing in control and Nav1.7 knockout cells, indicating that this compound affected other ion channels in those cells (Mcdermott et al., 2019). Similarly, point mutations may be introduced into ion channel genes to study their pharmacology in physiologically-relevant cell types. For instance, the hERG potassium channel mutant A561V does not traffic properly in cells and is associated with long QT syndrome. A small molecule screen in C. elegans worms expressing that mutant identified two potential correctors of A561V hERG trafficking, prostratin and ingenol-3,20-dibenzoate (IDB). To verify their efficacy in a disease-relevant setting, human iPSCs were edited with CRISPR/Cas9 to express A561V hERG and were subsequently differentiated into cardiomyocytes. The edited cells recapitulated the electrophysiological abnormalities of patient cells and both prostatin and IDB were able to correct those abnormalities by rescuing A561V hERG expression (Jiang et al., 2018). Conversely, CRISPR activation with catalytically dead Cas9 may be used for selective transcriptional activation (Gilbert et al., 2013). Enhanced expression of a specific ion channel in a disease-relevant cell type could be used to determine the need for functional agonists to reverse a disease phenotype. Finally, arrayed guide RNA libraries of human ion channels are available and constitute new tools to systematically probe the role of ion channels in specific physiological processes. The availability of ultra-high throughput and low-cost DNA sequencing has led to an explosion of genetic data in both the public and the private domains. The UK BioBank, for example, is an open source for genetic and phenotypic data collected from 500,000 individuals living in the United Kingdom (Bycroft et al., 2018). In addition, tens of millions of people have performed at-home mouth swabs for genetic testing by commercial companies. Some of these large data sets have been aggregated and analyzed for novel genetic associations and drug target validation (Diogo et al., 2018). The expansion of these genetic data sets will continue and fuel the discovery of additional functions for many genes, including ion channels. Marrying this information with gene editing and pharmacology will enable the development of better drugs in terms of disease efficacy and selectivity. For instance, such integration of pharmacological, gene editing, and genomic data recently uncovered biological pathways underlying anti-cancer drug efficacy in cellular models (Gonçalves et al., 2020).

1.06.7.2

Structural biology

For several decades, X-ray crystallography has been a must have technology for understanding the mechanism of ligand binding to soluble proteins and optimizing these ligands for binding affinity, efficacy, and selectivity. The field of ion channel biology had to wait until 1998 to see the first X-ray structure of an ion channel, the bacterial potassium channel KcsA, solved by the MacKinnon laboratory (Doyle et al., 1998; Mackinnon, 2003). Ion channel biophysicists could at last understand the structural basis for Kþ channel permeation. Since then, additional X-ray structures of ion channels have been solved but membrane protein structures still represent only 2% of all protein X-ray structures (Maveyraud and Mourey, 2020). It was not until breakthroughs in single-particle cryogenic electron microscopy (cryo-EM) in the early 2010s and their application to membrane protein structure that the field of ion channel structural biology really took off (Cheng, 2018). As of January 2020, there were about 380 ion channel structures in the Electron Microscopy Data Bank (EMDB, https://www.ebi.ac.uk/pdbe/emdb/), the major repository for cryo-EM structures, with over 220 structures released in 2018–2019 (Fig. 6). Cryo-EM structure resolution is also improving, approaching near-atomic resolution. Fifty percent of the ion channel structures released in 2019 had resolutions  3.6 Å versus none in 2015. While most cryo-EM studies have focused on mechanisms of gating and permeation, this level of resolution has allowed the localization and characterization of ligand binding sites in several ion channels. An important consideration for the structural analysis of ion channel function and pharmacology is the lipid environment of the purified protein. Ion channels are integral membrane proteins and critical information may be missed in detergent or other artificial systems. Furthermore, several ion channels are directly regulated by membrane lipids and their metabolites, such as phosphatidyl inositol lipids. For instance, cryo-EM structures of the GABAA channel in lipid nanodiscs have revealed a secondary benzodiazepine binding site that studies with solubilized channel proteins had not detected (Phulera et al., 2018; Zhu et al., 2018; Masiulis et al., 2019). Also, while the resolutions of most structures are sufficient for model building based on amino acid sequence, the map quality is often inadequate to precisely identify non-protein densities such as

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Fig. 6 Ion channel cryo-EM structures versus time, blue: number of ion channel sequences published to the Electron Microscopy Data Bank (EMDB, www.ebi.ac.uk/pdbe/emdb) through 2019, red: median resolution.

bound ligands or lipids. Therefore, ion channel cryo-EM studies include apo (ligand-free) and ligand-added samples to identify ligand binding sites by density map comparison. Mutagenesis studies often complement these initial structure determinations to further confirm binding site locations. For example, the determination of the structure of the Cav3.1 channel with and without a selective antagonist, Z944, allowed the identification of a binding site in a fenestration of the II–III domain linker, a region with low sequence homology between Cav channel subtypes (Zhao et al., 2019b). It therefore represents a potential Cav3.1specific druggable binding site.

1.06.7.3

Computational methods: Virtual screening, artificial intelligence, and machine learning

The increasing availability of high-resolution ion channel structures has opened the door to structure-based virtual screening for the discovery of novel ion channel ligands and binding sites. Feng and colleagues, for instance, used  2.5-Å cryo-EM structures of rat TRPV1 to build models of human TRPV1 ligand binding sites and conduct a virtual screen of  16,000 compounds. They identified and experimentally confirmed novel TRPV1 antagonists, validating this approach for TRPV1 ligand discovery (Feng et al., 2015). Similarly, 3.8- and 4.4-Å TRPV2 structures were used to identify three druggable sites in this channel and 200,000 compounds were screened virtually for binding to these sites. Six compounds out of fifty selected for testing showed inhibitory activity by whole-cell patch clamp and subsequent optimization of one of these compounds led to the identification of a novel and potent (TRPV2 IC50 ¼ 0.46 mM) TRPV2 inhibitor selective vs. TRPV1, TRPV3, and TRPV4 channels (Chai et al., 2019). Finally, Hughes and colleagues took a 3-Å resolution vanilloid binding pocket of TRPV5 and screened in silico a  12-million molecules library. They identified 65 unique scaffolds and tested 43 of these molecules in a whole-cell patch clamp assay. Two compounds inhibited TRPV5 currents. Testing analogs of the most active blocker identified a potent (TRPV5 IC50 ¼ 106 nM) and selective (TRPV6 IC50 > 10 mM) inhibitor. Mutagenesis studies coupled with structure determination of TRPV5 in the presence of this inhibitor and its parent compound revealed however two novel binding sites in that channel, highlighting limitations in that approach. Nevertheless, these binding sites represent an opportunity for the discovery of novel and selective TRPV5 antagonists (Hughes et al., 2019). Coupling structural data with automated electrophysiology methods and machine learning also represents an attractive alternative to conventional high-throughput methods for the discovery of novel ion channel ligands. It combines the gold standard method for studying these proteins, electrophysiology, to approaches that could potentially remove bottlenecks and bias in ligand discovery. Traditional methods to progress new compounds involve heuristics and efficiency measures such as lipophilic ligand efficiency (Hopkins et al., 2014). In contrast, computational approaches allow the systematic exploration of a wide range of parameters and provide a statistical framework for decision making. While very large compound libraries cannot be screened by automated electrophysiology, this method is amenable to iterative screening of tens to hundreds of thousands of compounds (Chambers et al., 2016).

1.06.8

Conclusion

Ion channels constitute a diverse set of pharmacological targets and, while druggable, have presented unique challenges to pharmacologists and drug hunters. Unlike 7TM receptors which, by definition, are well-defined structurally, ion channels are a heterogeneous family of proteins with minimal to extensive transmembrane regions and extremely diverse intra- and extra-cellular domains. However, ion channels within some families are highly homologous and ligand subtype selectivity is a major challenge, especially when affecting a specific subtype has undesirable physiological effects. In addition, channel activity and ligand binding are, in some cases, dependent on channel state or transmembrane voltage. The precise control of these parameters is only achievable with electrophysiological measurements. For a long time, ion channel studies were limited by the very low capacity of electrophysiological recordings and relied instead on less sensitive and less predictive methods such as ion flux or indirect fluorescence measurements. The advent of automated electrophysiology platforms in the early 2000s has allowed a broader application of

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electrophysiology. Though, despite these advances and extensive efforts, very few ion channel ligands have since successfully been developed into drugs. Recent developments in genetics, structural biology, and computational methods offer new avenues for the study of ion channel ligands, with better tools to genetically engineer ion channels and link them to disease or physiology, nearatomic resolution of ligand binding sites, and finally machine learning and artificial intelligence as new companions to pharmacologists. Thus, the field of ion channel pharmacology is poised to evolve and lead to novel medicines.

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Relevant Websites https://www.guidetopharmacology.org/GRAC/ReceptorFamiliesForward?type¼ICdIUPHAR/BPS Guide to PharmacologydIon Channels.

1.07

Nuclear Receptors

Sergio C. Chai and Taosheng Chen, Department of Chemical Biology and Therapeutics, St. Jude Children’s Research Hospital, Memphis, TN, United States © 2022 Elsevier Inc. All rights reserved.

1.07.1 1.07.2 1.07.3 1.07.3.1 1.07.3.2 1.07.3.3 1.07.4 1.07.5 1.07.6 1.07.7 1.07.8 1.07.9 1.07.10 1.07.11 1.07.12 References

Introduction Drug discovery of nuclear receptors Nuclear receptor domains DNA binding domain Activation function 1 Ligand binding domain Homo and heterodimerization of nuclear receptors Coregulatory recruitment to nuclear receptors Modulation of nuclear receptor activity Ligand binding to nuclear receptors Antagonism of nuclear receptor activity Diverging modulatory behavior of chemical analogs Allosteric sites for nuclear receptor antagonism Selectivity in nuclear receptor modulation Concluding remarks

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Glossary Agonist A chemical that activates the function of a receptor by binding to it. Antagonist A chemical that blocks the action of the agonist. Inverse agonist A chemical that causes the opposite response to that of the agonist. Ligand A small chemical that binds to a receptor eliciting a biological response. Transcription factor A protein that regulates transcription from DNA to mRNA by binding to specific DNA sequences.

1.07.1

Introduction

Nuclear receptors (NRs) are transcription factors that regulate a myriad of biological processes, including cell growth and development, metabolism, reproduction and inflammation (Evans, 2005; Laudet, 1997). Binding of small signaling molecules to NRs triggers a cascade of events that lead to a transcriptional response, including ligand-induced NR conformational changes, engagement to specific DNA sequences and recruitment of coregulatory proteins and transcriptional machinery (Tata, 2002). Unlike ligands that modulate the activity of cell surface receptors, NR ligands are sufficiently lipophilic to permeate into cells to interact with and alter the transcriptional activity of the corresponding NR. The first NR was biochemically identified in the 1960s as the receptor responsible for the cellular activity of estradiol, and two decades later the glucocorticoid receptor (GR) and estrogen receptor (ER) were among the first NRs to be cloned (Germain et al., 2006; Hollenberg et al., 1985; Greene et al., 1986; Green et al., 1986). Since then, 48 NRs have been recognized in humans (Fig. 1) (Germain et al., 2006), and phylogenetic studies indicate that NRs share a common ancestor that dates back to the earliest period of metazoan evolution (Laudet et al., 1992). Approximately, half of the 48 human NRs have identifiable ligands (Gronemeyer et al., 2004), such as hormones, retinoids and fatty acids. NRs with unknown or unspecified cognate ligands are described as orphan NRs (Robinson-Rechavi et al., 2003). However, it has been argued that the classification of NRs based on defined physiological ligands is vague and superficial (Olefsky, 2001). A unified nomenclature system for NRs has been proposed based on phylogenetic tree analysis (Auwerx et al., 1999), with the aim to avoid confusing nomenclature of the ever increasing number of discovered NRs; the NRs have been divided into seven subfamilies (Fig. 2). The widespread significance of NRs in human physiology and diseases is reflected by the extensive efforts in developing NR modulators as therapeutics. This book chapter provides highlights of NRs as targets in drug discovery, and it presents insights into the molecular mechanisms of NR modulation by ligands. The aim is to amalgamate basic principles and new breakthroughs governing the regulation of NR activity that can be utilized to broaden our understanding of how members of this superfamily function.

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Fig. 1

1.07.2

Nuclear Receptors

The NR superfamily. NR amino acid sequences have been clustered and displayed as circular tree.

Drug discovery of nuclear receptors

NRs play major roles in human diseases, and they have become important targets in drug discovery. At the molecular level, NRs provide attractive features for the development of therapeutics, because many of them contain well-defined pockets engulfed within the NR that are amenable for drug-like molecules to bind and exert a desired biological or pharmacological effect. There is also extensive mechanistic understanding of the ligand-induced gene regulation at the molecular and cellular levels. However, a single NR can control the regulation of several genes in complex pathways, and the challenge has been the discovery of NR modulators that can alter a selected subset of these genes (Huang et al., 2010). ERs are among the most studied NRs for therapeutic and clinical applications because of their involvement in cancer and the maintenance of reproductive organs, bone, cardiovascular and central nervous systems (Dahlman-Wright et al., 2006). The two subtypes, ERa and ERb, recognize the endogenous ligand 17b-estradiol (Huang et al., 2010), and they may be expressed similarly or differentially in tissues and cell types. The two forms have also distinct biological functions, and these differences in gene regulation sparked the interest in subtype-selective modulators. Selective ER modulators (SERMs) are a prominent group of ER ligands that can act as agonist or antagonist depending on the targeted tissue (Huang et al., 2010; Komm and Mirkin, 2014). Tamoxifen is arguably the most recognizable SERM, which is used in the treatment of ER-positive breast cancer as an ER antagonist, while its agonistic effect provides beneficial outcomes in bone density (Dahlman-Wright et al., 2006). In addition to having an effect on breast cancer, raloxifene and lasofoxifene are marketed for the treatment of osteoporosis.

Nuclear Receptors

Fig. 2

153

Classification of NRs. NRs have been classified into seven subfamilies. The formal and trivial nomenclature of NRs are indicated.

Androgen receptor (AR) is responsible for the development of male-specific differences and anabolism (Huang et al., 2010). It is activated by androgenic hormones, such as testosterone and the more potent metabolite 5a-dihydrotestosterone (Roy and Chatterjee, 1995). Androgens have been used therapeutically in the treatment of hypogonadism and in the promotion of muscle and bone development (Brown, 2004). Important applied areas of AR antagonists (antiandrogens) to counter the agonistic effects of androgens include acne, male pattern baldness and prostate cancer (Lu et al., 2006; Brown, 2004). Selective AR modulators (SARMs) aim to regulate AR activity in a tissue-selective manner in order to circumvent the duality of agonism and antagonism, thereby reducing undesirable effects (Brown, 2004). There is heightened interest in the development of nonsteroidal SARMs, particularly in the treatment of prostate cancer, such as the drugs flutamide, nilutamide and bicalutamide (Mohler et al., 2009). Moving away from steroidal scaffold would provide potential benefits in terms of selectivity and pharmacokinetic profiles (Brown, 2004). GR is one of the most intensely investigated NRs as a clinical target because of its participation in numerous physiological processes, including inflammation, glucose homeostasis, stress response, and the regulation of protein, carbohydrate and fat metabolism (Munck et al., 1990). Because GR regulates the expression of a multitude of genes, modulating the activity of GR results in considerable unintended side effects. Thus, the development of GR ligands that can affect the expression of a subset of GR target genes remains a major challenge in drug discovery (Schacke et al., 2002). The most relevant endogenous GR ligand in humans is cortisol, which is the predominant type of glucocorticoid in this species (Lu et al., 2006). Glucocorticoids have been among the most widely prescribed drugs, being used as anti-inflammatory agent and immunosuppressant (Jiang et al., 2015). A notable member of this class of drugs is dexamethasone, which is a synthetically modified hormone. The peroxisome proliferator-activated receptors (PPARs) are comprised of three isotypes: PPARa, PPARb/d and PPARg (Fruchart et al., 2019). Because of their importance in cellular metabolism, particularly in the metabolism and homeostasis of glucose and lipids, they have become drug targets against obesity and diabetes. Fibrates are a class of drugs with amphipathic carboxylic acids that are used for the control of elevated levels of cholesterol (hypercholesterolemia). Fibrates can be PPARa-selective, such as fenofibrate and gemfibrozil, or pan-agonists, such as bezafibrate (Fruchart et al., 2021). The thiazolidinediones are a class of heterocyclic compounds used to treat diabetes mellitus by targeting primarily PPARg. Several PPARd-selective agonists have been developed with the potential to have a positive impact in insulin resistance and obesity, with GW501516 being a candidate for clinical trials (Grewal et al., 2016). An approach analogous to the SERMs class of ER ligands has been implemented, known as selective PPAR modulators (SPPARMs), with the aim to improve potency, selectivity and tolerability (Fruchart et al., 2019). Some of the SPPARMs have been tested in clinical settings, and their combined use with pan PPAR ligands could promote significant therapeutic advancements (Shi, 2007).

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Pregnane X receptor (PXR) plays a prominent role in the detoxification system of xenobiotics and the homeostasis of endobiotics by regulating the expression of drug-metabolizing enzymes and transporters (Wang et al., 2012, 2013; Willson and Kliewer, 2002; Tolson and Wang, 2010). The detoxification process orchestrated by PXR provides a protective mechanism against deleterious compounds, but it can also decrease drug efficacy, increase drug resistance and induce toxicity from the drug-derived metabolic products. Considerable efforts have been vested in developing PXR antagonists that can be co-administered with therapeutics. In its role as xenosensor, PXR is highly promiscuous in binding and being activated by a vast array of compounds, which impeded the discovery of ligands that can abrogate PXR’s activity instead of activating it. SPA70 was reported as a highly selective and potent PXR antagonist (Lin et al., 2017; Li et al., 2021), and the discovery of such PXR antagonist demonstrates the feasibility of developing PXR-selective modulators amid the vast challenges due to PXR’s ligand promiscuity. In contrast, PXR agonists have been considered in the treatment of inflammatory bowel disease (IBD) (Cheng et al., 2012), and rifaximin was shown to provide significant improvements in clinical settings (Khan et al., 2011), leading to its approval by the FDA.

1.07.3

Nuclear receptor domains

NRs are composed of several domains and share common structural organization (Figs. 3 and 4). The most notable subunit is the ligand-binding domain (LBD), which is situated at the C-terminus and is moderately conserved (Bourguet et al., 2000a). The LBD recognizes ligands that direct the NR’s biological activity. The multifunctional LBD is connected to an N-terminal DNA-binding domain (DBD) through a highly flexible hinge. The core of the DBD is highly conserved, and it allows the NR to target specific DNA sequences encompassing a response element.

1.07.3.1

DNA binding domain

NRs can bind to DNA response elements as monomers or homodimers, or by heterodimerization with other NRs (Fig. 5). While dimerization allows for NRs to bind to target genes with higher specificity and affinity compared to the monomeric form, heterodimerization would expand the DNA sites that a homodimer would not be able to engage to (Germain et al., 2003). NRs can be arranged into three groups depending on how they interact with the target DNA (Helsen et al., 2012; Germain et al., 2003). NRs that bind DNA as homodimers are exemplified by GR (Meijsing et al., 2009) and AR (Shaffer et al., 2004). Several other NRs heterodimerize with the retinoid X receptors (RXR) in order to bind to DNA, including the retinoic acid receptor (RAR) (Rastinejad et al., 2000) and the vitamin D receptor (VDR) (Shaffer and Gewirth, 2004). A third group of NRs have the ability to interact with DNA as monomer, such as the steroidogenic factor (SF-1) (Little et al., 2006). NRs recognize specific DNA sequences within the response element comprised of variations of the hexameric motifs 50 -PuG(G/ A)(T/A)CA-30 and 5’-PuG(G/T)TCA-30 , which are half-sites recognized by a particular NR (Gronemeyer and Moras, 1995). Pairs of hexameric sequences can be present in different configurations, including direct, indirect, inverted repeats, and the sequences can be found as isolated half sites (Helsen and Claessens, 2014; Freedman and Luisi, 1993; Glass, 1994). For NRs that work in unison as a dimeric complex, the two half-sites can be separated by a spacer of varying length. Steroid receptors such as AR, GR and mineralocorticoid receptor (MR), bind as homodimer preferentially to the 50 -AGAACA-30 motif as inverted repeats with a spacer of 3 nucleotides (IR3) (Helsen and Claessens, 2014). Some NRs that form heterocomplex with RXR prefer direct repeats (DR) of the 50 -AGGTCA-30 motif of various lengths, such as RAR, VDR and PPAR. Variations in the sequence of the hexameric motif, changes in the relative orientation of the repeats and flanking sequences provide target gene specificity for a particular NR (Gronemeyer and Moras, 1995). The highly conserved DBD harbors two zinc-finger motifs: the N-terminal motif (CI) is composed of the sequence Cys-X2-CysX13-Cys-X2-Cys and the C-terminal motif (CII) contains Cys-X5-Cys-X9-Cys-X2-Cys, where the cysteine residues are separated by a X number of residues (Germain et al., 2003). Each one of them contains a zinc atom coordinated by four cysteine residues. These zinc-binding motifs are necessary for NR function and structural stability of the DBD (Freedman et al., 1988). In addition, several elements within the DBD contribute to the recognition specificity of DNA response elements, such as the P-box (proximal box), Dbox (distal box), A-box and T-box (Germain et al., 2003). In addition to making significant contact with DNA, the C-terminal extension (CTE) region accommodating the A-box and T-box participates in NR dimerization (Zechel et al., 1994a,b). Another noticeable

Fig. 3 Modular domains of NRs. The N-terminal activation function 1 (AF-1), DNA binding domain (DBD), hinge and the ligand binding domain (LBD) are depicted in contrasting colors.

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Fig. 4 The crystal structure of the full-length PPAR (cyan) in complex with the full-length RXR (pink). The heterodimer engages DNA (light brown) through the DBD (PDB code 3DZY).

feature in the structure of DBD is the presence of a pair of a helices. The N-terminal helix (helix 1) makes significant contact with DNA, particularly the major grooves, while the C-terminal helix (helix 2) is arranged perpendicularly to helix 1 and provides stability to the globular DBD structure (Glass, 1994; Freedman and Luisi, 1993).

1.07.3.2

Activation function 1

The N-terminal region of NRs that is adjacent to the DBD harbors the activation function 1 (AF-1) domain, which displays high amino acid sequence and length variability. For instance, the AF-1 of MR is approximately 7 and 25 times larger than that of RAR and VDR, respectively (Evans, 1988). The AF-1 may be involved in important transcriptional activation functions independently from ligand binding to the LBD.

1.07.3.3

Ligand binding domain

The LBD has several important functions related to ligand binding, including the recognition of ligands, protein–protein interactions with coregulatory partners and other NRs, and nuclear translocation (Baniahmad and Tsai, 1993). A prominent characteristic of the LBD is in the capacity to recognize particular ligands that reside in the ligand binding pocket, which is centrally located within the LBD (Huang et al., 2010). The LBD is composed of a cluster of a-helices (H1–H12), with some of the helices missing or modeled as an extended loop in some NRs (Ingraham and Redinbo, 2005; Buchman et al., 2018; Germain et al., 2003). The helices are arranged in a layered helical sandwich fold, with a small number of b strands (Huang et al., 2010). Most of the residues lining the ligand binding pocket are hydrophobic, and the few polar residues have been shown to participate in important hydrogen bond interactions with the ligand (Huang et al., 2010). There are considerable differences in the volume of the ligand binding pocket, which can expand in the presence of a ligand. For instance, the volumes of AR, PR and PXR are 420 Å3, 560 Å3 and 1544 Å3, respectively (Watkins et al., 2003a,b; Moore et al., 2006).

1.07.4

Homo and heterodimerization of nuclear receptors

The multifunctional LBD is seen to participate in oligomerization, where most NRs functions as homodimer or heterodimer (Fig. 6). The canonical dimerization surface is generally formed by helices H7, H9 and H10, and the interface area can span between 1000 Å2 and 1500 Å2 (Brelivet et al., 2012). Based on sequence analysis aided by structural information, two types of oligomeric behaviors have been defined (Brelivet et al., 2004). Class I corresponds to NRs that form homodimers, while class II encompasses those that participate in heterodimerization with RXR. Both classes display corresponding signature motifs that involves salt bridges. Fig. 6A illustrates the homodimerization of ERa (Brzozowski et al., 1997), while Fig. 6B shows the heterodimerization complex between RAR and RXR (Bourguet et al., 2000b). However, a differing homodimerization conformation is formed by a subgroup

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Fig. 5 (A) Binding of the DBD of GR to DNA as homodimer, with each monomer pictured in a different shade of blue (PDB code 3G99). (B) Binding of the DBDs of RAR (blue) and RXR (light orange) to DNA as heterodimer (PDB code: 1DSZ). (C) Binding of the DBD of SF-1 (blue) to DNA as a monomer (PDB code: 2FF0). The zinc atoms are illustrated as spheres (raspberry red) and DNA is represented as cartoon, with the backbone colored orange.

of steroid receptors that include AR, GR, progesterone receptor (PR) and MR. Unique structural differences that include the insertion of a beta sheet at the C-terminal helix disrupt the canonical homodimer pattern (Williams and Sigler, 1998). A unique homodimerization modality has also been observed in several PXR LBD structures, where the dimer interface is formed by the terminal b10 strands from each monomer, a structural element not observed in other NRs (Noble et al., 2006). One of the major mechanisms that allow for a stable PXR homodimer formation is the interlocking of the corresponding Trp-223 and Tyr-225 residues from each PXR monomer. Mutagenesis studies using Trp-223-Ala and Tyr-225-Ala double-mutant constructs confirmed the importance of these residues in homodimerization, which was reported to be necessary for the transcriptional activity of PXR. Biophysical and cell-based studies validated the numerous crystal structures that showed PXR as homodimer, which was assumed to be a crystallographic artifact. Retinoid X receptor a (RXRa) is a ubiquitous heterodimerization partner for several NRs (Bourguet et al., 2000b). Numerous LBD crystal structures of various NRs have been obtained with RXR, which reveal a relatively uniform orientation of the monomers with some differences in translation and rotation. The structure of the PXR LBD-RXRa LBD reveals a heterodimerization surface of approximately 1200 Å2, which is formed by PXR’s helices H5/H9/H10 and RXR’s helices H7/H9/H10 (Wallace et al., 2013). There seems to be an interplay between ligand binding, heterodimerization with RXR and coregulatory protein recruitment. It has been reported that ligand binding stabilizes the heterodimerization of VDR with RXR (Zhang et al., 2010). The PXR–RXRa complex was shown to have increased affinity for the steroid receptor coactivator 1 (SRC-1) coactivator peptide compared to the

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Fig. 6 NRs can form homodimer and heterodimer complexes. (A) Homodimerization of ERa, with each monomer colored in a different shade of blue (PDB code: 1ERE). (B) Heterodimerization of RAR (blue) with RXR (orange) (PDB code: 1DKF). (C) PXR displays a novel homodimerization interface, with each monomer displayed in differing shades of blue. Each PXR monomer in the homodimer complex interact with RXR (shades of orange) to form a heterodimeric set (PDB code: 4J5X).

monomeric forms (Wallace et al., 2013). However, interesting exceptions have been noted, such as the lack of heterodimerization disruption by the binding of the antagonist SR11023 and the partial inverse agonist SR10171 to the PPARg-RXRa complex (Frkic et al., 2018).

1.07.5

Coregulatory recruitment to nuclear receptors

NRs elicit their transcriptional response in partnership with coregulatory proteins (cofactors), such as coactivators and corepressors. The type of the coregulatory partner being recruited is contingent upon the nature of the ligand, which could be an agonist, antagonist, or inverse agonist. Upon ligand binding to the LBD, an agonist triggers conformational changes in the structure of the LBD that results in the recruitment of coactivator proteins (Dussault et al., 2002). A notable LBD structural feature is the positioning of the AF-2 helix in a specific active orientation, whereby exposing a favorable surface in the AF-2 region for coactivator engagement (Fig. 7A). The AF-2 helix caps the ligand binding pocket housing the agonist, whose position is stabilized by interactions between its residues and those of the LBD body, and in many cases, interactions with the agonist. Coactivators are recruited to agonist-bound NRs to initiate transcriptional activity. SRC-1 was the first coactivator identified (Onate et al., 1995), a member of a group of more than 200 coactivators reported to date (Huang et al., 2010). These coregulators have wide diversity in terms of function, and tissue and cellular distribution. The p160 family of coactivators include SRC-1, SRC-2 (also known as GRIP1 and transcription intermediary factor-2 (TIF2)) and SRC-3 (also known as RAC3, ACTR, pCIP and TRAM-1) (Chen et al., 2000; Germain et al., 2006). Coactivators contain the NR box motif Leu-Xxx-Xxx-Leu-Leu (where Xxx is any other amino acid residue) that is important for engaging the NR at the AF-2 surface. The LBD of many NRs have been crystallized in

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Fig. 7 Differential orientation of the AF-2 helix (red, cartoon) in the agonist-bound active conformation and the inactive apo form. (A) The agonist (yellow, spheres) resides in the ligand binding pocket within the LBD of PXR (blue, surface), triggering the positioning of the AF-2 helix in the active conformation favorable for coactivator recruitment (PDB code 5X0R). (B) The unliganded form of TR4 displaying the AF-2 helix in an inactive position, where it blocks coregulatory proteins from engaging in the AF-2 surface (PDB code 3P0U).

the presence of a coactivator peptide containing this motif. In the case of PXR, the SRC-1 peptide engages a groove formed by the helices H3, H4 and the AF-2 helix. The coactivator peptide is further stabilized by a charge clamp (Watkins et al., 2003a). Corepressors, being the functional counterparts of coactivators, are involved in transcriptional silencing of NRs. Notable members of this coregulatory group include the nuclear receptor corepressor (NCoR) and silencing mediator for retinoid or thyroid hormone receptors (SMRT) (Germain et al., 2006). NRs have displayed a preference for certain corepressor or CoRNR box (Hu et al., 2001), which is a segment within the corepressor containing the motif Ile/Leu-Xxx-Xxx-Ile/Val-Ile (Di Masi et al., 2009). The corepressor peptide interacting site has been mapped by structural studies in a region analogous to where the coactivator peptide engages the LBD (Xu et al., 2002).

1.07.6

Modulation of nuclear receptor activity

In the absence of ligand (apo state), most NRs have limited basal or nonexistent transactivation function because the AF-2 helix is not stabilized in the active conformation, which is a prerequisite for coactivator recruitment. The AF-2 helix orientation is therefore a dominant factor of NR activation and repression by guiding the association or dissociation of coregulators to the NR (Fig. 7). Except for those NRs with constitutive activity, the AF-2 of the apo NR is highly mobile and unable to secure its orientation in either the active or repressive conformation. The wavering character of the AF-2 has been examined in solution-based experiments, and its fluidity has been inversely correlated to ligand binding. The dynamic motion of the AF-2 has been observed in PPAR by fluorescence anisotropy (Kallenberger et al., 2003), in VDR by nuclear magnetic resonance (NMR) studies (Singarapu et al., 2011) and in thyroid hormone receptor (TR) by hydrogen/deuterium exchange coupled with mass spectrometry (HDX-MS) (Figueira et al., 2011). Therefore, the notion that the AF-2 helix is present in either one of two discrete states (the active or inactive states) is shortsighted, and accumulating data substantiates that it can adopt several intermediate configurations. Such multiplicity in AF-2 helix orientations in the unliganded NR has been concluded from NMR studies of PPARg, where the ligand binding reduces and limits the subset of AF-2 helix conformations (Johnson et al., 2000). Stabilization of the AF-2 helix in VDR in a graded manner by full and partial agonists have been reported based on HDX-MS (Zhang et al., 2010). The unsteadiness and ample mobility of the AF-2 helix in the unliganded NR seen in solution-based experiments cannot be easily observed by crystallography. In most of the crystal structures of NRs that have been obtained in the apo-form, the AF-2 helix has been modeled in the active conformation similarly as in structures of NRs with agonists (Frkic et al., 2018). This dissonance has been ascribed to biologically harsh conditions needed to obtain crystals for X-ray diffraction (Kojetin and Burris, 2013). However, a handful of crystal structures of various NRs have shown the AF-2 helix in a different orientation from that of the active state. The AF-2 helix in the structure of the apo RXRa is seen stretching away from the LBD in an arrangement that is notably distinct from that in the agonist-induced active form (Bourguet et al., 1995). A very different placement of the AF-2 helix has been observed in the structure of the unliganded testicular receptor 4 (TR4), where the AF-2 helix is lodged in the same area that coactivator peptides are known to engage with (Zhou et al., 2011) (Fig. 7B). The AF-2 helix in the unliganded form of the chicken ovalbumin upstream

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promoter-transcription factor II (COUP-TFII) is seen positioned in a similar orientation (Kruse et al., 2008). Consequently, the structures of TR4 and COUP-TFII are examples of self-inhibition conformations of the LBD because the AF-2 helix would prevent the recruitment of coregulatory proteins.

1.07.7

Ligand binding to nuclear receptors

The crystal structures of most NRs reveal that the ligand is completely engulfed deep within the core of the LBD, with the AF-2 helix forming a cap that seals the enclosed compartment. Analysis of such liganded NR crystal structures would indicate the lack of obvious ligand entry points to the ligand binding pocket (Martinez et al., 2005). For a ligand to access and be buried in the ligand binding pocket, the LBD must undergo structural rearrangements, which could indicate partial unfolding of the LBD. Molecular dynamics simulation studies have identified several flexible regions within the LBD of PPARg that could potentially serve as ligand entry gates (Aci-Seche et al., 2011). Similarly, ligand escape routes in the LBD of TR have been proposed involving conformational reorganizations of helices and b-sheets (Martinez et al., 2005). In addition to restraining the AF-2 helix, ligand binding appears to enhance the overall structural stability of the LBD. The apo form of the constitutive androstane receptor (CAR) appeared as partially unstructured when analyzed by protein thermal shift studies because no discernible melt curve was observed. However, the characteristic thermal shift curve appeared in the presence of a ligand, which presumably is needed for a more structured LBD fold (Cherian et al., 2018). Several malleable regions in PXR and CAR have been identified by HDX-MS that become more rigid in the presence of ligand as indicated by the level of deuterium exchange between solvent-exposed protein residues and the aqueous environment (Lin et al., 2017; Cherian et al., 2018). A fully folded and compact LBD structure would increase the stability at the protein–protein interface, which is needed for favorable interactions with coregulatory proteins (Johnson et al., 2000). There are some NRs that can recognize and bind to a wide array of chemicals. Among the most prominent NRs in this group are PXR and CAR, two transcription factors that regulate an array of genes involved in the metabolism and elimination of endogenous and exogenous compounds (Kliewer et al., 2002; Moore et al., 2000). They have the ability to interact with a variety of xenobiotics and physiological metabolites with differing physicochemical characteristics, including molecular scaffold, size and lipophilicity (Chai et al., 2020). They are branded as master xenobiotic sensors because of their promiscuous nature in ligand recognition, particularly PXR (Di Masi et al., 2009). Their promiscuity has been attributed to the highly flexible and plastic nature of the LBD, which can mold its shape to accommodate the large diversity of ligands. A growing number of crystal structures of PXR with agonists provide insights into the plasticity of the LBD (Chai et al., 2016). The ligand binding pocket of the apo PXR has a volume of approximately 1150 Å3, which is significantly larger than those of other NRs (Watkins et al., 2001). Depending on the bound ligand, the volume of the ligand binding cavity has been observed to expand to 1344 and 1544 Å3 in the presence of SR12813 and hyperforin, respectively (Watkins et al., 2003a,b). CAR has a smaller ligand binding pocket, with volumes ranging from 525 to 675 Å3, which could explain why CAR is less promiscuous than PXR (Suino et al., 2004; Xu et al., 2004; Shan et al., 2004). NRs with relatively spacious ligand binding pockets can accommodate the ligand in more than one orientation. The PXR agonist SR12813 has been shown to reside in the ligand binding site of PXR in three distinct poses, but the presence of a coactivator peptide locks the agonist in a single orientation (Watkins et al., 2001). It is plausible that the binding of the coactivator peptide to PXR’s LBD restricts the fluidity of the LBD. The 22S-hexyl analog of the VDR agonist 1a,25-dihydroxyvitamin D3 was designed to be a potent antagonist, but it resulted in being a partial agonist (Anami et al., 2015). Surprisingly, crystallography unveiled that the ligand was situated in the ligand binding site in several positions; some of these poses would induce agonism, while other orientations encourage antagonism.

1.07.8

Antagonism of nuclear receptor activity

NRs play important roles in physiological functions and in the pathology of disease, where ligand-induced activation of certain NRs lead to undesirable outcomes. Therefore, considerable effort has been devoted in the development of modulators that can antagonize NR function. These antagonists compete with agonists for occupancy in the same ligand binding pocket. In contrast to agonists, antagonists and inverse agonists fail to trigger conformational changes in the LBD to recruit coactivators. Two modes of antagonism have been proposed depending on how the antagonist prevents the AF-2 from being stabilized in the active conformation for coactivator association (Shiau et al., 2002). Active antagonists possess a bulky chemical group that protrudes from the ligand binding site, clashing with the AF-2 helix when it is positioned in the active configuration, thereby impeding the recruitment of coactivator (Kojetin and Burris, 2013). The VDR antagonists ZK168281 and related molecules, which are chemically related to the secosteroid scaffold of the potent endogenous VDR agonist 1,25(OH)2D3, have a bulky ester group that obstructs the AF-2 helix from attaining a proper orientation conducive to coactivator binding (Bury et al., 2000). The X-ray structure of ER in complex with the antagonist raloxifene reveals that the sizeable ligand cannot be comfortably contained within the ligand binding pocket, and it sterically perturbs the AF-2 helix from adopting a favorable configuration for coactivator recruitment (Brzozowski et al., 1997; Pike et al., 1999). The ERa antagonist 4-hydroxytamoxifen has a long chemical group that causes major structural distortions in the LBD that ultimately preclude the AF-2 helix from adopting the agonist-induced configuration (Shiau et al., 1998). In addition, these rearrangements favor the AF-2 helix to occupy the coactivator peptide-binding groove.

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The second type of antagonists are characterized by compounds that reside in the ligand binding site, but they fail to stabilize the AF-2 helix in the agonist-induced conformation because of poor or insufficient interactions between the antagonist and residues of the AF-2 helix (Hu et al., 2014). These passive antagonists also differ from the active antagonists in their inability to “actively” dislodge the AF-2 from occupying the active form. The lactone derivatives TEI9647, TET9648, and LAC67a are VDR antagonists that are unable to interact robustly with residues of the AF-2 helix (Yoshimoto et al., 2008; Miura et al., 1999). Crystallographic studies of VDR in complex with 22S-butyl-25,26,27-trinor-1a,24-dihydroxyvitamin D3 indicate poor interactions between the compound and the AF-2 helix in addition to the appearance of a small cavity in the ligand binding site that is not present in other VDR-ligand structures (Inaba et al., 2009). The novel ligand-induced cavity accommodates the butyl group of the compound, coined as the “butyl pocket,” and the formation of the butyl pocket strongly correlates with the agonism or antagonism of VDR by fine-tuning NR modulation (Yoshimoto et al., 2012).

1.07.9

Diverging modulatory behavior of chemical analogs

Many NRs are characterized for having ligand selectivity. However, it is interesting to note that within a series of ligands sharing a common chemical scaffold, minute modifications such as the insertion, removal or replacement of side chains can convert compounds that activate NR to chemicals that abrogate NR transcriptional function. These families of analogs provide invaluable insight into the mechanism of NR activation and inhibition, particularly when structure–activity relationship studies are combined with protein structural information. Agonism, partial agonism and antagonism have been observed in VDR by several 22-alkyl substituents of the hormone 1a,25-dihydroxyvitamin D3 (Sakamaki et al., 2010). Similarly, a large set of chemicals based on the scaffold of the selective PXR antagonist SPA70 encompass the various types of modulation, including full and partial agonism, antagonism, inverse agonism, and some with dual behavior (Li et al., 2021).

1.07.10 Allosteric sites for nuclear receptor antagonism Modulators that target NRs in regions other than the ligand binding pocket have been reported in a limited capacity. In principle, compounds that alter protein–protein interactions can be applied to either disrupt or enhance the recruitment of partner proteins. However, the difficulty of targeting such allosteric sites resulted in limited examples; some of these compounds are coined as NR alternate-site modulators (Moore et al., 2010). Certain pyrimidine compounds that obstruct the interactions of ERa with the SRC-1 peptide are among the earliest reported of such modulators (Rodriguez et al., 2004; Parent et al., 2008). The crystal structure of TRb in complex with HPPE shows the compound occupying a concave pocket on the AF-2 surface, thus blocking access to coactivator peptides (Estebanez-Perpina et al., 2007b). Similarly, crystallographic studies demonstrated that hydroxytamoxifen occupies the coactivator-binding groove of ERb (Wang et al., 2006), although the second site occupied by hydroxytamoxifen is speculated to be an artifact due to high compound concentrations used during crystallography. Certain compounds have been reported to compete with coactivators for access to the AF-2 surface of PXR, such as ketoconazole (Ekins et al., 2007; Wang et al., 2007; Huang et al., 2007) and SPB03255 and SPB00574 (Ekins et al., 2007). Chemicals have been identified to perturb allosterically coactivator association to AR by binding to a distinct hydrophobic site, known as binding function 3 (BF-3) (Estebanez-Perpina et al., 2007a).

1.07.11 Selectivity in nuclear receptor modulation A major challenge has been the modulation of a particular NR with minimal functional interference of other NRs; this obstacle is further confounded for isoforms and paralogues. For instance, ERa and ERb have distinct pharmacological behavior and are differentially expressed in tissues (Gronemeyer et al., 2004). Other prominent examples include the three forms of PPAR (PPARa, PPARb/ d and PPARg) and RAR (RARa, RARb and RARg). Extensive endeavors are underway in developing isotype-selective ligands that have shown much promise in eliciting a desired therapeutical outcome while reducing unwanted side effects, such as BMS453, which is a RARa antagonist and a RARb agonist (Chen et al., 1995). Fine-tuning NR modulation based on tissue or cell type is exemplified by SERMs, which are ER ligands that behave either as agonist or antagonist depending on the cellular context. It is postulated that SERMs, such as raloxifene, are capable of recruiting both coactivator and corepressor because the ligand can induce the AF-2 helix to be oriented in intermediate states of agonism and antagonism instead of the two discrete states of active and inactive conformation (Gronemeyer et al., 2004). This approach has been intensely tested in other NRs, resulting in the modulators SPRMs (Lee et al., 2020), SARMs (Brown, 2004) and SPPARMs (Fruchart et al., 2019) that target PR, AR and PPAR, respectively. Differences in NR modulation have been observed among species within a particular type of NR, which can be ascribed to significant differences in the amino acid sequences. The LBD of PXR is notable for exhibiting species-selective ligand induction (Ekins et al., 2002). For instance, rifampicin and SR12813 are strong human PXR (hPXR) agonists, but they have no effect on the mouse PXR (mPXR). On the other hand, PCN can robustly activate mPXR without having significant effect on hPXR (Moore et al., 2000). Even though there are numerous residues that are different between the hPXR and mPXR LBDs, a small number of critical residues

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are responsible for the selectivity between the two orthologs: mutations in selected residues in mPXR to mimic those in hPXR allowed for the mPXR mutants to be activated by SR12813 and not PCN (Watkins et al., 2001). Species-based modulation of NRs can have drastic impact on the pharmacology and functional investigations of ligands in organisms, especially when data from animal models are utilized to extrapolate potential effects of these modulators in humans. In the case of hPXR, humanized mouse models are employed in the evaluation of hPXR ligands. Strategies in the generation of humanized mouse models of PXR include insertion of the hPXR gene into the mouse genome lacking the mPXR gene (Xie et al., 2000), and the expression of a chimeric PXR containing mDBD and hLBD (Igarashi et al., 2012).

1.07.12 Concluding remarks NRs encompass a large number of transcription factors with complex biological roles, and they have been the focus of extensive drug discovery efforts. Challenges arise due to the regulation of multiple target genes by a single NR. In addition, many NRs have shown a certain degree of ligand promiscuity due to the inherent plasticity of the LBD, which complicates the development of NR selective modulators. Ligands that are dependent on tissue or cellular context hold great promise as therapeutics with improved pharmacological characteristics and tolerability. However, much of the mechanism of such modulators remain unclear at the cellular level. At the molecular level, structural studies using full length NRs to understand the communication pathways between domains will significantly enlighten the roles of flexible NR regions. The effect of NR post-translational modifications on agonism and antagonism is an area scantily explored and could impact the design of selective modulators. Another difficult and yet important aspect is to gain insight into the ligand-dependent cross talk between NRs.

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1.08 Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs Nikki J. Clauss and Lynette C. Daws, Departments of Cellular & Integrative Physiology and Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States © 2022 Elsevier Inc. All rights reserved.

1.08.1 1.08.2 1.08.2.1 1.08.2.2 1.08.2.3 1.08.2.4 1.08.3 1.08.3.1 1.08.3.2 1.08.3.3 1.08.3.3.1 1.08.3.3.2 1.08.3.4 1.08.3.4.1 1.08.3.4.2 1.08.3.4.3 1.08.3.4.4 1.08.3.4.5 1.08.3.4.6 1.08.3.4.7 1.08.3.4.8 1.08.3.4.9 1.08.3.4.10 1.08.4 1.08.4.1 1.08.4.2 1.08.4.3 1.08.4.3.1 1.08.4.3.2 1.08.4.3.3 1.08.4.4 1.08.4.4.1 1.08.4.4.2 1.08.4.4.3 1.08.4.4.4 1.08.4.4.5 1.08.4.4.6 1.08.5 1.08.5.1 1.08.5.2 1.08.5.3 1.08.5.3.1 1.08.5.3.2 1.08.5.4 1.08.5.4.1 1.08.5.4.2 1.08.5.4.3 1.08.5.4.4 1.08.6 1.08.6.1 1.08.6.2 1.08.6.3

Introduction Overview Location Function Structure Regulation Serotonin transporter Brain location Gene variants Interaction with psychoactive drugs Psychotherapeutics Stimulants Therapeutic application Depression Post-traumatic stress disorder (PTSD) and related anxiety disorders Autism Eating disorders Premenstrual disorders and menopausal vasomotor symptoms Myocardial infarctions Pain Gastrointestinal disorders Epilepsy COVID-19 Dopamine transporter Brain location Gene variants Interaction with psychoactive drugs Amphetamines Cocaine Synthetic cathinones Therapeutic application Attention deficit hyperactivity disorder (ADHD) Parkinson’s disease (PD) Psychostimulant abuse Obesity Topical anesthetic Analgesic Norepinephrine transporter Brain location Gene variants Interaction with psychoactive drugs Psychotherapeutics Stimulants Therapeutic applications Depression Smoking cessation Cardiovascular disorders Other indications GABA transporters Brain location Gene variants Interaction with psychoactive drugs

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1.08.6.3.1 1.08.6.3.2 1.08.6.3.3 1.08.6.4 1.08.6.4.1 1.08.6.4.2 1.08.6.4.3 1.08.7 1.08.7.1 1.08.7.2 1.08.7.3 1.08.7.4 1.08.7.4.1 1.08.7.4.2 1.08.7.4.3 1.08.8 1.08.8.1 1.08.8.2 1.08.8.3 1.08.8.3.1 1.08.8.3.2 1.08.8.3.3 1.08.8.3.4 1.08.8.3.5 1.08.8.4 1.08.8.4.1 1.08.8.4.2 1.08.8.4.3 1.08.8.4.4 1.08.9 Acknowledgments References Relevant Websites

Opioids Cannabinoids Anticonvulsant drugs Therapeutic application Seizures Anxiety and depression Pain management Glycine transporters Brain location Gene variants Interaction with psychoactive drugs Therapeutic application Hyperekplexia and glycine encephalopathy Schizophrenia Pain management Organic cation transporters Brain location Gene variants Interaction with psychoactive drugs Antidepressant drugs Antipsychotic drugs Psychostimulant drugs Anti-parkinsonian drugs Opioid analgesic drugs Therapeutic application Depression and stress-related disorders Psychostimulant use disorder Autism Parkinson’s disease Concluding remarks

186 186 186 186 186 186 186 187 187 187 187 187 188 188 188 188 188 189 189 189 189 189 190 190 190 190 190 191 191 191 192 192 204

Glossary Maximal velocity (Vmax) The transport rate attained when the transporter is saturated with substrate. Michaelis constant (KM) The concentration of a given substrate that is half the maximal velocity of transport. Missense mutation A mistake in the DNA when the change of a single base pair causes the substitution of a different amino acid in the resulting protein. This is a type of nonsynonymous mutation. Nonsynonymous mutation A nucleotide substitution that changes the corresponding amino acid in the protein. Stoichiometry The number of ions co-transported per substrate molecule. Synonymous mutation A nucleotide substitution that does not change the amino acid in the protein. More common than nonsynonymous mutations.

Nomenclature L/L Knockout [3H] Tritiated D/L Heterozygote D/D/WT Wild-type 2-AG 2-arachidonoylglycerol 4-MCC 4-methyl-N-methylcathinone (mephedrone) 5-HT 5-Hydroxytrytamine/Serotonin

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs

5-HTTLPR 5-HT gene-linked polymorphic region AD Autosomal dominant ADHD Attention deficit hyperactivity disorder Akt Protein kinase B AN Anorexia nervosa AR Autosomal recessive ASD Autism Spectrum Disorders ATPase Adenosine triphosphatase BED Binge eating disorder BGT1 Betaine GABA transporter BN Bulimia nervosa CaMKII Calcium calmodulin dependent protein Kinase II Chet Compound heterozygotes ClL Chloride CNS Central nervous system COOH Carboxyl group CSF Cerebral spinal fluid D22 Decynium-22 DA Dopamine DAT Dopamine transporter DTDS Dopamine transporter deficiency syndrome ERK1/2 Extracellular signal regulated kinase ½ FDA Food and Drug Administration GABA Gamma-aminobutyric acid GAD Generalized anxiety disorder GAT Gamma-aminobutyric acid transporter GI Gastrointestinal GlyR Glycine receptor GlyT Glycine transporter IC50 Half maximal inhibitory concentration JNK c-Jun NH2-terminal kinase KD Potassium Ki Inhibitory constant KM Michaelis constant L Long allele LeuT Leucine transporter MAO Monoamine oxidase MAPK Mitogen-activated protein kinase MDMA 3,4-methylenedioxymethamphetamine MDMC 3,4-methylenedioxy-N-methylcathinone (methylone) MDPV 3,4-methylenedioxypyrovalerone MI Myocardial infarction MPH Methylphenidate MPPD 1-methyl-4-phenylpyridinium NaD Sodium NAcc Nucleus accumbens NE Norepinephrine NET Norepinephrine transporter NH2 Amino group NKH Non-ketotic hyperglycemia nM Nanomolar NMDA N-methyl D-aspartate NMDAR N-methyl D-aspartate receptor NTTs Neurotransmitter transporters

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OCD Obsessive compulsive disorder OCT Organic cation transporter PAG Periaqueductal gray PCP Phencyclidine(phenylcyclohexyl piperidine) PD Parkinson’s disease PDZ Postsynaptic density 95/Discs-Large/Zona occludens domain PFC Prefrontal cortex PKA Protein kinase A PKC Protein kinase C PKG Protein kinase G PMAT Plasma membrane monoamine transporter PMDD Premenstrual dysphoria disorder PMS Premenstrual syndrome PQ2 D Paraquat PTSD Post-traumatic stress disorder S Short allele SAD Social anxiety disorder SERT/5-HTT Serotonin transporter SLC Solute carrier SN Substantia nigra SNARE Soluble N-ethylmaleimide-sensitive factor attachment protein SNCA Alpha-synuclein gene SNP Single nucleotide polymorphism SNRI Serotonin norepinephrine reuptake inhibitor SSRI Selective serotonin reuptake inhibitor TM Transmembrane VIAAT Vesicular inhibitory amino acid transporter VMAT Vesicular monoamine transporter Vmax Maximal velocity of transport VNTR Variable number of tandem repeats VTA Ventral tegmental area mM Micromolar

1.08.1

Introduction

Homeostasis of neurotransmitter signaling is essential for normal physiological and psychological function. Not surprisingly then, dysregulation of neurotransmitter signaling is linked to many disorders, including psychiatric and substance use disorders, as well as cardiovascular and gastrointestinal disorders, to name a few. For example, imbalances in extracellular serotonin (5hydroxytryptamine, 5-HT), both too much or too little, have been linked to depression, autism, anxiety and obsessive compulsive disorder (OCD), and high extracellular 5-HT can led to “serotonin syndrome” (discussed later), a very serious condition, which can result in death if untreated. When neurotransmitter is released into extracellular fluid, a primary mechanism controlling the strength and duration of signaling is uptake into neurons, as well as into glia. In neurons, the neurotransmitter is either repackaged into vesicles for reuse, or broken down enzymatically. In glia, neurotransmitter can be broken down into inactive metabolites, which in some cases can be returned to the neuron for reuse (e.g., glutamate; Chaturvedi et al., 2014). Uptake occurs via transport proteins located in the plasma membrane of presynaptic nerve terminals and glia. Transporters are dynamic proteins, capable of rapid trafficking into and out of the plasma membrane in response to a variety of stimuli (including drugs), and regulated by a number of mechanisms, including kinases, phosphatases, auto- and heteroreceptors. Transport proteins can also form oligomers and can exist in the plasma membrane in on and off states. These transporters, or solute carriers (SLC), comprise a large group of integral membrane proteins. There are approximately 350 SLC transporters, categorized into 55 families (for overview see Fredriksson et al., 2008; Kristensen et al., 2011). Here we focus on a subset of neurotransmitter transporters (NTTs) belonging to the SLC6 family; those that transport the monoamines, 5-HT, norepinephrine (NE) and dopamine (DA), abbreviated respectively as SERT (SLC6A4), NET (SLC6A2) and DAT (SLC6A), and those that transport the amino acid neurotransmitters, gamma-aminobutyric acid (GABA) and glycine. Of the four subtypes of GABA

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transporters (GAT) GAT1 (SLC6A1) and GAT3 (SLC6A11) are responsible for the majority of GABA clearance in the central nervous system (CNS). There are two subtypes of glycine transporters (GlyT) GlyT1 (SLC6A9) and GlyT2 (SLC6A5). The gene coding each of these transporter proteins is shown in italics, in parentheses. In addition, we discuss a subset of the SLC22 family of organic cation transporters (OCTs), OCT1 (SLC22A1), OCT2 (SLC22A2) and OCT3 (SLC22A3), and the SLC29 member, the plasma membrane monoamine transporter (PMAT, SLC29A4). We focus on these SLC6 transporters because of their important role in maintaining homeostasis of CNS 5-HT, NE, DA, GABA and glycine signaling, and because they are important therapeutic targets for a wide range of disorders. We highlight OCTs and PMAT because of their rapidly emerging importance in monoamine signaling and their potential as novel targets for development of new therapeutics. In the following sections, we provide a general overview of these transporters, and then discuss the pharmacology and therapeutic applications for each.

1.08.2

Overview

1.08.2.1

Location

It was originally thought that these NTTs were located in the synaptic cleft, but electron microscopy studies later showed them to be located extrasynptically (e.g., Pickel and Chan, 1999; Kristensen et al., 2011) (Fig. 1A). The extrasynaptic location of these NTTs implies that signaling occurs via paracrine (volume) transmission (see Fuxe et al., 2007; Borroto-Escuela et al., 2021). SERT, NET and DAT are located predominantly on dendrites and axons throughout brain, with their expression levels closely mapping

Fig. 1 Role of SLC6 neurotransmitter transporters (NTTs) in synaptic transmission. (A) Schematic representation of monoaminergic, GABAergic, and glycinergic synaptic terminals. In the presynaptic terminals of monoaminergic neurons, vesicular monoamine transporters (VMATs) belonging to the SLC18 gene family (Eiden et al., 2004) sequester 5-HT, DA, and NE into synaptic vesicles, whereas the vesicular inhibitory amino acid transporters (VIAATs) belonging to the SLC32 gene family (Gasnier, 2004) sequester GABA and glycine into synaptic vesicles in GABAergic and glycinergic neurons, respectively. After vesicular release, neurotransmitters exert their effects on post- and presynaptic receptors. The SLC6 NTTs are crucial for termination of neurotransmission by performing reuptake of the neurotransmitters from the synaptic cleft into presynaptic terminals or glial cells as well as for maintaining low tonic neurotransmitter concentrations outside synapses. The monoamine transporters (SERT, NET, and DAT) are localized to extrasynaptic sites (Torres et al., 2003), and to a lesser extent, glia (Inazu et al., 2001; Pickel and Chan, 1999) so are not shown here, whereas GATs and GlyTs are localized to synaptic and extrasynaptic sites in addition to glial cells (Supplisson and Roux, 2002; Conti et al., 2004; Madsen et al., 2010). (B) Chemical structures of the endogenous substrates for SLC6 NTTs and ion coupling stoichiometry for neurotransmitter reuptake. Reproduced with permission, from Kristensen AS, Andersen J, Jørgensen TN, Sørensen L, Eriksen J, Loland CJ, Strømgaard K, Gether U (2011) SLC6 neurotransmitter transporters: Structure, function, and regulation. Pharmacological Reviews 63(3): 585–640. PMID: 21752877 (web archive link), doi: https://doi.org/10.1124/pr.108.000869 (web archive link).

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to the location of their cognate neurotransmitter systems. These transporters are located primarily on neurons of their related neurotransmitter, with expression being more sparse on non-cognate neurons and glia. Expression patterns of GATs and GlyTs are more complex, varying in density throughout brain in both neurons and astrocytes. OCT3 and PMAT, located on neurons and glia, are broadly distributed in brain, often most densely in regions with high monoamine innervation. OCT1 and OCT2 are less widely expressed in brain. CNS distribution of these transporters is described in more detail in later sections.

1.08.2.2

Function

The primary function of all SLC6 transporters discussed in this chapter is to remove neurotransmitter from extracellular fluid. In doing so, they are the primary mechanism controlling the strength and duration of neurotransmission. SERT, NET, DAT, GAT and GlyT are secondary active transporters, deriving energy for neurotransmitter transport by co-transport into the cell with sodium (Naþ). The Naþ/potassium (Kþ) adenosine triphosphatase (ATPase) pump is responsible for maintaining the electrochemical potential difference across the cell membrane, maintaining high Naþ concentrations outside the cell. SLC6 transporters use this “free” energy, operating as symporters (i.e., transporting in the same direction) to transport molecules of neurotransmitter with Naþ ions into the cell. The stoichiometry of transport varies among these transporters. SERT, NET and DAT involve the movement of 5-HT, NE or DA with one or two Naþ ions and one Cl ion into the neuron. SERT is also an antiporter, transporting Kþ out of the cell during the transport cycle. GAT1 and GAT3 transport GABA into the cell with either two or three Naþ ions and one or two Cl ions. Likewise, GlyT1 and GlyT2 transport glycine into the cell with two or three Naþ ions, but only one Cl ion (Grouleff et al., 2015; Kristensen et al., 2011, see Fig. 1B). In contrast, OCTs and PMAT are Naþ independent, relying on the gradient of intra- versus extra-cellular neurotransmitter concentrations to drive transport (for comprehensive review, see Koepsell, 2020). These operate as electrogenic, facilitative transporters. The inside negative membrane potential of the cell helps drive transport of organic cations into the cell and accumulate at concentrations up to 10 times that in the extracellular domain (Wagner et al., 2016). Transport of substrate by secondary active transporters is a saturable process following Michaelis-Menton kinetics. The Michaelis constant (KM), defined as the concentration of substrate that is transported at half the maximal velocity (Vmax) of transport, is a measure of the affinity of the transporter for its substrate. KM values for SERT, NET and DAT for their native substrate range from approximately 0.2–5 mM, whereas GATs range from approximately 2–12 mM and GlyTs 20–100 mM (Kristensen et al., 2011). These NTTs are the high-affinity transporters for their native neurotransmitter. However, they have a relatively low capacity to transport their native neurotransmitter, meaning that the maximal turnover rate is quite slow (e.g., ranging from  1–3 (SERT, NET, DAT) to 20 (GlyTs) neurotransmitter molecules per second) (Kristensen et al., 2011). In contrast, OCTs and PMAT are lowaffinity polyspecific transporters, capable of transporting a host of cationic species, including monoamines. KM values for monoamines are dependent on monoamine (5-HT, DA or NE) and transporter type (i.e., whether PMAT or OCT and subtype of OCT) ranging broadly from 19 mM to 4400 mM (Koepsell, 2021). Importantly, these transporters have a very high capacity to clear monoamines from extracellular fluid, transporting many fold more molecules per second than SERT, NET or DAT. These transporters are therefore ideally suited to handling high extracellular concentrations of monoamines, and as discussed later, may serve to undermine the therapeutic utility of drugs that work by blocking SERT, NET and/or DAT to increase extracellular levels of the respective monoamines (see Daws, 2021). It is worth noting that SERT, NET and DAT are also promiscuous transporters, capable of transporting their non-cognate neurotransmitter, albeit with lower affinity. This may help explain why drugs that act by blocking both SERT and NET, or all three, are often therapeutically more efficacious than drugs that act to block only one of these monoamine transporters (Daws, 2009). Finally, the transporters discussed in this chapter are bidirectional, meaning that under certain conditions, neurotransmitter will be effluxed from the neuron or glial cell. This can occur in response to certain drugs, (e.g., amphetamine, 3,4methoxymethamphetamine (MDMA, Ecstasy)), or in the case of OCTs and PMAT, when the concentration of monoamine outside the cell is sufficiently low to allow movement of transmitter down its concentration gradient in spite of the inside negative charge of the plasma membrane.

1.08.2.3

Structure

In the early 1990s, there was an exponential increase in our understanding of these SLC6 transporters following the molecular cloning of DNA encoding them (Guastella et al., 1990, 1992; Blakely et al., 1991; Giros et al., 1991; Hoffman et al., 1991; Pacholczyk et al., 1991; Liu et al., 1992). With the exception of the GlyTs, which each contain around 700 amino acids, the SLC6 transporters described in this chapter are made up of approximately 600 amino acids, GAT have the fewest (599) and SERT the most (632). These transporters have 12 membrane spanning helices, with intracellular amino (NH2) and carboxyl (COOH) termini, also called N- and C-termini, as well as a large glycosylation loop (see Fig. 2C for an illustration of SERT). The intra- and extracellular loops contain numerous phosphorylation sites. The crystal structure of these NTTs remains elusive, largely due to the inability to obtain quantities that are sufficiently pure and stable for protein crystallization. However, much has been learned from crystallization of prokaryotic transporter proteins, in particular the leucine transporter (LeuT) (Yamashita et al., 2005), and more recently of the invertebrate, drosophila melanogaster, dDAT (Penmatsa et al., 2013). Studies such as these opened the doors for computational modelling of binding sites for many

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs a SLC6A4 gene

171

I425V

1A

1C 1B

3

2

6 7 4 5

8

9

10

11 12

13

14

5' 3'

SNPs rs25531, rs25532 5-HTTLPR (LA, LG, SA, SG)

G56A

VNTR (STin2: 9, 10 or 12 repeats)

Alternative polyadenylation sites

Alternative splicing

b SLC6A4/SERT variants

c SERT protein

Fold difference

Theoretical maximum 5 LL LL SS 4 LL SS 3 Actual LL SS 2 SS 1 0 5-HTTLPR G56A +rs25531 [+.75]

I425V [+.90]

Genotype

Extracellular TMs Intracellular

STin2: 12 [+1.00]

H 2N

HOOC

Nature Reviews ⎜Neuroscience

Fig. 2 (A) Structure of the SLC6A4 gene, including the sites of the major functional variants: the serotonin-transporter-gene-linked polymorphic region (5-HTTLPR), the variable number of tandem repeats (VNTR) (between 9 and 12 repeats can be found in intron 2 (STin2)) and the single nucleotide polymorphisms (SNPs). (B) Relative potential additive SERT expression and function for the major SLC6A4 polymorphisms. There is a theoretical 4.65-fold difference in function between individuals with a combination of the less active allele of each variant and individuals with the most active variants (Hu et al., 2006; Kilic et al., 2003; Lesch et al., 1996; Murphy et al., 2004; Prasad et al., 2005). The left-most two bars depict the actual differences in SERT expression and function that were measured in human lymphoblasts from large numbers of individuals, with the light-blue bar representing the 5-HTTLPR short/short (SS) þ rs25531 genotype and the yellow bar representing the 5-HTTLPR long/long (LL) þ rs25531 genotype; the six bars on the right combine this information with the functional consequences of the additional variants (G56A, I425V and STin2) that are predicted from in vitro measurements (dark-blue segments). (C) The SERT protein, with its 12 transmembrane (TM) segments, its extracellular loops and its intracellular amino- and carboxy-terminal tails. SNPs that change amino acids are denoted by red circles (except the functionally validated G56A and I425V SNPs, which are colored yellow); those in blue are synonymous. Panels (A) and (C) modified, with permission, from Murphy DL, Lerner A, Rudnick G, Lesch KP (2004) Serotonin transporter: Gene, genetic disorders, and pharmacogenetics. Molecular Interventions 4: 109–123. PMID: 15087484, doi: 10.1124/mi.4.2.8 © (2004) American Society for Pharmacology and Experimental Therapeutics. Data in part (B) from Hu XZ, Lipsky RH, Guanshan Z, Akhtar LA, Taubman J, Greenberg BD, Xu K, Arnold PD, Richter MA, Kennedy JL, Murphy DL, Goldman D (2006) Serotonin transporter promoter gain-of-function genotypes are linked to obsessive compulsive disorder. American Journal of Human Genetics 78: 815–826. PMID: 16642437, doi: 10.1086/503850; Kilic F, Murphy DL, Rudnick G (2003) A human serotonin transporter mutation causes constitutive activation of transporter activity. Molecular Pharmacology 64(2): 440–446. doi: 10.1124/mol.64.2.440. PMID: 12869649; Lesch KP, Bengel D, Heils A, Sabol SZ, Greenberg BD, Petri S, Benjamin J, Müller CR, Hamer DH, Murphy DL (1996) Association of anxiety-related traits with a polymorphism in the serotonin transporter gene regulatory region. Science 274: 1527–1531. PMID: 8929413, doi: 10.1126/science.274.5292.1527; Murphy DL, Lerner A, Rudnick G, Lesch KP (2004) Serotonin transporter: Gene, genetic disorders, and pharmacogenetics. Molecular Interventions 4: 109–123. PMID: 15087484, doi: 10.1124/mi.4.2.8; Prasad HC, Zhu CB, McCauley JL, Samuvel DJ, Ramamoorthy S, Shelton RC, Hewlett WA, Sutcliffe JS, Blakely RD (2005) Human serotonin transporter variants display altered sensitivity to protein kinase G and p38 mitogen-activated protein kinase. Proceedings of the National Academy of Sciences of the United States of America 102: 11545– 11550. PMID: 16055563, doi: 10.1073/pnas.0501432102. Fig. 2 is reprinted with permission from the Nature Publishing Group, Murphy DL and Lesch KP (2008) Targeting the murine serotonin transporter: Insights into human neurobiology. Nature Reviews. Neuroscience 9: 85–96. PMID: 18209729, doi: 10.1038/nrn2284.

psychotherapeutic drugs and drugs of abuse within the transport proteins. The reader is directed to Kristensen et al. (2011) and Grouleff et al. (2015) for more detailed reviews. Like the SLC6 transporters discussed here, OCT1, OCT2 and OCT3 have 12 predicted transmembrane domains, with intracellular N- and C-termini. They contain a large intracellular and a large extracellular loop. The extracellular loop contains at least one glycosylation site, and the intracellular loops contain numerous phosphorylation sites. The reader is directed to Koepsell (2020), for

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a comprehensive review. PMAT is predicted to have 11 transmembrane domains, with a long intracellular N-termini, and a short, extracellular C-termini. Like the other transporters, PMAT has numerous sites for phosphorylation and at least one glycosylation site. PMAT shares only 11–14% sequence homology with OCTs. The reader is directed to Vieira and Wang (2021), for a detailed review.

1.08.2.4

Regulation

These transporters are dynamically modulated by a range of cellular processes, including post-translational modifications and protein-protein interactions. Given the N- and C-terminal domains vary in length and generally lack sequence homology among these transporters, there is likely a great deal of transporter-specific regulation. Post-translational modifications include glycosylation and phosphorylation. The degree of glycosylation varies among tissue, brain region, cell type, developmental stage and species. For SLC6 transporters, removal of glycosylation sites generally reduces uptake activity due to a decrease in transporters on the plasma membrane, but does not tend to affect ligand binding affinities or intrinsic transporter function (see Kristensen et al., 2011 for review). Phosphorylation by protein kinase C (PKC) is the most extensively studied. In general, activation of PKC in SLC6 transporters leads to decreased plasma membrane expression of transporter and subsequent decrease in uptake activity. Other kinases shown to phosphorylate SLC6 transporters include calcium calmodulin-dependent protein kinase II (CaMKII) alpha, casein kinase II, c-Jun NH2-terminal kinase (JNK), cyclin-dependent kinase 5, extracellular signal regulated kinase 1/2 (ERK 1/2), protein kinase A (PKA), protein kinase B (Akt), protein kinase G (PKG), mitogen-activated protein kinase (MAPK) and tyrosine phosphorylation (Kristensen et al., 2011 for review). In contrast, tonic activity of protein phosphatases is thought to keep SLC6 transporters in a mostly dephosphorylated state. Studies using heterologous expression systems reveal that passage of SLC6 transporters through the endoplasmic reticulum and Golgi apparatus also influences their expression and function. This process is regulated by transporter oligomerization and by the Cterminus. In fact, these dynamic transport proteins are regulated by a vast ensemble of processes, including endocytic trafficking, regulated trafficking (e.g., by PKC-mediated phosphorylation and internalization), trafficking induced by substrates and inhibitors, interactions with membrane rafts (distinct plasma membrane domains with specialized lipid composition), numerous proteinprotein interactions, including with the soluble N-ethylmaleimide-sensitive factor attachment protein (SNARE), syntaxin 1A, CaMKII, postsynaptic density 95/Discs-Large/Zona occludens domain (PDZ) proteins (one of the most common protein recognition domains in the human genome), proteins in the early secretory pathway, alpha synuclein and a range of auto- and heteroreceptors (e.g., the DA D2 receptor) (see Kristensen et al., 2011 for review). OCTs and PMAT undergo similar kinds of regulation. The reader is directed to recent reviews by Koepsell (2020, 2021), Kölz et al. (2021) and Sweet (2021). In the following sections, we discuss each of these transporters in more depth, focusing on the pharmacology of drug action, therapeutic applications and role in addiction.

1.08.3

Serotonin transporter

1.08.3.1

Brain location

The serotonin transporter, commonly abbreviated as SERT or 5-HTT, is the high-affinity transport mechanism for clearing 5-HT from extracellular fluid. In doing so, it is a crucial player regulating the strength and duration of 5-HT signaling. SERT is primarily located on pre-synaptic nerve terminals, but is also present on glia (Inazu et al., 2001), platelets and lymphocytes (Marazziti et al., 2013). Importantly, SERT is located extrasynaptically (Pickel and Chan, 1999), meaning that serotonergic neurotransmission occurs via paracrine (volume) transmission (Fuxe et al., 2007; Borroto-Escuela et al., 2021). SERT is broadly distributed throughout brain, being expressed in highest density in cell body regions (dorsal and medial raphe nuclei). SERT is primarily concentrated in limbic structures, many of which are crucial for modulating mood and stress, including hippocampus, amygdala, and entorhinal cortex (Hensler et al., 1994). For comprehensive reviews of structure, function and regulation see Kristensen et al. (2011), Ramamoorthy et al. (2011), and Pramod et al. (2013).

1.08.3.2

Gene variants

Numerous variants of the human SLC6A4 gene have been identified. The most studied, and most common, of these variants is a 44base pair insertion/deletion polymorphism in the proximal region of the gene’s promoter. This polymorphism, known as the 5-HT gene-linked polymorphic region (5-HTTLPR), confers reduced transcriptional activity, resulting in reduced SERT expression in carriers of the short (S) allele, relative to those homozygous for the long (L) allele (Iurescia et al., 2017). This gene variant is further modulated by two single nucleotide polymorphisms (SNPs), rs25531 and re25532 (Fig. 2; Murphy and Lesch, 2008). These SNPs together with the 5-HTTLPR are located upstream of the transcriptional start site. The S allele is associated with a number of psychiatric disorders, including depression and other stress-related disorders, as well as predisposition to alcoholism (Caspi et al., 2003, 2010; Murphy and Lesch, 2008; Kristensen et al., 2011; Marcinkiewcz et al., 2016). Moreover, numerous clinical studies show that environmental factors, such as early life stress, can further increase the vulnerability of S allele carriers to these disorders (Caspi et al., 2010). Carriers of the S allele are often less effectively treated with commonly used therapeutics for these disorders, such as selective 5-HT reuptake inhibitors (SSRIs), than those homozygous for

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the L allele (Caspi et al., 2010; Serretti et al., 2007; Stevenson, 2018). Although the reason for this remains unclear, lessons continue to be learned from murine models. For example, constitutive SERT knockout (/) mice, and their heterozygote (þ/) counterpart, have been a tremendous resource, in particular SERTþ/ mice, which express 50% fewer SERT than wild-type (þ/þ) mice, thereby providing an excellent murine model for carriers of the human S allele (Murphy and Lesch, 2008; Gardier, 2009; Haenisch and Bönisch, 2011). Consistent with a reduced ability to capture released 5-HT, SERT / and SERTþ/ mice have 9- and 5-fold higher extracellular 5-HT, and lower tissue levels, than SERT þ/þ mice (Bengel et al., 1998; Mathews et al., 2004). SERT/ mice are viable, but have an anxiety-like phenotype, are less active, less aggressive and less social than SERT þ/þ mice. SERT þ/ mice exhibit a range of intermediate behaviors, which vary as a function of sex (see Murphy and Lesch, 2008 for review). Of note, OCT3 expression and function is upregulated in SERT þ/ and SERT / mice, likely to compensate for constitutive reductions in SERT (Baganz et al., 2008). Decynium-22 (D22), a non-selective OCT/PMAT inhibitor, produced antidepressant-like effects in SERT mutant mice, but not in wild-type mice, however D22 did augment the antidepressant-like effect of an SSRI in wildtype mice (Baganz et al., 2008; Horton et al., 2013). These findings suggest that a possible mechanism underlying the poor therapeutic response of carriers of the S allele to SSRIs is the increased activity of OCTs and/or PMAT, putatively OCT3, as discussed later in this chapter. Increased activity of OCT3 (or related transporter) in carriers of the S allele may serve to “keep a brake” on how high extracellular levels of 5-HT can climb in the presence of an SSRI, thereby undermining their therapeutic efficacy (see Daws, 2009, 2021; Daws et al., 2013). Interestingly, low extracellular 5-HT has long been associated as an etiological link to depression, however, findings in SERT mutant mice, which have higher extracellular 5-HT than wild-type mice bring this premise into question. Indeed, activity of upregulated OCT3 may explain why extracellular 5-HT concentrations are not higher than they are, as would be the prediction if SERT were the sole transporter for 5-HT (see Daws, 2009). Regardless, observations such as these bring into question the idea of a hyposerotonergic state precipitating depression, a dogma that has prevailed because drugs used to treat depression and related disorders act to increase extracellular 5-HT. If we can extrapolate from SERT mutant mice to humans, carriers of the S allele would be expected to have higher extracellular 5-HT, yet they are predisposed to depression and related disorders, and often less effectively treated by SSRIs. These apparent anomalies have led to the hypothesis that a hyposerotonergic state is not predictive of depression, but rather, therapeutic response is related to the magnitude of the net increase in 5-HT, regardless of what basal level might be (see Daws et al., 2013). Before leaving discussion of the 5-HTTLPR polymorphism, it is important to note that factors such as sex and ethnicity have not been systematically addressed. It is known that the frequency of the L and S allele vary with ethnicity, but the majority of clinical studies have been carried out in non-Hispanic Caucasian or East Asian populations (see Stevenson, 2018; Iurescia et al., 2016). It is therefore likely that conflicting reports in the literature can be attributable to differences in the cohorts sampled. In addition to the 5-HTTLPR polymorphism, numerous other variants have been identified including a variable number of tandem repeats (VNTR) polymorphism in intron 2 as well as many other SNPs that result in structural and/or functional alterations in SERT (for reviews see Murphy and Lesch, 2008; Bhat et al., 2021). Most of these variants are rare, but are linked with psychiatric disorders. For example, the I425V mutation is associated with obsessive-compulsive disorder (OCD) and other psychopathologies (e.g., Wendland et al., 2008), and the G56A variant with autism (e.g., Sutcliffe et al., 2005; Veenstra-VanderWeele et al., 2012). Both of these mutations are gain-of-function, causing SERT to increase its uptake activity (Kilic et al., 2003; Prasad et al., 2009). A summary of missense coding variants for SLC6 transporters, their transport phenotype and disease phenotype is provided in Table 1 (from Bhat et al., 2021). As well as the many gene variants of SLC6A4, epigenetic processes must also be considered. For example, methylation of the promoter is involved in depressive disorders, and gene x environment interactions, such as early life stress, can initiate epigenetic changes in gene expression (see Iurescia et al., 2017 for review). Continued investigation of phenotypes related to SLC6A4 gene variants, in combination with epigenetic regulation of SLC6A4, will help develop pharmacogenetic approaches with the goal to improve treatment of patients based on an individualized plan.

1.08.3.3

Interaction with psychoactive drugs

Two major classes of psychoactive drugs interact with SERT, psychotherapeutics and stimulants.

1.08.3.3.1

Psychotherapeutics

SSRIs are the major class of psychotherapeutic drug targeting SERT. SSRIs act by blocking SERT, preventing the uptake of 5-HT from extracellular fluid, thereby increasing the strength and duration of 5-HT signaling. The most commonly prescribed SSRIs include citalopram (Celexa), escitalopram (Lexapro), fluoxetine (Prozac), fluvoxamine (Luvox), paroxetine (Paxil, Paxeva) and sertraline (Zoloft) (Chu and Wadhwa, 2021). These compounds have inhibitory constants (Ki values) ranging from less than 1 nanomolar (nM) to 7 nM at SERT, whereas the majority have Ki values that are 25- to over 100,000-fold greater at DAT and NET. For example, escitalopram is especially selective for SERT having a Ki value of 3 nM, compared to > 100,000 nM for DAT and 6514 nM for NET. In contrast, sertraline has a Ki value of 0.1 nM for SERT, but also has some affinity for NET (Ki of 25 nM), but not DAT (420 nM) (Kristensen et al., 2011). Table 2 highlights Ki values for some of these commonly used SSRIs. Since the crystal structure of LeuT was solved (Yamashita et al., 2005; Penmatsa and Gouaux, 2014), and more recently, that for dDAT (Penmatsa et al., 2013) and hSERT (Coleman et al., 2016), little was known about the precise mechanism by which SSRIs blocked 5-HT transport (see Joseph et al., 2019 for review on structure and gating dynamics of SLC6 transporters). Gouaux and

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Mutations in human plasmalemmal SLC6 neurotransmitter transporters.

Gene

Protein name

SLC6A1

GAT1

SLC6A2 SLC6A3

SLC6A4

SLC6A5

SLC6A9

Missense coding variants

Transport phenotype Loss-of-function

NET DAT

9/45 variants characterized G94E, W235R, F270S, I272*, Y445C, W496*, G550R G234S, P361T A457P A559V, E602G, R615C

SERT

R85L, V158F, R219S, L224P, A314V, G327R, L368Q, G380_K384delinsE, G386R, P395L, R445C, Y470S, R521W, P529L, P554L V382A I312F, D421N O N336, T356M G56A, K605N

GlyT2

GlyT1

I425V, I425L, F465L, L550V N211S, V274I, F474L L90F 225R, Y297*, R439*, S477P, S477Ffs*9 P108Lfs*25, Y377*, V432Ifs*97, Q630* W151*, R191*, L198Rfs*123, L237P, P243T, E248K,S489Ffs*39, S513I, F547S, I655Kfs*1, Y656H, G657A A89E, A275T, L306V, T425M, W482R, Y491C, N509S,G787R P429L S510R, Y705C 310Ffs*31, S407G, Q573*

Loss-of-function Unaltered DA uptake, anomalous DA efflux Loss-of-function

Gain-of-function

? ? ? Loss-of-function

Trafficking to target membrane ? Reduced Reduced Unaltered Reduced

Unaltered Unaltered Unaltered Increased ? ? ? Reduced ?

Disease phenotype

Mode of inheritance

OMIM#

Myoclonic-atonic epilepsy

AD

616421

Chronic orthostatic intolerance ADHD, bipolar disorder

AD AD

604715

DAT deficiency syndrome (infantile Parkinsonism/dystonia)

AR

613135

ADHD Adult Parkinsonism with ADHD Autism Spectrum Disorder OCD, depression, Autism spectrum disorder, Asper’s syndrome, Tourette’s syndrome

AD CHet AD AD/AR

Anorexia nervosa-restrictive type

AD

Hyperekplexia

AR

614618

Glycine encephalopathy

AD AR

617301

164230 (1425 V)

Unaltered Reduced ?

?

AR, Autosomal recessive; AD, Autosomal Dominant; Chet, Compound heterozygotes Table taken from Bhat S, El-Kasaby A, Freissmuth M, Sucic S (2021) Functional and biochemical consequences of disease variants in neurotransmitter transporters: A special emphasis on folding and trafficking deficits. Pharmacology & Therapeutics 222: 107785. PMID: 33310157, with permission.

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs

Table 1

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs Table 2

175

Ki (in bold), Km (in italics) or IC50 values for drugs acting as substrates or inhibitors at SERT, DAT, NET, GAT1, GAT3, GlyT1, GlyT2, OCT1, OCT2, OCT3 and PMAT.

Transporter and compound

Type

SERT Amphetamine Substrate Citalopram Inhibitor Escitalopram Inhibitor Fluoxetine Inhibitor Fluvoxamine Inhibitor Imipramine Inhibitor MDMA Substrate Paroxetine Inhibitor Sertraline Inhibitor DAT Amphetamine Substrate Bupropion Inhibitor Cocaine Inhibitor Diethylpropion Inhibitor Methamphetamine Substrate Methylphenidate Inhibitor MDPV Substrate MDMA Substrate Pyrovalerone Substrate NET Amphetamine Substrate Atomoxetine Inhibitor MDMA Inhibitor Reboxetine Inhibitor GAT1 CI-966 Inhibitor Nipecotic acid Inhibitor SK&F 89976-A Inhibitor Tiagabine Inhibitor GAT2 CI-966 Inhibitor Nipecotic acid Inhibitor SK&F 89976-A Inhibitor Tiagabine Inhibitor GlyT1 Alx5407 Inhibitor Org 24,461 Inhibitor Org 24,598 Inhibitor Sarcosine Inhibitor GlyT2 Amoxapine Inhibitor Org 24,598 Inhibitor Sarcosine Inhibitor Organic Cation Transporter 1 Amisulpride Substrate/Inhibitor

Ki, Km, IC50

Species

References

45 mM 0.0007 mM 0.003 mM 0.00114, 0.0073 mM 0.002 mM 0.0013 mM 1.6 mM 0.0001 mM 0.0001 mM

hSERT rSERT hSERT hSERT hSERT hSERT hSERT hSERT hSERT

Kolaczynska et al. (2021) Plenge and Mellerup (1991) Owens et al. (2001) and Kristensen et al. (2011) Eshleman et al. (1999) and Kristensen et al. (2011) Tatsumi et al. (1997) and Kristensen et al. (2011) Owens et al. (1997) Kolaczynska et al. (2021) Eshleman et al. (1999) and Kristensen et al. (2011) Tatsumi et al. (1997) and Kristensen et al. (2011)

1.3 mM 2.9 mM 0.278 mM 14.99 mM 0.0823 mM 0.193 mM 0.03 mM 13 mM 0.05 mM

hDAT hDAT hDAT hDAT hDAT hDAT hDAT hDAT hDAT

Kolaczynska et al. (2021) Eshleman et al. (1999) Eshleman et al. (1999) Yu et al. (2000) Eshleman et al. (1999) Eshleman et al. (1999) Kolaczynska et al. (2021) Kolaczynska et al. (2021) Kolaczynska et al. (2021)

0.07 mM 0.005 mM 1.19 mM 0.003 mM

hNET hNET hNET hNET

Han and Gu (2006) and Kristensen et al. (2011) Andersen et al. (2009) and Kristensen et al. (2011) Han and Gu (2006) and Kristensen et al. (2011) Andersen et al. (2009) and Kristensen et al. (2011)

0.26 mM 19 mM 0.13 mM 0.11 mM

hGAT1 rGAT1; mGAT1 hGAT1 rGAT1; mGAT1

Dhar et al. (1994) and Kristensen et al. (2011) Kvist et al. (2009) and Kristensen et al. (2011) Dhar et al. (1994) and Kristensen et al. (2011) Kvist et al. (2009) and Kristensen et al. (2011)

1280 mM 113 mM 550 mM > 100 mM

rGAT2 rGAT1; mGAT1 rGAT1 rGAT1; mGAT1

Dhar et al. (1994) and Kristensen et al. (2011) Kvist et al. (2009) and Kristensen et al. (2011) Dhar et al. (1994) and Kristensen et al. (2011) Kvist et al. (2009) and Kristensen et al. (2011)

0.0028 mM 0.0065 mM 0.0069 mM 100 mM 3 mM 6, 8, 17, 18 mM

hOCT hOCT hOCT hOCT rOCT hOCT hOCT

(Continued)

176 Table 2

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs Ki (in bold), Km (in italics) or IC50 values for drugs acting as substrates or inhibitors at SERT, DAT, NET, GAT1, GAT3, GlyT1, GlyT2, OCT1, OCT2, OCT3 and PMAT.dcont'd

Transporter and compound

Ki, Km, IC50

Species

References

3.4, 28, 46 mM 197 mM 260 mM

hOCT hOCT hOCT

Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021)

167.9, 22, 168 mM

hOCT

Amitriptyline Inhibitor Amphetamine Substrate Citalopram Inhibitor Cocaine Inhibitor Corticosterone Inhibitor Decynium-22 Inhibitor Desipramine Inhibitor Dopamine Substrate/Inhibitor Estradiol Inhibitor Fluoxetine Inhibitor Imipramine Inhibitor Ketamine Inhibitor Norepinephrine Substrate/Inhibitor Serotonin Substrate Sulpiride Substrate Organic Cation Transporter 3 Amisulpride Substrate

0.5, 0.78, 9.1, 14 mM 0.7, 11, 20, 534 mM 12, 21, 155 mM 113 mM 5.4, 34, 80 mM 0.1–10 mM 0.34, 16, 75 mM 390, 420, 1400, 2300 mM >30, 85 mM 4.4, 17, 29 mM 0.3, 0.4, 6, 15, 29 mM 23, 34 mM 1500, 1900, 3568, 11,000 mM 80, 278, 290, 310 mM 26, 187, 390 mM

hOCT hOCT hOCT hOCT hOCT hOCT hOCT hOCT rOCT hOCT hOCT hOCT hOCT hOCT hOCT

Dos Santos Pereira et al. (2014); Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021)and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021)

26, 191.9 mM

hOCT

Amitriptyline Amphetamine Citalopram Cocaine Corticosterone Decynium-22 Desipramine Dopamine Estradiol Ketamine Morphine Norepinephrine Sertraline Sulpiride Serotonin PMAT Amitriptyline Corticosterone Decynium-22 Desipramine Dopamine Fluoxetine Imipramine Norepinephrine Serotonin Sertraline

Inhibitor Substrate Inhibitor Inhibitor Inhibitor Inhibitor Inhibitor Substrate Inhibitor Inhibitor Substrate Substrate/Inhibitor Inhibitor Substrate Substrate

>100 mM 24, 42, 363, 460 mM 145, 158, 188 mM >1000 mM 0.12, 0.29, 0.62 mM 0.07, 0.09, 0.20 mM 14, 68, 72 mM 384, 620, 800, 1033 mM 1.1, 2.9 mM 53, 226, 365, 440 mM 538, 583 mM 182, 434, 923, 2630 mM 7.4, 26 mM 160 mM 283, 900, 988 mM

hOCT hOCT hOCT hOCT hOCT hOCT hOCT hOCT rOCT hOCT hOCT hOCT hOCT hOCT hOCT

Dos Santos Pereira et al. (2014); Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021)

Inhibitor Inhibitor Inhibitor Inhibitor Substrate Inhibitor Inhibitor Substrate Substrate Inhibitor

23 mM 430, 450, 1,059 mM 0.1, 1.10 mM 15, 33 mM 201, 329, 406 mM 11, 23 mM 21 mM 510, 1078, 2606 mM 201, 114, 201, 283 mM 5.1, 14, 283 mM

hPMAT hPMAT hPMAT hPMAT hPMAT hPMAT hPMAT hPMAT hPMAT hPMAT

Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021) Reviewed in Koepsell (2021) and Bönisch (2021)

Type

Morphine Substrate Serotonin Substrate Sulpiride Substrate Organic Cation Transporter 2 Amisulpride Substrate/Inhibitor

colleagues have led the way in advancing knowledge of precisely how SSRIs interact with SERT (e.g., Coleman and Gouaux, 2018). SSRIs act by occupying the central substrate binding site, thereby stabilizing or “locking” SERT in outward-open conformation. In addition, there is an allosteric site, which when occupied, hinders ligand unbinding from the central site (Coleman et al., 2016), and the extracellular loops of SERT further control drug binding (Esendir et al., 2021). S-citalopram is an example of an allosteric ligand at SERT. Vilazodone, a SSRI with partial agonist activity at 5-HT1A receptors, was also recently found to be an allosteric inhibitor of SERT (Plenge et al., 2021). Studies such as these are providing roadmaps for the development of novel SERT inhibitors with potentially improved therapeutic efficacy (e.g., Plenge et al., 2020).

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs

177

Serotonin, the endogenous substrate for SERT, prevents PKC-induced phosphorylation and sequestration of SERT from the plasma membrane (Ramamoorthy and Blakely, 1999; Jayanthi and Ramamoorthy, 2005; Steiner et al., 2008). Non-transported compounds, including SSRIs, block this action of 5-HT. Thus, another way in which SSRIs act to increase extracellular 5-HT is by shifting the cellular distribution of SERT, such that fewer remain on the plasma membrane. These regulatory pathways have thus far been carried out using heterologous cell lines expressing SERT, or in murine tissue preparations from drug naïve animals. Studies from the Frazer group revealed that rats treated chronically with SSRI (21 days by minipump) to achieve plasma levels akin to those in humans treated with SSRIs, resulted in a robust, 80% decrease in SERT expression as measured by tritiated ([3H]) cyanoimipramine binding (Benmansour et al., 1999). This decrease was not due to decreases in SERT gene expression or neurotoxicity, and was not caused by other classes of antidepressant (NET blockers, monoamine oxidase (MAO) inhibitor). This dramatic loss of SERT occurred around 2 weeks after treatment began, and recovered approximately 10 days after treatment was stopped. Loss of SERT was associated with slower clearance of 5-HT from extracellular fluid in hippocampus, measured in vivo, using high-speed chronoamperometry (Benmansour et al., 2002). These studies suggest that the lag to significant reductions in SERT expression may contribute to the delay in therapeutic efficacy of SSRIs.

1.08.3.3.2

Stimulants

A variety of stimulants have activity at SERT. These come in two classes. Drugs that act as SERT blockers (e.g., cocaine, which is also a potent blocker of DAT and NET), and drugs that cause non-exocytotic release of 5-HT via reverse transport through SERT (e.g., 3,4methylenedioxymethamphetamine (MDMA, Ecstasy), fenfluramine, and synthetic cathinones (known as bath salts) such as methylone). These releasers are substrates for SERT and are transported into the nerve terminal. There, they interact with VMAT causing release of 5-HT into the cytosol, which is subsequently effluxed via SERT (Partilla et al., 2006). Thus, releasers serve to increase extracellular 5-HT by both competing with endogenous substrate, 5-HT, for uptake, and causing 5-HT release, whereas blockers act by preventing 5-HT uptake. Like the endogenous substrate 5-HT, psychostimulants that are substrates for SERT prevent PKC induced phosphorylation and sequestration of SERT (Ramamoorthy and Blakely, 1999; Jayanthi and Ramamoorthy, 2005; Steiner et al., 2008). Like SSRIs, stimulants that are blockers of SERT (e.g., cocaine), block this effect of substrates. Although SERT targeting stimulants act to elevate mood, they can also be toxic, both acutely and chronically. For example, MDMA acute overdose can lead to a severe hyperthermic response, which can lead to rhabdomyolysis, coma and death. MDMA can be neurotoxic as well, leading to loss of 5-HT nerve terminals (Simmler and Liechti, 2018). Recent modelling studies have begun to address the ligand-protein interplay between MDMA, and related drugs, and SERT, which are shedding new light on the molecular mechanism of action of these drugs (Islas et al., 2021a,b). Understanding the precise molecular mechanisms underlying their actions will be important in the design of stimulants for therapeutic use, which lack addictive and/or toxic effects.

1.08.3.4 1.08.3.4.1

Therapeutic application Depression

Depression is a debilitating disorder, which places a tremendous burden on the individual, their families, the healthcare system and economy. SSRIs are best known for their use in the treatment of depression, and are the most commonly prescribed drugs to treat depression, including bipolar depression and major depressive disorder. The Food and Drug Administration (FDA) has approved the following SSRIs for the treatment of depression; citalopram (Celexa), escitalopram (Lexapro), fluoxetine (Prozac), paroxetine (Paxil, Paxeva), sertraline (Zoloft), and vilazodone (Viibryd) but these are also frequently prescribed to treat related disorders including OCD, panic disorder, generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), bulimia and others (discussed below). Fluvoxamine (Luvox) is FDA approved for treatment of OCD. Although these drugs lead to rapid increases in extracellular 5-HT, they take at least 2 weeks, and sometimes months, to exert therapeutic benefit (if any). This implies that a persistent increase in extracellular 5-HT is needed to induce downstream events leading to neuroplastic adaptations required for therapeutic benefit. SSRIs are often the “go to” treatment for depression, and other disorders, because they have fewer side effects than other classes of antidepressant drugs (e.g., tricyclics, MAO inhibitors). However, a recent meta-analysis showed this to be dependent on the SSRI (Cipriani et al., 2018). For example, the SSRIs citalopram, escitalopram, paroxetine, fluoxetine and sertraline, as well as vortioxetine (a SERT blocker with activity at a number of other receptors) and agomelatine (a melatonin agonist and 5-HT2C receptor antagonist) are more tolerable than other antidepressants. In contrast, the SSRI fluvoxamine, dual 5-HT/NE reuptake inhibitors (SNRIs), duloxetine and venlafaxine, as well as the tricyclics, amitriptyline and clomipramine, which also block SERT and NET in addition to acting on a variety receptors, and the NET blocker, reboxetine, are not well tolerated, with dropout rates being highest for individuals prescribed these medications. Interestingly, of the seven most effective antidepressants determined in this meta-analysis, only two were SSRIs, escitalopram and paroxetine. Others included agomelatine, amitriptyline, mirtazapine (an atypical antidepressant that antagonizes adrenergic alpha2-receptors, histamine H1 receptor, 5-HT2A, 5-HT2C, and 5-HT3 receptors), venlafaxine and vortioxetine. Of the four least effective antidepressant, two were SSRIs, fluoxetine and fluvoxamine (Cipriani et al., 2018). Age is a caveat to the use of SSRIs, with the cost-benefit relationship for their use in children and adolescents remaining unclear (Murphy et al., 2021). Currently only two SSRIs are FDA approved for use in young people, fluoxetine and escitalopram.

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Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs

Therapeutic benefit is often less in children and adolescents than in adults, and adverse effects of these SSRIs, such as suicidal ideation, remains a concern in this population (Bowman and Daws, 2019; Edinoff et al., 2021).

1.08.3.4.2

Post-traumatic stress disorder (PTSD) and related anxiety disorders

Anxiety disorders are among the most common psychiatric disorder and include PTSD, GAD, social anxiety disorder (SAD) and panic disorder. As for depression, which is often comorbid with anxiety disorders, SSRIs are the front line treatment. A recent meta-analysis showed SSRIs to be therapeutically useful in treating anxiety (Jakubovski et al., 2019), making them a better choice over other anxiolytics, such as benzodiazepines (e.g., diazepam (Valium)), which have a less desirable side-effect profile. More recently, psychedelics including MDMA (Sessa et al., 2019) and psilocybin (a drug structurally similar to 5-HT that acts as a high-affinity 5-HT2A agonist) (Lowe et al., 2021) have been revisited as potential therapeutics for psychiatric disorders including anxiety and depression. The FDA approved MDMA for emergency treatment of PTSD patients resistant to other therapeutics. Phase III clinical studies using MDMA as a treatment for PTSD, in combination with psychotherapy, show promising results (Latimer et al., 2021). Clearly, there are many considerations to clinical use of MDMA (a “club-drug”). These include its abuse potential, and the possibility of inducing serotonin syndrome, particularly if taken together with an SSRI or other drug that serves to increase 5-HT signaling. Serotonin syndrome occurs when extracellular 5-HT levels rise too high and 5-HT2A receptors are overstimulated. This leads to a variety of symptoms including agitation, disorientation, tremors, hyperthermia, and tachycardia (Volpi-Abasie et al., 2013). Indeed, adolescents and young adults prescribed SSRIs may be at greatest risk if using MDMA recreationally (Dobry et al., 2013). However, under well controlled conditions and used at low dose, MDMA appears to be a promising path forward for the improved treatment of anxiety and depression (Sessa et al., 2019; Latimer et al., 2021).

1.08.3.4.3

Autism

Autism spectrum disorders are commonly treated with SSRIs, however their therapeutic utility remains controversial (Garbarino et al., 2019). A recent meta-analysis found that antipsychotics were superior in treating restricted and repetitive behaviors in autism (Zhou et al., 2021). Currently there are no treatments that ameliorate all symptoms of autism, underscoring the need for further research.

1.08.3.4.4

Eating disorders

There are three categories of eating disorder, anorexia nervosa (AN), bulimia nervosa (BN) and binge eating disorder (BED). SSRIs have some utility in treating these disorders (Crow, 2019). Fluoxetine is FDA approved for treatment of BN, though other SSRIs have shown some effectiveness (Milano and Capasso, 2019). A meta-analysis of the effectiveness of SSRIs in the treatment of BED found them to ameliorate the frequency of binge eating and to help remission rates, but without increasing patient body mass index (Ghaderi et al., 2018). In general, SSRIs are not effective in the treatment of AN. However, fluoxetine, and to some extent other SSRIs including citalopram and sertraline, can be useful in relapse prevention, and help to ameliorate psychiatric symptomatology in patients with restored weight (Marvanova and Gramith, 2018).

1.08.3.4.5

Premenstrual disorders and menopausal vasomotor symptoms

Premenstrual disorders are very common and include premenstrual syndrome (PMS) and premenstrual dysphoria disorder (PMDD). Though similar in nature, PMDD is the more severe form, causing extreme mood shifts (e.g., extreme sadness and hopelessness) and more extreme physiological effects (breast tenderness and bloating). SSRIs have proved to be very effective in treating these disorders (Appleton, 2018; Lanza di Scalea and Pearlstein, 2019; Marjoribanks et al., 2013). Vasomotor symptoms of menopause (e.g., hot flashes) have been traditionally treated with hormone replacement therapy. However, other treatments have been sought due to safety and compliance issues. SSRIs, paroxetine in particular, have proved effective in ameliorating the frequency and severity of vasomotor symptoms (Riemma et al., 2019).

1.08.3.4.6

Myocardial infarctions

There is a long history of association between depression and myocardial infarctions (MI) with inflammation appearing to be a key link (Malik et al., 2021). Although findings are not consistent, many studies report that SSRIs reduce risk of MI, particularly in individuals suffering both depression and cardiovascular disease (Fernandes et al., 2021; Kim et al., 2019; Undela et al., 2015). Though the mechanism is not understood, there is some evidence supporting attenuation of platelet activation by 5-HT (Helmeste et al., 1995). SSRIs should not be used as a standalone treatment for cardiovascular disease (Edinoff et al., 2021).

1.08.3.4.7

Pain

Opioids are often the front line treatment for nociceptive pain, but issues with development of tolerance, dependence and overdose can be problematic (Morgan and Christie, 2011). Animal and human studies have found fluoxetine to be beneficial in reducing these unwanted complications of opioids, and to be effective in treating inflammatory pain (Barakat et al., 2018 for review). With fewer adverse side-effects, fluoxetine offers a viable add-on to treatment of nociceptive pain with opioids, and to treat inflammatory pain. Vortioxetine, a SERT blocker, but with activity at a number of other 5-HT receptors, is showing promise as a treatment for chronic neuropathic pain (Adamo et al., 2021).

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs 1.08.3.4.8

179

Gastrointestinal disorders

Approximately 95% of 5-HT is synthesized in the lower intestine. In addition to being located on neurons and glia, SERT is found on enterocytes and platelets (Terry and Margolis, 2017). Not surprisingly then, SSRIs impact 5-HT signaling in the gastrointestinal (GI) system, which serves to control a number of hormonal, autocrine, paracrine, and endocrine actions (Gershon, 2013). Whether SSRIs are useful in the treatment of GI disorders remains unclear. Animal studies show that the SSRI fluvoxamine, through antioxidant and anti-inflammatory mechanisms, can protect against peptic ulcers (Dursun et al., 2009; Elsaed et al., 2018), and in human studies, some antidepressants can ameliorate ulcers (Olden, 2005). A recent meta-analysis showed that antidepressants, including SSRIs, were more effective than placebo in treating functional GI disorders in adults, but inferior in terms of tolerability (Xiong et al., 2018).

1.08.3.4.9

Epilepsy

Epilepsy and depression are highly comorbid, however at the current time it is unclear if treatment with SSRIs plays a role in reducing seizures. A recent review found some evidence that SSRIs do not exacerbate seizures (Maguire et al., 2021), but more research is warranted. In new developments, fenfluramine (a substrate for SERT) is emerging as a treatment for epilepsy. Fenfluramine was initially prescribed as a weight loss drug, but was later withdrawn from the market because it caused cardiac valvulopathy. In particular, fenfluramine has been repurposed, and approved by the FDA, to treat epilepsy in Dravet syndrome (Odi et al., 2021; Sullivan and Simmons, 2021).

1.08.3.4.10

COVID-19

Clinical trials are underway to evaluate repurposing of SSRIs for treatment of the novel coronavirus disease, which emerged in 2019 (COVID-19), and at the time of this writing, is continuing to pose a serious global health threat. SSRIs work to inhibit complications of COVID-19 via several mechanisms. In addition to blocking SERT, fluvoxamine also acts as an agonist at the sigma 1 receptor, serving as an anti-inflammatory agent by reducing actions of pro-inflammatory cytokines such as tumor necrosis factor alpha and interleukin 6. In turn, this protects against sepsis and its complications (for reviews see Meikle et al., 2021; Mouffak et al., 2021; Pashaei, 2021). Sertraline and fluoxetine are lysosomotropic agents, which neutralize endolysosomal pH. In turn, this interferes with viral replication. At higher doses, fluoxetine also acts as a functional inhibitor of acid sphingomyelinase. These inhibitors have been shown, in vitro, to prevent infection with influenza and Ebola virus. COVID-19 infects cells by activation of sphingomyelinase, so potentially inhibition of this enzyme could reduce viral infection (Carpinteiro et al., 2020). In addition, fluoxetine stops efflux of cholesterol from endosomes and lysosomes, resulting in lower cholesterol availability. Viruses are dependent on cholesterol for formation of their envelopes from the host membrane. In vitro studies using a cell culture model of COVID-19 showed that fluoxetine decreased viral load (Schloer et al., 2021). Importantly, fluoxetine exerts these effects at doses used to treat psychiatric disorders. Indeed, an observational clinical study found that risk of mortality and intubation was greatly reduced in patients taking antidepressant doses of fluoxetine and other SSRIs (Hoertel et al., 2021). These encouraging results await double-blind controlled randomized clinical trials, and of course, adverse effects of SSRIs must be considered before they are prescribed for any of the indications discussed here (Edinoff et al., 2021).

1.08.4

Dopamine transporter

1.08.4.1

Brain location

DAT is largely localized to dopaminergic neurons within the nigrostriatal, mesolimbic, mesocortical, and tuberoinfundibular systems, the four main dopaminergic pathways (Kristensen et al., 2011). Diverse patterns of DAT mRNA expression are observed in cell bodies of dopaminergic neurons within the substantia nigra (SN) and ventral tegmental area (VTA) (Richtand et al., 1995). Unlike SERT, DAT expression is lower in the cell bodies than in terminal field regions. DAT expression is especially rich in striatum and nucleus accumbens (NAcc) (Ciliax et al., 1999). Like SERT, DAT is located extrasynaptically, meaning that dopaminergic neurotransmission also occurs via paracrine (volume) transmission (Cragg and Rice, 2004; Rice and Cragg, 2008). For comprehensive reviews of structure, function and regulation (see Kristensen et al., 2011; Ramamoorthy et al., 2011, and Pramod et al., 2013).

1.08.4.2

Gene variants

Several missense coding variants lead to a reduction in DAT transport and are associated with the onset of diseases such as Parkinsonism and attention deficit hyperactivity disorder (ADHD) (see Table 1). It may seem surprising that loss of DAT function would lead to conditions characterized by a hypodopaminergic phenotype since a reduction in DAT function would generally be predicted to lead to excess extracellular DA. However, loss of DAT function may also lead to DA depletion due to impaired replenishment of the synaptic pool of DA. DA depletion is an essential component of diseases such as Parkinson’s disease (PD) due to its association with inactivating mutations of tyrosine hydroxylase (Diepold et al., 2005). Indeed, identification of several DAT coding variants have been found in individuals with infantile and juvenile forms of PD, collectively known as the DAT deficiency syndrome (DTDS) (Baga et al., 2021; Heidari et al., 2020; Kurian et al., 2009, 2011a,b; Kuster et al., 2018; Ng et al., 2014; Puffenberger et al., 2012; Yildiz et al., 2017). All the loss-of-function mutations associated with DTDS are inherited in an autosomal recessive manner, such that these individuals are either homozygotes or compound heterozygotes (Bhat et al., 2021). Moreover, mutations

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can have variable effects on basal uptake that leads to differences in the age of onset of the DTDS phenotype. For example, the A314V mutation has the highest uptake capacity ( 8% of wild-type (WT) DAT) among the known DTDS variants (Ng et al., 2014), and patients harboring the variant begin displaying the DTDS phenotype in adolescence. Similarly, Hansen et al. (2014) describe a patient with comorbid adult-onset PD and ADHD that expressed the mutations I312F and D421N, the co-expression of which leads to a reduction in DA uptake to  30% of WT DAT (Bhat et al., 2021). Other variants with preserved trafficking, but functional deficits include DN336 and V382A (see Table 1). The variant, DN336, is hypothesized to assume a half-open and half inward facing disposition that is rate limiting, leading to the abolishment of DA uptake, and was recently identified in a patient with autism spectrum disorders (ASD) (Campbell et al., 2019). V382A is associated with ADHD and shows significantly reduced uptake and binding affinity compared to the WT DAT (Lin and Uhl, 2003). For a more detailed review of missense coding variants in DAT and other neurotransmitter transporters, see Bhat et al. (2021).

1.08.4.3

Interaction with psychoactive drugs

The DAT is a principal target of various psychotropic, nootropic, and antidepressant drugs, as well as addictive psychostimulants. DAT ligands can be divided into two categories: inhibitors (blockers) of DA uptake and competitive inhibitors of DA uptake (see Table 2). Ligands in both categories lead to a rapid increase in dopaminergic neurotransmission by interfering with proper DAT function. The subsequent increase in dopaminergic signaling in limbic areas of brain is believed to mediate psychostimulants’ rewarding and addictive properties. Although psychostimulants, such as amphetamines and cocaine, affect SERT and NET as well, the rewarding properties and abuse potential of this class of drugs are believed to be primarily mediated through DAT and dopaminergic signaling (Giros et al., 1996; Wise, 1996; Torres et al., 2003).

1.08.4.3.1

Amphetamines

Amphetamine and amphetamine-like drugs have important medicinal and social uses, exert profound effects on mental function and behavior, and produce neurodegeneration and addiction. The chemical structures of common psychostimulants closely resemble those of endogenous monoamine neurotransmitters (Foley, 2005). They include a phenyl ring, a nitrogen group, and carbon side chains of various lengths (see Fig. 3). Amphetamines are therefore known as phenylalkylamines and have autonomic nervous system activity, mood-altering effects, activate receptors that generally bind catecholamines or 5-HT, and cause the release of catecholamines and 5-HT from nerve endings. Amphetamines are substrates for DAT and belong to the category of ligands that compete with DA uptake (see Table 2). While amphetamine is considered the prototypical stimulant, other compounds with similar chemical structures and physiological effects are often called amphetamines. Amphetamine and amphetamine-like substances that act as substrates for DAT include methamphetamine, methylphenidate (MPH, Ritalin), methylenedioxymethamphetamine (MDMA, ecstasy), and the herbs khat (which contains the labile alkaloid cathinone, considered the “natural” amphetamine) and ephedra (which contains ephedrine and norpseudoephedrine, also known as cathine) (Kalix, 1991). Under normal physiological conditions, DAT recycles DA after its release into the extracellular space, terminating the neurotransmitter signal. However, amphetamines acutely regulate DAT cell surface expression by causing redistribution of DAT as an active carrier on the plasma membrane to intracellular endosomal vesicles, leading to a concomitant loss of DAT activity (Saunders et al., 2000; Kahlig and Galli, 2003; Fig. 4). Amphetamines can also elevate extracellular DA by evoking neurotransmitter efflux through transporters such as DAT.

1.08.4.3.2

Cocaine

Cocaine is a benzoid acid ester whose chemical structure consists of three parts: a lipophilic group, a hydrophilic group, and an aliphatic group, which joins the first two groups. Unlike amphetamine-like substances, cocaine belongs to the category of ligands that prevents the reuptake of DA as it is a non-transported inhibitor of DAT (Eshleman et al., 1999; see Table 2). Notable, cocaine is a nonselective DAT blocker, blocking uptake of substrate via SERT and NET as well. Cocaine was initially used as a local anesthetic

Fig. 3

Chemical structures of common psychostimulants and endogenous monoamine neurotransmitters.

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Fig. 4 Regulation of dopamine (DA) transport by acute drug exposure. (A) Under basal conditions, DAT is expressed on both cell surface and intracellular membranes. (B) Amphetamine-like drugs compete with DA for uptake and reduce transporter expression on cell surface. (C) A reduction in transport capacity could lead to elevated extracellular DA concentration even after washout. (D) The DAT blocker cocaine inhibits DA uptake and increases transporter cell surface expression. (E) An increase in DA transport capacity could lead to decreased extracellular DA concentration following washout. Figure modified from Kahlig KM and Galli A (2003) Regulation of dopamine transporter function and plasma membrane expression by dopamine, amphetamine, and cocaine. British Journal of Pharmacology 479(1–3): 153–158, with permission.

but fell out of wide use because of its potent addictive qualities. When given in high systemic doses, it has mood-elevating effects that have contributed to its wide scale abuse. High doses of cocaine can lead to toxic reactions, including hyperthermia, rhabdomyolysis, shock, and acute liver injury, which can be fatal. Regulation of DAT by cocaine has been studied in vitro using brains of cocaine addicts obtained post-mortem, as well as in vivo using modern imaging techniques. Most reports demonstrate that chronic cocaine exposure increases DAT binding sites (Little et al., 1993, 1999; Staley et al., 1994). Interestingly, cocaine has been found to paradoxically increase DA uptake in vivo, in a manner that is dependent on concentration of cocaine and ambient extracellular DA (Zahniser et al., 1999; Daws et al., 2002). Cell culture experiments have determined that the increase in DAT transport activity upon cocaine exposure could be accounted for by a parallel increase in DAT cell surface expression (Daws et al., 2002). In addition, increased DAT membrane expression was observed in cocaine-treated NAcc neurons stably expressing DAT (Little et al., 2002). Similar increases in DAT cell surface expression were obtained in biotinylation experiments. Together, these studies indicate that acute exposure to DAT blockers increases DAT membrane expression. Therefore, it appears that cocaine acts to increase extracellular DA by blocking DA uptake while simultaneously increasing DAT cell surface expression. It follows then, that once cocaine is metabolized and eliminated from the system, extracellular DA may fall below baseline, as elevated DAT cell surface expression remains (see Fig. 4). This trafficking of the DAT may represent part of the cascade of changes that trigger a relapse of cocaine following abuse and withdrawal.

1.08.4.3.3

Synthetic cathinones

Similar to amphetamines and cocaine are new psychoactive substances, synthetic cathinones. Synthetic cathinones are lab-made chemical analogs of the khat plant, Catha edulis. Like amphetamine and cocaine, synthetic cathinones can either be substrates of DAT or DAT blockers. Both types have therapeutic and abuse potential (Baumann et al., 2018). For example, synthetic cathinones that are approved for therapeutic uses today in the United States include the anorectic agent, diethylpropion (DAT substrate, see Table 2), used under the brand name Tenuate for weight management (Cercato et al., 2009; Suplicy et al., 2014); the FDAapproved but rarely prescribed pyrovalerone (DAT and NET blocker, see Table 2), to treat chronic fatigue (Wander, 1963; Thomae, 1963; Seeger, 1967; Goldberg et al., 1973); and bupropion (DAT and NET blocker, and antagonist at nicotinic receptors, see Table 2), approved for the clinical treatment of depression under the brand name Wellbutrin (Mehta, 1974; Dhillon et al., 2008), as well as for the treatment of smoking addiction under the brand name Zyban (Dwoskin et al., 2006). In addition, there are a variety of designer synthetic cathinones used recreationally as substances of abuse (Baumann, 2014; De Felice et al., 2014). These are particularly troublesome, as many of them are complex preparations that are not scheduled under the 1961 Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances yet still pose a substantial threat to public

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health (Madras, 2016). One of the first examples of these drugs were “bath salts,” which consist of powders or crystals that are administered intranasally, intravenously, or orally to produce their psychoactive effects. Low doses of bath salts produce typical stimulant effects such as increased energy, elevated mood, and euphoria. However, higher doses induce serious symptoms including hallucinations, psychosis, tachycardia, hypertension, hyperthermia, and aggressive or violent behavior (Banks et al., 2014; Karch, 2015). Although the three major compounds being sold on the street in 2011 as “bath salts” were identified within a couple of years (e.g., 4-methyl-N-methylcathinone [4-MMC, mephedrone], 3,4-methylenedioxy-N-methylcathinone [MDMC methylone], and 3,4methylene dioxypyrovalerone [MDPV]) (Spiller et al., 2011; Shanks et al., 2012; Drug Enforcement Administration (DEA), Department of Justice, 2013) and quickly made illegal, new cathinone derivatives are being developed constantly in order to avoid legislative control. For example, as of 2017, more than 100 cathinones had been developed worldwide (United Nations Office on Drugs and Crime (UNODC), 2017). For a more detailed review regarding the neuropharmacology of synthetic cathinones, see Baumann et al. (2018).

1.08.4.4

Therapeutic application

The DAT plays an integral role in cognition, affect, behavioral reinforcement, and motor function (Volz and Schenk, 2005). Thus, DAT dysregulation is implicated in the etiology of diseases associated with abnormal DA levels, such as depression, bipolar disorder, PD, ADHD, and addiction (Volz and Schenk, 2005; Gainetdinov and Caron, 2003). Psychostimulants, which target the DAT, have many therapeutic uses in these diseases. Therapeutic uses for amphetamines are significantly more numerous than those for cocaine. Whereas amphetamines treat obesity, ADHD, and narcolepsy, cocaine is mainly restricted to pain relief drugs. Below we summarize some of the therapeutic uses for amphetamines and cocaine.

1.08.4.4.1

Attention deficit hyperactivity disorder (ADHD)

ADHD is a common neurodevelopmental disorder characterized by difficulties sustaining attention, impulsive behavior, and excessive activity (Biederman et al., 1991; Stein et al., 1995; Zametkin, 1995; Barkley, 2002; Barkley et al., 2002). There is a paucity of evidence regarding the causes and risk factors for ADHD. However, it involves dysregulation of the dopaminergic system, and genetic predisposition likely plays a role. The genes commonly thought to contribute to the development of ADHD predominantly incorporate those involved in the catecholamine pathways, including genes encoding the DAT (see Table 1). In addition to genetics, evidence exists for a handful of other risk factors, including prenatal exposure to specific teratogens, such as lead, alcohol, tobacco, premature delivery, low birth weight, and traumatic brain injury. Treatment plans for ADHD often involve the combination of psychotherapy and medication. The two most commonly prescribed medications for ADHD are the psychostimulants MPH (e.g., Ritalin) and amphetamine (e.g., Adderall, a mixture of amphetamine and dextroamphetamine). These drugs target DAT and NET and can be administered at behaviorally activating doses to potently increase extracellular levels of DA and NE throughout the brain (Kuczenski and Segal, 1994; Kuczenski et al., 1995). The fact that these predominantly DAT-targeting psychostimulants are used as pharmacotherapy for ADHD reinforces the potential role of DAT in forming the ADHD phenotype. Clinically relevant doses are essential to prevent the motor activating and reinforcing effects of higher psychostimulant doses (Spencer et al., 2015). Low, clinically relevant doses exert qualitatively different behavioral effects compared to higher, behaviorally activating doses. For example, the therapeutic dose range for MPH of approximately 10–40 ng/mL for humans, or 1–3 mg/kg (orally; 0.25–1.0 mg/kg i.p.) for rats, does not lead to behavioral activation or other arousal actions and improves prefrontal cortex (PFC)-dependent cognition (Kuczenski and Segal, 1994, 2001, 2002; Kuczenski et al., 1995). At these doses, MPH elicits significant increases in extracellular NE and DA in PFC without leading to addiction-promoting increases of DA levels in the NAcc or NE in the medial septum that occur at higher, clinically inappropriate doses (Kuczenski and Segal, 2001, 2002; Drouin et al., 2006). This lack of catecholamine increase in subcortical regions associated with addiction (i.e., NAcc and medial septum) (Seiden et al., 1993; Sulzer et al., 1995, 2005; Jones et al., 1998) that is elicited by clinically relevant doses of these psychostimulants is likely why individuals taking these medications do not develop dependence. Indeed, for individuals with ADHD, low dose psychostimulants appear more likely to reduce, rather than increase, drug abuse (Hammerness et al., 2017; Ginsberg et al., 2015; Krinzinger et al., 2019).

1.08.4.4.2

Parkinson’s disease (PD)

PD is a neurodegenerative disorder characterized by a progressive loss of nigrostriatal DA neurons, which results in bradykinesia, rigidity, resting tremor, and postural instability (Halliday and McCann, 2010). Treatment for PD includes administration of LDOPA or DA agonists. Evidence suggests that by the time a patient begins experiencing motor symptoms of PD, approximately 30–50% of dopaminergic cell bodies in the SN have been lost (Fearnley and Lees, 1991; Ishibashi et al., 2014). Moreover, human patients with PD display decreased DAT levels in the posterior putamen (Ishibashi et al., 2014) and an accumulation of Lewy bodies within the SN pars compacta containing a-synuclein (Braak et al., 2003). The production of a-synuclein is thought to involve the a-synuclein (SNCA) gene (Spatola and Wider, 2014), and a mutation in SNCA has been implicated in the heritable form of PD. The role for a-synuclein in regulating DAT is not fully elucidated. However, evidence suggests that a-synuclein recruits and stabilizes DAT at the plasma membrane and enhances Vmax under normal conditions (Fountaine and Wade-Martins, 2007). Additionally, Lee (2008) has hypothesized that the oxidative stress and terminal damage observed in PD patients is due to the overexpression of a-synuclein, leading to clustering and accelerated DA uptake. This DAT- a-synuclein interaction is disrupted by the ubiquitin

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183

ligase Parkin in cell models, which protects against the dopaminergic toxicity commonly induced by a-synuclein (Moszczynska et al., 2007; Bridi and Hirth, 2018). In addition to a role for a-synuclein, a link between mutations of the human SLC6A3 gene encoding DAT and infantile parkinsonism-dystonia (also known as DTDS; see Table 1) (Kurian et al., 2009, 2011b) has been observed. DTDS is a severe neurological syndrome that typically presents in early infancy with hypokinetic Parkinsonism (Adamsen et al., 2014). Later onset DTDS and Parkinsonism in adolescents and adults due to DAT missense mutations have also been observed (Hansen et al., 2014; Ng et al., 2014). Overall, evidence suggests that DAT is an important target for developing therapies to slow the progression of PD (e.g., Uhl et al., 2019).

1.08.4.4.3

Psychostimulant abuse

Stimulant drug abuse is a significant public health problem (United Nations Office on Drugs and Crime (UNODC), 2017), compounded by the lack of effective pharmacological treatment (e.g., Kufahl and Olive, 2011). Prolonged use of psychostimulants leads to a loss or dysfunction of DAT. For example, compared to control subjects, methamphetamine abuse leads to a persistent 15–25% decrease in caudate DAT densities (Volkow et al., 2001; Johanson et al., 2006; McCann et al., 1998). In addition, the development of PD is more likely among individuals who abuse amphetamines (Callaghan et al., 2012; Curtin et al., 2015). In contrast, individuals who abuse cocaine are not more likely to develop PD. However, clinical data regarding the effects of cocaine on DAT show mixed results, with some investigators reporting a reduction of DAT protein levels, but normal DAT binding in postmortem analyses (Wilson et al., 1996; Little et al., 2003); while others report increased DAT levels in striatal regions of recently abstinent cocaine users and in postmortem analyses (Mash et al., 2002; Crits-Christoph et al., 2008). Postmortem analyses also found elevated DAT function in subjects who abused cocaine (Mash et al., 2002). While these reports highlight the need to further investigate the effects of cocaine abuse and abstinence on DAT expression and function, Volkow et al. (2001) suggest that in some cases DAT downregulation in cocaine-addicted individuals might reflect an adaptation to overcome a reduction in DA release. Targets for DAT may possess therapeutic potential for treatment of psychostimulant addiction. The ability of DA reuptake inhibitors and releasers to treat dependency to cocaine and amphetamine have been investigated in several clinical trials. DAT inhibitors appear to be more effective than releasers for reducing psychostimulant use (Grabowski et al., 1997; Tiihonen et al., 2007). Alternatively, drugs that stimulate DA release are more effective at reducing use of DA inhibitors (Grabowski et al., 2001; Mooney et al., 2009; Longo et al., 2010; Galloway et al., 2011). Indeed, the DAT inhibitor MPH has been tested as a putative agonist replacement therapy for cocaine and methamphetamine/amphetamine dependence. Although clinical trials testing MPH substitution as a treatment for cocaine dependence are generally negative (see Grabowski et al., 1997; Schubiner et al., 2002), MPH may be effective for managing amphetamine use disorders (Tiihonen et al., 2007; Ling et al., 2014). Abstinence was not achieved in many of these participants, however, suggesting that DAT inhibitors may hold some therapeutic potential, but other DAT pharmacological manipulations should be explored for stimulant abuse treatment (i.e., Howell and Negus, 2014). Finally, drugs that mimic the neuropeptide neurotensin, a modulator of DA system activity, reduce animal self-administration of stimulants such as amphetamine and cocaine and are potential agents for the treatment of stimulant dependence (Richelson, 2003; Hanson et al., 2013). Although these neurotensin receptor agonists have been shown to block stimulant-induced increases in extracellular DA, their effects on DAT function have not been reported.

1.08.4.4.4

Obesity

Obesity is a significant public health concern that impacts approximately 42% of all adults in the United States (Hales et al., 2020). Obesity-related health conditions are among the leading causes of preventable and premature death. These conditions include but are not limited to heart disease, stroke, type 2 diabetes, and certain types of cancer. Treatments for obesity include diet and lifestyle changes, pharmacological intervention, and bariatric surgery (Ryan, 2016). However, diet and lifestyle changes often trigger several biological adaptations designed to prevent starvation. Such adaptations can be potent enough to undermine diet and lifestyle changes (Ochner et al., 2015). One such adaptation involves habituation to neural DA signaling that develops with chronic overconsumption of highly palatable food, which leads to a perceived reward deficit and subsequent compensatory increases in consumption. In cases such as these, amphetamines with action at DAT could be an effective treatment in addition to diet and lifestyle changes. Indeed, tesofensine, a triple action uptake inhibitor targeting SERT, NET, and DAT, has the potential to treat obesity. Individuals treated with tesofensine have experienced significant weight loss (Axel et al., 2010). Other DAT inhibitors that have demonstrated promising results for the treatment of obesity include bupropion and zonisamide. These drugs lead to modest weight loss when administered individually (Gadde et al., 2001, 2003; Anderson et al., 2002). However, there is evidence of an additive effect on weight loss when administered in combination with the other. Bupropion has shown promising results when administered with the opioid receptor antagonist naltrexone, which works by blocking the inhibitory feedback loop through pro-opiomelanocortin neurons, and is believed to limit sustained weight loss (Plodkowski et al., 2009; Won Son and Kim, 2020).

1.08.4.4.5

Topical anesthetic

Cocaine can be applied as a local anesthetic (i.e., Benowitz, 1993). Topical application of cocaine can cause loss of feeling or numbness, enabling specific procedures to be conducted without causing pain to the patient.

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1.08.4.4.6

Analgesic

Cocaine administered intranasally in the region of the sphenopalatine ganglion has been used to treat acute and chronic pain since 1909 (i.e., Benowitz, 1993). Additionally, cocaine has been advocated to relieve muscle spasms, neuralgia, vasospasm, reflex sympathetic dystrophy, lower back pain, sciatica, and angina. It has been successfully used to treat cluster headaches (Costa et al., 2000). However, the use of cocaine as an analgesic is not widespread because, while relatively rare, toxic reactions have been rapid, unexpected, and severe (Richards et al., 2016).

1.08.5

Norepinephrine transporter

1.08.5.1

Brain location

The NE transporter (NET) is the high-affinity transport mechanism for clearing NE from extracellular fluid. It can also take up DA with equivalent or better capacity than DAT (Morón et al., 2002). In doing so, it is a crucial player regulating the strength and duration of not only NE signaling, but also DA signaling, particularly in brain regions where DAT is sparsely expressed relative to NET (e.g., PFC) (Morón et al., 2002). NET is predominately located on pre-synaptic nerve terminals, but is also present on glia (Inazu et al., 2003a). Like SERT and DAT, NET is located extrasynaptically (Miner et al., 2003), meaning that noradrenergic neurotransmission occurs via paracrine (volume) transmission (Fuxe et al., 2007; Borroto-Escuela et al., 2021). NET is widely distributed throughout brain, being expressed in highest density in the cell body region, the locus coeruleus. NET is found in lower density in limbic structures including hippocampus, amygdala, and cortex, as well as hypothalamus (Tejani-Butt, 1992). For comprehensive reviews of structure, function and regulation see Mandela and Ordway (2006), Kristensen et al. (2011), Ramamoorthy et al. (2011) and Pramod et al. (2013).

1.08.5.2

Gene variants

Several rare variants of the gene encoding NET (SLC6A2) have been linked to a number of disorders including depression (Haenisch et al., 2009; Zhao et al., 2013; Marques et al., 2017) and ADHD (Kim et al., 2008; Hahn et al., 2009). Not surprisingly, given the important role of NE signaling in cardiovascular function, gene variants have been associated with cardiovascular disorders such as orthostatic intolerance, hypertension and long-QT syndrome (Halushka et al., 1999; Garland et al., 2002; Hahn et al., 2005; ShireyRice et al., 2013; Marques et al., 2017; Eikelis et al., 2018). Perhaps the most studied of these variants is the nonsynonymous SNP, A457P, which converts a highly conserved alanine in transmembrane domain 9, into a proline. This mutation renders NET 98% non-functional (Shannon et al., 2000). This variant is linked to orthostatic intolerance, a disorder characterized by hypotension, which occurs when standing up, evoking symptoms, such as lightheadedness, fainting and increased heart rate. Also see Table 1 (Bhat et al., 2021).

1.08.5.3 1.08.5.3.1

Interaction with psychoactive drugs Psychotherapeutics

There are three primary classes of NET acting psychotherapeutics, selective NET blockers, SNRIs and tricyclic antidepressants. Selective NET blockers FDA approved for therapeutic use include atomoxetine (Strattera) and reboxetine (Edronax). Reboxetine was the first truly selective NET inhibitor developed, with a Ki value for NET of  1 nM, compared to 129 nM and > 10,000 nM for SERT and DAT, respectively (Wong et al., 2000). Atomoxetine has Ki values of 5, 77 and 1451 nM at human NET, SERT and DAT, respectively (Bymaster et al., 2002). Several drugs used clinically have dual SERT and NET blocking activity. These are known as SNRIs and include bupropion (Wellbutrin), duloxetine (Cymbalta), desvenlafaxine (Pristiq) and venlafaxine (Effexor), the first SNRI developed. Tricyclic antidepressants such as amitriptyline (Elavil, Endep) desipramine (Norpramin), nortriptyline (Aventyl, Pamelor), protriptyline (Vivactil) and amoxapine (Asendin) also have dual SERT/NET blocking actions, though most are more selective for NET than SERT or DAT (Zhou, 2004). Table 2 provides Ki values for selective NET blockers. Though less research has focused on the structural basis for actions of NET acting drugs, studies using LeuT as a model indicate that SNRIs and tricyclics have a similar mode of action as SSRIs, in that they lock the transporter in an outward facing conformation (Wang et al., 2013). NET appears to be regulated by processes similar to SERT, for example, PKC induced phosphorylation of specific residues in the NET protein causes its internalization (Jayanthi et al., 2006; see Mandela and Ordway, 2006, for review). However, unlike 5-HT, which increases SERT activity (Ramamoorthy and Blakely, 1999), NE decreases NET activity (Zhu and Ordway, 1997) via oxidative stress pathways (Mao et al., 2004). Some, but not all antidepressants with NET blocking activity have been shown to cause downregulation the transporter after chronic treatment (Bauer and Tejani-Butt, 1992; Hébert et al., 2001; Gould et al., 2003, 2006, 2007; Benmansour et al., 2004). The reason why NET acting antidepressants do not uniformly have this effect remains unclear, as does the molecular mechanism underlying NET down regulation, though it appears that sequestration of the NET from the plasma membrane is involved (Mandela and Ordway, 2006).

Neurotransmitter Transporters and Their Role in the Pharmacological Actions of Therapeutic and Abused Drugs 1.08.5.3.2

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Stimulants

Stimulants that interact with NET are the same as those discussed for SERT and DAT. These are drugs that act as NET blockers (e.g., cocaine, which is also a potent blocker of DAT and SERT), and drugs that are competitive inhibitors (substrates) for NET (e.g., amphetamine, 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy)). Like the endogenous substrate, NE, amphetamine decreases NET expression and function (Zhu et al., 2000; Annamalai et al., 2010; Mannangatti et al., 2018). Effects of cocaine on NET expression are mixed, with increases, decreases or no change in NET expression reported (reviewed in Mandela and Ordway, 2006; Mannangatti et al., 2011, 2015).

1.08.5.4 1.08.5.4.1

Therapeutic applications Depression

The most common use for NET blockers, including SRNIs, is for treatment of depression. Current FDA approved drugs that have NET blocking capabilities include amoxapine, bupropion, desipramine, desvenlafaxine, duloxetine, nortriptyline, protriptyline and venlafaxine (Zhou, 2004). Their efficacy and tolerability are reviewed in Cipriani et al. (2018). This study showed the tricyclic, amitriptyline, and the SNRI, venlafaxine to be among the most effect antidepressants, whereas reboxetine was among the least effective.

1.08.5.4.2

Smoking cessation

Bupropion has proved to have efficacy in preventing tobacco smoking (Choi et al., 2021; Huecker et al., 2021).

1.08.5.4.3

Cardiovascular disorders

Use of NET blockers for cardiovascular disorders remains controversial and is contingent on the disorder. For example, NET blockers have been successful in preventing severe vasovagal reactions and syncope, a disorder that causes fainting due to a sudden drop in heart rate and blood pressure in response to certain triggers (e.g., sight of blood) (Lei et al., 2021). In contrast, NET blockers have been found to exacerbate postural tachycardia syndrome, a condition that occurs when moving from lying down to standing up, which causes lightheadedness, fainting and accelerated heart rate (Green et al., 2013).

1.08.5.4.4

Other indications

Although treatment of depression is the primary therapeutic application of NET blockers, these drugs are also prescribed for a variety of other indications. For example, duloxetine is indicated for treatment of GAD, diabetic peripheral neuropathic pain, fibromyalgia and chronic neuropathic pain. Tricyclic antidepressants such as amitriptyline and desipramine are used to treat neuropathic pain. Desipramine is used to treat BN, irritable bowel syndrome, overactive bladder, and ADHD.

1.08.6

GABA transporters

1.08.6.1

Brain location

Mammalian GATs include GATs 1–3 and the betaine GABA transporter (BGT1). However, GAT1 and GAT3 are the only GATs widely distributed throughout brain and will be the focus of this section (Ryan et al., 2021). GAT1 and GAT3 are present in neurons and astrocytes (Durkin et al., 1995; Borden, 1996; Scimemi, 2014a). GAT1 is the most highly expressed of these, with dense expression found in cerebellum, basal ganglia, olfactory bulb, and interpeduncular nucleus (Scimemi, 2014). In contrast, moderate expression is found throughout the neocortex, amygdala, septum, thalamus, zona incerta, subthalamic nucleus, hypothalamus, superior colliculus, dorsal tegmental nuclei, basal ganglia, nucleus of Darkschewitsch, pons, and medulla (Scimemi, 2014). GAT3 is less abundantly expressed than GAT1, with the highest expression found in the olfactory bulb and retina (Scimemi, 2014). However, it is moderately expressed in the septum, basal ganglia, subfornical organ, amygdala, thalamus, superior colliculus, ventral tegmental nucleus, basal ganglia, and medial vestibular nucleus (Scimemi, 2014). The cellular and subcellular distribution of GAT1 and GAT3 also differ. GAT3 is expressed mainly in astrocytes, with some expression in brain stem and cortical neurons (Pow et al., 2005; Ribak et al., 1996; Melone et al., 2005, 2015). GAT1 expression, on the other hand, is mainly limited to the axon terminals of symmetrical synapses in the neocortex (i.e., Minelli et al., 1995; Ribak et al., 1996). This pre-synaptic localization of GAT1 is essential for recycling GABA in the pre-synaptic terminal following a release event and indicates GABA uptake is crucial to sustaining GABAergic synaptic transmission. Additionally, postsynaptic GAT1 expression has been found in the dendrites and soma of non-GABAergic neurons (Yan et al., 1997). The function of this postsynaptic expression profile is not fully understood, but it may serve to limit extrasynaptic GABA activity.

1.08.6.2

Gene variants

Variations in the gene encoding GAT1 (SLC6A1) are associated with several neurodevelopmental disorders, including myoclonic atonic seizures, autism, and intellectual disability (see Table 1) (Bhat et al., 2021; Goodspeed et al., 2020). No disease relevant

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mutations have been identified in GAT3. Additionally, similar to other transporters, GAT knockout mouse models have provided invaluable information regarding the role that GATs play in these diseases, and indicate that GATs are a therapeutic target (Ryan et al., 2021).

1.08.6.3 1.08.6.3.1

Interaction with psychoactive drugs Opioids

Opioid withdrawal is characterized, in part, by hyperexcitation of GABAergic neurons and increased GABA release in the periaqueductal gray (PAG), both of which can be attenuated through inhibition or deletion of GAT1 (Bagley et al., 2005). Bagley et al. (2005) observed that some PAG neurons are hyperexcited by a GAT-dependent nonselective cation current during opioid withdrawal. GABA release increases in the PAG during opioid withdrawal (Ingram et al., 1998; Heck et al., 2003). Subsequent experiments revealed that GAT1 activity in nerve terminals was primarily responsible for increased GABA release in vitro, ruling out the possibility of pre-synaptic modulation by GABA receptors (Bagley et al., 2011). These experiments indicate that GAT1 currents in the PAG are important for exciting GABAergic neurons and nerve terminals to produce behavioral signs of opioid withdrawal (Ryan et al., 2021).

1.08.6.3.2

Cannabinoids

Chronic stimulation of cannabinoid receptors CB1 and CB2 during adolescence reduces GAT1 gene expression in the CA1 region of hippocampus (Higuera-Matas et al., 2015). Conversely, the endogenous endocannabinoid receptor agonist, 2-arachidonoylglycerol (2-AG), increases GABA uptake (Romero et al., 1998; Venderova et al., 2005). There is evidence that CB1 receptors interact or colocalize with b2 adrenergic receptors (Hudson et al., 2010), and b2 adrenergic receptor activation increases expression of GAT1 and subsequently GABA uptake (Martins et al., 2018). This increase in GABA uptake is inhibited by the cannabinoid receptor agonist WIN55, 212–2, as well as by the selective PKA inhibitor, H89, indicating that promotion of GABA uptake by b2 adrenergic receptors is mediated by a cannabinoid receptor controlled PKA pathway (Ryan et al., 2021).

1.08.6.3.3

Anticonvulsant drugs

Pharmacological compounds that modulate GABAergic neurotransmission, such as benzodiazepines and barbiturates, have demonstrated efficacy for treating anxiety and epilepsy (During et al., 1995; Dalby and Nielsen, 1997). Studies using stably transfected mammalian cell lines have demonstrated several inhibitors that are GAT1 selective, including cis-3aminocyclohexanecarboxylate, CI-966, nipecotic acid, NNC 05–711, SK&F 89976-A, and tiagabine (Borden et al., 1994, 1995). Each of these compounds leads to anticonvulsant activity in rodent models. Additionally, tiagabine is effective in the treatment of complex and refractory myoclonic seizures (Dalby and Nielsen, 1997; Bhat et al., 2021; Ryan et al., 2021). Table 2 summarizes the Ki/IC50 values for several of the drugs described in this section.

1.08.6.4 1.08.6.4.1

Therapeutic application Seizures

Dysregulation of GABAergic neurotransmission is associated with seizures and epilepsy (Meldrum and Stephenson, 1975; Grabenstatter et al., 2012). GABA transporters represent a viable target for the development of antiepileptic drugs. Indeed, seizures often occur due to increased excitation or decreased inhibitory neurotransmission mediated by GABA (Sarup et al., 2003). As previously discussed, the action of GABA is terminated by active removal of GABA from the synaptic space by high-affinity GAT proteins located on the pre-synaptic neuron and surrounding glial cells. GAT inhibition increases extracellular GABA, thereby prolonging access to synaptic and extrasynaptic GABAA receptors. One drug that effectively inhibits GAT and is widely used is tiagabine. Tiagabine is effective for treating seizures originating from cortex and limbic system but not for seizures originating from brain stem, consistent with selective GAT1 expression in brain (Dalby and Nielsen, 1997, Dalby, 2003; Madsen et al., 2009; Bhat et al., 2021; Ryan et al., 2021).

1.08.6.4.2

Anxiety and depression

GABA-uptake inhibitors lead to potent anxiolytic and antidepressant effects in animal models (Liu et al., 2007). Indeed, chronic administration of tiagabine, the GAT1 inhibitor, has a significant anxiolytic and antidepressant effect compared to the SSRI paroxetine. Female Naval Medical Research Institute (NMRI) mice that receive chronic administration of tiagabine also display increased GABAA and decreased GABAB receptor expression (Thomsen and Suzdak, 1995), suggesting that GAT1 may be a fruitful target for the development of drugs to treat anxiety and depression. However, other investigators have observed tremor, ataxia, and nervousness in GAT1 knockout mice (Chiu et al., 2005), indicating the need to elucidate further the role of GABA transporters in anxiety and depression (Zafar and Jabeen, 2018).

1.08.6.4.3

Pain management

GABAergic transmission plays an inhibitory role in the nociceptive process, especially in the dorsal horn and spinal cord (Smith et al., 2007). Therefore, it has been suggested that excitatory amino acid neurotransmission could be attenuated by inhibiting GAT1, resulting in an anti-nociceptive effect at the spinal cord level (Smith et al., 2007; Bowery and Smart, 2006). Indeed,

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GABAergic drugs are anti-nociceptive in animal models utilizing the hot plate paradigm (Smith et al., 2007; Łuszczki et al., 2003; Luszczki et al., 2007; Laughlin et al., 2002; Dudra-Jastrzebska et al., 2009). Moreover, analgesic properties of systemically administered tiagabine have been demonstrated in response to acute and chronic pain models (Smith et al., 2007; Łuszczki et al., 2003; Luszczki et al., 2007; Iversen, 2006; Laughlin et al., 2002; Cope et al., 2009). These data indicate the possibility of using GAT1 inhibitors to treat individuals with chronic pain (Gryzło et al., 2021).

1.08.7

Glycine transporters

1.08.7.1

Brain location

Two glycine transporter (GlyT) subtypes, GlyT1 and GlyT2, have been identified through cDNA cloning. While the GlyTs share approximately 50% amino acid sequence identity, they differ in pharmacology (Liu et al., 1993; Ponce et al., 1998; Smith et al., 1992) and tissue distribution (Guastella et al., 1992; López-Corcuera et al., 1998). GlyT1 is localized in astrocytes and largely represents an exclusive glial transporter isoform. Zafra et al. (1995) found that GlyT1 mRNA expression was abundant in glial cells from the hypothalamus, thalamus, diencephalon, retina, olfactory bulb, and cortex. In addition, GlyT1 is also highly expressed in brain regions devoid of strychnine-sensitive glycine receptors (GlyRs), where it co-localizes instead with the N-methyl D-aspartate (NMDA) subtype of glutamate receptors (Smith et al., 1992; Cubelos et al., 2005). In contrast, GlyT2 is exclusively expressed in neurons in strychnine-rich areas associated with inhibitory neurotransmission, such as the spinal cord, brainstem, and cerebellum (Jursky and Nelson, 1995; Araki et al., 1988).

1.08.7.2

Gene variants

Three GlyT1 variants have been identified among individuals suffering from an autosomal recessive disorder with a defective cleavage enzyme complex, called non-ketotic hyperglycemia (NKH) (Applegarth and Toone, 2006). These variants include K310Ffs*31, Q573*, and S407G (see Table 1) (Alfadhel et al., 2016; Kurolap et al., 2016; Bhat et al., 2021). The symptoms of people suffering from NKH parallel the deficits seen in GlyT1/ mice (see below for more details). Namely, individuals display symptoms such as encephalopathy, shallow breathing, hyperekplexia (exaggerated startle reflex, see below), hypotonia, and facial dysmorphism (Alfallaj and Alfadhel, 2019). There is a high mortality rate within the first year of life from respiratory failure, but those individuals who overcome respiratory failure and survive can have a normal lifespan, as observed in GlyT1 / mice (Gomeza et al., 2003a; Bhat et al., 2021). Individuals possessing loss-of-function mutations in key glycinergic systems display symptoms of hyperekplexia, or startle disease, and bear a striking resemblance to GlyT2 / mice (see below) (Bhat et al., 2021; Davies et al., 2010; Gill et al., 2011, 2012; Harvey et al., 2008). Several disease variants have been identified among individuals suffering from startle disease (see Table 1, Bhat et al., 2021). In addition to hyperekplexia, the phenotype of individuals with GlyT2 mutations often include hypotonia, recurrent infantile apneas, and developmental delays (Thomas et al., 2013). See Bhat et al. (2021) for a more detailed review of the consequences of specific GlyT gene variants.

1.08.7.3

Interaction with psychoactive drugs

The sensitivity of GlyT1 to N-methylglycine (sarcosine) provides a pharmacological distinction between the transport activities of GlyT1 and GlyT2 (Singer et al., 2009). Sarcosine acts as a competitive inhibitor, as it is a substrate of GlyT1 but not GlyT2 (Leonetti et al., 2006). The antidepressant amoxapine selectively inhibits GlyT2 (Mezler et al., 2008), as do several alkanols, including ethanol (Papouin et al., 2012). Unfortunately, these compounds do not display high GlyT-subtype selectivity. However, several compounds have been developed as novel high-affinity inhibitors for GlyT2, and their therapeutic potential is being evaluated (Tsai and Lin, 2010). The first potent and selective GlyT1 inhibitors reported include ALX5407, Org24461, and Org 24,598 (Lin et al., 2012; Javitt, 2008). N-[(3R)-3-([1,1’-Biphenyl]-4-yloxy)-3-(4-fluorophenyl)propyl-N-methylglycine hydrochloride (NFPS), the racemic form of ALX5407, and Org24461 increase glycinergic concentration in cerebrospinal fluid and reverse the phencyclidine (phenylcyclohexyl piperidine, PCP)-induced negative symptoms in rat models of schizophrenia (Heresco-Levy et al., 1999; Buchanan et al., 2007). NFPS also enhances NMDA receptor (NMDAR)-mediated excitatory transmission in hippocampal slice preparations and augments the downstream effects of NMDA receptor activation (Le Pen et al., 2003; Bullich et al., 2011). Overall, these data support a role for GlyT1 in regulating NMDA-mediated responses and strengthen the idea that GlyT1 inhibitors have considerable promise for treatment of negative symptoms of schizophrenia that result from impaired NMDA neurotransmission. See Table 2 for Ki and IC50 values of select compounds.

1.08.7.4

Therapeutic application

Current knowledge of GlyTs indicates an essential role in several disorders such as epilepsy, hyperekplexia, neuropathic pain, drug addiction, schizophrenia, stroke, and neurodegenerative disorders. For example, a GlyT antagonist that would increase extracellular glycine could have utility in treating neurological disorders associated with hyperactivity, and for sedation, narcosis, and analgesia. This is consistent with the observation that glycine reduces hyperalgesia and allodynia in animal models. Additionally, reduced

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activity of GlyT1 might improve neurological diseases associated with NMDAR dysfunction and, therefore, improve learning and memory as well as cognitive deficits.

1.08.7.4.1

Hyperekplexia and glycine encephalopathy

Homozygous GlyT KO mice imbued significant insight into the therapeutic potential of targeting these transporters. The hyperglycinergic state of GlyT1 knockout mice leads to symptoms like those associated with the human hereditary disease glycine encephalopathy. Glycine encephalopathy is characterized by degradation of excess intracellular glycine (Applegarth and Toone, 2001; Sakata et al., 1997). The hypoglycinergic state of GlyT2 knockout animals leads to symptoms like those observed in patients suffering from hyperekplexia. Hyperekplexia is a neuromotor disorder characterized by exaggerated startle responses. In severe cases, it results in a ‘stiff baby syndrome’, or a hypertonic phenotype that slowly dissipates over the first year of life. Hyperekplexia has been shown to involve mutations of the a1 and b subunit genes of the GlyR (Lynch, 2004). Notably, in several patients diagnosed with glycine encephalopathy or hyperekplexia, no defects in the glycine cleavage system or GlyR genes have been found (Applegarth and Toone, 2001; Vergouwe et al., 1997). This indicates that glycine encephalopathy- and hyperekplexia- like syndromes can be caused through different genetic mechanisms and suggests that some yet unclassified forms of these diseases might involve mutations in the human GlyT genes.

1.08.7.4.2

Schizophrenia

Schizophrenia is a severe psychiatric disorder characterized by an abnormal interpretation of reality. Individuals with schizophrenia can have various symptoms, including hallucinations, delusions, and extremely disordered thinking and behavior that impairs daily functioning. Pharmacological treatments for schizophrenia function primarily by blocking DA D2 receptors. However, an alternative formulation for schizophrenia focuses on disturbances of brain glutamatergic neurotransmission at NMDAR (Kantrowitz and Javitt, 2010). Evidence supporting NMDAR models of schizophrenia include the ability of agents such as PCP or ketamine to induce negative symptoms of schizophrenia in animals and humans (Jentsch and Roth, 1999). Moreover, the effects of NMDAR inhibition by PCP can be reversed via GlyT1 inhibition (Chen et al., 2003). Thus, Sur and Kinney (2004) suggested that pharmacological inhibition of GlyT1 could represent a therapeutic approach to treating people with schizophrenia. Indeed, mice treated with a schizophrenia-inducing agent displayed a reduction in schizophrenia symptoms such as hyperactivity and prepulse inhibition after administration of a GlyT1 inhibitor (Javitt et al., 2004; Kinney et al., 2003). Subsequent clinical trial results prompted the recommendation of a high glycine diet and a sarcosine prescription to manage schizophrenia symptoms. However, attempts to increase brain glycine and D-serine through dietary supplementation require extremely high doses to overcome rapid metabolism and the blood-brain barrier, which prevents their entry into brain. The dietary approach can also result in undesirable side effects due to glycine’s and D-serine’s involvement in several biochemical pathways. A rational alternative for increasing glycinergic activity is to inhibit reuptake of glycine by GlyT1. Data are being collected on several drugs in clinical trials (Cioffi, 2018).

1.08.7.4.3

Pain management

GlyT1 inhibitors might also have potential to increase efficacy of inhibitory neurotransmission in different pathological situations by transiently potentiating postsynaptic GlyR currents. For example, raising glycine levels at spinal synapses should reduce spontaneous motor activity (Gomeza et al., 2003a, Lynch, 2004) and decrease pain perception (Harvey et al., 2008). This might be exploited for muscle relaxation during narcosis or acute spastic syndromes and analgesia. Whether subsaturating doses of GlyT2selective inhibitors might also be beneficial is not clear at present.

1.08.8

Organic cation transporters

1.08.8.1

Brain location

Organic cation transporters (OCTs) are expressed in brains of multiple species, including humans and rodents. OCT1 has the smallest reported brain expression profile of the OCTs in all species investigated (Sweet, 2021). OCT1 mRNA is mainly confined to cortex and brain microvessels. However, it is also observed in the human choroid plexus and murine olfactory mucosa, as well as the primary brain vascular endothelial cells of mice, rats, and humans (Sweet et al., 2001; Slitt et al., 2002; Monte et al., 2004; André et al., 2012; Duan and Wang, 2013; Geier et al., 2013; Wu et al., 2015b; Chaves et al., 2020). In contrast to OCT1, OCT2 mRNA expression has been observed in several CNS regions and cell types, not limited to cortex and choroid plexus (rat, mouse, and human), murine olfactory bulb and olfactory mucosa, human neurons, and rat cerebellum, hippocampus and leptomeninges (Busch et al., 1998; Sweet et al., 2001; Monte et al., 2004; Amphoux et al., 2006; Duan and Wang, 2013; Miura et al., 2017). The most comprehensive protein expression data have been gathered in mice. These studies indicate OCT2 is expressed in noradrenergic and serotonergic neurons, in the amygdala, dorsal raphe, frontal cortex, hippocampus, thalamus, median eminence, and pituitary (Bacq et al., 2012; Courousse et al., 2015). OCT2 protein expression has also been observed in primary brain vascular endothelial cell preparations from rats, mice, and humans, as well as in the cerebral cortex, hippocampus, and neurons of mice and humans (Busch et al., 1998; Bacq et al., 2012; Courousse et al., 2015; Wu et al., 2015b). OCT3 is widely distributed throughout brain. OCT3 mRNA expression was found in the cerebral cortex and intact brain microvessels of humans, rats, and mice (Geier et al., 2013; Chaves et al., 2020). Specifically, in mice and rats, mRNA expression has also been found in several brain regions, not limited to the area postrema, choroid plexus, dorsal raphe, medial hypothalamus,

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cerebellum neurons, hippocampus, SN, VTA, subfornical organ, and thalamus (Sweet et al., 2001; Schmitt et al., 2003; Vialou et al., 2004, 2008; Amphoux et al., 2006; Gasser et al., 2006; Baganz et al., 2008; Miura et al., 2017). Protein expression has been detected in neurons, astrocytes, and ependymal cells of mice and rats (Vialou et al., 2004, 2008; Cui et al., 2009; Gasser et al., 2017); and for humans in the cerebellum, astrocytes, primary brain vascular endothelial cells, and intact brain microvessels (Inazu et al., 2003b; Geier et al., 2013; Li et al., 2013; Yoshikawa et al., 2013). OCT3 is unique in that there appears to be regional differences in expression compared to OCT1–2 and PMAT. PMAT is also broadly expressed in brain. Indeed, in all brain areas except for the caudate nucleus, human PMAT mRNA expression is significantly higher than the mRNA expression of OCT3. PMAT expression has been detected in the blood brain barrier, choroid plexus, medulla oblongata, pons, cerebellum, SN, putamen, caudate nucleus, cerebral cortex, hippocampus, and NAcc (Dahlin et al., 2009; Duan and Wang, 2010, 2013; Engel et al., 2004; Geier et al., 2013; Sekhar et al., 2019).

1.08.8.2

Gene variants

Numerous gene variants have been identified in OCTs. In terms of gene variants relevant to CNS function OCT2 variants rs316019 and rs316006 appear to play a role in smoking cessation (Bergen et al., 2014). Variant rs8177516 is thought to increase susceptibility to DA cell death and therefore could increase risk for PD (Taubert et al., 2007). CNS OCT3 gene variants have been less well studied, but an association between rs509707 and rs4709426 with polysubstance abuse in a Japanese cohort has been reported (Aoyama et al., 2006). In addition, two novel genetic variants of OCT3 have been found in patients with OCD (Lazar et al., 2008). For a detailed review of CNS OCT gene variants, the reader is directed to Kölz et al. (2021). The impact of polymorphisms or mutations in the PMAT gene in relation to psychiatric, neurodegenerative and substance use disorders has not been explored. It should however, be considered a candidate gene in disorders where monoamine dysfunction is hallmark. In support of this notion, three rare non-synonymous inherited mutations in PMAT have been found with high prevalence in individuals with ASD compared to unaffected counterparts (Adamsen et al., 2014, for review see Vieira and Wang, 2021).

1.08.8.3

Interaction with psychoactive drugs

OCTs and PMAT interact with several psychoactive drugs, including antidepressants, antipsychotics, psychostimulants, and opioids (see Table 3 in Bönisch, 2021; Table 2).

1.08.8.3.1

Antidepressant drugs

Antidepressant medications are used to treat a variety of conditions characterized by an imbalance of one or more neurotransmitters. Such conditions include major depressive disorder and anxiety disorders. These drugs can also be prescribed to help manage some addictions. Antidepressants that inhibit all three OCTs and PMAT include the tricyclic antidepressants amitriptyline, desipramine, imipramine, and tianeptine; several SSRIs, including fluoxetine, paroxetine, and sertraline; the dual DAT and NET inhibitor bupropion, the selective NE reuptake inhibitor reboxetine, and the 5-HT/NE reuptake inhibitor venlafaxine. There are also several antidepressants that inhibit all three OCTs but their relationship with PMAT has not yet been explored. Most of these are tricyclic antidepressants (e.g., doxepin, maprotiline, mianserin, mirtazapine, and trimipramine) or SSRIs (citalopram and fluvoxamine). However, there are a variety of others, including nefazodone, nisoxetine, nomifensine, and sertindole. Important to note, concentrations of these drugs needed to interact with OCTs/PMAT are considerably higher than those used clinically. Thus, it is unlikely these drugs exert their therapeutic action via OCTs and PMAT (Bönisch, 2021).

1.08.8.3.2

Antipsychotic drugs

Antipsychotic medications are mainly prescribed to treat psychosis symptoms, including delusions or hallucinations. These symptoms can occur as part of a variety of conditions including bipolar disorder, PTSD, schizophrenia, eating disorders, and OCD. The older, first-generation antipsychotic medications are often referred to as “typical” antipsychotics, whereas the newer, second generation antipsychotic medications are often referred to as “atypical” antipsychotics. Several typical and atypical antipsychotic medications have actions at OCTs and PMAT (Table 3 in Bönisch, 2021). Most of the antipsychotic drugs that inhibit both PMAT and all three OCTs are atypical antipsychotics, including clozapine, haloperidol, olanzapine, and risperidone (Bönisch, 2021). Levomepromazine is a typical antipsychotic that inhibits all three OCTs and PMAT. Atypical antipsychotics that inhibit all three OCTs but have not yet been explored for PMAT include amisulpride, quetiapine, remoxipride, sertindole, and zotepine (Bönisch, 2021). Additionally, amisulpride and sulpiride are both substrates of OCTs. Typical antipsychotic medications that inhibit all three OCTs include perazine, spiperone, thioridazine, and zuclopenthixol (Bönisch, 2021). It is important to note that none of these drugs show affinity for OCTs/PMAT in the submicromolar range, therefore their clinical relevance requires further investigation.

1.08.8.3.3

Psychostimulant drugs

Psychostimulants increase alertness, attention, and energy; as such, they are used therapeutically to treat ADHD. Psychostimulants also can lead to feelings of euphoria at higher doses, which contribute to their abuse potential. However, there is no evidence of increased abuse potential when psychostimulants are used as prescribed. Indeed, teenagers who are prescribed psychostimulants to treat legitimate conditions are reportedly less likely to abuse drugs than their counterparts (Hammerness et al., 2017; Ginsberg et al., 2015; Krinzinger et al., 2019). All three OCTs also interact with a number of psychostimulant drugs, including amphetamine,

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cocaine, methamphetamine, MDMA, and phencyclidine. Other psychostimulants act as substrates for specific OCTs. For example, cathine, dimethyltryptamine (DMT), ephedrine, and p-methoxy-methamphetamine are substrates for OCT2, and mescaline is a substrate for OCT1. Until recently, it was unknown if psychostimulants interact with OCTs/PMAT at behaviorally relevant concentrations. However, emerging evidence supports a role for OCT3 in the actions of amphetamine and the synthetic cathinone, mephedrone (Mayer et al., 2018, 2019), whereas OCT3 is insensitive to cocaine at behaviorally relevant concentrations (Mayer et al., 2018).

1.08.8.3.4

Anti-parkinsonian drugs

OCTs interact with a small number of drugs designed to treat symptoms of PD. Amantadine interacts with all three OCTs, but has a higher affinity for OCT1 and OCT2. Indeed, Bednarczyk and colleagues demonstrated that amantadine is a substrate for OCT2. OCT1 is inhibited by apomorphine, but no other OCTs are. There are mixed results regarding the interaction of pramipexole with the OCTs. For example, pramipexole was found to be a substrate for OCT1 by Ishiguro et al. (2005), while Diao et al. (2010) found that pramipexole was a substrate only for OCT2 and OCT3. Based on the results from their large population-based study, in which a specific OCT1 variant (rs622342) was linked to the prescription of higher doses of PD medication and a decreased survival time, Becker and colleagues suggested that the transport efficacy of these drugs at OCT1 is reduced in individuals with the rs622342 gene variant, though further work needs to be done to assess this hypothesis (Bönisch, 2021).

1.08.8.3.5

Opioid analgesic drugs

Opioids can be prescribed for pain management if the benefit to the patient outweighs the risk associated with the drug (Aubrun et al., 2019). Opioid analgesics mainly interact with OCT1, though there are some interactions at OCT2 and OCT3. For example, morphine acts as a substrate for OCT1, and also has a weak interaction at OCT3. Tramadol interacts with both OCT1 and OCT2, and levomethadone interacts only with OCT2. However, none of these drugs interact with IC50 values at these transporters in the submicromolar range (Bönisch, 2021).

1.08.8.4

Therapeutic application

As discussed, a large number of drugs used for a variety of therapeutic indications have activity at OCTs and PMAT, however, notably, at concentrations much higher than those needed for therapeutic benefit (Bönisch, 2021). These findings suggest that OCTs and PMAT are not key players in their therapeutic actions. However, as described below, growing evidence points to OCTs and PMAT as promising targets for the development of novel therapeutics.

1.08.8.4.1

Depression and stress-related disorders

The complexity of depressive disorders is indicated by the heterogeneity in presentation of symptoms among patients. Serotonin homeostasis plays an important role in the pathophysiology of depression (Cowen and Browning, 2015). As such most medications prescribed to treat depression influence the uptake or metabolism of 5-HT in brain. Drugs designed to selectively inhibit uptake of 5-HT by SERT, or SSRIs, are widely prescribed due to the essential role of SERT in maintaining 5-HT homeostasis. However, SSRI treatment often results in a poor or delayed response (Steffens et al., 1997; Al-Harbi, 2012). As previously discussed, OCTs and PMAT play a complimentary role in the homeostasis of neurotransmitters like 5-HT. Thus, they may have the therapeutic potential to improve the response to antidepressant medications (Schildkraut and Mooney, 2004; Zhou et al., 2007; Daws, 2009; Daws et al., 2013). Importantly, most antidepressants have IC50 values much too high at OCTs and PMAT to be of any therapeutic use (Haenisch et al., 2012; Haenisch and Bönisch, 2010; Zhou et al., 2007). In other words, at clinically relevant doses, plasma concentration of most antidepressant drugs is not high enough to inhibit OCTs or PMAT, and thus it is likely that antidepressants nearly exclusively inhibit the high affinity, low capacity uptake1 transporters (SERT, NET and DAT), and perhaps marginally the low affinity, high capacity, uptake2 transporters (OCTs, PMAT) (Bönisch, 2021). This indicates that OCTs and PMAT may delay the effect of antidepressants by preventing the extracellular increase in neurotransmitter required for therapeutic benefit (Daws, 2009). Studies to date suggest that targeting D22 sensitive transporters such as OCT3, OCT2, and PMAT, could provide therapeutic utility by increasing 5-HT neurotransmission in individuals with stress-related disorders like depression (Benton et al., 2021). Indeed, pharmacological studies that simultaneously inhibit uptake1 (i.e., via SSRIs) and uptake2 (i.e., via D22), and use OCT or PMAT knockout animals have demonstrated that D22 potentiates the effect of SSRIs to inhibit 5-HT uptake and improve antidepressant efficacy (Baganz et al., 2008; Duan and Wang, 2010; Horton et al., 2013). Additionally, Bowman et al. (2020) demonstrated that 5-HT clearance was significantly inhibited by ketamine in wild-type mice, but this effect was lost in both SERT and PMAT knockout mice. This work indicates that the antidepressant-like effects of ketamine in mice require SERT and PMAT, and demonstrates the importance of SERT and PMAT in the inhibition of 5-HT clearance and subsequent antidepressant effects of ketamine. Overall, evidence suggests that OCTs and/or PMAT could be viable targets for developing novel therapeutics to treat depression and other stress-related disorders, either as add-ons to existing antidepressants, or as standalone treatments.

1.08.8.4.2

Psychostimulant use disorder

Though it is too early for clinical trials, preclinical studies support OCT3 as a novel target to treat dependence on amphetamine and its congeners. As noted earlier, Mayer and colleagues found that amphetamine and mephedrone exert their effects on monoamine

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signaling not only via DAT, but also via OCT3 (Mayer et al., 2018, 2019). Indeed, pretreatment of mice with the OCT/PMAT inhibitor, D22, robustly attenuated amphetamine-induced hyperlocomotion, an effect that was lost in OCT3 / mice (Mayer et al., 2018). More recently, Clauss et al. (2021) showed that D22 abolished conditioned place preference for amphetamine in male mice, an effect that was also lost in OCT3 / mice. In contrast, PMAT appears to be at play in conditioned place preference for amphetamine in female mice. These findings encourage further research into the utility of targeting OCT3 and PMAT for treatment of amphetamine use disorder.

1.08.8.4.3

Autism

Symptom presentation for the neurodevelopmental disorder, autism spectrum disorder (ASD) is very heterogeneous and can vary from mild to severe. Even so, ASD can be generally characterized by deficits in verbal and nonverbal communication, social interaction difficulties, and the presentation of repetitive behaviors. The etiology of autism is unclear. There is strong evidence that ASD is heritable. However, the few genes that have been found to be associated with ASD do not explain the majority of cases (Muhle et al., 2004; Sandin et al., 2017; Waye and Cheng, 2018). Interestingly, individuals with ASD often display 5-HT-related abnormalities such as hyperserotonemia, which is associated with atypical maturation of the serotonergic system (Cook and Leventhal, 1996; Leboyer et al., 1999; Whitaker-Azmitia, 2001; Cross et al., 2008). This implication of abnormal 5-HT neurotransmission in the etiology of ASD opens the possibility of the involvement of the OCTs or PMAT, which play a role in 5-HT homeostasis. Indeed, three rare non-synonymous inherited mutations in PMAT have been found with high prevalence in individuals with ASD compared to unaffected counterparts (Adamsen et al., 2014). Individuals with these mutations presented low cerebral spinal fluid (CSF) 5HIAA levels and elevated blood- and platelet- 5-HT levels. Thus, PMAT dysfunction may play a significant role in ASD development by compromising 5-HT clearance and promoting high prenatal 5-HT exposure (Vieira and Wang, 2021). High prenatal 5-HT levels can disturb development of the 5-HT network, and its subsequent local synthesis, via negative feedback through autoreceptors. Such a process could eventually culminate in low brain 5-HT.

1.08.8.4.4

Parkinson’s disease

As mentioned previously, PD is a neurodegenerative disorder that affects nigrostriatal dopaminergic neurons, and is characterized by symptoms such as shaking, stiffness, and difficulties with walking, balance, and coordination (Halliday and McCann, 2010). Genetic and environmental factors both influence the etiology of PD. Indeed, CNS exposure to environmental or endogenously produced 1-methyl-4-phenylpyridinium (MPPþ)-like toxins has been implicated in the etiology of PD (Nagatsu, 1997; Collins and Neafsey, 2000). For example, 1-benzyl-1,2,3,4-tetrahydroisoquinoline is an example of a natural analog of MPPþ (an endogenously produced neurotoxin) that is elevated in CSF of individuals with PD (Kotake et al., 1995). Moreover, many MPPþ-like neurotoxins are substrates for PMAT (Engel et al., 2004; Ho et al., 2011; Wu et al., 2015a), and PMAT-expressing cells are significantly more sensitive to the toxic effects of those compounds (Ho et al., 2011). Additionally, the PMAT-rich choroid plexus plays a major role in the maintenance of CSF homeostasis and clearance of potentially harmful compounds (Duan and Wang, 2013). Thus, it is possible that PMAT may play a protective role in the CNS by clearing cationic neurotoxins and lowering risk for PD (Vieira and Wang, 2021), though more work needs to be done to determine if this is the case. OCT3 may play a similar protective role. Another environmental toxin that plays a role in the etiology of PD is paraquat (PQ2 þ, N,N0 -dimethyl-4–40 -bipiridinium) (Ritz et al., 2009; Tanner et al., 2011), a toxic chemical that is commonly used in herbicides. PQ2þ bears a striking structural similarity to MPPþ (Rappold et al., 2011), and leads to several PD-like indicators such as the induction of a loss of nigral DA neurons when it is injected into mice (McCormack and Di Monte, 2003), and the induction of a-synuclein up-regulation and aggregation (Manning-Bog et al., 2002). Moreover, Ritz et al. (2009) found that individuals who carry certain DAT gene variants have an increased risk of developing PD after PQ2 þ exposure. Work by Rappold et al. (2011) demonstrates that DAT and OCT3 may work in concert to mediate nigrostriatal damage induced by PQ2 þ.

1.08.9

Concluding remarks

We have provided a brief overview of the function, structure and regulation of key transporters maintaining monoamine homeostasis (SERT, DAT, NET, OCTs and PMAT) as well as those that transport the amino acid neurotransmitters GABA and glycine (GATs, GlyTs). We then delved deeper into their location in brain, the impact of gene variants coding these transporters for CNS disorders, their interaction with psychoactive drugs and their therapeutic application. It is clear that polymorphisms and mutations of these genes can have moderate to severe impacts on human CNS disorders, ranging from subtle to dramatic phenotypes (e.g., DTSD). Some can impact therapeutic efficacy (e.g., carriers of the S allele of the 5-HTTLPR are reportedly less effectively treated by SSRIs). Moreover, some of these variants interact with environmental stimuli to exacerbate the phenotype (e.g., the 5-HTTLPR together with early life stress increases predisposition to depressive disorders). A clearer understanding of not only how gene variants affect treatment response, but also gene x environment interactions, will be important for individualizing treatment plans for a variety of disorders. Likewise, furthering our understanding of how drugs interact with these transport proteins on a structural level, will be crucial to the design of more efficacious treatments, for example, by exploiting allosteric binding sites. Importantly, in terms of monoamine signaling it is becoming increasingly apparent that all players must be taken into account, particularly when considering therapeutic efficacy of drugs. Not only are SERT, DAT and NET promiscuous for each other’s cognate neurotransmitter, but OCTs and PMAT are

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now recognized to play an important role in homeostasis of monoamine neurotransmission. Research into all of the transporters discussed here continues at the cutting edge, and will inevitably lead to improved treatments for the diverse array of disorders that are linked to imbalances in these neurotransmitters.

Acknowledgments The authors would like to acknowledge the following awards from the National Institutes of Health (NIH), R01 MH093320, R21 DA049044; R21 DA046044 and U18 DA052527 to LCD. NJC is supported by T32 DA031115.

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Relevant Websites http://www.ittsociety.org/dInternational Transmembrane Transporter Society. https://www.mdpi.com/journal/cells/special_issues/neuro_transdNeurotransmitter Transporters in Health and Disease. https://www.unodc.org/unodc/en/data-and-analysis/wdr2021.htmldWorld Drug Report 2021.

1.09

Pharmacological Receptor Theory

Terry Kenakin, Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States © 2022 Elsevier Inc. All rights reserved.

1.09.1 1.09.2 1.09.2.1 1.09.3 1.09.4 1.09.4.1 1.09.4.2 1.09.4.3 1.09.4.4 1.09.4.5 1.09.5 1.09.5.1 1.09.5.2 1.09.6 1.09.7 1.09.8 References

Introduction Mass action building blocks The Langmuir adsorption isotherm and receptor isomerization Pharmacodynamics: Historical perspective Pharmacodynamics: Orthosteric interaction Agonism: The Black/Leff operational model Orthosteric competitive and non-competitive antagonism Agonist-antagonist hemi-equilibria Truly irreversible antagonism Antagonist efficacy: Partial and inverse agonism Pharmacodynamics: Allosteric interaction Allosteric effects on ligand binding Allosteric effects on receptor function Molecular dynamics and probabilistic models of receptor function Fitting pharmacodynamic models to determine drug parameters Conclusions

206 206 207 209 212 212 213 214 215 215 217 217 219 220 221 223 224

Glossary Agonist A molecule that when added to a functional pharmacological system produces cellular response. Allosteric A mode of interaction between two molecules on a single drug target occurring through binding to separate sites for each molecule to produce a conformational change in the target. Antagonist A molecule that does not produce an overt change in the functional pharmacological system directly but does reduce the response produced by agonists. Constitutively active Referring to the spontaneous production of receptor that’s that, in the absence of a ligand, activate signaling systems in the cell and thus elevated basal response. Efficacy The pharmacological definition of efficacy is the property of a molecule that when bound to the target, changes the target’s behavior toward its host (cell). Extended ternary complex model An extension of the ternary complex model whereby the receptor can spontaneously exist in an active and inactive state. G protein coupled receptors A family of cell surface receptors that interact with extracellular ligands and transfer signals to the cytoplasm to activate signaling pathways. Early studies suggested that G proteins were the main signaling pathway activated but subsequent work has shown other pathways (i.e. b-arrestin) to be activated as well. They are also named for their unique seven transmembrane loop structure as seven transmembrane receptors. Hemi-equilibrium kinetics A condition whereby a portion of the receptor population re-equilibrates with the agonist and antagonist according to mass action and a portion of the receptors are pseudo-irreversibly bound to the antagonist and do not re-equilibrate with the agonist. KA (equilibrium dissociation constant of the ligand-receptor complex) This is the ratio of rate of offset of the ligand out of the binding pocket (k2) and the rate of diffusion of the molecule toward the binding pocket (k1). It is also the concentration of ligand that occupies 50% of the available binding sites. Linkage models A system of receptor models whereby the protein species are identified and then the inter-converting paths between them are defined with equal energy transfer. Mass action A mechanism of conversion of states of a protein interacting with ligands with conservation of mass. Protein ensemble A collection of inter-convertible conformations for a given protein (i.e. receptor) of comparable free energy but differing capabilities to recognize ligands. Ternary complex model A scheme whereby receptors, when activated by an agonist, go on to couple to G proteins to form a signaling complex of receptor/ligand/G protein.

Comprehensive Pharmacology, Volume 1

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1.09.1

Pharmacological Receptor Theory

Introduction

The collective quantitative models and equations written to describe the interaction of ligands with membrane bound receptors have been given a general label of “receptor theory.” Specifically, these ideas describe the interaction of molecules emanating from the extracellular space binding to G protein Coupled Receptors (GPCRs) to transmit chemical information to the cytosol of the cell through interaction of the ligand-bound receptor with intracellular signaling proteins. The moniker GPCR (derived from the fact that the first prominent signaling protein receptors were shown to interact with were G protein, thus G Protein Coupled Receptors) is a historical product of early studies on the signaling properties of these receptors; later studies indicated that these receptors trigger signals to other cytosolic signaling species as well such as ion channels and b-arrestin. To include this broader scope of receptor signaling, a more recent and better name for these receptors (based on their structure) emerged based on their structure, namely seven transmembrane receptors (7TMRs). The descriptor “receptor theory” suggests that it is only relevant to theoretical considerations related to the interaction of drugs with receptors but it is important to note that drug receptor theory forms the basis of eminently practical models of drug action that can be applied experimentally to identify universal descriptors of drug action. This is especially important to pharmacologicallybased therapy since drugs, unlike the substrates of enzymes, are not changed by their interaction with receptors. Rather, they bind to the receptor, produce an effect while bound, and then diffuse to leave the system much the same as how they found it (after recovery from possible desensitization). Thus, a theory had to be devised which would allow for drug-related effects to occur subject to the presence and absence of the drug. A very important aspect of the pharmacodynamic action of drugs is the interplay between the drug and receptor and the sensitivity of the resulting reporting system that allows us to see the result of the drug action. Thus, drugs can have very different actions depending on the sensitivity of the organ system to which they are exposeddthis will be discussed further in Section 1.09.4.1. Seven transmembrane receptors interact with a bewildering range of input signals from large proteins, large and small peptides, small molecules such as hormones and neurotransmitters, ions and even light. There are several hundred types of receptors controlling a myriad of cellular processes making them an extremely fruitful source of drug targets for therapy. In addition, these proteins reside on the extracellular surface of cells thus making make them tractable drug targets. In fact, historically pharmacological receptor theory was developed on the basis of data obtained from 7TMR systems. There are two main operations important to drug action; (1) binding of the drug to the receptor and (2) transduction of information from that binding process to the cell to induce possible response. It is worth considering these separately; the first process to be considered is the binding of the drug to the receptor. A fundamental building block in the construction of drug receptor models to this end is the mass action equation.

1.09.2

Mass action building blocks

Pharmacodynamic models are fundamentally based on the mass action equation first published by Guldberg and Waage (1864, 1879) given as: AþB

0

%A þB

0

(1)

which simply states that two reactants make products with possibly different forms but of equal mass, i.e. mass is neither created nor destroyed. As stated by these authors “. the amount of substance in the ‘sphere of action’ or, put another way, the concentration in the medium” (Guldberg and Waage, 1864). An equilibrium is created by equating the rates of the forward and backward reactions. The pharmacology of drug receptor interaction takes on the following form of the mass action equation:

(2)

where the drug ligand is A, R is the receptor, AR is the ligand-receptor complex, k1 the rate constant for association of the drug with the receptor and k2 the rate constant for dissociation of the bound drug away from the receptor. Assuming that drug A binds to receptor R with a rate of onset of k1 (in s 1 M 1) and the rate of drug  receptor dissociation is given by k2[A][R], then at equilibrium these rates are equal (k1[A][R] ¼ k2[AR]) to yield the amount of receptor bound to drug through an equilibrium dissociation constant in the form of KA ¼ k2/k1. The equation to calculate the amount of drug receptor complex ([AR]) as a fraction of total receptor ([Rtot] ¼ [R] þ [AR]) denoted rA is given by: rA ¼ ½A ½R tot =ð½A  þ KA Þ

(3)

Pharmacological Receptor Theory

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In pharmacological terms, the KA is the concentration needed to bind to 50% of the available receptors; therefore, KA 1 is a representation of the affinity of A for the receptor. This can be deduced from the relative magnitudes of KA values. For instance, if 10 6 M of a given ligand A binds to 50% of the receptors and 10 8 M of another ligand B binds to the same fraction of receptors, then clearly the affinity of ligand B is greater than the affinity of ligand A since a lower amount of ligand is necessary to bind the same fraction of receptors. Pharmacologically, the following assumptions must be made to make the KA a valid description of drug binding: (1) all receptors are equally accessible to ligands; violation of this assumption leads to incomplete assessment of ligand binding, (2) the binding is reversible; violation of this assumption precludes calculation of valid KA values (3) receptors are either free or bound to ligand, and there is not more than one affinity state or states of partial binding (the ligand and receptor must exist in only two states, bound or unbound) and lastly, and importantly, (4) binding does not alter the ligand or receptor; violation of this assumption also leads to ambiguity in the assignment of potency values (system-dependent potency). It is useful to consider how pharmacological violation of these precepts affects what we assess as ligand affinity. A preamble to this discussion involves the basic assumptions of the Langmuir isotherm as it applies to ligand binding.

1.09.2.1

The Langmuir adsorption isotherm and receptor isomerization

The binding equation was derived by Hill (1910); 10 years later it was again derived by the chemist Irving Langmuir as the Langmuir adsorption isotherm which describes the binding of molecules to an inert metal surface (Langmuir, 1916). As stated by Langmuir, molecules have an intrinsic property that will cause them to adsorb to a surface, a process he called “condensation” (controlled by a rate constant a) and that once bound, molecules intrinsically diffuse away from the surface, a process he called “evaporation” (governed by a rate constant V1). The first-order rate of condensation is given by ma(1  q), where m is the concentration and q is the fraction of surface already bound by molecule; the rate of evaporation is qV1. Rearrangement of terms leads to Langmuir’s version of the adsorption isotherm q ¼ am/(am þ V1) which corresponds to Eq. 3 with the substitutions rA ¼ qm (fraction of receptor bound as a fraction of metal surface bound), m ¼ [A], and KA ¼ V1/a. Eq. 3 predicts a semilogarithmic sigmoidal relationship between drug concentration (as Log[A]) and fraction of receptor bound to drug (rA) having a midpoint equal to KA which is qualitatively similar to curves obtained experimentally (Fig. 1). A critical factor in this measurement to be stable is the immutability of the quantity of [AR] driving the backward reaction (k2). When Langmuir derived the adsorption isotherm to define KA he was modeling the adsorption of molecules onto an inert metal surface that did not change upon ligand binding. Under these circumstances, the measurements made truly define the affinity of the molecule for the interacting metal since k1 and k2 adequately characterize the rates of binding. The situation is quite different for proteins as in many cases drug binding leads to the production of a subsequently modified protein species. Therefore, if ligand binding leads to the removal of [AR] from the equilibrium to form another species (i.e. if ligand binding changes the receptor), then the backward rate of offset is reduced thereby driving the forward reaction further forward. This produces a concomitant reduction in the effective equilibrium dissociation constant, i.e. increased affinity. Therefore in quantitative terms, the observed potency of A can deviate from KA due to this violation of a major assumption of the Langmuir isotherm when applied to binding to proteins. Under these circumstances the observed value describing the complete process will arithmetically be lower than KA thus producing an increase in effective affinity; this process has been described by Colquhoun as “receptor isomerization” (Colquhoun, 1985). The process can be shown as: isomerization

k1

zfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflffl{ g

½A  þ ½R  % ½AR  %½AR   k2

4

(4)

Fig. 1 Curves representing the binding of a drug to a receptor (solid line), the observation of that effect when the receptor changes as a result of drug binding (processes described by g and 4-dashed line), and the observation of binding when the result leads to the formation of a ternary complex between the drug, receptor and membrane signaling protein, in this case a G protein (dashed-dotted lines). The actual locations of the curves are determined by the avidity of the secondary processes occurring after drug-receptor binding.

208

Pharmacological Receptor Theory

If the isomerization process were not in place, the affinity would be determined by KA (k2/k1). However, in the presence of isomerization, the observed affinity is given by: Kobserved ¼

KA 1 þ g=4

(5)

It can be seen that if the isomerization from [AR] to [AR*] is facilitated by g > 4, then the Kobserved will always be arithmetically lower than the KA-see Fig. 1. Under these circumstances the affinity of the complete process will always be greater than just the affinity of the ligand for the non-isomerized receptor. Isomerization-mediated changes in affinity have been shown experimentally in receptor binding studies where binding receptors to nanobodies (to mimic G protein docking) significantly alters the affinity of receptors for extracellular molecules (Staus et al., 2016). For receptors, a form of isomerization commonly takes place whereby a ligand-bound receptor may go on to bind to another species in the cell membrane to form a ternary complex; these species usually are G proteins (DeLean et al., 1980) or b-arrestin: k1

k3

k2

k4

A þ R # AR þ G # ARG

(6)

Defining KA as k2/k1 and KG as k4/k3, in terms of the ultimate ternary complex species formed (ARG), the observed potency of the drug A is then given by: Kobserved ¼

KA 1 þ ½G=KG

(7)

It can be seen from Eq. 7 that the observed binding potency is now affected by a secondary factor, namely the quantity of the second species interacting with the receptor ([G]/KG)dFig. 1. It can also be seen that Eq. 6 is actually a system made of a two mass action reactions in series. The fact that receptors are conduits for energy transfer between other species (i.e. ligand and signaling proteins such as G proteins and b-arrestin) necessitates the inclusion of these other species in models of receptor function; the mass action reaction is thus used as a building block for systems either in series or parallel or both. Parallel mass action reactions are found in ion channel models where the channel pre-exists as a mixture of open and shut channels determined by an equilbrium constant. The receptor counterpart to this is the receptor in pre-existing active and inactive states. The extended ternary complex model of receptor function (Samama et al., 1993) incorporates both series and parallel mass action reaction as shown in Fig. 2. It is assumed that cellular response emanates from the ARaG species therefore the fraction of receptor that produces response is given by (Samama et al., 1993): ½AR a G L½G=KG ð1 þ ag½A =KA Þ ¼ rARa G ¼ ½R total  ½A =KA ð1 þ aLð1 þ g½G=KG Þ þ Lð1 þ ½G=KG Þ þ 1

(8)

It can be seen that the production of the ternary complex (ARaG) depends upon the two reactants with the receptor; the ligand A and the signaling protein [G]/KG. The extended ternary complex also highlights another principle of model building within pharmacodynamics, namely the inclusion and exclusion of protein species; usually this is a matter of expediency and experimental verifiability. As can be seen from the Fig. 2, only the active state receptor Ra is allowed to interact with the G protein which, actually violates the laws of thermodynamics. Specifically, because the [ARiG] and the [RiG] species exist, they should have a possible finite interaction with G protein as well. Therefore, a more thermodynamically complete model of the one shown in Fig. 2 is the cubic ternary complex model which allows all receptor species to interact with G protein (see Fig. 3). This model predicts that tissue response is given by (Weiss et al., 1996a,b,c): ½AR a G adgbL½G=KG ½A =KA ¼ rARa G ¼ ½R total  ½A =KA ð1 þ aLð1 þ g½G=KG ð1 þ abdLÞÞ þ ½G=KG ð1 þ bLÞ þ L þ 1

(9)

Fig. 2 The extended ternary complex model of 7TMR function showing how it is comprised of mass action building blocks in series and in parallel. Model from Samama P, Cotecchia S, Costa T, and Lefkowitz RJ (1993) A mutation-induced activated state of the b2-adrenergic receptor: Extending the ternary complex model. The Journal of Biological Chemistry 268: 4625–4636.

Pharmacological Receptor Theory

209

Fig. 3 The cubic ternary complex model showing how the species in red form the cubic ternary complex model (Weiss JM, Morgan PH, Lutz MW, and Kenakin TP (1996a) The cubic ternary complex receptor occupancy model. I. Model description. Journal of Theoretical Biology 178: 151–167.) which is a thermodynamic compliment of the extended ternary complex model (in black: Samama P, Cotecchia S, Costa T, and Lefkowitz RJ (1993) A mutation-induced activated state of the b2-adrenergic receptor: Extending the ternary complex model. The Journal of Biological Chemistry 268: 4625–4636). In the cubic model the inactive species of the receptor (Ri) is allowed to interact with the G protein.

The trade off between thermodynamic accuracy and practical utility lies in the ability of the data to allow the estimation of the model parameters. As more protein species are added for thermodynamic accuracy, so too are the number of parameters that are needed to fully describe the system. If the aim of the model is to furnish drug activity parameters, then a surfeit of unestimatable parameters leads to a loss in the practical applicability of the model. An extreme case of this condition is the description of a ligand A interaction with a receptor (R existing in two states) concomitant with the interaction of that receptor binding to a G protein (G) and an allosteric modulator (B, defined as a ligand that binds to it’s own site on the receptor separate from the binding site of A). The full thermodynamic description of this system yields an equation describing what would be receptor-agonist response (Chistopoulous and Kenakin, 2002). The large number of parameters needed to fully describe this model behavior coupled with the fact that these parameters cannot be independently verified makes the model heuristic but not useful pharmacodynamically to describe drug action. This article will describe practical pharmacodynamic models that can be used to estimate the activity of drugs in universal terms, i.e. predict activity in organs of varying sensitivity. The described equations and models thus far have all been confined to the binding of the drug to the receptor. This is the first and necessary step in the pharmacodynamic process and the chosen model for drug binding is used as the front end of the complete model to describe the cellular action of drugs. This latter process is much more complex and requires a number of assumptions as the biochemical steps leading to drug response are largely unknown. While the binding models described to this point are relatively conventional in thermodynamic and biochemical terms, the translation of how the activated receptor complex induces cellular response is uniquely within the realm of pharmacological pharmacodynamics. It is useful at this point to describe the historical progress of this complex process.

1.09.3

Pharmacodynamics: Historical perspective

The pharmacological literature is rich with mathematical descriptions of the interaction of ligands with receptors and papers by Ariens (1954, 1964), MacKay, 1987, 1990, Stephenson (1956), Clark (1933, 1937), Gaddum (1957), Gaddum et al. (1955), Arunlakshana and Schild (1959), Furchgott et al. (1966), and Furchgott (1972) formed the early basis of receptor theory. These pharmacological theories of drug action needed to accommodate two observed effects of drugs; some drugs produce cellular response (and are termed agonists) and some do not but rather block the effect of drugs that do produce response (these are antagonists). As defined by Stephenson (1956), these behaviors were postulated to be meditaed by two fundamental drug properties; (1) affinity, which causes the drug to bind to the receptor and (2) efficacy, the property responsible for activation and response. It was realized that the property of agonists responsible for cellular response is largely independent of the binding affinity of the ligand, i.e. affinity and efficacy are separate properties of the ligand. This was deduced by Stephenson (1956) who observed that a series of alkyltrimethylammonium muscarinic agonists that had the same affinity for the receptor had very different abilities to induce cellular resplonse. The main experimental findings supporting the concept of efficacy are the large differences found between agonist binding and functional response curves. For example, Fig. 4 shows the relative binding and effect curves of isoproterenol for b-adrenergic receptor activation. It can be seen that the effect curve is shifted to the left by a factor of 100,000 from the binding curve. The cause of this striking dissimulation between binding and effect is the magnitude of the efficacy of the complex as it produces agonist response. In this example, there is a 105-fold sinistral displacement of the effect curve from the binding curve which also describes a “receptor reserve” for isoproterenol in this assay as only 0.001% of the receptor population needs to be activated by this agonist to produce maximal response. Fig. 5 gives a direct demonstration of receptor reserve for muscarinic M1 acetylcholine receptors

210

Pharmacological Receptor Theory

Fig. 4 Comparison of the b-adrenoceptor binding curve for isoproterenol (dotted line) and label free dynamic mass redistribution functional responses in A431 cells (solid line, filled circles). Unpublished data from Ye Fang, Corning Inc.

Control

FRACT. MAX

1

2 PM POB 16 PM POB 32 PM POB

0.8 0.6

11% 1%

0.4

0.3%

0.2 0 −9

−8

−7

−6

−5

−4

Log[ACh]:M Fig. 5 Muscarinic M1 receptor activation by acetylcholine to produce increased accumulation of IP1 in recombinant CHO cells. Control curve (filled circles) and curves after irreversible alkylation of receptors with phenoxybenzamine (POB) (2 mM, filled triangles), 16 mM (open triangles) and 32 mM (filled diamonds). Percentages next to curves indicate remaining receptors after alkylation. Redrawn from Bdioui S, Verdi J, Pierre N, Trinquet E, Roux T, and Kenakin, T. (2019) The pharmacologic characterization of allosteric molecules: Gq protein activation. Journal of Receptors and Signal Transduction. https://doi.org/10.1080/10799893.2019.1634101.

transfected into CHO cells. Specifically it can be seen that, as receptors are irreversibly removed from the system through alkylation with the b-haloalkylamine phenoxybenzamine, the dose response curve to acetylcholine is shifted to the right along the concentration axis by a factor of 10 but still shows the tissue maximal response (Bdioui et al., 2019). Further reduction of the receptor population then does produce depression of the maximal response as the reduction is beyond the receptor reserve for this system. This type of effect is accommodated by the inclusion of a proportionality factor in the drug binding equation referred to as the “efficacy” (e) of the drug. Thus, under these circumstances, the effect of an agonist is decribed as (Stephenson, 1956): Response ¼ ½A e=ð½A  þ KA Þ

(10)

5

For the example shown in Fig. 4, e ¼ 10 . While modifications of this approach were added in subsequent years (i.e. the definition of the efficacy imparted to the system by a single agonist-bound receptor is termed “intrinsic efficacy” 3 as 3 ¼ e/[Rtotal] where [Rtotal] is the total receptor density in the tissue) were incorporated into pharmacodynamic receptor theory; Furchgott et al. (1966), no substantial advances in the quantification of the ability of drugs to induce cellular response were introduced until the publication of the Black/Leff operational model (Black and Leff, 1983)dvide infra. With respect to antagonism, there are two mechanisms by which a ligand can block the response produced by an agonist; (1) orthosteric antagonism whereby the antagonist competes for the same binding site as the agonist and (2) allosteric antagonism whereby the antagonist binds to another site on the receptor to induce a conformational change that blocks the ability of the agonist to produce response. For both of these mechanisms the process of drug antagonism is dealt with through mass action equations. The most simple mechanism is orthosteric antagonism (steric hindrance) whereby an antagonist competes with the agonist for the same binding site on the receptor to produce “competitive antagonism”; see Fig. 6. Historically competitive antagonism was described in very simple terms by Gaddum (1937) who presented his famous Gaddum equation for the fractional receptor occupancy of an agonist A (rA) in the presence of a competitive antagonist B as: rA ¼ ½A =KA =ð½A =KA þ ½B=KB þ 1Þ

(11)

where KA and KB are the respective equilibrium dissocation constants of the agonist and antagonist receptor complexes. Eq. 11 predicts that the concentration response curve describing the agonist receptor occupancy is shifted to the right with increasing concentrations of antagonist with no diminution of maximal response to the agonist (see Fig. 6). The key to this competitive

Pharmacological Receptor Theory

211

Fig. 6 Receptor system where two ligands (A, B) compete for the same binding site on the receptor. If A is an agonist and B a competitive antagonist, this translates to a parallel dextral displacement of the agonist concentration response curve with no diminution of maximal response. For this to occur, the receptors must rapidly re-equilibrate with the agonist and antagonist within the timeframe needed to capture response.

behavior is that the kinetics of binding of both the agonist and antagonist are rapid and reversible to allow re-equilibration of receptor occupancy with the two ligands present, i.e. the two ligands truly compete for the same binding site. A special case of this mechanism arises when the antagonist persistently binds to the receptor (does not rapidly dissociate) to effectively pseudo-irreversibly block agonist occupancy. Under these conditions, the antagonism is non-competitive described by a modified equation also presented by Gaddum (1957) and Gaddum et al. (1955); rA ¼ ½A =KA =ð½A =KA ð1 þ ½B=KB Þ þ ½B=KB þ 1Þ

(12)

Depending on the magnitude of the receptor reserve for the agonist, a diminution of the maximal response to the agonist is observed (see Fig. 7). The other major mechanism for antagonism of receptor response is allosteric where the antagonist binds to its own site on the receptor to induce a conformational change in the protein which then antagonizes the ability of the agonist to produce response (Fig. 8). Protein allosterism was well documented for proteins and enzymes before the idea was applied to receptors (Changeux, 1964; Monod, 1966; Stadtman, 1966). The earliest applications of these theories to receptor function were made by Stockton et al. (1983) and Ehlert (1988). These treatments depicted systems whereby the agonist (A) binds to the receptor concomitantly with an allosteric modulator (B); since these ligands bind at separate sites, the species ABR (two ligands bound to the same receptor) is

Fig. 7 Receptor system where two ligands (A, B) bind to the same binding site on the receptor but one of the ligands is dominant temporally in terms of binding (slowly dissociates from the receptor to hinder re-equilibration with agonist). If A is an agonist and B an antagonist, this translates to a mixture of dextral displacement of the agonist concentration response curve and concomitant diminution of maximal response. In this case, within the time needed to measure agonist response, the antagonist does not appreciably dissociate to allow re-equilibration with the two ligands.

212

Pharmacological Receptor Theory

Fig. 8 Allosteric binding model whereby two ligands (A, B) bind to separate sites on the receptor and where the binding of one ligand modifies the affinity of the other ligand by a factor a. From Stockton JM, Birdsall NJ, Burgen AS, and Hulme EC (1983) Modification of the binding properties of muscarinic receptors by gallamine. Molecular Pharmacology 23: 551–557 and Ehlert FJ (1988) Estimation of the affinities of allosteric ligands using radioligand binding and pharmacological null methods. Molecular Pharmacology 33: 187–194.

present in the system and actually this is the species of pharmacological interest. The equation for agonist receptor occupancy (rA) in the presence of an allosteric ligand B is given by (Stockton et al., 1983; Ehlert, 1988): rA ¼ ½A =KA ð1 þ a½B=KB Þ=ð½A =KA ð1 þ a½B=KB Þ þ ½B=KB þ 1Þ

(13)

where a is the co-operative effect between co-binding of A and B on the receptordsee Fig. 8.

1.09.4

Pharmacodynamics: Orthosteric interaction

The models and equations describing the interactions of agonists and antagonists utilize quantitative models rooted in the literature described above. However, significant changes of these ideas were made that must be considered as they greatly extend the original application(s) of the pharmacodynamic equations published previously; these can be divided into three areas: (1) agonism, (2) orthosteric antagonism, and (3) allosteric modulation. In addition, strides were made in the 1970s to delineate the basic mechanisms of 7TMR activation of G proteins for the production of cellular response. Thus, binding of an agonist (A) to a receptor (R) produced a complex that then went on to bind to a G Protein to form a ternary complex; this ternary complex activated the G protein which then dissociates to activate various cellular processes. The basic model, designated as the ternary complex model, is given as (DeLean et al., 1980): A þ AR # AR þ G # ARG Ka

Kg

(14)

where cellular response is mediated by the ARG complex. This model provided the best depiction of 7TMR cellular agonism until it was later supplanted by the Extended Ternary Complex model (Samama et al., 1993) (vide infra).

1.09.4.1

Agonism: The Black/Leff operational model

The operational model was introduced by James Black and Paul Leff to accommodate the ad hoc insertion of “e” into the binding equation to explain drug response and give a mechanistic framework to the efficacy concept (Black and Leff, 1983). The physiological basis of this model begins with the experimentally observed fact that most ligand concentration-response curves are hyperbolic in nature and can be described by the general form: Response ¼ ½A $Em =ð½A  þ sÞ

(15)

where the drug is A, Em is the maximal response capability of the system and s the midpoint of the concentration-response curve for the drug. For mass action binding, the concentration of the drug also can be expressed with a metameter of the Langmuir adsorption isotherm (Eq. 3) which when substitued for A in Eq. 15 yields: Response ¼ ½AR $Em $KA =ð½AR ðKA  sÞ þ ½R total sÞ

(16)

For a real solution that is not linear, KA must be > s leading to the root relationship: ðResponse=½R total Þ ¼ ½AR =ð½AR  þ KE Þ

(17)

where KE is a fitting parameter for the hyperbolic response between receptor occupancy and tissue response. Substituting [AR] from the Langmuir adsorption isotherm (Eq. 3) leads to the equation for the operational model: Response ¼

½A $½R total $Em ½A ð½R total  þ KE Þ þ KA KE

(18)

Pharmacological Receptor Theory

213

An alternative way to conceive the operational model is by comparing the interaction of drugs with receptors to the MichaelisMenten model of enzyme kinetics (Michaelis and Menten, 1913). The similarity of agonist concentration-response curves to Michaelis-Menten enzyme velocity curves (on a linear scale) suggest that a cell could be modeled as a virtual enzyme converting a substrate (which in this case is the agonist-receptor complex formed at the cell membrane) to a product (cellular response). Substituting the agonist-complex as the substrate ([S]) in the Michaelis-Menten enzyme equation from the Langmuir adsorption isotherm (Eq. 3) and rearranging yields the Black/Leff operational model equation for agonism:   ½A $½R total  ½AþKA $V max ½S$V max  Velocity ¼ ¼ (19) ½ S þ K M ½A $½R total  þ K m ½AþKA Black and Leff defined the enzyme terms Vmax and KM in general pharmacological terms to describe cellular response as Em ¼ for Vmax and KE for KM. Defining efficacy as sA ¼ [Rtotal]/KE leads to the operational model equation (Furchgott et al., 1966): Response ¼

½A sA Em ½A ð1 þ sA Þ þ KA

(20)

The Black/Leff operational model is an extremely useful model for the description of pharmacological agonism since it illustrates and incorporates the critical interplay between the power of the agonist to induce response (intrinsic efficacy) and the sensitivity of the system. Specifically, system sensitivity is incorporated in the efficacy term sA defined as [Rtotal]/KE where the sensitivity of the sysem is described by the receptor density on the cell membrane and by the efficiency of the transduction of receptor activation to cellular response through KE. The drug specific component of efficacy is also incorporated in KE. This provides a facile method of manipulating the sensitivity of the response system to examine the drug specific efficacy of agonists for comparison of agonists and prediction of therapeutic effect. One of the most important applications of the Black/Leff model is the fact that it accurately describes the effects of agonists in system of varying sensitivity. As is shown in Fig. 5, diminishing receptor density (through chemical alkylation with the b-haloalkylamine phenoxybenzamine) where the receptor density is reduced to 11% of control value produces a curve that is shifted to the right with no depression of maximal response. This indicates a 89% receptor reserve in this system. Further reduction of the receptor density to 1% and 0.3% of control, however, does depress the maximal response. These changes can be quantified with the Black/Leff operational model by applying different efficacy values to the agonist, in this case sA ¼ 300, 35, 3 and 1. Before the publication of the operational model, agonism was quantified by the relative potency of agonists, specifically the midpoint of the agonist concentration response curve on the concentration scale denoted as the EC50 (effective concentration of agonist producing 50% maximal response). When both agonists produce full maximal response (are full agonists), this midpoint comparison is system independent since it can be shown that the cellular factors affecting agonist response cancel for both agonists and the ratio of EC50 values (the potency ratio) depends only on the agonist-specific properties of affinity and efficacy. However this method devolves into a system dependent scale when one or both of the agonists are partial agonists and this technique cannot be used to quantitatively compare such agonists in a system-indepent manner. This problem is solved with the operational model since this model can be used to produce system-independent measures of both full and partial agonists. Through a fit of the operational model equation (Eq. 20), a single number denoting the power of an agonist to induce response can be calculated in the form of Log(sA/KA) (Kenakin et al., 2012). An even more simple index can be used for agonists that produce > 30% maximal response and have curves of slope > 0.5 in the form of Log(max/EC50) where max refers to the maximal response of the agonist as a fraction of the maximal response window of the asssay and EC50 is the potency of the agonists (Kenakin, 2017). Thus agonism from any agonist (full or partial) can be compared wth a single number allowing statistical comparisons of efficacy. The other useful feature of the operational model is that it gives pharmacologists the ability to predict the agonist response to an unknown agonist in different systems. This is done through quantification of the KA value for the agonist and a reference agonist and then measurement of the ratio of the efficacy of the agonist and reference agonist in a test system. This ratio is system-independent therefore if a concentration response curve is available in another system, the concentration response curve of the test agonist can be predicted; this procedure is shown in Fig. 9.

1.09.4.2

Orthosteric competitive and non-competitive antagonism

Orthosteric antagonism occurs when the antagonist binds to the same site as that utilized by the natural agonist and displaces it to reduce agonist receptor occupancy and subsequently response. The basic equations were derived in the 1930s and 1940s but there have been modifications in the modern era to further describe this type of receptor blockade. These modifications relate to (1) the relative kinetics of agonist and antagonist binding and (2) the antagonist-mediated creation of conformational changes in the receptor to create low levels of agonism (partial agonism) or reversal of spontaneous agonist-independent activation of receptors (inverse agonism). When the agonist response is measured in the presence of a concentration of antagonist in an ideal situation, the relative effects are determined by the concentrations of the agonist, the antagonist and their respective equilibrium dissocation constants. Thus, the receptor occupancy rapidly equilibrates as a function of these factors to yield agonist response; this is simple competitive

214

Pharmacological Receptor Theory

Fig. 9 Prediction of a test agonist concentration response curve with the Black/Leff operational model using an estimate of the relative efficacies of the agonists in the test system. (Panel A) Reference agonist (filled circles) and test agonist (open circles) concentration response curves fit with the model and the values of s and KA shown. This yields a ratio of s values of 0.26. (Panel B) Concentration response to the reference agonist (filled circles) fit in therapeutic system. Application of the KA value for the test agonist and the s ratio yields the predicted curve to the test agonist in this system as the dotted line.

antagonism (Paton and Rang, 1965). This mechanism predicts that the agonist concentration-response curve will be shifted to the right (along the agonist concentration axis) by the presence of the antagonist but that the maximal response to the agonist will not be changed (see Fig. 6)dthis pattern of curves is referred to as surmountable antagonism. The shift to the right of the agonist concentration response curve can be quantified by a metameter called a dose ratio (DR) which is the EC50 of the agonist in the absence of the antagonist divided by the EC50 of the agonist in the presence of the antagonist. A facile method to determine the equilibrium dissociation constant of the competitive antagonist receptor complex (KB) was derived by Heinz Schild and is referred to as the “Schild plot” (Arunlakshana and Schild, 1959). By manipulation of the simple competitive antagonism equation (Eq. 11) Schild and colleagues showed that a linear equation relating the dextral displacement of the agonist concentration-response curves (DR) to the concentration of antagonist ([B]) and the equilibrium dissociation constant of the antagonist-receptor complex (KB) in the form of the Schild equation (Arunlakshana and Schild, 1959): LogðDR  1Þ ¼ Log½B þ pKB

(21)

where pKB is  Log KB. It can be seen that Eq. 21 is in the form of an equation for a straight line therefore a regressions of Log(DR  1) values upon the logarithm of the antagonist concentrations that produce the various DR values yields a straight line of slope unity and an intercept of the  pKB. This manipulation forms a “Schild plot” which was and still is an extrememly valuable tool for the analysis of simple competitive antagonism. Specifically, it dictates that the regression must be linear and also have a slope equal to unity for the mechanism to be truly consistent with simple competitive antagonism. Irregularities from this behavior have been used to detect various non equilibrium condition in pharmacological experiments (Kenakin, 1982). The Schild method was specifically developed to yield a linear regressional analysis since the computational methods of the time did not allow for adequate handling of non linear data. A Schild plot is now not necessary to obtain estimates of pKB and curves such as those shown in Fig. 6 can be fit directly to Eq. 11 through non linear analysis. A different pattern of agonist concentration response curves is obtained when the kinetics of the agonist and antagonist are not matched and the antagonist binds more persistently to the receptor. In this case, there is little re-equilibration between agonist and antagonist in the presence of the agonist and the agonist basically binds only to the remaining receptors not bound by the antagonist; this is referred to as non-competitive antagonism and is shown in Fig. 7. Under these circumstances, the maximal response of the agonist can be depressed below the control value as the concentration response curves shift to the right in the presence of the antagonist, a pattern of curves referred to as insurmountable antagonism. While early treatments of this type of antagonism provided linear analyses similar to the Schild method (Gaddum et al., 1955), modern methods fit the data directly to the equation directly describing the mechanism: rA ¼ ½A =KA =ð½A =KA ð1 þ ½B=KB Þ þ ½B=KB þ 1Þ

1.09.4.3

(22)

Agonist-antagonist hemi-equilibria

The previous discussions describe two kinetic extremes of antagonist kinetics: (1) rapid kinetics yielding competitive antagonism and (2) slow kinetics leading non-competitive antagonism. In experimental pharmacology there are kinetic conditions between those two extremes whereby the antagonist re-equilibrates with the agonist on only a portion of the receptor population; this is referred to as hemiequilibrium. The key to this effect is that there is insufficient time within the confines of the experiment to allow for complete re-equilibration (Paton and Rang, 1965); a notorious experimental condition where this is the case is with the measurement of transient calcium responses whereby only the first few seconds of agonist response is captured experimentally

Pharmacological Receptor Theory

215

Fig. 10 Receptor system where two ligands (A, B) bind to the same binding site on the receptor and where the two ligands partially re-equilibrate during the time needed to capture agonist response. If A is an agonist and B an antagonist, this translates to dextral displacement of the agonist concentration response curves with concomitant diminution of maximal response to a new partially depressed steady-state. In this case, the agonist A rapidly binds to unoccupied receptors and then slowly competes with antagonist-bound receptors.

(Bdioui et al., 2018; Trinquet et al., 2006, 2011). Under these conditions a pattern of curves midway between surmountable and insurmountable antagonism are produced where the agonist concentration response curves shift to the right but the maximama depress to a new non zero level (Paton and Rang, 1965; Kenakin et al., 2006)dsee Fig. 10. The extent of the depression of the maximal response is inversely related to the rate of offset of the antagonist from the receptor once bound, i.e. very slow offset antagonists will produce more depression of maxima than faster offset antagonists. The explicit equations describing this type are complex and involve knowledge of the rate constant for antagonist offset but an estimate of the pKB can be derived from a Schild analysis of the dextral displacement of the agonist concentration response curves in the presence of the antagonist in the parallel region of responses to yield a Schild plot (Bdioui et al., 2019).

1.09.4.4

Truly irreversible antagonism

Under equilibrium conditions there is always the possibility that a non zero steady-state can be achieved at low concentrations of antagonist, i.e. a concentration of antagonist can be identified that produces a submaximally depressed agonist maximal response (indicative of non-competitive insurmountable antagonism) which comes to a steady state after a period of antagonist equilibration that still allows the agonist to produce response. However, if the rate of offset of the antagonist from the receptor is so low that it never leaves the receptor, then a truly irreversible condition is produced whereby the antagonist never comes to equilibrium with the receptors. Under these conditions an accurate estimate of the antagonist affinity cannot be obtained and the antagonist binding process essentially becomes an irreversible chemical reaction removing receptors from the system but never stopping until either all of the receptors are blocked or the antagonist reacts with another species (i.e. protein, water) and is depleted. Under these circumstances, a pKB value for the antagonist cannot be estimated. This type of interaction is detected by extending the equilibration period of the antagonist with the system to determine if the response to the agonist is completely blocked or reaches a non zero steadystate. Chemical reactions generally go to completion and this will be evident in antagonism that does not reliably reach submaximal levels in a range of concentrations near the equilibrium dissocation constant of the antagonist-receptor complex (Fig. 11).

1.09.4.5

Antagonist efficacy: Partial and inverse agonism

Protein dynamics dictates that receptors exist in a collection of conformations of related free energy but otherwise different in terms of ligand recognition called “ensembles” (Dror et al., 2010, 2011). Thus receptor “activation” is depicted as being due to conformational selection (Burgen, 1981; Vogt and Di Cera, 2013), whereby the selective affinity of a ligand for various states stabilizes those states at the expense of others to create a new ligand-bound ensemble. Protein dynamics also suggests that affinity and efficacy are thermodynamically linked and this leads to an imperative relationship between the binding of a ligand and a change in the make-up of the ensemble. Thus, any ligand bound to the receptor will change the conformation and thus change the ensemble, i.e. binding is not a passive process (Kenakin and Onaran, 2002). Under these circumstances, it cannot be assumed that an antagonist binding to the receptor will not change receptor conformation and simply interfere with agonist binding; in fact, it must be assumed that some change in the receptor ensues and that this may result in a positive or negative direct effect on receptor function.

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Pharmacological Receptor Theory

Fig. 11 Receptor system where two ligands (A, B) bind to the same binding site on the receptor but one of the ligands irreversibly binds to the receptor. If A is an agonist and B an antagonist, this translates to a mixture of some dextral displacement of the agonist concentration response curve and concomitant diminution of maximal response. In this case, within the time needed to measure agonist response, the antagonist does not dissociate to allow re-equilibration with the two ligands and no sub-maximal inhibited steady-state can be observed, i.e. this is a chemical reaction that goes to completion.

The existing model of 7TMR activation (ternary complex model, Eq. 14) dictates that the agonist binds to only one form (conformation) of the receptor. However, data from Costa and Herz (1989) clearly indicated that the receptor can spontaneously form an active state to produce elevated basal response in the absence of agonist. Under these circumstances, the agonist has at least two receptor conformations from which to choose therefore the model for 7TMR function had to be modified to the extended ternary complex model of GPCR function published in 1993 (Samama et al., 1993)dsee Fig. 12. In this model, receptors adopt two states, an inactive state Ri which does not signal to the cell and an active state Ra which can spontaneously bind to G proteins and signal even in the absence of agonist through the RaG state. If the system has a sufficient amount of spontaneous active state receptor it will couple to G proteins to produce an elevated basal cellular response; this is referred to as constitutive activity (Costa and Herz, 1989).

Fig. 12 Ligand-mediated alteration of receptor conformations in an equilibrium system where the receptor exists in an active (Ra) and inactive (Ri) state and where the active state can couple to a signaling protein (G) to induce response. The ligand has differential affinities for the two states denoted by the ratio a (a ¼ (Ka for active state)/(Ka for inactive state)). If a >1 then the ligand will promote formation of the active state (ARaG) and produce agonist response. If a < 1, then the ligand will stabilize the inactive state (form ARi). In cases where there is a significant amount of spontaneously formed active state receptor (enough to raise basal response constitutively), this ligand will remove this spontaneous active state and diminish the elevated basal response (produce inverse agonism).

Pharmacological Receptor Theory

217

This model incorporates a general tenet of biochemistry and pharmacology, namely that conformational selection (Burgen, 1981) will change the receptor ensemble to creates signaling species in the cell to produce cellular response. Specifically, it can be shown that if a protein exists in two states and the ligand has a differential affinity for those two states (a difference denoted by a ratio a), then the binding of the protein will necessarily modify the relative amounts of the two states according to the relative affinity of the ligand. The scheme below shows a protein in states Ri and Ra existing in an equilibrium denoted by L (where L ¼ [Ra]/[Ri]):

(23)

It can be shown that the ratio of Ra to Ri state in the absence and presence of a saturating concentration of A(rN/r0) is given by (Kenakin, 2013): rN =r0 ¼ að1 þ LÞ=ð1 þ aLÞ

(24)

where a is the differential ratio of affinities of the ligand A for the two states. It can be seen from Eq. 24 that a must be unity (the ligand must have identical affinities for the two states) for the ratio of receptor species not to change. If a > 1, then Ra will be enriched; if a < 1, Ra will be diminished. Therefore, if an antagonist encounters an ensemble of proteins, it is likely to have differential affinities for the various states leading to a redistribution of states resulting from antagonist binding. In an ensemble world where there are multiple conformations of a receptor for the antagonist to choose from, it is even more likely that antagonist binding will modify the conformations of the receptor because there will be an a value for every conformation and Eq. 24 becomes:   n n P P aiþ1 Liþ1 1 þ Liþ1 rN i¼1 i¼1  ¼ (25) n n P P r0 aiþ1 Liþ1 Liþ1 1þ i¼1

i¼1

It is extremely unlikely that a will be identical for every conformation thereby essentially ensuring that antagonist binding will alter overall receptor conformation. That being the case, the change in conformation could either promote a positive agonist response to lead to partial agonism or stabilize signaling inert inactive receptor conformations. A caveat to this prediction is the fact that not all receptor conformations are relevant to signaling therefore, some antagonists may modify the distribution of non signaling states and not modify cell response; this type of antagonist is referred to as a neutral antagonist. At present, the majority of all known orthosteric antagonists do modify signaling in some way and are either (weak) partial agonists or inverse agonists but some neutral antagonists have been described. If positive conformations are stabilized, the ARaG form of the receptor increases and partial agonism will be seendsee Fig. 12. Stabilization of inactive states to form a predominance of ARi normally only produces antagonism with no visible response except in the circumstance that a significant level of spontaneoulsy active receptor is present that couples to G protein on it’s own to produce an elevated response due to the RaG species (constitutive activity). In this case, an antagonist that stabilizes the inactive state to produce ARi will diminish the RaG species and produce a diminution of basal response; this is referred to as inverse agonismdsee Fig. 12. This effect will only be seen in systems that are constitutively active; otherwise, inverse agonists will behave exactly as competitive antagonists.

1.09.5

Pharmacodynamics: Allosteric interaction

The other way in which a ligand can interact with a protein target is to bind to a site separate from that of the endogenous agonist; such ligands are called allosteric modulators. In keeping with the previous discussion, the binding of an allosteric modulator will change the conformation of the receptor and thus change its interaction with other ligands binding to it (referred to as probes as in radioligands or agonists). There is no a priori reason that an uniform effect will be produced by an allosteric modulator; it may inhibit, not change or potentiate the interaction of the receptor with the other ligand. Usually the other ligand is the orthosteric natural agonist for the receptor thus a modulator may block response (be a negative allosteric modulator referred to as a NAM) or potentiate the agonist response (positive allosteric modulator referred to as a PAM). The model for these interactions is shown in Fig. 9 where the agonist A binds to the receptor according to equilibrium dissocation constant KA in the absence of modulator (B) and aKA in the presence of the modulator (similarly the modulator binds according to KB in the absence of A and aKB in the presence of A).

1.09.5.1

Allosteric effects on ligand binding

The fractional binding of the agonist A to the receptor form the AR species as a fraction of the total receptor species (R, AR, ABR, BR) and this is given by:

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rAR ¼

½A =KA ð1 þ aL½B=KB Þ ½A =KA ð1 þ a½B=KB Þ þ ½B=KB þ 1

(26)

Eq. 26 allows the calculation of agonist (A) binding in the presence of an allosteric modulator (B) (Stockton et al., 2013; Ehlert, 1988). As seen in Fig. 13A, a modulator that negatively affects the binding of the agonist by a factor of 30 produces diminution of agonist affinity in a concentration dependent manner up to a maximal effect of a 30-fold reduction of affinity. This shows one of the most important features of allosteric modulators, namely that they re-set the affinity of proteins to a new level but otherwise do not overwhelm the system. Specifically, even in the presence of extremely high concentrations of the modulator, no further effect will be elicited on the receptor other than a 30-fold diminution of affinity for the agonist. This is in contrast to an orthosteric competitive antagonist which will produce ever-increasing antagonism in response to increasing concentrations (Fig. 13B). This highlights one of the two most important effects of allosteric modulators (as opposed to orthosteric antagonists). Specifically, for allosteric modulators the effect will not necessarily be linked to the concentration as a saturation of effect is produced through saturation of binding to the allosteric site. Therefore, no matter how high the concentration of modulator is present in the receptor compartment, an effect no greater than that defined by the factor a will be achieved. The other important feature of allosteric modulators is that they are probe dependent, i.e. they have different effects for different probes. This is because the magnitude of a depends on the nature of the two molecules binding to the receptor. Fig. 14A shows the effects of a NAM/agonist pair where a ¼ 0.2; in this case the modulator produces a maximal 5-fold shift to the right of the agonist (Agonist 1) concentration response curve. In contrast, the same NAM may produce a maximal 50-fold shift to the right of another agonist (Agonist 2)d see Fig. 14B. This can be enormously important therapeutically as probe dependence may allow a NAM to block pathological behaviors of receptors without changing their natural therapeutic functions. For instance, the chemokine receptor CCR5 normally mediates chemotaxis in response to the chemokines CCL3, CCL4, CCL5 and CCL3L1 but also is the mediator of HIV-1 entry to cause AIDs. While loss of CCR5 receptors is not lethal to the host, it has been shown that a robust CCR5 chemokine response is beneficial to patients infected with HIV-1 in that it postpones progression to AIDs (Gonzalez et al., 2005). Therefore, an allosteric modulator that blocks the usage of CCR5 by HIV-1 but otherwise allows the receptor to function normally with respect to the chemokine system would be superior to a simple overall antagonist of CCR5; there are modulators that do this and spare CCR5 function (to a certain extent) while blocking HIV-1 entry (Muniz-Medina et al., 2009). Allosteric effects are produced by changes in the conformation of the receptor protein; only one aspect of these effects is reflected in the affinity of other species (e.g. ligands). The other possible effect of allosteric modulation is a change in the quality of the protein as it interacts with other membrane signaling proteins; this can be reflected in the efficacy of agonists. The description of these interactions requires allosteric modulation to be studied with receptor function with a functional allosteric model.

Fig. 13 Comparison of antagonistic effects of a negative allosteric modulator (NAM) which diminishes the affinity of the receptor for the agonist by a factor of 33 (panel A) and an orthosteric simple competitive antagonist (panel B). The NAM will produce a maximal dextral displacement of the curves of 33-fold whereas the displacement produced by the orthosteric antagonist is limitless and dependent upon antagonist concentration.

Fig. 14 Probe dependence: Effects of a NAM on responses to two different agonists. The affinity for agonist 1 is reduced by a factor of 5 while the affinity of agonist 2 is reduced by a factor of 50.

Pharmacological Receptor Theory 1.09.5.2

219

Allosteric effects on receptor function

Incorporation of the Stockton-Ehlert allosteric binding scheme (Stockton et al., 1983; Ehlert, 1988) (Fig. 8) with the Black/Leff operational model for receptor function (Black and Leff, 1983) provides the most straightforward model for assessing the functional effects of allosteric modulation (Kenakin, 2005; Ehlert, 2005; Price et al., 2005)dsee Fig. 15. With this model, an assessment of the quality of protein complex can be made through a ratio b, namely the ratio of the efficacy of the agonist with the modulator bound vs the efficacy with the modulator not bound. Thus when b ¼ 2.5, it means that the efficacy of the agonist is 2.5-fold greater than the control. With this model, the effects of an agonist (A) in the presence of a modulator (B) is given by (Kenakin, 2005; Ehlert, 2005; Price et al., 2005): Response ¼

ð½A =KA sA ð1 þ ab½B=KB Þ þ sB ½B=KB ÞEm ½A =KA ð1 þ a½B=KB Þ þ sA ð1 þ ab½B=KB Þ þ ½B=KB ð1 þ sB Þ þ 1

(27)

where a is the effect of the modualtor on the affinity of the agonist (and reciprocally the effect of the agonist on the modulator) and b is the effect of the modulator on the efficacy of the agonist (and the effect of the agonist on the efficacy of the modulator). The direct efficacy of the modulator for production of response is sB; Em is the maximal window for response. Eq. 27 illustrates the flexibility of the functional allosteric model in describing any and all effects of a modulator on agonist concentration response curves. This is a requirement of allosteric models since allosterism describes changes in the conformation of proteins and those have no prescribed or predicted effects on the response of the protein to other probe molecules. The manipulation of sB (direct efficacy of the allosteric modulator), a (effect of modulator on agonist affinity) and b (effect of modulator on agonist efficacy) can produce a myriad of effects on agonist concentration response curves thus fulfilling this requirement. One of the main therapeutically relevant effects of allosteric modulators is the revitalization of failing physiological systems through potentition of ambient natural agonist response (PAM activity). Fig. 16 shows the effects of two types of PAMs; one increases the affinity of the agonist for the agonist (Fig. 16A) and the other the efficacy of the agonist (Fig. 16B). It can be seen that if a PAM is designed to increase the agonist response of a failing response (e.g. Alzheimers disease and cholinergic transmission; Anand and Singh, 2013) then an increased efficacy of the endogenous agonist would be required. This is because the remaining

Fig. 15 The functional allosteric model made up by the Stockton-Ehlert allosteric binding model (Fig. 9) providing the receptor species for the Black/Leff operational model (red). Response is produced by the agonist (ARE), the agonist binding to the receptor with modulator also bound (ABRE), and direct agonism by the modulator (BRE).

Fig. 16 Two different mechanisms of positive allosteric modulation. (Panel A) The modulator increases the affinity of the receptor for the agonist (a effect). (Panel B) Modulator increases the efficacy of the agonist (b effect).

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Pharmacological Receptor Theory

Fig. 17 The effect of the functional system sensitivity to a NAM. (Panel A) The system is sensitive to the agonist and there is a receptor reserve for maximal response, i.e. only a fraction of the receptors needs to be activated to produce maximal response. (Panel B) A much less sensitive system will yield insurmountable antagonism by the same NAM.

response in a failing system is low to begin with and a will only lower the concentration of the natural agonist producing the low level response but will not increase the magnitude of the response. Fig. 16 illustrates a subtle difference between PAMs; if the endogenous response is weak, then increasing the affinity of the receptor for the endogenous agonist will do little to increase overall response and revitalize the physiology; what is needed is an effect on efficacy (positive b) to increase the effectiveness of the low level response remaining in the natural system. The functional allosteric model is useful to illustrate the effects of a modulator in systems of varying sensitivity. For example, this can be demonstrated as different antagonistic effects for NAMs if they decrease agonist efficacy (b < 1). Fig. 17 shows the effect of a NAM with fractional b values (decrease in efficacy) in a system that is very sensitive to the agonist. In systems with receptor reserve the maximal response can be produced by activation of only a fraction of the receptors (since there are many extra receptors present). Under these circumstances, diminution of a portion of the receptor population will not cause a decrease in the maximal response to the agonist (but rather create surmountable antagonism)dsee Fig. 17A. The same NAM, however, will produce insurmountable antagonism in a system with no receptor reserve (Fig. 17B).

1.09.6

Molecular dynamics and probabilistic models of receptor function

All pharmacodynamic models prior to 2000 were “linkage models” which are comprised of a set of protein species (ligand bound and unbound) pre-defined and linked together such that the energy between their formation and dissolution is neutral (i.e. energy is neither created nor destroyed). Linkage models have evolved from the simple agonist-receptor complex leading to response to systems where the receptor can exist in two states (simple two state model; Katz and Thesleff, 1957; Del Castillo and Katz, 1957; Changeux et al., 1967; Karlin, 1967; Thron, 1973, full two state model; Podleski and Changeux, 1970; Ross et al., 1977; Heidenreich et al., 1980; Iyengar et al., 1980) and does so while coupling to signaling proteins (simple ternary complex model; Ariens, 1964; MacKay, 1987; Mayo et al., 1989; Ross, 1989; Birnbaumer et al., 1990), complete ternary complex model DeLean et al., 1980; Jacobs and Cuatrecasas, 1976; Boeynaems and Dumont, 1977; Wregget and DeLean, 1984; Costa et al., 1992; Lefkowitz et al., 1993, extended ternary complex model (Samama et al., 1993), cubic ternary complex model (Weiss et al., 1996a,b,c). In fact, all of the existing models can be shown to be facets of a cubic structure comprised of receptor, signaling coupling protein and ligand. There are two serious limitations to these types of models. The first is these models become extremely complicated with the addition of each new protein state which carries with them a set of parameters to describe them; these parameters become so numerous and codependent that they cannot be independently verified (note see Fig. 4). The second limitation is the need to pre-identify the protein species. In an ensemble-based dynamic view of receptor systems there could very many conformations available for drug binding (the ligand essentially enters a conformational cafeteria) so the need to pre-set the number of conformations limits the model considerably, especially in terms of biased signaling systems. This limitation is overcome with the introduction of molecular dynamic systems identifying the probability that ligand binding to ensembles produces changes (Onaran et al., 2000, 2017). There are two important reasons why the introduction of these types of models is important to pharmacology: (1) the evidence was mounting that ligands formed different receptor states upon binding the nature of which could not be pre-determined in terms of reactivity toward signaling proteins in the cell membrane, and (2) distinct experimental data to show that molecules could produce some but not all signaling processes that are coupled to a given receptor. For example, whereas previously it was assumed that receptor activation was a required pre-requisite to receptor internalization, new assays measuring receptor internalization directly indicates that antagonist ligands that do not produce response can avidly internalize receptors (Roth and Chuang, 1987; Willins et al., 1999; Roettger et al., 1997). For these reasons, models other than standard linkage models have been proposed based on the probability of creating undefined protein states. These models do not define actual receptor species but they can influence the probability of a ligand changing existing states in an ensemble. For example, a model proposed by Onaran et al. (2000, 2017) begins with the definition of a root

Pharmacological Receptor Theory

221

receptor state [R0]; the affinity of a ligand A for that state is given by AK0 ¼ [AR0]/[R0][A]. The receptor is an allosteric species binding to both the ligand and a signaling protein in the cell membrane (in this case denoted as a G protein G) therefore the affinity of the R0 state for the G protein is given by GK0 ¼ [GR0]/[R0][G]. The probability of the receptor being in the R0 state is defined as p0 and the probability of the receptor forming another state (R1) is defined as p1. The ratio of the probabilities of changing from the R0 to the R1 state is defined as j1 (j1 ¼ p1/p0), i.e. the magnitude of j1 defines the energy of transition from the R0 to the R1 state. Just as with affinities for the various states for ligand A and G protein, the transition driven by ligand A to convert states is defined as A j1 ¼ Ap1/Ap0 and, similarly, by G protein as Gj1 ¼ Gp1/Gp0. With these operators the fractional stabilization of any state (defined as b) is given by the ratio of j values. Thus the stabilization factor for the R1 states produced by ligand A is defined as Ab1 ¼ Aj1/Aj0 and by G protein as Gb1 ¼ Gj1/Gj0; the vector values of b for each receptor state determine the ligand affinity and efficacy as probabilities of transition to various states. These probability vectors are defined as U; the probabilty of spontaneous formation of the Ri state (from R0) defined as U ¼ 1 þ Sji and the vectors for ligand transition as UA ¼ 1 þ USAbipi and G protein as UG ¼ 1 þ USGbipi. For the transition in the presence of both ligand and G protein UAG ¼ 1 þ USAbi Gbipi. These functions yield expressions for macroaffinity as: Macroaffinity ¼ ðKÞ ¼

A

k 0 UA ðUÞ1

(28)

where k0 refers to the free energy of interaction between the ligand-bound state and the reference microstate. Efficacy, as defined by a change in the receptor behvior toward its host, namely the cell, is then given as: Efficacy ¼ ðaÞ ¼ ðUUAG ÞðUA UG Þ1

(29)

One of the conclusions drawn from this model is that affinity and efficacy are related in terms of energy (note UA and U are elements common to both expressions). This is not surprising since the thermodynamic forces that control ligand binding also involve the forces that control the protein binding pocket (Kenakin and Onaran, 2002). Therefore, the fact that a particular efficacy is not observed in a functional assay does not necessarily mean that the ligand does not possess efficacy, only that the assay used to detect effect does not register the change in receptor function. A corollary to this idea is that all possible efficacies should be explored for any ligand that binds to a receptor as a conformation may be stabilized that alters physiological function. A valuable feature of this type of model is that there is no compusory linearity between states, i.e. receptor activation need not be a pre-requisite for receptor internalization but rather, the internalization state has it’s own probability of formation in the presence of a ligand that is independent of the probability for ligand-induced activation. Under these conditions the ligand produces effects through stabilization of different receptor states in the ensemble (Dror et al., 2010, 2011; Boehr et al., 2009; Motlagh et al., 2014; Nygaard et al., 2013; Park, 2012) to form dynamic systems (Onaran et al., 2000, 2017; Vardy and Roth, 2013; Manglik and Kobilka, 2014; Manglik et al., 2015) controlled by allosteric linkages between receptors, ligands and signaling proteins (Monod et al., 1965; Changeux and Edelstein, 2005). Thus ligands practice conformational selection to stabilize ensembles, in essence the receptor roams on an energy landscape and is biased into energy wells of stabilized or preferred energy conformations (Frauenfelder et al., 1988, 1991; Woodward, 1993; Dill and Chan, 1997; Freire, 1998; Hilser and Freire, 1997; Hilser et al., 1998, 2006; Miller and Dill, 1997dsee Fig. 18). In this scenario, collateral efficacy is practiced by receptors in that some conformations can “skip” efficacies and practice “collateral” efficacy (Kenakin, 2002). This type of behavior is essential to account for biased receptor signaling.

1.09.7

Fitting pharmacodynamic models to determine drug parameters

The impetus for developing mathematical pharmacodynamic models is to describe the complex pharmacology of drugs and derive parameters that quantify drug activity so that medicinal chemists can use these as a scale to optimize those activities. An important

Fig. 18 Energy landscape with arrows showing possible receptor routes to various energy wells (stable conformational states). Note that there is no preferred energy route but rather, depending on the energy of the ligand bound receptor, the complex may form any number of individual states without a pre-requisite formation of another state.

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Pharmacological Receptor Theory

consideration in this process is the fact that drugs interact with systems to modify ongoing system behavior, i.e. physiology has an ambient behavior that modifies the effect of the drug. For example, the sensitivity of the cell to activation affects the type of response an agonist produces in that a sensitive cell may yield a powerful response to an agonist and a cell of lower sensitivity may yield no response at all. This requires that the pharmacodynamic models have the capability of varying this sensitivity; for functional receptor systems this is done with the Black/Leff operational model by manipulating the magnitude of sA, the efficacy of the agonist. This value is an amalgam of the inherent power of the agonist to induce response and the sensitivity of the system in the form of the number of cell surface receptors available to produce response and the efficiency with which those receptors are coupled to biochemical stimulus-response mechanisms in the cell cytosol. The key to the application of these pharmacodynamic models is to set the system sensitivity with a reference agonist to which all other agonists may be compared. It is important to determine the effects of drugs in systems of varying sensitivity for two reasons: (1) this can unveil hidden properties of the drug that are evident only in some select tissues of defined sensitivity, and (2) a consistent set of pharmacological parameters can be obtained that characterize the activity of the molecule for medicinal chemists in lead optimization programs. Testing drugs in systems of varying sensitivity yields a complete picture of what a drug may do in vivo where it will encounter organs of varying sensitivity. Fig. 19 shows the effects of the PAM-agonist BQCA (Ma et al., 2009; Mistry et al., 2016) on responses to acetylcholine in transfected cell lines of three different sensitivities. It can be seen that the allosteric model (Eq. 9) can be fit to the curves in all of the systems with essentially the same set of parameters (a,b,sB,KB) yet the effects of BQCA in these different systems are quite different (Bdioui et al., 2019). This highlights another advantage of fitting experimental data to pharmacodynamic models, namely that parameters that are system independent can be derived which characterize drug action in vivo for all organs of varying sensitivity.

Fig. 19 Effects of the muscarinic M1 receptor PAM-agonist BQCA on acetylcholine IP1 responses in transfected CHO cells. System sensitivity controlled by alkylation of various fractions of the muscarinic receptor population. (A) control state: BQCA produces positive agonism, sinistral displacement of the concentration response curve and no change in maximal response. (B) Controlled alkylation with phenoxybenzamine (POB, 2 mM) produces a less sensitive cellular assay. BQCA produces no agonism, sinistral displacement of the curve and no change in maximum. (C) Further alkylation with POB (30 mM) yields a system whereby BQCA produces no agonism, sinistral displacement of the curve and an increased maximal response. For all three systems, the functional allosteric model can fit the data to yield a consistent set of sB, a, b and KB values. Redrawn from Bdioui S, Verdi J, Pierre N, Trinquet E, Roux T, and Kenakin, T. (2019) The pharmacologic characterization of allosteric molecules: Gq protein activation. Journal of Receptors and Signal Transduction. https://doi.org/10.1080/10799893.2019.1634101.

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Fig. 20 Cellular processing of receptor stimulus producing concentration response curves of varying slope. Depending on which type of biochemical cascade is initiated in the cell, different steady-states of response will be observed. The production of a second messenger through an unsaturable process leads to a curve with slope of unity. If the process is saturable (Michaelis-Menten kinetics), then a curve with slope of 3 can be produced. Redrawn from Tyson JJ, Chen KC, Novak B (2003) Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell. Current Opinion in Cell Biology 15: 221–231.

The association of concentration-response curve patterns to mathematical models requires verisimilitude between what is predicted by the model and what is observed in the experiment. For this to occur, the model must accommodate nuances of the experiment and one of these is the modification of agonist response by the biochemical cascades in the cell as it yields response. Cellular functional systems can modify receptor signaling to alter the slopes of concentration response curves (Tyson et al., 2003); this does not affect the pharmacodynamic analysis of drug-receptor interactions but must be accommodated to fit experimental data to the models. Fig. 20 shows one example of how cellular processing of receptor response can change a one to one association of agonist to receptor (which should yield a concentration response curve with a slope of unity) to a much more steep curve with a Hill coefficient of 3. Cellular response is a steady state of the production of biochemical mediators in the cell and degradation of those mediators and there are numerous ways this can occur. As shown in Fig. 20, a process such as an unsaturable dephosphorylation of a protein leads to a concentration response curve of unit slope. In contrast, if the dephosphorylation step is saturable (i.e. follows MichaelisMenten kinetics), then the resulting concentration response curve has a slope of 3. Such varying slopes due to cellular processing of agonist stimulus must be accommodated to fully fit experimental data and there are metameters of the previously discussed equations published that can do so. For example, the Black/Leff operational model was subsequently modifed to describe concentration response curves with slopes different from unity (Black et al., 1985): Response ¼

½A n sA n Em ½A sA n þ ð½A  þ KA Þn n

(30)

where n is the observed slope of the concentration response curve to the agonist.

1.09.8

Conclusions

Drugs can initiate, block, modulate, and potentiate normal physiological response to change the type, strength, duration, or location of the signal in the body. The key factor in this scheme is that drugs work in partnership with already ongoing physiological processes in the human body and the ambient activity of those processes can modify the effect of drugs. This being the case, mathematical models of drug-receptor interaction must be flexible and must also involve more than the single target protein binding the drug, i.e. the other protein(s) and chemical species in the receptor compartment become integral players in the final outcome of cellular drug response. From the simple mass action binding model to more complex systems involving receptors, ligands, third species interactants and cellular signaling proteins, the pharmacological literature is rich with mathematical schemes that describe agonism, antagonism and modulation. The fruits of these labors are robust techniques that can be used to measure universal drug

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activity parameters that can then be used by pharmacologists and medicinal chemists to optimize pharmacological activity for therapeutic advantage.

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1.10

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

Sam R.J. Hoare, Pharmechanics LLC, Owego, NY, United States © 2022 Elsevier Inc. All rights reserved.

1.10.1 1.10.2 1.10.2.1 1.10.2.2 1.10.2.3 1.10.3 1.10.3.1 1.10.3.2 1.10.3.3 1.10.3.4 1.10.3.5 1.10.3.6 1.10.4 1.10.4.1 1.10.4.2 1.10.4.3 1.10.4.3.1 1.10.4.3.2 1.10.4.3.3 1.10.4.3.4 1.10.5 1.10.5.1 1.10.5.2 1.10.5.3 1.10.5.4 1.10.5.5 1.10.6 1.10.6.1 1.10.6.2 1.10.6.2.1 1.10.6.2.2 1.10.6.2.3 1.10.6.2.4 1.10.7 1.10.7.1 1.10.7.2 1.10.7.3 1.10.8 References

Introduction Mechanics of target-ligand binding kinetics Dissociation Association Relationship between binding kinetics and affinity In vivo efficacy and target binding kinetics Introduction A long residence time can enable once-a-day dosing of a rapidly-eliminated drug A long residence time can enable lower-dosing of a chronically-dosed drug Residence time has little impact on pharmacodynamics if elimination is slower than dissociation A short residence time is required for rapid and transient drug effects controlled by pharmacokinetics Summary Other kinetic effectsdMicro PK-PD relationships, fluctuating endogenous ligand concentration, and post-binding events Rebinding and the association rate constant Endogenous ligand dynamics Post-binding target events Covalent binding Conformational change Signal transduction Target degradation and inactivation Impact of binding kinetics on in vitro assays of drug effect Introduction Slow dissociation can result in underestimation of affinity Drug discovery implications of mis-estimation of affinity resulting from lack of equilibration Case study of mis-estimation of affinity owing to lack of equilibrationdCRF1 receptor antagonist development Insurmountable antagonismdReduction of maximal response in signaling assays Measuring receptor-ligand binding kinetics Detecting slow dissociation Measuring receptor binding kinetics using binding assays Preferred assay modalities for measuring receptor binding kinetics Quantifying kon and koff from a direct ligand binding assay Competition kinetics for quantifying kon and koff of unlabeled ligands Quantifying more complex binding kinetic mechanisms Functional assays for measuring binding kinetics Enzyme assays for measuring inhibitor binding kinetics Signaling response assays for measuring receptor antagonist dissociation rate Washout methods Concluding remarksdWhen to measure binding kinetics in drug discovery

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Glossary Association Process of formation of the ligand-target complex. The association rate is the rate of ligand recognition by the target. Association rate constant (kon) Microscopic rate constant for receptor-ligand association. Dissociation Process of breakdown of the ligand-target complex. The dissociation rate is the rate of breakdown of the complex. Dissociation rate constant (koff) Rate constant for dissociation of target-ligand complex. Dissociation t½ A measure of the duration of drug-target complexes. Average half-life of a population of receptor-ligand complexes (equal to 0.693/koff).

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Observed association rate constant (kon(obs)) Macroscopic rate of receptor-ligand association (equal to [L]
kon þ koff, where [L] is ligand concentration). Pharmacodynamic/pharmacokinetic model (PK/PD model) Mathematical model of in vivo drug effect and how it changes over time. Drug effect is described by parameters that define the concentration of ligand and the interaction of ligand with target. Residence time Another measure of the duration of drug-target complexes (equal to 1/koff).

1.10.1

Introduction

Quantifying the interaction of ligand with target is central to the modern process of drug discovery (Kenakin, 2009). Target interaction parameters are used in medicinal chemistry campaigns to optimize candidate therapeutic molecules. Typically, affinity or its surrogates such as potency are used to quantify target interaction. Affinity defines the extent of target occupancy, for example Kd or Ki defines the amount of ligand needed to occupy 50% of the target population. However, affinity tells us nothing about the timing of target-ligand interactiondhow long it takes the ligand-target complex to form (the association rate) and how long the ligand-target complex remains together once formed (the residence time, defined by the dissociation rate). This is the study of the kinetics of target binding. Kinetics has been studied since the beginning of research into the activity of biological molecules, including enzymes and receptors (Clark, 1926b; Michaelis et al., 2011; Morrison and Walsh, 1988; Paton and Rang, 1966). In the last 20 years or so, major efforts have been made to establish the value of binding kinetics to modern drug discovery (Copeland et al., 2006; Schuetz et al., 2017; Swinney, 2004; Sykes et al., 2019b; Tonge, 2018; Vauquelin and Charlton, 2010; Zhang and Monsma, 2010). The purpose of this article is to present this information in a manner that will allow drug discovery scientists and basic science researchers to assess the value of binding kinetics to their targets of interest. The in vivo effect of drugs can in certain circumstances be influenced by receptor binding kinetics, particularly the residence time (Dahl and Akerud, 2013; Daryaee and Tonge, 2019; De Witte et al., 2016). For example, a long residence time can prolong the duration of action of drugs if the duration of occupancy is longer than the duration of the effective concentration of drug in the body. The extent to which residence time can impact drug effect in vivo is presented in Section 1.10.3. This is done using literature examples, and pharmacodynamic/pharmacokinetic (PK/PD) modeling that incorporates binding kinetics. To aid investigators exploring these relationships for their own targets, simulators are provided in the Annex that are simple to use (in Microsoft Excel). The significance of target kinetics for in vivo effect can be influenced by additional drug effects including partitioning and other “Micro PK/PD” phenomena (Vauquelin and Charlton, 2010); fluctuating endogenous ligand concentrations (Vauquelin and Van Liefde, 2006); and post binding target events such as covalent binding, signal transduction and target degradation (Swinney, 2004). These effects are considered in Section 1.10.4. Next the effect of target binding kinetics on in vitro drug effect is considered (Section 1.10.5). It is often unappreciated that kinetics can negatively impact the in vitro assessment of ligand-target interaction. For example, a long residence time can distort the measurement of affinity in the routine assays used to define structure-activity relationships in drug discovery, especially lead optimization. This is a result of pharmacology “Small print”dfor the assay to report affinity correctly, it must closely approach equilibrium. If the residence time of ligand on the target is long, assays can be far from equilibrium and affinity of compounds dramatically underestimated. This is illustrated using a drug discovery case study (Section 1.10.5.4), where discovering prolonged residence time of lead compounds solved a disconnect between in vitro and in vivo drug activity and led to the optimization of improved molecules (Hoare et al., 2020a). Functional assays of receptor activity are also impacted by binding kinetics; antagonists that dissociate slowly can reduce the maximal response to agonists, a mode of inhibition termed insurmountable antagonism (Kenakin et al., 2006; Vauquelin et al., 2002). This behavior could potentially impact drug effect in vivo and can be used to identify and quantify slow antagonist dissociation. Applying binding kinetics to drug discovery and target research requires assays and data analysis methods that can be applied in reasonable throughput. Fortunately, measuring binding kinetics has become reasonably routine for most target classes (Vauquelin et al., 2015; Zhang et al., 2016). Dramatic improvements in workflow efficiency have resulted from continuous read technologies for measuring target-ligand interaction and target activity over time; rather than sacrificing an assay plate/vessel for each time point, in continuous read assays the same plate/vessel is read repeatedly (Sykes et al., 2019b; Georgi et al., 2018; Holdgate and Phillips, 2020). Data analysis equations have been derived for quantifying the rates of unlabeled compounds in competition assays, enabling familiar competition binding and activity assays to be extended to quantify binding kinetics (Morrison and Walsh, 1988; Kenakin et al., 2006; Copeland, 2013a; Motulsky and Mahan, 1984). In Sections 1.10.6 and 1.10.7, the assays and analyses used to quantify target binding kinetics are presented. This includes description of screening type assays to rapidly identify slow target-ligand dissociation. Finally in the concluding remarks (Section 1.10.8) the issue of when to measure binding kinetics in the drug discovery cascade is discussed, in light of the impact of binding kinetics on drug activity and the resources it takes to measure the binding rates. It is argued that, given the potential positive and negative impacts of kinetics to drug discovery, kinetics should be assessed reasonably

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early in drug discovery projects for key compounds, for example at the advanced lead stage. Collectively, this material should enable scientists engaged in studying target-ligand interaction to assess the significance of binding kinetics to their targets of interest, and to develop assays to quantify the kinetic pharmacology.

1.10.2

Mechanics of target-ligand binding kinetics

1.10.2.1

Dissociation

In order to understand the impact of binding kinetics on drug discovery it is necessary to understand the underlying theory and mechanics of binding kinetics. Historically, these concepts and the equations describing them emerged from chemical principles (the law of mass action and the Langmuir adsorption model, Érdi and Tóth, 1989; Langmuir, 1918) and pharmacological theory (the receptor concept, Rang, 2006). The simplest ligand-target binding mechanism is illustrated in Fig. 1. Here a single ligand molecule interacts with a single target molecule in a one-step interaction. Dissociation is considered first because it is conceptually more straightforward. Dissociation of a population of ligand-target complexes is shown in “Video 1 in the online version at https://doi. org/10.1016/B978-0-12-820472-6.00011-6 dissociation animation” in the online Annex at https://doi.org/10.1016/B978-0-12820472-6.00011-6. This video is a simulation based on single-molecule imaging studies of the dissociation process, for example (Tokimoto et al., 2007). The population breaks down by an exponential decay process, evident in the graph in the video, and shown in Fig. 2. The decline of the target-ligand complex population is easily quantified by the dissociation half-time (t½). This is the time required for half the population of target-ligand complexes to break down. This is analogous to the familiar half-time of radioactive decay. The rate of dissociation varies for different ligand-target interactions. This is shown in the dissociation graph shown in Fig. 2. A simulator is provided that enables investigators to examine the curve shape when varying the dissociation t½ value (“Kinetic binding assay simulation” (Data 7 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6) in the online Annex at https://doi.org/10.1016/B978-0-12-820472-6.00011-6). The faster the dissociation rate, the higher the dissociation rate constant value and the lower the dissociation t½. It is important to note that dissociation is a stochastic process, meaning it is defined by a random probability distribution. In practical terms this means some of the complexes in the population will break down more rapidly than the t½ and some will break down more slowly, which is evident in the video. It is not the case that all the complexes break down simultaneously at the dissociation t½. Formally, half the complexes will break down before this time and half will break down after. For this reason, the dissociation t½ can be formally defined as the median amount of time taken for a target-ligand complex to dissociate. Dissociation is quantified by koff, the dissociation rate constant. This can be measured by fitting dissociation data (e.g., Fig. 2) to the following exponential decay function (Eq. 1): ½RL ¼ ½RLt¼0 ekoff t

(1)

where [RL] is the amount of target-ligand complex at time t and [RL]t ¼ 0 the amount at the initiation of dissociation. (Experimentally, dissociation is usually measured by combining receptor and ligand for long enough for receptor-ligand complex to form, then the dissociation phase is started by either removing the unbound ligand or outcompeting it with a large excess of a competitive inhibitor, see Section 1.10.6.2.2). koff is in the rather abstract units of inverse time (t 1). It is convenient to express dissociation in terms of the duration of the complex since this is more intuitive, using a parameter in terms of time (seconds, minutes, etc.). Such a parameter reflects the average amount of time the complex stays together. Two such parameters are the dissociation t½ and the residence time. The dissociation t½ can be calculated from koff as follows: Dissociation t

¼

1 2

¼

ln2 koff

0:693 koff

Fig. 1 The basic one-site, one-step target-ligand binding mechanism. Ligand (in orange) binds to the target (in green) by a simple reversible interaction. The rate of ligand association with the target is defined by kon (the association rate constant). Dissociation of the ligand from the targetligand complex is defined by koff (the dissociation rate constant). Dissociation is also quantified by measures defining the duration of the targetligand complex, the dissociation half-time (0.693/koff) or the residence time (1/koff).

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

Target occupancy (%)

230

100

Dissociation t1/2

80

6 min 20 min 60 min

60 40 20 0 0

30

60 Time (min)

90

120

Fig. 2 Ligand dissociation data. In a dissociation experiment, target and ligand are incubated together to form the target-ligand complex, then the dissociation phase (shown in the graph) is initiated by removing unbound ligand or adding an excess of competing ligand. Dissociation time courses of three compounds are shown, with varying dissociation rates (dissociation t½ as indicated.) The dissociation curve is an exponential decay curve for a single-step, single-site interaction.

(Note the second equation defines t½ to three significant figures). The residence time is simply the reciprocal of koff (i.e., 1/koff). Both of these terms are used in the literature and here both terms will be used for the purposes of readability. However, in all cases in this article the number quantifying dissociation will be the dissociation t½. It is easy to interconvert dissociation t½ and residence time. Residence time is equal to 1.44 times the dissociation t½ (to three significant figures). The dissociation t½ of numerous drugs in clinical use is shown in Fig. 3 and Table 1 for a variety of target classes. This shows that the stability of the target-ligand complex varies enormously, the dissociation t½ varying from under a second to more than a day. This finding has been reported in numerous surveys of drug dissociation rate, usually conducted by target class, including enzymes (Morrison and Walsh, 1988; Copeland et al., 2006; Dahl and Akerud, 2013; Lu and Tonge, 2010), protein kinases (Georgi et al., 2018), G-protein-coupled receptors (GPCRs) (Guo et al., 2014) and monoamine transporters (Tsuruda et al., 2010). The physicochemical mechanisms governing the rate of dissociation include the bonds between target and ligand (e.g., electrostatic, hydrogen bonding, hydrophobic interactions), the conformational dynamics of the target-ligand complex, and water molecule dynamics. Detailed discussion of the mechanics underlying structure-activity relationships of the dissociation rate are beyond

Fig. 3 Dissociation half-life of drugs in clinical use. This survey indicates a very large range of dissociation ratedfrom less than a second to several days. (Note the log60 scale of the time axis.) The majority of drugs dissociate in the range of a few seconds to a few minutes. Some drugs, notably enzyme inhibitors, form long-lasting complexes with the target that dissociate very slowly, for example opicapone (100 h dissociation t½) (Palma et al., 2012). See Table 1 for values and references.

Kinetics of Drug-Target Binding: A Guide for Drug Discovery Table 1

231

Residence time survey of drugs in clinical use.

Drug Enzymes Ritonavir1,2 Trimethoprim3,4 Triclosan5,6 Methotrexate7,4 Oseltamivir8,4 Aliskiren9,10 Saxagliptin11,12 Pentostatin13,14 Abiraterone15,16 Opicapone17,18 G-protein-coupled receptors Diphenhydramine19,20 Clozapine21,4 Morphine22,4 Propranalol23,24 Ipratropium25,26 Candesartan27,28 Calcitonin29,30 Desloratadine31,32 Aclidinium33,34 Maraviroc35,36 Protein kinases Imatinib37,38 Erlotinib37,39 Vemurafenib37,40 Dabrafenib37,41 Dasatinib37,42 Nilotinib37,43 Nuclear receptors Raloxifene44,45 Dexamethasone46,47 Ion channels Verapamil48,4 Phenytoin48,49 Ketamine50,51 Granisetron52,4 Amlodipine53,54 Transporters Atomoxetine55,4 Citalopram55,4 Empagliflozin56,57

Target

Indication

Dissociation t½

Elimination t½

HIV-1 protease DHFR (E. coli) ENR (F. tularensis) COMT Viral neuraminidase Renin DPP4 Adenosine deaminase CYP17A1 COMT

HIV Bacterial infection Bacterial infection Autoimmunity, cancer Influenza Hypertension Type 2 diabetes Leukemia Prostate cancer Parkinson’s disease

5.3 min 8.4 min 28 min 35 min 46 min 1.7 h 3.5 h 29 h 42 h 100 h

4h 9.6 h 21 h 3.9 h 1.8 h 40 h 2.8 h 5.6 h 24 h 1.2 h

H1 histamine receptor Dopamine D2 receptor m opioid receptor b2 adrenergic receptor Muscarinic M3 receptor Angiotensin AT1 receptor Calcitonin receptor Histamine H1 receptor Muscarinic M3 receptor CCR5 receptor

Allergy, insomnia Schizophrenia Pain Hypertension, arrhythmia COPD Hypertension Osteoporosis Allergy COPD HIV

15 s 25 s 30 s 1.5 min 11 min 3.3 h 4h 9h 9.6 h 9.6 h

4.3 h 8h 2h 4.5 h 2h 9h 1.1 h 27 h 1h 11 h

ABL kinase EGF receptor kinase B-Raf B-Raf ABL kinase ABL kinase

Leukemia Non-small cell lung cancer Melanoma Melanoma Leukemia Leukemia

32 s 43 s 28 min 58 min 7.7 h 69 h

18 h 36 h 57 h 8h 4h 17 h

Estrogen receptor Glucocorticoid receptor

Breast cancer Inflammation, allergies

9 min 58 min

33 h 4h

L-type calcium channel Sodium channels NMDA receptor 5HT3 channel L-type calcium channel

Hypertension, arrhythmia Epilepsy Anesthesia, depression Nausea and vomiting Hypertension, arrhythmia

0.24 s 1.4 s 9.4 s 24 min 9h

2.8 h 19 h 3h 5.2 h 33 h

Norepinephrine transporter Serotonin transporter SGLT2 transporter

ADHD Depression Type 2 diabetes

2.5 min 11 min 58 min

5.2 h 33 h 12 h

For amlodipine and desloratadine, the dissociation t½ was extrapolated from the observed degree of dissociation at the final time point. For erlotinib, vemurafenib, dabrafenib and dasatinib, the dissociation t½ is a minimum estimate (limit of detection in the assay). The superscripts in the first column denote the references for binding kinetics (first superscript) and pharmacokinetics (second superscript). (1) Shuman et al. (2004) (2) Hsu et al. (1998), (3) Lewandowicz et al. (2003), (4) Obach et al. (2008), (5) Lu et al. (2009), (6) SandborghEnglund et al. (2006), (7) Appleman et al. (1988), (8) Kati et al. (2002), (9) Gossas et al. (2012), (10) Vaidyanathan et al. (2008), (11) Kim et al. (2006), (12) Neumiller and Campbell (2010), (13) Agarwal et al. (1977), (14) Major et al. (1981), (15) Cheong et al. (2020), (16) Benoist et al. (2016), (17) Palma et al. (2012), (18) Rocha et al. (2013), (19) Bosma et al. (2018), (20) Paton and Webster (1985), (21) Sykes et al. (2017), (22) Pedersen et al. (2020), (23) Sykes et al. (2014), (24) Lalonde et al. (1987), (25) Casarosa et al. (2009), (26) Belal and Al-Badr (2003), (27) Fierens et al. (2002), (28) Hubner et al. (1997), (29) Hilton et al. (2000), (30) Beveridge et al. (1976), (31) Anthes et al. (2002), (32) Affrime et al. (2002), (33) Casarosa et al. (2009), (34) Jansat et al. (2009), (35) Swinney et al. (2014), (36) Walker et al. (2005), (37) Georgi et al. (2018), (38) Cohen et al. (2002), (39) Johnson et al. (2005), (40) Kim et al. (2014), (41) Amaria and Kim (2014), (42) Brave et al. (2008), (43) Hazarika et al. (2008), (44) Kwok and Cheung (2010), (45) Morello et al. (2003), (46) Hogger and Rohdewald (1994), (47) FDA (2019), (48) Swinney (2004), (49) Gugler et al. (1976), (50) Parsons et al. (1995), (51) Sinner and Graf (2008), (52) Akuzawa et al. (1998), (53) Sugiyama et al. (1996), (54) Williams and Cubeddu (1988), (55) Tsuruda et al. (2010), (56) Grempler et al. (2012), (57) FDA (2018).

the scope of this article. Useful reviews include the following: Bernetti et al. (2019), Cusack et al. (2015), Georgi et al. (2017), Lu et al. (2018), Pan et al. (2013), and Schiele et al. (2015b). The single-step dissociation process represented in Fig. 1 is likely a simplification of the pathway of ligand dissociation from a target, which likely proceeds by a series of linked steps. That the dissociation appears to be a single-step process in most cases likely reflects a dominating single rate-limiting step in the dissociation pathway. Sometimes dissociation data can appear multiphasic and this can result from more than one transition contributing to the timing of the overall dissociation process (Section 1.10.6.2.4).

232 1.10.2.2

Kinetics of Drug-Target Binding: A Guide for Drug Discovery Association

The association process is illustrated in “Video 2 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6 Association animation” in the online version Annex at https://doi.org/10.1016/B978-0-12-820472-6.00011-6 and by the graph in Fig. 4A. The time course is an exponential association curve; the number of target bound receptors increases rapidly at first, then the rate of increase slows down and finally, given sufficient time, the curve approaches a plateau at which equilibrium is attained. At the plateau the binding is not static, as shown in the video. Rather, the ligand keeps associating and dissociating. Association is conceptually more complex than dissociation. The timing of association is governed by three factorsdthe intrinsic microcscopic association rate constant kon, the ligand concentration, and the dissociation rate constant koff. That the ligand concentration contributes to the timing of association makes intuitive sensedthe more ligand is present, the more rapidly the targets will become occupied. The observed time it takes for a population of receptors to become occupied is defined by the observed association rate constant, kon(obs). It is important to note kon(obs) is not the same as kon (the association rate constant), which can be a source of confusion. kon(obs) is related to kon, koff and the ligand concentration by the following equation (Eq. 2): konðobsÞ ¼ kon ½L þ koff

(2)

[Ligand] (nM)

(A)

Target occupancy (%)

100

32 10 3.2 1 0.32 0.1

80 60

Half-time 40 20 0 0

(B)

10

20

30 Time (min)

40

50

60

1.2

kon(obs) (min -1)

1.0 0.8

Gradient = kon = 0.0347 nM-1min-1 = 3.47 × 107 M-1min-1

0.6 0.4

y intercept = koff = 0.0347 min-1

0.2 0.0 0

10

20 [Ligand] (nM)

30

Fig. 4 Ligand-target association data. (A) Association time course for various ligand concentrations. The degree of target occupancy (y axis) increases over time then approaches a plateau at which occupancy approaches its equilibrium value. The graph shows how increasing the ligand concentration increases the rate of target occupancydthe curve steepens, and the t½ (time required to reach half the plateau equilibrium occupancy) decreases (circles). As expected, the plateau increases with increasing ligand concentration as the target becomes saturated. (B) Plot of observed association rate versus ligand concentration. The time course data in (A) were fit to an exponential association equation to determine the observed rate constant for association (kobs, y axis). The plot is a straight line where the gradient is equal to kon and the y intercept is equal to koff. Note the y intercept is slightly above zero. This shows that the observed association rate constant at low concentrations approaches the dissociation rate constant.

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

233

Fig. 4 shows the effect of ligand concentration on the association time course. As the ligand concentration increases, association speeds up, evident by the increased steepness of the initial phase of the association curve. In other words, the higher the ligand concentration the more rapidly the targets become occupied. The equilibrium plateau also increases with increasing ligand concentration, as the target becomes saturated with the ligand. The association time course can be analyzed by curve fitting to determine kon(obs), using the following equation (Eq. 3):
 (3) ½RL ¼ ½RLeq 1  ekonðobsÞ t where [RL]t is the amount of target-ligand complex at time t and [RL]eq the amount at equilibrium (the plateau). Details on the analysis can be found in Section 1.10.6.2.2. From kon(obs) the t½ for association can be calculated (as 0.693/kon(obs)). This t½ is shown Fig. 4A (circles); the t½ decreases as the ligand concentration increases, demonstrating again the dependence of association on the ligand concentration. A simulator is provided to enable investigators to see the effect on the association curve of varying kon, koff and the ligand concentration (“Kinetic binding assay simulation” (Data 7 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6) in the online Annex at https://doi.org/10.1016/B978-0-12-8204726.00011-6). It is instructive to plot the kon(obs) values versus the ligand concentration (Fig. 4B). kon can be determined from this plotdit is the gradient of the line. This plot also demonstrates a rather surprising property of the association process. The y intercept, when the ligand concentration is zero, is not zero (evident by close inspection of Fig. 4B). In fact, the y intercept value is equal to the dissociation rate constant. In practical terms, this means that the timing of association for low concentrations of ligand is defined in large part by the dissociation rate constant. In simple terms, the longer the residence time, the longer it takes low concentrations to associate with the receptor. This surprising fact also means that compounds with long residence times take longer to reach equilibrium, which has profound implications for the reliability of in vitro assays to accurately report binding affinity (see Sections 1.10.5.2–1.10.5.4). The physicochemical mechanisms defining the association rate constant include the rate of diffusion, the efficiency of desolvation of the ligand and the binding site, the accessibility of the binding site within the protein, and conformational flexibility/rigidity of the target and ligand (Bernetti et al., 2019; Cusack et al., 2015; Georgi et al., 2017; Lu et al., 2018; Pan et al., 2013; Schiele et al., 2015b). There is an upper limit to kon, which is determined by the rate of diffusiondligand cannot access the target faster than the diffusion limit. The limiting value is of the order of 109–1010 M 1 min 1 (depending on the conditions, e.g., temperature and molecular size) (Alberty and Hammes, 1958; Hill, 1975). Below this limit, kon can vary considerablydvalues as low as 8
102 M 1 min 1 have been reported (Braz et al., 2010). The association process to form the final bound state likely proceeds along multiple steps. For example, for many targets there is an initial association step, forming the so-called encounter complex (Gabdoulline and Wade, 1999), which then undergoes transitions to form the stable bound state (Tummino and Copeland, 2008). If the rate of one of these steps dominates the overall pathway then the interaction is defined by a single overall rate. However, as for the dissociation process, sometimes more than one step can define the timing of the association process, resulting in multiphasic association curves (see Section 1.10.6.2.4).

1.10.2.3

Relationship between binding kinetics and affinity

Binding affinity is the standard metric of ligand-target interaction used in optimization and development of new therapeutics. In pharmacology, affinity is usually defined as the concentration of ligand required to occupy 50% of the targets at equilibrium (the Kd, or Ki in an inhibition assay). The Kd is directly related to the binding kineticsdKd is simply koff divided by kon: Kd ¼

koff kon

Examining this equation indicates that in principle changes of binding affinity can be achieved by changing only koff, only kon, or both. All three of these scenarios have been encountered in ligand optimization (reviewed in De Witte et al., 2016; Georgi et al., 2017; Schiele et al., 2015b; Schoop and Dey, 2015). The relative contribution to affinity of koff and kon can be observed in largescale surveys of ligand binding kinetics, which indicate very broad ranges of the parameter combinations (Georgi et al., 2018; Miller et al., 2012). The equation also indicates that the same affinity can be obtained with different combinations of koff and kon. A “Slowon, slow off” compound can have the same affinity as a “Fast-on, fast-off” compound. This scenario has been observed, for example in a series of HIV protease inhibitors (Markgren et al., 2002). The relationship between affinity and kinetics enables an alternative method for measuring affinitydinstead of attempting to incubate an assay until it reaches equilibrium, which can be challenging for slowly-dissociating ligands, the rates can be measured instead and affinity calculated using the equation above (see Section 1.10.5.4). Kinetics are also related thermodynamically to binding affinity in transition state theory (Lu and Tonge, 2010; Bernetti et al., 2019; Georgi et al., 2017; Pan et al., 2013; Schoop and Dey, 2015). Affinity is the free energy difference between the free target and ligand and the target-ligand complex. Kinetics considers the free energy of the transition state, the energy barrier which must be overcome to form the bound state. kon is related to the energy barrier between free target/ligand and the transition state. koff is related to the free energy difference between the transition state and the final bound state.

234

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

1.10.3

In vivo efficacy and target binding kinetics

1.10.3.1

Introduction

Control of drug pharmacodynamics is critical to obtain the appropriate magnitude, onset time and duration of drug effect. This is required to maximize therapeutic efficacy and minimize toxicity. Drug effect in the body is defined by the concentration of the drug and how it changes over time (pharmacokinetics) and by the strength of drug-target interaction/modulation (e.g., binding affinity). These inputs are used in pharmacodynamic-pharmacokinetic modeling (PK-PD modeling) to analyze and predict in vivo drug activity (Mager et al., 2003). In modern drug discovery, successfully predicting human pharmacodynamics is a critical translational step. The science of human dosing prediction enables selection of optimal clinical candidates and has dramatically reduced the number of candidates that fail in Phase 1 trials for inadequate PK. Typically, PK/PD models assume a constant equilibrium between target-bound and free drug, with the change of drug concentration being the determinant of the time course of drug effect. The kinetics of drug-target binding can also impact the duration, onset and magnitude of drug effect under certain circumstances. This has been systematically evaluated in a number of recent studies (Dahl and Akerud, 2013; Daryaee and Tonge, 2019; De Witte et al., 2016; Vauquelin, 2018). The extent to which the kinetics of target binding impacts pharmacodynamics is dependent on the relationship between the target residence time and the pharmacokinetics of the compound. For most drugs, the dissociation is relatively rapid (t½ of the order of minutes) relative to the duration of the drug concentration (elimination t½ of the order of hours) (Dahl and Akerud, 2013). This is indicated in Table 1, which lists the elimination and dissociation t½ values of numerous drugs in clinical use. (An interactive table “Supplementary Table S1 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6 Drug binding kinetics interactive table” is available in the online Annex at https://doi.org/10.1016/B978-0-12-820472-6.00011-6.) Consequently, for these drugs the rate-limiting step is elimination, and the residence time does not contribute appreciably to the PK-PD relationship. For some drugs, the opposite is truedthe residence time is longer than the elimination time and must be considered to accurately interpret and predict drug effect in vivo (Cheong et al., 2020; Rocha et al., 2013; Gavalda et al., 2014; Walkup et al., 2015; Yassen et al., 2006). A long residence time on the target can be a benefit or a liability depending on the circumstances. It is a benefit if prolonged efficacy is required and drug elimination is rapid (e.g., opicapone, see Section 1.10.3.2 and Fig. 7 (Rocha et al., 2013)). Conversely, a long residence time is a liability for a short-acting drug, whose duration of action is controlled pharmacokinetically by means of rapid elimination (e.g., sleep drugs, see Section 1.10.3.5). For chronically-dosed drugs, the effect of residence time is dependent on the pharmacokinetic stability (i.e., elimination rate). If the elimination rate is moderate, a long residence time can enable lower dosing (Section 1.10.3.3). If the elimination rate is long, residence time has little effect (Section 1.10.3.4). The interplay between binding kinetics and pharmacokinetics is not trivial and is dependent on many variables, including the PK and binding rates, the amount of target occupancy at the Cmax of the drug, the frequency of dosing, and the required amount of occupancy for drug effect (so-called “Target vulnerability”) (Tonge, 2018; De Witte et al., 2016; Vauquelin, 2018; De Witte et al., 2018a; Folmer, 2018). Simulations are highly useful for exploring the potential impact of binding kinetics on pharmacodynamics under the circumstances of interest (Dahl and Akerud, 2013; Daryaee and Tonge, 2019; Vauquelin, 2018; Walkup et al., 2015; De Witte et al., 2018a; De Witte et al., 2017; Vauquelin, 2016; Yin et al., 2013). To aid the investigator, simulators are provided in the online Annex (Data 2–5 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6). These tools are easy to use, being simple spreadsheets in Microsoft Excel. The simulators enable investigators to determine how binding kinetics impacts target occupancy for whatever combination of parameters is of interest or relevance to the target under investigation. These simulators are used below for four scenarios. The results highlight that knowing the residence time of the drug can enable benefits of binding kinetics to be realized, and liabilities to be avoided.

1.10.3.2

A long residence time can enable once-a-day dosing of a rapidly-eliminated drug

In the first simulation we consider the effect of residence time on pharmacodynamics of a drug that is rapidly eliminated. The simulation model is shown in Fig. 5. The model is simple, comprising a simple one compartment oral dosing pharmacokinetic model coupled with the one-step kinetic binding mechanism. The PK is defined by the dose, absorption rate constant and elimination rate constant. Target interaction is defined by kon and koff. The pharmacodynamic readout is target occupancy by ligand, expressed as a percentage. Fig. 6 shows the simulated PK time course of a rapidly eliminated compound (elimination t½ of 30 min, grey dashed line). The in vivo target occupancy was then simulated for compounds with a range of target residence time. (The affinity of the compounds was held constant so differences of occupancy could be ascribed solely to the residence time.) Data were generated using the simulator in the online Annex “BK-PK simulator oral dosing.” The simulation clearly shows that residence time can determine the duration of occupancy when it is longer than drug elimination (Fig. 4). This has been demonstrated in numerous studies (Tonge, 2018; Vauquelin and Charlton, 2010; Dahl and Akerud, 2013; Georgi et al., 2018; De Witte et al., 2018a). Occupancy is sustained for a compound with a long residence time (24 h dissociation t½, blue line)d58% of the targets are occupied 24 h after dosing (Fig. 6). By this time, the free drug has effectively disappeared (concentration < 1 fM). This simulation shows how slow dissociation from the target could enable once-a-day dosing of a compound that is very rapidly cleared from the body.

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

235

Fig. 5 Simple target occupancy model combining pharmacokinetics and target binding kinetics. Dosed drug is absorbed into the compartment where the target is located and subsequently eliminated. The rates of absorption and elimination are defined by ka and kel, respectively. Binding to the target is defined kinetically by the association and dissociation rate constants (kon and koff).

125

80

100

24 hr

60

40

75

50

9 hr

20

10 min

25

1 hr

3 hr

Compound concentration (nM)

Target occupancy (%)

Compound concentration 100

0

0 0

8 16 Time post dose (hours)

24

Fig. 6 Slow drug dissociation from the target can determine the duration of target occupancy of a rapidly-eliminated drug. In this simulation of a simple single compartment oral dosing model (Fig. 5) the drug elimination was rapid (kel t½ 30 min), giving the PK curve (dashed grey line, concentration on right y axis). Dissociation was varied from relatively rapid (t½ 10 min) to very slow (t½ 24 h) and target occupancy plotted on the left y axis. The slower the dissociation from the target, the longer the duration of occupancy. A dissociation t½ of 24 h enables >50% occupancy to be maintained for a day in this simulation, despite the rapid elimination (t½ 30 min). Data were simulated as described in the simulator “BK-PK simulator oral dosing” in the online Annex, with the following parameter values: ka t½, 0.4 h; kel t½, 0.5 h; drug dose concentration, 300 nM; drug Kd, 2 nM; dissociation t½, see numbers on figure.

An example of this phenomenon is opicapone. This compound is used for Parkinson’s disease as an adjunctive therapy to levodopa, increasing levodopa exposure by inhibiting an enzyme that degrades it, catechol-O-methyltransferase (COMT) (Ferreira et al., 2016). This compound has a short half-life (elimination t½ of 1.2 h) but is able to achieve the sustained COMT inhibition necessary to enable once-a-day dosing (Rocha et al., 2013). This is a result of a very long residence time on the target (100 h) (Palma et al., 2012). The human pharmacodynamic data is shown in Fig. 7 (reproduced from Rocha et al., 2013). In the trial, subjects were dosed once daily for 8 days and then, following the final dose, the COMT inhibition measured for a further 6 days (Rocha et al., 2013). As expected, the drug concentration had fallen below the lower limit of quantification 12 h after the final dose (Fig. 7) (Rocha et al., 2013). In stark contrast, the target engagement was sustained for several daysdCOMT inhibition was still detectable 6 days after the final dose (Fig. 7) (Rocha et al., 2013). The t½ for decay of the PD effect (in excess of 100 h) was in good agreement with the dissociation t½ of the compound (100 h) (Palma et al., 2012). These data provide a clinical example of how a long residence time can enable once-daily dosing of a compound that would otherwise be too short-acting owing to its rapid elimination.

236

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

80

60

40

20

0

300

Plasma compound (ng/ml)

% inhibition COMT

100

200

100

0 0

1

2

3 Time (days)

4

5

6

Fig. 7 PK/PD relationship for opicapone, a rapidly-eliminated, very slowly-dissociating drug. Healthy human volunteers were administered opicapone (30 mg/kg) for 8 days. The plasma concentration and target engagement of opicapone were then measured for 6 days after the final dose. Target engagement was quantified as the inhibition of erythrocyte soluble catechol-O-methyltransferase (COMT) activity. Note the sustained target engagement over several days, long after elimination of the drug, consistent with the very slow dissociation of opicapone from COMT (t½ of 100 h). % inhibition of COMT activity was determined by subtracting the % baseline data in the original study from 100. Data are from Rocha JF, Almeida L, Falcao A, Palma PN, Loureiro AI, Pinto R, Bonifacio MJ, Wright LC, Nunes T, Soares-Da-Silva P (2013) Opicapone: A short lived and very long acting novel catechol-O-methyltransferase inhibitor following multiple dose administration in healthy subjects. British Journal of Clinical Pharmacology, 76: 763–75.

While opicapone is probably the most extreme example, there are other drugs with an appreciably longer target occupancy half-time than pharmacokinetic half-life (Table 1). Examples include the M3 muscarinic antagonist aclidinium (9.6-fold longer dissociation t½ than elimination t½), the chemotherapeutic adenosine deaminase inhibitor pentostatin (5.2-fold), the ABL1 kinase inhibitor nilotinib (4.1-fold), the osteoporosis drug salmon calcitonin (3.6-fold), and the prostate cancer therapeutic abiraterone (1.8-fold). In some cases, it has been argued that the long residence time contributes to the clinical pharmacodynamic effect of the drug (Cheong et al., 2020; Gavalda et al., 2014). It is important to note that extending the target residence time is not the only way to produce sustained target engagement. Prolonged occupancy can also be obtained pharmacokinetically, by forcing a high degree of target occupancy at Cmax; when the target population is saturated with drug, the loss of occupancy lags behind the loss of drug (De Witte et al., 2018a; Folmer, 2018). However, this forcing approach is undesirable if there is the potential for target-mediated toxicity. Of course if the goal is a once-a-day dosing the simplest strategy is to optimize the PK of the compound series. For most targets it is likely to be easier to optimize for PK than for a long target residence time: compounds with suitably long clearances are much more common than compounds with hours-long residence times on the target (e.g., Table 1). However, there are scenarios where this might not be the case. New therapeutic modalities can require drugs that are larger and more difficult to optimize pharmacokinetically, such as proteolysis targeting chimera (PROTAC) compounds (Bondeson and Crews, 2017). There are also modalities where rapid clearance of the drug is highly desirable, e.g., cytotoxic radiopharmaceuticals. For these drugs, the ideal compound is one rapidly cleared from the body (to minimize radioactive toxicity) but tightly bound to the target (to maximize damage to the target cells).

1.10.3.3

A long residence time can enable lower-dosing of a chronically-dosed drug

Now we consider repeated dosing. Drugs for chronic conditions are dosed repeatedly, typically once per day. Numerous studies have examined the effect of binding kinetics on repeat-dosing PK/PD relationships (Schuetz et al., 2017; Dahl and Akerud, 2013; De Witte et al., 2016) Usually the pharmacodynamic goal is to obtain target engagement above a threshold throughout the 24 h interval after dosing. However, the drug concentration is not constantdas the drug is cleared from the body, the occupancy of the target will decline. In order to obtain sufficient target occupancy at the lowest drug concentration at the end of the dosing interval (the trough), it can be necessary to force a high degree of target occupancy towards the beginning of the dosing interval, i.e., at Tmax for the drug concentration (De Witte et al., 2018a; Folmer, 2018). This requires a higher concentration of drug than is necessary for therapeutic efficacy at the Tmax. This is shown in Fig. 8. Here the PK/PD relationship was simulated for a compound with an elimination t½ of 6 h, dosed orally every 24 h for 7 days. The target occupancy threshold for efficacy is 60%. This simulation was performed with the simulator (Data 7 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6) in the online Annex, “BK-PK simulator repeated oral dosing.”

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Fig. 8 Slow dissociation can enable lower dosing of a compound with moderate stability on repeated dosing. In this repeated dosing simulation, compounds with moderate stability are dosed once daily for 7 days (elimination t½ of 6 h). The upper graphs (A and B) show a compound that dissociates rapidly (t½ 5 min) and the lower graphs (C and D) show a compound with the same affinity and pharmacokinetics but a much slower dissociation rate (t½ 24 h). The goal is to maintain at least 60% occupancy across the time course. The concentration of drug required to achieve this was determined (by manually iterating the simulator). For the rapidly-dissociating compound, a dose concentration of 188 nM was required (yielding a Cmax of 142 nM on Day 7). By contrast, for the slowly-dissociating compound, only a third of this dose was required (59 nM dose, 45 nM Cmax on Day 7). This difference was also manifest in the peak occupancy required to maintain occupancy at trough (93% and 72% for rapidly and slowly dissociating compounds, respectively). Data were simulated with the “BK-PK simulator repeated oral dosing” simulator available in the online Annex, with the following parameters: Absorption t½, 1 h; elimination t½, 6 h; binding affinity, 10 nM; dissociation t½ 5 min or 24 h; dose concentration 188 or 59 nM; dosing interval, 24 h.

First we consider a compound that dissociates rapidly from the target (dissociation t½ of 5 min) (Fig. 8A and B). A dose of compound was identified that achieved 60% occupancy at trough after 7 days of dosing (Day 7 data in Fig. 8B) (dose concentration of 188 nM). At the trough (24 h after dosing) a drug concentration of 15 nM is required to achieve 60% occupancy on Day 7. By this time, much of the drug has been eliminated; 24 h is four elimination half-lives of the drug. In order to achieve 15 nM drug at 24 h, the Cmax of the drug needs to be 142 nM (Fig. 8B). This means the Cmax is 9.5-fold higher than is necessary to achieve therapeutic occupancy. The occupancy at this high Cmax concentration is 93%, far above the 60% required for efficacy. The potential negative consequence of this Cmax forcing is a reduced therapeutic window, owing to too much occupancy of the target if there is mechanismbased toxicity, and/or occupancy of undesired targets if the compound has limited target selectivity (for example, a protein kinase inhibitor). Now we consider a compound that dissociates slowly from the target (dissociation t½ of 24 h) but which binds with the same affinity and has the same PK as the rapidly-dissociating compound (Fig. 8C and D). Here again a dose of compound was identified that gave 60% occupancy at trough after 7 days of dosing (Day 7 data shown in Fig. 8D). The dose concentration required was 59 nM. Note this is just a third of the dose required for the rapidly-dissociating compound (188 nM). This shows how prolonging target residence time (without changing affinity) can enable a lower dose of compound to be used. This could potentially be

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beneficial to improve therapeutic window and reduce occupancy of undesirable targets. On examining the whole 7 day time course (Fig. 8C), the occupancy profile is quite different from that of the rapidly dissociating compound (Fig. 8A). First, the peak height for occupancy is reduced, evident as a reduced range of occupancy between peak and trough (i.e., a reduced amplitude, 60–72% occupancy on Day 7, compared with 60–93% for the rapidly-dissociating compound). Second, it takes 2 days for the threshold trough occupancy to be achieved. This rather surprising effect is a result of the observed association of drug and target being dependent on the dissociation rate constant, as described in Section 1.10.2.2 (see Fig. 4B): at low/moderate levels of occupancy, the time it takes target to become occupied is determined largely by the dissociation rate constant, which in this case is slow (24 h), which delays occupancy relative to a rapidly-dissociating compound.

1.10.3.4

Residence time has little impact on pharmacodynamics if elimination is slower than dissociation

Next we examine the effect of residence time if the compound has optimal once-daily dosing pharmacokinetics. The once-a-day dosing regimen is usually achieved pharmacokinetically by optimizing the elimination rate. Fig. 9 shows the PK and target occupancy simulation for a compound with an elimination t½ of 12 h, dosed once-daily for 7 days. First we consider a compound with a short residence time (dissociation t½ of 5 min). This compound fits the conventional assumptions of rapid target-ligand equilibration, and of occupancy dynamics being driven solely by the change of drug concentration. The target occupancy threshold was 60% across the whole day (on Day 7). A dose was identified that satisfied this criteria (42 nM dose concentration), giving a Cmax of 46 nM on Day 7, in moderate excess of the ligand Kd of 10 nM. The change of target occupancy across the day was moderate (ranging from 60% to 82% on Day 7). Now we consider the effect of a long residence time. A dissociation t½ was chosen that is long relative to most drugs (Table 1) but shorter than the elimination t½. The value chosen was 6 h (compared with the elimination t½ of 12 h). The PK, dose and affinity of the compound was set to be the same as that of the rapidly-dissociating compound so the effect of residence time alone could be determined. Under these conditions, a long residence time had little effect on pharmacodynamics. Fig. 9B and C shows the target occupancy (orange curve) was similar to that of the rapidly-dissociating compound (green curve), in terms of the shape of the time course, and the change of target occupancy across Day 7 (65–81%, compared with 60–82%). The only substantial difference was on Day 1 where there was a delay in target occupancy for the slowly-dissociating compound (Fig. 9B) (expected owing to the dependence of the observed association rate on the dissociation rate constant, see Section 1.10.2.2 and Fig. 4B). Consequently, under these conditions the long residence time has little effect and so is neither a benefit nor a liability. This is reflected in the broad range of dissociation t½ of drugs used to treat chronic conditions (e.g., 5.3 min for ritonavir used to treat HIV infection, and 7.7 h for dasatinib used to treat leukemia, Table 1).

1.10.3.5

A short residence time is required for rapid and transient drug effects controlled by pharmacokinetics

Some drugs are designed to work transiently, rapidly producing a physiological effect that is then maintained for a period of time, followed by wearing off of the response. Classic examples include anesthetics, sleep drugs, and cardiovascular agents in emergency medicine. The time course of these agents is controlled pharmacokinetically; the duration of response is controlled by duration of drug in the body. In particular, the elimination rate of the drug defines how long the drug effect lasts. This enables straightforward prediction of drug effect and selection clinical of candidates, since drug concentration is reasonably straightforward to quantify. Clearly in this case a long target residence time of the drug is undesirable since it could prolong drug effect beyond that predicted by the drug concentration (see above). The effects of a long residence time are simulated in Fig. 10 (Data 7 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6). This simulation highlights the surprising issue with long-residence time drugs, a delayed onset of action. Here we simulate a sleep drug’s concentration and target occupancy, based on the GABAA agonist zolpidem. The desired pharmacodynamic profile is rapid onset, necessary for improving sleep initiation; efficacy for a time interval long enough for sleep maintenance; followed by rapid decay of the drug effect in order to minimize impairment of next-day performance (Nicholson and Pascoe, 1986). This is achieved by controlling the PK, exemplified in Fig. 10 (grey line), with a rapid rate of absorption (t½ 30 min) and an appropriate rate of elimination (t½ 2 h). For a rapidly-dissociating ligand (10 min dissociation t½, red line) the target occupancy follows almost the same time course as the compound concentration (Fig. 10). This enables the duration of target engagement to be defined by the PK. The target occupancy threshold for sedative-hypnotic action of GABAA agonists is approximately 30% (Abadie et al., 1996). In the simulation, this is reached rapidly (by 24 min, Fig. 10). Occupancy is sustained above the 30% threshold and then falls below it 5 h after dosing. This provides the ideal target engagement for the desired pharmacodynamic profile. Now we consider a compound with a long residence time (4 h dissociation t½, blue line). The most startling result is a dramatically prolonged onset time (Fig. 10). The threshold occupancy is not reached until 3 h after dosing. This is a result of the effect described in Section 1.10.2.2, that the observed association rate of the ligand is determined largely by the dissociation rate constant when target occupancy is low. Occupancy remains above the threshold for too long remaining above it until 6.5 h post-dose. Obviously, this is inappropriate for the desired pharmacodynamics. The worst-case scenario is that such a compound is advanced into clinical trials without knowledge of the slow dissociation from the target and then fails the efficacy endpoints.

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Fig. 9 Dissociation rate has little effect on occupancy of a once-daily dosed compound with a long elimination half life. In this simulation, compound was dosed once a day for 7 days. (A) PK profile. The profile of the compound is ideal for maintaining target engagement using once-daily dosing (elimination t½ 12 h). The concentration at trough (15 nM) is sufficient to occupy 60% of the targets, being 1.5-fold the Kd concentration (10 nM). (B) Target occupancy. The target occupancy for a rapidly-dissociating compound (5 min t½) oscillates between 60% and 82% after repeated dosing. For a slowly-dissociating compound (6 h t½) the occupancy curve closely overlies that of the rapidly-dissociating ligand. (C) On the seventh day, after repeated dosing, the occupancy curves for rapidly and slowly-dissociating ligands are similar. Data were simulated with the “BK-PK simulator repeated oral dosing” simulator available in the Annex, with the following parameter values: ka t½, 1 h; kel t½, 12 h; drug dose concentration, 42 nM; drug Kd, 10 nM; dissociation t½, 5 min or 6 h; dosing interval, 24 h.

1.10.3.6

Summary

These considerations show how the impact of target residence time on therapeutic efficacy is dependent on the interplay between pharmacokinetics and binding kinetics (Dahl and Akerud, 2013; De Witte et al., 2016; Vauquelin, 2016). The scenarios considered here demonstrate that a long residence time can be a benefit, a liability, or of little consequence, depending on the desired pharmacodynamics. If prolonged efficacy is required, a long residence time can rescue a compound that would otherwise be discarded due to rapid elimination (e.g., opicapone). A long residence time can also enable a lower dose to be used for a drug dosed repeatedly if the elimination t½ is shorter than the dissociation t½. If the elimination t½

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Fig. 10 Rapid dissociation enables occupancy to be controlled by pharmacokinetics. This example illustrates the pharmacodynamics of a sleep drug, based on the GABAA channel modulator zolpidem (Nicholson and Pascoe, 1986). Rapid onset of occupancy is required for sleep onset, then sufficient occupancy is required to maintain sleep for a period of time. Subsequently, occupancy must decline below the sedative threshold, to avoid next-day residual effects that can impair performance. Here it is assumed the threshold for efficacy is >30% occupancy (dashed line) (Abadie et al., 1996). The rapidly-dissociating compound (t½ 10 min) enables occupancy to be controlled by PKdthe red occupancy curve closely matches the grey PK curve. Target occupancy is reached in 24 min, then is sustained up to 5 h post-dose. After this point, occupancy declines below the sedative threshold, enabling normal next-day performance. Slow dissociation (t½ 4 h) dramatically disrupts the pharmacodynamics (blue line). First, it takes much longer for the threshold to be reached (3 h), then the occupancy takes longer to decay. Data were simulated with the “BK-PK simulator oral dosing” simulator available in the online Annex, with the following parameter values: ka t½, 0.5 h; kel t½, 2 h; dose concentration, 40 nM; Kd, 23 nM; dissociation t½, 10 min or 4 h.

is longer than the dissociation t½, residence time has little effect. If a short duration of action is required, a long residence time can be a liability by prolonging efficacy beyond the desired time window, and by delaying the onset of action. Clearly many other scenarios beyond those discussed here are encountered in drug discovery. These can be evaluated using the simulators provided in the online Annex. More extensive evaluation of the binding kinetic/pharmacokinetic interaction can be found in recent review articles (Dahl and Akerud, 2013; Daryaee and Tonge, 2019; De Witte et al., 2016; Vauquelin and Van Liefde, 2006; De Witte et al., 2017; Vauquelin, 2016).

1.10.4 Other kinetic effectsdMicro PK-PD relationships, fluctuating endogenous ligand concentration, and post-binding events Drug effect can be influenced by other kinetic processes, including the association rate constant and rebinding of drug to target. These factors influence pharmacodynamics when the architecture of the target environment constrains free diffusion of the drug and the target, concentrating the drug in the vicinity of the target. This results in so-called “Micro PK/PD” phenomena, researched extensively by Vauquelin, Charlton, de Witte and colleagues, summarized in Section 1.10.4.1. Another factor, for inhibitory drugs, is the dynamics of the endogenous agonist the compound is competing against. Large changes of agonist concentration can displace rapidly-dissociating ligands but not slowly-dissociating ones (Vauquelin and Van Liefde, 2006). This process is simulated and discussed in Section 1.10.4.2. Finally, events that occur after the initial target-ligand interaction affect the kinetics of drug response, including covalent binding, slow conformational changes, signal transduction and target degradation. As argued by Swinney, drug action involving these events cannot be quantified and predicted solely on the basis of equilibrium mass-action target binding (Swinney, 2004; Swinney, 2006). These events are elaborated in Section 1.10.4.3.

1.10.4.1

Rebinding and the association rate constant

Local phenomena can control the free drug concentration in the vicinity of the target, which can modify the influence of binding kinetics on pharmacodynamics. This is particularly evident for membrane-localized targets (Vauquelin and Packeu, 2009). The cell membrane can act to concentrate the drug in a layer at the cell surface (Berg and Purcell, 1977; Gherbi et al., 2018), particularly for ligands that interact electrostatically with phospholipid head-groups (Kane et al., 2008). This effect can be amplified by targets in the membrane acting as a sink for the drug (Gherbi et al., 2018). The membrane itself can act as a drug sink as a result of passive partitioning into the lipid bilayer (Sykes et al., 2014; Vauquelin and Packeu, 2009; Anderson et al., 1994; Seydel et al., 1994). These effects are amplified in locations with little convective stirring, such as synapses and interstitial spaces (reviewed in Vauquelin and Charlton, 2010).

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The kinetics of target binding are influenced by these concentrating phenomena. First, the same ligand can bind repeatedly to the target population. This phenomenon, termed rebinding, can extend the duration of target occupancy (Vauquelin and Charlton, 2010; Vauquelin, 2016). Second, the duration of effect can become dependent on the association rate constant, kon (since the rate of rebinding is determined by kon) (Vauquelin and Charlton, 2010; De Witte et al., 2016; Vauquelin, 2016). Third, complex PK-PD relationships emerge, defined by the properties of the drug compartments, target density and all the associated PK and binding rate constants. A useful guide to these PK/PD relationships was recently provided by De Witte and colleagues (De Witte et al., 2016). An example of their application is to the efficacy and side-effect liabilities of antipsychotics and their relationship to the association and dissociation rate constants for the D2 dopamine receptor (Sykes et al., 2017).

1.10.4.2

Endogenous ligand dynamics

For drugs designed to inhibit binding of an endogenous ligand (e.g., receptor antagonists), the degree of target engagement by the drug is dependent on the concentration of the endogenous ligand. High concentrations of endogenous ligand will compete better against the drug than low concentrations, blocking the drug from binding to the target. This effect can change over time when the endogenous ligand concentration is subject to temporal control. Such time-dependence of the endogenous ligand concentration is very common in biology, occurring over a broad range of timescalesdfrom milliseconds for synaptic neurotransmitter release, to seconds and minutes for homeostatic factors, and to hours for hormones subject to diurnal rhythm. The changes in concentration can be dramatic, with peak concentrations sufficient to effectively saturate the target. In principle this effect can change how the drug dissociation rate effects pharmacodynamics. Occupancy of a drug that dissociates slowly can be more resistant to increases of agonist concentration than that of a drug that dissociates rapidly (Vauquelin and Van Liefde, 2006). Here the effect of changing agonist concentration is simulated. The simulator used is available in the online Annex to enable investigators to explore their conditions of interest (“BK-PK simulator changing endogenous ligand concentration”). The agonist dynamics follow a rise-and-fall time course profile that models a graded increase of agonist concentration that is then cleared over time. For the sake of simplicity, the inhibitor concentration is assumed to be at steady-state (i.e., does not change over time). The inhibitor-target occupancy is assumed to be at steady-state prior to the appearance of the agonist. Fig. 11 shows the change of inhibitor-target occupancy over time (green line) as the agonist concentration changes (black line). At the start, inhibitor occupancy is at 90% and the agonist concentration is zero. Agonist concentration then increases rapidly (rise t½ of 1 min), peaks at 2.4 min, then declines (decline t½ of 3 min). Agonist concentration at peak is high, at 450 nM, which is 45fold the Kd (10 nM), sufficient to occupy 98% of the targets in the absence of antagonist. Agonist binding is assumed to be rapidly-equilibrating. Now we consider how this affects occupancy of the target by the inhibitor. Before agonist appears, 90% of the targets are occupied by inhibitor (drug concentration 100 nM, drug Kd 11 nM, Fig. 11). First, we consider a rapidly-dissociating inhibitor (dissociation t½ 10 s). As the agonist occupancy rises, occupancy by inhibitor declines rapidly (green line, Fig. 11A). This makes sensedrapid dissociation yields the free target, which becomes bound by the agonist since it outcompetes the inhibitor. At peak agonist concentration, occupancy by inhibitor is dramatically reduced, to 16% (Fig. 11A), and agonist occupies 82% of the targets (not shown). Later in the time course, the agonist concentration falls and so occupancy by the inhibitor returns, because of reduced competition by the agonist (Fig. 11A). Inhibitor occupancy ultimately returns to the pre-agonist exposure level (90%) once all the agonist has cleared. Now we simulate a slowly-dissociating inhibitor (dissociation t½ 90 min, Fig. 11B). As for the rapidly-dissociating inhibitor, target occupancy is 90% before agonist appears (since the affinity is the same). However, as the agonist concentration increases, there is much less effect on inhibitor occupancy, which declines only slightly, to 80% (Fig. 11B). This is easily explaineddin order for agonist to bind and displace the inhibitor, inhibitor first has to dissociate from the target. If there is minimal dissociation over the time course of the agonist pulse, there will be minimal displacement of the inhibitor by the agonist, and so the inhibitor occupancy will remain largely unaffected. In this way, slow inhibitor dissociation can protect the therapeutic effect against transient elevations of the agonist concentration. This mode of inhibition is often called insurmountable inhibitiondthe agonist cannot outcompete the inhibitor no matter how much the agonist is in excess over the inhibitor (see Section 1.10.5.5). For the rapidly-dissociating inhibitor, protection can be obtained by increasing the dose. Fig. 11C shows occupancy with a 20fold increase of the dose for the rapidly-dissociating ligand (2000 nM, 10 s dissociation t½). This results in an initial occupancy of 99.5%. Now when the agonist concentration is elevated the effect on inhibitor occupancy is much lessdtrough occupancy by drug is 80%. Now when we compare this result with that for the slowly-dissociating drug the benefit of slow dissociation becomes apparent (Fig. 11B and C). For both ligands, trough occupancy is 80%, but for the slowly-dissociating ligand this is achieved at a 20-fold lower dose compared with the rapidly-dissociating drug, despite the affinity of the two drugs being identical. Consequently, the slowly-dissociating drug could provide an improved therapeutic window. An interesting example of this model is encountered in chemotaxis, such as by leukocytes at sites of inflammation, as described in Ref. (Vauquelin and Charlton, 2010). In this case the drug target is a receptor on a cell circulating in the bloodstream. The change of agonist concentration is a manifestation of a chemotactic gradient of the agonist that the cell passes through at a site of inflammation. This is illustrated in the animated PowerPoint slide show “Data 1 in the online version at https://doi.org/10.1016/B978-012-820472-6.00011-6 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6” in the online Annex called “Slow dissociation and chemotactic gradients” (view in Slideshow mode). In the absence of inhibitor, the high concentration of

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Fig. 11 Slow dissociation protects against elevation of endogenous ligand concentration. In this simulation, the endogenous ligand increases over time (black line). Inhibitor drug is at steady-stateddrug concentration is constant over time, as is target occupancy by drug before the appearance of endogenous ligand (90% occupancy). (A) When the endogenous ligand concentration increases, occupancy of a rapidly-dissociating drug drops substantially because free receptor generated upon drug dissociation is occupied almost exclusively by endogenous ligand. Occupancy troughs at 16%. Later, as endogenous ligand is cleared, occupancy by drug returns to 90%. (B) For a slowly-dissociating drug (t½ 90 min) with the same affinity, the increased endogenous ligand has little effect (trough occupancy 80%) because few of the receptors become free owing to slow drug dissociation. (C) For a rapidly-dissociating drug, competition by endogenous ligand can be overcome by increasing the dose. A 20-fold increase of dose gives the same degree of protection as a 90 min dissociation t½ (trough occupancy of 80%). This shows how slow dissociation can improve the therapeutic window when there are large excursions of endogenous ligand concentration. (D) Model used for simulation. The simulation values used were, AP, 780 nM; ka t½, 1 min; kel t½, 3 min; KA, 10 nM; k2 t½, 10 s (A,C) and 90 min (B); KB (¼ k2/k1), 11.1 nM; [B], 100 nM (A,B) and 2000 nM (C). The simulator used is available in the online Annex (“BK-PK simulator changing endogenous ligand concentration”).

chemotactic agent binds the receptor on the passing leukocyte, resulting in migration into the inflammatory site and the appropriate immune response. The inhibitor binds the receptor throughout the circulation so the receptor is loaded with inhibitor when the cell encounters the chemotactic gradient. If the dissociation rate of inhibitor is slow, few receptors will become free and so the chances of migration will be diminished, and the chances of the cell continuing to circulate will be increased. By contrast for a rapidlydissociating inhibitor, the free receptors will become occupied by the chemotactic agonist, which outcompetes the inhibitor, resulting in loss of inhibitory efficacy. Potential targets impacted by this mechanism include the CXCR2 chemokine receptor (Chapman et al., 2007) and the DP2 prostanoid (CRTH2) receptor (Sykes et al., 2016). More sophisticated modeling of fluctuating agonist concentration and the effect of inhibitor kinetics has been carried out, for example on synaptic dopamine signaling dynamics and the kinetics of block of the D2 dopamine receptor by antipsychotic drugs (De Witte et al., 2018b). This systems-biology approach included dynamics of the second messenger cAMP and identified a limit of dopamine fluctuation frequency at which the kinetics of antipsychotic binding could impact signaling dynamics.

1.10.4.3

Post-binding target events

After the initial complex between ligand and target has formed, events can take place that are irreversible or poorly reversible on the timescales of drug effect measurement and effective drug concentration in the body (Fig. 12). Such events include: (1) covalent bond formation with the target for covalent inhibitors; (2) conformational change of the drug-target complex to form

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Fig. 12 Post-binding events that do not equilibrate. Once target has bound ligand, events can take place that are irreversible, or poorly reversible on the timescale of the drug effect measurement. These include covalent binding, conformational changes of the target-ligand complex, degradation of the target, and signal transduction (depending on the target and the ligand modality). Swinney has argued that the prevalence of these events requires a kinetic, rather than a steady-state, interpretation of drug action (Swinney, 2004; Swinney, 2006).

a tightly-bound state; (3) signal transduction and gene expression changes, for drugs that activate receptors (i.e., agonists); (4) degradation or inactivation of the target, e.g., targeted protein degrader compounds. Swinney has argued that such nonequilibrium events indicate that drug action cannot be quantified and predicted solely on the basis of equilibrium massaction target binding (Swinney, 2004; Swinney, 2006). These events occur for the majority of drugs; in a survey of new molecular entities approved by the Food and Drug Administration between 2001 and 2004, 80% of the drugs operated by non-equilibrium mechanisms (Swinney, 2006).

1.10.4.3.1

Covalent binding

Covalent binding of an inhibitor to a target renders the binding event irreversible (Swinney, 2004; Ghosh et al., 2019; Singh et al., 2011) (Fig. 12). This means that no amount of elevation of the endogenous ligand concentration will overcome the block because the endogenous ligand is unable to displace the inhibitor. This improves the efficiency of the inhibitor relative to readily-reversible inhibitors in situations where the endogenous ligand concentration can increase (particularly for enzymes, where inhibition will increase the substrate concentration if a substrate elimination mechanism is not available, Swinney, 2004; Westley and Westley, 1996). New synthesis of the protein is required to overcome the block by covalent inhibitors and this synthesis rate is a key variable in models that describe the pharmacodynamics of such compounds. Numerous widelyused drugs bind covalently to their targets, including aspirin, a cyclooxygenase inhibitor; clopidogrel, a P2Y12 receptor antagonist; penicillin, which inhibits bacterial DD-transpeptidase; omeprazole, a gastric Hþ/Kþ ATPase inhibitor; and ibrutinib, a Bruton’s tyrosine kinase inhibitor (reviewed in Ghosh et al., 2019; Singh et al., 2011). Drug development of covalent ligands has been associated with toxicity risks, including irreversible binding to other targets and formation of immunoreactive ligandprotein complexes. However, the attractiveness of irreversible block for conditions where strong and long-lasting inhibition is desirable has stimulated a resurgence of interest in this modality, for example for kinase inhibitors in oncology indications (Abdeldayem et al., 2020).

1.10.4.3.2

Conformational change

Another mechanism that results in prolonged drug-target interaction is a conformational change to form a tightly bound drug-target complex (Figs. 12 and 21A) (Morrison and Walsh, 1988; Tummino and Copeland, 2008; Copeland, 2013b; Vauquelin, 2017). In this mechanism, the ligand first forms a reversible complex with the target (RL). The target-ligand complex then undergoes a conformational change to form a long-lived complex, R’L. It is assumed the ligand cannot dissociate from the R’L complex. Instead, for the complex to break down it must first transition back to the reversibly-bound state RL (defined by the rate constant k4 in Fig. 21A). When this transition is slow, the observed dissociation rate is k4, which becomes the rate limiting step in the dissociation pathway. This mechanism has been commonly observed and broadly applied to a class of enzyme inhibitors, the so called “Slow binding” inhibitors (Morrison and Walsh, 1988; Copeland, 2013b) (see (Morrison and Walsh, 1988) for a large survey of examples). The R’L complex can be exceptionally long-lived. For example, the k4 t½ for abiraterone interaction with CYP17A1 is 42 h (Cheong et al., 2020). This is longer than the elimination t½ of the drug (24 h) (Benoist et al., 2016). The stable R’L complex was incorporated into a PK/PD model that accounts for the prolonged androgen-lowering effect of the drug in man (Cheong et al., 2020).

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This mechanism has been identified for other target classes, for example GPCRs. The AT1 angiotensin receptor antagonist candesartan produces prolonged inhibition of this receptor via a two-step binding interaction (Takezako et al., 2004; Vauquelin et al., 2001). The k4 t½ for this interaction was 1.9 h (Vauquelin et al., 2001). The mechanism has also been invoked to explain the binding kinetics of muscarinic agonists and antagonists (Schreiber et al., 1985a; Kloog and Sokolovsky, 1978; Schreiber et al., 1985b).

1.10.4.3.3

Signal transduction

Agonist drugs activate receptors, resulting in signal transduction and gene expression, leading to changes of cellular behavior. The receptor classes mediating these effects are GPCRs, catalytic receptors (for example receptor tyrosine kinases), and nuclear receptors (Alexander et al., 2019a; Alexander et al., 2019b; Alexander et al., 2019c). The kinetics of signal transduction impacts drug effectiveness in vivo and the quantification of drug effect in vitro. The signal generation and cellular response does not continue indefinitelydrather, the responses become dampened over time to prevent overstimulation of the cell. This means that the response is usually only produced for a certain window of time. The regulation mechanisms that dampen the responses include receptor desensitization, dephosphorylation of proteins activated by upstream kinase enzymes, degradation or sequestration of second messenger molecules, and degradation of gene expression products, i.e., mRNA and proteins. These mechanisms act to prevent excessive signaling, either in the continuous presence of the ligand (“Fade”) or on repeated exposure (tachyphylaxis). The kinetics of response can impact the effectiveness of agonist drugs in vivo. In the simplest case, if the agonist strongly desensitizes the signaling response, it may lose effectiveness on repeated dosing. This can require the use of increasing concentrations of drug to maintain effectiveness (tolerance), the classic example being the opioid analgesics (Morgan and Christie, 2011). More complex mechanisms have recently emerged that have been correlated with drug responsiveness. GPCRs can produce prolonged signaling after being internalized into the cell (Calebiro et al., 2009; Ferrandon et al., 2009; Hothersall et al., 2016; Irannejad et al., 2013; Lobingier and Von Zastrow, 2019; Mullershausen et al., 2009). For the parathyroid hormone-1 receptor (PTH1 receptor), ligands that produce prolonged signaling, such as PTH(1–34), have been associated with a favorable therapeutic profile for the treatment of osteoporosis compared with ligands that signal only transiently, such as PTH-related protein(1–36) (Ferrandon et al., 2009; Tay et al., 2018). This mode of action is attracting considerable attention for numerous therapeutically-attractive GPCRs, including the b2 adrenoceptor (Irannejad et al., 2013), calcitonin gene-related peptide receptor (Yarwood et al., 2017), m-opioid receptor (Stoeber et al., 2018), sphingosine 1-phosphate S1P1 receptor (Mullershausen et al., 2009) and tachykinin NK1 receptor (Jensen et al., 2017). Of potential concern, in vitro measures of drug effect can also be affected by the signaling kinetic profile of the ligand. Specifically, the potency and efficacy (EC50 and Emax, respectively) in signaling assays can be dependent on the time at which the response is measured (Hoare et al., 2020b; Klein Herenbrink et al., 2016; Zhu et al., 2019). This was highlighted in a study of biased agonism of the D2 dopamine receptor (Klein Herenbrink et al., 2016). Biased agonism is the capacity of a ligand to selectively activate one or more signaling pathways relative to other pathways activated by the receptor (Kenakin, 2019). It was found that in some cases bias estimates were reversed if the signals were recorded at later time points (Klein Herenbrink et al., 2016). Presently, efforts are being made to interpret signaling pharmacology in kinetic terms (Hoare et al., 2020b; Zhu et al., 2019; Bridge et al., 2018; Finlay et al., 2020; Hoare et al., 2018; Hoare et al., 2020c; Zhao and Furness, 2019; Zhao et al., 2020). This involves recording the time course of the signaling response, which has become a lot more straightforward with the development of biosensors of signaling that enable continuous reading of the signal over time in live cells (Greenwald et al., 2018; Hoare and Hughes, 2021; Lohse et al., 2012; Namkung et al., 2018; Olsen et al., 2020; Tewson et al., 2016).

1.10.4.3.4

Target degradation and inactivation

The final nonequilibrium drug action considered here is degradation or inactivation of the target. A therapeutic modality of considerable current interest is targeted protein degradation by PROTAC compounds (proteolysis targeting chimera) (Bondeson and Crews, 2017; Sakamoto et al., 2001). These bifunctional small molecules bind to the target and also interact with E3 ubiquitin ligase. Within the ternary complex of small molecule, target and ligase, the target protein is ubiquitinated, which leads to target degradation by the proteasome. This leads to a reduction in the level of pre-existing target protein with the duration of drug effect determined by the synthesis rate of new protein molecules (Watt et al., 2019). This can result in the duration of effect extending beyond that anticipated from the elimination rate of the compound in vivo (Mares et al., 2020). An interesting question is how the binding kinetics of the PROTAC ligand can influence the kinetics of degradation. In a recent example it was demonstrated that the lifetime of the ternary complex was correlated with the initial rate of degradation (Roy et al., 2019). Another class of compounds that results in target degradation are the selective estrogen receptor downregulators (Bondeson and Crews, 2017; Shagufta et al., 2020). An alternative mechanism for GPCRs utilizes the receptor desensitization process, for example the gonadotropin releasing hormone (GnRH) receptor agonist leuprorelin. This ligand initially produces a flare of gonadotropin release owing to receptor activation but subsequently produces a prolonged suppression of release owing to desensitization of the signaling pathway (Plosker and Brogden, 1994).

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

1.10.5

Impact of binding kinetics on in vitro assays of drug effect

1.10.5.1

Introduction

245

In vitro assays are used to establish the structure-activity relationships used by medicinal chemists to optimize new therapeutics. These assays quantify the activity of the compound on the target, usually in terms of potency (the amount of compound required to half-maximally occupy, inhibit or modulate the target). The reliability of these assays to quantify the target-compound interaction is critically important in drug development. If the in vitro assay is distorted by artifacts it can result in stalled drug discovery programs. Binding kinetics is one of the factors that can impact in vitro assays of ligand-target interaction. A long residence time can greatly prolong the time it takes assays to attain equilibrium in an in vitro assay (necessary to accurately quantify binding affinity). In functional assays, slow dissociation can result in an unusual type of antagonism, where compounds reduce the maximal response of the agonist (insurmountable antagonism). If kinetics are not considered, these effects can distort structure activity relationships by providing inaccurate estimates of target-ligand interaction. By contrast, if kinetics are considered in assay design and data interpretation, potency can be accurately reported and valuable modalities of target modulation by the drug can be discovered.

1.10.5.2

Slow dissociation can result in underestimation of affinity

In medicinal chemistry campaigns, compounds are tested for activity on the target using high throughput target interaction assays. Compound potency is obtained from these assays, i.e., the concentration of compound required to produce a specified level of effect (usually 50%, e.g., Kd, IC50, EC50). Potency is the primary metric of compound activity, used to determine SAR, prioritize compounds for in vivo testing, and predict human dosing. Potency is usually assumed to be related to the affinity of drug-target interaction. Now we consider the pharmacology “Fine print.” Affinity is an equilibrium parameter. Formally it is the free energy of binding of ligand to target when the rate of the forward reaction (association) has reached the rate of the backward reaction (dissociation). It is this equilibrium free energy that underlies the thermodynamics of structure-activity relationships used by medicinal chemists to optimize new molecules. Equilibrium affinity is also the parameter used in PK/PD models used in human dosing prediction. Consequently, it is very important that the in vitro system used to quantify target interaction reliably reports the equilibrium potency/ affinity of the ligand. However, target interaction assays are not always at equilibrium and this can result in underestimation of affinity (Motulsky and Mahan, 1984; Aranyi, 1979; Aranyi, 1980; Hulme and Trevethick, 2010). It takes time for equilibrium to be closely approached. Rather surprisingly, the amount of time required to approach equilibrium is dependent on the dissociation rate constant. The slower the dissociation of target-ligand complex, the longer it takes for the assay to closely approach equilibrium (Motulsky and Mahan, 1984; Aranyi, 1980). (Formally equilibrium is never reached since it is an asymptote, requiring infinite time.) In a direct targetcompound binding assay, equilibrium is reasonably well approximated after three dissociation half-lives of the receptor ligand complex. By this time, occupancy will reach 97% of its equilibrium value. This means long incubation times are required to accurately report affinity of ligands that dissociate slowly. Sometimes routine assay incubation times of 1–2 h are insufficient and affinity is underestimated for such ligands (Hoare et al., 2020a; Sullivan et al., 2006). This concept is demonstrated in Fig. 13. Here target-ligand interaction is measured over time using a direct target-ligand binding assay, for a compound with a dissociation t½ of 4 h (Fig. 13A). This is done for a range of concentrations of ligand spanning the Kd (0.032–100 nM, Kd of 1 nM). At the 12 h time point (right-hand dashed line), equilibrium is closely approacheddthe binding curves for all concentrations of ligand closely approach the plateau. Now we examine an early time point, 30 min (left-hand dashed line in Fig. 13A). Here, at the highest concentration (blue line) the equilibrium occupancy plateau is still closely approached (because the observed association rate is proportional to the ligand concentration and at 100 nM we are at 100-fold excess over the Kd of 1 nM). However, at the lower concentrations the binding does not approach the plateau by 30 min. For example at the Kd concentration of 1 nM (pink line), occupancy is just 8%, rather than the equilibrium value of 50% (Fig. 13A). This demonstrates occupancy can be underestimated if the incubation time is not long enough. If the assay is incubated for 4 h, this effect is lessened but still persistsdthe equilibrium plateau is closely approached at 10, 32 and 100 nM, but binding is still underestimated at the Kd concentration (38% at 1 nM) (Fig. 13A). In Fig. 13B this effect is shown using the standard concentration-response saturation plot, as would be done in a routine SAR assay. This shows the effect of incubation time on affinity measurement. At 30 min, the curve is shifted to the right, which is a result of the underestimation of occupancy at lower concentrations (see above). Fitting these data to a sigmoid curve equation gives an apparent Kd of 8.6 nM. This means affinity of the compound is underestimated by almost an order of magnitude because the incubation time isn’t long enough. At 4 h (equal to the dissociation t½) the performance is better but the curve is still slightly rightshifted, with a fitted Kd of 1.5 nM. At 12 h, three times the dissociation t½, the assay accurately reports the affinity (fitted Kd of 1.0 nM). Now we extend this analysis to demonstrate how this underestimation of affinity can distort structure-activity relationships for a series of compounds with a range of residence times (dissociation t½ varying from 1.5 min to 48 h). This is shown in Fig. 14. In this example it is assumed residence time determines affinity, i.e., that the association rate constant is the same for all compounds (107 M 1 min 1). The true affinity of the compounds is shown by the saturation curves in Fig. 14A (Kd values ranging from 24 pM to 46 nM). Now suppose a rather short incubation time of 20 min is used for the assay (Fig. 14B). Under this condition, the assay is

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Fig. 13 The assay incubation time affects affinity estimation for a compound that dissociates slowly from the receptor. (A) Time course of targetligand binding at various concentrations of ligand, for a compound with a dissociation t½ of 4 h and affinity of 1 nM. The dashed lines indicate 30 min, 4 h and 12 h incubation times. At early times (e.g., 30 min), the degree of occupancy by intermediate concentrations (e.g., 1 nM) is less than that at later time points when equilibrium is being approached (e.g., 12 h). (B) Ligand binding saturation curves at different incubation times (data transposed from A). The reduced binding at an early time point at the lower concentrations results in a substantial underestimation of affinity (30 min, green symbols, fitted Kd 8.6 nM vs. true Kd of 1.0 nM). When the incubation time equals the dissociation t½ (4 h), affinity is reasonably well estimated (Kd 1.5 nM). At threefold the t1.2 (12 h), the incubation time is sufficient to accurately quantify affinity (fitted value 1.0 nM) and the binding curve overlies that for the equilibrium condition.

no longer detecting a difference of affinity of the compounds with the highest affinity (slowest dissociation). The curves for the high affinity ligands overlie one another (Fig. 14B). In other words, the assay reaches a floor, at approximately 3 nM. Beyond this, a compound might have a higher affinity but the assay is unable to detect it. Now we consider a longer incubation time of 2 h (Fig. 14C). At this time point, the assay is more reliable in that compounds can be discriminated down to a lower Kd, but the floor effect is still present, at approximately 0.5 nM. This effect is further illustrated in Fig. 14D by plotting the measured affinity versus the true affinity for a range of assay incubation times. The performance of the assay for discriminating the affinity of the compounds improves as the incubation time improves, evident by the increase of the plateau (the assay floor). This plot also demonstrates a practical issue. For very long residence times, impractically long incubation times for modern workflows and reagent stability are required to accurately measure affinity (e.g., 24 h). For such interactions, kinetic assays can be used to quantify affinity (Section 1.10.6.2.3).

1.10.5.3

Drug discovery implications of mis-estimation of affinity resulting from lack of equilibration

If ligand affinity is underestimated in the target binding/activity assay, the consequences for drug discovery can be profoundly negative. The worst-case scenario is that the mis-estimated affinity value is used in the prediction of human dosing and that the mis-estimation is only revealed by the results of human safety or efficacy testing. This is a particular concern if there is targetmediated toxicity potential at high target occupancy: The equilibration issue results in an underestimate of affinity, which would

Kinetics of Drug-Target Binding: A Guide for Drug Discovery Equilibrium

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Fig. 14 Effect of incubation time on precision of affinity estimation for a series of ligands with a range of residence time. Here a theoretical series of ligands is considered in which the affinity is determined by the residence time; the dissociation t½ and Kd varies between the ligands (values shown on figure) whereas kon is the same for all ligands (at 107 M 1 min 1). (A) Binding saturation curves at equilibrium, demonstrating the true range of affinity of the ligand series. (B) Binding curves for a 20 min assay incubation. Note the curves for the highest affinity ligands are rightshifted, indicating a lower measured affinity. The assay hits a floor at a measured Kd of approximately 3 nM, indicated by the overlying curves for the highest affinity ligands. (C) Binding curves at 2 h. Note the curves for the highest affinity ligands are still right-shifted but the floor is lower at an apparent Kd of approximately 0.5 nM. (D) Apparent affinity versus true affinity. Fitted apparent Kd values at four incubation times are plotted against the true affinity. Note the assay performance improves as the incubation time increases. For ligands with the slowest dissociation/highest affinity, long incubation times are required (24 h) to accurately measure affinity, which might not be practical. Measuring affinity kinetically offers an alternative approach requiring shorter incubation times (see Section 1.10.6.2.3).

translate to excessive occupancy of the target in man if the underestimate was used in the human dosing prediction. Such excessive occupancy would likely be noticed in preclinical animal models if the compound affinity for the human target and preclinical species orthologues are the same. However, it is conceivable that species selectivity issues could hide a higher true affinity solely for the human receptor. Underestimation of affinity can also impact interpretation of preclinical animal model resultsda series of compounds can have the same apparent affinity in the target binding/activity assay but display widely divergent activity in vivo. This can result if the true affinity of some of the compounds is higher but the higher affinity is masked by an assay floor owing to lack of equilibration. This is demonstrated by the case study below in Section 1.10.5.4. Clearly mis-estimation of affinity will impact SAR detection and interpretation. The affinity floor resulting from lack of equilibration can result in the highest affinity ligands being unrealized. This means chemical groups on the ligand beneficial for target engagement might not be incorporated into preclinical/clinical candidate molecules. In addition, the highest-affinity molecules might not be tested in the animal model if they are indistinguishable from lower-affinity compounds, meaning highly active molecules in vivo can be missed. This lack of detection of higher potency is a potentially missed opportunity for improving the therapeutic window. The assay floor also impacts assessment of target selectivity. It is conceivable that the apparent affinity for the target

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protein and undesired proteins is the same, but that the true affinity for the target protein is higher and that this difference is hidden by lack of equilibration in the target affinity assay. This issue is a concern for protein kinase inhibitors, where selectivity is an issue and the residence time can be long (Georgi et al., 2018). It is also possible that SAR breakthroughs during early phase SAR could go unnoticed: An affinity floor of 10 nM resulting from slow equilibration could be erroneously interpreted as “Flat” SAR, a false physicochemical limit of affinity for the target. In the worst-case scenario, undetected selectivity and SAR breakthroughs could result in fruitful series and targets being abandoned.

1.10.5.4

Case study of mis-estimation of affinity owing to lack of equilibrationdCRF1 receptor antagonist development

A practical example of how binding kinetics impacts SAR assays in drug discovery is provided by a recent case-study. This study described the consequences of lack of equilibration, how it was discovered, and how properly accommodating binding kinetics resulted in the development of a clinically-effective molecule (Hoare et al., 2020a). The target was the corticotropin releasing factor type 1 receptor (CRF1 receptor), a GPCR. Antagonists for this receptor were being developed by numerous groups for the treatment of neuropsychiatric and endocrine disorders, based on the role of CRF as a primary behavioral and endocrine regulator of the stress response (Gilligan et al., 2000; Grigoriadis, 2005; Kehne and De Lombaert, 2002). In the case study, an effective molecule had been identified (NBI 30775) but was discontinued for toxicological reasons. An SAR campaign was in progress to identify new active molecules. The efficacy of NBI 30775 was evident in preclinical and clinical endpoint and biomarker measurements (Chen et al., 2004; Gutman et al., 2003; Heinrichs et al., 2002; Held et al., 2004; Zobel et al., 2000). Fig. 15 shows the activity of the compound in the animal model used to test in vivo efficacy, inhibition of adrenocorticotropin (ACTH) release in rats. (CRF released from the hypothalamus acts on pituitary corticotropes to stimulate ACTH production and release into the circulation.) NBI 30775 produced a large and sustained inhibition of ACTH release in the model (Fleck et al., 2012; Schwandt et al., 2016); 2 h after dosing, ACTH was reduced by 91% at the highest dose tested (30 mg/kg) and inhibition was sustained for at least 6 h (Fig. 15). The dose response was quantified using the AUC of the time course from 0 to 4 h. From this, the % ACTH inhibition was calculated, then this was plotted against the AUC of the compound concentration in the plasma (Fig. 16). New compounds were being synthesized and tested. These compounds were tested in an in vitro competition binding assay to measure affinity for the CRF1 receptor. The conditions were typical for a binding assay used in drug discoverydthe assay was incubated for 90 min at room temperature (see (Fleck et al., 2012) for details). Five new compounds were selected for in vivo testing in the rat ACTH assay based on the Ki in the binding assay. The measured in vitro Ki for all compounds was similar to that of NBI 30775 (10 nM, see Fig. 16 for new compound Ki values). The new compounds had improved pharmacokinetic properties compared with NBI 30775, for example higher plasma exposures (Hoare et al., 2020a). However, when tested in vivo, the compounds were much less active than NBI 30775 despite the higher plasma concentrations in the animals (Fig. 16). Numerous factors were tested to see if they could account for the lack of effect, including protein binding and species selectivity, but none of the factors could explain it. The disconnect between in vitro and in vivo activity of the compounds was a considerable problem for the project because there was no way to predict the in vivo activity of the molecules using in vitro assays.

Vehicle NBI 30775

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Fig. 15 CRF1 antagonist in vivo assay, showing inhibition of plasma ACTH release by NBI 30775. CRF released from the hypothalamus activates the CRF1 receptor on pituitary cells resulting in the production of ACTH and its release into the circulation (see Ref. (Fleck et al., 2012) for method details). NBI 30775 (30 mg/kg, oral dosing) produced a strong and sustained reduction of ACTH in the model. Data are from Fig. 2A of Schwandt ML, Cortes CR, Kwako LE, George DT, Momenan R, Sinha R, Grigoriadis DE, Pich EM, Leggio L, Heilig M (2016) The CRF1 antagonist Verucerfont in anxious alcohol-dependent women: Translation of neuroendocrine, but not of anti-craving effects. Neuropsychopharmacology, 41: 2818–2829.

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

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Fig. 16 In vivo activity and in vitro Ki of initial compounds in a CRF1 antagonist backup campaign. The in vivo activity was inhibition of ACTH release in rats (see Fig. 15). Here the AUC for ACTH release inhibition over 0–4 h was plotted against the AUC of the plasma compound concentration. Note the new compounds (Compounds 1–5) were less active in vivo than the original lead NBI 30775 despite higher exposure and similar measured affinity in vitro (Ki). Affinity was measured using typical conditions (90 min incubation at room temperature). It was subsequently discovered NBI 30775 was not close to equilibrium under these conditions owing to a long residence time and that the true affinity of the compounds differed by more than two orders of magnitude (see Fig. 17A). The dashed line is a sigmoid curve fit to the NBI 30775 in vivo data. Data are from Hoare SRJ, Fleck BA, Williams JP, Grigoriadis DE (2020a) The importance of target binding kinetics for measuring target binding affinity in drug discovery: A case study from a CRF1 receptor antagonist program. Drug Discovery Today, 25: 7–14.

At this time the target binding properties of the antagonists were being evaluated using radiolabeled analogues of the compounds. 3H-NBI 30775 had been synthesized, and as part of the standard characterization of radioligand binding a dissociation assay was conducted. This assay revealed that dissociation of 3H-NBI 30775 was remarkably slow (Fleck et al., 2012). At room temperature, the temperature used to quantify affinity of the compounds, the dissociation t½ was more than 5 h. In fact, only 10% of the radioligand had dissociated by the 5 h time point (Fleck et al., 2012). From this value, an extrapolated dissociation t½ of 33 h can be calculated. This is an unusually slow dissociation event for a GPCR ligand (compare with values in Table 1). The implication of this result was that the assay used to quantify NBI 30775 affinity was very far from equilibrium, the incubation time of 90 min being far less than the calculated dissociation t½ of 33 h. This meant that the affinity of NBI 30775 was likely underestimated substantially, which could potentially explain the in vivo results. If the true affinity of NBI 30775 was much higher, this could explain why it was more potent in vivo than the new compounds. In order to test this hypothesis it was necessary to develop methods to accurately quantify the affinity, methods that could be accommodated within the daily workflow of a drug discovery project. This is very challenging when the residence time is so long. Fortunately, the problem was made more tractable by increasing the assay temperature. At 37  C, dissociation was faster, the dissociation t½ of 3H-NBI 30775 reduced to 3.2 h (Fleck et al., 2012). However, this was still too long to be accommodated by a standard equilibrium binding assaydto closely approach equilibrium and so accurately measure affinity an incubation time of three times the dissociation t½ is needed, which translated to an incubation time of 9.6 h. To solve this problem, the affinity was measured kinetically. As described in Section 1.10.2.3, affinity is related to the binding kinetics by the equation Kd/i ¼ koff/kon. A method was developed to measure koff and kon of the new compounds using the competition kinetics approach (Motulsky and Mahan, 1984; Aranyi, 1980) (as described in Section 1.10.6.2.3). Unlabeled test compound was competed against 3H-NBI 30775 at 37  C at multiple time points, and data fit to an equation to determine koff and kon of the unlabeled compound (Fleck et al., 2012). These koff and kon values were then used to calculate the Ki, which was termed “Kinetic Ki.” These experiments demonstrated that the affinity of NBI 30775 was much higher than originally estimated under the nonequilibrium conditions of the binding assay. The kinetic Ki was 0.76 nM, compared with 10 nM under the nonequilibrium conditions (Hoare et al., 2020a; Fleck et al., 2012). They also demonstrated that the affinity of the new compounds was much lower than that of NBI 30775, ranging from 18 to 140 nM (Hoare et al., 2020a). This dramatic difference between the initial non-equilibrium Ki results and the true Ki from the kinetic assay is illustrated in Fig. 17A. This result provided a simple explanation for why the new compounds were less active in vivo – they simply bound with a lower affinity. (The experiments also demonstrated a lower affinity of some compounds at 37  C compared with room temperature, potentially a consequence of the markedly accelerated dissociation at 37  C.) We extended this comparison to literature standards (Fig. 17B) (Fleck et al., 2012). Eleven compounds were tested, compounds advanced to clinical or late preclinical testing that had been assumed to bind with similar affinity using the conventional in vitro binding assay. The measured affinity under non-equilibrium conditions in a side-by-side comparison varied by ninefold (Fig. 17B). However, the kinetic assay revealed the true affinity range was much broader, at 500-fold (Fig. 17B) (Fleck et al., 2012). Thus the kinetic affinity assay revealed previously unappreciated differences between compounds that are used extensively to examine CRF1 receptor physiology and pharmacology. Now that the problems of the in vitro/in vivo potency disconnect and of measuring affinity accurately had been solved, attention was turned to identifying compounds which were as active as NBI 30775 in the in vivo model. For this purpose, the kinetic

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Kinetics of Drug-Target Binding: A Guide for Drug Discovery

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Fig. 17 Comparison of apparent affinity under non-equilibrium conditions and true affinity measured kinetically of CRF1 receptor antagonists. (A) Initial leads from CRF1 antagonist backup campaign. The data in the left column show the similar apparent affinity from the assay used initially to determine affinity (90 min incubation at room temperature), conditions that were not close to equilibrium owing to slow compound dissociation. The right column shows the true affinity of the compounds determined in a kinetic binding assaydkon and koff were measured then Ki calculated as koff/ kon. Note the kinetic assay was run at 37  C, which resulted in a reduction of affinity for some compounds. (B) Literature standards. In the initial non-equilibrium assay (left column) the affinity of the compounds differed by ninefold. However, the kinetic assay revealed the true affinity differed by 500-fold (right column). (A) Reproduced with permission from Fig. 4 in Hoare SRJ, Fleck BA, Williams JP, Grigoriadis DE (2020a). The importance of target binding kinetics for measuring target binding affinity in drug discovery: A case study from a CRF1 receptor antagonist program. Drug Discovery Today, 25: 7–14. Elsevier Ltd. 2019. Data in (B) are originally from Fleck BA, Hoare SR, Pick RR, Bradbury MJ, Grigoriadis DE (2012) Binding kinetics redefine the antagonist pharmacology of the corticotropin-releasing factor type 1 receptor. The Journal of Pharmacology and Experimental Therapeutics, 341: 518–531.

competition assay was used to quantify koff, kon and the kinetic Ki of the compounds. This provided a convenient in vitro assay for triaging compounds to test in vivo. Compounds were identified that bound with kinetic Ki in the range of that for NBI 30775 and that dissociated slowly from the receptor. These compounds were tested in the in vivo rat ACTH model and numerous molecules were identified that were similarly potent to NBI 30775 for suppressing ACTH. One of these compounds, verucerfont (NBI 77860) (Tellew et al., 2010), was advanced to clinical testing. This compound, with a kinetic Ki of 15 nM and dissociation t½ of 1 h (Hoare et al., 2020a), reduced ACTH in healthy human volunteers (Schwandt et al., 2016), and reduced ACTH and downstream hormones in patients with an endocrine disorder, congenital adrenal hyperplasia (Turcu et al., 2016). This case study demonstrates how knowledge of binding kinetics can impact drug discovery. A problemdinability to predict in vivo efficacydwas solved by discovering that compounds dissociated slowly from the target, indicating the original assay was underestimating affinity owing to lack of equilibration. Incorporating kinetics into the drug discovery cascade by measuring the binding rates and the kinetic Ki enabled good prediction of in vivo efficacy. This resulted in the identification of highly-active new molecules, one of which demonstrated efficacy in a human disease population.

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

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Fig. 18 Surmountable and insurmountable antagonism. Antagonists of receptor signaling can display different modes of inhibition depending on the dissociation rate. In the experiment, the effect of antagonist on the concentration-response of the receptor agonist is determined. (A) Rapidlydissociating compounds (dissociation t½ of 10 s in this case) produce a rightward shift of the concentration response (i.e., an increase of agonist EC50) without affecting the maximal response to the agonist (Emax). This means that the antagonist effect can always be overcome by increasing the concentration of the agonist. This is called surmountable inhibition. (B) By contrast, an antagonist that dissociates slowly from the target, relative to the duration of assay, produces a reduction of the maximal response (as well as a rightward shift of the curve). The dissociation t½ in this case is 2 h, with an assay incubation time of 20 min. This reduction of maximal response means that increasing the agonist concentration cannot overcome block by the antagonist. This is called insurmountable inhibition and is potentially relevant in vivo when the agonist concentration fluctuates (Section 1.10.4.2, Fig. 11). Data were simulated using Eq. 7 (Eq. 1 of Kenakin et al., 2006) with the following parameter values: KA, 3.16 nM; KB, 1 nM; koff, 4.16 min 1 (A) and 0.00578 min 1 (B); incubation time, 20 min; s, 3; Em, 133 response units.

1.10.5.5

Insurmountable antagonismdReduction of maximal response in signaling assays

For receptor signaling systems, slow dissociation of antagonist drugs can be manifest in the classical experiment used to quantify the antagonist effect (Arunlakshana and Schild, 1959; Clark, 1926a; Gaddum, 1926). In this matrix experiment (Fig. 18), the signaling response is measured for multiple concentrations of agonist to generate an agonist concentration-response curve (CRC). The CRC is determined in the absence of antagonist and in the presence of multiple concentrations of antagonist (Fig. 18). The antagonist shifts the agonist CRC and EC50 to the right, i.e., to higher concentrations, owing to mass-action competitiondmore agonist is required to overcome block by the antagonist. Historically, this experiment has provided a wealth of information on the identification and classification of receptors (Kenakin, 1997b; Kenakin, 1997c). It has also provided insights into the mechanisms of receptor-ligand interaction (Kenakin, 1997a), including binding kinetics of competitive antagonists (Paton and Rang, 1966; Kenakin et al., 2006; Paton and Waud, 1967). The experiment also provides an estimate of the maximal effect of the agonist (i.e., the Emax) and it is in this parameter that binding kinetics can be manifest. Specifically, the speed of ligand dissociation determines whether the antagonist affects the Emax. Antagonists that dissociate rapidly from the receptor, relative to the duration of the response measurement, do not affect the Emax. This makes sense because the agonist can outcompete the antagonist if added in sufficient excess owing to mass-action

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competition. Every time the antagonist dissociates it will be replaced by the agonist if the latter is in sufficient excess. Since dissociation is rapid, the entire population of receptors will become free and so available to the agonist. In practical terms, this means the effect of the antagonist can always be overcome by increasing the concentration of the agonist. This type of inhibition is described as surmountable antagonism (Kenakin et al., 2006; Vauquelin et al., 2002; Gaddum et al., 1955). By contrast, if the antagonist dissociates slowly from the receptor, relative to the duration of the response measurement, it can reduce the maximal response to the agonist. This behavior is called insurmountable antagonism (Kenakin et al., 2006; Vauquelin et al., 2002; Gaddum et al., 1955). In practical terms, no amount of increase of the agonist can overcome block by the antagonist. This property potentially increases the magnitude of the drug effect of the antagonist, relative to a surmountable antagonist, when the receptor is exposed briefly to the agonist. The reduction of Emax makes sense because a fraction of the receptors will not become available to the agonist over the duration of the response measurement owing to slow dissociation of the antagonist. Since this fraction does not become free to bind the agonist, no amount of increase of agonist concentration can overcome block by the antagonist. This concept enables insurmountability to be used as a metric to quantify the dissociation rate of antagonists, as elaborated in Section 1.10.7.2. (Note this section includes description of how to discriminate slow dissociation from allosteric inhibition, which can also reduce the agonist Emax Kenakin et al., 2006; Vauquelin et al., 2002). The timing of the signaling assay is a key determinant regarding the detection of insurmountable antagonism (Kenakin et al., 2006; Guo et al., 2014; Christopoulos et al., 1999). First, the receptor must be pre-incubated with the antagonist prior to application of the agonist for insurmountable antagonism to be apparent (see for example Fierens et al., 1999). Only if the receptors are pre-blocked with antagonist can the fraction of receptors continuously bound by antagonist be produced. If instead receptor is exposed simultaneously to agonist and antagonist, the whole population of receptors is available to the agonist, which can then fully outcompete the antagonist by mass-action competition. The second factor is the duration of the response measurement. In principle, insurmountable antagonism resulting from slow dissociation can be overcome simply by increasing the duration of agonist application (Kenakin et al., 2006). Given sufficient time, all the antagonist-receptor complexes will dissociate and so all the receptors will become accessible to the agonist. As a result, the inhibition will become surmountable. In practical terms this means the shorter the response measurement, the more sensitive the assay is for detecting insurmountable inhibition. This is demonstrated for Gq-coupled GPCRs. Mobilization of intracellular calcium ions is often quantified within a minute or two of agonist application. Consequently, this response is known to be particularly sensitive for the detection of insurmountable antagonism (Christopoulos et al., 1999; Charlton and Vauquelin, 2010). Longerduration responses of the Gq pathway can be measured, for example the accumulation of inositol phosphates over a period of tens of minutes. In this assay, certain compounds display surmountable antagonism that are insurmountable in the Ca2þ mobilization assay (Bdioui et al., 2018). Given that insurmountable antagonism can be overcome with sufficient time, the in vivo effectiveness of this mechanism has been questioned. If the duration of the drug effect in vivo is many hours, insurmountable antagonism detected in vitro over minutes might not be relevant to the therapeutic effectiveness of the drug. Here a critical determinant could be the dynamics of the agonist concentration in vivo, i.e., how the agonist concentration changes over time (Section 1.10.4.2). If the agonist is present continuously in vivo, then the insurmountable antagonism detected in vitro over minutes likely will not translate to improved efficacy in vivo over hours. However, if the agonist concentration changes over time, e.g., due to pulsatile release, then insurmountable antagonism detected in vitro could translate to improved efficacy, if the duration of the agonist pulse is similar to the duration of the in vitro assay. This was demonstrated by simulation in Section 1.10.4.2 and Fig. 11 and in Vauquelin and Van Liefde (2006). (Here improved efficacy means increased in vivo occupancy of the slowly-dissociating antagonist relative to a rapidlydissociating antagonist of the same affinity.)

1.10.6

Measuring receptor-ligand binding kinetics

In order to assess and apply binding kinetics to drug discovery, assays and data analysis methods are required that are of sufficient throughput and precision. Fortunately, numerous methods are available owing to technological advances in measuring target binding and activity, and the derivation of equations to analyze time course data. Two general approaches are considered here. First, detection of slow dissociation is described. Given the potential impact of slow dissociation on drug discovery, from quantifying affinity to predicting human dosing, methods to quickly identify whether slow dissociation is an issue in a medicinal chemistry campaign are desirable. These methods are semi-quantitative and are relatively rapid to implement, requiring only slight adaptations of the routine SAR assays used to quantify potency. These approaches include running assays at two different time points (e.g., the Ki shift assay) and testing for functional insurmountability of receptor antagonists. The second approach is to implement assays to measure kon and koff. The methods applied depend on the type of assay available, for example direct target-ligand binding assays, competition ligand binding assays, and target function assays. Assays suitable for kinetic studies have been thoroughly reviewed extensively elsewhere (Schuetz et al., 2017; Sykes et al., 2019b; Vauquelin et al., 2015; Zhang et al., 2016; Holdgate and Phillips, 2020; Georgi et al., 2017) and so will be introduced briefly here. Data analysis methods are required for estimating the rate constants of target-ligand interaction from kinetic assay data (i.e., kon and koff). This typically involves nonlinear regression curve fitting of time course data to kinetic equations. The analysis routines are understandable to investigators familiar with curve fitting of pharmacological data, such as concentration-response datadthe software used is the same (e.g., GraphPad Prism) and the equations are of the same mathematical format. However, a learning curve is

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required to understand the limitations of the analyses, the reliability of the fitted parameters, and how to troubleshoot failed fits. In addition, analysis methods that are often unknown to the pharmacologist are available for more complex binding interactions and for nonstandard assay designs. Here the data analysis methods for quantifying kinetics will be presented in detail. Simulators are provided in the Annex to enable investigators to see how the binding rates are manifest in binding data.

1.10.6.1

Detecting slow dissociation

Methods for detecting slow dissociation take advantage of the assay artifacts this property introducesdthe time-dependence of measured potency values (Section 1.10.5.2), and insurmountable antagonism for receptor antagonists (i.e., the reduction of agonist maximal response, Section 1.10.5.5). The potency shift assay reports the difference of measured ligand affinity, for example the Ki in a competition binding assay, at two time points. The assay is run at an early time point and a much later time point. If the measured Ki value at the later time point is less than at the earlier time point, this can indicate a slow rate of ligand dissociation from the target (see Fig. 13B for simulated example). This assay was used for the GnRH receptor to identify slowly-dissociating compounds (Fig. 19) (Heise et al., 2007). Test compounds were competed against radiolabeled GnRH and the apparent Ki determined at 30 min and 10 h. Slowlydissociating compounds display a rightward shift of the curve at 10 h, relative to 30 min, indicating a lower Ki (i.e., a higher apparent affinity). Seventy eight compounds were screened using this method. Nine compounds were found to display a Ki (30 min)/Ki (10 h) ratio of greater than 6, suggesting slow dissociation. Five of these were tested in a kinetic binding assay to determine koff and all five dissociated slowly (t½ > 1 h). Seven of the compounds with a low Ki (30 min)/Ki (10 h) ratio of < 6 were also tested and six of these dissociated more rapidly from the receptor (koff t½ < 1 h) (Heise et al., 2007). This approach can also be used for enzyme inhibitorsdenzyme is preincubated for various times with inhibitor, and a time dependent increase in activity of the inhibitor can indicate slow dissociation (Copeland, 2013b). A simple approach useful for high-throughput screening for identifying slowly dissociating compounds is the kinetic rate index (KRI) method (Guo et al., 2013). Here, compound is competed against a tracer in a competition binding assay and binding measured at two time points. The first time point is when the association curve for the tracer in the absence of compound begins to approach equilibrium (for example, reaching 90% of equilibrium occupancy). The second time point is when the tracer association has plateaued for a considerable period, ideally taking a time point as late as possible within workflow and assay stability limits. The KRI method relies on an unusual feature of the tracer association curve in the presence of a slowly-dissociating inhibitor: the curve displays an overshoot phenomenon (see Fig. 20A red line for example)dit rises to a peak then falls to a plateau that is lower than the peak. For such a compound, tracer binding at the first time point will be higher than tracer binding at the later time point (see Fig. 1 of Guo et al., 2013), giving a KRI value greater than 1. By contrast, in the presence of a rapidly-dissociating compound, binding of tracer ligand is lower at the first time point than at the second, later time point (Fig. 20E), giving a KRI value less than 1. This approach was used to rapidly screen 35 ligands for the A1 adenosine receptor, identifying 7 compounds with a slow dissociation rate (Guo et al., 2013). Insurmountable antagonism (Section 1.10.5.5) can also be used to identify slowly-dissociating ligands (Sum et al., 2004). This method for receptor antagonists has the benefit of not requiring a binding assay to be developed for the target. Consequently, it is useful for early-stage drug discovery projects on receptors, where a signaling assay is usually the first assay to be developed. Signaling is measured over a concentration-response curve for the agonist and this is done in the absence and presence of the antagonist test compound (Fig. 18). Slowly-dissociating antagonists reduce the maximal response to the agonist, i.e., the agonist Emax, whereas rapidly-dissociating antagonists do not (assuming the antagonists competitively inhibit agonist bindingdsee below) (Fig. 18). In setting up the assay, it is essential that the receptor be pre-incubated with antagonist compound before application of the agonist (see for example Fierens et al., 1999). Also, the shorter the agonist incubation with the receptor the more sensitive the method is, i.e., the larger the reduction of agonist Emax (Kenakin et al., 2006). For this reason, rapidly-generated responses such as Ca2þ mobilization are ideal (Christopoulos et al., 1999; Charlton and Vauquelin, 2010; Bdioui et al., 2018). Examples where the insurmountability method has been used to identify slow dissociation of antagonists include the AT1 angiotensin receptor (Morsing et al., 1999; Vanderheyden et al., 1999), CRF1 receptor (Ramsey et al., 2011), endothelin receptors (Gatfield et al., 2012), muscarinic receptors (Christopoulos et al., 1999; Naline et al., 2018; Riddy et al., 2015), and the OX2 orexin receptor (Mould et al., 2014). The insurmountability data can be used to determine the koff value by applying an equation (Kenakin et al., 2006; Riddy et al., 2015; Mould et al., 2014), as described in Section 1.10.7.2.

1.10.6.2

Measuring receptor binding kinetics using binding assays

The simplest way to quantify binding kinetics is to use a target binding assay. Here interaction of the compounds of interest with the target is measured either directly, recording a binding signal upon target-ligand interaction, or indirectly, by measuring inhibition of tracer ligand binding. Equations are available for curve fitting to quantify kon and koff for both the direct and indirect modalities, assuming a simple one-site, one-step binding interaction. More complicated binding mechanisms can also be quantified using binding assays, including the two-step post-binding conformational change model. In this section, the binding assay technologies preferred for measuring kinetics are presented (Section 1.10.6.2.1). Next, the data analysis methods for quantifying kon and koff are shown, for direct and indirect binding assays (Sections 1.10.6.2.2 and 1.10.6.2.3). Finally, methods for quantifying more complex binding mechanisms are presented (Section 1.10.6.2.4).

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Fig. 19 Ki shift assay for detecting slow ligand dissociation. Competitive displacement of [125I]-His5, D-Tyr6 GnRH binding to the GnRH receptor was measured using the scintillation proximity assay format as described in (Heise et al., 2007). (A and B) Dose-response curves of 2 compounds measured at 30 min (n) and 10 h (B). (C) Ratio of the Ki value determined at 30 min divided by the Ki value determined at 10 h for all 78 compounds tested. Ki values were calculated from dose-response curves using the Cheng-Prusoff equation. Each assay was performed in singlet and normalized to 0% and 100% specific [125I]-His5, D-Tyr6 GnRH binding. Reproduced with permission from Fig. 1 in Heise CE, Sullivan SK, Crowe PD (2007) Scintillation proximity assay as a high-throughput method to identify slowly dissociating nonpeptide ligand binding to the GnRH receptor. Journal of Biomolecular Screening, 12: 235–239. SAGE Publishing, 2007.

1.10.6.2.1

Preferred assay modalities for measuring receptor binding kinetics

Recent articles have thoroughly reviewed binding assay technologies and their suitability for kinetic measurements (Schuetz et al., 2017; Sykes et al., 2019b; Vauquelin et al., 2015; Zhang et al., 2016; Holdgate and Phillips, 2020; Georgi et al., 2017). In principle, any assay that quantifies the target interaction of the ligand can be adapted to investigate binding kinetics. The only requirement is

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107 M 1 min 1; k2, 0.0231 min 1; k3, 9.24
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to run the assay at multiple time points. Up until recent times this was laborious for many assaysdeach time point required a separate assay sample, so running an assay at 10 ten points, for example, required 10 times as much reagent and manual processing time. For example, a radioligand binding rapid filtration assay usually requires a separate tube or plate for each time point. This inefficiency contributed to a lack of engagement in evaluating binding and signaling kinetics, particularly for certain target classes, such as GPCRs. In the last 20 years or so, new assay modalities have emerged that solve this problem. Using optical or other physical readouts, the same assay sample is measured repeatedly over time. For example, binding of a fluorescent ligand to a target is detected by a change of fluorescence in the assay vessel, and the time-dependence can be recorded by simply placing the vessel in a suitable reader and measuring the fluorescence change repeatedly over time. This approach is referred to as “Continuous read” or “Real time” detection. Such technologies allow a time course assay to be run for the same cost in reagents and consumables as a single time point assay. One of the most useful technologies for measuring binding kinetics is surface plasmon resonance (SPR), introduced for routine application using the Biacore platform (Holdgate and Phillips, 2020; Myszka et al., 1998; O’Shannessy et al., 1994; Rich and Myszka, 2011). This approach directly detects interaction of unlabeled ligand with target. Target is immobilized on a surface and the ligand perfused across it. When ligand binds to the target it causes a change in refractive index that is detected by a specialized reader. The SPR signal is recorded continuously. Ligand association is measured by continuous perfusion of a constant concentration of ligand. Dissociation is then simply determined by stopping perfusion of the ligand and instead perfusing assay buffer across the target. In this way association and dissociation can be quantified using the same sample. Data are fitted to association and dissociation exponential equations, often modified to incorporate the ligand transport processes of diffusion and flow, to determine kon, koff and Kd (Myszka et al., 1998; O’Shannessy et al., 1994). SPR is the method of choice for systems in which purified target is available and large changes of molecular weight occur on ligand binding, for example for profiling of therapeutic antibody binding (Malmqvist, 1993). Other methods that directly detect unlabeled compound-target binding include calorimetry, in which the binding energy is quantified directly as a change of heat output (Holdgate and Phillips, 2020; Freyer and Lewis, 2008). Often direct measurement of unlabeled ligand binding is technically or logistically challenging, since these methods usually require a purified and well-behaved target sample. As an alternative, binding can be detected using a labeled ligand. This ligand can either be the compound of interest or, more usually, is a suitable tracer against which test compounds are competed. Recently, fluorescent ligands have become the tracer ligands of choice for kinetic studies (Sykes et al., 2019b; Georgi et al., 2017; Robers et al., 2019; Soave et al., 2019). Interaction of fluorescent ligand with target results in an optical signal, which can be detected in plate readers. This enables high throughput (through the use of microtiter plates) and a high efficiency workflow (continuous reading of the binding interaction in the same plate). The ligand is modified to incorporate a fluorescent moiety and binding is detected using two methods. In the first, binding is detected directly as a change in fluorescence of the fluorophore, such as emission intensity or polarization. In the second, which is becoming increasingly popular, the target is modified to incorporate a fluorescence donor and binding detected by resonance energy transfer (RET) between the ligand fluorescence acceptor and the target donor. Donors include chemical groups, such as terbium cryptate (Schiele et al., 2015a; Zwier et al., 2010), and luminescent proteins such as NanoLuc (Robers et al., 2019; England et al., 2016). RET methods offer very high signal-to-noise ratios and lack of interference from intrinsically fluorescent ligands in competition binding assays. In addition, chemical donors offer a very high quantum yield that enables exceptionally short read times and so a large number of reads to be collected. This high read frequency can be important to improve the precision of rate constant estimates (Georgi et al., 2019; Sykes et al., 2019a). Radioactive ligands were the first tracer ligands to be developed, in the early 1970s (Cuatrecasas, 1971; Pert and Snyder, 1973; Rodbell et al., 1971). The archetypal radioligand binding assay employs filtration to harvest the target-bound ligand (Auld et al., 2004), which is inefficient and laborious for kinetic studies requiring multiple time points. However, kinetic measurements were facilitated by an advance to this approach, the scintillation proximity assay, where target is immobilized on a bead and radioligand binding detected by proximity to scintillation fluid in the bead (Auld et al., 2004; Hart and Greenwald, 1979; Xia et al., 2016). The assay vessel containing the beads is placed in a scintillation counter and the signal read repeatedly over time. This approach can suffer from technical limitations including long read times (which reduces the read frequency), and the concentrating of targets on the bead that can lead to rebinding and consequent distortion of rate constant estimates (see Section 1.10.4.1).

1.10.6.2.2

Quantifying kon and koff from a direct ligand binding assay

If binding is measured directly, quantifying kon and koff is reasonably straightforward for a simple single-site, single-step interaction (Fig. 1). The experiment, the data analysis and troubleshooting are discussed in depth in Hulme and Trevethick (2010) and Hoare (2021). In the association experiment, ligand and receptor are combined and the time course of the target-ligand complex concentration determined. The curve shape is an association exponential, as shown in Section 1.10.2.2dbinding increases rapidly at first, then slows down, then approaches a plateau which defines the equilibrium level of binding (Fig. 4A). Sufficient data points should be included to define the curve shape; in particular, the time course should extend long enough to properly define the plateau. After subtraction of background/nonspecific binding signals, the amount of target-ligand complex is plotted against time, and the data are analyzed using the association exponential equation introduced in Section 1.10.2.2 (Eq. 3):
 ½RL ¼ ½RLeq 1  ekonðobsÞ :t

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This equation is available in commonly-used curve fitting software programs, including Prism from GraphPad Software, Inc., XLfit from ID Business Solutions Ltd., and SigmaPlot from Systat Software, Inc. The analysis provides fitted values of the equilibrium level of binding, [RL]eq (i.e., the plateau) and the observed association rate constant, kon(obs). For guidance on how to analyze association data using Prism, see Hoare (2021). A simulator is provided (Data 7 in the online version at https://doi.org/10.1016/B9780-12-820472-6.00011-6) in the online Annex, “Kinetic binding assay simulation”. In order to quantify kon, the association curve is measured for multiple concentrations of ligand (Fig. 4A). Analysis of each curve using Eq. 2 yields kon(obs) values for each concentration of ligand. The kon(obs) value is then plotted versus the ligand concentration (Fig. 4B). The resulting plot is a straight line for a single-site, single step interaction. (If the plot is not a straight line, this can indicate a more complex binding interactiondsee Section 1.10.6.2.4). The kon value is then determined by linear regression – kon is the slope of the line (Fig. 4B). koff is measured in a ligand dissociation assay. In this experiment, ligand and target are first incubated together until the equilibrium level of binding is reached (i.e., the plateau). At this point, two processes are occurringdligand is associating with and dissociating from the target (see “Association animation” video in the online Annex at https://doi.org/10.1016/B978-0-12820472-6.00011-6. An experimental intervention is then used to enable dissociation to be measureddthe association process is blocked, meaning the only process occurring will be dissociation. This is done in one of two ways: (1) the unbound ligand is removed, e.g., by washing. (2) In the case of tracer ligand binding, the unbound tracer is completely outcompeted for binding to the target by the application of a large excess of unlabeled ligand. After this intervention, the level of target-ligand complex is measured over time. The resulting time course is a single-phase exponential decay curve for a simple single site, single step interaction (Fig. 2). Data are analyzed with the exponential decay equation, which is included in commonly-used curve fitting software programs (Eq. 1): ½RL ¼ ½RLt¼0 ekoff :t This analysis provides a fitted estimate of koff. The koff value is the same for all ligand concentrations. To properly define the curve it is necessary to extend the time course sufficiently to capture the bottom of the curve, i.e., the lower asymptote where dissociation is effectively complete. The curve should approach zero. If it plateau’s above zero, this can suggest a more complicated binding interaction (for example, isomerization to a pseudo-irreversible state, Section 1.10.6.2.4), or an artifact such as an inaccessible washresistant compartment that prevents removal or blockade of the unbound ligand (Hoare, 2021).

1.10.6.2.3

Competition kinetics for quantifying kon and koff of unlabeled ligands

An alternative to measuring binding directly is to measure binding of unlabeled compounds by competition versus a labeled tracer ligand, labeled with a fluorescent moiety or radioisotope (Fig. 20F). Competition binding assays are used routinely to measure affinity of compounds for the target (i.e., the Ki value). In the early 1980s, methods were developed to measure binding kinetics using the competition approach. An equation was developed to enable kon and koff of the unlabeled ligand to be estimated by routine curve-fitting (Motulsky and Mahan, 1984; Aranyi, 1980). This method is now being used extensively for a broad array of target classes, including GPCRs (Sykes et al., 2019b), protein kinases (Georgi et al., 2018) and transporters (Martin et al., 2008). Moderate throughput can be achieved using the assay, sufficient for lead optimization campaigns, enabling kinetic SAR and broad surveys of target classes. For example, in a recent study, 270 inhibitor compounds were tested on 40 protein kinases to illuminate the role of kinetics in the efficacy and drug development progression of this class of targets (Georgi et al., 2018). The assay is not trivial to set up. To aid the unfamiliar investigator, numerous guides and best-practice recommendations have been published (Georgi et al., 2019; Sykes et al., 2019a; Hoare, 2021; Sykes and Charlton, 2018). In the standard competition kinetics experiment (Fig. 20), the time course of tracer ligand association with the target is measured in the presence of the unlabeled test ligand at multiple concentrations (e.g., three). The concentrations of unlabeled ligand used in the assay are usually selected specifically for each compound and this requires the IC50 of the compound to be determined in an initial competition assay (run at a single time point). Concentrations of 3-fold, 1-fold and 0.33-fold the IC50 concentration will give sufficient inhibition, but not too much, to enable reliable analysis (Fig. 20). Association is also measured in the absence of unlabeled ligand as a control. The target is exposed simultaneously to the tracer and unlabeled ligands, meaning in practice the target is added last to the assay. This is obligatory for application of the original equation but recently the equation has been modified to permit alternative orders of reagent addition (Hoare, 2018; Shimizu et al., 2016). A large number of time points are required to accurately quantify kon and koff of the unlabeled compound, at least 16 but ideally many more. A read frequency of several times per minute is ideal, especially for accurately quantifying rapid dissociation (Georgi et al., 2019; Sykes et al., 2019a). For this reason, continuous read binding assay modalities are preferred, e.g., fluorescent ligand binding (Georgi et al., 2018; Robers et al., 2019; Soave et al., 2019; Schiele et al., 2015a; Zwier et al., 2010; Sykes and Charlton, 2018). The first time point should be read as quickly as achievable and autoinjectors on the reader are convenient for this purpose (Sykes et al., 2019a). The shape of the time course in the presence of unlabeled compound can qualitatively diagnose how rapidly it dissociates, relative to the tracer ligand (Motulsky and Mahan, 1984). In the online Annex, a simulator is provided to enable investigators to see how the parameter values affect the curve shape (“Competition kinetics binding assay simulation” (Data 6 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6) at https://doi.org/10.1016/B978-0-12-820472-6.00011-6). If the unlabeled compound dissociates more slowly than the tracer, an unusual curve shape results in which tracer binding overshoots its equilibrium value (Fig. 20A and B). The curve rises, then reaches a peak, then declines down to a plateau value at the end of the time

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course. When the compound koff equals that of the tracer, an exponential curve results where the compound reduces the tracer binding plateau without affecting the t½ of tracer association (Fig. 20C). When the compound dissociates more rapidly than the tracer, the curve is a biphasic exponential; there is a rapid initial rise phase and then a second slower phase as binding slowly approaches the plateau (Fig. 20D, particularly visible for the 30 nM curve). If the compound dissociates much more rapidly than the tracer, then the curve approximates a single-phase exponential curve where the compound reduces the tracer binding plateau and increases the association t½ (Fig. 20E). The time course data are fit to an equation, providing fitted estimates of the unlabeled ligand kon and koff (Motulsky and Mahan, 1984; Aranyi, 1980). The equation is rather complex and is as follows (Eq. 4):   Bmax ½Lk1 k4 ðKF  KS Þ k4  KF KF t k4  KS KS t (4) ½RL ¼ þ e  e KF KS KF  KS KF KS where

 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  KF ¼ 0:5 KA þ KB þ ðKA  KB Þ2 þ 4½L½Ik1 k3  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  KS ¼ 0:5 KA þ KB  ðKA  KB Þ2 þ 4½L½Ik1 k3 KA ¼ ½Lk1 þ k2 KB ¼ ½Ik3 þ k4

k3 and k4 are the kon and koff values for the unlabeled ligand, respectively, and k1 and k2 are the corresponding values for the tracer ligand. Bmax is the number of ligand binding sites and [L] is the tracer concentration. KF, KS, KA and KB are composite parameters used to simplify writing of the equation. This equation is built-in to the curve-fitting program Prism (Motulsky, 2019b) and can be entered manually into other programs. The analysis is advanced because it involves fitting with two independent variables (unlabeled compound concentration and time). This is reasonably straightforward in commonly-used curve-fitting programs including Prism (Motulsky, 2020a), XLfit (IDBS, 2020) and SigmaPlot (Software, 2020). The analysis results should be examined with care because there are limits of sensitivity associated with the method. Specifically, there is an upper and lower boundary of reliable fitted kon and koff values of the unlabeled ligand. The boundaries are dependent on multiple variables, including the read frequency, how quickly the first time point can be measured, the duration of the assay, and the kinetics of the tracer (Georgi et al., 2019; Sykes et al., 2019a). For example, unreliable estimates of the koff value of slowlydissociating ligands can result if the assay is not run for long enough. In addition, unreliable koff values of rapidly-dissociating compounds can result if the first time point is too late and/or the assay is not read frequently enough. If the fitted values are outside of the reliable range they will typically be associated with large variability, for example with large fitted standard error values (Motulsky, 2019c) and confidence intervals (Motulsky, 2019a). Examining the fit standard error of the parameter value provides a quick way to assess reliability of the fitted value. For more systematic evaluation of the limits of sensitivity, Monte Carlo analysis can be used as described (Georgi et al., 2019; Sykes et al., 2019a). For ligands that dissociate too rapidly for koff to be quantified accurately, an alternative equation is available that returns a value of the compound affinity (Ki) (Hoare, 2021; Motulsky, 2020b). Finally, the competition kinetics assay provides an alternative means of quantifying affinity of slowly-dissociating ligands. In standard concentration-response binding experiments run at a single time point, unrealistically long incubation times can be required to accurately quantify Ki of slowly-dissociating ligands (Section 1.10.5.2). Instead, the Ki can be determined from kon and koff using the equation Kd ¼ koff/kon.

1.10.6.2.4

Quantifying more complex binding kinetic mechanisms

The analysis methods above assume a single-site, single step binding interaction (Fig. 1). However, often the ligand binding mechanism is more complex. Fortunately, many such mechanisms are amenable to quantification because equations have been derived with which to analyze binding assay data to estimate the rate constants. These mechanisms include the post-binding conformational change mechanism introduced in Section 1.10.4.3.2 (Schreiber et al., 1985a); the two independent receptor states model (Guo et al., 2018); and a dimeric target-ligand interaction model (White and Bridge, 2019). These mechanisms are illustrated in Fig. 21. In all cases they yield binding data that are incompatible with the equations for the single-site, single step-interaction. For example, the association time course is biphasic for these more complex mechanismsdthere appears to be a rapidlyassociating and a more slowly-associating phase of binding (Fig. 22) (Data 8 in the online version at https://doi.org/10.1016/ B978-0-12-820472-6.00011-6). The two-step binding mechanism is considered first. As described in Section 1.10.4.3.2, this mechanism is relatively common, particularly for enzyme inhibitors (Morrison and Walsh, 1988; Copeland, 2013b; Szedlacsek and Duggleby, 1995) and also for GPCR ligands (Schreiber et al., 1985a; Vauquelin et al., 2001; Schreiber et al., 1985b). Ligand binding proceeds by two stepsdthere is an initial target-ligand association event that forms a target-ligand complex (RL, Fig. 21A). Subsequent to this, there is a change in the complex to form a second bound state (R’L, Fig. 21A). It is usually assumed this is a conformational change of the complex (and so this is sometimes referred to as a conformational induction model). However this is not obligatory and the model as formulated

Kinetics of Drug-Target Binding: A Guide for Drug Discovery

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(A)

k1

R

k3

RL

k2

k4

R’L

(B)

k1

T

T

k2

k3

R

R

R

k4

R

(C)

k1A

RA

k2A

RA L

k1B

RB

k2B

RB L

Fig. 21 More complex kinetic binding mechanisms for which equations have been derived for estimating the rate constants by curve fitting. (A) Two-step binding process. Ligand association forms an initial target-ligand complex (RL), which then transitions to a second bound state (R’L). See (Schreiber et al., 1985a) for equation. (B) Dimeric target-ligand interaction model. Binding of the first molecule of ligand to the dimer changes the interaction of a second molecule of ligand to the second subunit of the dimer. The model can accommodate positive and negative cooperativity. Here positive cooperativity is shown via the Monod Wyman Changeux model (Monod et al., 1965), where T is the conformation with lower affinity for ligand and R the conformation with higher affinity. See (White and Bridge, 2019) for equation. (C) Two independent conformational states model. Ligand binds with different kinetics to two independent conformations of the target (RA and RB). See (Guo et al., 2018) for equation.

Ligand-target complex

80

RL total RL R'L

60

40

20

0 0

60

120

180 240 Time (min)

300

360

420

Fig. 22 Kinetic binding data for the two-step binding mechanism. In this mechanism, commonly encountered for enzyme inhibitors and GPCR ligands, ligand first forms an initial complex with the target (RL), which then undergoes a transition to form a final bound state (R’L) (see Fig. 21A for diagram). The graph shows the time course of occupancy for total bound ligand (RL total), the initial complex (RL) and the final complex (R’L) at a single concentration of ligand. Association is shown on the left of the dashed line and dissociation is shown on the right. Note the bi-phasic curves for RL totaldthere is an initial rapid phase and a slower phase later in the time course. Data were simulated using the “Two step binding simulation” simulator available in the online Annex, with the following parameter values: First step affinity, 10 nM; k2 t½, 3 min; k3 t½, 30 min; k4 t½, 60 min; ligand concentration, 10 nM; RTOT, 100 receptor units.

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can also accommodate other changes such as shuttling of a ligand from an initial encounter site to a second site (Jakubik et al., 2000). The mechanism is defined by four rate constantsdthe association and dissociation rate constants for formation of the initial complex RL (k1 and k2, respectively, in the model in Fig. 21A), the rate constant for transition to the second state R’L (k3) and the rate constant for transition back to the initial state (k4). In a binding assay this mechanism is manifest by biphasic association curves, assuming both of the bound states are detected in the assay (Schreiber et al., 1985a). This is illustrated in Fig. 22 and can also be visualized using a simulator provided in the online Annex (“Two step binding simulation” (Data 8 in the online version at https://doi.org/10.1016/B978-0-12-820472-6.00011-6) at https://doi.org/10.1016/B978-0-12-820472-6.00011-6). An equation was derived for analyzing association time course data for estimating the four rate constants in the model (Eq. 5, adapted from Eqs. 4 and 5 of Schreiber et al., 1985a): ½RLTOT ¼ ½RL þ ½R 0 L

  Bmax ½Lk1 k4 KP  KQ k4  KP KP t k4  KQ KQ t ½RL ¼ þ e  e KP  KQ KP KQ KQ KP   Bmax ½Lk1 k3 KP  KQ 1 1 þ eKP t  eKQ t ½R0L ¼ KP KQ KP  KQ KP KQ where,

(5)

 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  KP ¼ 0:5 K1A þ k3 þ k4 þ ðKA þ k3 þ k4 Þ2  4ðK1A k4 þ ½Lk1 k3 Þ  qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  KQ ¼ 0:5 K1A þ k3 þ k4  ðKA þ k3 þ k4 Þ2  4ðK1A k4 þ ½Lk1 k3 Þ K1A ¼ ½Lk1 þ k2

Although not immediately apparent, this is a two-phase exponential equation, the two exponential terms being e KPt and e KQt. Time course data can be fit to this equation to estimate the values of k1, k2, k3 and k4 (see for example Schreiber et al., 1985a; Schreiber et al., 1985b). Dissociation for this mechanism is also biphasic providing k4 is not much smaller than k2 and k3 (S.R.J. Hoare, unpublished observations.) The two-step mechanism has also been evaluated extensively by numerical solution of the differential equations (Vauquelin et al., 2001). Eq. (5) can be adapted for numerous scenarios. A competitive binding variant has been developed for quantifying the rates of an unlabeled compound, inhibiting binding of a tracer (Schreiber et al., 1985a; Schreiber et al., 1985b). Under certain circumstances, only the R’L state is detected (for example in filtration binding assays where dissociation of ligand from RL is so fast that it is washed off during the filtration procedure) (Strickland et al., 1975). When only R’L is detected, the association time course is monophasic but the plot of the observed association rate versus ligand concentration is a hyperbola rather than a straight linedit approaches a plateau at high ligand concentrations (Strickland et al., 1975). Another common more complex mechanism is the dimeric target, a scenario encountered in certain target classes such as receptor tyrosine kinases, Family C GPCRs and regulated enzymes. Recently, an equation describing association kinetics of two molecules of a ligand binding to a dimeric target was solved (White and Bridge, 2019). The mechanism is illustrated in Fig. 21B. Binding of ligand to the first subunit in the dimer causes a change in the complex that alters the binding of the second molecule of ligand to the second subunit. The model describes this kineticallydthe alteration of the binding of the second ligand is in terms of its association and dissociation rate constants (White and Bridge, 2019). The model can accommodate positive and negative cooperativity, i.e., ligand binding to the first subunit enhancing or attenuating ligand binding to the second subunit. Interestingly, one scenario this model can describe is the Monod-Wyman-Changeux model of positive cooperativity (Monod et al., 1965). This is illustrated in Fig. 21B. Binding of ligand to the first subunit causes a concerted conformational change across both subunits that increases the binding of ligand to the second subunit. With this new equation such mechanisms can be analyzed in kinetic terms by non-specialists using familiar curve-fitting software. The equation is a two-phase exponential equation and the model’s manifestation in binding data is detailed in Ref. (White and Bridge, 2019). A third more complex model is the two independent conformations model (Guo et al., 2018). Here two conformations of the target exist and they do not interconvert. This situation can be encountered in GPCR binding assays for agonist ligands. The receptor in membrane preparation exists in two predominant states, one which is coupled to G-protein and binds agonist with higher affinity, and the uncoupled state which binds agonist with lower affinity. The application of the model equation enables the kinetics of agonist binding to these states to be quantified, and a competition kinetics equation for this mechanism has been derived (Guo et al., 2018).

1.10.7

Functional assays for measuring binding kinetics

While binding assays are the most direct method for quantifying binding kinetics, such assays are often not available, especially for newly-discovered targets where often the only available assay is a functional assay of the target’s biological activity. Functional assays were also used historically to quantify binding interactions before the advent of binding assay reagents and technologies.

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Methods and equations for analysis are available for quantifying inhibitor compound kon and koff by measuring blockade of target activity. These activities include enzyme activity, signal transduction for receptors, and ion channel conductance. These approaches are not as straightforward as binding assays for quantifying the kinetics. In particular, knowledge of the biological principles and analytical frameworks is necessary, and this knowledge is specific to each target class. Communication with experts in the target class area is recommended before considering these experimentsdfor example, enzymologists, receptor pharmacologists, and electrophysiologists.

1.10.7.1

Enzyme assays for measuring inhibitor binding kinetics

The activity of enzymes is quantified by measuring the generation of product (or occasionally depletion of substrate). The kinetics of inhibitor binding can be determined by measuring the inhibition of this activity over time (Morrison and Walsh, 1988; Szedlacsek and Duggleby, 1995). The time course data are fit to equations that yield estimates of the inhibitor’s binding kinetics. For competitive inhibitors analytical methods have been established for this purpose, as introduced below. However, the analysis can be complicated by the often-complex mechanisms of inhibitor binding to the enzyme, which often include post-binding conformational changes. For this reason in particular, familiarity with enzyme mechanisms and enzyme kinetics analysis is recommended for successful quantification of inhibitor binding kinetics. One reasonably routine method developed for quantifying the inhibitor dissociation rate constant is the jump-dilution assay (Zhang et al., 2016; Copeland, 2013a). In this experiment, illustrated in Fig. 23, enzyme and inhibitor are first incubated together, 1. Incubate enzyme (blue) and inhibitor (orange) together to form enzyme-inhibitor complex

2. Perform large dilution into a solution containing substrate (green). This reduces the inhibitor concentration sufficiently that it no longer associates with enzyme.

koff t1/2

30

No inhibitor control 1 min 10 min 40 min 2 hr 6 hr 24 hr

Product

20

10

0 0

20

40

Time (min)

60

3. As inhibitor dissociates, substrate can access enzyme and product is generated (yellow).

Fig. 23 Jump dilution method for quantifying koff of enzyme inhibitors. For details, see Section 1.10.7.1 and Copeland et al. (2011). Data in the graph were simulated using the jump dilution kinetic equation (Eq. 6) using Prism 8, with the following parameter values: vs, 0.45 product units min 1; vi, 0 product units min 1; koff, 0.693, 0.0693, 0.0173, 0.00578 and 0.00193 and s 1.

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Kinetics of Drug-Target Binding: A Guide for Drug Discovery

in the absence of substrate, to form the inhibitor-enzyme complex. This is usually done at an inhibitor concentration sufficient to occupy approximately 90% of the population of enzyme molecules. The mixture is then diluted substantially. This dilution step greatly reduces the concentration of free inhibitor, to a concentration sufficient to occupy less than 10% of the enzyme population. The reduction of concentration substantially slows down association of the inhibitor with the enzyme, since the rate of association is dependent on concentration (Section 1.10.2.2, Fig. 4). As a result, after the jump dilution step, dissociation of the enzyme-inhibitor complexes formed in the pre-incubation is strongly favored over the association interaction. The concentration of inhibitor-enzyme complex can be determined by measuring enzyme activity. Enzyme substrate is included in the diluent used for jump dilution and the time course of product formation is measured. When inhibitor is bound to the enzyme it reduces enzyme activity and this reduction can be used to quantify the time course of inhibitor dissociation from the enzyme. The resulting time course data is illustrated in Fig. 23. An equation has been derived to fit the time course data under initial rate conditions, i.e., restricted to the linear portion of the enzyme activity progress curve (Morrison and Walsh, 1988; Copeland et al., 2011). This is Eq. (6):  vi  vs
1  ekoff t (6) ½P  ¼ vs t þ koff where [P] is product, vs is the rate of enzyme activity at the free concentration of inhibitor after jump dilution, and vi the rate of activity at the free concentration of inhibitor immediately before jump dilution. In practice, conditions are often employed such that vs approximates activity in the absence of inhibitor and vi approximates activity in the presence of a saturating inhibitor concentration (e.g. Kumar and Lowery, 2017). The assay does reach limits of sensitivity, as can be seen in the graph in Fig. 23. Specifically, when the inhibitor dissociates rapidly, the progress curve approaches that of the no inhibitor control (red and black curves, respectively). When inhibitor dissociates slowly, the curve approaches baseline (pink 24 h curve in Fig. 23). Examples of the method’s application include the following: Walkup et al. (2015), Kumar and Lowery (2017), Chang et al. (2013), and King et al., 2015). An alternative approach is to incubate enzyme, inhibitor and substrate together and to analyze the resulting progress curves to determine the rate constants. This requires a thorough understanding of the enzyme mechanism and detailed discussion is beyond the scope of this article. Equations are available to analyze these data under initial rate conditions, including for multi-step inhibitor-enzyme binding mechanisms (Morrison and Walsh, 1988; Szedlacsek and Duggleby, 1995). If the whole progress curve is included in the analysis, this usually requires fitting the data to differential equations by numerical solution. A commerciallyavailable program that can perform this type of analysis is DynaFit (BioKin Ltd., Watertown, MA).

1.10.7.2

Signaling response assays for measuring receptor antagonist dissociation rate

As described in Sections 1.10.5.5 and 1.10.6.1, in functional assays of receptor signaling responses slow antagonist dissociation can result in insurmountable antagonism. The antagonist reduces the maximal response to the agonist (Fig. 18B). Insurmountable antagonism data can be analyzed using an equation to estimate the antagonist dissociation rate constant. In the experiment, the concentration-response to the agonist is measured in the presence of a series of concentrations of the antagonist (and the agonist is tested alone as a control) (Fig. 18). Antagonist is pre-incubated with the receptor prior to application of the agonist. The resulting data are then fit to the following equation (Eq. 7):

  ½A =KA 1  w 1  ek2 Ft  rB ek2 Ft sEm



Response ¼ (7) 1 þ ½A =KA þ ½A =KA 1  w 1  ek2 Ft  rB ek2 Ft s where w¼

½B=KB 1 þ ½A =KA þ ½B=KB rB ¼

F¼

½B=KB 1 þ ½B=KB

1 þ ½A =KA þ ½B=KB 1 þ ½A =KA

Response is the signal in response to agonist; [A] and KA are the agonist concentration and equilibrium binding affinity, respectively; [B] and KB the corresponding parameters for antagonist; k2 the antagonist dissociation rate constant; t the assay incubation time; s the agonist efficacy; and Em the maximal response capability of the system. This equation was derived in Kenakin et al. (2006) based on earlier work (Paton and Waud, 1967). The equation accounts for the pharmacological property of receptor reserve inherent in receptor signal transduction systems. Receptor reserve is the separation of the degree of receptor occupancy from the degree of functional response owing to signal amplification down the transduction cascade (Kenakin, 2009). (Owing to receptor reserve, 50% of the maximal response to the agonist can be achieved at less than 50% occupancy of receptor.) This is done using the operational model of agonism (Black and Leff, 1983). Data are fit to Eq. (7) using agonist concentration and antagonist concentration as independent variables, as described previously (Riddy et al., 2015; Mould et al., 2014).

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It is necessary to ensure that the mechanism of insurmountability is slow dissociation rather than allosteric modulation, an alternative mechanism that can reduce the agonist Emax (Kenakin et al., 2006; Vauquelin et al., 2002). This can be done by running a control experiment in which receptor is exposed simultaneously to both antagonist and agonist. Under this condition, insurmountable inhibition by a slowly-dissociating antagonist will disappear (Fierens et al., 1999), whereas it will be retained for an allosteric modulator.

1.10.7.3

Washout methods

The washout method is commonly used for measuring dissociation of inhibitor ligands from targets in cell preparations, particularly ion channels and GPCRs (Parsons et al., 1995; Bosma et al., 2017; Malany et al., 2009; Sahlholm et al., 2016), and also recently kinase enzymes (Robers et al., 2019). Inhibitor ligand is applied to the cells and incubated long enough for the target-ligand complex to form (association phase). The free ligand, i.e., that not bound to the receptor, is then washed away. Two approaches can then be used to assess dissociation of the target-inhibitor complex. In the first, the preparation is incubated for various intervals of time during which the inhibitor dissociates (dissociation phase). Following this, an agonist for the target is applied and a target activity assay is performed, e.g., ion channel conductance or GPCR signaling (activity phase). Activity is measured after as short an incubation as possible, to minimize dissociation of inhibitor during the activity phase. The amount of activity stimulated by the agonist or amount of tracer binding is proportional to the degree of dissociation of antagonist from the complex during the dissociation phase of the experiment. From this the degree of inhibition can be calculated. The degree of inhibition is then plotted against the time interval during the dissociation phase and from this the dissociation rate of the inhibitor can be assessed (Bosma et al., 2017; Sahlholm et al., 2016). In the second approach, the washed preparation is used immediately for a signaling or a tracer binding assay (Robers et al., 2019; Malany et al., 2009; Packeu et al., 2010; Uhlen et al., 2016). The time course of signaling or tracer binding is measured. Dissociation of inhibitor is determined by the delay of signaling or binding resulting from the need for inhibitor to first dissociate before agonist or tracer can bind. For binding assays, an equation has been derived to analyze these data to estimate the inhibitor koff (Malany et al., 2009). Equations are also available for receptor signaling assays when there is no signal amplification (e.g., for arrestin interaction assays) (Hoare et al., 2018). Caution is recommended when using this method with microtiter plates, owing to incomplete washout (Hoare, 2021). The method requires an efficient washing protocol to remove as much free inhibitor ligand as possible. If sufficient free ligand remains after washing, it can block the agonist response or tracer binding in the activity phase and this block results in overestimation of the inhibitor residence time of on the target. In principle this can result in a false positive in the search for slowlydissociating ligandsdresidual free compound can make a very rapidly dissociating ligand appear to dissociate very slowly if the unwashed ligand is not considered (Hoare, 2021). This issue is exacerbated for ligands that partition into cell membranesdso called “wash-resistant” ligands (Section 1.10.4.1). Effective washing is feasible for electrophysiological and whole tissue assays where large volumes are applied (to perfusion chambers and organ baths). However, with the advent of microtiter plates the wash method has become problematic owing to the dramatically reduced assay volume (and increased surface area to volume ratio). It is recommended that after the wash phase a sample of the assay medium in the dissociation phase be taken and tested for whether it can significantly block target activity, which would indicate insufficient washout of free ligand.

1.10.8

Concluding remarksdWhen to measure binding kinetics in drug discovery

This article highlights the potential benefits and pitfalls of binding kinetic activity for defining drug efficacy in vivo and for measuring drug activity in vitro. This provides a perspective for guiding the practical and logistical considerations of when to measure binding kinetics in the drug discovery cascade. Kinetic assays take time to develop, implement and run, especially for the throughout required in lead optimization, and adding kon and koff to the list of compound activities to optimize (such as affinity and pharmacokinetic properties) could add a burden to the drug development process. The significance of binding kinetics to drug discovery remains a matter of debate (Swinney, 2004; Folmer, 2018; Copeland, 2016) and in the section below a personal perspective is presented. Binding kinetics can affect in vivo drug activity under certain circumstances. While these circumstances are of high potential value (see below) the significance of residence time needs to be considered in light of broader drug development considerations. For most drugs, the elimination rate is the rate limiting step in defining the duration of drug activity in simple PK/PD scenarios (Table 1) (Dahl and Akerud, 2013). This suggests that from a medicinal chemistry perspective, for the regimen of long-term once-daily dosing it is likely to be easier to develop compounds with day-long stability rather than day-long residence time. Furthermore, when it comes to prediction of human dosing for first-time-in-human studies, translating pharmacokinetics from preclinical to clinical models is well-established and the risks well-defined. By contrast, translating residence time from preclinical to clinical settings is an emerging concept. For example, it is rarely known how residence time in a binding assay translates to residence time at the target in humans. Having a human target engagement assay that is robust and straightforward is likely to be highly valuable for clinical translation and measurement of the target residence time. This will also be facilitated by clinical PK/PD modeling incorporating target residence time, which has been done extensively for some targets, for the example the m opioid receptor (Yassen et al., 2006; Yassen et al., 2007).

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The circumstances in which a long residence can benefit pharmacodynamics are of high potential value. Under certain conditions, a long residence time can enable a lower compound concentration to be used compared with a drug with the same affinity but a short residence time (Sections 1.10.3.3 and 1.10.4.2). This provides a way to reduce unwanted engagement with related targets that produce side effects, a current priority for protein kinase inhibitor development. In principle a long residence time can also protect against large surges in concentration of endogenous ligands for the target, via insurmountable inhibition. This is potentially valuable for targets in tumors exposed to very high endogenous ligand concentrations, and for targets on immune cells which become exposed to high gradients of endogenous ligand at target tissues. For short acting drugs, residence time might need to be considered for the opposite reasonda long residence time can in principle impair the pharmacodynamics of a short-acting drug (Section 1.10.3.5). A long residence time can delay onset of action and prolong drug effect beyond the desired time window. In this case a long residence time on the human target is a development risk and this can be discovered quickly and cheaply by assessing the residence time in vitro (Section 1.10.6.1). Efficacy studies in preclinical species do not fully de-risk the potential for impaired efficacy in humans because residence time can be species-dependent (Kohout et al., 2007). The second aspect of drug discovery impacted by binding kinetics is the in vitro quantification of drug effect. The in vitro assays used to establish SAR in ligand optimization and to predict in vivo drug effect can be profoundly distorted by a long residence time (Section 1.10.5.2). If the residence time is much longer than the incubation time, compound affinity for the target will be underestimated. This can have early and late consequences for drug discovery success. Most seriously, in late development it could affect clinical trials because the wrong value of affinity would be used in the calculations used to predict human dosing. Substantially greater target occupancy would result because affinity would be underestimated. This could result in mechanism-based toxicity. In the early stages, incorrectly measuring affinity can result in SAR being misunderstood, potential breakthroughs of affinity or selectivity going unrealized, failure to test the highest affinity ligands in vivo, and confusion resulting from the disconnect between in vitro and in vivo measures of drug effect. This negative effect of binding kinetics impacts potentially the most valuable molecules, the highest-affinity ones. In terms of measuring target binding kinetics, technological advances and the derivation of useful equations have dramatically reduced the resource burden (Sections 1.10.6 and 1.10.7). The use of continuous-read assay modalities enables the whole time course to be measured for the same cost in reagents and consumables as measuring a single time point. The throughput is usually sufficient to enable kinetic characterization of every compound in a lead-optimization campaign, should that be necessary. For purified proteins direct compound-target interaction can be measured kinetically using surface plasmon resonance and other technologies. Tracer binding assays have evolved into practical kinetic assays with the use of fluorescence readouts and the application of the competition kinetics equation to quantify the binding rates of unlabeled compounds. Kinetics can also be assessed using target activity assays, usually the first assay developed during a new project on a novel target. However, assay development time and the learning curve associated with analyzing, interpreting and troubleshooting kinetic data analysis are a significant resource commitment. To ease the initial assessment of binding kinetics, assays are available that just require running an established assay at two time points, or that use familiar concentration-response analysis of functional assays to potentially reveal a long residence time (Section 1.10.6.1). Given the potential significance of kinetics to drug discovery and the recently reduced resource burden, a generic recommendation is that binding kinetics be assessed relatively early in the drug discovery process, for example at the advanced lead stage when compounds are first being assessed in vivo. This represents a realistic trade-off between value and resource commitment. Qualitative resource-light methods can be used at first to determine whether there is long residence time issue for the chemical series and target. If kinetics is an issue of sufficient significance, quantitative assays can then be developed and implemented. Obviously, there are exceptions to this recommendation depending on the goals of the project. It may be decided before a project commences that a long residence time is a highly desirable feature for the therapeutic mode of action, and kinetic assays will therefore be builtin to the project plan from the start. Ultimately the significance of target binding kinetics is likely to be highly target- and scenario-specific. The developments in the field reviewed here and the resources in the Annex will enable investigators to assess this themselves for their targets and ligands of interest.

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Vauquelin, G., 2018. Link between a high K on for drug binding and a fast clinical action: To be or not to be? Medicinal Chemistry Communications 9, 1426–1438. Vauquelin, G., Charlton, S.J., 2010. Long-lasting target binding and rebinding as mechanisms to prolong in vivo drug action. British Journal of Pharmacology 161, 488–508. Vauquelin, G., Packeu, A., 2009. Ligands, their receptors and ... plasma membranes. Molecular and Cellular Endocrinology 311, 1–10. Vauquelin, G., Van Liefde, I., 2006. Slow antagonist dissociation and long-lasting in vivo receptor protection. Trends in Pharmacological Sciences 27, 356–359. Vauquelin, G., Morsing, P., Fierens, F.L., De Backer, J.P., Vanderheyden, P.M., 2001. A two-state receptor model for the interaction between angiotensin II type 1 receptors and non-peptide antagonists. Biochemical Pharmacology 61, 277–284. Vauquelin, G., Van Liefde, I., Vanderheyden, P., 2002. Models and methods for studying insurmountable antagonism. 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1.11

Orthosteric Receptor Antagonism

Terry Kenakin, Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States © 2022 Elsevier Inc. All rights reserved.

1.11.1 1.11.2 1.11.3 1.11.3.1 1.11.3.2 1.11.3.3 1.11.3.4 1.11.4 1.11.4.1 1.11.4.2 1.11.4.3 1.11.5 1.11.6 1.11.7 References

Introduction What is “steric hindrance”? Kinetics, competitive and non-competitive antagonism Competitive antagonism Non-competitive antagonism Hemi-equilibrium antagonism Irreversible antagonism Antagonists with efficacy Partial agonism Inverse agonism Pharmacological resultant analysis Verisimilitude to allosteric antagonism Antagonist target coverage in vivo Conclusions

272 273 274 275 280 282 283 284 285 286 289 291 292 295 295

Glossary Allosteric Where binding to a separate site on a protein changes the conformation of that protein to affect the interaction of a second molecule with that same protein at a second binding site. Competitive A condition whereby two ligands compete for a single binding site, each diffusing toward and away from the binding site. The kinetics of both ligands are such that the relative occupancy of the sites depends upon the concentrations and affinities of the ligands. Hemi-equilibrium kinetics A condition whereby a portion of the receptor population re-equilibrates with the agonist and antagonist according to mass action and a portion of the receptors are pseudo-irreversibly bound to the antagonist and do not re-equilibrate with the agonist. Inverse agonism The ligand-induced reduction of elevated basal cellular activity (caused by constitutively activated receptors). IC50 Molar concentration of antagonist that reduces a pre-existing agonist response (or pre-existing level of radioligand binding) by 50%. This is an empirical measure of antagonist potency. KB (Equilibrium dissociation constant of the antagonist-receptor complex) This is the ratio of rate of offset of the antagonist out of the binding pocket (k2) and the rate of diffusion of the molecule toward the binding pocket (k1). It is also the concentration of antagonist that occupies 50% of the available binding sites. Non-competitive Two ligands are targeted for binding to a single target but one of the ligands shows persistent binding to the extent that when bound it does not allow the binding of the other ligand. Orthosteric Binding to the same locus on a protein target, i.e., an antagonist binding to the binding site utilized by the natural endogenous agonist. Schild analysis (Schild plot(s)) Procedure published by Arunlakshana and Schild (1959) whereby the dextral displacement of an agonist dose response curve with varying concentrations of a competitive antagonist produces a linear plot the intercept of which is the KB.

1.11.1

Introduction

The human body has multitude of ongoing hormone/neurotransmitter interactions with protein receptors leading to a collection of biochemical reactions and a large mode of therapeutic action of drugs is the inhibition of these with antagonists that bind to the natural agonist binding site and preclude signaling. This mechanism of action is termed “orthosteric” to denote the commonality of the binding sites for the endogenous agonists and the antagonist(s). The main index of antagonist activity is potency, i.e., the concentration of antagonist needed to produce a defined amount of receptor antagonism. A second important indicator of activity, especially in in vivo systems, is the kinetics of interaction of the antagonist with the receptor. This can determine the pattern of effect

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the antagonist has on agonist dose response curves and also the “target coverage” of the antagonist in vivo. These factors will be considered in detail in this chapter. The actual mechanism of orthosteric antagonism can simply be described as “steric hindrance” whereby the agonist binding is precluded because of the obstruction of the binding site by the antagonist.

1.11.2

What is “steric hindrance”?

For an antagonist to prevent the binding of an agonist through steric hindrance there has to be the opportunity for it to access the receptor binding site and for that to be the case, the agonist must allow the binding site to be open for a period of time. This is actually what happens as binding is really a process of a ligand approaching and diffusing away from the binding site according to chemical forces between the ligand and protein. As shown in Fig. 1A, molecules in turn approach and diffuse away from the binding site according to the energy of their position within the binding pocked. The rate constant for approach of the molecule is denoted k1 while the rate constant for diffusion out of the pocket is k 2. Thus the rate of diffusion into the pocket is given by k1[A]q where [A] is the concentration of ligand and q the fraction of binding pocket open for binding while the rate of diffusion away from the pocket is given by k2(1
q). At equilibrium, these rates are equal and it can be shown that the fraction of binding pockets bound with ligand (rA) is given by [A]/([A] þ (k2/k1)). It can be seen that the ratio k2/k1 (denoted as KA and referred to as the equilibrium dissociation constant of the ligand-receptor complex) is actually a concentration for when [A] ¼ KA, then half of the number of binding pockets are occupied by the ligand A. Thus, KA effectively becomes a measure of the tightness of binding of the ligand, i.e., when KA has a very low value then the rate of diffusion out of the pocket is much smaller than the rate of approach toward the pocket, i.e., the ligand binds tightly in the pocket. A low value for KA (tight binding) means that a low concentration of the ligand can bind 50% of the binding sites, i.e., it is a potent molecule in terms of binding. Thus, the equilibrium dissociation constant of an antagonist (denoted KB) is the measure of the potency of that antagonist as a blocker of agonist response. For an antagonist to displace an ongoing association between an endogenous agonist in the receptor compartment in vivo and the receptor, it must bind to the pocket in the periods when the agonist ligand has diffused away to allow antagonist binding. The probability of a molecule being found within the binding pocket (see Fig. 1B) is a function of its equilibrium dissociation constant and its concentration thus the probability of response production by an endogenous agonist in the presence of an antagonist is globally a function of the concentrations of the agonist and antagonist and their respective KA and KB values.

Fig. 1 (A) Binding of an antagonist ligand (black circle) to a receptor protein binding site. The position of the molecule within the binding site is a function of strength of attraction to the protein (denoted by rate constant k1) and strength of repulsion away from the binding site once bound (binding rate constant k2). Shown below is a potential energy diagram for a molecule with an equilibrium distance of 5 Å. (B) At any one instant, the number of molecules residing at various distances within the protein binding pocket can be represented by a Boltzmann diagram.

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Kinetics, competitive and non-competitive antagonism

Kinetics are critical to the production and maintenance of pharmacological response in vivo since real time is a factor, i.e., concentrations are never at equilibrium but rather vary with the strength of physiological signal (amount of hormone or transmitter present in the receptor compartment due to secretion or release) and the absorption and clearance of the antagonist. Therefore, the actual rate constants for binding of both the agonist and the antagonist become important to the understanding of overall therapeutic antagonism. The differential equation describing the rate of receptor occupancy by an antagonist B interfering with the binding of an agonist A in vivo is given by (Paton and Rang, 1965): 
  ½B ð1
rB Þ½A =KA
rB ð1
rB Þ
(1) vrB =dt ¼ k 2 KB ½A =KA þ 1 where rB is the fractional receptor occupancy by the antagonist at equilibrium. Upon integration of Eq. (1), the fractional receptor occupancy by the antagonist with time (rBT) is given by:
  
k2 ½B=KB þ½A=KA þ1 t ½A =KA þ1 ½B=KB ½B=KB ½B=KB

e rBT ¼ (2) ½B=KB þ ½A =KA þ 1 ½B=KB þ ½A =KA þ 1 ½B=KB þ 1 where t is time and k2 is the rate of offset of the antagonist. It is assumed that the rate of onset and offset of the agonist is much greater than that of the antagonist in accordance with normal physiology where neurotransmitter and hormone function is rapid; later in the chapter the reverse case of slow agonist-dominated diffusion will be considered. Eq. (2) can be used to calculate the fractional receptor occupancy with time for the antagonist upon addition of a fast acting agonist. Under ideal competitive conditions, the agonist and antagonist are both rapid onset and offset and the ligands reequilibrate according to mass action, i.e., they compete. Eq. (2) predicts that the receptor occupancy for such a fast acting antagonist will essentially reach equilibrium with the agonist within 5 min (see Fig. 2). This kinetic extreme (fast offset antagonist, time ¼ [k2
1; the exponential term in equation 2 / 0) converts Eq. (2) to the well known famous equation for simple competitive antagonism first published by Gaddum (1937): rA ¼ ½A=KA =ðð½A =KA Þ þ ½B=KB Þ þ 1Þ

(3)

where rA is the fractional receptor occupancy of the agonist in the presence of the antagonist. The essential feature of Eq. (3) is that high concentrations of agonist ([A]/KA / N) can overcome the antagonism by B and return the agonist maximal response, i.e., as ([A]/KA / N, rA / 1). Thus, a competitive antagonist will block the agonist response but the maximal response to the agonist will not be depressed, i.e., the antagonism will be surmountable; this pattern of agonist dose response curves is shown in Fig. 3A. As shown in Fig. 2, for antagonists of slow rates of offset (i.e., 10
4 s
1) there will be little readjustment of receptor occupancy between the agonist and antagonist within a period of time reasonable for the measurement of pharmacological response. Thus, the other kinetic extreme for Eq. (2) sets t  k2 which brings the exponential terms in equation 2 / 1. This is the condition for noncompetitive antagonism whereby the antagonist will not allow the agonist to bind to the receptor and reduces Eq. (2) to the well known equation for non-competitive antagonism described by Gaddum (1957) and Gaddum et al. (1955) as: rA ¼ ½A =KA =ðð½A =KA Þ ð1 þ ½B=KB ÞÞ þ ½B=KB þ 1Þ

Fig. 2 Kinetics of adjustment of receptor occupancy upon addition of a fast acting agonist to system equilibrated with antagonists with different rates of offset. Ordinates: Fractional receptor occupancy by the antagonist with time. Abscissae: Time available for measurement of response. Concentration of agonist added ¼ [A]/KA ¼ 20; antagonist ¼ [B]/KB ¼ 3. Note that the agonist virtually does not compete with the slow offset antagonist (k2 ¼ 10
4) whereas for the fast offset antagonist (k2 ¼ 10
2), the system equilibrates within 350 s.

(4)

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275

Fig. 3 Agonist concentration response curves showing orthosteric blockade with antagonists having three different rates of offset from the receptor. (A) The antagonist rapidly binds to and rapidly diffuse off of the receptor to compete with the agonist thus yielding simple competitive antagonism. (B) The antagonist is persistent and has a slow rate of offset from the receptor once bound. Tis leads to non-competitive antagonism of the agonist. (C) Hemi-equilibrium whereby a partial offset of antagonist binding yields a situation where some competitivity is observed but a depression of agonist maximal response occurs where high agonist receptor occupancy is required.

It can be seen from Eq. (4) that non zero values of [B] can cause rA / (1 þ [B]/KB)
1, a value < 1, i.e., the antagonist will depress the maximal response to the agonist to produce insurmountable antagonism. This pattern of antagonism is shown in Fig. 3B. If the rate of offset is midway between raid and very slow, a condition referred to as a “hemiequilibrium” is created whereby the surmountability and insurmountability of the antagonism depends upon the concentration of agonist (vide infra). Under these conditions a pattern of partial depression of the maximal response coupled with dextral displacement of the dose-response curve is produced as shown in Fig. 3C. Each of these kinetic conditions will be discussed in further detail in terms of how the potency of these antagonists is measured in pharmacological experiments. There are two approaches to observing drug antagonism; binding and function. The equations for competitive, non-competitive and hemiequilibria given above (Eqs. 3 and 4) represent the fractional occupancy of a probe (i.e., radioligand, agonist) in the presence of the antagonist and therefore are relevant to receptor binding studies. Those receptor occupancies, if for an agonist, also relate to the production of agonist response in a cell but for this to be considered, a model of what happens after receptor binding must be included to relate agonist/antagonist occupancy with cellular response. The standard model for this is the Black/Leff operational model of agonism (Black and Leff, 1983). This model relates receptor occupancy to cellular response through a hyperbolic function representing the biochemical cascades in the cell that transform receptor stimulus to response. Thus, agonist response is given as (Black and Leff, 1983): Response ¼

½A sA Em ½A ð1 þ sA Þ þ KA

(5)

where sA is the efficacy of the agonist, Em is the maximal response window of the measuring system and KA is the equilibrium dissociation constant of the agonist-receptor complex. Applying the Black/Leff model to the blockade of receptor response leads to equations for competitive and non-competitive antagonism; the resulting equation the competitive antagonism of agonist cell response is: Response ¼

ð½A =KA ÞsA Em ð½A =KA Þð1 þ sA Þ þ ½B=KB þ 1

(6)

where B is the antagonist and KB the equilibrium dissociation constant of the antagonist/receptor complex. The corresponding equation for function for a non-competitive antagonist is: Response ¼

1.11.3.1

ð½A =KA ÞsA Em ð½A =KA Þð1 þ sA þ ½B=KB Þ þ ½B=KB þ 1

(7)

Competitive antagonism

In binding studies, competitive antagonism will result in a surmountable antagonism (parallel dextral displacement with no diminution of maximal response) of the radioligand saturation binding curve (i.e., see Fig. 3A). While this type of experiment can be done, it would be unduly expensive and labor intensive due to the requirements of radioligand binding; the alternative is to seed the assay with a pre-determined amount of radioligand and then cumulatively add the antagonist to reduce the binding of that basal binding. This yields an inverse sigmoidal curve the midpoint of which relates to the potency of the antagonist; this is referred to as the IC50 which is defined as the molar concentration producing 50% reduction in the basal radioligand binding (see Fig. 4). For competitive antagonists, the IC50 is a conditional measure of antagonist potency. This is because its magnitude depends on the amount of radioligand put into the assay, i.e., the more radioligand there is present to compete with, the more antagonist will

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Agonist DR Curve

(A)

IC50 Curve

(B)

FRACT. MAX.

1.0 0.8

1.0

0.6

0.8 0.6 0.4 0.2 0.0

0.4 0.2 0.0 –9

–8

–7 –6 Log [Agonist]

–5

–4

–9

–8 –7 –6 –5 Log [Antagonist]

–4

Fig. 4 IC50 curves to quantify antagonism. (A) Effects of a simple competitive antagonist on agonist concentration response curves. Open circles show the response to one concentration of agonist (0.5 mM) in the presence of various concentrations of antagonist. (B) Response to 0.5 mM agonist as a function of a range of concentration of the antagonist. The antagonism defines an inverse sigmoid curve and the potency of the antagonist is quantified by the midpoint designated as the IC50 (inhibitory concentration producing 50% inhibition).

be needed to displace it. However, the IC50 can be converted to a system independent equilibrium dissociation constant for the antagonist (denoted as KI, a molecular quantity that is not radioligand concentration-dependent) through a conversion equation first published by Cheng and Prusoff (1973) referred to as the Cheng-Prusoff correction: KI ¼ ½IC50 =ðð½A  =KA Þ þ 1Þ

(8)

where [B] is the concentration of antagonist, [A*] is the concentration of radioligand and KA the equilibrium dissociation constant of the radioligand/receptor complex. A slightly different approach is used to measure competitive antagonism in functional experiments. This same approach can be used for functional antagonism. Specifically, a concentration of agonist is added (usually to give a submaximal level of response between 50% and 80% maximum) and then a succession of increasing concentrations antagonist added to the assay until the response is completely blocked. As with binding, the IC50 from the antagonism curve is a measure of the potency of the antagonist and can be converted through a correction to the KB value (equilibrium dissociation constant of the antagonist-receptor complex). However, due to the fact that cells can modify the slope of the agonist concentration response curve, a correction method for functional IC50’s better than that used for binding where the slope of the binding curve is assumed to be unity (Cheng-Prusoff correction) has been presented by Leff and Dougall (1993): KB ¼ IC50 =ðð2 þ ð½A =EC50 Þn Þ1=n –1Þ

(9)

where n is the slope of the agonist dose-response curve and EC50 is the concentration of agonist producing 50% maximal response. If a rapid re-equilibration between the agonist, antagonist and receptor population is created then surmountable effects on the agonist dose-response curve are produced as shown in the Fig. 3A. The most straightforward approach to the measurement of the potency of a competitive antagonist is to fit binding or functional data to the simple competitive antagonism model (Eq. 6) but this necessitates fitting non-linear curves to data, a process not easily done in the early years when this mechanism was first presented. Accordingly, Heinz Schild and colleagues proposed a method whereby the dose-response curve pattern could furnish data for a linear metameter of the competitive equation to produce a straight line analysis amenable to regressional analysis (Arunlakshana and Schild, 1959). Thus, the values for the EC50 (effective agonist concentration producing half maximal response) for the curves in the absence and presence of a range of concentrations of antagonist were used to furnish dose ratios (DR where DR ¼ EC50 (in presence of antagonist)/EC50 (in absence of antagonist)) which were then utilized in a linear regression of Log(DR
1) values as a function of the logarithm of the antagonist concentration in the Schild equation: LogðDR
1Þ ¼ nLog½B
pKB

(10)

where the pKB is the negative logarithm of the equilibrium dissociation constant for the antagonist-receptor complex. The pKB is the measure of antagonist potency; for example, a pKB value of 8.0 represents an antagonist with an equilibrium dissociation constant of 10
8 M. An example of this procedure is shown in Fig. 5. Irrespective of whether a Schild regression is used to measure the pKB or the data is fit directly to the competitive antagonism equation, the Schild equation is instructive as a means to test the assumptions of simple competitivity between agonist, antagonist and the receptor population. For instance, required criteria consistent with the antagonism being truly competitive is that the regression be linear, have a slope of unity and that the agonist concentration response curves be parallel and have the same maxima. These latter two criteria easily can be tested statistically. For example, the curve may be fit to a metameter of Eq. (7) which can fit a variable slope and maxima vs. curves with a common slope and maximum. The resulting sum of squares of the deviations between the fit and the actual data subjected to Akaike’s information criterion (Kenakin, 2019) to determine whether the data statistically adhere to a model of parallel curves with a common maximum. There are other applications of Schild regressions that can detect non equilibrium steady-states and thereby inform experimenters if the resulting measurements truly reflect the properties of the system and the antagonist. One of these is the equilibration time required to achieve true binding equilibrium between the antagonist and the receptors; this is required to yield a correct estimate of the pKB.

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Fig. 5 Schild analysis to quantify the affinity of a simple competitive antagonist. (A) Concentration response curves to the agonist are shifted to the right by increasing concentrations of competitive antagonist. The degree of shift is quantified by the dose ratio (DR) which is defined as the ratio of the EC50 agonist response in the presence and absence of the antagonist). (B) Schild regression. The dextral displacement of the agonist concentration response curves shown in panel A (as ordinate values in the form Log(DR
1)) a plotted as a function of the logarithm of the molar concentration of the antagonist producing each of the respective DR values to yield a straight line. The intercept on the abscissal axis is the logarithm of the KB, equilibrium dissociation constant of the antagonist-receptor complex.

The true potency of an antagonist is correctly measured only if it has come to equilibrium with the receptor population; if the equilibration time is inadequate for this, then the potency of the antagonist will be under-estimated. The Schild regression is a useful tool to detect such inadequate equilibration times. The Schild regression for an antagonist at various times of equilibration (denoted t) is (Kenakin, 1980): " #   1
e
k2 ðð½B=KB Þþ1Þt   LogðDR t
1Þ ¼ Log½B
LogKB þ Log (11) 1 þ ð½B=KB Þ e
k2 ðð½B=KB Þþ1Þt The application of Eq. (11) to detect inadequate equilibration times depends on the first order nature of antagonist onset, i.e., higher concentrations equilibrate with receptors more rapidly than do lower concentrations. Therefore, Schild regressions obtained at inadequate equilibration times will be curvilinear which, with discreet data points often translate into Schild regressions with slopes greater than unity (see Fig. 6). An examination of Eq. (6) shows that for simple competitive antagonism there is a strict relationship between the concentrations of agonist and antagonist ([A] and [B]) and their respective equilibrium dissociation constants (KA and KB). Therefore, if there is an alteration in the system brought about by aberrations in the concentrations of agonist and/or antagonist or the nature of the receptor population (production of response from another receptor subtype in the system), then the Schild regression is an excellent tool to detect such conditions. Fig. 7A shows the observed Schild regression for a competitive antagonist in a system where the agonist activates a mixed population of receptors (Kenakin, 1992). Thus, a linear Schild regression will be observed until the agonist

Fig. 6 Failure to allow a sufficient period of pre-equilibration of the antagonist with receptors results in a curvilinear Schild regression with steep (slope > 1) slope. Fine dotted line is an adequately pre-equilibrated system showing a linear Schild regression with slope of unity. Heavy dashed line is the regression when the pre-equilibration period is inadequate. Open circles are experimental data points for three concentrations of antagonist not pre-equilibrated adequately with the receptor. In absence of data to define the curved regression, this would result in an apparent linear regression (solid line) with a slope > 1.

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Fig. 7 Application of Schild analyses to detect (A) multiple receptor populations and (B) uptake and removal mechanisms for agonists. (A) The antagonist interacts with a primary receptor to yield a linear Schild regression of slope unity until the agonist encounters the second (less sensitive to the antagonist) receptor. At this point, a decrease in slope to the point where the antagonist blocks the secondary receptor ensues. (B) In a system where there is a saturable removal of the agonist, a Schild regression blocking receptors under these conditions will be observed. However, as the concentration of agonist increases (due to blockade of receptors and dextral displacement of the curve) and the removal mechanism becomes saturated, the increased agonist concentration in the receptor compartment causes a decrease in the antagonist potency made evident by an increased slope.

encounters the second receptor population in which case a diminution of antagonist potency is encountered. This is seen as a curvilinear Schild regression as shown in Fig. 7A (Kenakin, 1992). Other non-equilibrium steady-states can be identified through aberrations in Schild regressions. For example, if there is a saturable removal of agonist from the receptor compartment then curvilinear Schild regressions like those shown in Fig. 7A will be observed. Alternatively, if there is a saturable removable mechanism for the antagonist then the increased free antagonist available for blockade of receptors after saturation of the removal process (i.e., adsorption, uptake removal) will be seen as an increase in the ordinate values of the Schild regression (see Fig. 7B). The simple competitive antagonism model assumes a single mechanism of action for the antagonist, i.e., reversible binding to the receptor. Thus the Schild regression is a useful method of detecting other off target activities for the antagonist. For instance, the muscarinic antagonist ambenonium is also an acetylcholinesterase inhibitor and this effect sensitizes the functional systems to substrates of acetylcholinesterase such as acetylcholine (Kenakin and Beek, 1985). This, in turn, cancels a portion of the antimuscarinic effects of ambenonium as a receptor blocker and this is manifest as a dextral displacement of the Schild regression. Fig. 8A shows how there is a sixfold under-estimation of the potency of ambenonium as a receptor antagonist when the acetylcholinesterase substrate acetylcholine is used as the agonist as opposed to if bethanechol (not a substrate for acetylcholinesterase) is used. Fig. 8B shows how this disparity is eliminated through prior inhibition of acetylcholinesterase with neostigmine. In general, the Schild regression and the prediction that parallel dextral displacement of agonist dose response curves is produced by simple competitive antagonism is an excellent way to detect non-equilibrium steady-states in functional assays. These assays

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Fig. 8 Concomitant blockade of muscarinic receptors and acetylcholinesterase by ambenonium. (A) Schild regressions for ambenonium with acetylcholine (filled circles) and bethanechol (open circles) as the agonist. The reduced potency of ambenonium for acetylcholine is caused by potentiation of response through acetylcholinesterase blockade. (B) Cancellation of ambenonium’s acetylcholinesterase effect through pretreatment with the acetylcholinesterase inhibitor neostigmine. Data redrawn from Kenakin TP and Beek D (1985) Self-cancellation of drug properties as a mode of organ selectivity: The antimuscarinic effects of ambenonium. The Journal of Pharmacology and Experimental Therapeutics 232(3): 732–40.

Orthosteric Receptor Antagonism

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Fig. 9 Temporal sensitivity of b-adrenoceptor blockade of isoproterenol responses by atenolol. (A) Agonist isoproterenol (10 nM) inotropy blockade by addition of atenolol (10 mM). (B) Lack of blockade of isoproterenol (10 nM) lusitropy by atenolol (10 mM). Data redrawn from Corsi M and Kenakin T (2000) The relative importance of the time-course of receptor occupancy and response decay on apparent antagonist potency in dynamic assays. Journal of Autonomic Pharmacology 20(4): 221–7.PMID: 11260360.

depend on the assumption that the agonist and antagonist have only a single property, an assumption that quite often may be wishful thinking. Thus, deviation from the simple competitive model can provide evidence of secondary effects of agonists and/or antagonists that can be extremely useful in the characterization of possible therapeutic drug effect. For example, Fig. 9 shows how the order of addition of agonist and antagonist can be useful to determine agonist mechanism of action. It can be seen from Fig. 9A, the inotropic (effect on isometric contractile tension) and lusitropic (rate of relaxation of myocardial response) effects of isoproterenol b-adrenoceptor antagonist atenolol, are blocked equally well; this is consistent with both effects being mediated by activation of b-adrenoceptors by isoproterenol. However, if isoproterenol is added before atenolol, then it can be seen that the inotropic effects are readily reversed by atenolol (Fig. 9) whereas the lusitropic effects are not (Fig. 9) (Corsi and Kenakin, 2000). This suggests that atenolol will have differential effect on inotropic vs. lusitropic effects in vivo where the intervention would be the result of addition of the antagonist to an ongoing physiological function. Orthosteric antagonists also can be useful in determining the site of action of new receptor agonists. Fig. 10A shows the activation of Gi protein by the muscarinic orthosteric (binds to the same site as the natural agonist acetylcholine) agonist carbachol; it can be seen that the orthosteric antagonist QNB blocks the carbachol response. In contrast, the allosteric agonist alcuronium, which binds to a site distinct from that utilized by carbachol and QNB, is not blocked by QNB thereby illustrating the allosteric nature of the alcuronium activation of the receptor (Jakubík et al., 1996). While Schild regressions can be useful tools to determine non equilibrium states in systems and measuring the potency of competitive antagonists, non-linear fitting of agonist concentration response curves obtained in the absence and presence of a range of concentrations of antagonist is the most common method of determining antagonist competitivity and the potency of the

Fig. 10 Effects of orthosteric receptor antagonism on responses to the orthosteric muscarinic agonist carbachol (panel A) and allosteric agonist alcuronium (panel B). Curves shown in the absence (filled circles) and presence (open circles) of the orthosteric antagonist quinuclidinyl benzilate (100 nM). Data redrawn from Jakubík J, Bacáková L, Lisá V, el-Fakahany EE and Tucek S (1996) Activation of muscarinic acetylcholine receptors via their allosteric binding sites. Proceedings of the National Academy of Sciences of the United States of America 93: 8705–8709.

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Fig. 11 Effects of increasing concentrations of a simple competitive antagonist on agonist responses in a system where the concentration response curve slope is unity (black lines) and where the slope is ¼ 2 (grey lines) equations to the right of the figure from respective Black/Leff operational model systems describing response.

antagonist. Thus fitting data directly to Eq. (6) for concentration response curves with slopes not significantly different from unity yields the value of KB for the antagonist. As discussed in Chapter 1.14, cells can modify receptor stimulus to yield concentration response data with slopes significantly different from unity (Tyson et al., 2003); under these circumstances, data is fit to a version of Eq. (6) with a variable slope n: Response ¼

n

½A  sA

n

½A n sA n Em þ ð½A  þ KA ð1 þ ½B=KB ÞÞn

(12)

A comparison of models with slope ¼ and s to unity is shown in Fig. 11.

1.11.3.2

Non-competitive antagonism

If the offset rate of the antagonist is much slower than that of the agonist, then a condition whereby the agonist and antagonist do not compete occurs. If the antagonist is pre-equilibrated with the receptor system before addition of the agonist, then the agonist basically binds to the open receptors (not bound by agonist) and then slowly re-equilibrates with the remaining antagonist bound receptors. Since the antagonist offset is slow, the agonist cannot achieve the receptor occupancy dictated by normal mass action and the antagonist dominates. As shown by Eq. (4), the fractional receptor occupancy by the agonist in the presence of the noncompetitive antagonist will always be < 1 and if complete receptor occupancy is required for the agonist to produce maximal response, then a non-competitive antagonist will always depress the maximal receptor occupancy of the agonist. This depression of maximal receptor occupancy is translated into an IC50 curve that is insensitive to the concentration of radioligand in the binding assay (Fig. 12). Thus, unlike the situation with competitive antagonists, whatever the level of radioligand concentration, the IC50 is actually equal to the KB and becomes a system independent measure of non-competitive antagonist potency. Experimentally, in studies measuring agonist functional responses this is not always the case because systems can have a “receptor reserve” whereby the agonist need occupy only a fraction of the receptors to produce maximal response. This is evident

Fig. 12 IC50 curve to quantify antagonism for a non-competitive orthosteric antagonist. (A) Effects of a non-competitive antagonist on agonist concentration response curves. Open circles show the responses to one concentration of agonist (0.1 and 1 mM) in the presence of various concentrations of antagonist. (B) Response to both concentrations agonist as a function of a range of concentration of the antagonist. The antagonism defines an inverse sigmoid curve and the potency of the antagonist is quantified by the midpoint designated as the IC50 (inhibitory concentration producing 50% inhibition). Note that there is no effect of increasing agonist concentration on the potency of the non-competitive antagonist.

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281

Fig. 13 Functional antagonistic effects of a non-competitive antagonist in systems of varying receptor reserve. Top panel: IC50 curves for the antagonist in systems with no receptor reserve (sA ¼ 1: see panel A) and a large receptor reserve (sA ¼ 300; see panel B). With no receptor reserve, non-competitive insurmountable antagonism will be observed. In systems with a large receptor a non-competitive antagonist will produce surmountable (apparent competitive) antagonism until the free receptor population diminishes below the level of the reserve needed to produce maximal response.

in the functional response counterpart of the non-competitive receptor occupancy equation; thus the response to an agonist in a functional system in the presence of a non-competitive antagonist in a system where the slopes of the concentration responses are not significantly different from unity is given by Eq. (7). The maximal response to the agonist from Eq. (7) is given by: Maximal Response ¼ sA =ð1 þ sA þ ½B=KB Þ

(13)

It can be seen that for high values of sA (sA [ 1), there will systems where the maximal response to the agonist will still be maximal (Maximal response ¼ 1) even in the presence of the non-competitive antagonist, i.e., a non-competitive antagonist will not depress the maximal response to the agonist. Since sA is defined as [Rtotal]/KE where [Rtotal] is the receptor density of the system and KE the efficiency of receptor coupling in the cell (Black and Leff, 1983), it can be seen that in tissues with high receptor expression and/or very efficient receptor coupling (i.e., a high effective receptor reserve for the agonist), non-competitive antagonists will produce surmountable blockade at concentrations near the KB. The relationship between the agonist maximal response and the efficacy of the agonist (i.e., corresponding to the receptor reserve for the agonist) is shown in Fig. 13. For a low efficacy agonist (with no receptor reserve) such as sA ¼ 1 (Fig. 13A), the non-competitive antagonist depresses the maximal response at all concentrations. In contrast, for the high efficacy agonist with a high receptor reserve (sA ¼ 300, Fig. 13B), there is a range of antagonist concentrations that produce essentially surmountable antagonism before depression of the maximal response is observed. For this reason, whether or not the agonist maximal response is depressed can be an unreliable indicator of competitive vs. noncompetitive antagonism. The corresponding equation for functional non-competitive antagonism in systems with concentration response curves that have slopes not equal to unity is: Response ¼

½A n sA n Em ½A sA n þ ð½A ð1 þ ½B=KB Þ þ KA ½B=KB þ KA Þn n

(14)

A comparison of non-competitive antagonism in systems with n ¼ 1 and n s 1 is shown in Fig. 14. While there are clear differences between competitive and non-competitive antagonism in vitro, these differences are not relevant for the in vivo antagonism of physiological agonism in vivo. As seen in Fig. 13, if it is assumed that the in vivo physiological system normally operates within the sensitive range of the agonist dose-response curve, then whether the antagonism is competitive or non-competitive makes little difference to the in vivo effect of the antagonist. Specifically, as the antagonist is absorbed, blockade of endogenous effect will be seen; as the antagonist is cleared from the receptor compartment, the response returns; essentially an inverse bell shaped curve is observed (Fig. 15). Whether the antagonist is competitive or non-competitive makes little difference to the acute in vivo antagonism. However, insofar as in vitro non-competitive antagonism reflect the rate of offset of the antagonist from the receptor, it should not be considered that the non-competitive nature of the antagonist is not relevant to the in vivo effects of the molecule. Specifically, if the non-competitive antagonism seen in vitro reflects a slow offset of the antagonist from the receptor, then the non-competitive nature of the molecule will predict a better target coverage of the molecule in vivo (vide infra).

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Fig. 14 Effects of increasing concentrations of a non-competitive antagonist on agonist responses in a system where the concentration response curve slope is unity (black lines) and where the slope is ¼ 2 (grey lines) equations to the right of the figure from respective Black/Leff operational model systems describing response.

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Fig. 15 In vivo effects of competitive and non-competitive orthosteric antagonists. (A) Blockade of agonist response (either dextral displacement or depression of agonist concentration response curves) in an in vivo system with an ambient level of endogenous agonist producing a baseline response will cause a depression of in vivo ambient response while the antagonist binds to the receptors followed by a waning of antagonism as the antagonist is cleared from the receptor compartment. (B) The in vivo profiles of antagonism for competitive and non-competitive antagonists are very similar thereby indicating that the in vitro profile of surmountable vs. insurmountable antagonism has little impact on the in vivo blockade by the antagonist.

1.11.3.3

Hemi-equilibrium antagonism

The previous sections describe kinetic extremes for the rate of offset of the antagonist; rapid (competitive) and slow (noncompetitive). Experimentally, there can be conditions whereby an intermediate rate of offset can produce a unique pattern of antagonism. This pattern is related to the temporal window made available by the assay to observe the binding of the agonist to produce measurable pharmacologic response. Specifically, if the rate of antagonist offset is sufficiently slow to produce (with respect to the agonist binding) partial offset from the receptor population and the window for observation of the kinetic process (in this case seen as agonist response) is truncated (i.e., the window to observe response is closed before the agonist/antagonist equilibrium is

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283

Fig. 16 Hemi equilibria with orthosteric antagonism. (A) Effects of a competitive antagonist with a slow rate of offset from the receptor in a system with a restricted period of response collection (3 s). As antagonism increases with increasing concentrations, the maximal response of the shifted curves depress to a new steady-state below the true maximum. (B) The size of the temporal window available to collect response controls the degree of maxima depression to the antagonist. With short times (1, 3 s) the maximum are depressed but the same antagonist in a system with a longer period of response collection (i.e., 30 s) would not produce a depressed maximal response.

allowed to come to completion), then a unique pattern of antagonism is produced. This is a kinetic condition referred to as hemiequilibrium and the relative receptor occupancies of the antagonist and agonist determine the overall observed agonist occupancy, i.e., there will be a differential effect of the slow antagonist offset determined by the required agonist occupancy. This produces a selective non-competitive antagonism of the agonist concentrations required to produce maximal response where the occupancy of the agonist is optimal. The result is a series of agonist concentration-response curves that are shifted to the right in a manner similar to competitive antagonism but with maximal responses to the agonist depressed to a new equilibrium level (see Fig. 16A). The unique confluence offset rates of the antagonist and agonist coupled with the window of time available for the measurement of response leads to the pattern of antagonism seen in Fig. 16A. The effect of the temporal window available to capture agonist response (denoted as time t) is given by Kenakin et al., 2006):       Maximal Response ¼ 1
e
k2 t sA þ 1 = 1
e
k2 t sA þ 1 (15) where sA is the efficacy of the agonist, k2 the offset rate of the antagonist and t the time allowed for agonist effect before measurement of the response (temporal window for response collection). Various assays may have different temporal windows and this may, in turn affect the relative surmountability and insurmountability of the antagonism. An assay that is particularly sensitive to this effect is the calcium transient response. The assay that measures this presents only the first few seconds of response to the agonist and this truncation of response window routinely produces insurmountable antagonism due to hemiequilibrium effects. Fig. 16B shows the effect of magnitudes of response windows on the antagonism of a slow acting antagonist; it can be seen that this response window, a property of the functional assay, can dictate the degree of insurmountable antagonism seen.

1.11.3.4

Irreversible antagonism

Non-competitive antagonism occurs when the antagonist has a slow offset from the receptor and full equilibration between the agonist, antagonist and receptor population cannot occur within the time frame needed to observe response. However, there is an equilibrium reached whereby the antagonist, though slow, will diffuse off of the receptor and an agonist may bind. A kinetic extreme of this situation is when the antagonist does not come off of the receptor when bound, i.e., it is truly irreversible. Under these circumstances, the response to the receptor occupancy by an agonist (rA/B) is given by: 
½A =KA e
k1 ½Bt rA=B ¼ (16) ½A =KA þ 1 This essentially describes a chemical reaction that does not reach equilibration but rather progresses to completion. Under these circumstances, given enough time, an irreversible antagonist will block all receptors in the system and there cannot be a condition whereby the agonist produces a response (even with a depressed maximum). Therefore the discerning property of a non-competitive antagonist vs. an irreversible antagonist is that concentrations of the former can be found that produce a depressed but stable concentration response to the agonist that does not collapse to a totally insensitive receptor system (Fig. 17B and C). A caveat to this requisite is that a truly irreversible antagonist, if it reacts with multiple species in the system (i.e., water), may be depleted to a condition of partial inactivation of the system and still yield a concentration response curve to the agonist. Fig. 17A shows a chemically reactive beta haloalkylamine molecule which cyclizes to an aziridinium ion to alkylate proteins. Molecules of this type such as phenoxybenzamine are used to irreversibly block a-adrenergic and muscarinic receptors (Kenakin and Cook, 1976). While the aziridinium ion alkylates groups on proteins, it also reacts with water to form a chemically unreactive alcohol therefore in

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Fig. 17 Irreversible vs. pseudo-irreversible orthosteric antagonism. (A) Alkylating agents such as b-haloalkylamines form a reactive aziridinium ion in solution which can then react with proteins (to inactivate receptors) or water (to form an alcohol). (B) Such irreversible receptor alkylation diminishes response to produce dextral displacement and depression of maxima; given sufficient time, the receptors will be completely inactivated and the response will diminish to zero. (C) A pseudo-irreversible non-competitive antagonist, which has a slow rate of offset from the receptor but does not truly irreversible inactive it, will come to a depressed maximum steady-state and not necessarily diminish response to zero.

solution there is a competition that can deplete the alkylating agent and produce incomplete alkylation (Cook et al., 1980). However, these depressed concentration response curves most likely will not conform to the strict pattern for non-competitive antagonist given by Eq. (14), i.e., a range of concentrations of antagonist should yield a strict pattern of depressed curves according to the non-competitive model. It is not possible to measure an equilibrium dissociation constant to gauge affinity since there is no equilibrium reached however the avidity of such agents to irreversibly block function can still be measured. One scenario where this is operative is in time dependent inhibition of cytochrome P450 enzymes in pharmacokinetics. Specifically, drug interactions can occur when two drugs interfere with a common metabolizing enzyme and a serious problem can occur if one of those interferences in an irreversible manner. This is referred to as time dependent inhibition because an equilibrium is never attained but rather the potency of the molecule increases over time and does not reach a steady state. Most reversible reactions reach equilibrium within 3 h and the potency of an reversible inhibitor will not change after that time period. If the potency steadily increases over long periods of time this suggests that the inhibition is irreversible and that a chemical reaction is taking place. For instance, the time dependent inhibition of Cyp2C9 by erythromycin does not come to equilibrium over a period of 70 h (McGinnity et al., 2006). Fig. 18 illustrates a method to quantify irreversible inhibition, in this case the time dependent inhibition of Cyp3A4 by the antiarrhythmic drug amiodarone (Cheong et al., 2017). The rate of inactivation is measured (Fig. 18A) and plotted as a function of the concentration of amiodarone causing the inactivation to produce a curve (Fig. 18B). The midpoint and maximum of this curve furnishes a value for the maximal rate of inactivation and concentration producing half maximal inactivation which can be used to characterize the process.

1.11.4

Antagonists with efficacy

Molecular dynamics predicts that a ligand binding to a receptor will necessarily modify that receptor in terms of the thermodynamic ensemble dynamics of the various receptor microstates, i.e., binding is not a passive process (Kenakin and Onaran, 2002). Given this, and in light of the many possible effects changes in receptor conformation can have in terms of the interaction of the receptor with cytosolic biochemical pathways, there is a high probability that ligand binding will be associated with some sort of cellular efficacy. This efficacy may be excitatory for the primary agonist signaling (the antagonist will be a partial agonist), inhibitory for the primary agonist signaling (the antagonist will be an inverse agonist) or secondary to agonist-receptor interaction (i.e., b-arrestin activation, ERK activation). It is worth considering these in turn as efficacy is an integral part of the properties for any antagonist.

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285

Fig. 18 Estimation of the potency and maximal effect of a truly irreversible antagonist. (A) The antiarrhythmic amiodarone produces an irreversible inhibition of the cytochrome P450 enzyme CYP3A4. Panel shows the rate of enzyme inhibition by various concentrations of amiodarone. (B) The various rates of inhibition of CYP3A4 is plotted as a function of amiodarone producing the inhibition to produce a hyperbolic curve. The midpoint of this curve furnishes the potency of amiodarone as an irreversible inhibitor and the maximal asymptote the maximal effect of amiodarone. Date redrawn from McGinnity DF, Berry SJ, Kenny JR, Grime K and Riley RJ (2006) Evaluation of time-dependent cytochrome P450 inhibition using cultured human hepatocytes. Drug Metabolism and Disposition 34: 1291–1300.

1.11.4.1

Partial agonism

If the antagonist activates the receptor with a low level of efficacy while it binds to produce antagonism, then a pattern of receptor blockade is produced characterized by elevated basal response coupled with dextral displacement of agonist concentration response curves-see Fig. 19. The potency of a partial agonist can be obtained in the same manner as for silent competitive antagonists, namely

Fig. 19 Antagonism of responses to a full agonist by a partial agonist. Concentration response curves to the full agonist are shifted to the right by increasing concentrations of a partial agonist. The addition of the partial agonist also elevates the baseline due to the direct response produced by the partial agonist. Tracking of a single response of the full agonist (shown by the open circles at 1 mM) defines an inverse sigmoidal IC50 curve that does not diminish to zero response due to the direct agonist activity of the partial agonist.

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through an estimate of the IC50 or a Schild regression (see Fig. 19). If an IC50 curve is determined, it may saturate as a bottom level greater than zero response; this will occur when the response reaches the response determined by the direct efficacy of the partial agonist. Thus, failure to completely displace an agonist in a functional IC50 curve by an unknown antagonist is presumptive evidence of partial agonism. The affinity of a partial agonist also can be estimated from the agonist EC50 (concentration producing 50% of the maximal response to the partial agonist). The potency of an agonist is given by: EC50 ¼ KA =ð1 þ sA Þ

(17)

where KA is the equilibrium dissociation constant of the partial agonist-receptor complex and sA is the efficacy of the partial agonist. Since partial agonists produce less than the system maximal response then sA usually is low thus as sA / 0, EC50 / KA. Thus, for low efficacy partial agonists, the EC50 is usually a close approximation of the KA. This provides a useful internal check of the potency of a partial agonist since the agonist EC50 should be equal to the affinity determined from a Schild regression (see example in Fig. 20 for the muscarinic receptor partial agonist alvameline, Bdioui et al., 2018). The response to an agonist obtained in the presence of a partial agonist is defined within the operational model for concentration response curves with slopes equal to or not equal to unity as shown in Fig. 21. Finally, a distinction should be made between observed partial agonism and the efficacy of the partial agonist. The observation of agonist response is a product of the interplay between the efficacy of the molecule and the sensitivity of the system. Therefore, in functional systems of low sensitivity a low efficacy partial agonist may not produce agonist response at all but rather will resemble a simple competitive antagonist (i.e., see Fig. 5). Simple competitive antagonists will always reduce ambient natural endogenous agonism in vivo but a more complex pattern of agonism and antagonism may be seen with partial agonists. Specifically, depending on the magnitude of the efficacy of the partial agonist, the partial agonist may produce increased response (point A in Fig. 20), no response (point. B) or antagonism (point. C) of response in vivodsee Fig. 22. This is seen in the interplay of b-adrenoceptor partial agonists pirbuterol, prenalterol, and pindolol in vivo under conditions of different resting heart ratedsee Fig. 22 (Kenakin and Johnson, 1985).

1.11.4.2

Inverse agonism

Efficacy is defined as the property of a ligand that causes the receptor to change it’s behavior toward it’s host (the cell). The key is to recognize that receptors themselves have behaviors and with the advent of the discovery of receptor constitutive activity, a receptor

Fig. 20 Comparison of the agonist and antagonist potencies of a partial agonist. The EC50 for the direct agonist response to a partial agonist should, under equilibrium conditions, equal the antagonist potency of the partial agonist as measured by Schild analysis. This is demonstrated by the muscarinic partial agonist alvameline. Data redrawn from Bdioui S, Verdi J, Pierre N, Trinquet E, Roux T, Kenakin T (2018) Equilibrium assays are required to accurately characterize the activity profiles of drugs modulating Gq-protein-coupled receptors. Molecular Pharmacology 94: 992–1006.

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Fig. 21 Effects of increasing concentrations of a partial agonist on agonist responses in a system where the concentration response curve slope is unity (black lines) and where the slope is ¼ 2 (grey lines) equations to the right of the figure from respective Black/Leff operational model systems describing response.

Fig. 22 Effects of partial agonists on endogenous in vivo responses to ambient levels of natural agonist. (A) In systems where the endogenous agonist response is low, a partial agonist may produce agonism (point A). No response may be produced if the efficacy of the partial agonist coincides with the endogenous level of response (point B) whereas in systems with a high level of endogenous response, the partial agonist may produce antagonism (point C). (B) In vivo heart rate responses to three b-adrenoceptor partial agonists of varying efficacy (pirbuterol > prenalterol > pindolol) in systems of low endogenous heart rate (105 bpm; chloralose-pentobarbital anaesthesia) and high endogenous hear rate (225 bpm; urethane anaesthesia). Data redrawn from Kenakin TP and Johnson SF (1985) The importance of the alpha-adrenoceptor agonist activity of dobutamine to inotropic selectivity in the anaesthetized cat. European Journal of Pharmacology 111: 347–54.

behavior was discovered that forms an active state that can activate signaling pathways; selective stabilization of an inactive form of the receptor would therefore reduce cellular response in a constitutive system. This was discovered by Costa and Herz (1989) where it was observed that some opioid receptor ligands (i.e., ICI174) produce a concentration-dependent depression of basal cellular response mediated by the constitutively active opioid receptor. This type of effect required rewriting of receptor theory to allow receptors to spontaneously form active states; high expression of such receptors causes an abundance of active state receptors that can then go on to couple to signaling pathways and produce elevated basal cellular activity even when there is no agonist ligand being present. The ability of some ligands to reduce this spontaneously elevated basal response is linked to the relative affinity of the ligand for the inactive (Ri) and active (Ra) state of the receptors in the system. Specifically, a constitutively active receptor system can be depicted as: L Ri

Ra DKa

Ka A

A

(18)

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where L is the allosteric constant determining the relative spontaneous level of active state receptors (L ¼ [Ra]/[Ri]) and a represents the differential affinity of the ligand A for the two receptor states. Under these circumstances, the ratio of receptor species found in the absence (r0) and presence of a saturating concentration of ligand A (rN) is given by: rN að1 þ LÞ ¼ ð1 þ aLÞ r0

(19)

It can be seen from Eq. (18) that unless a ¼ 1, the ratio of states in the active vs. inactive state will change upon addition of ligand A. If the ligand prefers the inactive state, then rN/r0 will be < 1, i.e., there will be an enrichment of the inactive state. Receptors have an intrinsic propensity to form an active state determined by the magnitude of the allosteric constant L; usually L has a very low magnitude and the level of spontaneously formed active state receptors in any functional system is low. However, if the receptor expression is high, then there may be a sufficient number of receptors to allow L to result in a substantial level of active state receptors. For example, for L ¼ 10
4, in a system of 104 receptors, there would be 1 receptor in the active state. However, if the receptor level were to be increased to 107, 10 there would be 1000 active state receptors. The active state receptors present in the system can spontaneously couple to signaling proteins and produce an elevated basal level of cellular response; this is referred to as constitutive (receptor) activity. In constitutively active systems where the basal response is elevated, an inverse agonist preferentially binds to the inactive state of the receptor and removes [Ra] from the system; this produces a decrease in the basally activated response referred to as inverse agonism (Samama et al., 1993). Fig. 23 shows the effect of an inverse agonist on agonist concentration response curves in a constitutively active system. It can be seen that concomitant with the dextral displacement of the curve, a decrease in the basal response is observed. This translates to an IC50 curve that descends below the apparent level of zero response (since this is actually an elevated response due to constitutive activity). The affinity of inverse agonists is measured as it is for competitive antagonists, namely in a Schild regression utilizing the parallel dextral displacements of the agonist concentration response curves. Just as with partial agonists, the EC50 for functional inverse agonism should correspond to the affinity of the inverse agonist as measured in a Schild regression (see Fig. 24). The response to an agonist obtained in the presence of an inverse agonist is defined within the operational model for concentration response curves with slopes equal to or not equal to unity as shown in Fig. 23. The phenomenon of inverse agonism was first reported as a receptor mediated negative efficacy by Costa and Herz (1989). Subsequent to the report, a great deal of investigative experimentation was done to determine the possible therapeutic relevance of the property but there are few if any constitutively active organ systems in vivo where the effect is relevant; in nonconstitutively active systems inverse agonists function as simple competitive antagonists. However, in many cancers, there is a tremendous over-expression of receptors in tumors which could lead to constitutive activity contributing to tumor growth. For instance, vasoactive intestinal peptide (VIP) surface receptor levels are increased 100,000 times in pancreatic epitheloid carcinoma cells (Virgolini et al., 1994). Most natural tissues have a receptor expression level where there is little if any constitutive activity. However, a 100,000-fold increase could easily produce a resting elevated basal response due to constitutive receptor activity consistent with a pathophysiological mechanism by which tumor growth is supported by constantly elevated metabolic activity (Kenakin, 2001). In such cases only an inverse agonist would decrease the errant elevated metabolic response if the elevated activity is due to constitutive receptor activity. In fact, an antagonist of VIP has been shown to decrease tumor size although in this case it is not clear whether the effect is through inverse agonism or blockade of cell-secreted VIP (Virgolini et al., 1994).

Fig. 23 Effects of inverse agonists on constitutively active functional systems. (A) Agonist concentration response curves are shifted to the right and the constitutively active elevated basal response of the system is decreased by increasing concentrations of inverse agonist. (B) Tracking the effects of a single concentration of agonist (in this case 1 mM) plotted as a function of the inverse agonist concentrations yields an inverse sigmoidal IC50 plot. The maximal effect of the inverse agonist goes beyond the constitutively elevated basal response of the system.

Orthosteric Receptor Antagonism

289

Fig. 24 As with partial agonists, the inverse agonism direct effect EC50 should match the potency of the inverse agonist blocking agonist response at equilibrium.

While there is a paucity of instances where inverse agonism can be linked to uniquely useful therapeutic antagonism profiles, it has been linked to a negative property of some antagonists. Specifically, H2 histamine receptor antagonists for the treatment of ulcers have been shown to demonstrate tolerance with chronic use (Nwolko et al., 1990; Deakin and Williams, 1992; WilderSmith et al., 1990). This tolerance was subsequently shown to be the result of an increased cell surface expression of histamine H2 receptors causing an increased sensitivity of oxyntic cells to histamine, more acid release and escape from histamine receptor antagonism (Nwolko et al., 1991). This effect is associated with inverse agonists but not neutral antagonists that do not prefer the inactive receptor state (Smit et al., 1996). Selective stabilization of the inactive receptor state by inverse agonists prevents natural receptor activation, phosphorylation and internalization; in the face of continued receptor synthesis and transport to the cell surface this can lead to elevated cell surface receptor presence. It should be noted that this phenomenon is receptor type and cell type dependent and not seen with all inverse agonists. The previous discussions relate to efficacies associated with the primary receptor signaling event linked to the antagonist activity, i.e., G protein activation. With the advent of new functional assays with the capability to detect receptor efficacies beyond a single G protein coupling event, has come the realization that ligands can have a number of “efficacies” linked to the unique receptor conformation stabilized by the antagonist binding and the interaction of that unique conformation with a range of cellular coupling and signaling proteins, i.e., efficacy is “pluridimensional” (Galandrin and Bouvier, 2006). Some of these can affect the observed blocking activity of the antagonist as in the case of ambenonium which simultaneously blocks acetylcholinesterase to potentiate acetylcholine responses and muscarinic receptors to reduce acetylcholine response (Kenakin and Beek, 1985)dsee Fig. 8. Other efficacies are not as obvious such as the ERK activating properties of b-blockers. For example, the beneficial effects of carvedilol could also be influenced by its b-arrestin-mediated partial agonist activity for activation of extracellular regulated kinase 1/2 (ERK1/2) (Wisler et al., 2007; Kim et al., 2008). Other b-blockers have similar profiles such as nebivolol (Erickson et al., 2013), alprenolol (Kim et al., 2008), and propranolol (Baker et al., 2003; Azzi et al., 2003) similarly have efficacies associated with ERK activation that my significantly contribute to their therapeutic profiles of activity.

1.11.4.3

Pharmacological resultant analysis

If an orthosteric competitive antagonist has more than one activity and if the secondary activity interferes with the primary antagonism, then a procedure referred to a “resultant analysis” can be done to elucidate the original receptor blocking potency of that antagonist; in essence, this procedure removes the obfuscating second activity (Black et al., 1986). The key to this procedure is to use the multiple activity antagonist (referred to as the “test” antagonist denoted [C]) in conjunction with a reference single activity antagonist (referred to as the reference antagonist, denoted [B]). There is a strict relationship between the concentrations of two competitive antagonists interacting with an agonist that predicts additive dose ratios (Paton and Rang, 1965) and the way this is

290

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perturbed by the secondary effect of the test antagonist is used to uncover this secondary effect. Schild regressions for a range of concentrations of the reference antagonist are carried out with added concentrations of the test antagonist and the Schild regressions used to delineate the receptor blocking activity of the test antagonist. Thus, as seen with Eq. (3), the agonist receptor occupancy in a system containing a concentration [B] of reference antagonist and [C] test antagonist is given by: rA=BþC ¼ ½A 0 =KA =ðð½A 0 =KA Þ þ ð½B=KB Þ þ ð½C=KC Þ þ 1Þ

(20)

where [A0 ] is the concentration of agonist and KC is the equilibrium dissociation constant of the test antagonist-receptor complex. In the absence of the reference antagonist, the receptor occupancy of the agonist in the presence of the test antagonist is: rA=C ¼ ½A =KA =ðð½A=KA Þ þ ð½C=KC Þ þ 1Þ

(21)

Eqs. (20) and (21) can be used to calculate equiactive agonist dose ratios (r0 ) as: ½A 0 =½A  ¼ r 0 ¼ 1 þ ð½B0 =KB Þ=ð1 þ ½C=KC Þ

(22)

In the absence of the test antagonist, the dose ratios are: r ¼ 1 þ ð½B=KB Þ

(23)

r0 ) indicating equal levels of antagonism, an equiactive dose ratio of antagonist

For equal values of r from Eqs. (22) and (23) (r ¼ can be defined ([B0 ]/[B] defined as “y”); this number compares the concentration of reference antagonist needed to produce a given level of blockade in the absence and presence of the test antagonist for the same level of blockade. Thus: ½B0 =½B–1 ¼ y
1 ¼ ½C=KC

(24)

Converting this to a logarithmic metameter yields a straight line plot referred to as a ‘resultant’ plot which can be used to determine the pure receptor binding affinity of the test antagonist without the secondary obfuscating activity: Log ðy
1Þ ¼ Log½C þ Log KC

(25)

Fig. 25 shows this technique applied to the measurement of adenosine receptor antagonism by the phosphodiesterase inhibitor isobutylmethylxanthine (IBMX); in this case, the reference antagonist is 8-sulfophenyltheophylline (8-SPT). IBMX blocks receptors to decrease sensitivity to adenosine agonists (in this case 2-chloroadenosine) and inhibits phosphodiesterase to potentiate adenosine receptor activated response to cancel the antagonism (Fig. 25). Therefore, Schild regressions for 8-SPT in the absence and presence of a range of concentrations of IBMX produces an array of regressions (Fig. 25) that can be used to estimate y values and produce a resultant plot (Fig. 25). In this case, the receptor affinity for IBMX is found to be 3.5 mM (Kenakin and Beek, 1987). This procedure can only be done with competitive antagonists since it relies on the principle of additive dose-ratios (Paton and Rang, 1965).

Fig. 25 Pharmacological resultant analysis. (A) The antagonist blocks agonist response through blockade of the receptor and concomitantly potentiates agonist response through blockade of the enzyme that degrades the agonist-mediated signaling messenger producing response. In this case, the agonist increases cyclic AMP and the antagonist blocks phosphodiesterase blocking the degradation of cellular cyclic AMP produced by the agonist. (B) Schild regressions to a simple competitive antagonist of adenosine receptors (S-SPT) produced in the absence (filled circles) and presence of increasing concentrations of the dual adenosine receptor antagonist and phosphodiesterase inhibitor isobutylmethylxanthine (IBMX). Schild regressions obtained in the absence (filed circles) and presence of IBMX (10 mM, open circles, 30 mM, filled triangles, 100 mM open triangles). (C) Resultant plot for IBMX unveiling adenosine receptor antagonism without the obfuscating effects of PDE inhibition showing the pKB for adenosine antagonism by IBMX of 5.4. Data redrawn from Kenakin TP and Beek D (1987) Measurement of antagonist affinity for purine receptors of drugs producing concomitant phosphodiesterase blockade: The use of pharmacologic resultant analysis. The Journal of Pharmacology and Experimental Therapeutics 243(2):482–6.

Orthosteric Receptor Antagonism

1.11.5

291

Verisimilitude to allosteric antagonism

An antagonist can interfere with agonist interaction at the receptor through steric hindrance (orthosteric antagonism) but also through an allosteric action whereby the antagonist stabilizes a different conformation of the receptor with an altered (reduced) affinity for the agonist (allosteric antagonism). This second mechanism is very different from the former but can appear to be very similar to the former within a limited range of antagonist concentrations. It is very important to differentiate orthosteric from allosteric antagonism since these different mechanisms of antagonism can result in very different profiles of activity in the therapeutic realmdsee Table 1. The point is that both orthosteric and allosteric antagonism can present profiles of surmountable or insurmountable antagonism that are very similar. The underlying principles important to the differentiation of orthosteric from allosteric antagonism are: 1. Allosteric antagonism uniquely is saturable which is necessarily associated with the saturable binding to the allosteric site, i.e., unlike orthosteric antagonism which is a competition that can theoretically go on indefinitely with different ratios of agonist and antagonist, allosteric effects cease when the allosteric site is saturated. Therefore, antagonism will come to a limit unlike orthosteric antagonism. 2. Allosteric antagonism is uniquely linked to the nature of the agonist, i.e., allosteric probe dependence dictates that different agonists will be blocked to a different extent. Orthosteric antagonists do not discriminate between agonists and block all equally well. These properties define the tenets that can be used to discern orthosteric from allosteric antagonism. Specifically, the concentrations of antagonists tested should be as high as possible to detect possible saturation of effect and as many agonists as practical should be tested to possibly detect probe dependence. Even then these are one way experiments in that if saturation of effect or probe dependence is not discovered, then it may be that the range of antagonism tested was not large enough (very low values for a) or the right probes (agonists) were not chosen to detect probe dependence (Fig. 26 and 27). Fig. 27 shows the procedure for detecting allosteric antagonism when orthosteric competitive antagonism is initially indicated. In general, a range of concentrations of an antagonist produces dextral displacements of agonist concentration-response curves. The resulting dextral displacements of the agonist concentration-response curves indicate a linear Schild regression consistent with orthosteric simple competitive antagonism. For orthosteric antagonism any two agonists will produce the same regression line (Fig. 27A). However, for an allosteric antagonist further testing of higher concentrations of the antagonist may indicate a saturation of effect shown by a diminution in the dextral displacement of the agonist concentration-response curves with higher concentrations of antagonist (Fig. 27B) resulting in an obvious curvilinearity of the Schild regression. Moreover, testing with another agonist may show a different pattern of dextral displacement of the agonist concentration-response curves which results in yet another Schild regression (Fig. 27B). These data clearly show that the antagonism tested in these experiments is actually a NAM and not an orthosteric competitive antagonist.

Table 1

Differences between orthosteric and allosteric antagonists.

Orthosteric antagonists

Allosteric antagonists

Completely block agonist effect: Target activity diminishes to zero in the presence of high concentrations of orthosteric antagonists Indiscrimately block agonist effect of all activators interacting with targets, i.e., all agonists blocked equally well Ligands “hijack” the target and antagonist efficacy is the only efficacy expressed at the target

Modify drug effects: Target activity is altered but need not be diminishes to zero (may just be modulated, not changes or even potentiated) Can show probe dependence: Not all activators need be affected by the allosteric modulator in the same way. The nature of the agonist matters with respect to what overall effect is seen Texture in antagonism is seen: allosterically modified targets may have different conformations from each other which may lead to differences in resistance profiles with chronic treatment Duration and intensity of effect may be dissociated: (i.e., duration can be prolonged through receptor compartment loading with no concomitant overdose) Greater potential for receptor subtype selectivity: Allosteric binding may occur at auxiliary binding sites on the receptor and these may be dissimilar between receptor subtypes Activation through allosteric mechanisms may produce more selective activation and fewer secondary side effects

Mandatory link between duration of action and intensity of effect Less probability of receptor subtype selectivity: Binding site for the endogenous agonist may offer less potential for differential activity at target subtypes No texture in effect: patterns of signaling may not be preserved. Blanket agonism (for orthosteric partial agonists) results and the only texture comes from varying signal strength (due to receptor density differences or differences in receptor coupling efficiency)

292

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Fig. 26 Effects of increasing concentrations of an inverse agonist on agonist responses in a system where the concentration response curve slope is unity (black lines) and where the slope is ¼ 2 (grey lines).

Fig. 27 Differentiating orthosteric from allosteric antagonists. (A) Schild regression for an orthosteric antagonist blocking responses to two agonists. (B) Schild regressions produced by an allosteric antagonist blocking two agonists clearly show non-linearity at higher concentrations of antagonist that varies with agonist thereby indicating probe dependence and an allosteric mechanism of effect for the antagonist.

1.11.6

Antagonist target coverage in vivo

Measurement of antagonist potency and effectiveness is done in vitro with the concentration held constant so that the measurement can be made. However, antagonists used therapeutically in vivo are never used under such steady-state conditions. Rather, the concentration of the antagonist in vivo changes constantly as the antagonist is absorbed and cleared therefore the kinetics of ligand offset in real time is an important property of antagonists. In general, the kinetics of interaction of antagonists in vivo are slower than kinetics in vitro as in vivo there are diffusion barriers encountered by the antagonist as it diffuses through the body which hinder receptor offset and favor antagonist re-binding in restricted areasdsee Fig. 28 (Vauquelin and Charlton, 2010). The persistence of binding of an antagonist to the receptor is an important property and should be considered in new antagonist progression studies. Antagonists can have significantly different rates of offset from the receptor. For example, Fig. 29A shows the in vivo offset rates of the 5-HT2 receptor antagonists altanserin and ritanserin. These molecules are of similar potency (altanserin ¼ KB ¼ 0.43 nM, ritanserin ¼ 0.89 nM) but very different rates of offset from the receptor (altanserin t1/2 ¼ 15 min; ritanserin t1/2 ¼ 160 min) (Leysen and Gommeren, 1986). Thus, in vivo, ritanserin will have a much greater target coverage (defined as the amount of time the molecule is bound to the target) and presumably be a better therapeutic utility.

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293

Fig. 28 The influence of receptor density on offset of receptor offset kinetics. Antagonists diffuse near the receptor and, when sufficiently close to the binding site, chemical forces defining affinity cause binding to the receptor. As the antagonist diffuses off of the receptor random Brownian motion dictates in some cases return to the cell surface; if there is a receptor present at the site of return, the antagonist will bind again. If there is no receptor present, the antagonist will diffuse out of the receptor compartment. Therefore, high receptor density systems will lead to receptor rebinding and continued receptor occupancy.

Fig. 29 Target coverage by different orthosteric receptor antagonists. (A) Rate of offset of receptor occupancy by ritanserin (t1/2 ¼ 160 min) and altanserin (t1/2 ¼ 15 min). It can be seen that ritanserin has much greater target coverage in vivo as after 100 min, there is no receptor antagonism by altanserin and still 70% antagonist receptor occupancy with ritanserin. (B) Two hypothetical antagonists of equal potency but differing rates of receptor onset and offset. It can be seen that these antagonists will produce different levels of target coverage in vivo irrespective of their equal potencies.

The persistence of antagonist binding is a completely separate property from antagonist potency. Antagonist affinity is a ratio of the rate of offset and rate of onset of the molecule to the receptor but there can be equiactive antagonists with very different kinetics, i.e., potent fast acting and potent slow acting molecules. Fig. 29B shows two theoretical molecules of identical potency (KB ¼ 50 nM) but very different kinetics. Thus, while these molecules would be deemed identical in vitro, they would behave very differently in vivo. For this reason it is important to determine the rate of offset of antagonists in addition to their potency and efficacy profile. This can be done in vitro through antagonist washout studies. For example, Fig. 30 shows the equilibrium antagonism of an agonist by a competitive antagonist (indicated by the dextral parallel displacement of the agonist concentration-response curves) followed by the reversal of that antagonism by washing with antagonist-free media. The effect of washing can be tracked through the measurement of the response to a single concentration of agonist throughout the wash period. The magnitude of the response change with time can then be used to reconstruct the complete agonist curve and the dextral displacement of the theoretical curve used to estimate the fraction of receptors still bound with antagonist. This is done with the Schild equation where the observed dose ratio (DRt) with time yields the receptor occupancy (rt) with the equation: rt ¼ DR t =ðDR t
1Þ

(26)

The fractional receptor occupancy by the antagonist then can be calculated by using the DR found in the presence of the antagonist (DRe) to yield the equilibrium receptor occupancy (re); the kinetics of receptor offset can then be estimated with a plot of ln(rt/re) on time (Fig. 30B).

294

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Fig. 30 Measurement of antagonist receptor offset rate for a competitive antagonist. (A) Concentration response curves to an agonist in the absence (left hand curve) and presence (right hand curve) of a competitive antagonist. Washing with drug free medium causes a reduction in the dextral displacement of the curve as the antagonist diffuses off of the receptors; this can be tracked by observing the response to a single concentration of agonist (open circles, at 0.1 mM). These single responses are then used to reconstruct concentration response curves with time which are then used to calculate antagonist receptor occupancy with time. (B) A regression of the antagonist receptor occupancies (as logarithmic values) with time yields a straight line with a slope equal to the rate of offset of the antagonist from the receptor.

If the antagonist produces non-competitive or pseudo-irreversible antagonism, the rate of reversal can still be measured but in this case, the apparent changes in receptor available to the agonist with time are used to generate the kinetic plot. The operational model utilizes the term s to fit agonism; s ¼ [Rt]/Ke where [Rt] is the population of receptors and Ke the apparent coupling constant of the receptors to the stimulus-response mechanisms of the cell. Therefore, as the agonist response is diminished, this can be modeled with a new reduced value of s which, in turn, reflects a reduction in the number of receptors. Thus, an estimate of the receptor density is embodied in the new value of s at time t denoted st. The return of agonist response with washing after treatment with a non-competitive antagonist can therefore be used as a reflection of the return of viable receptors as the antagonist leaves the receptor (Fig. 31A). Thus the kinetic plot for return of viable receptors (i.e., the washout of the non-competitive antagonist) is given by a plot of ln(1
(st/se)) vs. time where st is the s for the curve at time t and se the value of s in the presence of the antagonist (see Fig. 31B).

Fig. 31 Measurement of antagonist receptor offset rate for a non-competitive antagonist. (A) Concentration response curves to an agonist in the absence (left hand curve) and presence (right hand curve) of a competitive antagonist. Washing with drug free medium causes a reversal of antagonism as the antagonist diffuses off of the receptors. Each curve at each time point is fit to the Black/Leff operational model to yield a new value for s which in turn yields a new value for the fraction of operable receptors in the system. The changes in the operable receptors with time yields values for the antagonist receptor occupancy with time. (B) A regression of the antagonist receptor occupancies (as logarithmic values) with time yields a straight line with a slope equal to the rate of offset of the antagonist from the receptor.

Orthosteric Receptor Antagonism

1.11.7

295

Conclusions

This chapter discusses the properties of orthosteric antagonists in terms of the methods to characterize them and their impact on antagonist effect. In general, the answer to four questions about a new antagonist entity need to be answered: 1. 2. 3. 4.

What is the potency of the antagonist (equilibrium dissociation constant of the antagonist-receptor complex)? Does the antagonist possess positive (partial agonism) or negative (inverse agonism) efficacy? What is the rate of offset of the antagonist from the receptor once bound (target coverage in vivo)? What is the mechanism of action (orthosteric or allosteric) of the antagonism and if the antagonist is allosteric does it induce signaling bias in the natural system?

If these equations can be answered for any new antagonist, then the therapeutic effects of that antagonist may be predicted in most physiological and pathological situations.

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McGinnity, D.F., Berry, S.J., Kenny, J.R., Grime, K., Riley, R.J., 2006. Evaluation of time-dependent cytochrome P450 inhibition using cultured human hepatocytes. Drug Metabolism and Disposition 34, 1291–1300. Nwolko, C.U., Smith, J.T.L., Gavey, G.C., Sawyer, A.M., Pounder, R.E., 1990. Tolerance during 29 days of conventional dosing with cimetidine, nizatidine, famotidine or ranitidine. Alimentary Pharmacology & Therapeutics 4, 29–45. Nwolko, C.U., Smith, J.T.L., Sawyer, A.M., Pounder, R.E., 1991. Rebound intragastric hyperacidity after abrupt withdrawal of histamine H2-receptor blockade. Gut 32, 1455–1460. Paton, W.D.M., Rang, H.P., 1965. The uptake of atropine and related drugs by intestinal smooth muscle of the guinea pig in relation to acetylcholine receptors. Proceedings of the Royal Society of London - Series B: Biological Sciences 163, 1–44. Samama, P., Cotecchia, S., Costa, T., Lefkowitz, R.J., 1993. A mutation-induced activated state of the b2-adrenergic receptor: Extending the ternary complex model. The Journal of Biological Chemistry 268, 4625–4636. Smit, M.J., Leurs, R., Alewijnse, A.E., Blauw, J., Amerongen, G.P.V., Vandevrede, Y., Roovers, E., Timmerman, H., 1996. Inverse agonism of histamine H-2 antagonists accounts for up-regulation of spontaneously active histamine H-2 receptors. Proceedings of the National Academy of Sciences of the United States of America 93, 6802–6807. Tyson, J.J., Chen, K.C., Novak, B., 2003. Sniffers, buzzers, toggles and blinkers: Dynamics of regulatory and signaling pathways in the cell. Current Opinion in Cell Biology 15, 221–231. Vauquelin, G., Charlton, S.J., 2010. Long-lasting target binding and rebinding as mechanisms to prolong in vivo drug action. British Journal of Pharmacology 161, 488–508. Virgolini, I., Yang, Q., Li, S., Angelberger, P., Neuhold, N., Nierderle, B., Scheithauer, W., Valent, P., 1994. Crosscompetition between vasoactive intestinal peptide and somatostatin for binding to tumor cell membrane receptors. Cancer Research 54, 690–700. Wilder-Smith, C.H., Gennoni, T.E., Zeyen, B., Halter, F., Merki, H.S., 1990. Tolerance to oral H2-receptor antagonists. Digestive Diseases and Sciences 35, 976–983. Wisler, J.W., DeWire, S.M., Whalen, E.J., Violin, J.D., Drake, M.T., Ahn, S., Shenoy, S.K., Lefkowitz, R.J., 2007. A unique mechanism of b-blocker action: Carvedilol stimulates b-arrestin signaling. Proceedings of the National Academy of Sciences of the United States of America 104, 16657–16662.

1.12

Allosteric Modulation

Dario Doller, Alcyoneus ScienceWorks, LLC, Sparta, NJ, United States © 2022 Elsevier Inc. All rights reserved.

1.12.1 1.12.2 1.12.2.1 1.12.2.1.1 1.12.2.2 1.12.2.2.1 1.12.2.3 1.12.2.3.1 1.12.2.3.2 1.12.2.3.3 1.12.2.4 1.12.3 1.12.3.1 1.12.3.2 1.12.3.3 1.12.3.4 1.12.4 1.12.5 1.12.6 1.12.6.1 1.12.6.2 1.12.6.3 1.12.6.4 1.12.7 Acknowledgments References

Introduction The geography of allosterism Localization of the binding site of an allosteric ligand Where an allosteric drug binds to its receptor: Does it matter? Allosteric modulation of enzymes: The case of kinases What allosteric enzyme inhibitors can do Allosteric modulation of membrane-bound receptors Topologies of GPCR allosteric modulation Ligands functionally selective for a dimeric receptor: mGluR2/mGluR4 PAMs GPCR bitopic ligands: Muscarinic M1 PAMs Chemical properties of allosteric and orthosteric ligands Types of macroscopic functional response by allosteric modulators V-type and K-type allostery in enzymes a and b types in GPCR allosteric modulators Type I and type II ion channel modulators Allosteric modulation of membrane transporters Probe dependence: Can the functional attributes of a drug be defined independently of its chemical context? Functional shifts: A bug or a feature? Beyond allosterism Drug design in complex allosteric systems Function is a system property, not “owned” by any individual partner Molecular dynamics as an input to function Putting it all together: Allosterism and system chemistry Summary: Allosterism and more effective drug discovery

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Glossary Ago-potentiator An allosteric ligand that enhances the functional effects of an orthosteric agonist, and can also activate the same receptor in the absence of the orthosteric agonist. Often referred to as Ago-PAM. Allosteric agonist A ligand that binds to an allosteric site on a receptor macromolecule and causes an enhancement in the functional response without requiring the presence of the endogenous natural agonist. Allosteric modulator A ligand that modifies the biological effects caused by an orthosteric ligand upon binding to an allosteric site on the receptor macromolecule. A positive allosteric modulator (PAM) increases a specific effect (e.g., affinity, efficacy, gating) of an orthosteric ligand, whereas a negative allosteric modulator (NAM) decreases it. Allosteric sites A binding site on a receptor macromolecule defined by a number of amino acid residues that is totally or partly nonoverlapping and spatially distinct from the orthosteric binding site. Upon binding to this site, a ligand may impact conformation, dynamics and function of the whole receptor complex. Ideally, reciprocity exists in the allosteric interaction between orthosteric and allosteric ligands. Biased ligand A compound that preferentially stimulates receptor coupling to specific signaling pathways (e.g., G proteins or barrestins for GPCRs) or that promotes changes in permeability for ion channels or transporters (e.g., Cl
vs. HCO
3 for GABAA ion channels). Bitopic ligand A hybrid molecule that concomitantly binds to an orthosteric and a nearby allosteric site on a receptor macromolecule. Neutral allosteric ligand A ligand that upon binding to an allosteric site on a receptor does not produce a change in a specific biological effect caused by an orthosteric ligand. This neutral allosteric ligand (NAL) may prevent the binding of other allosteric ligands to the same or different allosteric sites, and may be functionally non-silent towards other orthosteric ligands or allosteric ligands that bind to a different allosteric site on the receptor macromolecule. NALs have been also referred to as silent allosteric modulators (SAMs), the use of which is discouraged.

Comprehensive Pharmacology, Volume 1

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Orthosteric agonist A ligand that binds to a biological receptor in a site that overlaps with that where the natural, endogenous ligand binds and is necessary and sufficient to cause a biologic response. Conventional orthosteric agonists increase receptor activity, whereas orthosteric inverse agonists reduce it. Orthosteric site The location or volume of space within a receptor macromolecule, defined by a relatively small number of amino acid residues that recognize and provide energetically favorable interactions to enable the binding of an endogenous agonist.

1.12.1

Introduction

Unfortunately that branch of molecular chemistry that seems most closely connected with physiology has of late years received but little attention, and is still in a rudimentary state (Blake, 1884).

What is allosterism, in essence? Let’s think of it as a framework of drug action at the molecular level that aims to explain experimental observations that don’t always fit earlier models. Why embark in this exercise? The answer is simple: to exploit the evergrowing tension between the descriptive and explanatory branches of science, and hopefully develop new knowledge that inspire us to imagine new questions, and conduct novel experiments, that will continue expanding the cycle of human progress. The earlier premise of drug action, known as the “lock & key” hypothesis, is based on the interaction between one molecule recognizing one binding site in a biological receptor, in such a way that the ligand (an enzyme substrate, a GPCR agonist) matches perfectly the shape and polarity of the binding site, like a lock and a key would. As a model of drug action, allosterism keeps some of the attributes of the lock & key model (e.g., an orthosteric site recognizing an endogenous agonist or substrate), but it adds a second ligand binding at a second, somewhat distant region: the allosteric binding site. A number of consequences arise due to this expansion. Some of these were initially foreseeable, but other were not. From a basic perspective, allostery can be explained arguing that the binding of an allosteric ligand to a receptor causes changes that impact the interactions between the receptor and the orthosteric ligand. The effects of the allosteric modulator occur via small changes in conformational dynamics, and are transmitted along across the amino acid residues in the protein, impacting and regulating its functional activity. This is called the allosteric transition. However, the evolution of allosteric drug discovery demonstrated this view is too simplistic, and more sophisticated theories are emerging. One goal of this article is to present and explain to the reader some examples of these newer theories. For example, in a simple system (Fig. 1) composed by one ligand (A) and one binding site at a receptor (R), the functional pharmacology observed is derived from a single species: the AR complex. However, in a system with one orthosteric ligand (A) and one allosteric ligand (B), a number of functionally distinct molecular entities will coexist, which may or may not be at equilibrium.

Fig. 1 Comparison of a binary and an allosteric ternary model of drug action. Two molecules, orthosteric ligand A and allosteric ligand B, bind the receptor separately to produce binary complexes AR or RB thermodynamically governed by KA and KB. These may go on to form ternary complex ARB, the affinity dimension of which is subject to the cooperativity factor a. Functionally, AR, RB, ARB and R (for the case of constitutive activity) may contribute their different inputs to the overall functional readout, according to their individual efficacy cooperativity factors b. The parameters sA and sB are related to the efficacy of each ligand alone.

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These species are represented by R, AR, BR, ARB. Each one of these may exert its own, distinct functional effects. Therefore, the total functional response measured experimentally will represent a combination of the individual functional responses of each species. Consider the case of establishing the broad selectivity of a drug candidate. In a world where only competitive interactions exist, two molecules cannot occupy the same binding site. Therefore, this task would confidently be carried out using comprehensive panels of biochemical or radioligand binding displacement assays at relevant receptors expressing the orthosteric ligand binding domain. Thus, displacing a radioligand would imply interaction of the test article with the receptor. However, for novel ligands acting at allosteric sites, these assays may not provide the appropriate system (e.g., by using a truncated protein target construct not representing the full length enzyme, and thereby lacking the allosteric site) or the drug under study may interact with the protein allosterically and not display binding cooperativity with any of the previously developed radioligands. Therefore, claims of selectivity of allosteric ligands, especially for chemical probes, may need to be re-qualified in reference to the system against which tests were conducted. Panels including functional assays in agonist, PAM and antagonist modes, considering diverse signaling cascades, for a broad number of safety or mechanistically related targets may be necessary to support claims of superior selectivity or to interpret mechanistically studies with designer chemical probes. Clearly, the bar to qualify a drug’s selectivity just got a lot higher! The reversible association of three independent molecular entities in a cellular matrix is part of a number of important biochemical processes. Three-component systems have been recognized as more complex than their two-component counterparts. A general model for ternary complex equilibria has been developed, and its retrospective application to previously published data provided new insights into highly complex systems, including the non-intuitive bell-shaped concentration-response curve seen with ternary and higher-order equilibria, in which increasing the total concentration of an intermediate species may, under certain conditions, cause a decrease in ternary complex concentration (known as the hook effect) (Douglass et al., 2013). For the case of GPCRs, in order to understand as a whole the effects derived from allosteric ligands, it is useful to independently consider two orthogonal dimensions: the binding affinity of the ligands involved, and the functional efficacy produced by the system. From the binding perspective, the difference may be visualized by chemical equations shown in Fig. 1. The parameter a represents the binding cooperativity between A and B, or the ratio of the affinity of A to R in the absence and the presence of saturating concentrations of B. A number of books (Bindslev, 2008; Bowery, 2006; Doller, 2016; Ehlert, 2014; Kenakin, 2004; Zhang and Nussinov, 2019) and articles in the primary literature (Buchwald, 2017; Christopoulos and Kenakin, 2002; Gregory et al., 2020; Hulme and Trevethick, 2010; Jakubík et al., 2020; Johnstone and Albert, 2017) present detailed derivations of different mathematical models similar to the one shown in Fig. 1. The interested reader is invited to consult the cited references to take a deeper dive, as these varied equations and models are outside the scope of this article. Another interesting question comes from comparing the nature of the functional effects of receptor modulation using orthosteric ligands alone or in the presence of allosteric ligands. Will the later just augment in intensity the exact same effects observed in its absence? Or will the modulator alter additional dimensions of the specific nature of the functional effects? In fact, many still believe that receptor modulation only magnifies or reduces the exact pharmacology of the orthosteric ligands. However, there is evidence suggesting otherwise. Indeed, it might well be that the effects of each allosteric ligand on a biological target are unique and different from the effects of all other allosteric ligands. If this is the case, it is possible that a disease arising from (for example) hyperfunction of a receptor caused either by excess agonist or by excess endogenous positive allosteric modulator (PAM) may require two different chemical entities to restore healthy homeostasis, depending on which mechanisms generated the dysfunction. In other words, “not all the cellular dysfunctions are created equal.” It is our intention to discuss a number of these examples, and to discuss their implications in drug discovery. As a consequence of extensive efforts across scientific disciplines for over half a century now (Fig. 2), many examples exist of small molecule allosteric modulators for different families of polypeptide biomolecules (receptors), including enzymes (soluble and membrane-bound), ligand- and voltage-gated ion channels, G protein-coupled receptors, transporters, and nuclear hormone receptors. Some patterns and early knowledge are emerging. While examples of key concepts are presented in areas involving allosteric modulation of enzymes, the majority of the discussions will be in the area of GPCRs and ion channels. Part of the goal is to expose the reader to the similarities and differences in allostery for different type of biological targets of polypeptide naturedwhether they are membrane-bound or cytosolic. We hope everyone understands this is a field in development, where new lessons are learned frequently. Anticipating the impact that the concept of allostery would have as a fundamental law of Nature acting ubiquitously at the interface between chemistry and biology, Monod called it “the second secret of life” (Fenton, 2008; Monod, 1971). Comparable to the essential role of nucleic acids (DNA, RNA) as major factors enabling life, this statement recognized the revolutionary changes that have occurred since. These conceptual advances have enhanced our understanding of the causes of human diseases at the molecular level, and established new paradigms in drug discovery. One of our goals is to look at what’s been learned in allosteric drug research across different types of receptors, and identify patterns of similarities or differences. It would be impossible to cover all ongoing aspects of allosteric modulation in the depth they deserve. We aim to strike a balance between breadth and depth, present some of the most striking case studies in allosterism. We hope to knit these observations together in support of a common theme: the superbly subtle way in which small molecules interact with polypeptide biomolecules to produce alterations to the system’s functional response that plays a role in healthy and disease states. For the purpose of consistency in communication within this work, we have compiled definitions of key terms relevant to allostery in Table 1.

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PubMed hits from searches related to allosterism in drug discovery for different types of targets (accessed on May 18, 2020).

Table 1

Additional definitions pertinent to allosterism in drug discovery.

Silent agonist Allosteric transition

Competitive interaction

Probe dependence

Allosteric pluripotency Functional switch Molecular glue

Pharmacological chaperones Cryptic sites Morpheeins Reciprocity Single target pathway pleiotropy

Epistasis

Term coined for ion channel ligands that bind at the orthosteric site and very weakly activate the receptor, but show substantial desensitization. They antagonize the effects of full agonists. They may be silent in the ionotropic process but act as agonists in the metabotropic transduction pathway (Horenstein and Papke, 2017). Proteins are at the core of complex receptor systems. They display a basal level of activity resulting from different multiple states in the absence of ligand, including constitutive receptor activity. Orthosteric and/or allosteric ligands reset the balance among receptor states, causing a shift in the overall functional vectors. Interconversions between discrete conformational states occurs in concert for all subunits define allosteric transitions. An interaction between ligands that bind to the same receptor macromolecule, such that both compounds cannot be bound simultaneously. A competitive interaction can occur between different orthosteric ligands, an orthosteric and an allosteric ligand or between different allosteric ligands provided that each class shares a similar recognition domain on the receptor macromolecule. It occurs when the direction and magnitude of the effect of an allosteric ligand on an orthosteric ligand varies for different orthosteric ligands. Thus, the functional attributes (activation, inhibition) of a compound upon a receptor cannot be asserted independently of the chemical system in which it exists. Different allosteric effects may be observed for the same modulator, acting at the same receptor but with different orthosteric ligands. Dependence of the functional response of a signaling system to an allosteric stimulus on subcellular conditions (Byun et al., 2020) Experimental observation of a functionally distinct (often times opposite) receptor response exerted on a receptor by ligands in the same chemical class, driven by structural changes generally thought of as subtle (e.g., -H for -CH3 or -F; -CH3 for -CH2CH3; -NHCH3 for -NH2) (Wood et al., 2011). Compounds that bind protein surfaces or interfaces perturbing or promoting the formation of novel protein-protein interactions by modifying extensive contact surfaces with a second protein, ultimately resulting in the activation or suppression of a cellular response (Che et al., 2018; Hughes and Ciulli, 2017). Often called protein-protein interaction (PPI) modulators. Small molecules that bind to nascent protein targets to facilitate their biogenesis, and assist in the folding and subsequent forward trafficking of disease-causative protein misfolding mutants (Leidenheimer, 2018). Surface pockets appropriately sized for drug binding that occur in the ligand-bound but not in the unbound protein (apo-protein). They form upon conformational changes induced by ligand binding, and may provide tractable drug target sites (Vajda et al., 2018). Homo-multimeric proteins that can come apart, change shape, and reassemble differently with functional consequences (Jaffe, 2020). The principle that the effect of a bound allosteric ligand on the binding of the orthosteric ligand must be identical in sign and magnitude to that of the bound orthosteric agonist on the binding of the allosteric ligand (Fenton, 2008; Fisher, 2012). The ability of a ligand, upon binding to a single receptor, to activate distinct cellular processes and pathways. This is explained arguing that if a protein exists in an ensemble of receptor conformations, ligand binding will modify that set of conformations to a different make up, which then go on to produce each a distinct response (Kenakin, 2008a,b). A phenomenon that occurs when the sequence background into which a mutation is introduced in a protein changes the functional effect of that mutation (Nishikawa et al., 2020). Understanding the underlying mechanisms behind protein allostery and non-additivity of substitution outcomes is critical when aiming to predict the functional impact of mutations, particularly at non-conserved sites (Campitelli and Ozkan, 2020).

Allosteric Modulation

1.12.2

301

The geography of allosterism

In the early days of pharmacology some drugs were conceived through chemical structure changes to endogenous, naturally occurring compounds, generally considered as functional agonists, and aimed to either mimick or antagonize their actions (Black et al., 1986). The term “orthosteric” has since been used to qualify ligands recognizing the site where endogenous agonists bind to their respective receptors. This was followed by a different design strategy, studying compounds that mediate their pharmacological effects by interacting with a different, non-overlapping set of amino acid fragments localized in a spatially distinct region, forming an allosteric site. In principle, multiple distinct allosteric sites could exist in a given receptor protein. The realization that small molecules may exert functional modulation of a pathophysiologically relevant receptor by binding at different sites than the endogenous agonist has major implications in drug discovery. Allosteric regulation of protein function is now considered as being a ubiquitous mechanism in cellular biology, but the fundamental principles governing this phenomenon are only beginning to be appreciated, and are definitely not understood to the same depth than for their orthosteric counterparts (Bhat et al., 2020).

1.12.2.1

Localization of the binding site of an allosteric ligand

While endogenous orthosteric agonists are generally acknowledged as binding in well-defined sites in a receptor or enzyme, that is not demonstrated for most allosteric ligands. Indeed, distinct allosteric sites are known to exist for certain receptors, often but not necessarily linked to different chemical cores. Furthermore, their pharmacology may also be significantly different. Several examples exist for some of the best studied and most disease relevant GPCRs and ion channels for which ligands within the same chemotype may display radically different functional phenotypes (see below, Functional Switches section). When it comes to interpreting the quantitative pharmacology of allosteric systems, the stoichiometry of binding is yet another source of complexity. The homo- or hetero-oligomeric nature of many biological receptors is directly linked to producing more than a single binding site for an orthosteric ligand in every receptor complex unit. This multiplicity of orthosteric binding sites occurs with many metabotropic and ionotropic receptors, including GABAA, GABAB, NMDA, mGluRs (Ferré et al., 2014; Zhou and Giraldo, 2018). This may lead to concentration-response curves that are biphasic in nature, as the effects of the first equivalent of ligand binding to the receptor may be quite different (both in terms of affinity and functional response) from those resulting from the binding of the second equivalent of ligand. Operational models of agonism and allosterism for receptors with multiple orthosteric binding site were recently developed and validated, which provide tools to accurately quantify drug actions (Gregory et al., 2020). A number of approaches exist to identify allosteric sites. Some of these are based on static structures of proteins acquired using nuclear magnetic resonance, X-ray crystallography or Cryo-EM. These may be very useful approaches for the design of allosteric enzyme inhibitors, as it was shown for a new class of inhibitors of BRAF monomers and dimers. First, the FDA-approved drug ponatinib (1) was shown to bind the BRAF dimer and stabilize a distinct aC-helix conformation through interaction with a previously unrevealed allosteric site. This led to a novel class of dimer selective inhibitors, with the discovery that PHI1 (2) has discrete cellular selectivity for BRAF dimers, with enhanced inhibition of the second protomer when the first protomer is occupied (Cotto-Rios et al., 2020; Gunderwala et al., 2019).

1

2

These and similar biophysical methods have provided actionable information for the case of some GPCRs (Christopher et al., 2019; Deflorian et al., 2020), ion channels (García-Nafría and Tate, 2020) and transporters (Cheng and Bahar, 2019). Such biophysical methods are especially powerful when applied in an integrated way (Shimada et al., 2019). However, they haven’t been as useful to predict allosteric binding sites for these membrane-bound receptors as they have been for enzymes. Newly developed computational tools can estimate the druggability of allosteric sites in these receptors, but the prediction of hydrophobic sites in the interface between the protein and the membrane remains a challenge (Renault and Giraldo, 2020). Molecular dynamics simulations are used to explore large scale interdomain movements. Such movements can be correlated with protein function and, when defined by the slowest frequency modes, present intrinsic features of protein function reflecting distant allosteric couplings (Tobi and Bahar, 2005). Even when amino acid residues defining an allosteric pocket on a certain protein are well-established experimentally, alternative sites might exist that are not obviously revealed, yet might provide unique opportunities for drug design (Ayyildiz et al., 2020). Whole-protein site-directed mutagenesis, generally initiated with an alanine-scan, is a powerful tool to identify residues contributing in allosteric mechanisms, and to explore possible outcomes following systematic amino acid probing of allosteric proteins, even when the design and interpretation of such studies is sometimes controversial (Tang et al., 2017). Practically, the use of

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mutations becomes challenging for proteins where extended regions are involved and as the number of positions to be probed increases. However, recently developed high-throughput techniques enabled mutagenesis studies involving large datasets of mutant proteins (Jones et al., 2020).

1.12.2.1.1

Where an allosteric drug binds to its receptor: Does it matter?

Given the decades of experience accumulated studying interactions between orthosteric ligands and residues forming their binding sites, it is very tempting to extrapolate this knowledge to allosteric ligands. Basically, the 3-dimensional shape of the effector molecule places key functional groups in the right spatial relationship so as to be recognized by complementary interactions with residues that are substituents of specific amino acid residues in the receptor sequence, providing the thermodynamic wherewithal to bind. Removal or modification of the substituents in some of these amino acids (e.g., in a mutant) would alter the affinity. Is this true also for allosteric ligands? As mentioned earlier, biophysical techniques like X-ray crystallography and Cryo-EM are being used to establish unambiguous support for the site where an allosteric ligand binds its target. Unfortunately, the existence of physically overlapping binding sites between two ligands is often asserted based on in vitro binding assays wherein displacement of a radioligand by a test article in a concentration-responsive manner is observed. This practice may have to be re-evaluated. Research with allosteric ligands has shown that binding to an allosteric site can induce conformational changes such that the orthosteric radioligand has lost significant affinity (negative binding cooperativity). When a protein and a small molecule bind, a unique conformer population is established, defining a downstream pathway for cellular events (Cesa et al., 2015). A classic example of this behavior is for neurosteroids acting as GABAA PAMs, that inhibit the binding of the radioligand [35S]-TBPS (3), which binds yet to a distinct non-orthosteric site (Hawkinson et al., 1994). Furthermore, these conformational changes are responsible for the efficacy of allosteric modulators acting as protein-protein interaction (PPI) inhibitors (Ni et al., 2019a, 2019b). In spite of long held beliefs to the contrary (Arkin et al., 2014; Scott et al., 2016), binding of small molecules at allosteric sites can indeed produce pharmacologically relevant inhibition of PPIs. This is particularly important in drug design, given the observation that PPI inhibitors targeting apoprotein conformations tend to associate with challenging PPI interfaces, and have weak affinity and large surface areas, resulting in poor druggability scores (e.g., as in Lipinski’s rule of five) (Shin et al., 2020). Among the most remarkable examples of PPI inhibition by allosteric mechanisms is the small molecule maraviroc (4), an FDA-approved antiretroviral drug used in the treatment of HIV infection. The chemokine receptor CCR5 is an essential co-receptor for most HIV strains and necessary for the entry of the virus into the host cell. By acting as a CCR5 receptor NAM, maraviroc blocks the HIV protein gp120 from associating with the receptor, rendering HIV unable to enter human macrophages and T cells (Kenakin, 2007).

3

4

In some complex receptor systems, the number of distinct allosteric sites is such that two different allosteric drugs may bind concomitantly and change significantly the properties of each allosteric ligand alone (allosteric drug/allosteric drug interaction, Fig. 3). These interactions have major relevance, as they may lead to significant differences in the functional output produced by either allosteric ligand alonedin both directions. An example involves the combination of two GABAA PAMs, the neurosteroid SAGE-217 (5, zuranolone) and the benzodiazepine diazepam (6), using in vitro electrophysiology assays with Ltk cells expressing GABAA subtype a1b2g2, in the presence of 2 mM GABA. Under these conditions, SAGE-217 alone was characterized by an EC50 ¼ 844 nM and Emax ¼ 511%; and diazepam alone by EC50 < 100 nM, Emax ¼ 150%. Addition of 100 nM diazepam with varying concentrations of SAGE-117 decreased the EC50 for SAGE-217 to 109 nM (more than sevenfold lowering). Moreover, the maximal potentiation (Emax) was doubled with the SAGE-117 plus diazepam combination (from 511% to 1084%) (Althaus et al., 2020). Isoallopregnanolone (7) and its 3a-diastereoisomer, allopregnanolone (8), are both naturally occurring endogenous neurosteroids. Their functional interactions, at the GABAA receptor are remarkable (Fig. 3). When studied alone, isoallopregnanolonehad no effect on basal Cl
uptake in rat cortical homogenates, nor did it affect GABA-stimulated Cl
uptake. As expected for the GABAA PAM allopregnanolone (8), Cl
uptake was increased by 10 mM of GABA in a concentration-dependent manner. Interestingly, the enhancement of Cl
uptake by allopregnanolone was inhibited by isoallopregnanolone. These inhibitory effects were not seen when flunitrazepam (9) or pentobarbital (10) were used as the GABAA PAM. Furthermore, the inhibitory effects were not likely due to competitive displacement of allopregnanolone by isoallopregnanolone, as the in vitro displacement of [35S]-TBPS by allopregnanolone (IC50 ¼ 58 nM, Imax ¼ 99%) was not impacted by addition of 30 mM isoallopregnanolone (IC50 ¼ 62 nM, Imax ¼ 103%). Altogether, these results suggest different binding sites for 7 and 8 at the GABAA receptor in spite of their similar chemical structures (Lundgren et al., 2003).

Allosteric Modulation

Fig. 3

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Examples of allosteric drug-allosteric drug interactions, leading to non-additive functional effects.

In another related study, epipregnanolone (11, yet another related diasteroisomer) acted as a mild PAM potentiating the effects of GABA currents in Xenopus oocytes expressing recombinant GABAA receptors containing a1, b2, and g2L subunits, yet, epipregnanolone (11) non-competitively inhibited the effects of allopregnanolone (8) in that system (Fig. 3) (Wang et al., 2002). Therefore, functional effects due to pairs of allosteric modulators acting at the same receptor are not necessarily easy to predict. This is particularly important for systems known to include endogenous allosteric modulators. Indeed, a detailed analysis was recently conducted on the different binding sites for neurosteroids with activity at GABAA receptors, from their initial molecular modeling to the latest findings, aiming at rationalizing the molecular basis of the receptor functional modulation (Alvarez et al., 2019). Among their conclusions, the authors suggest that some synthetic analogs may not bind at the same sites as endogenous ligands, and point to the dynamic behavior of the receptor/ligand complex as a likely key determinant of the system’s functional response.

Therefore, allosteric and orthosteric ligand binding may be molecular events sharing some of their basic characteristics, but with significant fundamental differences between them. Thus, a nuanced explanation of allosteric drug effects may be more complex than that for their orthosteric counterparts. An orthosteric ligand may not require major, energetically costly, conformational rearrangements to fit an evolutionarily optimized binding site, and the apo structure of the protein can productively be used to guide the synthesis of new analogs, at least in a number of cases. On the other hand, allosteric ligands may act by requiring allosteric binding site conformation different from the apo structure (Cesa et al., 2015). As we shall discuss next, simultaneous binding of two drugs at the orthosteric and allosteric site is also being used in the oncology area, to mitigate the resistance caused by orthosteric site mutations.

304 1.12.2.2

Allosteric Modulation Allosteric modulation of enzymes: The case of kinases

Protein kinases phosphorylate substrate proteins by catalyzing the transfer of the terminal g-phosphate of the co-substrate adenosine 50 -triphosphate (ATP) to the hydroxyl acceptor group on the side chains of serine, threonine, or tyrosine residues of the substrate proteins. Because of the important physiological and pathological role of kinases, the human kinome has major implications for drug discovery in oncology and other therapeutic areas, including inflammation, autoimmune diseases, and metabolic disorders. Accordingly, the number of FDA-approved small molecule kinase inhibitors grew from 13 in 2011 to 40 in 2018 (Ward and Goldberg, 2018). The catalytic domain of kinases is characterized by a high degree of amino acid sequence homology, especially for kinases within the same family. These enzymes share a common (orthosteric) ATP binding site, working through a conserved activation loop and displaying similar three-dimensional architecture. It follows that crafting into a molecule acting at the orthosteric site a meaningful level of selectivity versus the nearly 600 family members, represents a major challenge. In addition, to compete successfully for the same binding site with high concentrations of ATP in the cell, especially under pathophysiological conditions, ATP site ligands must demonstrate exceptionally high affinity. In this context, ligands binding in pockets away from the ATP site, highly divergent among the different kinases, are creating new opportunities to deliver selective ligands and increase the probability of success (Gavrin and Saiah, 2013). Identification of novel allosteric sites, and small molecules binding to them, is now a major effort in kinase drug discovery. With strong support by technologies enabling structure-based design, a number of medicinal chemistry kinase inhibitor strategies emerged. These strategies can be organized according to the spatial relationship between the probe molecule and the ATP binding pocket (Zuccotto et al., 2010). Indeed, many different classifications of kinase inhibitors appear in the literature (Garuti et al., 2010; Panicker et al., 2019; Roskoski, 2016; Wu et al., 2015). For the purpose of this discussion, we consider five distinct types (Cowan-Jacob et al., 2014). The first wave of kinase inhibitors was conceived to mimic the adenine ring in ATP in its interactions with the hinge residues of the protein in its active form, and led to type I inhibitors. These compounds compete directly with ATP and are often characterized by binding to an open conformation of the activation loop, which is referred to as “DGF-in” based on a conserved sequence aspartate-phenylalanine-glycine (DFG). In the type I binding mode, the aspartate side chain of the DFG motif faces into the active site of the kinase. Type II inhibitors bind to an extended ATP site in an inactive conformation, resulting from the displacement of the DFG motif (“DFG-out”) of the activation segment of the kinase, and including an adjacent, less-conserved allosteric site. In type II binding mode, the DFG motif undergoes a 180 degrees rotation, which projects the DFG aspartate side chain out into the solvent and opens up a new pocket exploited by many type II inhibitors (Chaikuad et al., 2014; Foda and Seeliger, 2014). These inhibitors are also competitive with ATP. As the DFG-out conformation is adopted by many kinases, the challenge to deliver selective compounds in these two types persists, and the issues derived from off-target side effects remain significant for both type I and type II inhibitor classes (Fischer, 2004). Type III ligands bind to a site on the kinase that is proximal but does not overlap with the ATP binding site. Ligands binding at this site, show no direct interaction with the hinge region that connects the N- and C-lobes of the kinase domain, and are therefore allosteric. They may be competitive with ATP if their mechanism involves the stabilization of a conformation incompatible with ATP binding. However, some of these ligands may be not competitive and bind to the ATP-bound form of the kinase. From a functional standpoint, these ligands may act as either enzyme inhibitors or activators. Type IV inhibitors or activators are allosteric ligands binding farther away from the ATP site, and reaching into other domains of the target kinase or to other interacting proteins, therefore behaving as molecular glues (Table 1) which interfere with a kinase partner, disrupting regulation or activation of the kinase (Zhang et al., 2010). Bisubstrate and bivalent inhibitors (type V) combine characteristics of different inhibitor classes, and can potentially yield drugs featuring both, the high affinity characteristic of type I/II inhibitors as well as the selectivity of allosteric inhibitors engaging the protein kinase surface outside the ATP cleft (Cox et al., 2010; Gower et al., 2014; Lamba and Ghosh, 2012). Bivalent kinase inhibitors contain an active site-directed fragment tethered to a different group which interacts more distant from the active site. Bisubstrate kinase inhibitors are composed of an ATP-competitive ligand covalently tethered through a linker to a pseudopeptide substrate of the protein kinase of interest. Such compounds may provide chemical biology tools for the study of new kinases (Fig. 4). It must be emphasized that establishing without ambiguity the location where an enzyme inhibitor binds is a highly nuanced process, requiring thoroughly validated biochemical, biophysical, and structural biology evidence. Often, compounds are assigned as binding to an allosteric site based on their acting noncompetitively in biochemical assays (as discussed previously). However, these data can be misinterpreted, especially because compounds of relatively weak affinity or acting as nonspecific inhibitors often exhibit similar noncompetitive behavior (McGovern and Shoichet, 2003). An example of misclassification of an allosteric ligand was recently reported in the area of inhibitors of CK2a, a ubiquitous, highly promiscuous protein serine/threonine kinase that is among the most highly conserved proteins in nature. This enzyme can utilize either ATP or GTP as substrates (Ahmad et al., 2008, p. 2), and is constitutively active. and not requiring phosphorylation for activation (Pinna, 2002). Its ubiquitous role in diseases like cancer and fibrosis made CK2a a target of high interest in drug discovery, and several high-affinity inhibitors in diverse chemical space have been reported and characterized structurally as ATP-competitive (Panicker et al., 2019). Recently, a series of CK2a inhibitors (e.g., 12) was reported a to bind in a novel site just outside the highly conserved ATP site (Bestgen et al., 2019a). The allosteric nature of the inhibition was postulated based on a number of enzymatic, native mass spectrometry, and

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Fig. 4

305

Cartoons representing different types of kinase inhibitors.

competitive ligand-based NMR studies (Bestgen et al., 2019b). However, the similarity of chemical structure with known ATPcompetitive inhibitors (e.g., 13, 14) motivated additional work wherein structural and biophysical analyses showed no evidence that these inhibitors bind to the originally proposed allosteric site. Indeed, crystallographic structures, competitive isothermal titration calorimetry, hydrogen-deuterium exchange mass spectrometry, and cheminformatic analyses were all found consistent with these compounds binding in the ATP pocket (Brear et al., 2020).

1.12.2.2.1

What allosteric enzyme inhibitors can do

Based on findings from allosteric kinase programs (type III and IV), a number of patterns are emerging in literature reports. Since the amino acids making up the allosteric pockets outside the ATP-binding site are often less conserved throughout the kinome, ligands binding there tend to be more selective and display minimal off-target pharmacology compared with ATP-competitive inhibitors (Cowan-Jacob et al., 2014). Sub-type selectivity within the same kinase family is generally lacking or hard to obtain (Lu et al., 2019), yet examples of isoform selectivity were reported, even if serendipitously obtained (Karpov et al., 2015), This is particularly relevant for the qualification of chemical biology probes, often used in target validation efforts (Panicker et al., 2019). From the perspective of structural novelty, molecules recognized by allosteric pockets to produce kinase functional modulation tend to belong in a different chemical space than for the densely interrogated ATP-competitive kinase inhibitors. Furthermore, since the ATP pocket is rather flat and hydrophobic, ligands tend to be characterized by poor aqueous solubility at physiologically relevant conditions, high molecular weight and problematic cross-reactivity with safety-relevant targets, particularly the hERG channel. Conversely, allosteric pockets may be rather flexible and produce a three-dimensional environment favorable to the design of compounds with improved physicochemical properties. Pharmacological properties of allosteric kinase inhibitors present rather interesting features. In terms of functional selectivity, when using a type I or II compound, the phosphorylation of all potential substrates is inhibited concurrently, cascading into all downstream effects of the corresponding phosphorylated substrates. On the other hand, allosteric ligands may function as either kinase activators or inhibitors for different substrates, and therefore lead to differential effects in signaling pathways. Finally, it is well known that upon prolonged drug treatment with ATP-competitive inhibitors, active site mutations may compromise the drug’s efficacy. Likewise, mutations leading to the development of resistance to allosteric ligands have also been established, rendering these drugs ineffective (Lu et al., 2020). However, the combined used of one orthosteric and one allosteric kinase inhibitor provides the opportunity to overcome mutation-associated drug-resistance (Pisa and Kapoor, 2020; Zhang et al., 2010). This dual strategy is gaining momentum in the oncology drug discovery space (Eide et al., 2019; Ni et al., 2020; Wylie et al., 2017, p. 001). In a recent study, structural evidence emerged reporting the first crystal structures where both, orthosteric and allosteric ligands are simultaneously bound to mutant epidermal growth factor receptor (EGFR). Osimertinib (15) is a third-generation tyrosine kinase inhibitor considered currently as the gold-standard for the treatment of non-small cell lung cancer (NSCLC) harboring T790M-mutated EGFR. Unfortunately, the emergence of the C797S resistance mutation limit the drug’s efficacy. As discussed previously, this motivated the search for allosteric inhibitors. While these were not effective as monotherapy, a combination of osimertinib and the allosteric inhibitor JBJ-04-125-02 (16) was able to effectively deal with the mutated EGFR (Niggenaber et al., 2020).

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Again, this work illustrates that the co-administration of two inhibitors is as a promising approach to circumvent resistance, distinct from single drug administration for EGFR-driven NSCLC patients, and supports the potential for other hybrid design in future tyrosine kinase inhibitors.

15

16

It is remarkable that two general mechanisms exist that can lead to drug resistance associated with allosteric mutations within or outside the allosteric sites. The first, and most obvious, is the case in which amino acid mutations directly negate modulator binding. However, a second, more indirect mechanism exists whereby the ability of the allosteric modulator to bind is not affected by the mutation, but instead it enables a population shift in the balance between active or inactive states of the enzyme target. This mechanism modifies the allosteric transitions between orthosteric and allosteric sites, leading to a mutant enzyme functionally unresponsive to the binding of an allosteric inhibitor. Examples of these two mechanisms have been reviewed (Lu et al., 2020).

1.12.2.3

Allosteric modulation of membrane-bound receptors

The group formed by combining drugs that act through GPCRs, ion channels or transporters, will cover the largest percentage of FDA-approved small molecule therapeutics. These receptors share two mechanistically important attributes. One, that they exert their effects by sensing extracellular signals and communicating these to the interior of the cell. The molecular machinery enabling this communication, also known as signal transduction, allows highly sophisticated ways to regulate cellular function via endogenous metabolites or xenobiotic drugs (Lindsley and Rutter, 2006). Two, the subtleties of the different modes of action of these three types of receptors provides a highly powerful cellular regulation toolbox. While triggered by a natural effector (an endogenous orthosteric ligand, voltage change or substrate), the exact nature of the functional effect can be precisely modulated by allosteric ligands, which act by modifying molecular dynamics and impacting functional effects. For example, for GPCRs, signal bias characteristic of a natural agonist may be modified by the presence of an allosteric ligand, allowing the propagation of different signaling pathways (Leach et al., 2007; Luttrell and Kenakin, 2011; Slosky et al., 2021). For ion channels, an allosteric ligand may modify the transitions between active, inactive and de-sensitized states that regulate the intensity and duration of channel opening (Pierce et al., 2020). For transporters, differences in clinical efficacy or tolerability of some drugs which target them have been linked to the allosteric nature of drug binding, producing functional effects differing from those derived from ligands acting by orthosteric mechanisms (Sanchez et al., 2014).

1.12.2.3.1

Topologies of GPCR allosteric modulation

Similarly as described for kinases, the distance between the orthosteric and allosteric binding pockets in GPCRs plays an important role in the design of ligands with specific attributes (Fig. 5). However, an important difference between GPCRs and kinases is that the former may be able to form homo- or hetero-dimeric species, with functional properties quite different from those for the monomeric species (Borroto-Escuela and Fuxe, 2019). These interactions are known as receptor-receptor interactions (RRI). Its increasing awareness has expanded our understanding of the role of GPCRs in intercellular communication, as different incoming signals could already be integrated at the plasma membrane level via direct allosteric interactions between the protomers that form the oligomeric complex (Guidolin et al., 2019). Examples of functional modulation of these different molecular systems by small molecules are presented below.

1.12.2.3.2

Ligands functionally selective for a dimeric receptor: mGluR2/mGluR4 PAMs

The mGluRs are class C GPCRs of major importance in CNS drug research (Azam et al., 2020). A number of chemical probes and drug candidates have been discovered, both orthosteric or allosteric, and several have made it to advanced clinical studies (Li et al., 2015, p. 2; Nickols and Conn, 2014). Indeed, some of these are currently the focus of repurposing efforts in new indications (Caprioli et al., 2018; Goodwani et al., 2017). Some efforts are still ongoing targeting mGluR4 PAMs for the treatment of motor deficits in Parkinson’s disease (PD) and as potential antipsychotic agents (Doller et al., 2020; Panarese et al., 2019a). VU0418506 (17) and Lu AF21934 (18) were initial lead compounds independently obtained by two research groups based on screening efforts using

Allosteric Modulation

(A)

(B)

(C)

(D)

Monomeric GPCR

Monomeric GPCR

Homodimeric GPCR

Heterodimeric GPCR

Bivalent ligand

Hetero-bivalentligand

Orthosteric and allosteric ligands

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Bitopic ligand

Fig. 5 Schemes representing different cases of GPCR, orthosteric and allosteric ligand scenarios. (A): Classic view of a monomeric GPCR with an orthosteric and an allosteric binding sites occupied by independent molecules. (B) A monomeric GPCR with its orthosteric and allosteric sites occupied simultaneously by a single molecule of a bitopic ligand, where a tether links two pharmacophores, one for the allosteric site and one for the orthosteric site. This model may also apply to a dimeric GPCR with a bitopic molecule binding the orthosteric and allosteric sites of the same protomer unit. (C) A bivalent ligand binding the two transmembrane orthosteric sites of a homodimeric GPCR complex. (D) A bivalent ligand binding the allosteric sites of two different protomers, (A) and (B).

functional tests in cell lines overexpressing only mGluR4. Interestingly, the evaluation of their functional effects in more complex and translationally relevant systems expressing the mGluR4 homodimer and the mGluR2/mGluR4 heterodimer suggested these compounds have different PAM properties at the two oligomeric receptor complexes (Moreno Delgado et al., 2017; Niswender et al., 2016; Yin et al., 2014). Compound 18 is a PAMs of both the mGluR4 homodimer and the mGluR2/mGluR4 heterodimer, and demonstrated efficacy in behavioral animal models of anxiety and psychosis. In contrast, compound 17 only activates homodimeric mGluR4 and did not show efficacy in these models, yet showed robust activity in models of antiparkinsonian-like behaviors (Fulton et al., 2019). With these observations in mind, a series of potential bivalent ligands were designed for the mGluR2/mGluR4 heterodimer by linking with a variable length tether the selective mGluR2 PAM BINA (19) and the mGluR4 PAM 20. Using bioluminescence resonance energy transfer (BRET)-based assays, this set of analogs was tested as potential enhancers of mGluR4 homodimers, mGluR2 homodimers, and mGluR2/mGluR4 heterodimers using either the mGluR2 agonist DCG-IV (21) or the mGluR4 agonist L-AP4 (22). The patterns of functional activity within these compounds are complex. While, most flexible tethered ligands were able to access functionally active binding sites, one analog (VU6023804, 23, n ¼ 8) showed preference for mGluR2/mGluR2 homodimers over the other dimeric species (Fulton et al., 2020).

1.12.2.3.3

17

18

21

22

19

20

23

GPCR bitopic ligands: Muscarinic M1 PAMs

Preclinical and clinical evidence developed since the 1990s strongly suggests that compounds that activate muscarinic acetylcholine receptors (mAChRs) have therapeutic potential for symptomatic treatment of Alzheimer’s disease (AD) (Moran et al., 2019; Scarpa et al., 2020). During that decade, the M1/M4 nAChR-selective partial agonist xanomeline (24) demonstrated modest improvements in cognitive function in AD patients (Bodick et al., 1997), consistent with the so-called cholinergic hypothesis in AD. Unfortunately, cholinergic side effects, both peripherally and centrally driven, were too severe. This led to a course-correction in the path towards these medicines, that motivated a number of groups to focus on the development of M1 nAChR-selective PAMs. M1 nAChR PAMs are thought to enhance binding affinity or functional signaling driven by the endogenous orthosteric ligand, acetylcholine (Ach). Interestingly, the short distance between the orthosteric site and an allosteric site led to the discovery of bitopic PAMs with extremely high binding cooperativity (a), in the 1000–2000 range, and functional cooperativity b close to 1. However, the first clinical compound MK-7622 (25), failed due to the same cholinergic side effects seen with the agonist xanomeline. This was associated

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Fig. 6

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Chronology of the path from xanomeline (24) discovery to low-efficacy M1 nAChR-selective PAMs.

with the Ago-PAM effects of MK-7622 (a ¼ 338, sB ¼ 1.067) (Beshore et al., 2018), prompting the design of “pure” PAMs (compounds with smaller a values (a  1000) and sB around 0). A chronology of the search for such compounds involved chemical tools, PET ligands and clinical candidates, and is depicted in Fig. 6 (not all relevant compounds shown). In the end, most observations support the concept that M1 mAChR-selective “pure” PAMs with low levels of agonist activity would be most likely to provide clinical efficacy and least likely to show centrally-driven adverse events (Bradley et al., 2018). The complex challenge of designing a PAM binding at an allosteric pocket very close to the orthosteric site, while avoiding concurrent binding at the orthosteric site, motivated the search for a different, more distant allosteric site. In other words, the design strategy moved away of the bitopic ligand, towards a “classic” PAM. Indeed, a clever screening strategy led to the discovery of a low fold-shift M1 nAChR PAM (“Comp 22”, 26, Fig. 6) (Flohr et al., 2017). At the time of this writing, two low-efficacy M1selective PAMs are progressing into phase 2 clinical trials: VU319 (27, structure undisclosed) and TAK-071 (28, a ¼ 199; sB > 0). Time will tell whether these earn regulatory approval.

1.12.2.4

Chemical properties of allosteric and orthosteric ligands

When considering amino acid sequences in GPCRs, ion channels or transporters, amino acid fragments in regions exposed to an aqueous matrix (extracellular, cytoplasmic) are generally more polar than those forming the transmembrane section, which is embedded within the lipophilic cellular membrane (Szlenk et al., 2019). For example, the orthosteric binding sites for some of the most polar neurotransmitters (glutamic acid, g-aminobutyric acid, glycine, or Ca2þ) are localized in the polar extracellular matrix. However, monoaminergic binding sites (e.g., dopamine, serotonin, adrenaline) are within the transmembrane region (Chan et al., 2019). Naturally, these evolutionarily optimized relationships between the properties of an orthosteric ligand and its binding sites creates physicochemical constraints for other ligands binding at that site. This is a key piece of knowledge for medicinal chemists intending to mimic these polarity requirements, albeit using fragments that increase the drug-like properties of their molecules. But are there any trends linking allosteric function with the physicochemical properties of ligands? The answer to this question will highlight a key driver of allosteric modulation by small molecules: the changes introduced to the molecular dynamics of proteins (which already exist in an ensemble of conformations in response to the thermal energy of the system). Upon binding, ligands modify the ensemble of preferred conformations, which then interact differently with their molecular partners within the cell to provide a unique and distinct set of pharmacological signals. Under this thermodynamic model, the site of ligand binding needs not be predestined, nor uniquely responsible for the changes in functional response. Rather, it is the combined effect of drug, core receptor, and ancillary partners in the matrix as a whole that determine the net observable effect. And, in theory, any part of the protein may become a ligand binding site which, upon binding of a cognate molecule, drives the ensemble to a given pharmacological endpoint (Kenakin, 2008a,b). Under this model, the physicochemical properties of different allosteric ligands for a target protein may differ significantly from each other, and may occur within a broad range of chemical structures. In addition, multiple allosteric sites might exist for a given core protein. Is this line of thought supported experimentally? Valuable insights to begin answering this question were obtained from mother Nature itself. For GPCRs (Doller, 2017; Thal et al., 2018; Wootten et al., 2012), the systematic study of the chemical

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diversity of naturally occurring endogenous compounds acting as allosteric modulators includes ions (e.g., Naþ, Cl
) (Strasser et al., 2015; Zarzycka et al., 2019), peptides (RAMPs or MRAPs) (Berruien and Smith, 2020; Pioszak and Hay, 2020; Webb and Clark, 2010), amino acids (Conigrave et al., 2004; Leach et al., 2020; Lee et al., 2007), purinergic nucleotides (Li et al., 2002), carbohydrates (Medina et al., 2016), and lipids (Doller, 2017; Leach et al., 2007). For ion channels such as GABAA, a number of endogenous modulators have been grouped under the name “endozepines,” mostly of peptide nature (Farzampour et al., 2015; Tonon et al., 2020). Among these, diazepam-binding inhibitor (DBI) is an 86-amino acid polypeptide capable of displacing [3H]-diazepam binding to brain membranes, as well as an octadecaneuropeptide that behaves as a GABAA PAM, and is derived from endoproteolytic cleavage of DBI/ACBP complex. Likewise, NMDAR endogenous modulators have been reviewed, including small molecules (neurosteroids, polyamines, kynurenic acid and Zn2þ) and autoantibodies (Jorratt et al., 2020; Skiba and Kruse, 2020). The study of the selective functional effects of Naþ on class A GPCRs uncovered the existence of a conserved binding site, including evidence from high-resolution GPCR crystallographic structures (Katritch et al., 2014). This has been exploited to design modulators for the opioid, A2 adenosine, D2 dopamine, and b2 adrenergic receptors, among others (Selent et al., 2010; Shang et al., 2014). Recently, Cl
ions were found to act as PAMs in group II and III mGluRs by binding to a site near the orthosteric glutamate site (DiRaddo et al., 2015; Tora et al., 2015). Indeed, the discovery that Cl
interacts with a fairly large number of proteins and directly modifies their activity, led to the idea that Cl
is a bona fide natural signaling ion (Lüscher et al., 2020). Di- and trivalent ions (Ca2þ, Zn2þ, Hg2þ, SO4
2, PO4
3) are also known to play key roles modifying the function of a number of GPCRs by allosteric binding (Zarzycka et al., 2019). Among the most important examples of endogenous peptides allosterically modifying receptor function, the receptor activity modifying peptide-1 (RAMP-1) combines with the class B GPCR calcitonin receptor-like receptor (CLR), to form the calcitonin gene related peptide (CGRP) receptor. In recent years, several biologics acting as antibodies towards this receptor and two small molecules antagonists, rimegepant (29) and ubrogepant (30) have been approved as effective treatments for migraine. Structural biology studies confirmed that RAMP-1 acts as an allosteric modulator of CLR through limited direct contact with its extracellular domain and stabilizing the receptor complex (Liang et al., 2018). Based on the structures of CGRP-bound and small-molecule antagonist bound receptor with telcagepant (31), olcegepant (32), and HTL22562 (33), it was suggested that antagonists do not preclude the formation of the CLR/RAMP-1 complex, but they rather induce a conformational change that prevents key interactions at the interface of CLR and RAMP-1, rendering the complex functionally silent (Bucknell et al., 2020; ter Haar et al., 2010).

The endogenous neuropeptide L-prolyl-L-leucylglycinamide (33), or melanocyte stimulating hormone release inhibiting factor 1 (MIF-1), derives from the cleavage of the hormone oxytocin. This neuropeptide is found in CNS, where it acts as a dopamine D2 receptor PAM, as suggested by in vitro enhancement of the specific binding of the agonists [3H]-N-propylapomorphine (NPA) and [3H]-quinpirole, and functional enhancement of cAMP accumulation in the presence of dopamine, but not on its own (SampaioDias et al., 2019). SAR studies around the structure of MIF-1 (33) produced the tripeptide PAOPA (34), also a D2R PAM thought to bind at the extracellular fluid-facing surface of the receptor (Daya et al., 2018). Interestingly, conformation constrained analogs like 35 switched their PAM effects to functional inhibition (see below) (Bhagwanth et al., 2012). These tripeptides are structurally very different from 36 and 37, lipophilic D2 and D3 receptor PAMs, suggesting different allosteric binding sites. At both D2 and D3 dopamine receptors, these PAMs potentiate both [3H]-dopamine binding, and dopamine-stimulated [35S]-GTPgS binding in

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membranes from CHO cells expressing human dopamine D2 receptor. Moreover, these tripeptides enhance the dopaminedependent decrease in cAMP in the presence of forskolin. Again, functional switches were observed upon SAR exploration: while the R enantiomer 36 had no effects alone but potentiated the effects of dopamine at the D2 receptor, the S enantiomer 37 attenuated either the PAM effects of the R enantiomer, as well as activation by dopamine, and therefore likely acts as a D2 receptor NAM (Wood et al., 2016).

Carbohydrates and glycosides are another structurally distinct group of allosteric modulators. For example, the ligand gated ion channel P2X7 plays an important role regulating immune cell responses during inflammation and infection (Di Virgilio et al., 2017). Activation of P2X7 in infected macrophages is thought to promote microbial killing in pathogens such as Mycobacterium tuberculosis, Toxoplasma gondii and Leishmania amazonensis. P2X7 PAMs were optimized using a HEK-hP2X7 stable cell line and a YO-PRO-1 dye uptake assay, with ATP as the agonist. The starting PAMs, ginsenosides CK (38) and Rd. (39) showed two distinct effects on concentration-response curves for ATP-induced responses at hP2X7: an increase in the maximum response and a shift to the left (see below, type I and II ion channel modulators, respectively). The aglycone PPD (40) had no effect, suggesting the carbohydrate moieties play a key role determining these functional effects at P2X7. Gypenoside XVII (41) has a type II PAM activity, whereas gypenoside XLIX (42) increased the maximum response by 1.9-fold (type I PAM). Among glycosides with monosaccharide attachments, ginsenoside CK (43) increased the maximum response and reduce the EC50 value for ATP, a mixed type I/II effect. Interestingly, ginsenoside F1 (44), differing from ginsenoside CK only by an extra hydroxyl group on C-6, did not potentiate P2X7 responses (Piyasirananda et al., 2020).

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On the other end of the polarity spectrum, highly lipophilic lipids have also been reported as allosteric modulators. First off, cholesterol (45), was thought to exert only non-specific effects on membrane fluidity. However, evidence eventually emerged suggesting stereoselective interactions with membrane proteins (Fantini et al., 2019). These effects are so ubiquitous, that a number of amino acid motifs were identified in several proteins known to bind cholesterol, defining essentially an allosteric pocket, coined “cholesterol-recognition amino acid consensus” (CRAC), or its mirror image, referred to as the “CARC” consensus motif (Di Scala et al., 2017). Cholesterol metabolites, produced endogenously by auto-oxidation, enzymatic processes, or both, have also been characterized as allosteric modulators of key receptors, including nuclear receptors, GPCRs, ligand-gated ion channels and transporters (Wang et al., 2020a,b). Similar to the situation with cholesterol, there appears to be a complex picture, with multiple binding sites for oxysterols on these receptors, localized to the cysteine rich domain pocket as cholesterol or within transmembrane regions. For example, 24S-hydroxycholesterol (46) acts as a mild PAM at the NMDA receptor, binding at a different site than other NMDA receptor allosteric ligands. Furthermore, its glutamate-enhancing effects are antagonized by its isomer, 25hydroxycholesterol (47), also binding at a distinct, non-overlapping allosteric site (Linsenbardt et al., 2014).

A combined analysis of X-ray crystallographic and near full-length cryo-EM structures of the GABAB receptor in an inactive state revealed the presence of two large endogenous phospholipids embedded within the transmembrane domains (48 and 49). They are thought to maintain receptor integrity and play a physiological role modulating receptor function acting as NAMs by stabilizing the inactive conformation of GABAB2, as mutation of amino acid residues forming hydrogen bonds with the phosphate head group yielded mutant receptor exhibiting a small gain of function despite reduced cell surface expression. Support for this observation may be derived from a structure of the GABAB receptor in the active state (Park et al., 2020).

While the list of endogenous and synthetic allosteric modulators could be more extensive, these examples demonstrate that the ability to allosterically modulate receptor function is not reserved to certain types of chemical structures or limited to one narrow volume of the physicochemical property space. Reports to the contrary might just be reflecting whatever was explored in the past, rather than what’s actually possible (van Westen et al., 2014).

1.12.3

Types of macroscopic functional response by allosteric modulators

While there is broad agreement of the central role played by allostery in living systems, our understanding of the mechanistic details at the molecular and atomic level of how allosteric regulation of individual proteins occurs is far from complete. In particular, how distant regions between two communicating binding sites on a protein contribute to allostery and how that interaction leads to diverging functional cooperativity vectors (activation, inhibition, silent) is a topic to which scientists in quite different fields within life sciences are devoting major efforts. It is noteworthy that the distance between orthosteric and allosteric binding sites can range from a few Angstroms (e.g., in bitopic GPCR or transporter ligands), to tens of Angstroms (as in bivalent dimeric GPCR ligands) to hundreds of Angstroms (e.g., mGluRs or type V kinase inhibitors). However, when comparing the literature of allosteric modulators acting on different types of receptors, it is remarkable that different researchers have independently described analogous phenomena, albeit with different terminology.

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1.12.3.1

V-type and K-type allostery in enzymes

The difference in catalytic activity produced by an allosteric modulator of an enzyme may be due to variations in either of the two classical Michaelian kinetic parameters, Km or Vmax, as a function of the concentrations of substrate and effectors. Accordingly, two types of allosteric modulators of enzymes have been described: V-type allostery (for V ¼ velocity), in which effector binding alters the rate of catalysis at substrate saturation (Vmax), and K-type allostery, in which effector binding alters the kinetically defined affinity of one or more substrates (for Km) (Carlson and Fenton, 2016; Monod et al., 1965). Most of the early data was on Ktype enzymes. One such example is Pyruvate Kinase A (PykA), the dominant isoform in P. aeruginosa, characterized by potent Ktype allosteric activation by glucose 6-phosphate and by intermediates from the pentose phosphate pathway (Abdelhamid et al., 2019). Unexpectedly, structural biology studies based on the X-ray structure of PykA at 2.4 Å resolution, revealed binding of glucose 6-phosphate in a different pocket than previously reported for the PK from Mycobacterium tuberculosis, the only available precedent for bacterial PK bound glucose 6-phosphate. This example highlights the risks of extrapolating binding site information across different biological systems. Among the early examples of V-type allostery reported was the enzyme phosphoglycerate dehydrogenase (PGDH) derived from Escherichia coli. PGDH catalyzes the first step in serine biosynthesis, the formation of 3-phosphohydroxypyruvate from D-3phosphoglycerate using NAD/NADH as a cofactor, and is allosterically inhibited by serine (Schuller et al., 1995). Interestingly, enzyme activity may vary as a function of pH or matrix component concentration, leading to differential ionization of amino acid residues and conformational modifications in the enzyme that result in changes in the V-type or K-type functionality. For example, functional changes of murine intestinal brush border sucrase-isomaltase (sucrose D-glucosidase) in response to pH and Naþ ions were investigated. While at pH 5.0, the enzyme activation by allosteric Naþ ions was V-type, it changed to K-type at pH 7.2, whereas at pH 8.5, Naþ ions acted as uncompetitive inhibitors of the enzyme, affecting both the Km and Vmax components (Gupta et al., 2008). Allosteric enhancers of enzyme activity offer potential clinical utility in diseases caused by functional downregulation of an enzyme, be that due to mutations or insufficient protein synthesis. One excellent example is AG-348 (mitapivat, 50), an allosteric activator of the enzyme pyruvate kinase (PK). Mutations in PK cause deficiencies in red blood cell glycolysis, leading to a disease known as PK deficiency. Mitapivat was shown to increase the activity of wild type and mutant PK enzymes in biochemical assays. As seen on Fig. 7, this allosteric drug did not affect the Vmax values of the different enzymes tested, but rather their KM (K-type effects) (Kung et al., 2017). Mitapivat is currently undergoing later stage clinical studies for the treatment of this rare disease, as well as for its

(B) (A)

(C)

(D)

Fig. 7 (A) Chemical structure of AG-348. (B) Plot of activity of recombinant WT PK-R enzyme stimulated with PEP with or without preincubation by AG-348 (5 mM). (C) and (D) Kinetic parameters Vmax and KM from recombinant PK enzymes measured with or without treatment with AG-348 (5 mM). Vmax is the maximum velocity; KM is the substrate concentration needed to achieve half maximal velocity. All graphics re-plotted using published data (Kung et al., 2017).

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potential use in the treatment of thalassemia and sickle cell disease (Agios, n.d.). The effects seen in vitro were confirmed in a more translationally relevant ex vivo test conducted during clinical studies for the treatment of PK deficiency, on red blood cells and erythroid precursors from PK-deficient patients. In 15 patients treated with AG-348, enzymatic activity increased in all patient cells after 24 h by a mean value of 1.8-fold (Rab et al., 2020).

1.12.3.2

a and b types in GPCR allosteric modulators

The fundamental allostery model represented in Fig. 1, or variations thereof, are becoming more broadly used in the study of allosteric modulators for GPCRs (Roche et al., 2014). As expected for a drug action model with increasing number of functionally relevant chemical partners, the different resulting mathematical treatments are relatively complex (Eq. 1, Table 1) (Leach et al., 2007). Conceptually however, the take-home message is that the effects of an allosteric modulator may be effectively defined using three independent macroscopic parameters: a, the binding cooperativity; b, the efficacy cooperativity; and for compounds with allosteric agonist activity, the term sB defining the maximal efficacy the ligand can produce in the absence of the orthosteric agonist (Fig. 8). Since many of these parameters vary significantly, even among structurally similar analogs, a larger set of functional profiles is feasible (Kenakin, 2012).

(1) (A)

(B)

(C)

(D)

Fig. 8 Simulated concentration-response curves exemplifying four select types of GPCR allosteric modulation. Red is the CRC for agonist alone, shades of blue are CRCs for different concentrations of allosteric modulator added on top of agonist. (A) Leftward shift (a > 1, b ¼ 1). (B) Upward shift, or EMAX enhancement above 100% of agonist (a ¼ 1, b > 1). (C) Allosteric agonist (sB > 0, a ¼ 1, b ¼ 1). (D) Negative allosteric modulator with b < 1.

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Fig. 9 Simulation showing three different allosteric modulators with similar functional enhancement at the agonist EC20 (1 mM) but different profiles at the EC80 concentration (30 mM).

While typically studied at a single agonist concentration (e.g., EC20) and establishing the “agonist fold-shift,” these graphical representations of three-ligand models suggest that quantifying allosteric effects this way may provide an incomplete characterization of an allosteric ligand. Indeed, compounds having different combinations of a and b profiles may give similar potentiation at a low agonist concentration but quite different effects at higher agonist concentrations (Fig. 9). Thus, the full set of concentration response curves across a range of agonist and allosteric ligand concentrations may be the best practice in this area, especially for the characterization of key probe molecules or high-value advanced clinical candidates. Preclinically, the study of a group of mGluR4 ligands acting as a-PAMs and/or b-PAMs shed some light on the link between these parameters and in vivo efficacy in rodent PD models (Huang et al., 2014). Using rat brain slice electrophysiology, the mGluR4preferring agonist LSP1-2111 (51) was first characterized by an EC50 ¼ 12 mM and maximal reduction of fEPSP EMAX ¼ 25%. Next, using EC20 and EC80 agonist concentrations (3.7 mM and 19 mM, respectively), the selective, mixed type a þ b mGluR4 PAMs ADX88178 (52), VU0161737 (53) or Lu AF21934 (18) were added, showing an enhancement of the EMAX to 66%, 50% and 45%, respectively. In vivo, these three compounds showed efficacy in three preclinical models used in PD drug discovery: reversal of haloperidol-induced catalepsy, and unilateral full and partial 6-OHDA lesion model in rat (Kalinichev et al., 2014). On the other hand, the pure a-type chemical probe 51, as well as the mGluR4-inactive enantiomer of compound 18, used as a control, showed no efficacy either in in vitro electrophysiology or in in vivo models. These results link compounds with significant super-physiological enhancements of mGluR4 function in vitro to translationally relevant efficacy models (Huang et al., 2014). Of note, the pure a-type mGluR4 PAM foliglurax (55) was recently discontinued from clinical development after missing primary and secondary endpoints in a PD levodopa off-time phase II study (Charvin et al., 2017, 2018). Lundbeck Reports Headline Results From Phase IIa AMBLED Study of Foliglurax in Parkinson’s Disease (n.d.). On the contrary, valiglurax (56) is another mGluR4 PAM in advanced preclinical development, characterized by robust b effects (Log b ¼ 1.34, around 300% of glutamate EMAX) (Panarese et al., 2019a,b). This compound may provide an avenue to test in the clinic the validity of the hypothesis linking mGluR4 PAM b effects with efficacy in PD treatment.

Allosteric Modulation 1.12.3.3

315

Type I and type II ion channel modulators

Similar observations have been made regarding the macroscopic effects of allosteric modulators for ion channels of high importance in drug discovery. Diverse modes of NMDAR positive allosteric modulation have been observed. Each mode described has potential consequences in terms of desirable and undesirable effects on brain function due to their differences in effects upon kinetics of channel deactivation and changes of agonist affinity for different types of PAMs (Hackos and Hanson, 2017). Type I PAMs were defined as compounds that enhance the maximum activity or efficacy of a receptor without altering the agonist EC50, whereas type II PAMs do shift the agonist EC50 to lower values (left shift). The tool compound PYD-106 (57), acting on top of a maximally effective concentration of glutamate and glycine, increased the response of GluN1/GluN2C NMDA receptors in HEK-293 cells to 221%, while only slightly altering the agonist glutamate EC50, thereby functioning as a type I PAM. At the single-channel level, PYD-106 works by increasing the frequency of opening and the mean open time, thus indicating stabilization of the open state (Khatri et al., 2014). Compounds GNE-6901 (58) and GNE-8324 (59) are GluN2A-selective NMDAR PAMs derived from medicinal chemistry optimization. Despite sharing the same thiazolopyrimidinone core, the PAM functional effects of these two analogs are different in nature. GNE-6901 produces type I PAM effects, with only minimal increases in agonist Glu potency and minimal effects on deactivation, whereas GNE-8324 shows mixed type I and type II PAM effects, both slowing deactivation kinetics and significantly increasing Glu potency. Structural biology analysis of these relatively close analogs did not clarify the mechanistic origin of the different effects on agonist Glu potency (Hackos and Hanson, 2017).

Likewise, based on the effects evoked by the agonist acetylcholine (60) on macroscopic currents at a7 nicotinic acetylcholine receptors (nAChRs), PAMs have been classified as type I and type II. Both types of PAMs enhance the agonist-elicited peak currents, but whereas type I PAMs exert minimal or no changes in desensitization, Type II PAMs decrease the desensitization rate and reactivate receptors from desensitized states. Another reported difference between a7 nAChRs type I and II PAMs is the ability of the latter to reactivate the receptor from the desensitized state (Chatzidaki and Millar, 2015), and the long-term agonist treatment-induced upregulation of a7 nAChRs was shown to be inhibited by type II, but not type I PAMs (Thomsen and Mikkelsen, 2012). When using single-channel recordings, more sensitive to kinetic changes than macroscopic recordings, potentiation of a7 NAChRs by both types of PAMs is detected by the increase in burst and open durations. The impact of both types of PAMs on a7 kinetics covers a wide spectrum of enhanced durations. Type II PAMs produce a more profound potentiation and, in some cases, they can increase the activation episodes from the sub millisecond range into the range of seconds. Type I PAMs typically exert more modest effects on the scale of several milliseconds, which, in turn, make them more suitable and promising for therapeutic use (Nielsen et al., 2020). To illustrate the limitations of this rough analysis based on macroscopic data, a detailed kinetic study of PNU-120596 (61) and A-867744 (62), two (putative) type II a7 nAChR PAMs was conducted using a fast perfusion method suited for high temporal resolution. This allowed the thorough characterization of the type of functional modulation produced by these probe compounds. Major differences were found between the modulation mechanisms by PNU-120596 and A-867744. For example, using brief agonist pulses, A-867744 caused a type I modulation, while PNU-120596 caused a type II prolonged activation. Even though both compounds were considered as type II PAMs based on early studies, in depth characterization suggested major pharmacological differences, including their onset and offset kinetics, state preference, and single channel open time (Pesti et al., 2019). These results exemplify the depth of insights that can be uncovered by thorough characterization of allosteric ligands, especially important before embarking upon very expensive clinical studies. Subtle details of the mechanism of action may be significant in correctly assessing the therapeutic utility and provide translationally valuable information.

316

Allosteric Modulation

AMPAR PAMs have also been broadly grouped by their impact on receptor activation, as low impact (type I) or high impact (type II). High impact potentiators produce comparatively more robust increases in AMPAR activation, but at the expense of producing convulsions, disruption of motor coordination, and neurotoxic phenotypes, comparable to those observed by orthosteric AMPAR agonists. Low impact AMPAR PAMs produce small increases in synaptic currents by decreasing AMPAR deactivation (channel closing), and have few adverse effects. High impact AMPAR PAMs show larger effects by decreasing both deactivation and desensitization together to enhance and prolong synaptic currents (Roberts et al., 2010). These two types of AMPAR PAMs also show different properties in electrophysiology studies, with low impact ampakines having essentially no effect on the half-width of the field excitatory postsynaptic potential (fEPSP), and high impact compounds increase it markedly. Examples of advanced low impact AMPAR PAMs include aniracetam (63) and CX516 (64), whereas BIIB-104 (or PF-04958242, 65) and ORG-26576 (66) are some examples of clinical high-impact ampakine PAMs (Partin, 2015).

Recent work aimed at threading the needle between efficacy and safety for high impact AMPAR PAMs addressed some challenges of compounds in the biarylsulfonamide chemotype (e.g., LY451646, 67), such as bell-shaped concentration-response curves in various pharmacological tests and narrow safety margin against seizures. It was hypothesized that the bell-shaped response of 67 was linked to agonist activity showed by these PAMs triggering BDNF production in primary neurons (Kunugi et al., 2018). Consistent with this observation, HBT1 (68) demonstrated weaker agonist activity in the BDNF assay and lower risk of bellshaped response. Further efforts to lower such agonistic effects led to the discovery of TAK-137 (69), a promising AMPAR PAM with potent pro-cognitive effects and lower risks of bell-shaped response and seizure. The improved pharmacology was attributed to a binding pose where 69 forms a hydrogen bond with S518 in the ligand binding domain of the AMPAR (Suzuki et al., 2019a). Follow up studies using electrophysiology in rat primary cultured hippocampal neurons showed that in the absence of AMPAR agonist, LY451646, but not TAK-137, induced large inward currents, suggesting reduced agonistic properties for TAK-137 over LY451646 (Suzuki et al., 2019b).

67

68

69

Formerly introduced glycolipid hP2X7 PAMs (38–44) also showed two distinct effects on concentration-response curves for ATP-induced responses: an increase in the maximum response (Emax or type I) and a shift to the left (EC50 or type II) (Piyasirananda et al., 2020, p. 7). From all these examples and other not discussed here, it seems that small molecule allosteric modulators of different ion channels share some common patterns in terms of subtly affecting parameters used to quantify the functional effects of their biological targets in primary in vitro assays. These effects vary even among structurally close compounds, and may be optimized in a way that maximizes the benefit/risk ratio (Stokes et al., 2020). Although classifying allosteric modulators as belonging to a given type may be practically useful, this representation derives from an empirical description of readily observable functional effects by a ligand on the agonist concentration response relationship. Such empirical classifications should not be confused with the complex changes in

Allosteric Modulation

317

molecular mechanistic parameters. The examples previously discussed highlight important differences in the functional phenotypes caused by allosteric ligands, which may have translational relevance and physiological consequences during a drug discovery project.

1.12.3.4

Allosteric modulation of membrane transporters

Transporters regulate the passage of chemicals (e.g., water, nutrients, hormones, drugs) across membranes, and are key to maintaining cellular homeostasis. There are two main transporter groups: solute carrier (SLC) transporters, and ATP-binding cassette (ABC) transporters (efflux transporters). To the former group belong several approved drugs, such as dopamine, norepinephrine and serotonin reuptake inhibitors for the treatment of depression and sodium/glucose co-transporter (SGLT2) inhibitors for the treatment of diabetes. The ABC transporters, including P-glycoprotein (P-gp), play major roles in hepatobiliary and urinary excretion, intestinal absorption, and blood-brain barrier penetration of drugs. They use ATP hydrolysis to provide the energy required for the translocation of substrates across membranes. Generally speaking, the study of allosteric modulators of transporter function has followed a different trajectory than other types of membrane-bound receptors or soluble enzymes. That’s not to suggest that excellent progress is not also being made with a broad number of small molecule transporters (Niello et al., 2020; Wang et al., 2020a,b). Major discoveries in the structural biology of these transporters bound to their substrate and other ligands are driving the understanding of their structure-function properties (Xing et al., 2020). However, gaps still exist in our ability to develop predictive knowledge linking allosteric ligand-directed effects at the molecular level with physiological and behavioral effects. Research exploring the function and pharmacology of their allosteric ligands continues, moving beyond competitive inhibitory mechanisms and introducing new models that account for experimental observations of partial efficacy through allosteric modulation of these transporters (Aggarwal and Mortensen, 2017; Hasenhuetl et al., 2019). A key finding in this area was the discovery of the allosteric enantioselective effects of (R)-citalopram negatively modulating the function of its enantiomer, escitalopram (Sánchez et al., 2004). Recently, the structurally related first high-affinity allosteric SERT inhibitor, Lu AF60097 (70), was characterized by its potentiation of the inhibitor imipramine (71) binding to the orthosteric site S1, and functional enhancement of hippocampal serotonin concentration in rat brain (Plenge et al., 2020). UCPH-101 (72), an allosteric EAAT1 inhibitor with sub-mM potency inhibiting [3H]-aspartate cellular uptake assay (Abrahamsen et al., 2013), displaying non-competitive and long-lasting effects through an allosteric binding mode established by structural studies (Canul-Tec et al., 2017). GT949A (73), was identified as a glutamate EAAT2 PAMs by combining molecular dynamics calculations that identified five residues hypothesized to define the allosteric pocket, an in silico screen, and an in vitro glutamate uptake assay. In the uptake assays in COS-7 cells expressing EAAT2, the compounds showed nM levels of functional potency and selectively increased Vmax of transport, without changing substrate affinity (Kortagere et al., 2018). Consistent with the mode of action, the compound (as a racemate) also displayed neuroprotective properties in models of glutamate excitotoxicity (Falcucci et al., 2019). Interestingly, this chemotype displayed functional shifts (see below), as out of 10 compounds prioritized by the virtual screen, three were found to be PAMs and 4 NAMs (Kortagere et al., 2018).

In summary, the examples discussed in these sections suggest two points. First, that allostery is a fundamental property of how small molecules interact with biological receptors systems (at least for polypeptides). It seems as though similar patterns of functional effects are seen for different compounds and different type of receptors. Second, these interactions are very diverse in nature, and play a key mechanistic role enabling the subtle modulation of cellular physiology of the cells or organism under study. Indeed, the inherently unpredictable nature of the link between structural changes to allosteric ligands and their functional effects, both a blessing and a curse, may lead to drugs with truly unique pharmacological properties, impossible to emulate using orthosteric agonists.

1.12.4 Probe dependence: Can the functional attributes of a drug be defined independently of its chemical context? Probe dependence is another major characteristic attribute of allosteric modulation (Table 1). Basically stated, the direction and magnitude of the functional effects caused by an allosteric ligand may differ when the orthosteric ligand varies. The implications of this attribute are major, as they break the linear tractability that many expect when conducting SAR studies. In the extreme case, the allosteric properties of a compound may not be defined independently of the system in which it is testeddits chemical

318

Allosteric Modulation

contextdincluding both small molecule ligands as well as ancillary biomolecules and matrix components. Given how ubiquitously allosteric mechanisms regulate cell homeostasis, it is safe to infer that understanding probe dependency is a key component of “knowing your drug,” (Bunnage et al., 2015) and predicting its physiological effects. Consistent with some experimental observations, early thoughts around probe dependence aimed to explain qualitative changes in the magnitude of allosteric effects on the basis of the intrinsic functional efficacy of the orthosteric agonist partner. The expectation, based on this principle, was that a particular PAM would potentiate full (higher efficacy) agonists to a greater extent than it would partial or inverse agonists (lower efficacy). For example, at the M1 mAChR, the PAM BQCA (74) potentiated the full agonists acetylcholine (60) or carbachol (75) to a greater extent than the partial agonists pilocarpine (76) or xanomeline (24), and inhibited the effects of the inverse agonist NMS (77) (Canals et al., 2012). Another muscarinic PAM, LY2033298 (78), allosterically potentiated the M2 mAChR signaling of the agonist oxotremorine-M (79) but for xanomeline, it inhibited the signaling (Valant et al., 2012).

However, findings inconsistent with this principle were observed in subsequent probe dependence studies, prompting different thinking. LY2033298 (78) is also an M4 mAChR PAM, and it had provoked great interest as a potential clinical treatment for schizophrenia. As such, and given differences in receptor amino acid sequence for rodent and human, a number of in vitro studies were conducted in CHO cells stably expressing human or mouse M4 mAChRs, using assays of agonist-induced ERK1/2 or GSK-3a phosphorylation, [35S]-GTPgS binding, or radioligand binding of [3H]-NMS or [3H]-ACh. To investigate potential probe dependence, acetylcholine (60), oxotremorine (80), or xanomeline (24), were used as the orthosteric agonist. These studies led to the conclusion that the mechanistic basis for species selectivity in the actions of LY2033298 at the M4 mAChR were not linked to binding affinity, but rather differences in functional cooperativity at the rodent and human receptors (Suratman et al., 2011). These observations led to the novel hypothesis that the allosteric effects of LY2033298 at the congener M2 mAChR subtype varied according to the ligand used as the orthosteric partner, not just in magnitude, but also in the direction of the functional vector. In these M2 mAchR studies, radioligand binding displacement assays using the orthosteric antagonist [3H]QNB suggested a strong negative binding cooperativity (a’ ¼ 0.001) with LY2033298 at M2 mAchR. By itself, this is indistinguishable from a competitive interaction between ligands binding at overlapping sites. On the other hand, when using [3H]NMS (an orthosteric antagonist), LY2033298 caused an enhancement in radioligand specific binding at the receptor, corresponding to a positive binding cooperativity a0 ¼ 3.2. This remarkable example of probe dependency can be explained by arguing that the two radioligands, while chemically related, bind to the orthosteric pocket in different orientations, and therefore produce dissimilar allosteric transitions with the allosteric ligand LY2033298. While infrequent, as many highly educational natural experiments (Natural Experiment, 2020), this example has significant implications in the complex narrative of molecular mechanisms of drug action. Whether or not a drug discovery project aims for drugs acting at allosteric sites, it is increasingly clear that most biomolecules that are known to act as receptors are components of a complex system formed by a number of endogenous chemicals. Given the reciprocal nature of allosteric transitions, functional effects of drugs designed to act at orthosteric sites will depend on the endogenous chemicals available to interact with the receptor at allosteric sites. Therefore, functional effects caused by a synthetic orthosteric ligand in an in vivo system, may plausibly be expected to differ from those measured in a simpler system lacking the full complement of physiologically relevant endogenous modulators. Furthermore, the functional effects could conceivably change according to disease progression. This clearly has translational implications in drug discovery projects. Besides highlighting the potential for mis-characterization of a drug’s on- and off-target allosteric effects, these findings should create awareness and discipline to evaluate the allosteric properties of valuable chemical tools or clinical compounds as fully as possible based on the disease hypothesis and existing metabolomics knowledge, including translationally relevant endogenous chemicals. This should be a reasonable mitigating strategy before investing millions of dollars and, more importantly, exposing patients to a new drug. Probe dependence may become an advantage and invite novel strategies in drug discovery. Recent disclosures by two independent groups in the area of potentiation of the glucagon-like peptide 1 (GLP-1) receptor provide interesting examples. The GLP-1 receptor is the biological target of a handful of injectable peptide agonists effective in the treatment of type 2 diabetes mellitus,

Allosteric Modulation

319

including semaglutide, exenatide, liraglutide, lixisenatide, albiglutide, and dulaglutide. The requirement of parenteral administration motivated the search of small molecules acting as GLP-1 PAMs, seeking to emulate improvements in glycemic control, reductions in body weight and potential cardiovascular benefits. The native peptide agonist, GLP-1(7–36), has a very short half-life of only 1–2 min, as the enzyme dipeptidyl-peptidase 4 (DPP4) removes the N-terminal His7 and Ala8. The resulting metabolite, GLP-1(9–36), shows circulating concentrations higher than full-length GLP-1, but it is a functionally weaker agonist. This led to the search for probe-dependent PAMs that enhance the activity of GLP-1(9–36) at the GLP-1 receptor. Earlier studies on GLP1-PAMs had shown the possibility that PAMs designed with an eye towards the potentiation of the endogenous agonist may as well behave as PAMs of a metabolite of such agonist (Wootten et al., 2012). Further exploration of this strategy lead to remarkable learnings. In vitro, the PAM LSN3160440 (81) enhances GLP-1R-induced cAMP signaling from a very low efficacy to full agonism, left-shifting the EC50 by  1500-fold. Studies of the effects of LSN3160440 on the enhancement of radioligand binding of [125I]GLP-1(9–36) and [125I]GLP-1(7–36) showed negligible binding cooperativity effects on the native parent peptide, and a 70-fold increase in affinity of the truncated peptide metabolite (Bueno et al., 2020). In ex vivo cultures of isolated islets from wild-type, but not from GLP-1R knockout mice, treatment with LSN3160440 and GLP-1(9–36) in the presence of glucose produced insulin levels comparable to those seen with full-length GLP-1. No effects were seen at low glucose concentration, by any ligand, alone or in combination (Bueno et al., 2020). Similar efforts from a different group led to the discovery of a structurally different GLP-1 PAM (82) but showing similar probe dependency towards the metabolite GLP-1(9–36) (Méndez et al., 2019). Compound 82 was shown to act as a potent GLP-1 R PAM in various functional assays, effectively combining with the significantly less active metabolite GLP1(9–36) to activate GLP-1R in a comparable way to the native peptide (Méndez et al., 2019). Unfortunately, proof of concept studies in vivo fell short and only delivered mechanistic support, as for both compounds 81 and 82, the observation of insulinotropic effects occurred only with concomitant addition of exogenous peptide metabolite GLP1(9– 36) (Bueno et al., 2020; Méndez et al., 2019). These results suggest that the amounts of metabolite generated under physiological conditions are too low for the level of potentiation that these two PAMs can provide in vivo, and question the feasibility of this approach.

1.12.5

Functional shifts: A bug or a feature?

During traditionally orthosteric drug discovery projects, SAR is developed to converge from initial hits to a clinical development candidate. Indeed, one of the attributes of an optimizable chemotype was thought to show “tractable SAR” patterns. This implied that lead optimization was conducted in an orderly fashion, where lead compounds could be divided in fragments (e.g., West and East), which would be optimized independently, and then the optimized fragments would be combined and culminate in compounds with the expected linear properties. When this strategy was used for the design of allosteric modulators, only a few were met with success. Indeed, the many failures contributed to generating a taboo: optimization of allosteric ligands may not be tractabledat least may not be in a linear fashion, as it was the case with orthosteric drugs. Among the non-tractability issues encountered, functional switches quickly gained notoriety as a major challenge, and seemed to be adding insult to injury for medicinal chemistry efforts. Not only were SAR efforts non-linear (or not additive), but upon minimal structural changes to an allosteric modulation, the functional vector was switching unexpectedly from positive to negative or silent. Soon enough, the perception developed that functional switches were a built-in attribute of allosteric modulators, at least for the case of GPCRs and ion channels (Lindsley et al., 2016). In this section, we present examples of functional switches upon modest structural changes to all types of ligands, independently of their binding location. Indeed, agonist-to-antagonist shifts within a chemotype are not unusual in the literature (Dosa and Amin, 2016; Fujioka and Omori, 2012; Lindsley, 2014; Sugg, 1997). We hope to persuade the reader that such changes are a feature of all systems under allosteric regulatory control, independently of the binding site towards which chemists are designing ligands. We also seek to extract valuable information regarding what lessons these changes teach us about drug action. The initial definition of functional switches referred to “allosteric ligands scaffolds that unexpectedly modulate pharmacology and raise concerns over metabolism and the pharmacology of metabolites” (Orgován et al., 2020; Wood et al., 2011). To clarify this definition: the reader must be made aware that the term “molecular switches” is also used in a different context in the area of GPCRs,

320 Table 2

Allosteric Modulation Examples of functional switched within mGluR5 chemotypes. R

2-F 3-F 4-F 2-Cl 2,6-F2

Comp

Allosteric drug profile

EC50; Emax

IC50; Imax

Affinity (uM)

83

NAM

–

4.8 nM; 88%

0.51 mM

84

PAM

7.5 nM; 97%

–

0.67 mM

85 86 87 88 89

Low FS PAM High FS PAM Ago-PAM NAL NAM

1.5 nM 0.8 nM 0.4 nM >3000 nM >3000 nM

>3000 >3000 >3000 >3000 27

1.9 nM 1.5 nM 1.7 nM 0.6 nM 2.4 nM

90

Inhibitor

–

8.2 mM; 63%

n/a

91

PAM

4.6 mM; 82%

–

n/a

From Sams AG, Mikkelsen GK, Brodbeck RM, Pu X and Ritzén A (2011) Efficacy switching SAR of mGluR5 allosteric modulators: Highly potent positive and negative modulators from one chemotype. Bioorganic & Medicinal Chemistry Letters 21: 3407–3410. doi: 10.1016/j.bmcl.2011.03.103; Huang H, Degnan AP, Balakrishnan A, Easton A, Gulianello M, Huang Y, Matchett M, Mattson G, Miller R, Santone KS, Senapati A, Shields EE, Sivarao DV, Snyder LB, Westphal R, Whiterock VJ, Yang F, Bronson JJ and Macor JE (2016) Oxazolidinonebased allosteric modulators of mGluR5: Defining molecular switches to create a pharmacological tool box. Bioorganic & Medicinal Chemistry Letters 26: 4165–4169. doi: https://doi. org/10.1016/j.bmcl.2016.07.065 and the only reported for mGluR4 allosteric ligands Utley T, Haddenham D, Salovich JM, Zamorano R, Vinson PN, Lindsley CW, Hopkins CR and Niswender CM (2011) Synthesis and SAR of a novel metabotropic glutamate receptor 4 (mGlu4) antagonist: Unexpected “molecular switch” from a closely related mGlu4 positive allosteric modulator. Bioorganic & Medicinal Chemistry Letters 21: 6955–6959. doi: 10.1016/j.bmcl.2011.09.131.

to describe the structural detail enabling the passing of the signal from the agonist binding site, usually located close to the extracellular surface, to the intracellular part of the receptor (Filipek, 2019). This is not how the term is used in the present discussion of allosteric functional switches. Many highly remarkable cases of functional switches caused by allosteric ligands were reported during in vitro investigations of modulators of mGluR5, deservedly earning that receptor a “temperamental” reputation. Given the binding/function gap characteristic of allosteric ligands, rigorous inferences cannot be made based on functional potency alone, and also require the measurement of binding affinity. Table 2 exemplifies this behavior for select compounds within the alkyne containing chemotype, which may be considered early leads (83 and 84) (Sams et al., 2011) or optimized molecules (85–89) (Haas et al., 2017; Huang et al., 2016). However, it wouldn’t be fair to extrapolate this propensity in functional switching to all GPCRsdnot even to all mGluRs! For example, mGluR4 PAMs have also been studied by many groups during the last decade (Célanire and Campo, 2012; Volpi et al., 2018), and only one example of functional switching was found and reported in the open literature. This is the compound VU0448383 (90), derived by a switch from mGluR4 PAM 91, characterized by weak IC50 of 8.2 mM and inhibiting an EC80 glutamate response by 63%. Indeed, it took more than 5000 compounds screened by the authors while exploring the structure activity relationships of other mGluR4 PAM scaffolds before they observed this pharmacology switch (Utley et al., 2011, p. 4). So, if these switches are a feature of some GPCR allosteric modulators, what about their orthosteric counterparts? Functional switches for orthosteric ligands of GPCRs have been systematically investigated for a set of compounds differing by small structural modifications that result in major changes in functional activity (Dosa and Amin, 2016). The GPCRs in that survey include the opioid and the nociception receptors, the ghrelin receptor, the chemokine-3 (CCR3) receptor, mGluR2 and 3, the cholecystokinin 2 (CCK2) receptor, the vasopressin V2 receptor, a2 adrenergic receptor, the melatonin MT1 and MT2 receptors, H3 and H4 histamine receptors, the angiotensin AT1 and AT2 receptors, the serotonin 5-HT1A, 5-HT2B, 5-HT4, 5-HT6, and 5-HT7 receptors, the complement C3a receptor, the melanocortin-4 (MC4) receptor, A3 adenosine receptor, muscarinic M2 and M3 receptors, the bradykinin B2 receptor, the cannabinoid CB1 and CB2 receptors. An interesting recent example of subtle changes in CXCR3 agonism is also included (Wijtmans et al., 2012). With the caveat that some of the examples discussed were based on the biological activity of racemates, which may be a confounding factor, many were achiral or enantiopure (presented here). These remarkable examples of functional switches and their explanations should illuminate our understanding of the mechanisms by which small molecules exert their effects on receptor complex functional response. The diversity of chemotypes and targets suggests the phenomenon of functional switches for orthosteric ligands is far from unusual.

Allosteric Modulation

321

322

Allosteric Modulation

Allosteric Modulation

323

Similar examples of functional switches were reported for ion channels (e.g., 126 and 127) (Thorat et al., 2021) and nuclear hormone receptors (e.g., 22S-alkyl vitamin D analogs) (Anami et al., 2014; Yamamoto, 2019). So, we propose that this phenomenon may be an attribute of all ligands binding complex receptor systems or membrane bound receptors, not necessarily exclusive to allosteric modulators. Interestingly, these functional shifts often occur without significant changes in binding affinity, suggesting the existence of a mechanistic gap between the binding conformation of a certain chemotype and the resulting functional profile.

1.12.6

Beyond allosterism

Over the last few years, a number of novel chemical modalities emerged and were quickly adopted by drug discovery organizations (Blanco and Gardinier, 2020; Ward et al., 2020). These include small molecule interacting with RNA (or SMIRNAs) (Disney, 2019), engineered proteins (e.g., monoclonal antibodies, modified endogenous proteins, bispecific antibodies, and fusion proteins) (Stanimirovic et al., 2014), hetero-bivalent protein degraders (PROTACs or molecular glues) (Che et al., 2018; Paiva and Crews, 2019), cyclic peptides (Qian et al., 2017; Rubin and Qvit, 2016), antibody drug conjugates (Nasiri et al., 2018), and gene-based therapies (Blanco and Gardinier, 2020). This may be seen as evidence that the field of therapeutic drug design is evolving towards more complex molecules (Rock and Foti, 2019). While not all of these have demonstrated clinical utility, they should be evaluated as potential “modality of choice” in every new drug discovery project, sometimes following more than one path simultaneously. Thus, allosteric drugs have strong competitorsdand possibly offer important lessons to be learned across chemical modalities. However, the pharmacology observed when targeting a disease via the same biological target but with different modalities may have differences. Can these different outcomes be predicted, or at least potential differentiation between modalities be hypothesized in the early days of a new project?

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Perhaps a step in the right direction would be to re-conceptualize mechanisms of drug action by challenging accepted knowledge. What do we mean? Essentially, to avoid the temptation of extrapolating our experience from one chemical modality to a different one. For example, targeted protein degraders tend to be long and flexible molecules, with high molecular weight, polar surface area and numerous rotatable bonds, and low solubility and cellular permeability. Their physicochemical properties belong to the “beyond rule of 5” space (Doak et al., 2014, p. 5). As expected, this type of drug faces complex absorption, distribution and metabolism challenges to achieve oral bioavailability when compared with traditional small molecules. Yet, careful analysis allowed the formulation of a strategy to overcome the hurdles dealing with this new modality (Rock and Foti, 2019). In addition, an exact and general mathematical description for quantitative measurement of target engagement by PROTACs was disclosed recently. This suite of new mathematical tools empowered the field by enabling mechanistic understanding for many common challenges associated with developing PROTACs as therapeutic agents (Han, 2020). Furthermore, the challenges remaining to be addressed to fulfill the promise of the PROTAC modality continue to be aggressively pursued (Bondeson et al., 2018; Paiva and Crews, 2019; Testa et al., 2020). Although the ability of RNA to interact with small molecules has long been recognized (Harvey et al., 2002), major efforts are ongoing towards understanding the fundamental biological principles of disease-causing RNAs and how to identify appropriate small molecule druggable binding pockets. While structured regions exist throughout the transcriptome, human RNA has been considered recalcitrant to small molecule targeting. Early observations on the physicochemical properties of ligands that render this modality “roughly as difficult as for protein targets” were recently disclosed. This disclosure was, however, accompanied by a warning that “no one currently knows how to target RNA with drug-like small molecules in a scalable and reproducible way.” Serendipity has also had a major enabling role for this modality. Because the most drug-like RNA-targeting molecules were initially discovered using target-agnostic phenotypic assays, and were only later found to bind to RNA, the authors issue a series of provocative guidelines (Warner et al., 2018). Designing RNA targeting small molecules is hampered by the conformational flexibility of RNA and the shallow nature of three-dimensional binding pockets within RNA compared to proteins. The mere goal of achieving high binding affinity and selectivity constitutes a formidable challenge, particularly given the abundance of cellular RNAs with similar structural motifs. The consensus in this novel area is that “the evolution of medicinal chemistry fundamentals to successfully and routinely target RNA with small molecules will be a critical factor in determining the overall impact” of this modality (Ernst et al., 2019). Criteria to assess the quality of RNA chemical probes, methodologies enabling the evaluation of target engagement and to establish functional efficacy against RNA-mediated phenotypes in vitro and in vivo are essential to push the field forward. These criteria are at the core of current efforts recently reviewed (Morgan et al., 2018a; Ursu et al., 2019). At this point, it should be clear that developing new chemical modalities requires major efforts to understand the fundamental nature of the specific ligand-target interactions, and cannot be simply extrapolated from our many decades of drug discovery designing orthosteric ligands for polypeptide targets.

1.12.6.1

Drug design in complex allosteric systems

In any area of drug discovery research, the general consensus is that the fundamental human biology that regulates cell and tissue function, and the pathologies that produce disease are incompletely understood. But how large is this knowledge gap? Or, in other words, “how much information lies hidden in biological data that remains unaccounted for?” A frustratingly correct answer given was: “Probably quite a lot” (Mak, 2017). Indeed, that would seem the case after a recent report of more than 2 million peptides and nearly 350,000 proteins identified in a 100-species proteomics study (Müller et al., 2020). Such a large, diverse system is unlikely to lead in the foreseeable future to highly predictable methods to translate discoveries from in vitro systems into therapeutic drug treatments. What to do then? How about trying to identify the Goldilocks point, where our models go beyond simplicity but still contain as much complexity as can be managed with existing technologies? A continuum exists going from simpler systems at the low range of translational value (Fig. 10, bottom left; faster and cheaper), to advanced clinical studies expected to provide registration-enabling support for a drug therapy (top right, slower and more expensive). Among the former are the highly chemically tractable structure-based drug discovery projects, whose hypotheses are formulated based on a “one drug-one receptor” model. On the other hand, in phenotypic drug discovery projects based on disease-relevant human tissues, an attempt is made to reconstruct the complex system where the drug would work using a mechanistically agnostic strategy that does not need to know the actual drug target(s). The latter generally possess a favorable risk profile, short of having confirmatory clinical evidence. In between these two, the use of complex systems formed by the “core receptor,” including its endogenous ligands (orthosteric and allosteric), and accessory proteins, appears to be a risk-managing compromise, depending on how well the system is understood (Kenakin and Miller, 2010; Rosenbaum et al., 2020; Thal et al., 2018). Several strategies have been used to target receptor complexes with variably productive outcomes. Historically, many such drugs were discovered using phenotypic screens, that are agnostic to the mechanism of action (Childers et al., 2020). However, regulatory requirements and business models have changed markedly over time, and projects that succeeded decades ago might not do so in today’s environment. Likewise, our ability to interrogate target identification using new technologies has greatly increased. In particular, access to human tissues in healthy and disease states has improved, leading to screens with much more translationally accurate power. This is key to clinical success, given that a phenotypic project based on in vitro screens in nonhuman tissues or in vivo models in preclinical species may be reflecting a mechanism either unrelated to or completely absent in the human pathophysiology (Vincent et al., 2020).

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Fig. 10 Scheme showing the balance between the translational value of different drug discovery approaches and the relative level of complexity of the models supporting the biological hypothesis interrogated.

Efforts at the chemistry/biology interface are improving our ability to “reconstitute” an endogenous complex system, by identifying individual molecules of any kind that impact the functional response of the system. When the core protein itself is the missing or defective component in the receptor complex system, genomic studies can readily provide a link to a disease, generally falling within the rare disease group. Recent advances in structural chemistry provide clarity in our understanding of the architecture of protein complexes, thus enabling the identification of allosteric binding sites of GPCR (monomers, homodimers and heterodimers), ion channels and transporters. However, using human disease tissue, within a functionally relevant phenotypic screen, appears to be the most practical path to exploit the power of this knowledge for drug discovery. A remarkable example of small molecule modulation of a complex receptor system is in the area of phosphoprotein phosphatase modulators. Such phosphatases are enzymes that act as tumor suppressors, countering the activity of oncogenic kinases. Identification of small molecule phosphatase modulators has faced major challenges, in particular for phosphatase activators. Protein phosphatase 2A (PP2A) enzymes are able to suppress tumors. However, in human cancers they get widely downregulated or inactivated by inhibitory proteins overexpressed across multiple types of cancer (Leonard et al., 2020). PP2A is has three subunits, among which the regulatory B-subunit determines substrate selectivity. Among more than 40 B-subunits, the small molecule DT-061 (128) selectively stabilizes a single heterotrimeric form. This stabilization is large enough to produce observable effects cellularly and in vivo, and to enable the determination of a cryo-EM structure of the heterotrimeric phosphatase. In this structure, DT-061 binds at the interface of all three subunits, providing the molecular basis of PP2A activation towards selective dephosphorylation of c-Myc, but not towards other substrates (Leonard et al., 2020). A group of small-molecules named “iHAPs” (an acronym of “improved heterocyclic activators of PP2A”) were recently disclosed that allosterically assemble a specific heterotrimeric PP2A holoenzyme, and with the ability to kill leukemia cells (Morita et al., 2020). In particular, studying a series of compounds chemically related to known antipsychotics yielded compound iHAP1 (129), which activates this complex with good selectivity over inhibition of dopamine D2 receptor unlike its nonselective precursor, the antipsychotic perphenazine (PPZ, 130) (Morita et al., 2020).

326 1.12.6.2

Allosteric Modulation Function is a system property, not “owned” by any individual partner

Historically, compounds have been qualified in “classes” based on two main factors: the functionality demonstrated in a test involving their primary receptor target, and the overt observed effect (e.g., full agonist vs. antagonist, PAM vs. NAM vs. silent modulator) (Kenakin, 2008a,b). For a while, this was an effective way to communicate among scientists, enabling a dialog that matched the level of existing complexity in our understanding of drug action. However, as time passed by and our toolbox evolved based on the development of sophisticated technologies, it became apparent than characterizing a drug’s functional activity based on its behavior in a single system (e.g., cellular vs. tissue, healthy vs. disease) had likely become an inappropriate scientific practice. Our vocabulary had to catch up to science if we wanted to keep communication straight (easier said than done). For some compounds showing functionally selective biased agonism at GPCRs, this difference is in the relative magnitude by which different cellular pathways are activated (e.g., G protein vs. b-arrestin). A notable case, still undergoing scrutiny (Gillis et al., 2020; Stahl and Bohn, n.d.) is the G protein-biased m opioid receptor agonist oliceridine (TRV130, 131), recently approved for the intravenous treatment of acute moderate-to-severe post-operative pain (Trevena Announces FDA Approval of OLINVYKÔ (oliceridine) injection, n.d.). Other examples show that a given ligand acting at one receptor complex can have opposite efficacies (i.e., activation/inhibition) towards two different signaling pathways. Propranolol (132) is an inverse agonist at both the b1- and b2-adrenergic receptor for the adenylyl cyclase (AC) signaling pathway, but a partial agonist towards both receptors in the extracellular signal-regulated kinase (ERK) activity (Galandrin and Bouvier, 2006). Likewise, the H3 histamine receptor proxyfan (133) acts as a partial agonist promoting histamine release, but as inverse agonist in a GTPgS binding assay. In behavioral models in rodents and cats, proxyfan displays a spectrum of activity ranging from full agonism to full inverse agonism (Gbahou et al., 2003). Furthermore, allosteric modulators showing opposing cooperativity effects towards orthosteric ligand binding and efficacy have been reported for the case of the NMDAR antagonist ifenprodil (134) (Kew et al., 1996).

Many GPCR antagonists have been shown to actually work by internalizing their receptor, thus actively downregulating both cell surface expression and intracellular functional signaling (Kenakin and Miller, 2010). Likewise, clinically used kinase inhibitors such as erlotinib (135), lapatinib (136), vemurafenib (137), and sorafenib (138) were found to induce the downregulation of their corresponding targets, acting by preventing access of their client kinase to the chaperone system, resulting in ubiquitination and degradation of the target besides the expected competitive inhibition. In fact, these compounds act as “small molecule PROTACs,” and suggest that enzyme degraders may display enhanced efficacy over traditional ATP site inhibitors in certain contexts and cell types (Jones, 2018).

From a practical point of view, this would suggest that risk mitigation strategies in drug discovery programs may be corrected towards better assessments. For example, clinical PET studies used to assess receptor occupancy (essentially confirming a drug is binding to its receptor in the desired level) may benefit from being supplemented with imaging or biomarker studies to confirm

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the functionality resulting from target engagement is consistent with the biological hypothesis of disease. Such practices are consisted with best practice proposed as risk-mitigating strategies from a chemical biology perspective (Bunnage et al., 2013; Morgan et al., 2018b). These observations suggest that drug efficacy is a more complex parameter than initially anticipated. Efficacy should always be considered in the context of the diverse signaling pathways that may be available to a given receptor subtype, and the entire complex effector system may need to be included in its description (Galandrin and Bouvier, 2006). Therefore, every small molecule drug discovery project will minimize the risk of translational failure by characterizing the drug candidate as deeply as possible. Often times this implies testing in a large number of systems, including different animal species used in preclinical efficacy and safety studies. This is not cheap, but avoiding this expense only increases the chances of failure for the whole program.

1.12.6.3

Molecular dynamics as an input to function

In biochemistry textbooks, our understanding around protein structure and function is conveniently presented in four steps of increasing order of complexity. Primary structure consists of the chemical sequence of amino acids in the protein from amino to carboxy termini. Secondary structure describes how segments of amino acids fold into small, stable, distinct elements. Examples are alpha helices, beta sheets, or canonical turns, common to many different proteins. Tertiary structure is how the whole protein, including secondary structure features and other amino acids, folds in a tridimensional space to confer a certain conformation to the protein. Quaternary structure corresponds to assembly of multiple discrete polypeptides into a functional complex. As appealing as this picture is, it does not explain all experimental facts around protein function, and further model development is needed. Many proteins actually exist as collections of tertiary conformations, known as ensembles, which can be represented by a funnel-shaped “energy landscape” (Fig. 11). It has been shown that small differences among these conformations lead to meaningful effects on the activity of enzymes and receptors. Indeed, the induced-fit theory suggests that protein flexibility is an essential characteristic of receptors, in open contrast with the rigid “lock & key” theory of Emil Fischer (Koshland, 1998). Structures obtained from crystallographic models impress as having rock-like solidity, and represent the most probable form of a protein, certainly not its only form. Thermodynamics requires that other possible structural forms must also exist. Protein will, over time, explore a vast ensemble of possible structural states (Englander and Kallenbach, 1983; Lawson et al., 2018). The relative proportion, and the functional natures of given conformations depend on changes in thermal energy and chemical composition of the system. Thus, a fifth dimension of protein function must be added, reflecting that they are dynamically fluctuating biomolecules constantly changing conformations (“breathing” patterns) and taking conformational excursions away from a canonical native structure (Gunasekaran et al., 2004; Jaffe, 2020).

1.12.6.4

Putting it all together: Allosterism and system chemistry

As has been stated elsewhere, “the idea that efficacy is an exclusive affair of ligand-receptor interaction hardly makes any sense. It is time for a cleanup” (Onaran and Costa, 2012). Technology enhancements in the areas of X-ray crystallography and cryo-EM, as well as high field multidimensional NMR methods, are enabling enhanced understanding in structural chemistry. As a consequence, multiple static structures of GPCRs, ion channels, transporters, and enzymes in complex with various signaling partners have been reported

Fig. 11 Free energy versus conformation diagrams. (A) Classical system representing one active and one inactive protein conformation and the changes derived by binding of an allosteric ligand (PAM in this case). (B) Model considering an ensemble of receptor conformations, each one providing its own distinct contribution to the global functional response. Binding of an allosteric ligand creates a comparable number of local changes, impacting the conformational landscape and global function.

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Fig. 12 Cartoon depicting the architecture of receptor complexes and factors that impact its functional response. These include several components. 1 A core receptor, that can adopt different conformation and functionalities, as well as form homo- or hetero-oligomers. 2 Endogenous (native) orthosteric and allosteric ligands, as well as their metabolites. 3 Allosteric drugs. 4 Accessory proteins (may be localized extracellularly, intracellularly, or within the cell membrane). 5 Post-translational modifications. 6 Intracellular proteins involved in signal transduction. 7 Membrane effects due to change in its chemical composition (membrane rafts). A range of pharmacological and toxicological effects may occur depending on the system’s chemical composition.

(Van Drie and Tong, 2020). While these provide remarkable snapshots, they don’t fully explain the functional complexity of many membrane-bound complex systems, which depends on their flexibility and capability to transition between conformations (Lawson et al., 2018; Torrens-Fontanals et al., 2020). Therefore, the functional output produced by a complex receptor system will result from the combined effects of the environment (temperature, pH, ionic strength, membrane composition), and all ligands existing within physical reach (extracellular, membrane-bound, architectural, intracellular ancillary proteins or transduction proteins), to bind and impact the dynamics of the system (Fig. 12). While the increased complexity presented in this scheme may feel overwhelming, the good news is that it suggests that understanding the actual chemicals in a system may lead to disease-relevant biomarkers or ways to segment the population ahead of conducting clinical trials, so that the mechanism of the therapeutics chosen matches the deficiency in the patient, thus maximizing the probability of success.

1.12.7

Summary: Allosterism and more effective drug discovery

In this article, we have presented examples of how small molecules designed to act as therapeutic drugs or tool compounds by interacting allosterically with their (mostly polypeptide) targets. We covered enzymes, GPCRs, ion channels and transporters. When appropriate, we drew a comparison with drugs acting as orthosteric ligands. We have tried to make the case that allosterism is broadly present in Nature, ubiquitously acting to regulate protein function, and possibly also certain functions of nucleic acids. While sharing some common aspects of their interactions with receptors, the overlap between molecular mode of action of orthosteric and allosteric drugs is not perfect. Some of the effects caused by these two types of molecules are driven by fundamentally different factors, yet they converge to provide the optimal functional output of a complex receptor system. Small molecules have the capacity to develop functionally productive interactions with non-orthosteric segments of a biological receptor that go beyond the concept of allosterism, including areas such as molecular glues, misfolding correctors, potentiators, trafficking. The effects of allosteric modulators on the global protein are transmitted across a range of distances, from contiguous binding sites to binding at different enzyme domains or components of an oligomeric system. Whether endogenous, natural products or synthetic, allosteric modulators may occupy a very dissimilar chemical space and bind in multiple different sites within a receptor complex. From a high-level perspective, allosteric ligands seem to exert similar effects on the two or three parameters used to characterize the functional response of their biological targets. However, upon digging deeper into the nature of these effects, a complex network of molecular mechanisms is revealed, which enables a highly sophisticated regulation of cellular function and differentiation of structurally related analogs. The phenomena of probe dependence and functional switches are not merely practical challenges along the way to discover a drug. They reflect the fundamental fact that the functional properties of a system under allosteric regulation

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cannot be defined by a single component in a static manner, but rather depend upon the ligand constituency as a whole, and including molecular dynamic aspects. Finally, we discussed potential strategies to reduce this knowledge to practice in ways that maximize the probability of success. For those of us working to understand the molecular details of functional regulation of polypeptide receptors by small molecules, our endeavor is intellectually stimulating and practically relevant. We mentioned in the beginning of this article that Monod called allostery “the second secret of life,” giving nucleic acids the top hierarchical priority. Nucleic acids research led to the development of gene editing using CRISPR and related technologies, with incredible potential to impact human health. What will allostery lead to? At this point we still have work to do to fully understand how small molecules interact with biological targets. Clearly, much evolution of thought is needed to attain the mentality required to advance beyond the current state of affairs, and to make significant progress for treating, let alone curing, highly complex diseases from Alzheimer’s to Amyotrophic Lateral Sclerosis (ALS). Working beyond repurposing of existing drugs, and expanding truly collaborative efforts to uncover new scientific precepts that lead to refreshed ideas, will surely help. Born at the interface of enzymology and chemistry, and subsequently expanding into many other sciences, allostery has the potential to enable major progress in our quest to improve the human experience. Let’s galvanize our interdisciplinary efforts and boost our exciting journey! So, since this is work in progress, I’d like to leave you with a quote that hopefully, will make our path forward swift and productive: There are many examples of old, incorrect theories that stubbornly persisted, sustained only by the prestige of foolish but well-connected scientists. Many of these theories have been killed off only when some decisive experiment exposed their incorrectness. Thus the yeoman work in any science is done by the experimentalist, who must keep the theoreticians honest (Kaku, 1994).

Acknowledgments I’d like to acknowledge Dr. Larry Hardy for his expert contribution to the segment on K-type and V-type enzyme allosteric modulation and critical review of this article. I am indebted to Dr. Robb Brodbeck and Dr. Xinyan Huang for everything we learned during our research at Neurogen Corporation (RB) and Lundbeck Research USA (both). I have been very fortunate to count them as my friends and colleagues. I would like to thank Prof. Terry Kenakin for being a member of our project teams through the many teachings from his thoughtful publications, and for the opportunity to work on this article. I am grateful to Dr. Stevin Zorn for his personal support and the freedom he gave to our project teams at Lundbeck Research USA.

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1.13 Analysis of the Function of Receptor Oligomers by Operational Models of Agonism Jesu´s Giraldoa,b,c, *, Bin Zhoua,b,c, David Roched, Carles Gile, Jordi Ortizb,c,e, Isaias Lansa,f, James Daltona,b,c, and Pedro Renaulta,b,c, a Laboratory of Molecular Neuropharmacology and Bioinformatics, Biostatistics Unit and Institute of Neurosciences, Universitat Autònoma de Barcelona, Bellaterra, Spain; b Translational Neuroscience Unit, Parc Taulí University Hospital, Parc Taulí Research and Innovation Institute (I3PT), Institute of Neurosciences, Universitat Autònoma de Barcelona, Barcelona, Spain; c Instituto de Salud Carlos III, Center for Biomedical Research in Mental Health Network, CIBERSAM, Madrid, Spain; d Department of Economic and Social Sciences, International University of Catalonia, Barcelona, Spain; e Laboratory of Molecular Neuropharmacology and Bioinformatics, Institute of Neurosciences and Department of Biochemistry & Molecular Biology, Universitat Autònoma de Barcelona, Bellaterra, Spain; and f Biophysics of Tropical Diseases, Max Planck Tandem Group, University of Antioquia, Medellín, Colombia © 2022 Elsevier Inc. All rights reserved.

1.13.1 1.13.2 1.13.3 1.13.4 1.13.5 1.13.6 1.13.7 1.13.8 References

Introduction Mathematical modeling of GPCR oligomerization The operational model of agonism Extending the operational model of agonism to account for receptor oligomerization Inclusion of allosterism and constitutive activity in the mathematical modeling of receptor oligomerization An operational model for receptor homodimers An operational model for receptor heterodimers Concluding remarks

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Glossary Activation cooperativity Parameter that measures the influence of a ligand already bound to the oligomer on the ability of a subsequent ligand to activate the same oligomer. Basal activity Effect of the receptor with no bound ligand. When basal activity is present it is said that the receptor has constitutive activity. Binding cooperativity In the case of a receptor dimer, this parameter indicates how the binding of a first ligand affects the binding of the second. Values equal, greater or lower than 1 indicate that the first ligand has no effect, favor or disfavor the binding of the second ligand to the receptor dimer. Em Maximum response of the system. This is a measure of the efficacy of the system. Em can only be asymptotically reached by full agonists at increasing high concentrations. Gradient G The derivative of the E/[A] function in semi-logarithmic (base 10) mode at the mid-point of the curve after normalization (dividing by the asymptotic Top value). Top (asymptote of effect) The asymptotic effect achieved by a particular ligand when increasing its concentration. Top is always lower than Em. Top values close to Em define full agonists whereas top values significantly lower than Em define partial agonists. Top values equal to basal activity define neutral antagonists whereas top values lower than basal activity define inverse agonists.

Nomenclature CB1R Cannabinoid receptor type 1 CRD Cysteine rich domain ECL Extracellular loop GPCR G protein-coupled receptor GRK GPCR kinase

*

Jesús Giraldo is the corresponding author.

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ICL Intracellular loop MD Molecular dynamics mGlu Metabotropic glutamate receptor NAM Negative allosteric modulator TM Transmembrane VFT Venus flytrap

1.13.1

Introduction

G protein-coupled receptors (GPCRs) are integral membrane proteins responsible for signal transduction from outside to inside the cell. Thus, their functional conditions may determine the healthy or pathologic state of living organisms, in particular humans. Because of this, GPCRs are the target of about one third of current marketed medicines (Hauser et al., 2017; Sriram and Insel, 2018). There are about 800 human GPCRs that can be classified into five classes or families: A (Rhodopsin), B (Secretin), C (Glutamate), Adhesion and F (Frizzled) (Alexander et al., 2019). GPCRs share a common structure consisting of a single polypeptide that includes an extracellular N-terminus, an intracellular Cterminus and seven transmembrane helices (TM1-TM7) that are connected to each other by three extracellular loops (ECL1-ECL3) and three intracellular loops (ICL1-ICL3) (Alexander et al., 2019). GPCRs, as proteins, are flexible entities and this flexibility can be used by agonists to promote the activation of signaling pathways by the respective binding to the receptor of G proteins, GPCR kinases (GRKs), and arrestins (Weis and Kobilka, 2018; Yoo et al., 2020). Thus, a particular receptor can activate different signaling pathways if adopts the required conformation. This phenomenon, known as functional selectivity, can be exploited by biased agonists to promote beneficial therapeutic pathways over those leading to adverse effects (Costa-Neto et al., 2016; Smith et al., 2018; Wisler et al., 2018). Receptor activation, because involves the binding of the receptor to particular transducer proteins, needs the receptor to reach specific and distinct conformations. These conformations can be different depending on the signaling pathway and the receptor class (Picard et al., 2019). In this regard, activation of Class A GPCRs through G proteins involves the best established mechanism currently available: receptor activation is mainly characterized by a large outward movement of TM6 from the central TM3 and a smaller inward movement of TM7 (Weis and Kobilka, 2018). Functional selectivity or biased agonism is an example of the inherent complexity of GPCRs because of both their structure and their molecular environments. GPCRs are a multitasking cellular machine whose modes of function are regulated by a number of factors. To this respect, ions play a role either inhibiting (Naþ) or enhancing (Ca2 þ, Mg2þ) GPCR activation (Chan et al., 2020; Hu et al., 2020; Ye et al., 2018; Yu et al., 2020). Lipids may also act as allosteric modulators of receptor function (Bruzzese et al., 2020, 2018; Duncan et al., 2020; Sejdiu and Tieleman, 2020). Yet, there is another way by which allosterism allows the modulation of GPCR function: receptor oligomerization. Receptor homo- and heterodimerization is an effective way by which the function of a receptor protomer can be altered through physical interactions with a second one. Thus, receptor oligomerization can be a valuable structural element available to the cell for allosteric modulation of biological processes (Wodak et al., 2019). There are examples in which GPCR homodimerization and heterodimerization is constructed through obligated dimers involving strong interactions between their partners, and, thus, there is no doubt about their existence. This occurs in Class C GPCRs, in particular, in metabotropic glutamate (mGlu) and GABAB receptors (Pin and Bettler, 2016). However, when oligomerization results from weak interactions, as it happens in Class A GPCRs, the hypothesis of their existence in living organisms has led to vivid debates (Bouvier and Hebert, 2014a, b; Lambert and Javitch, 2014) and is still open (Ferré et al., 2020; Vischer et al., 2015). This controversy has been sustained by conflicting results (Calebiro et al., 2013; Kasai and Kusumi, 2013; Lan et al., 2015; Mercier et al., 2002) concerning the stability, stoichiometry, ligand-dependence and even existence itself of GPCR oligomers either homomers (Milligan et al., 2019; Möller et al., 2020) or heteromers (Gomes et al., 2016; Qian et al., 2018). As stated in (Walsh et al., 2018), a possible explanation for these controversial results may lie in the fact that the majority of GPCR oligomerization studies have been performed in living cells, in which many environmental effectors may influence oligomerization. Obviously, the aim of biologists is the understanding of living systems but it would be necessary to separate the variability associated to the main system from the variability associated to the environments and, importantly, particular attention should be given to the presence of confounding factors. In their publication (Walsh et al., 2018), the authors provided a comprehensive analysis of GPCR oligomerization that was designed to be unbiased by cellular complexity and intra-reconstitution proteoliposome heterogeneities (Walsh et al., 2018). By using three Class A GPCRs (b2-adrenergic receptor, cannabinoid receptor type 1 (CB1R), and opsin), the authors confirmed that receptor oligomerization was not influenced by random collisions of receptors. This means that we cannot attribute receptor aggregation to a pure stochastic process yet receptor oligomerization is dependent on 3D protein structure and, thus, is preformed in the amino acid composition of GPCRs. Importantly, they arrived to the conclusion that the inconsistencies present in the published literature could be due to environmental effectors, such as local protein density and membrane curvature. Significant differences in GPCR oligomerization were found between highly curved and more planar membranes. Whereas the latter were more prone to host oligomeric receptors the former were found to decrease both the propensity

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of GPCRs to oligomerize and the size of the oligomers (Walsh et al., 2018). Thus, the same receptor can display different oligomeric states in the endosome (highly curved membrane) and in the plasma membrane (more planar). Interestingly, the authors also found that GPCR oligomerization is receptor specific with distinct oligomer stoichiometries and association constants. This means that receptor oligomerization is a molecular process in which the receptors recognize themselves through the spatial disposition of their amino acid sequences. Receptor dimers can be stable as Class C mGlu and GABAB receptors or unstable and transient as Class A GPCRs. The strength of receptor aggregation seems weak and dynamic in the case of Class A GPCRs because of the physical forces involved with transient encounters being the feature that mainly characterize them (Milligan et al., 2019). The process of Class A GPCR dimerization not only can be dynamic but also agonist-specific (Möller et al., 2020). Both stable/strong and unstable/weak GPCR dimerization are fundamental in receptor function, in particular, in receptor activation. mGlu receptors are composed of eight subtypes assembled into three groups: Group I (mGlu1 and mGlu5), Group II (mGlu2 and mGlu3) and Group III (mGlu4, mGlu6, mGlu7 and mGlu8). mGlu receptors are receptor dimers constituted by three protein domains in each of the protomers: the extracellular Venus flytrap (VFT) domain, the transmembrane (TM) domain, and the cysteine rich domain (CRD). The VFT domain includes the orthosteric site where glutamate and synthetic agonists and antagonists bind. The TM domain includes the seven helices typical of all GPCRs and the intracellular regions responsible for G protein recognition. The CRD connects the VFT and TM domains (Pin and Bettler, 2016). mGlu receptors are obligated or stable dimers due to a disulfide bond linking both VFTs (Romano et al., 1996) together with strong hydrophobic interactions between the upper lobes of both VFT subunits (Levitz et al., 2016). The activation process initiated by orthosteric ligands at the VFT domains involves an intersubunit TM conformational change leading to a TM6/TM6 interface. This proposal (Hlavackova et al., 2012; Xue et al., 2015) was confirmed recently by a combination of X-ray crystallography, cryo-electron microscopy and signaling studies of full-length mGlu5 dimer (Koehl et al., 2019). However, what happens within each of the protomers? Are there any changes in order to facilitate the binding of the G protein? According to (Hlavackova et al., 2012; Grushevskyi et al., 2019), an intrasubunit rearrangement should sequentially occur after the intersubunit conformational change but this was not identified in (Koehl et al., 2019), probably because of the absence of the G protein in the agonist-receptor complex. Nevertheless, some features potentially associated to TM activation were found. In particular, an upward movement of TM3, a slight outward movement of TM5 and a destabilization of the ionic lock (Koehl et al., 2019). The point is: should the activation mechanism of Class C GPCRs include the main structural feature characterizing Class A as it is the large displacement of TM6? Molecular dynamics (MD) simulations performed by our group on TM models of mGlu4 and mGlu5 receptors (Dalton et al., 2017) identified distinguishing structural features between Class C and Class A GPCRs with potential implications in the mechanism of activation. However, the concomitant bending of TM6 in Class A GPCRs was not observed in these mGlu models. As the authors noted, TM6 in both mGlu4 and mGlu5 receptors is relatively short and rigid. This makes it unlikely that TM6 in mGlu receptors plays the same role as in Class A GPCRs (Dalton et al., 2017). Further computational work of our group on the TM domain of mGlu5 through potential of mean force calculations linked to MD simulations (Lans et al., 2020) also discarded TM6 displacement as the conformational output reflecting mGlu TM activation. Instead, the importance of intracellular loops was highlighted. The authors found that polar interactions between ICL1 and the intracellular side of TM6 had to be disrupted to allow the cavity formation at the intracellular side of the transmembrane domain, which is likely to be necessary for G protein coupling (Lans et al., 2020). These computational results (Dalton et al., 2017; Lans et al., 2020) needed experimental validation to be fully credible. Interestingly, structural confirmation came through data by another Class C GPCR, the GABAB receptor. The GABAB receptor is an obligated heterodimer of GB1 and GB2 subunits with each subunit composed of VFT and TM domains. In a very recent paper (Mao et al., 2020), cryo-EM structures of human full-length heterodimeric GABAB receptor in an antagonist-bound inactive state and in an active state complexed with agonist and positive allosteric modulator and in the presence of Gi protein were included. The structures revealed relevant structural features of the activation mechanism: agonist binding stabilizes the closure of GB1 VFT, which in turn triggers a rearrangement of TM interfaces between the two subunits from TM3-TM5/TM3-TM5 in the inactive state to TM6/ TM6 in the active state and finally induces the opening of ICL3 and the shifting of TM3, 4 and 5 helices in GB2 TM domain to accommodate the a5-helix of Gi. Interestingly, the authors noted that unlike class A, B and F GPCRs, TM6 held its position in response to activation, constrained by the TM6/TM6 interface in the active state. Computational and experimental results indicate that there may be differences in the activation mechanism of GPCRs depending on the class to which they belong. An appealing point is the relevance of receptor dimerization in the activation mechanism and the conditioning characteristics that dimerization imposes and offers. Thus, although the formation of receptor dimers can be transient (Class A GPCRs), the fact that their stability is influenced by ligands as it happens in the dopamine D2 receptor, where agonists and antagonists stabilize and destabilize dimer formation, respectively (Kasai et al., 2018; Wouters et al., 2019) or in the m-opioid receptor, where the process of receptor dimerization is agonist-dependent (Möller et al., 2020), gives an extra complexity and versatility to ligand action and, consequently, widens the therapeutic ligand space (Milligan et al., 2019). The evidences in favor of GPCR homo-oligomerization, see (Milligan et al., 2019) for review, raise the question about the extent and physiological relevance of GPCR hetero-oligomerization (Gomes et al., 2016; Ferre et al., 2009; González-Maeso et al., 2008; Lee et al., 2020). It seems plausible that if particular Class A GPCRs homo-oligomerize in a receptor-specific way (Walsh et al., 2018) the same may happen for receptor hetero-oligomerization. It was observed that the dopamine D2 receptor, for example, can form a tetramer with the Ghrelin receptor (GHSR) in vitro (Damian et al., 2018). Two copies of each receptor and two heterotrimeric G proteins were assembled in the complex. The heteromer affected the conformation of the D2-bound Gi, but the effect of heteromerization upon Gi activation was not detected in the absence of the GHSR-bound Gq, suggesting a direct contact between the two G

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proteins in the complex (Damian et al., 2018). In a recent review (Gomes et al., 2016), some criteria were required to be accomplished to establish heteromer function in vivo. The criteria are the following: (1) Heteromer components should colocalize and physically interact. (2) Heteromers should exhibit properties distinct from those of the protomers. (3) Heteromer disruption should lead to a loss of heteromer-specific properties. At the time of the publication (2016), very few Class A receptor combinations had fulfilled these criteria and then provided evidence for GPCR heteromerization in native tissue. As the authors mentioned, more data would be expected with technical advances in the field, including proximity-based assays, membrane-permeable peptides that target the dimerization interface, and heteromer-selective antibodies (Gomes et al., 2016). mGlu receptors have provided relevant information concerning heteromerization. In neurons expressing mGlu1 and mGlu5 receptors cooperative signaling was found between the two receptors consistent with interactions between homodimers but not between protomers in a heterodimer (Sevastyanova and Kammermeier, 2014). In this regard, the presence of an mGlu1/5 complex in hippocampus and cortex brain regions was confirmed by co-immunoprecipitation and super-resolution microscopy (Pandya et al., 2016). Moreover, using biochemical and pharmacological approaches, a hetero-complex of mGlu2 and mGlu4 receptors was found in native rat and mouse tissues exhibiting a distinct pharmacological profile (Yin et al., 2014). In line with this research, it was later found that mGlu2 and mGlu3 form strict heterodimers in heterologous cells via inter-VFT interactions (Levitz et al., 2016). In agreement with previous results (Doumazane et al., 2011), it was found by single-molecule analysis that mGlu2 heterodimerizes not only with mGlu3 (Group II) but also with Group III members. However, it does no heteromerizes with Group I. Finally, the authors found that mGlu receptors prefer intra- over intergroup assembly. Interestingly, an asymmetric activation mechanism was found in the mGlu2/3 heterodimer which makes that the basal response depends on the dynamics of one of the subunits, the mGlu3 (Levitz et al., 2016). Asymmetry and unique conformational dynamics relative to their parent dimers has also been found in mGlu2/4 heterodimers (Liu et al., 2017; Moreno-Delgado et al., 2017). All these data on mGlu receptors have been recently confirmed (Lee et al., 2020), the authors analyzed the relative homo- and heterodimer propensities across group-I, -II, and -III mGlus by quantitative, fluorescence-based assays. They found a strong preference for heterodimerization in a number of cases, including mGlu2 with mGlu3, which were confirmed in frontal cortex using in situ RNA hybridization and co-immunoprecipitation. All these results support the biological relevance of mGlu heterodimerization and the complex modulation their relative populations exert in brain (Lee et al., 2020). There have been proposals of GPCR heteromerization not only within Class A and Class C but also between them. In particular, a serotonin 5HT2A-mGlu2 heteromer was proposed to be involved with schizophrenia (González-Maeso et al., 2008). The authors found that the two receptors form functional complexes in brain cortex and that the serotonin 5HT2A-mGlu2 complex triggers unique cellular responses when targeted by hallucinogenic drugs thus becoming a promising target for the treatment of psychosis. The authors also identified the TM helices responsible for the interactions between the protomers in the heteromer thus confirming a physical interaction between the receptors (González-Maeso et al., 2008). Further work of the authors (Fribourg et al., 2011) provided a mechanistic interpretation of the serotonin 5HT2A-mGlu2 heteromer function in terms of the signaling pathways, Gq (5HT2A-linked) and Gi (mGlu2-linked), involved in the system. The authors proposed that schizophrenia is expected to be associated with a variable disruption of the normal Gi-Gq balance leading to a decrease in Gi and an increase in Gq. Drugs may have effects not only in the protomer they bind but also in the heteromeric partner. The authors found that, in general, dominant (strong) agonists enhance signaling through the receptor they target as part of the complex but inhibit signaling of the heteromeric receptor partner. On the contrary, inverse agonists inhibit signaling through the receptor they target as part of the complex but enhance signaling of the heteromeric partner. These results suggested the authors that a combination of mGlu2 dominant agonists with 5HT2A inverse agonists is likely to synergize in vivo to achieve an optimal signaling ratio in cases where a suboptimal Gi-Gq signaling balance exists. This pointed out the potential beneficial use of combination therapy in treatment-resistant schizophrenia (Fribourg et al., 2011). Combination therapy in the context of receptor heteromers has also been suggested for other receptor systems and diseases. In particular, heteromerization of m-opioid receptor (MOR) with either CB1R or mGlu5 in relation with pain have been proposed (Akgün et al., 2013; Le Naour et al., 2013). In both cases, the authors have proposed the combination of a MOR agonist with either a CB1R antagonist or mGlu5 negative allosteric modulator (NAM) linked by a spacer of proper length. This concept of bivalent ligands is a particular application of combination therapy because the two molecules converge into scaffolds of a single molecule. This may result into a compound of higher affinity because the binding of the first scaffold to the assigned protomer in the heteromer guarantees the proximity of the second scaffold within a distance established by the spacer to the binding site of the second protomer. This may be true if the spacer does not conveys a steric hindrance to the binding event. All these results from different families of GPCRs can be integrated into common messages. Receptor oligomerization involves specific receptor-receptor interactions thus giving a physicochemical structural contextualization to pharmacological functional data. GPCR heteromerization adds a new level of complexity to GPCR homomerization allowing the opening of a new pharmacological space with distinct properties from the partner protomers. Heteromerization makes use of allosterism to mutually modulate the conformational landscape of the protomers providing, in this way, a mechanistic way to regulate the signaling space and the opportunity for biased agonism. Receptor heteromerization offers new possibilities to pharmacology and therapeutics through new structure-based drug screening and the design of proper drug combinations or, more specifically, bivalent ligands targeting not only specific receptor structures but specific signaling pathways. Pharmacological research and, in particular, receptor oligomerization needs well-established theoretical frameworks in which experimental data can be mechanistically explained. To this end, we need the proper development and application of mathematical models to understand and quantify the interactions involved in these complex molecular systems.

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

1.13.2

341

Mathematical modeling of GPCR oligomerization

Mathematical modeling is a suitable technique for the quantification of GPCR function, in particular, for GPCR oligomers. Many approaches and applications can be found in the literature concerning the quantification of GPCR oligomerization function through mathematical modeling: empirical or mechanistic models and, in the latter cases, equilibrium or kinetic approaches can be considered (Moreno-Delgado et al., 2017; Brea et al., 2009; Casadó-Anguera et al., 2019; Chidiac et al., 1997; Franco et al., 2006; Giraldo, 2008, 2013; Monod et al., 1965; Rovira et al., 2008, 2009, 2010; Strange, 2005; Zhou and Giraldo, 2018a, b). In the present study, we are not aiming at providing a comprehensive recapitulation of published work but, instead, we will comment on a few examples of our contributions to GPCR oligomerization through mathematical modeling, and with the operational model of agonism as the common thread.

1.13.3

The operational model of agonism

The operational model of agonism (Black and Leff, 1983) constitutes a seminal work in quantitative pharmacology for the description of functional agonism. The model includes two steps: the binding of an agonist to the receptor and a transducer function for the conversion of receptor occupation into response, the E/[AR] relationship (Fig. 1 and Appendix Eq. A2). Eq. (1) represents the E/[A] relationship in the operational model of agonism after choosing the transducer function 1 in Fig. 1, with Em being the maximum possible effect of the system; K, the dissociation equilibrium constant; s, the operational efficacy and A, the agonist. E¼

Em s ½A  Em s½A  ¼ 1þs K þ ½A  K þ ð1 þ sÞ½A  1þs

(1)

It is worth noting several characteristics of the operational model: (i) although binding and efficacy are attributed to two different parameters, K and s, respectively, there is some overlap between them: K is a functional constant and includes in its definition the generation of receptor active conformation after ligand binding. Thus, K incorporates events related with receptor activation (Roche et al., 2013b). (ii) The model falls within the conceptual framework of induction effect: the observed effect is induced by ligand binding. Thus, the ligand does not select an active receptor conformation because these conformations are not present in the absence of the ligand. As a corollary of this, quantification of inverse agonism is not possible. (iii) The E/[A] function corresponds to a rectangular hyperbola (or Hill equation, E ¼ a[A]m/(bm þ [A]m), with m ¼ 1). The latter aspect was the consequence of the decision of the authors of choosing a rectangular hyperbola for the E/[AR] relationship (Black and Leff, 1983) (Appendix Eq. A2). (iv) The conformational plasticity of the receptor is reduced to a single conformation that produces a single effect through a single signaling pathway. Thus, the possibility of biased agonism is not contemplated (Roche et al., 2013b). Eq. (1) yields theoretical E/[A] curves that are characterized by 4 geometric parameters: the asymptotic minimum response (Basal ¼ E for [A] ¼ 0); the asymptotic maximum response (Top ¼ E for [A] / N); the midpoint or agonist concentration for half maximum response ([A50] ¼ [A] for E ¼ (Top-Basal)/2); and when the curve is represented in a semilogarithmic (base 10) scale, the normalized derivative at the midpoint (G). Basal is equal to 0 by model definition. Top, [A50] and G reflect the efficacy (through Em and s parameters), potency (through K and s parameters) and the sensitivity of the system at the midpoint, that is, the changes in effect resulting from small changes in agonist concentration (through the G parameter), respectively. The G value for Eq. (1) is ln(10)/4 ¼ 0.576 and can be taken as a reference of curve steepness (Appendix Eqs. A4 to A7).

Fig. 1 The operational model of agonism includes 2 steps: the binding of the agonist A to the receptor R and a transduction E/[AR] function for the conversion of receptor occupancy into effect. K is the dissociation equilibrium constant and KE is a parameter linking receptor occupation ([AR]) with the observed physiological effect (E). Three E/[AR] transduction functions are proposed. Eq. (1): rectangular hyperbola. Eq. (2): hyperbolic function. Eq. (3): nonhyperbolic function. These E/[AR] functions yield the corresponding E/[A] equations (Eqs. 1 to 3 in the main text) when the [AR] ¼ [RT] [A]/(Kþ[A]) relationship, with [RT] ¼ [R] þ [AR], is included.

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Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

Eq. (1) is suitable for the description of experimental data following a rectangular hyperbola. To allow for the description of steeper or flatter curves the authors (Black and Leff, 1983) proposed the transducer E/[AR] function 2 in Fig. 1. By doing so, Eq. (2) is obtained. E¼

Em st ½At Em ½A t t t t ¼ t ðK þ ½AÞ þ s ½A  Kþ½A þ ½A t s

(2)

By comparing Eq. (2) with the general hyperbolic or Hill equation E ¼ a[A]m/(bm þ [A]m), we see that Eq. (2) with t s 1 does not belong to this curve set because the b-term is not constant. Parameter t influences the effect of the s parameter on the values of Top, [A50] and G geometric descriptors (Appendix Eqs. A11 to A13). When t > 1, G decreases with decreasing s whereas when t < 1, G increases with decreasing s. As expected G ¼ 0.576 when t ¼ 1. Moreover, when s is very large, G ¼ 0.576 t, which is the expected value of E/[A] following a E ¼ Top[A]t/([A50]t þ [A]t) hyperbolic relationship (Black et al., 1985). Eqs. (1) and (2) are not sufficient to describe the whole pharmacological space. Eq. (1) describes a rectangular hyperbola (G ¼ 0.576) whereas Eq. (2) describes steep (G > 0.576) or flat (G < 0.576) nonhyperbolic curves. However, steep or flat hyperbolic curves (Hill equations with t s 1) are not included. To address this issue, the transducer E/[AR] function 3 in Fig. 1 was proposed (Roche et al., 2016). By doing so, the Hill E/[A] Eq. (3) is obtained. t Em s Em st ½A t ð1þsÞt ½A  ¼   t Kt þ ð1 þ sÞt ½A t K þ ½A t 1þs t

E¼

(3)

Comparing Eq. (3) with the empirical Hill equation E ¼ Top[A]t/([A50]t þ [A]t), we see that Top ¼ Emst/(1þ s)t, [A50] ¼ K/(1 þ s) and G ¼ 0.576 t (Appendix Eqs. A17 to A19). Eqs. (2) and (3) allow the description of nonhyperbolic and hyperbolic curves, respectively. Eq. (1) is a particular case of Eqs. (2) and (3) with t ¼ 1. Moreover, Eq. (2) tends to Eq. (3) in the region [A]  K when s [ 1. Eq. (3) was presented recently as an extension of the original operational model to account for hyperbolic steep or flat curves. This is not just an academic proposal but also a practical one. When fitting Eq. (2) to an experimental steep or flat hyperbolic curve the parameters progressively adapt their values to accommodate the theoretical curve points to the experimental ones. Because Eq. (2) tends to Eq. (3) when [A]  K and s [ 1 then overestimated K and s values can be obtained when using the nonhyperbolic Eq. (2) to fit an experimental hyperbolic curve. Indeed, simulations performed with these equations showed that this is the case (Roche et al., 2016). Interestingly, overestimations of both parameters canceled each other within the combined parameter s/K (Roche et al., 2016), a quantity which, in logarithmic form, has been proposed as a parameter for biased agonism quantification (Kenakin et al., 2012). Analytical comparison between E/[A] equations together with performed simulations suggest that an analysis of the shape of the curves should be performed previously to the application of operational models (Roche et al., 2016). If experimental data follow hyperbolic Hill equations (E ¼ a[A]m/(bm þ [A]m)) then Eq. (3) is preferred whereas if experimental data do not follow hyperbolic Hill equations then Eq. (2) should be used. A requisite to follow hyperbolic curves is that their semi-logarithmic versions be symmetrical (the midpoint matches the inflection point). A detailed analysis of this property can be found in (Giraldo et al., 2002). To graphically illustrate the effect of parameter values on E/[A] curves arising from Eqs. (1) to (3), a set of parameter values was chosen (Fig. 2). If we take Eq. (1) (the rectangular hyperbola) as a reference, inclusion of the slope parameter t ¼ 2 has the effect of

Fig. 2 Simulation of E/[A] curves for operational models using Eq. (1) to (3). Black line: Eq. (1). Blue line: Eq. (2). Red line: Eq. (3). Parameter values used: Em ¼ 1; s ¼ 10; K ¼ 10 6; t ¼ 2. Top values: Black line, 0.91; Blue line, 0.99; Red line, 0.83. log([A50]): Black line, 7.04; Blue line, 6.96; Red line, 7.04. Steepness, as measured by the G parameter: Black line, 0.576; Blue line, 1.048; Red line, 1.151.

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343

increasing the top value when using Eq. (2) and decreasing the top value when using Eq. (3). With respect to the location of the curves along x-axis (log([A50])), Eq. (2) displaces this point to the right whereas Eq. (3) has no effect. With respect to the steepness as measured by the G parameter, both Eqs. (2) and (3) increase the steepness of the reference curve through their t > 1 parameter.

1.13.4

Extending the operational model of agonism to account for receptor oligomerization

In all the models leading to Eqs. (1) to (3) it has been assumed that the ligand-receptor binding process is a bimolecular reaction in which the receptor bears a single binding site. Thus, in the case of receptor oligomers, the question arises on whether the fitting of these models to experimental curves may lead to wrong parameter estimates. To address this issue, a recent study extended the operational model of agonism (Black and Leff, 1983) by including receptor oligomerization (Gregory et al., 2020). Following the same rationale as in the previous section, we consider the possibility of different transducer functions (Fig. 3 and Appendix Section A.2). For the sake of simplicity we assume that the receptor is either free or fully occupied. Following the rationale of the original version of the operational model (Black and Leff, 1983), we first assume a rectangular hyperbola (Fig. 3 Transducer function 1 and Appendix Eq. A22) for the transduction of receptor occupancy into effect. Eq. (4) is obtained. E¼

n Em s Em s½A n 1þs ½A  ¼ n n n K Kn þ ð1 þ sÞ½A  1þs þ ½A 

(4)

It is worth noting that n corresponds to the number of receptor binding sites. Thus, n cannot be lower than 1. Then, either rectangular hyperbolas (Hill equation with n ¼ 1) or steep hyperbolic functions (Hill equations with n > 1) are obtained. In Appendix Eqs. (A24) to (A27), the analytical expressions of the geometric parameters of the E/[A] function are shown. The steepness of the curves is reflected in the G parameter. As expected, rectangular hyperbola (G ¼ 0.576) or steep hyperbolic curves (G ¼ 0.576n, with n > 1) are obtained. We see that the steepness of the curves are strictly related with the number of binding sites. Moreover, the n parameter modulates the midpoint but not the asymptotic top value. If, as in the original version of the operational model (Black and Leff, 1983), a general logistic function is chosen (Fig. 3 Transducer function 2 and Appendix Eq. A28) then Eq. (5) is obtained. E¼

Em st ½A nt n t

ðKn þ ½A  Þ þ st ½Ant

Em ½A nt t Kn þ½An þ ½A nt s

¼

(5)

Functional effect depends on two slope parameters, the binding parameter, n, and the transducer parameter, t. If n ¼ 1 (receptor with 1 binding site), Eqs. (1) or (2) are obtained depending on whether t is equal to or different from 1, respectively.

Fig. 3 The operational model of agonism for a receptor oligomer R with n orthosteric binding sites includes two steps: the binding of the agonist A to the receptor and a transduction E/[AR] function for the conversion of total receptor occupancy into effect. A simplification is included: the receptor is either free or fully occupied. K is the nth root of the total dissociation constant Kn, thus, representing the average binding of one ligand. KE is a parameter linking receptor occupation ([AnR]) with the observed physiological effect E. Three E/[AnR] transduction functions are proposed. Eq. (1): rectangular hyperbola. Eq. (2): hyperbolic function. Eq. (3): nonhyperbolic function. These E/[AnR] functions yield the corresponding E/[A] equations (Eq. 4 to 6 in the main text) when the [AnR] ¼ [RT][A]n/(Kn þ [A]n) relationship, with [RT] ¼ [R] þ [AnR], is included.

344

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism Table 1 s ¼ 10 n 1 1 1 2 2 2

The operational model of agonism extended for receptor oligomerization: Fig. 3, transducer function. t 0.5 1 2 0.5 1 2

s ¼ 100 n 1 1 1 2 2 2

G 0.34 0.58 1.05 0.69 1.15 2.10

t 0.5 1 2 0.5 1 2

G 0.31 0.58 1.14 0.62 1.15 2.28

Gradient values G for (n, t, s) combinations.

In Appendix Eqs. (A30) to (A33), the analytical expressions of the geometric parameters of the E/[A] function are shown. The n and t parameters modulate the midpoint whereas the asymptotic top value is affected only by the t parameter. The steepness of the curve depends on s, n, and t. Because of its complexity we have not included the G(s, n, t) function explicitly. Instead, a table with particular numeric values was constructed. Table 1 shows the numerical values of G for different conditions (s ¼ 10, 100; n ¼ 1, 2; t ¼ 0.5, 1, 2). As expected, we see that for n ¼ 1 (receptor monomer), G takes values as those produced by the original operational model (Black et al., 1985), Appendix Eq. (A13). Thus, for n ¼ t ¼ 1, G ¼ 0.576 and is independent of s. For n ¼ 1 and t s 1, G depends on s: G increases with increasing s when t > 1 and G decreases with increasing s when t < 1 (Black et al., 1985). We see also that n has a greater influence than t on the steepness of the curve. When s ¼ 10, G values of 1.15 and 1.05 are obtained for (n ¼ 2, t ¼ 1) and (n ¼ 1, t ¼ 2) combinations, respectively. As s increases the influences of n and s converge. Increasing the value of s to 100 yields G values of 1.15 and 1.14 for (n ¼ 2, t ¼ 1) and (n ¼ 1, t ¼ 2) combinations, respectively. Thus, we see that large s values produce G values approaching those corresponding to a Hill equation with a slope parameter of nt. In the present ln ð10Þ

ln ð10Þ

case, in the limit as s increases, G for nt ¼ 2 is G ¼ 4 nt ¼ 4 2 ¼ 0:576  2 ¼ 1:15. As it happened with the original operational model (Black and Leff, 1983), Eq. (5), which is the analogous to Eq. (2) for receptor oligomers, does not account for steep or flat hyperbolic E/[A] functions. To address this issue, Fig. 3 Transducer Eq. (3) or Appendix Eq. (A34) is chosen. This leads to Eq. (6). nt Em s Em st ½A nt ð1þsÞt ½A  ¼   t Knt þ ð1 þ sÞt ½A nt Kn þ ½A nt 1þs t

E¼

(6)

Eq. (6) follows a hyperbolic or Hill equation with a Hill coefficient of nt, with n equal or greater than 1. Geometric descriptors are shown in Appendix Eqs. (A36) to (A39). Top is influenced by parameter t whereas [A50] is influenced by parameter n. The   nt ¼ 0:576nt , for any s. product nt determines the value of the steepness G ¼ ln ð10Þ 4 To graphically illustrate the effect of parameter values on E/[A] curves arising from Eqs. (4) to (6), a set of parameter values was chosen (Fig. 4). If we take Eq. 4 (n binding sites and transduction slope parameter t equal to 1) as a reference, inclusion of parameter

Fig. 4 Simulations of E/[A] curves for operational models Eqs. (4) to (6). Black line: Eq. (4). Blue line: Eq. (5). Red line: Eq. (6). Parameter values used: Em ¼ 1; s ¼ 10; K ¼ 10 6; n ¼ 2; t ¼ 2. Top values: Black line, 0.91; Blue line, 0.99; Red line, 0.83. log([A50]): Black line, 7.04; Blue line, 6.96; Red line, 7.04. Steepness, as measured by the G parameter: Black line, 1.151; Blue line, 2.100; Red line, 2.303.

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345

t with a value of 2 has the effect of increasing the asymptotic top value when using Eq. (5) and decreasing it when using Eq. (6). With respect to the location of the curves along x-axis (log([A50])), Eq. (5) displaces this point to the right whereas Eq. (6) has no effect. With respect to the steepness as measured by the G parameter, both Eq. (5) and (6) increase the steepness of the reference curve through their t > 1 parameter. Comparison between those curves on Figs. 4 and 2 that use the same t values leads to the following observation: the top values are the same. However, inclusion of oligomerization has an effect both in the location of the curves along the x-axis (displacement to the right) and their steepness (increase).

1.13.5 Inclusion of allosterism and constitutive activity in the mathematical modeling of receptor oligomerization The operational model of agonism (Black and Leff, 1983) has extensively been used for the analysis of agonism (Kenakin, 2017) and successfully applied to signaling pathway drug discovery through the recently proposed log(s/K) transduction parameter for biased agonism (Kenakin et al., 2012). The operational model of agonism has been extended by including the concept of allosteric binding sites in receptor monomers (Giraldo, 2015; Kenakin, 2007; Leach et al., 2007) thus providing parameters for binding and functional cooperativity between orthosteric and allosteric ligands. Mathematical modeling of allosterism in receptor monomers is out of the scope of the present paper, which is focused on the analysis of the functionality of orthosteric sites both in receptor homomers and heteromers. It is worth noting that the different versions of the operational model of agonism herein presented (Black and Leff, 1983; Roche et al., 2016; Gregory et al., 2020) suffer from some limitations: (i) the possibility of constitutive receptor activity is not included. Thus, the physiological effect of the receptor in the absence of agonist is zero and, as a consequence, inverse agonism cannot be addressed. (ii) When extending the original model (addressed to receptor monomers) (Black and Leff, 1983) to include receptor oligomerization (Gregory et al., 2020), it was assumed that there is only one bound-receptor state: the fully occupied receptor. Obviously, this is a limiting case assuming an extreme cooperativity of binding. In general, partially bound receptor states are expected to be present. If this is the case, the possibility that all bound-receptor states can signal should be considered.

1.13.6

An operational model for receptor homodimers

Fig. 5 and Appendix Section A.3 present a model for receptor homodimers (Zhou and Giraldo, 2018a) that includes the following features: (i) the receptor can be singly or doubly occupied. This allows the definition of binding cooperativity through the a parameter. (ii) All the receptor species including the free receptor can contribute to stimulus S and, consequently, be part of the observed effect E. Inclusion of the free receptor species in the stimulus expression allows for constitutive receptor activity and, consequently, for quantification of inverse agonism. (iii) Intrinsic efficacies 3A and 3AA are defined for singly- and doubly-bound receptors, respectively. Note that, implicitly, 3RR ¼ 1. Then, the intrinsic efficacies of the bound-receptor species are defined in relation to the free receptor, which is taken as a reference. Moreover, defining 3AA ¼ 3A3Ad allows d to be identified as activation cooperativity. d equal, greater and lower to 1 indicates absence, increase and decrease of activation capacity of the combined presence of the 2 ligand molecules with respect to their activation capacity considered as independent entities. (iv) A rectangular hyperbola is proposed for the conversion of stimulus into physiological effect. Em is the maximum possible effect of the system and KE is the value of the stimulus for half Em. Eq. (7) is obtained.
Em c K2 þ 2K3A ½A  þ ad3A 2 ½A 2 E¼ 2 (7) K ðc þ 1Þ þ 2Kðc3A þ 1Þ½A  þ aðcd3A 2 þ 1Þ½A 2 The conditions imposed in the model and embodied by the corresponding parameters in Eq. (7) translate into E/[A] curves. As shown in Appendix Eqs. (A44) and (A45), asymptotic analytical expressions for basal (Emc/(c þ 1)) and top

Em cd3A 2 = cdεA 2 þ1 responses, with c ¼ [RRT]/KE, are obtained. It is worth noting the influence of c on both basal and top responses thus being a general parameter of receptor functionality. Moreover, in addition to the system parameters Em and c, Top is determined by d3A2, the intrinsic efficacy of the doubly-bound receptor, that is, the receptor species most populated at large [A]. As expected, Top is not dependent on the dissociation constant K. Also, because constitutive receptor activity is considered in the model, Top can be lower than Basal if d3A2 is lower than 1. Further, 3A and d are determined by the identity of the agonist in the considered receptor. 3A measures the intrinsic efficacy of an agonist as an individual ligand and d the activation cooperativity between the ligands. Because, in the definition of stimulus S (Fig. 5), the intrinsic efficacy of the free receptor RR is taken as 1, neutral antagonists, agonists and inverse agonists are revealed when both 3A and 3AA are equal, greater or lower than 1, respectively. Moreover, because 3AA ¼ 3A2d, extra complexity can be shown along x ¼ log[A] axis when 3A and d have opposite order relations (> or 1 value, which includes more complexity to the system: a neutral a ¼ 1 would produce parallel curves (Fig. 8B).

Fig. 8 Simulations of E/[A] curves for the receptor heterodimer model using Eq. (8). It is considered that signaling pathway 1, which is associated to R1 and to which agonist A binds, is measured. Ligand B binds to R2. (A) Parameter values Em ¼ 1; c ¼ 0.2; 3 ¼ 1; K ¼ 10 6; M ¼ 10 6; 3A ¼ 10; 3B ¼ 0.1; a ¼ 10; d ¼ 5. (B) The same parameter values as Fig. 8A except a ¼ 1.

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

349

The developed heterodimer model (Zhou and Giraldo, 2018b) can be applied to some published experimental data. As an example, we can mention an excellent work related with the serotonin 5HT2A-mGlu2 heteromer and its potential relationship with schizophrenia (Fribourg et al., 2011). For this heteromer, a Gq signaling pathway is linked to the 5HT2A protomer whereas a Gi signaling pathway is linked to the mGluR2 protomer. In normal circumstances, a determined Gi-Gq balance is present that is disrupted by a decrease in Gi and an increase in Gq under schizophrenia conditions. The authors found that, in general, dominant (strong) agonists enhance signaling through the protomer they target as part of the heteromer but inhibit signaling of the heteromeric receptor partner. On the contrary, inverse agonists inhibit signaling through the protomer they target as part of the heteromer but enhance signaling of the heteromeric receptor partner (Fribourg et al., 2011). Thus, to restore normal balance in a person with schizophrenia, an mGlu2 dominant agonist would be appropriate to increase Gi signaling and supplementarily decrease Gq signaling. In the same way, a serotonin 5HT2A inverse agonist would be appropriate to decrease Gq signaling and supplementarily increase Gi signaling. Further, a combination of the two ligands would synergistically favor the desired effect (Fribourg et al., 2011). Following our notation, we can design Gi signaling as pathway 1 and Gq signaling as pathway 2. Under schizophrenia conditions the normal Gi-Gq balance is disrupted and Gi decreases and Gq increases. This can be reflected in our model by a decrease of Basal1 through lowering the c131 product and an increase of Basal2 through increasing the c232 product. Ligand A is an mGlu2 dominant agonist whereas ligand B is a serotonin 5HT2A inverse agonist. For pathway 1 (Gi), parameter values 3A1 > 1, 3B1 > 1 and d1 > 1 would be expected whereas for pathway 2 (Gq) parameter values 3A2 < 1, 3B2 < 1 and d2 < 1 would fit. These parameter values would lead to E1 > Basal1 and E2 < Basal2 thus restoring normal conditions. It is worth noting the double effect that the heteromer arrange favors. On the one hand, a synergistic effect between ligands is seen within each signaling pathway. On the other hand, because two pathways are involved in schizophrenia the proper regulation of each of them favors the proper regulation of the whole and, consequently, a more effective treatment of this severe mental disorder is achieved.

1.13.8

Concluding remarks

The operational model of agonism (Black and Leff, 1983) constitutes a cornerstone in quantitative pharmacology and receptor theory allowing the analysis of receptors and agonists by the estimation of affinity and efficacy parameters. However, because of the definition used in its original formulation, some regions of the functional pharmacological space were omitted. For instance, the possibility of experimental data following steep or flat hyperbolic curves was not contemplated (Black and Leff, 1983; Black et al., 1985). To solve this lack it was recently proposed an E/[AR] function that properly yields the desired E/[A] curves thus enlarging the pharmacological space and producing more reliable affinity and efficacy estimates (Roche et al., 2016). Also with the aim of providing more accurate estimates of agonist pharmacological parameters an operational model for receptor oligomers with multiple orthosteric sites has been recently presented (Gregory et al., 2020). This study was developed with the aim of extending the original operational model of agonism which was strictly designed for receptor monomers with a single orthosteric site. A question that may arise in agonist function analysis is whether the steepness of particular experimental E/[A] curves results from cooperativity at the signal transduction or at the agonist binding levels. In order to answer this question a model including the possibility of more than one binding site is needed. The operational model for receptor oligomers (Gregory et al., 2020) includes two parameters related with the slope of the E/[A] curves, one coming from the binding step and the other from the transduction step. Then, if properly used, this model may quantify both contributions to the steepness of the curves and, as a consequence, provide more accurate parameter estimates. The operational model (Black and Leff, 1983) presents also the issue of not including constitutive receptor activity in its definition and, thus, is not applicable to inverse agonists. This drawback was properly tackled by Slack and Hall by providing an extension of the original model (Slack and Hall, 2012) (see also a review on mathematical modeling of agonism and allosterism (Roche et al., 2013a)). We have included these ideas in the development of operational models for receptor homodimers (Zhou and Giraldo, 2018a) and heterodimers (Zhou and Giraldo, 2018b), which in both cases include receptor constitutive activity in their formulations. Importantly, these models provide mechanistic parameters for the pharmacological concepts of binding and activation cooperativities thus allowing the interpretation of biphasic and bell-shaped curves that sometimes have been considered as anomalous data. As a general idea, it is worth noting that mathematical modeling has two goals: to provide meaningful mechanistic descriptions of biological systems and to provide parameter estimates that accurately quantify pharmacological properties when fitting to experimental data. This requires a level of complexity that needs to be carefully weighted to avoid overfitting problems. In this piece of work we have put together a set of models generated from the operational model of agonism with the aim of constructing a framework for the quantification of receptor oligomerization function. Incorporation of biased agonism (Hall and Giraldo, 2018; Zhou et al., 2019) into these models would be of value to construct a robust and accurate methodology for a mechanistic description of current pharmacological complexity. Finally, important efforts are currently being dedicated to the simulation of oligomeric systems by structural modeling methods (Qian et al., 2018; Wouters et al., 2019; Oliveira et al., 2017). Structural modeling techniques and, particularly, MD methods allow the visualization of the molecular ligand-receptor interactions responsible for the observed functional effects. Thus, atomistic descriptions provided by MD simulations can, in principle, be associated to parameter estimates provided by mathematical modeling. It is expected that MD simulations of oligomeric systems bound with their corresponding ligands may detect the distinct allosteric interactions between protomers induced by specific ligands thus illustrating the parameter

350

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

values for cooperativity provided by mathematical modeling. In our opinion, combination of structural and mathematical models into a general computational structure would represent a great help for drug discovery programs, constructed for the design of new medicines which, ideally, would be pathway-oriented and absent of side-effects. This approach may represent a promising route in the quest for a new pharmacology.

A

Appendix

A.1 The operational model of agonism In its original version (Black and Leff, 1983), the operational model of agonism contains a single chemical equilibrium, the binding of the ligand to the receptor.

A.1.1

The equilibrium constant of the model K

A þ R 4 AR;

K¼

½A ½R  ½AR 

(A1)

K is the dissociation equilibrium constant.

A.1.2

The observed effect depends on the transduction function

A.1.2.1 Transduction function 1 A rectangular hyperbolic equation is proposed (Black and Leff, 1983) for the relationship between the concentration of bound receptors and the observed effect. E¼

Em ½AR  KE þ ½AR 

(A2)

Em is the maximum possible effect of the system and KE is the value of [AR] for E ¼ Em/2. If we substitute [AR] by the correspond-

T ½A  ing relationship derived from ligand binding and take into account that [RT] ¼ [R] þ [AR], that is, ½AR  ¼ ½R Kþ½A then

E¼

Em s ½A  Em s½A  ¼ 1þs K K þ ð1 þ sÞ½A  1þs þ ½A 

(A3)

½R 

With s ¼ KTE . If we compare the operational E/[A] relationship with the rectangular hyperbola, empirical Hill equation, E ¼ a[A]/(b þ [A]), we see that the operational model of agonism follows a Hill equation with a ¼ Ems/(1 þ s) and b ¼ K/(1 þ s). The rectangular hyperbola is a symmetric curve when displayed in a semi-logarithmic scale because the midpoint matches the point of inflection (Giraldo et al., 2002). Geometric descriptors of the curves

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

(A4)

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

Top ¼

Em 1 þ 1s

(A5)

s defines the operational efficacy of the ligand in a particular receptor system, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em.

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2) ½A 50  ¼

K 1þs

(A6)

[A50] defines the potency of the agonist. [A50] depends on the affinity (K) and the efficacy (s) of the agonist. [A50] is lower than K and as more efficacious is the agonist greater is the difference between K and [A50].

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

•

351

E/[A] curve gradient: G

The E/[A] curve gradient (G) is defined as the derivative of the E/[A] function in semi-logarithmic (base 10) mode at the midpoint of the curve after normalization (dividing by the asymptotic Top value) (Black et al., 1985). G provides a measurement of the slope of the curve at the midpoint. G¼

lnð10Þ ¼ 0:576 4

(A7)

This is the value corresponding to a rectangular hyperbola or Hill equation with a Hill coefficient of 1. A.1.2.2 Transduction function 2 A general logistic function is proposed (Black and Leff, 1983) for the relationship between the concentration of bound-receptors and the observed effect. E¼

Em ½AR t KE t þ ½AR t

(A8)

If we substitute [AR] by the corresponding relationship derived from ligand binding and take into account that [RT] ¼ [R] þ [AR], that is, ½AR  ¼

½R T ½A Kþ½A  ,

then E¼

Em st ½A t Em ½A t ¼  t t t ðK þ ½AÞ þ st ½A  Kþ½A þ ½A t s

(A9)

½R 

With s ¼ KTE :If we compare the operational E/[A] relationship with the general logistic function, empirical Hill equation, E ¼ a [A] /(bm þ [A]m), we see that the operational model of agonism does not follow a Hill equation because the b term is not a constant. Geometric descriptors of the curves m

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

(A10)

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

Top ¼

Em 1 þ s1t

(A11)

s defines the operational efficacy of the ligand in a receptor system, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em. The t parameter modulates the influence of s.

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2)

½A 50  ¼

K ð2 þ st Þ1=t  1

(A12)

As with Top asymptotic maximum response, the t parameter modulates the [A50] value.

•

E/[A] curve gradient: G

G¼

  0:576 t ð2 þ st Þ ð2 þ st Þ1=t  1 ð2 þ st Þ1=t ð1 þ st Þ

(A13)

The slope of the curve is determined by t and s. Note that when t ¼ 1 (the E/[A] function corresponds to a rectangular hyperbolic curve or Hill equation with a Hill coefficient of 1) G takes the value 0.576 (see (Black et al., 1985) for detailed description). A.1.2.3 Transduction function 3 A function for the relationship between the concentration of complexed receptors and the observed effect, which was predesigned in order to obtain a Hill equation for the E/[A] relationship, is proposed (Roche et al., 2016).

352

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

E¼ KE t



Em ½AR t t  t AR  KE 1  ½½R þ 1 þ ½AR t  ½R  T T

(A14)

If we substitute [AR] by the corresponding relationship derived from ligand binding and take into account that [RT] ¼ [R] þ

T ½A  [AR], that is, ½AR  ¼ ½R Kþ½A  then

t Em s Em st ½A t ð1þsÞt ½A  E¼ t t t t ¼ K þ ð1 þ sÞ ½A  K þ ½A t 1þs t

(A15)

With s ¼ ½RKTE : If we compare the operational E/[A] relationship with the general logistic, empirical Hill equation, E ¼ a[A]m/(bm þ [A]m), we see that the operational model of agonism follows a Hill equation with a ¼ Emst/(1 þ s)t, b ¼ K/(1 þ s) and m ¼ t. Geometric descriptors of the curves

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

(A16)

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

Top ¼ 

Em 1 þ 1s

(A17)

t

s defines the operational efficacy of the ligand, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em. The t parameter modulates the influence of s.

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2) ½A 50  ¼

K 1þs

(A18)

[A50] is lower than K and as more efficacious is the agonist greater is the difference between K and [A50].

•

E/[A] curve gradient: G G ¼ 0:576t

(A19)

The slope of the curve at the midpoint is determined by t. In comparison with the rectangular hyperbola or Hill equation with a Hill coefficient of 1, steeper curves are obtained when t > 1 and flatter curves when t < 1.

A.2 The operational model of agonism for receptor oligomers The original version of the operational model of agonism (Black and Leff, 1983) was extended by including the possibility that the receptor bears more than one single orthosteric binding site (Gregory et al., 2020).

A.2.1

The equilibrium constant of the model

The receptor oligomer contains n orthosteric binding sites. K1

K2

K3

Kn

nA þ R 4 AR þ ðn  1ÞA 4 A 2 R þ ðn  2ÞA 4 A 3 R þ ðn  3ÞA.A n1 R þ A 4 A n R K1 ¼

½A½R  ½A ½AR  ½A ½A 2 R ½A ½A n1 R ; K2 ¼ ; K3 ¼ ; /; Kn ¼ ½AR  ½A 2 R ½A 3 R ½A n R

K1 to Kn are the dissociation equilibrium constants for the corresponding agonist-receptor complexes. We see that K1 K2 K3 /Kn ¼

½An ½R  ½A n R

¼ Kn , where K is the geometric mean of the individual equilibrium dissociation constants.

For simplicity, the receptor is considered either empty (R) or fully occupied (AnR):

(A20)

353

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism Kn

nA þ R 4 A n R; Kn ¼

A.2.2

½A n ½R  ½A n R

(A21)

The observed effect depends on the transduction function

A.2.2.1 Transducer function 1 In accordance with the operational model of agonism (Black and Leff, 1983), a rectangular hyperbola is proposed for the transduction of receptor occupancy into response: Em ½A n R (A22) E¼ KE þ ½A n R Em is the maximum possible effect of the system and KE is the value of [AnR] for E ¼ Em/2. If we substitute [AnR] by the corresponding relationship derived from ligand binding and take into account that [RT] ¼ [R] þ [AnR], that is,½A n R ¼ E¼

Em s½A n ¼ Kn þ ð1 þ sÞ½A n

½R T ½An Kn þ½An

, then

n Em s 1þs ½A  n n K 1þs þ ½A 

(A23)

With s ¼ ½RKTE : If we compare the operational E/[A] relationship with the general logistic, empirical Hill equation, E ¼ a[A]m/(bm þ [A]m), we see that the operational model of agonism follows a Hill equation with a ¼ Ems/(1 þ s), b ¼ K/(1 þ s)1/n and m ¼ n. Geometric descriptors of the curves

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

(A24)

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

Top ¼

Em 1 þ 1s

(A25)

s defines the operational efficacy of the ligand, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em.

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2) ½A 50  ¼

K

(A26)

ð1 þ sÞ1=n

[A50] is lower than K and as more efficacious is the agonist greater is the difference between K and [A50]. The n parameter modulates the [A50] value.

•

E/[A] curve gradient: G G ¼ 0:576n

(A27)

The slope of the curve at the midpoint is determined by n. In our mechanistic hypothesis n is equal or greater than 1, monomeric or oligomeric receptor, respectively. Thus, in comparison with the rectangular hyperbola or Hill equation with a Hill coefficient of 1, steeper curves are obtained when n > 1; flatter curves (n < 1) are not contemplated in the model. A.2.2.2 Transducer function 2 In accordance with the scheme of the operational model of agonism (Black and Leff, 1983), the logistic function for the transduction of occupancy into response can be defined as: E¼

Em ½A n Rt KE t þ ½A n Rt

(A28)

Em is the maximum possible effect of the system and KE is the value of [AnR] for E ¼ Em/2. If we substitute [AnR] by the corresponding relationship derived from ligand binding and take into account that [RT] ¼ [R] þ [AnR], that is,½A n R ¼ E¼

Em

st ½A nt t

ðKn þ ½A n Þ þ st ½Ant

¼

nt

Em ½A  t þ ½A nt

Kn þ½An s

½R T ½An , Kn þ½An

then (A29)

354

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

With s ¼ ½RKTE . If we compare the operational E/[A] relationship with the general logistic function, empirical Hill equation, E ¼ a[A]m/ (bm þ [A]m), we see that the operational model of agonism does not follow a Hill equation because the b term is not a constant. Geometric descriptors of the curves

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

(A30)

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

Top ¼

Em 1 þ s1t

(A31)

s defines the operational efficacy of the ligand, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em. The t parameter modulates the influence of s.

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2) K ½A 50  ¼  1=n 1=t t ð2 þ s Þ  1

(A32)

G ¼ f ðn; t; sÞ

(A33)

[A50] is regulated by K, s, n and t parameters.

•

E/[A] curve gradient: G

The slope of the curve is determined by n, t and s. We have not included explicitly the G function because of its complexity. Because n  1 and 0  t different slope values at the midpoint can be obtained depending on the combinations of parameters (see Table 1 in the main text). A.2.2.3 Transduction function 3 A function for the relationship between the concentration of bound-receptors and the observed effect, which was predesigned in order to obtain a Hill equation for the E/[A] relationship in the case of receptors with a single binding site (Roche et al., 2016), is now adapted for receptor oligomers. E¼

KE t



Em ½A n Rt    1  ½A½RnTR t þ 1 þ ½RKET  t ½A n Rt

(A34)

Em is the maximum possible effect of the system and KE and t regulate the transduction of receptor occupancy into physiological effect. If we substitute [AnR] by the corresponding relationship derived from ligand binding and take into account that [RT] ¼ [R] þ [AnR], that is,½A n R ¼

½R T ½An Kn þ½A n

, then nt Em s Em st ½A nt ð1þsÞt ½A  E ¼ nt ¼   t K þ ð1 þ sÞt ½A nt Kn þ ½A nt 1þs t

(A35)

With s ¼ ½RKTE : If we compare the operational E/[A] relationship with the general logistic, empirical Hill equation, E ¼ a[A]m/(bm þ [A]m), we see that the operational model of agonism follows a Hill equation with a ¼ Emst/(1 þ s)t, b ¼ K/(1 þ s)1/n and m ¼ nt. Geometric descriptors of the curves

•

Left asymptote (Basal response: E for [A] ¼ 0) Basal ¼ 0

The receptor does not have constitutive activity. The model cannot describe the function of inverse agonists.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E) ½A /N

(A36)

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

Top ¼ 

Em 1 þ 1s

t

355

(A37)

s defines the operational efficacy of the ligand in the receptor system, as greater is s greater is the asymptotic top values of the agonists. Full agonists yield top values close to Em whereas partial agonists produce top values significantly lower than Em. The t parameter modulates the influence of s, increasing (t > 1) or decreasing (t < 1).

•

The midpoint, the [A] value for half maximum effect (E ¼ Top/2) ½A 50  ¼

K ð1 þ sÞ1=n

(A38)

[A50] is lower than K and as more efficacious is the agonist greater is the difference between K and [A50]. We see the differential influence of n and t parameters: whereas t regulates the top value (efficacy) n regulates [A50] (potency).

•

E/[A] curve gradient: G G ¼ 0:576nt

(A39)

The G value is that corresponding to a hyperbolic function or Hill equation with a Hill coefficient of nt. Because n  1 and 0  t different slope values can be obtained depending on the combinations of parameters.

A.3 The operational model of agonism for receptor homodimers including constitutive activity The receptor consists of a dimeric arrangement of 2 protomers: RR. Each of the protomers contains a binding site.

A.3.1

The equilibrium constants of the model

K¼

½RR ½A  ½RR ½A  K ½RRA½A  ½ARR ½A  ¼ ; ¼ ¼ ½RRA ½ARR  a ½ARRA ½ARRA

(A40)

K is the dissociation equilibrium constant of the binding of the first agonist to the receptor. a is the binding cooperativity. Positive cooperativity: a > 1, absence of cooperativity a ¼ 1 and negative cooperativity: 0 < a < 1.

A.3.2

Receptor species generate a stimulus S ¼ ½RR  þ 3A ½RRA þ 3A ½ARR  þ 3AA ½ARRA

A.3.2.1

(A41)

Transduction function of stimulus into effect E¼

Em S KE þ S

(A42)

Em is the maximum possible effect of the system and KE is the value of S for E ¼ Em/2. If we substitute S by A41 and take into account that [RRT] ¼ [RR] þ [ARR] þ [RRA] þ [ARRA], then
Em c K2 þ 2K3A ½A  þ ad3A 2 ½A 2 E¼ 2 (A43) K ðc þ 1Þ þ 2Kðc3A þ 1Þ½A  þ aðcd3A 2 þ 1Þ½A 2 T With c ¼ ½RR KE : Eq. (A43) does not follow a general logistic, empirical Hill equation, E ¼ a[A]m/(bm þ [A]m). Asymptotic values of the curves

356

•

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

Left asymptote (Basal response: E for [A] ¼ 0). Basal ¼

Em c cþ1

(A44)

Basal response or constitutive receptor activity depends on Em and c. Agonists increase basal response whereas inverse agonists decrease it.

•

Right asymptote, the asymptotic value as [A] increases (Top: lim E). ½A /N

Top ¼

Em cd3A 2 1 þ cd3A 2

(A45)

If we compare A45 with A5 (Top ¼ Ems/(1 þ s) with s defining operational efficacy), we see that now the concept of operational efficacy includes the c, d and 3A terms. Moreover, comparing A45 with A44, we see that d3A2 is equal, greater and lower than 1 for a neutral antagonist, agonist and inverse agonist, respectively.

A.4 The operational model of agonism for receptor heterodimers including constitutive activity The receptor consists of a dimeric arrangement of two different receptor protomers: R1R2. Each of the protomers contains a binding site.

A.4.1

The equilibrium constants of the model

K¼

½R 1 R 2 ½A  ½R 1 R 2 ½B K ½R 1 R 2 B½A  M ½AR 1 R 2 ½B ;M ¼ ; ¼ ; ¼ ½AR 1 R 2  ½R 1 R 2 B a ½AR 1 R 2 B a ½AR 1 R 2 B

(A46)

K and M are the dissociation equilibrium constants for the binding of ligands A and B to protomers R1 and R2, respectively. a is the binding cooperativity between the two ligands in their corresponding receptors. Positive cooperativity: a > 1, absence of cooperativity a ¼ 1 and negative cooperativity: 0 < a < 1.

A.4.2

Receptor species generate a stimulus Sn ¼ 3n ½R 1 R 2  þ 3An ½AR 1 R 2  þ 3Bn ½R 1 R 2 B þ 3ABn ½AR 1 R 2 B

(A47)

Sn denotes the stimulus on pathway n (1 or 2). A.4.2.1

Transduction function of stimulus into effect En ¼

Emn Sn KEn þ Sn

(A48)

A rectangular hyperbolic function transduces stimulus Sn into response En. En represents the effect for pathway n and Emn denotes the maximum possible effect of the system for pathway n. KEn is the value of Sn for half Emn, therefore it measures the efficiency of transducing stimulus into response. If we substitute Sn in A48 by its expression in A47 and take into account that [R1R2T] ¼ [R1R2] þ [AR1R2] þ [R1R2B] þ [AR1R2B], then En ¼

Emn cn ðKM3n þ M3An ½A  þ K3Bn ½B þ adn 3An 3Bn ½A ½BÞ KMðcn 3n þ 1Þ þ Mðcn 3An þ 1Þ½A  þ Kðcn 3Bn þ 1Þ½B þ aðcn dn 3An 3Bn þ 1Þ½A ½B

With cn ¼ ½RK1 REn2 T : Asymptotic values of the curves

(A49)

357

Analysis of the Function of Receptor Oligomers by Operational Models of Agonism

•

Left asymptote (Basal response: En for [A] ¼ [B] ¼ 0). Basaln ¼

Emn cn 3n cn 3n þ 1

(A50)

Basal response or constitutive receptor activity depends on Emn, cn and 3n. Agonists increase basal response whereas inverse agonists decrease it.

•

Right asymptote, the asymptotic value as [A] and/or [B] increase (TopnA,B:

lim

½A;½B/N

En ; TopnA: lim En ; TopnB: lim En Þ: ½A /N

½B/N

Emn cn dn 3An 3Bn ¼ 1 þ cn dn 3An 3Bn

(A51)

Topn;A ¼

Emn cn ðM3An þ adn 3An 3Bn ½BÞ Mðcn 3An þ 1Þ þ aðcn dn 3An 3Bn þ 1Þ½B

(A52)

Topn;B ¼

Emn cn ðK3Bn þ adn 3An 3Bn ½A Þ Kðcn 3Bn þ 1Þ þ aðcn dn 3An 3Bn þ 1Þ½A 

(A53)

Topn;A;B

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Sequential inter- and intrasubunit rearrangements during activation of dimeric metabotropic glutamate receptor 1. Science Signaling 5, ra59. Hu, X., Provasi, D., Ramsey, S., Filizola, M., 2020. Mechanism of m-opioid receptor-magnesium interaction and positive allosteric modulation. Biophysical Journal 118, 909–921. Kasai, R.S., Kusumi, A., 2013. Single-molecule imaging revealed dynamic GPCR dimerization. Current Opinion in Cell Biology 27C, 78–86. Kasai, R.S., Ito, S.V., Awane, R.M., Fujiwara, T.K., Kusumi, A., 2018. The Class-A GPCR dopamine D2 receptor forms transient dimers stabilized by agonists: Detection by singlemolecule tracking. Cell Biochemistry and Biophysics 76, 29–37. Kenakin, T., 2007. Allosteric agonist modulators. Journal of Receptor and Signal Transduction Research 27, 247–259. Kenakin, T., 2017. Theoretical aspects of GPCR-ligand complex pharmacology. Chemical Reviews 117, 4–20. Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A., Novick, S., 2012. 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Bivalent ligands that target m opioid (MOP) and cannabinoid1 (CB1) receptors are potent analgesics devoid of tolerance. Journal of Medicinal Chemistry 56, 5505–5513. Leach, K., Sexton, P.M., Christopoulos, A., 2007. Allosteric GPCR modulators: Taking advantage of permissive receptor pharmacology. Trends in Pharmacological Sciences 28, 382–389. Lee, J., Munguba, H., Gutzeit, V.A., Kristt, M., Dittman, J.S., Levitz, J., 2020. Defining the homo- and heterodimerization propensities of metabotropic glutamate receptors. Cell Reports 31, 107605. Levitz, J., Habrian, C., Bharill, S., Fu, Z., Vafabakhsh, R., Isacoff, E.Y., 2016. Mechanism of assembly and cooperativity of homomeric and heteromeric metabotropic glutamate receptors. Neuron 92, 143–159. Liu, J., Zhang, Z., Moreno-Delgado, D., Dalton, J.A., Rovira, X., Trapero, A., et al., 2017. Allosteric control of an asymmetric transduction in a G protein-coupled receptor heterodimer. eLife 6. Mao, C., Shen, C., Li, C., Shen, D.D., Xu, C., Zhang, S., et al., 2020. Cryo-EM structures of inactive and Gi-coupled GABAB heterodimer. Cell Research 1–10. Mercier, J.F., Salahpour, A., Angers, S., Breit, A., Bouvier, M., 2002. Quantitative assessment of beta 1- and beta 2-adrenergic receptor homo- and heterodimerization by bioluminescence resonance energy transfer. The Journal of Biological Chemistry 277, 44925–44931. Milligan, G., Ward, R.J., Marsango, S., 2019. GPCR homo-oligomerization. Current Opinion in Cell Biology 57, 40–47. Möller, J., Isbilir, A., Sungkaworn, T., Osberg, B., Karathanasis, C., Sunkara, V., et al., 2020. Single-molecule analysis reveals agonist-specific dimer formation of m-opioid receptors. Nature Chemical Biology. Monod, J., Wyman, J., Changeux, J.P., 1965. On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology 12, 88–118. Moreno-Delgado, D., Moller, T.C., Ster, J., Giraldo, J., Maurel, D., Rovira, X., et al., 2017. 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Relevant Websites https://www.rcsb.org/dProtein Data Bank. https://gpcrdb.org/dGPCRdb. https://submission.gpcrmd.org/home/dGPCRmd. http://grip.b.dendai.ac.jp/grip/dGRIP. https://campagnelab.org/software/gpcr-okb/dGPCR-OKB.

1.14

Agonism and Biased Signaling

Terry Kenakin, Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States © 2022 Elsevier Inc. All rights reserved.

1.14.1 1.14.2 1.14.3 1.14.3.1 1.14.3.2 1.14.4 1.14.5 1.14.5.1 1.14.6 References

Introduction Receptor protein dynamics forming different conformational states Measuring the magnitude of agonist efficacy Observed agonism and system sensitivity Quantitative models of agonism The Black/Leff operational model of Agonism Agonist biased signaling The molecular mechanism of biased signaling Conclusions

360 361 363 363 364 365 367 369 369 370

Glossary Agonist A molecule possessing efficacy and that causes a discernible response when bound to a target. Allosteric The interaction of two bodies through one binding site that affects the interaction of the complex with a third body at another binding site. Biased Receptor Signaling The production of a receptor-mediated response by an agonist that can emphasize the response from one (or more) of the pleiotropically linked signaling pathways to the receptor at the expense of others. Conformational Induction The production of a new conformational state in a protein. Conformational Selection The selective binding of a ligand to a co-existing array of protein conformations. Constitutive Receptor Activity The spontaneous production of active state receptors in the absence of any ligand present. EC50 The effective concentration of agonist producing 50% of the maximal response to the agonist. Efficacy The property of a ligand that when bound to the target changes the behavior of that target to it’s host (the cell). Full Agonist An agonist that produces a maximal response that is equal to the full response that can be obtained from a functional assay. Intrinsic Activity A scale of drug maximal response corresponding the fraction of the assay maximal response window. Intrinsic Efficacy A delineation of Stephenson’s ‘efficacy’ into the amount of efficacy per a single receptor. Inverse Agonist A ligand that reduces the basal response in a functional assay when that basal response is elevated due to constitutive receptor activity. Partial Agonist An agonist that produces a maximal response that is less than the full response that can be obtained from a functional assay. Protein Ensemble A collection of tertiary conformations of a protein of similar free energy but differing recognition properties for ligands. Receptor Isomerization The creation of a new receptor species (in terms of ligand recognition) through conformational change. Stimulus The overall signal produced by a receptor-agonist complex to the cell that is processed by the cell into response.

1.14.1

Introduction

Agonism, as defined by the activation of a receptor protein to cause that protein to initiate cellular response, is one of the greatest mysteries of pharmacology. To account for this extraordinary phenomenon some basic fundamental ideas about the interaction of ligands (agonists) and protein (receptors) must be assumed. Perhaps the first is the idea that a given protein conformation, when it exists in the cell membrane, will spontaneously interact with other nearby proteins in the cell to cause the activation of those (signaling) proteins. The key to this type of system is the versatility of the proteins involved, especially the receptor, to form different tertiary conformations. Presumably the complimentarity of these conformations facilitates the receptor and the signaling protein to bind to form a complex that then, in turn, facilitates activation of the signaling protein. The first step in the process of agonism therefore is the proclivity with which the receptor protein forms different tertiary conformations (now to be referred to as receptor states).

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Receptor protein dynamics forming different conformational states

Early depictions of drug action leading to a cellular response (agonism) implicitly described agonist binding to the receptor as a process whereby the binding would induce a new (active) conformation to the receptor, a process described as ‘induction’. These ideas emanated from early descriptions of protein allostery whereby ligands and proteins, as they bound together, mutually facilitated their binding by changing the conformations of the principles, a process referred to by Koshland as ‘induced fit’ (Koshland Jr, 1958). The binding of a ligand to an ‘inactive’ receptor (denoted R) resulting in formation of an active state (R*) ligand-bound conformation can occur through two mechanisms as shown in Fig. 1. A mechanism of induction would have the ligand binding to the inactive receptor state R to form AR which then goes on to change conformation to AR*; in another scenario termed ‘conformational selection’, the ligand pre-selects the spontaneously formed active state receptor R* to for the AR*dsee Fig. 1. In general, conformational selection as a mechanism for efficacy agrees much more with thermodynamic theory and physiology. Selection requires that there be a choice of receptor states, one of which is the receptor ‘active’ state (R*) leading to cellular response and the existence of these states have been confirmed in many experimental systems. For instance, in systems with high levels of expressed receptors, the existence of the R* state is made evident by the spontaneous coupling of this state to signaling proteins to produce an elevation of basal cellular response (Costa and Herz, 1989). This has been shown for b-adrenoceptors where high levels of receptor have been shown to couple to Gs protein to activate adenylyl cyclase and produce elevated basal cyclic AMP (Samama et al., 1993). Conformational selection is also supported by the kinetics of conformational changes in that only conformational selection allows formation of AR* by ligand binding within a timeframe commensurate with one to sustain life in cells (Bosshard, 2001; Burgen, 1981; Vaidehi and Kenakin, 2010; Vogt and Di Cera, 2013). Within this theoretical framework agonist ‘efficacy’ is defined as the differential affinity a ligand has for various conformations of the receptor (Burgen, 1981). The next question to consider is how many conformations does the ligand have to choose from, i.e. what is the size of the conformational cafeteria available to the ligand? Historically, ideas around the binding of molecules to proteins has been dominated by so called ‘lock and key’ models which emphasize the fit of the molecule to a structured protein. However, this implies a rigidity to both ligands and proteins to maintain such structure which is in direct opposition to modern concepts of molecular dynamics. Receptors are Nature’s prototype allosteric protein whose only function is to transmit energy between an extracellular ligand and an intracellular signaling protein. Thus receptors have functionally-related conformational flexibility (Frauenfelder et al., 1979; Tang and Dill, 1998; Williams et al., 2006) which results in an array of receptor states comprising of a pre-existing ensemble of similar but different conformations (Park, 2012; Nygaard et al., 2013; Motlagh et al., 2014; Boehr et al., 2009; Dror et al., 2010, 2011). This array forms a dynamic system called a receptor protein ensemble with which ligand interact in cells (Vardy and Roth, 2013; Manglik and Kobilka, 2014; Manglik et al., 2015). Assuming that ligands will have differential affinity for different receptor conformations, a ligand entering this array of different conformations will bind to the various species according to the affinity for the various species. Thus, ligands that selectively bind to certain conformations will stabilize those conformations at the expense of other conformations and the equilibrium of the ensemble shifts toward the stabilized species according to Le Chatelier’s principledsee Fig. 2. In general, the energy required for proteins to form states they already do not spontaneously make is inordinately high making induction to a unique ligand-bound state highly unlikely. While induction may appear to be the mechanism for the appearance of a unique receptor state, it would likely be the result of the stabilization of rare but still spontaneously formed state made by the protein (Kenakin, 1996).

Fig. 1 A system of a ligand A binding to two receptors co-existing in two states (active R* and inactive R); response results from the AR* state. The time course for changes in states given by the integration of the differential equations d[AR]/dt ¼ k1[A][R] - k 1[AR] and d[AR*]/dt ¼ k2[AR] – k 2[AR*]. For an agonist A, the binding to the R state will be weak (i.e. k1 is 104 M 1 s 1). Considering a timecourse for changes in protein conformation of 102 s 1 and integrating the differential equations yields a half time for the formation of AR of 2.5 h. In contrast, the mechanism for conformational selection is 80 s, a timecourse much more in keeping with normal physiology (Bosshard, 2001).

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Fig. 2 The conformational cafeteria. Reducing a receptor ensemble to only two states (squares, circles) ligands (small blue circles) preferentially bind to the receptor states for which they have the highest affinity. If the ligand is an agonist, it will preferentially bind to the active states, removing it from the natural equilibrium and driving the equilibrium toward more active state (circles) receptor to produce cellular response.

Within the concept of conformational selection, agonist efficacy is then defined as the agonist having a high affinity for the ‘active’ state of the receptor, that active state being defined as one that is able to further interact with signaling proteins to produce cellular response. This secondary coupling to another species significantly compliments this scheme in that it further increases the effective affinity of the agonist for the receptor. This can be seen from an examination of the rudimentary steps in the agonist binding process. The most simple model to describe affinity is the Hill equation, also referred to as the Langmuir adsorption isotherm (Colquhoun, 2006; Kenakin, 2016). This defines the production of an agonist-receptor complex ([AR]) as the product of a bimolecular reaction between drug [A] and receptor [R] with the avidity of that process characterized by an equilibrium dissociation constant of the drug-receptor complex (KA); affinity is the reciprocal of the KA. The rate of offset of bound ligand A from the receptor is given by the product of the concentration of drug-receptor complex [AR] multiplied by the rate constant for offset k2; the rate of production of the [AR] complex is given by the product of concentration of drug A, the rate constant k1 and the number of free receptors. Those rates equate at equilibrium and the system is described by the KA which is the ratio of k2 divided by k1. A critical factor in this measurement to be stable is the immutability of the quantity of [AR] driving the backward reaction (k2). When Langmuir derived the adsorption isotherm to define KA he was modeling the adsorption of molecules onto an inert metal surface that did not change upon ligand binding. Under these circumstances, the measurements made define the affinity of the molecule for the interacting metal. The situation is quite different for proteins as in many cases drug binding leads to the production of a subsequently modified protein species. Therefore, if ligand binding leads to the removal of [AR] from the equilibrium (i.e. if ligand binding changes the receptor), then the backward rate of offset is reduced thereby driving the forward reaction further forward and reducing the backward reaction. This produces a concomitant reduction in the effective equilibrium dissociation constant. Receptors are nature’s prototypic allosteric protein existing only to bind extracellular molecules and change their conformation to correspondingly present an altered binding domain for cytosolic signaling proteins therefore [AR] in a cellular system quickly can move on to interact with membrane components (i.e. for example, G proteins) to form a ternary complex. Under these circumstances the observed value describing the complete process will arithmetically be lower than KA thus producing an increase in effective affinity. This process has been described by Colquhoun as ‘receptor isomerization’ (Colquhoun, 1985). When the receptor changes upon ligand binding, the equilibrium kinetics of binding change. The backward reaction in the equilibrium is reduced due to removal of one of the driving forces in the reaction, namely the concentration of [AR] complex, thus enhancing the forward reaction (ligand binding) to cause the observed affinity to increase. The process can be shown as:

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If the isomerization process were not in place, the affinity would be determined by KA (k2/k1). However, in the presence of isomerization, the true affinity is given by: Effective K ¼

KA 1 þ 4g

It can be seen that if the isomerization from [AR] to [AR*] is facilitated by g > 4, then the Effective K will always be arithmetically lower than the KA. Under these circumstances the affinity of the complete process will always be greater than just the affinity of the ligand for the non-isomerized receptor. Such isomerization-mediated changes in affinity have been shown experimentally in receptor binding studies where binding receptors to nanobodies (to mimic G protein docking) significantly alters the affinity of receptors for extracellular molecules (Kruse et al., 2013). Since receptors are allosteric proteins, they can and do alter their properties when a second body is bound to them. Therefore, another aspect of isomerization is that natural allosteric alterations can occur upon subsequent cytosolic signaling protein binding with the receptor that change the affinity of ligands through allosterism. Considering the cytosolic signaling proteins as allosteric modulators of receptor activity, standard allosteric theory predicts that the affinity of the receptor for ligands can change through an allosteric affinity co-operativity factor imposed on the receptor by the protein (denoted with the parameter a) (Kenakin, 2005; Ehlert, 2005; Price et al., 2005). This need not necessarily increase the affinity of the receptor for ligands since a can range from values less than to greater than unity. This brings into consideration the second tenet for drug-protein binding (association of a protein with another body necessarily changes the complex in terms of interactions with subsequent bodies) that relates the nature of the co-binding ligands to the magnitude of affinity. It will be seen that for receptors and enzymes, the nature and the relative stoichiometry of co-binding species can affect the affinity of targets for the drug (in accordance with allosteric probe dependence). Specifically, allosteric modulators can have varying effects on different molecules interacting with the receptor protein (vide infra). For example, Staus et al. (2016) have shown the effects of two different nanobodies on the affinity of noradrenaline to b2-adrenoceptors; specifically, aNb80 enhances affinity 1000-fold, aNb60 reduces affinity 100-fold. It will be seen that in the consideration of agonist signaling bias the nature of the signaling protein can have profound effects on the affinity of the ligand for the receptor. This has implications for differences in agonist affinities as the ligand-bound receptors interact with different signaling proteins in the cell, a concept that will further be discussed in the context of biased signaling.

1.14.3

Measuring the magnitude of agonist efficacy

An important property of agonists is their power to induce cellular response. Assuming that the efficacy of an agonist is an intrinsic property of the agonist, the strength of signaling capability becomes an important characteristic of the ligand. However, what is observed as agonism is an interplay between the agonist and the sensitivity of the system.

1.14.3.1

Observed agonism and system sensitivity

As a pre-requisite to the discussion of these effects, the main tool used to assess this should be considered, namely the concentration-response curve. In an assay system that is sensitive enough to display an agonist response to a ligand, the addition of increasing concentrations of the ligand will yield a sigmoidal curve consisting of a threshold, midpoint and maximal asymptote. The maximal effect occurs as the agonist saturates the receptor population available in the tissue; the ligand produces what is termed ‘stimulus’ to the cell which then returns some observable response. What is observed in any given functional assay however need not necessarily be the complete range of stimulus to the cell but rather each assay has a window to show response that once exceeded, yields the same maximal response for all agonists that exceed the window. Fig. 3A shows the production of stimulus by four agonists; two the agonists produce stimuli that exceed the maximal window for observation of response. What then is observed from the assay is shown in Fig. 3B: agonists that produce the full assay maximal response are termed ‘full agonists’ while those that do not are called ‘partial agonists’. These sigmoidal concentration response curves are used to characterize agonists in terms of potency (the concentrations that actually produce response) and maximal effect. The concentration response curves representing partial and full agonists are very different in terms of what information can be gained from them. The most information is gained from the concentration response curve to a partial agonist. The two important properties of agonists is their affinity (the concentration producing 50% maximal receptor occupancy) and efficacy (the power to induce cellular response). For partial agonists, the maximal response is solely determined by the efficacy of the agonist (Fig. 4) and the sensitivity of the system therefore differences in maxima of two partial agonists in the same system are indicative of difference in the efficacy of the two agonists. Also, the concentration of partial agonist producing half maximal (maximal being the maximal response to the partial agonist) response denoted as the EC50 is a reasonably accurate estimate of the affinity of the partial agonistdsee Fig. 4. In contrast, once the system sensitivity is such that the agonist produces full agonism, then the maximal response ceases to provide any information about the efficacy of the agonist other than it is sufficiently high so as to exceed the window of response production of the assay. In addition, the EC50’s of full agonists are complex products of affinity and efficacy that cannot be delineated into either of those molecular propertiesdFig. 4. Therefore, in lead optimization experiments designed to determine the

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Fig. 3 Effects of different agonists on cellular systems as seen at the level of the cell and from the vantage point of a functional pharmacological assay. From the cellular viewpoint, stimulus depends upon the receptor density and the efficacy of that agonist; however, there may be a limit to the maximal response that can be reported to the functional assay (i.e. limitation in cellular signaling proteins, etc). The dotted line represents the maximal effect that the assay can report. Therefore, from the assay vantage point, anything that exceeds the window for response in the assay yields the same maximal response.

Fig. 4 Characteristics of concentration-response curves for partial and full agonists. For partial agonists (red), the midpoint of the curve (EC50) represents the affinity of the partial agonist for the receptor. The maximal response is solely a function of the magnitude of efficacy of the partial agonist and the sensitivity of the system. For full agonists (blue), the maximal response is simply the maximal window of response for the assay and the EC50 is a complex function of the affinity and efficacy of the agonist.

affinity and efficacy of new agonists, assays where partial agonism is produced are much more valuable than those where the molecules produce full agonism. The interplay between agonist efficacy and sensitivity of the assay system can produce an array of observed agonist responses from full to partial agonism. Fig. 5 shows the observed effects of a n agonist in systems of varying receptor density (i.e. the greater the number of receptors the greater the sensitivity to agonism). There is an important difference in the sensitivity of the systems to agonism with increasing receptor density. Specifically, if the agonist produces partial agonism, the maximal response increases with increasing receptor density and the sensitivity to agonism increases (as seen as a decrease in the EC50 for agonism). Once full agonism is achieved, then there is no further change in the maximal response and the EC50 diminishes with increasing receptor density. However, it should be noted that the changes in EC50 with increasing receptor density for partial agonists vs full agonists differs in that larger changes are seen for full agonists. This is important when the relative potency of agonists is considered since it precludes the comparison of relative EC50 values of full to partial agonistsdsee Fig. 5.

1.14.3.2

Quantitative models of agonism

Agonists can have differing effects in systems of varying sensitivity; for example the low efficacy b-adrenoceptor agonist prenalterol produces no agonist response in the guinea pig extensor digitalis longus muscle, partial agonist activity in guinea pig left atria and nearly full agonism in thyroxine-treated guinea right atria (Kenakin, 1985). Thus, the observed agonist activity of prenalterol leads to a confusing list of possible identifiers from antagonist, partial agonist to full agonist. This type of ambiguity made it clear that some method of quantifying the intrinsic power of agonists to induce cellular response is needed that would supersede the impact of system sensitivity. Historically, the first system to quantify agonism relied upon the observed effects of agonists in individual system. Thus, if an agonist produced partial agonism, then the maximal response to the partial agonist was calculated to be a fraction of the maximal response window of the assay; this provided a measure referred to as the ‘intrinsic activity’ of the agonist (Ariens, 1954, 1964). Thus, the maximal response of the agonist, calculated as a fraction of the maximal response window of the assay, formed a scale to grade agonists in terms of their power to induce response in any one system. This system was clearly flawed by the fact that once an agonist achieved full agonism in any system, then any measure of efficacy is lost. This problem was examined by Stephenson (1956) who

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Fig. 5 The effects of cellular receptor density on observed agonist response. Low levels of receptor density yield partial agonism (red curves) where the major effect of increasing receptor density is an increased maximal response with concomitant small effect on EC50. Once full agonism is achieved, there is no further increase in maximal response with increasing receptor density but a sinistral displacement of the concentration response curve leading to diminutions in the EC50.

devised a scale of agonism based on ‘efficacy’ (denoted ‘e’), a measure of response production dependent upon the intrinsic power of the molecule to induce response and the sensitivity of the system to demonstrate the effect. In Stephenson’s system, efficacy was a property of both the agonist and the sensitivity of system. Furchgott modified this theory to define ‘intrinsic efficacy’ (denoted 3) as the power of an agonist to impart stimulus to a single receptor (i.e. e ¼ 3 x [Rt] where [Rt] is the receptor density in the system) (Furchgott et al., 1966). This was an extremely useful way to compare agonists as comparison of agonist response within the pharmacodynamic equations describing agonism provided a way to cancel the tissue specific effects controlling agonist response and accessing the pharmacologically relevant effects of agonists. Stephenson’s efficacy is basically a proportionality constant placed as a multiplier for receptor occupancy to model an observed functional agonist concentration response. Thus, if receptor occupancy (rA) is given by the Langmuir adsorption isotherm: rA ¼ ½A =ð½A  þ KA Þ

(1)

Where [A] is the concentration of agonist and KA is the equilibrium dissociation constant of the agonist-receptor complex, then Stephenson described a quantity he termed ‘stimulus’ as: StimulusA ¼ e ½A =ð½A  þ KA Þ

(2)

He assumed that tissue response is a saturable function of stimulus (usually hyperbolic): ResponseA ¼ f ð e ½A =ð½A  þ KA ÞÞ

(3)

Fig. 6 shows the relationship between agonist receptor occupancy and response within this theoretical framework. This allowed estimates of the relative efficacy of agonists to be made beyond just partial agonists. However, there are two shortcomings of this model: (1) it does not allow the quantitative comparison of full and partial agonists and (2), it has not physiological rationale. Both of these shortcomings were addressed by a new theory proposed to describe agonism by Sir James Black and Paul Leff termed the ‘operational model’.

1.14.4

The Black/Leff operational model of Agonism

The Black/Leff operational model is currently the commonly applied model of agonism in pharmacology. It allows the comparison of the relative agonism of all agonists (including partial to full agonists) and can be used to predict agonist response in therapeutic systems. The theoretical basis for this model is the fact that most agonist dose-response curves are hyperbolic in nature of the form:

Fig. 6 The application of efficacy in the Stephenson model of agonism. The broken line curve agonism represents the occupancy of the receptor population by the agonist. The solid line marked response represents the observed agonist response. The difference between the curves represents the efficacy of the agonist as it occupies the receptor and produces stimulus to the cell.

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Agonism and Biased Signaling Response ¼ ½A  Emax =ð½A  þ lÞ

(4)

When combined with the fact that the formation of the agonist-receptor complex ([AR]) is described by the Langmuir adsorption isotherm: ½AR  ¼ ½A  ½R t =ð½A  þ KA Þ

(5)

Where [Rt] is the receptor density and KA s the equilibrium dissociation constant of the agonist-receptor complex; this leads to: Response ¼ ð½AR  Emax KA Þ=ð½AR  ðKA  lÞ þ ½R t  l

(6)

For physiologically reasonable behavior of such system, KA > l leading to the equation: Response ¼ ½AR =ð½AR  þ KE Þ

(7)

Where KE represents the coupling of agonist-bound receptors to the cell machinery producing response. Substitution for [AR] from Eq. (5) and defining a term s as [Rt]/KE leads to the Black/Leff operational equation for agonism (Black and Leff, 1983): Response ¼ ½A  s Emax =ð½A  ðs þ 1Þ þ KA Þ

(8)

For systems where the agonist concentration response curves have slopes that differ from unity, a variable slope version of the Black/Leff operational model is (Black et al., 1985): Response ¼ ½A n sn Emax =ð½A n sn þ ð½A  þ KA Þn Þ

(9)

The Black/Leff model is based on the fact that an agonist concentration response curve, when plotted on a linear abscissal scale, resembles a Michaelis-Menten enzyme kinetic curve. Thus, essentially, the cell is considered a virtual enzyme with the amount of agonist-receptor complex being the substrate and cellular response the enzyme velocity. This view adds a physiological rationale that is not present in the Stephenson model which simply inserts a proportionality constant to adjust receptor occupancy and functional response. There are a number of practical advantages to fitting agonist concentration response curves to the Black/Leff model (either Eqs. 8 or 9). Once values can be assigned to the efficacy and affinity of an agonist, then ratios of those values in any one functional system should be constant for the ratios for those same agonists in every other system. Under these circumstances, the values of s and KA become predictors of agonism in all functional tissues. As a pre-requisite to the application of this idea, the application of the Black/ Leff model to experimental data should be considered. For partial agonists, unique values for s and KA can be obtained through fitting (i.e. an initial estimate for KA will be the EC50 of the partial agonist and then s is adjusted to fit to accommodate the rest of the curve)dsee Fig. 7A. However, for full agonists, no individual unique values for s and KA can be derived since there are an infinite set of s and KA ratios that can fit a full agonist curve. The model is still useful since the ratio log(s/KA) becomes the unique identifier of full agonism (Kenakin et al., 2012). Thus, while individual values of s and KA do not emerge from the fit, the ratio s/KA is a unique molecular identifier of agonismdsee Fig. 7B. The sensitivity of functional systems is adjusted through values of s, a term encompassing the intrinsic power of the agonist to induce response and the sensitivity of the system through [Rt], i.e. s ¼ [Rt]/KE). As shown in Fig. 7A, two partial agonists are fit to the Black/Leff operational model to yield respective values for efficacy and affinity of s ¼ 4 and KA ¼ 30 mM and s ¼ 1 and KA ¼ 200 mM. What is assumed within this theoretical framework is that the ratio of efficacies (s values) and the respective affinities of these agonists will be constant in all systems, Thus, in a tissue 100-fold more sensitive (a 100-fold increase in receptors or equivalent increase in the efficiency of receptor cell coupling) both agonists will be essentially full agonists (Fig. 7B) but adhering to the same KA values and the same ratio of s values (4 to 1). Also, what will remain constant is the s/KA ratio for both agonists. It will be

Fig. 7 The effect of the sensitivity of the functional assay on the observed effects of an agonist. A. In a low sensitivity system (low receptor density, low efficiency of coupling of receptors to cell stimulus-response mechanisms) the two agonists shown both produce partial agonism. In another system of higher sensitivity (larger number of receptors or more efficient coupling of receptors to stimulus response), Both agonists are full agonists; the operational model predicts the relative activity of the agonists in this new system.

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Fig. 8 Application of the Black/Leff operational model to predict agonism of a test agonist in a therapeutic system. A. In a test system, a reference agonist (filled circles) and a test agonist (open circles) are tested to obtain estimates of efficacy and affinity. This yields a ratio of efficacies that is applied to other systems. B. The experimentally obtained concentration response curve to the reference agonist is fit to the model to yield and estimate of efficacy in this system (using the same KA value as that found in the test system). The efficacy ratio is then applied to the test agonist efficacy and the KA from the test system for the test agonist is used to predict the concentration response curve of the test agonist in the therapeutic system (dotted line).

seen in the subsequent discussion of biased signaling, that s and KA values are unique to the signaling system activated by the agonist-receptor complex therefore, variations from the previously mentioned ratios actually are taken as presumptive evidence for biased agonism (vide infra). The operational model also can be used to predict against response indifferent tissues through use of measured ratios of efficacy (see Fig. 8).

1.14.5

Agonist biased signaling

Historically, agonist efficacy was dealt with in terms of whole cell response, i.e. considered an agonist-mediated signal of uniform quality. In terms of the Black/Leff operational model, the potency of an agonist (EC50, the concentration producing half maximal response) is given as: Agonist Potency ¼ EC50 ¼ KA =ð1 þ sÞ

(10)

For full agonists where s [ 1, the EC50 becomes KA/s, an expression derived from the ratio of the affinity and efficacy of the agonist. It follows that the potency ratio of two agonists (Agonist1 and Agonist2) measured in the same functional system is: P:R: ¼ KA1 s2 =KA2 s1

(11)

which is a ratio comprised of solely the drug related parameters affinity and efficacy. Thus, the potency ratio of two full agonists should be constant in all cellular backgrounds. With the knowledge that receptors are pleiotropic with respect to the signaling proteins with which they interact, has come the realization that not all receptor active states will trigger the same cascade of cellular responses. The key to linking this behavior to specific agonists is the concept that receptors can form multiple ‘active’ states and that not all agonists stabilize the same active states. This idea was verified in a reverse manner by the study of agonist potency ratios. If all agonists produce the same receptor active state, then potency ratios should be uniform between functional systems, in essence the activated receptor is simply a uniform stimulus to the system. This idea formed the basis of an extremely useful tool in receptor pharmacology, namely the agonist potency ratio. Thus, in terms of a uniform receptor state, the relative potency of two agonists should be constant in all systems. This was and still is a very useful tool in drug discovery since potency ratios can be measured in test systems for prediction of relative potencies in therapeutic systems. With the advent of recombinant cell systems and the extensive testing of receptors in different cellular backgrounds came data that did not agree with the constant potency ratio rule. As early as 1987, Roth and Chuang (1987) noted that different relative potencies were found for different signaling pathways and wrote an augur for what is now known as biased signaling . ‘..the possibility is raised that selective agonists and antagonists might be developed which have specific effects on a particular receptor-linked effector system’. In 1989, Kenakin and Morgan (1989) published a theoretical model to account for agonist specific activation of different G proteins in the form of ideas describing the stabilization of different receptor active states by different agonists. A definitive demonstration of system dependence of agonist potency ratios was published by Spengler et al. (1993) in a study of the activation of the PACAP receptor. Specifically, two response pathways controlled by this receptor (cyclic AMP and inositol phosphate signaling) were measured for two agonists, PACAP1–27 and PACAP1–38. A reversal of relative potency for these agonists was seen between the two pathways, a finding that is totally incompatible with a single receptor active state. Instead, this clearly showed that PACAP1–27 and PACAP1–38 produced different active states with differential activating properties for cAMP and IP1. These and other data led to the idea that the stabilization of different receptor active states by different agonists produces biased agonism (Kenakin, 1995).

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The main impetus to the discovery of biased agonism is the timely availability of new functional assays that allow the measurement of multiple receptor responses; once these became utilized it was seen that agonists activate these pathways with differential strengths. The net result of this phenomenon is that overall agonist potency ratios are not as constant as they would be if the efficacies of the agonists involved was a homogenous signal. A number of experimental groups have noticed this effect and have given various names to the phenomenon (i.e. ‘stimulus trafficking (Kenakin, 1995), biased signaling (Jarpe et al., 1998), functional selectivity (Lawler et al., 1999), collateral efficacy (Kenakin, 2005), functional dissociation (Whistler and von Zastrow, 1999), biased inhibition (Kudlacek et al., 2002), and differential engagement (Manning, 2002). While there were a diversity of names for the effect, basically it described the same phenomenon, namely that agonist potency ratios do not adhere to a simple ratio as agonists are tested in different systems; the most common name for this effect at present is biased signaling or functional selectivity. The realization that receptors could differentially traffic activation to different signaling proteins with variable agonist occupancy led to a revolution in the application of agonism for therapeutic advantage. Specifically, It was realized that only useful components of receptor signaling could be targeted for therapeutic activity and harmful (‘side reactions’) of agonist activity could be eliminated. There were four basic scenarios within this new view of agonism that were exploited in discovery pharmacology: 1. Augmentation of useful receptor signaling: The emphasis of a favorable signal (over other signals) can lead to a better therapeutic agonist. For instance, data to show that parathyroid hormone does not build bone in b-arrestin knockout mice (Ferrari et al., 2005; Gesty-Palmer et al., 2006, 2009) leads to the conclusion that a b-arrestin biased PTH agonist would be beneficial for treatment of osteoporosis. Similarly, for the GKP-1-mediated release of insulin in diabetes, b-arrestin-1 is implicated as the important signaling mediator thereby suggesting that a b-arrestin-1 biased GLP-1 ligand would be beneficial in diabetes (Sonoda et al., 2008). 2. De-emphasis of a debilitating cellular signal: The m-receptor agonist morphine is known for producing respiratory depression, however, in b-arrestin knockout mice, much less respiratory depression is observed (Raehal et al., 2005; DeWire et al., 2013) suggesting that a G protein biased opioid would be a better analgesic. Similarly, there are data to suggest that k-opioid agonists could be useful in the treatment of affective and psychotic disorders and drug addiction but have dysphoric effects linked to b-arrestin activation suggesting that k-opioid agonists biased toward G proteins would offer a better alternative therapeutically (White et al., 2014). 3. De-emphasis of a debilitating cellular signal and blockade of the ability of the natural agonist to produce that same signal: TRV120027, the angiotensin A1 receptor blocker inhibits harmful chronic vasoconstriction in congestive heart failure while providing useful b-arrestin-mediated cellular signaling (Violin et al., 2006, 2010). 4. Prosecution of drug targets otherwise not otherwise possible: The k-opioid receptor has been implicated in many central nervous system mechanisms with potential relevance to antidepressant, anti-psychotic and other possible beneficial CNS activities. However, k-opioid agonists also are known to cause serious dysphoric effects thereby precluding them as possible therapeutic agents. The association of the dysphoric effects of k-opioid agonists with b-arrestin activation has opened the possibility that G protein k-opioid agonists may provide a useful therapeutic venue for the therapeutic exploitation of this receptor (White et al., 2014). In addition to the pre-conceived exploitation of biased activity, the operational application of this potential is applied in drug discovery. Specifically, once a compound library has been screened to detect possible ligands for a given target, the active ‘hits’ for that target can be re-screened in another functional assay for that same target to detect possible biased activity. In most cases there is a diverse scatter of activities of the compounds in the different assays reflecting biased signaling from the molecules; extreme examples of these then can be advanced to more complex assays with the hope of greater probability of observing unique phenotypes of response. Biased signaling has highlighted that agonist efficacy, as well as having a quantity, also has a quality in the mix of signaling passed on to the cell. Thus, one agonist may impart a primarily G protein signal to a cell while another agonist for the same receptor could impart a primarily b-arrestin signal. This type of fine tuning of agonist signals is used in normal physiology for receptors with multiples agonists. A prominent example is the previously discussed example in LLC PK1 cells for two natural peptides for this receptor, PACAP1–27 and PACAP1–38 yielding the opposing relative potencies for cyclic AMP and IP3 signaling (Spengler et al., 1993). Differences in the quality of efficacy of apparently redundant multiple agonists can be found in the chemokine receptor system where 19 receptors are activated by 47 chemokines. For example the CCR5 receptor interacts with seven natural chemokines two of which also interact with CCR2 and three of which also interact with CCR1 (Wells et al., 2006). Distinct differences in signaling have been reported for some of these ‘redundant’ agonists such as in the case of the two natural agonists for the CCR7 receptor, namely CCL19 and CCL21. In this system both agonists activates G proteins but only CCL19 causes receptor agonist-dependent phosphorylation and recruitment of b-arrestin to terminate the G protein stimulus (Kohout et al., 2004; Byers et al., 2008; Hauser and Legler, 2016). Biased signaling also can be found in the expression of splice variants of CXCR3 where four natural agonists for this receptor (CXCL4, CXCL9, CXCL10, CXCL11) produce different biased signaling on these variants to affect cell-based signaling selectivity (Berchiche and Sakmar, 2016). Other examples of natural signaling bias can be found for melanocortin receptors (Yang and Tao, 2016) and protease activated receptor 2 (Jiang et al., 2017; Suen et al., 2014; Zhoa et al., 2014). Natural biased signaling also is evident for metabolites of natural agonists. This produces modified signaling after agonist metabolism as in the case of the metabolism of adenosine to inosine to yield altered signaling (Welihinda et al., 2016).

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This has significant implication for the development of synthetic agonists in that it cannot be assumed that a synthetic agonist for a given receptor will produce the same signaling as the natural agonist for that receptor. This may be a good or a bad thing but it should be considered before the synthetic agonist is progressed to the clinic. This also raises the specter of whether biased signaling is a common or uncommon phenomenon that should be expected with new agonists. To address this, it is useful to consider the molecular mechanism of biased signaling.

1.14.5.1

The molecular mechanism of biased signaling

Biased signaling is the result of well known allosteric probe dependence. Allostery is the change in conformational tertiary conformation of a protein and resulting change in the interaction of that altered protein with other components of the cell signaling system. What is special about allosteric interactions is the change in tertiary conformation of the protein is unique to every other interactant available to the protein thereafter. For example, Fig. 9 shows the effect of the allosteric modulator aplaviroc on the interaction of the CCR5 receptor with two chemokine agonists. Fig. 9A shows that aplaviroc blocks be binding of the chemokine MIP-1a with the receptor; Fig. 9B shows that aplaviroc does not affect the binding of the chemokine RANTES with the same receptor (Watson et al., 2005). This is classical probe dependence known for many allosteric-receptor interactions. Allosteric probe dependence extends to multiple binding site interactions of receptors and modulatory proteins; as seen in Fig. 10, if two modulatory proteins interact with the receptor, an allosteric alteration of the receptor conformation need not (and in fact usually does not) alter the interactions of ligands at the two binding sites in an identical manner. When this probe dependence is operative at the level of a cytosolic allosteric vector (allosteric interactants yield differences in cellular signaling), then ligand-induced differences in cellular signaling can result. Fig. 10A shows CCR5-mediated the activation of cAMP and b-arrestin by the agonist CCL3L1; Fig. 10B shows the same for the agonist CCL3. It can be seen that CCL3L1 produces more relative activation of the b-arrestin system for a given activation of the cAMP system, than does CCL3. This is classical based agonism and indicates that CCL3L1 stabilizes a different receptor active state than does CLC3. In view of the fact that allosteric probe dependence is a well known and ubiquitous effect in protein function, it is clear that biased agonism by allosteric agonists of GPCRs should be an expected phenomenon and not a rare event.

1.14.6

Conclusions

The description of ligands that initiate cellular response through interaction with receptor targets (i.e. agonists) has undergone considerable modification over the past few years in light of the introduction of molecular biology into pharmacology as a description of receptor active states. Specifically, the idea that agonists selectively stabilize certain conformations from an array of spontaneously formed receptor conformations has led to the notion that as ligands produce different collections of stabilized

Fig. 9 Allosteric probe dependence. The allosteric CCR5 receptor modulator aplaviroc blocks the binding of the CCR5 binding chemokine 125I-MIP1a (Panel A). In contrast, aplaviroc does not affect the binding of the CCR5 binding chemokine 125I-RANTES (Panel B). Data from Watson, C., Jenkinson, S., Kazmierski, W., Kenakin, T., 2005. The CCR5 receptor-based mechanism of action of 873140, a potent allosteric noncompetitive HIV entry inhibitor. Molecular Pharmacology 67(4), 1268–1282. Epub 2005 Jan 11.PMID: 15644495.

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Fig. 10 Allosteric probe dependence resulting in biased agonism. A. Activation of the CCR5 receptor by CCL3L1 to activate the cAMP and b-arrestin signaling systems. B. Activation of the same receptor system by the chemokine CCL3 yielding a lower relative activation of the b-arrestin signaling system indicative of biased agonist signaling. Data courtesy of Harumi Sato and Marc Caron, Duke Department of Pharmacology.

conformations presented to the cell; this results in different qualities of agonism produced by these different agonists. Through quantitative measurement of the relative stabilization of these conformations then an active control of agonist-mediated ensemble-conformation could result in a controllable quality of agonist efficacy to the cell.

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1.15

The Pharmacology of WNT Signaling

Evangelos P. Daskalopoulosa and W. Matthijs Blankesteijnb, a Pôle de Recherche Cardiovasculaire (CARD), Institut de Recherche Expérimentale et Clinique (IREC), Université catholique de Louvain (UCLouvain), Brussels, Belgium; and b Department of Pharmacology&Toxicology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands © 2022 Elsevier Inc. All rights reserved.

1.15.1 1.15.2 1.15.2.1 1.15.3 1.15.3.1 1.15.3.2 1.15.3.3 1.15.3.4 1.15.4 1.15.4.1 1.15.4.2 1.15.4.2.1 1.15.4.2.2 1.15.4.2.3 1.15.4.2.4 1.15.4.3 1.15.4.4 1.15.5 1.15.5.1 1.15.5.2 1.15.5.3 1.15.5.4 1.15.5.5 1.15.6 1.15.7 1.15.7.1 1.15.7.2 1.15.7.3 1.15.7.4 1.15.7.4.1 1.15.7.4.2 1.15.7.4.3 1.15.8 1.15.8.1 1.15.8.2 1.15.8.3 1.15.8.4 1.15.8.5 References

The discovery of WNT signaling WNT proteins Post-translational modifications of WNT proteins Receptors for WNT proteins Frizzled proteins Binding of WNT proteins to Frizzled LRP5/6 act as co-receptors, involved in the interaction between WNT and FZD ROR and Ryk can act as co-receptors of WNT signaling Signal transduction The Disheveled proteins WNT/b-catenin signaling Characteristics of b-catenin The b-catenin destruction complex Regulation of b-catenin levels by activation of WNT signaling Regulation of gene expression by b-catenin WNT/planar cell polarity signaling WNT/Ca2þ signaling Regulation of WNT signaling by internal mediators Regulation of the activity of WNT proteins Secreted Frizzled-related proteins WNT inhibitory factor-1 Regulation of the Frizzled protein density at the cell membrane Dickkopf proteins WNT signaling in stem cells WNT signaling in disease WNT signaling in cancer WNT signaling in bone metabolism WNT signaling in Alzheimer’s disease WNT signaling in cardiovascular diseases WNT signaling in atherosclerosis WNT signaling in cardiac hypertrophy WNT signaling in myocardial infarct healing Drugs targeting the WNT signaling pathway WNT synthesisdSecretion level Extracellular level Intracellular level Nuclear level Challenges in developing WNT-based compound strategies

374 374 374 376 376 377 378 378 379 379 379 379 380 380 380 380 382 382 382 383 383 383 384 384 385 385 385 386 386 386 387 387 387 388 388 393 394 395 396

Glossary b-Catenin Signal transduction proteins in the WNT/b-catenin signaling arm; component of adherens junctions between cells. DKK Dickkopf protein, named after the phenotype of Xenopus larvae (Dickkopf is German for “fat head”). Acts as an antagonist for LRP proteins. Frizzled Receptors for WNT, named after a mutant Drosophila phenotype showing aberrant orientation of wing hairs and bristles. LRP LDL receptor-related protein, a co-receptor for WNT proteins. Ror Receptor protein, sharing characteristics with Tyrosine Kinase receptors, with an extracellular ligand binding domain similar to Frizzled proteins.

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Ryk Receptor protein, sharing characteristics with Tyrosine Kinase receptors, with an extracellular ligand binding domain similar to WIF. sFRP Soluble Frizzled-related protein. These proteins share the WNT binding site, but not the transmembrane part, with Frizzled proteins. TCF/LEF T-cell factor/Lymphoid enhancer factor. Transcription factor localized in the nucleus that can interact with b-catenin for WNT-induced gene expression. WIF WNT inhibitory factor. Secreted protein capable of binding WNT proteins in the extracellular space. WNT binding site differs from that in Frizzled/sFRPs. WNT Ligand proteins for WNT signaling. The name is derived from a combination of wingless (a mutant Drosophila phenotype) and int-1, the gene having preferential insertion region for the mouse mammary tumor virus.

1.15.1

The discovery of WNT signaling

WNT signaling was discovered in the early 1980s by two simultaneous discoveries: (1) the preferential insertion site of the mouse mammary tumor virus in the promotor region of a gene named int-1 by Nusse and Varmus, and (2) the Drosophila segment polarity gene wingless, responsible for wing development, by Nüsslein-Volhard and colleagues. Subsequent analyzes revealed that the int-1 gene was in fact the same gene as the mammalian homolog of the wingless gene, resulting in renaming the genes to the hybrid name “WNT” (Nusse and Varmus, 2012). WNT signaling is an essential component in the control of animal development, but also has been implicated in a wide variety of pathologies. The signaling is activated by the secretion of local mediators, the WNT proteins, acting on neighboring cells and affecting cell proliferation, differentiation and formation of the embryonic axis. WNT proteins have been identified in every branch of the animal kingdom, with the same number of subfamilies being present in Hydra and vertebrates, nevertheless, they are absent from plants and single-cell organisms. WNT genes are likely to have been present in genomes before the division of the animal kingdom into protostomes and deuterostomes, some 600 million years ago. In this article, we use the nomenclature for members of the WNT signaling pathway according to the Wnt homepage (http://web. stanford.edu/group/nusselab/cgi-bin/wnt/). For the general indication of WNT pathway members, the human spelling (i.e., all capitals) is used, but when we refer to a specific species, the spelling is adapted accordingly. Gene names are written in italic.

1.15.2

WNT proteins

After the discovery of the first WNT genes, multiple orthologues were identified in a wide variety of animals by homology searches. This resulted in the identification of 19 different WNT genes in mammals, which are highly conserved in evolution. Several WNT genes (including WNT2, 3, 5, 7, 8, 9 and 10) show duplications, resulting in a clustering of the WNT genes in 12 well-conserved subfamilies, as depicted in Fig. 1 (Qian et al., 2003). Typically, WNT proteins are cysteine-rich, 350-400 amino acids long, and carry a N-terminal signal peptide required for secretion. Functionally, WNT proteins can be divided into two groups: those that induce body axis duplication in Xenopus embryos and oncogenic transformation (WNT1, 3, 3a, 7a, 7b and 8), and those that do not (WNT4, 5a, 6 and 11). This division turned out to be related to different signal transduction pathways that are activated by these two groups: The WNT1-class typically activates b-catenin-mediated WNT signaling, whereas the WNT5a-class typically signals through b-catenin-independent signaling pathways, as will be discussed in detail in Section 1.15.4 (Croce and McClay, 2008).

1.15.2.1

Post-translational modifications of WNT proteins

WNT proteins show extensive post-translational modifications, some of them being essential for their biological activity. First, addition of a lipid moiety has been reported at two different sites of most of the WNT proteins: O-acylation with a saturated palmitate group at an N-terminal Cysteine residue (equivalent to Cys88 in WNT1) and S-acylation with a mono-unsaturated palmitoleate group at a C-terminal Serine residue (equivalent to Ser219 in WNT1). Although the biological function of the acylation of the Cys residue is not clear, the addition of palmitoleic acid to the C-terminal Ser residue was found to be essential for biological activity. This lipid modification requires the activity of the membrane-bound O-acyltransferase Porcupine (Porcn), localized in the endoplasmic reticulum, as shown in Fig. 2. Inactivation of this enzyme by genetic or pharmacologic interventions results in intracellular accumulation of immature WNT proteins because the presence of the palmitoleate modification is required for the interaction with the transporter protein Wntless (Torres et al., 2019). Moreover, this acylation is essential for the interaction with Frizzled (FZD) proteins (Janda et al., 2012), as will be discussed in Section 1.15.3. Another post-translational modification of WNT proteins is N-glycosylation. The function of this modification is less well characterized, although this may be required for interactions with extracellular heparan sulfate proteoglycans, affecting the spreading of WNT proteins over the plasma membrane (Driehuis and Clevers, 2017).

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Fig. 1 Phylogenetic tree of the 19 WNT proteins, based on sequence homology. Several WNT genes show recent duplications, resulting in 12 subfamilies. The blue WNT proteins are frequently associated with WNT/b-catenin signaling, whereas the green WNTs are more commonly linked to b-catenin independent signaling. For the WNT proteins depicted in black no clear association with a signaling pathway has been reported. Figure adapted from Qian J, Jiang Z, Li M, Heaphy P, Liu YH and Shackleford GM (2003) Mouse Wnt9b transforming activity, tissue-specific expression, and evolution. Genomics 81(1): 34–46.

Fig. 2 Schematic representation of the synthesis, post-translational modification and secretion of WNT proteins. After transcription and translation of WNT genes, the Porcupine enzyme attaches a palmitoleate group to WNT proteins in the endoplasmic reticulum (ER). The palmitoleated WNT proteins subsequently bind to the chaperone protein Wntless (WLS), facilitating their transport through the Golgi complex and migration to the plasma membrane in secretory vesicles. Because of the strongly lipophilic nature of WNT proteins, several extracellular binding partners have been identified which can shield the lipid moiety and facilitate the transport towards their site of action, including SWIM, exosomes and lipoprotein particles. Palmitoleated WNTs can also integrate in the plasma membrane or associate with glypicans.

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The attachment of the lipophilic acyl group to WNT proteins obviously affects their water-solubility. This not only compromised the purification of biologically active WNT proteins for many years, but also necessitates additional mechanisms to transport WNT proteins to their site of action. At least four of these mechanisms were described to shield the lipid moiety during transport, as illustrated in Fig. 2: (1) association with lipoprotein particles such as Drosophila lipophorin (Panakova et al., 2005), (2) binding to the secreted WNT-interacting protein SWIM, a member of the lipocalin family (Mulligan et al., 2012), (3) transport of WNT proteins in exosomes (Gross et al., 2012) and (4) insertion of the lipid group in the plasma membrane, allowing the spreading over the cell surface (Stanganello et al., 2015). The latter mechanism allows the presentation of WNT proteins to neighboring cells via cytonemes, thin, cellular projections involved in exchange of signaling proteins between cells (Torres et al., 2019).

1.15.3

Receptors for WNT proteins

After the discovery of the family of WNT proteins, the identity of their receptors remained an enigma for more than a decade. Eventually, the binding of the Drosophila Wingless protein to DFz2, a seven transmembrane receptor protein encoded by one of the Drosophila Frizzled genes, was demonstrated (Bhanot et al., 1996). It is interesting to note that the phenotype of a mutation in what is now known as the Frizzled gene was already described in Drosophila by Bridges and Brehme in 1944, because of the deranged orientation of the cuticle wing hairs and bristles they observed (Bridges and Brehme, 1944). The regulation of planar cell polarity is now recognized as one of the major functions for WNT proteins, as will be discussed in Section 1.15.4.3. After this discovery, several other receptor proteins were identified, the characteristics of which will be discussed in the following paragraphs.

1.15.3.1

Frizzled proteins

Frizzled (FZD) proteins form a well-conserved group of 10 different family members, clustered into 5 subfamilies based on structural homology: FZD1/2/7, FZD3/6, FZD5/8, FZD9/10 and FZD4. The proteins share a common building plan, where a conserved 120 amino acid domain located in the extracellular part of the receptor contains 10 highly conserved Cys residues. The disulfide bonds formed between these Cys residues determine a spherical structure, generally referred to as the cysteine-rich domain (CRD). This CRD, connected to the remainder of the receptor protein via a rather poorly conserved linker region of 70–120 amino acids, is involved in the interaction with WNT proteins (MacDonald and He, 2012). Because of this unusual characteristic, the International Union of Basic and Clinical Pharmacology (IUPHAR) has categorized the FZD proteins in a separate class of seven transmembrane (7TM) receptors, the F or Frizzled class, together with the smoothened protein (a member of the Hedgehog family) that shares the same building plan (Schulte, 2010). For detailed information on individual FZD family members, we refer the reader to the IUPHAR/BPS Guide to Pharmacology database (https://www.guidetopharmacology.org/GRAC/FamilyDisplayForward? familyId¼25). CRD domains are not unique to FZD proteins, but have also been observed in other proteins such as the sFRPs and the receptor tyrosine kinase-like orphan receptor (ROR), where they are also involved in the binding of WNT proteins. Similar to the class A, B and C of G-protein coupled receptors, FZD proteins contain seven stretches of lipophilic amino acids that form the transmembrane regions. These transmembrane regions are connected via three intracellular and three extracellular loops. Cysteine residues present in extracellular loop 1 and 2 form disulfide bonds, stabilizing the receptor protein, whereas the intracellular loops contain several structures required for binding of intracellular signaling molecules. The presence of a conserved KTxxxW domain in a helical structure near the C-terminus of FZD proteins is thought to be required for the interaction with signaling proteins from the Disheveled (DVL) family (Fig. 3). The large number of FZD homologs raises the question of functional redundancy, particularly within the subfamilies. This question has been addressed in targeted mutagenesis experiments in mice, where the expression of FZD proteins was blocked. Some of these mutants displayed a phenotype, such as a cleft palate in Fzd2 /, severe retinal vascularization defects in Fzd4 /, placental insufficiency in Fzd5 / and aberrant orientation of hair follicles in Fzd6 /. The phenotype of Fzd2 / mice could be aggravated by cross-breeding them with Fzd1 / mice. This indicates that some of the functions of Fzd2 in the developing mouse can be taken over by Fzd1 (Wang et al., 2016). Surprisingly little is known about the affinity and specificity of the interaction between certain members of the WNT ligand family and FZD receptor proteins. A complicating factor in investigating this specificity is the poor water solubility of the ligand, hampering a traditional ligand-binding approach to determine the binding characteristics of ligand/receptor pairs. Instead, most of the data on the WNT/FZD interactions are derived from immunoprecipitation or functional assays, mainly using reporter cell lines, as summarized in Table 1 (Dijksterhuis et al., 2014). From this table, it can be deduced that the distinction between activation of either WNT/b-catenin or b-catenin independent signaling does not appear to reside at the level of the ligand-receptor complex, since both the prototype WNT/b-catenin ligands (WNT1 and WNT3a) and the b-catenin independent signaling ligands (e.g., WNT5a) can interact with the majority of FZD homologs. Recently, it was suggested that the signaling specificity of certain WNT/FZD pairs does not depend much on selective binding but rather on variations in the conformational change of the ligand/receptor complex, exposing specific intracellular domains for interaction with certain signaling molecules (Kozielewicz et al., 2020).

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Fig. 3 Schematic representation of the building plan of Frizzled (FZD) receptors and their sites of interaction with WNT and Disheveled. At the extracellular N-terminal side, a cysteine-rich domain (CRD) forms the actual binding site for WNT proteins. Note that this interaction takes place at two sites of the CRD, which is pinched by the ‘thumb’ and ‘index finger’ extensions of the WNT protein. The thumb site consists of a hydrophobic groove, allowing the interaction with the palmitoleate moiety of WNT, which is depicted as a red bar. The CRD region is connected to the 7transmembrane part of the receptor via a linker region, and three intracellular and three extracellular loops can be distinguished. Extracellular loop 1 and 2 contain conserved Cys residues, which are likely to form a disulfide bond. The third intracellular loop and the C-terminal tail of the protein are involved in the interaction with the Disheveled protein. Modified from MacDonald BT and He X (2012) Frizzled and LRP5/6 receptors for Wnt/betacatenin signaling. Cold Spring Harbor Perspectives in Biology 4(12):a007880; Schulte G (2010) International Union of basic and clinical pharmacology. LXXX. The class frizzled receptors. Pharmacological Reviews 62(4): 632–667.

1.15.3.2

Binding of WNT proteins to Frizzled

Crystallography studies of the N-terminal CRD region of FZD proteins in the presence of WNT ligand yielded important information on their physical interaction. By solving the crystal structure of the Xenopus Wnt8 in complex with the CRD of mouse FZD8, Janda et al. demonstrated that the 3-dimensional shape of WNT resembles a horseshoe. Interaction with the CRD of FZD takes place at the open end of the horseshoe, at two separate binding sites, as depicted in Fig. 3. Using the analogy of a human hand, the regions of WNT required for the interaction with FZD were named “thumb” and “index finger” and are connected by the “palm” region. Interestingly, the conserved palmitoleated Ser residue in the WNT proteins appeared to be localized at the tip of the thumb region and was found to interact with a lipophilic groove on the CRD of the FZD protein. This observation explains the absence of biological activity of WNT proteins lacking this acyl group in their thumb region. On the other hand, the index finger is formed by two

Reported interactions between WNT and FZD family members. A direct binding (shown by e.g., immunoprecipitation) is indicated with “a,” whereas a functional interaction (e.g., in a reporter assay) is marked with “b.”

Table 1 WNT

1

2

FZD1 FZD2 FZD3 FZD4 FZD5 FZD6 FZD7 FZD8 FZD9 FZD10

a

a b

a b

a

2B

3

3A

b b

a b a b a a a a

a a

a

4

a

5A

5B

a a a b a a b

b

b

6

7A

7B

8A

b

b b

a b

b

a a b b

8B

9A

9B

b

10A

10B

11

b

b a

Adapted from Dijksterhuis JP, Petersen J and Schulte G (2014) WNT/frizzled signalling: Receptor-ligand selectivity with focus on FZD-G protein signalling and its physiological relevance: IUPHAR review 3. British Journal of Pharmacology 171(5): 1195–1209.

16

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anti-parallel b-strands, held together by several disulfide bonds formed between Cys residues. This index finger interacts via several hydrophobic contacts with a second binding site on the CRD of FZD proteins (Janda et al., 2012). More recently, this relatively simple 1:1 interaction of a WNT protein and a FZD CRD was challenged by high resolution structural information on the complex between human WNT3 and mouse Fzd8 CRD, providing evidence for a symmetrical dimer in which 2 CRDs form a complex with 2 WNT molecules (Hirai et al., 2019). This study confirmed an earlier observation that the unsaturated fatty acid moiety of WNT can stabilize the dimerization of two CRDs (Nile et al., 2017). On the other hand, Petersen et al. showed the transient dissociation of a FZD6 dimer when exposed to WNT5a. The time course of FZD6 dimer dissociation appeared to coincide with the phosphorylation of ERK1/2, suggesting a role for the dimer dissociation in signaling (Petersen et al., 2017). When interpreting these data, one has to keep in mind that most of the crystallization experiments were performed using isolated CRDs, not connected to the 7TM domains of FZD, and in the absence of co-receptors. Moreover, multiple chemical modifications were introduced to enhance expression and solubility (Hirai et al., 2019). Therefore, the results from these studies may differ from the actual interaction between WNT and FZD in a physiological setting. An unresolved issue in the initiation of the interaction between WNT and FZD is a thermodynamic one. In order to interact with the hydrophobic groove in the CRD region, the palmitoleate moiety of WNT has to migrate from its protective transporter (see Fig. 1) through an aqueous phase, into the CRD. Since this process requires energy, it is tempting to speculate that the interaction of WNT and FZD at other points, e.g., at the index finger, are the actual driving force for this transfer of the palmitoleate moiety (Kozielewicz et al., 2020).

1.15.3.3

LRP5/6 act as co-receptors, involved in the interaction between WNT and FZD

Our understanding of the physical interaction between WNT and FZD is further complicated by the discovery of co-receptors. Mutagenesis studies in Drosophila resulted in the identification of the arrow gene which, when mutated, shows a phenotype similar to that of Wingless mutants. Arrow turned out to be homologous to LRP5 and -6, members of the family of low density lipoprotein (LDL) receptors. Functional analysis revealed that the presence of LRP5 or -6 is required for the activation of WNT/b-catenin signaling. These LRP proteins are over 1600 amino acids in size, with a single transmembrane domain. The extracellular part of LRP5 and -6 contains four repeats of a b-propeller domain, coupled to an epidermal growth factor (EGF) domain. These four repeats are coupled to the transmembrane domain via three LDL-receptor type A repeats, as discussed in more detail below. The cytoplasmic side of the receptor proteins consists of a stretch of  200 amino acids, with five conserved PPPSPxS motifs. These motifs can be phosphorylated by GSK3 and CK1, kinases that will be discussed in more detail in Section 1.15.4.2. Mutagenesis studies have shown that phosphorylation of these motifs is required for the binding of axin and subsequent activation of the WNT/b-catenin signaling. On the other hand, deletion of the extracellular part led to constitutive activation of this signaling cascade (MacDonald and He, 2012). Mapping studies have shown that the extracellular part of LRP5 and -6 can be divided in three segments: b-propeller/EGF domain 1 and 2, b-propeller/EGF domain 3 and 4, and the LDL receptor type A repeats. This subdivision is of relevance for the interaction with WNT proteins: Most WNT proteins interact with b-propeller/EGF domain 1 and 2, but some (including WNT3 and WNT3a) interact with b-propeller/EGF domain 3 and 4. For other WNT proteins such as WNT5a and -5b, no interaction with LRPs has been reported (MacDonald and He, 2012). Although the domain of WNT proteins that can interact with LRP5 or -6 is less well characterized, there are indications from structural studies that this domain is located in the palm region, distant from the “thumb” and “index finger” (Hirai et al., 2019; Janda et al., 2012).

1.15.3.4

ROR and Ryk can act as co-receptors of WNT signaling

ROR1 and 2 and Ryk are single transmembrane proteins, sharing characteristics with Tyrosine Kinase receptors in that they have a ligand binding domain at the extracellular side and an intracellular domain that is potentially involved in signaling. The ligand binding domain of the ROR protein bears close homology to the CRD domain in FZD protein, whereas the ligand binding domain of Ryk resembles that of the WNT inhibitory factor (WIF) binding domain; WIF will be discussed in Section 1.15.5 of this chapter (Green et al., 2014). ROR proteins are expressed in many tissues during development. In mice lacking ROR2, craniofacial abnormalities and shortening of limbs and tails were observed, a phenotype resembling that of WNT5a-deficient mice. This led to the hypothesis that WNT5a may act as a ligand for ROR proteins (Ho et al., 2012). The phenotype of the ROR2-deficient mice can be aggravated by simultaneously inactivating ROR1, suggesting some redundancy between the two homologs (Green et al., 2014). Binding of WNT5a to ROR2 can induce the formation of a complex with FZD and subsequent phosphorylation of the cytoplasmic domain of ROR2. In fact, this process is quite similar to the association of WNT3a to LRP5/6 and the subsequent complex formation with FZD. This has led to a model where the type of WNT ligand determines whether a LRP/FZD or ROR2/FZD complex (or both) is formed. This could also explain why WNT5a is capable of inhibiting WNT/b-catenin signaling, although the precise mechanism for this inhibition has not yet been elucidated (Grumolato et al., 2010). Alternatively, ROR2 can also interact with the PCP signaling component Vangl2, where the intracellular kinase domains of ROR2 phosphorylate Vangl2 to regulate its function, a process particularly important in limb formation (Gao et al., 2011); for a detailed description of PCP signaling, we refer the reader to Section 1.15.4.3 of this chapter.

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In mammals, a single Ryk gene is present, whereas in Drosophila and C. elegans, multiple homologs have been identified. Although Ryk shares its building plan with ROR1 and -2, no kinase activity has been reported for this receptor. This suggests that Ryk acts as a co-receptor, and indeed, the formation of a complex with WNT and FZD was documented. Similar to ROR2, Ryk can also bind to the PCP gene Vangl2. Ryk activity is required for axon guidance and neurite outgrowth, and a phenotype typical for defective PCP signaling has been observed in Ryk-deficient mice, suggesting a role in this signaling pathway as well (Green et al., 2014).

1.15.4

Signal transduction

The complexity of WNT signaling is strikingly illustrated by the variety of signal transduction pathways that can be activated. The pathways are typically distinguished by the use of b-catenin as a second messenger; b-catenin mediated WNT signaling is often referred to as the “canonical” WNT signaling pathway, whereas for non-b-catenin mediated WNT signaling the term “noncanonical” is often used. Here we will indicate the most relevant signaling pathways by the terms WNT/b-catenin, WNT/Planar Cell Polarity (WNT/PCP) and WNT/Ca2þ signaling. Although the WNT/b-catenin pathway is studied the most extensively, many of the phenotypes observed in Drosophila lending their names to components of WNT signaling actually refer to disturbances in non-b-catenin mediated WNT signaling. The most important signaling pathway in this context is the PCP pathway, its main function being to provide directional clues to the developing embryo. Both WNT/b-catenin and WNT/PCP signaling require the intracellular scaffold protein Disheveled (DVL). In contrast, for WNT/Ca2þ signaling, a third signaling transduction arm, DVL does not seem to be required.

1.15.4.1

The Disheveled proteins

Disheveled was originally described as a segment polarity gene, required for Wingless signaling in Drosophila (Klingensmith et al., 1994; Noordermeer et al., 1994). Analysis of vertebrate genomes showed that three Disheveled protein homologs are present in mammals, named DVL1–3. The DVL proteins have a highly conserved domain structure, with a DIX (disheveled-axin) domain at the N-terminal side and a DEP (disheveled-EGL10-plekstrin) domain at the C-terminal side. These two domains are linked together by a third domain called PDZ, located centrally in the protein and involved in the interaction with the KTxxxW motif of FZD proteins (Fig. 3). In WNT/b-catenin signaling, DVL proteins facilitate the complex formation between the WNT/FZD/ LRP receptor complex via the PDZ domain and Axin via the DIX domain. Axin is the scaffold protein that forms the core of the b-catenin destruction complex, as will be discussed below. WNT/PCP signaling on the other hand also makes use of the PDZ domain for interaction with the receptor complex but requires the DEP domain rather than the DIX domain for signaling. This DEP domain can bind to a bipartite motive in the third intracellular loop of FZD, inducing a conformational change of the protein and subsequent dimerization of DVL by DEP domain swapping (Gammons et al., 2016). Apart from their role in WNT signaling, DVL proteins are also involved in other cellular functions such as anchoring of ciliary bodies and antagonizing Notch signaling. DVL can also be transported to the nucleus, although the function of this remains unclear (Mlodzik, 2016). The three DVL homologs in mammals appear to be highly redundant for WNT/b-catenin signaling. Only a single allele of one of the three Dvl genes is sufficient for a normal b-catenin mediated WNT signaling in mice. Targeted mutation of single Dvl genes in mice results in different phenotypes, that can all be attributed to a defective PCP signaling (Wynshaw-Boris, 2012). The phenotype of the Dvl1 knockout mouse is relatively mild, with abnormalities in social behavior (Lijam et al., 1997). In Dvl2 and Dvl3 mutants, cardiac outflow tract defects, transposition of the great arteries and persistent truncus arteriosus were observed. Combining Dvl3 mutants with a deficiency in another Dvl gene further aggravated the PCP phenotypes (Etheridge et al., 2008; Hamblet et al., 2002).

1.15.4.2 1.15.4.2.1

WNT/b-catenin signaling Characteristics of b-catenin

b-Catenin is an 80 kDa protein that is highly conserved throughout the animal kingdom. Its structure consists of 12 centrally located, so-called Armadillo repeats (Armadillo is the Drosophila homolog of b-catenin), flanked by N- and C-terminal domains. Together, these Armadillo repeats form a long, positively charged and relatively rigid groove which serves as an interaction platform for the many binding partners of b-catenin. The protein is subject to extensive post-translational modifications, regulating its degradation and localization inside the cell. For an extensive description of the characteristics and functions of b-catenin, we refer the reader to this comprehensive review article (Valenta et al., 2012). Here, we will briefly summarize its main features. b-Catenin has two main functions inside the cell, which are quite different in nature. First, it plays a structural role in the cadherin-catenin cell adhesion complex, where it forms a complex with a-catenin, binding to the N-terminal region, and Ecadherin, binding to the central region. This is where typically most of the b-catenin is located in a cell, as the integration in the adhesion complex protects b-catenin from destruction. Second, b-catenin can modulate gene expression by interacting with the T-cell factor/Lymphoid enhancer factor (TCF/LEF) transcription factor complex. Normally, free cytoplasmic b-catenin is rapidly degraded by the b-catenin destruction complex, but this degradation can be attenuated by activating WNT signaling (Valenta et al., 2012).

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1.15.4.2.2

The b-catenin destruction complex

When WNT signaling is inactive, a protein complex called the b-catenin destruction complex, also called the Axin degradasome, is formed in the cytosol. This complex consists of the scaffold protein Axin, to which the adenomatous polyposis coli (APC) protein and two kinases, casein kinase-1a (CK1a) and glycogen synthase kinase-3b (GSK3b) are bound. When b-catenin associates with this destruction complex, multiple phosphorylations take place. First, the protein is phosphorylated at Ser45 by CK1a, which is a prerequisite for the subsequent phosphorylations of residues Ser33, Ser37 and Tyr41 by GSK3b. The phosphorylated motif of b-catenin is recognized by the E3 ubiquitin ligase b-Trcp and ubiquitinated, the first step of its degradation by the ubiquitin-proteasome complex (Clevers and Nusse, 2012).

1.15.4.2.3

Regulation of b-catenin levels by activation of WNT signaling

As the name implies, the b-catenin protein can act as a second messenger in the branch of WNT signaling that is referred to as b-catenin mediated or WNT/b-catenin signaling. The regulation of the turnover of b-catenin is quite unusual in that its synthesis is relatively constant, but its degradation is regulated by WNT signaling: when WNT activates the FZD/LRP receptor complex at the plasma membrane, the b-catenin destruction complex is recruited to this activated receptor complex and can bind to the DVL protein located at the intracellular side of the receptor complex. The kinases, present in the destruction complex, phosphorylate the PPPSPxS motifs in the intracellular tail of the LRP5/6 co-receptor (Wolf et al., 2008). The relocation of the destruction complex to the plasma membrane inhibits the ubiquitination of b-catenin, saturating the complex with phosphorylated b-catenin. Therefore, newly formed b-catenin cannot be phosphorylated by the complex, resulting in cytoplasmic accumulation of unphosphorylated b-catenin (Clevers and Nusse, 2012). This b-catenin can migrate to the nucleus where it can initiate the transcription of WNT-related genes.

1.15.4.2.4

Regulation of gene expression by b-catenin

In order to be transported into the nucleus, proteins typically make use of specific carriers in the nuclear membrane called karyopherins, which facilitate the transport through nuclear pores. However, b-catenin appears to be an exception to this rule as it can shuttle between the nucleus and cytoplasm without making use of a specific carrier proteins. Instead, b-catenin was shown to interact directly with components of the nuclear pore complex via the armadillo repeats 10–12 (Jamieson et al., 2014). Phosphorylation of the Tyr654 residue in the last armadillo repeat was shown to stimulate the transport of b-catenin towards the nucleus, at the same time reducing its association with the cell adhesion complex (Sharma et al., 2016). When b-catenin enters the nucleus, it can activate the transcription of target genes of WNT/b-catenin signaling. b-Catenin cannot directly bind to the promoter regions of these target genes, as it lacks a DNA binding domain. Instead, b-catenin associates with TCF/ LEF, which provides the interaction with the WNT-responsive elements in the DNA, and several transcriptional co-activators. In mammals, 4 TCF genes are present which can be alternatively spliced, giving rise to a large number of variants. TCF proteins belong to the high mobility group DNA-binding factors and when the b-catenin/TCF complex is bound to the WNT-responsive element (CCTTTGWW, where W stands for T or A) an alteration in the chromatin structure is induced that promotes gene transcription (Fig. 4). Multiple copies of the WNT-responsive element, linked to reporter genes such as luciferase or b-galactosidase, are frequently used in reporter assays which demonstrate activated WNT/b-catenin signaling (MacDonald et al., 2009). In the absence of b-catenin, TCF forms a complex with Groucho/TLE, which represses the expression of WNT target genes. In the presence of b-catenin, however, Groucho is displaced from TCF and a multimeric transcription complex is formed with b-cateninassociated coactivators. These co-activators can either interact with the N-terminal part (such as BCL9 and Pygopus) or C-terminal part (such as p300/CBP and TRRAP/TIP60 histone deacetylases) of b-catenin. The complex interplay between the b-catenin/TCF complex, its co-activators and its repressors is likely to provide a high degree of fine-tuning of the expression of WNT target genes (MacDonald et al., 2009).

1.15.4.3

WNT/planar cell polarity signaling

PCP is the term used to describe a controlled orientation of specialized structures in the plane of the epithelium. Examples are the polarity of cuticular hairs and bristles, as well as the ommatidia of the compound eye in Drosophila, but also the orientation of hair follicles in mammals. Orientation of these structures into a certain direction requires the rearrangement of the cytoskeleton. As an example of FZD-PCP signaling, disruption of the FZD6 gene resulted in a nearly randomized orientation of hair follicles in the developing mouse, which gradually reoriented into whorls in the first postnatal weeks (Adler, 2012; Wang et al., 2016). Also, the polarization of the mechanosensory hair cells in the mammalian cochlea is a result of PCP signaling (Curtin et al., 2003). Decades of study of Drosophila mutants displaying PCP phenotypes have resolved many questions regarding the WNT/PCP signaling pathway. In vertebrates, PCP signaling isdnext to regulation of epithelial polaritydalso involved in the regulation of the anterior-posterior body axis during gastrulation (Yang and Mlodzik, 2015). The importance of PCP signaling in mammalian development is illustrated by the cardiac outflow tract defects and incomplete closure of the neural tube, observed in WNT/PCP mutants (Phillips et al., 2005; Wang et al., 2019b). Six core proteins have been identified that are essential for PCP signaling, as depicted in Fig. 5A. Three of them are transmembrane proteins: the 7TM proteins FZD and CELSR (Cadherin, EGF LAG seven-pass G-type receptor; the vertebrate orthologue of the Drosophila Flamingo protein) and the 4TM protein Vangl-like (Vangl, the vertebrate orthologue of Drosophila Van Gogh protein). The other three proteins are located in the cytoplasm: DVL, Prickl (Pk) and Inversin/diversin (called Diego in Drosophila). These proteins are clustered in two complexes, located at opposite sides of the cell, with the FZD-DVL-CELSR-Diego complex at one side of

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Fig. 4 Schematic representation of b-catenin-mediated WNT signaling. In the “Off” state (left panel), the b-catenin that is continuously produced in the cell associates with the destruction complex, consisting of GSK3b, APC, CK1 and the scaffold protein Axin. This complex phosphorylates b-catenin, targeting it for ubiquitination and degradation. In the “On” state, the complex formed between WNT, FZD and LRP5/6 recruits components of the destruction complex to the plasma membrane. This prevents the degradation of b-catenin, promoting its accumulation in the cytoplasm and its migration to the nucleus, where it can promote the transcription of WNT target genes.

the cell and the Vangl-CELSR-Pk complex at the other side. These complexes can interact inside the cell, where Vangl and Pk can inhibit the formation of the FZD-DVL complex. Inversin/diversin on the other hand can interact with Pk, thereby stabilizing the FZD-DVL complex. These mutual inhibitory effects of the two complexes are thought to contribute to their asymmetric localization

Fig. 5 Schematic representation of the planar cell polarity (PCP) pathway and WNT/Ca2þ signaling. (A) The WNT/PCP signaling depends on the formation of two receptor complexes at the opposing sides of the cell. On one side of the cell, a complex of FZD and CELSR, Disheveled (DVL) and Inversin/Diversin is formed. On the other side, the complex consists of CELSR, Vangl and Prickle (Pk). The interaction of the extracellular parts of the receptor complexes results in activation of ROCK and JNK/P38 MAPK signaling. The presence of WNT proteins (not shown) is thought to interfere with the Vangl-FZD interaction, thereby disrupting the PCP signaling. (B) For signaling via the WNT/Ca2þ pathway the interaction with G-proteins and activation of the phospholipase C (PLC) and p38 MAPKinase signaling is required.

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inside the cell (Yang and Mlodzik, 2015). Recently, the stoichiometry of the two complexes has been studied in more detail. For the FZD-DVL-CELSR-Inversin/diversin complex, a 1:2:1:1 ratio was reported, whereas for the Vangl-CELSR-Pk complex this ratio was found to be 6:1:1 (Humphries and Mlodzik, 2018). In vertebrates, two Vangl homologs and three CELSR homologs have been identified in vertebrates, with CELSR1 playing a key role in epithelial PCP, whereas CELSR2 and -3 are required for ciliogenesis and axon guidance. FZD3 and -6 appear the most prominent FZD homologs involved in PCP signaling in mammals (Goffinet and Tissir, 2017; Wang et al., 2016). A consequence of the localization of the two PCP complexes at the opposite sides of a cell is that the extracellular parts of PCP complexes of two neighboring cells face each other and come into close contact. This allows for the interaction of the extracellular domains of FZD and Vangl, thereby activating the FZD protein. Moreover, both complexes contain a CELSR protein and the long N-terminal extracellular tail of these proteins are thought to interact as well, thereby mutually stabilizing the complexes in the neighboring cells and their interaction. This intercellular communication would also facilitate the propagation of PCP signaling from cell to cell (Yang and Mlodzik, 2015). Activation of FZD by interaction with Vangl located in an adjacent cell activates the FZD-PCP signaling, requiring the PDZ and DIX domains of the DVL protein. The effects on cytoskeletal organization are mediated via the small GTPases Rho, Rac and Cdc42. For this interaction, DVL forms a complex with Disheveled activator of morphogenesis-1 (DAAM1) and the guanine nucleotide exchange factor WGEF to activate downstream effectors such as Rho-GTPase and Rho-associated kinase (ROCK). On the other hand, activating the signaling via JNK/p38-type MAPK and Jun-Fos downstream of DVL can modulate transcriptional responses (Yang and Mlodzik, 2015). In our discussion of WNT/PCP signaling, the role of WNT has not been addressed so far. Until recently, WNTs were even considered not to be involved in PCP signaling, but this opinion has changed by two observations: First, the presence of the CRD domain in FZD –its site of interaction with WNTdwas found to be critical for WNT/PCP signaling and second, PCP axes appear to be oriented towards the source of WNT expression. This has led to a model where WNT proteins can antagonize the interaction between the extracellular domains of FZD and Vangl, serving as instructive regulators for this interaction. Using ectopic expression models, the asymmetric localization of PCP proteins was demonstrated to align with the source of WNT (Humphries and Mlodzik, 2018). To illustrate the importance of WNT proteins in the regulation of PCP signaling in vertebrate embryogenesis, opposing gradients of WNT5a and sFRP1 (see Section 1.15.5.2 for a description of the sFRPs) have been shown to be required for the proper breaking of the left-right symmetry in the ventral node. Because sFRPs can act as WNT antagonists, these opposing gradients may help to fine-tune the amount of WNT protein and thereby the PCP signaling during this polarization of the node cells along the anterior-posterior axis of the mouse embryo (Minegishi et al., 2017).

1.15.4.4

WNT/Ca2D signaling

The building plan of FZD receptors shows many similarities to other G-Protein coupled receptors (GPCRs), raising the question whether FZD receptors are actually capable of signaling via heterotrimeric G-proteins. Ca2þ-mediated signaling of FZD proteins was already observed in the early days of WNT/FZD research (Cook et al., 1996; Slusarski et al., 1997) and two distinct pathways have been implicated as shown in Fig. 5B: the phospholipase C/inositol triphosphate (PLC/IP3) pathway and the cyclic guanosine monophosphate/p38 MAP-kinase pathway (Ma and Wang, 2007). For a detailed description of FZD/G-protein signaling, we refer the reader to (Dijksterhuis et al., 2014).

1.15.5

Regulation of WNT signaling by internal mediators

From the large numbers of ligands, receptors, and co-receptors and the regulatory role of WNT signaling in development and disease, it can be deduced that extensive regulatory mechanisms are in place to regulate the activity of the pathway. Indeed, regulation can take place at the level of the WNT ligands, the (co)receptors, and the signal transduction complex. In the following paragraphs, we will discuss the internal mediators regulating the activity of WNT signaling at these different levels in the pathway, as illustrated in Fig. 6.

1.15.5.1

Regulation of the activity of WNT proteins

To regulate the biological activity of WNT proteins, two enzymes have been identified. The first enzyme is the carboxylesterase Notum, capable of the enzymatic removal of the palmitoleate moiety from WNT proteins. Because this modification is essential for the biological activity of WNT, enzymatic removal of this acylation inactivates WNT proteins (Kakugawa et al., 2015). A second enzymatic inactivation of WNT proteins is through proteolytic inactivation. This can be achieved by WNT-specific metalloproteinases called Tiki, for which two homologs exist in vertebrates. These enzymes cleave of the first 9–20 amino acids from the N-terminal side of a wide variety of WNT proteins, thereby rendering them inactive (Zhang et al., 2016). These enzymes allow the further fine-tuning of WNT signaling in a time- and space-related fashion.

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Fig. 6 Different mechanisms can regulate the activity of WNT signaling. (A) the enzymes Notum and TIKI respectively can remove the palmitoleate moiety or cleave the WNT protein, rendering it inactive; (B) sFRPs can scavenge WNT proteins, preventing their interaction with the CRD from FZD; (C) WNT inhibitory factor (WIF) also can prevent the interaction of WNT with FZD by binding WNT; (D) Proteins from the Dickkopf (DKK) family interact with LRP5/6, preventing the formation of the signaling complex.

1.15.5.2

Secreted Frizzled-related proteins

The Secreted Frizzled-related proteins (sFRPs) are a group of 5 related proteins, named sFRP1–5 and ranging in size from 295 to 346 amino acids. A characteristic feature of the sFRPs is that they contain a CRD at the N-terminal side of the protein with 30–50% sequence homology to FZD proteins. Like in the FZD proteins, the disulfide bridges between 10 conserved Cys residues contribute to the tertiary structure of the CRD (Cruciat and Niehrs, 2013). The C-terminal side of the protein contains a Netrin-related motif, rather than the 7 transmembrane domains of the FZD proteins, which offers heparin-binding properties. This netrin-related motif has also been demonstrated to serve as an additional binding site for WNT proteins (Bhat et al., 2007). Initially, sFRPs were thought to act as scavengers of WNT proteins, preventing their interaction with FZD receptors and thereby acting as functional antagonists. However, follow-up studies have shown that the mechanism of action of the sFRPs is likely to be more complex. Therefore, four mechanisms of action have been proposed for the sFRPs: 1. Scavenging of WNT proteins, thereby preventing their interaction with FZD and inhibiting WNT signaling; 2. A direct interaction of the CRD of the sFRP with the CRD of FZDs, preventing the interaction with WNT and thereby inhibiting WNT signaling; 3. Formation of homo- or heterodimeric sFRP complexes, favoring WNT signaling; 4. Binding WNT to the C-terminal netrin-motif and presenting it to the CRD of FZD proteins, which would activate the signaling. It is unclear how the mechanism of action of sFRPs can be directed in any of these four directions (Bovolenta et al., 2008).

1.15.5.3

WNT inhibitory factor-1

WNT inhibitory factor-1 (WIF1) is a secreted protein with a mechanism of action that resembles the sFRPs in that it can bind WNT proteins. The protein consists of a N-terminal signaling sequence, the WNT binding domain, five epidermal growth factor-like repeats and a C-terminal hydrophobic domain (Hsieh et al., 1999). WIF1 can interact with multiple WNT proteins, both of the WNT1 (b-catenin dependent) and the WNT5a (b-catenin independent) subclasses (Cruciat and Niehrs, 2013). In contrast to the sFRPs, the WNT binding domain of WIF1 does not resemble the CRD of FZD proteins, but is similar to the WNT binding domain of the RYK tyrosine kinase receptor. The interaction between WNT and its binding domain at WIF1 also takes place at two sites, similarly to the interaction with the CRD on FZD proteins and sFRPs (Kerekes et al., 2015). Although the biological actions of WIF1 are not fully understood, its silencing in different tumors suggests a role in cancer (Cruciat and Niehrs, 2013).

1.15.5.4

Regulation of the Frizzled protein density at the cell membrane

Activation of WNT signaling not only depends on the available amount of WNT ligand, but also on the receptor density on the cell membrane. This density can be decreased by endocytosis of receptor proteins, making them inaccessible for the ligand, as illustrated in Fig. 7. Two single transmembrane proteins, the E3 ligases RNF43 and ZNRF3, were found to promote the endocytosis of FZD proteins. These proteins can transfer ubiquitin groups to a lysine residue, located at the intracellular loops of the FZD proteins, targeting them for endocytosis (Hao et al., 2012; Koo et al., 2012). The density of RNF43 and ZNFR3 at the cell membrane is also

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Fig. 7 Regulation of the FZD density on the plasma membrane. (A) Ubiquitination and subsequent internalization of the WNT-FZD-LRP5/6 complex by the E3 ligase RNF43 or ZNRF3, in the absence of R-spondin. (B) In the presence of R-spondin, the E3 ligase RNF43 or ZNRF3 bind to LGR4/5, preventing the internalization of the WNT-FZD-LRP5/6 complex.

subject to regulation by 7-transmembrane receptors from the leucine-rich repeat-containing G-proteins coupled receptor (LGR) family, particularly LGR4 and -5. When activated by R-spondin, the ligand of LGR4 and -5, the complex of RNF43 or ZNRF3 and the activated LGR is rapidly internalized. The consequence of this is a reduced ubiquitination of FZD proteins, leading to a higher density of FZD and LRP5/6 at the cell membrane and an augmented b-catenin mediated WNT signaling (de Lau et al., 2014). R-spondin can also augment WNT/PCP signaling, but via a different mechanism. In this case, R-spondin binds to syndecan 4 and promotes the clathrin-mediated endocytosis of the WNT/FZD complex. This endocytosis of the receptor complex is an essential step in the activation of WNT/PCP signaling (Cruciat and Niehrs, 2013).

1.15.5.5

Dickkopf proteins

The name of the Dickkopf (DKK) family of inhibitors of WNT signaling is derived from its phenotype in Xenopus embryos, where it induces head formation (Glinka et al., 1998). In the meantime, 4 family members have been identified in vertebrates, named DKK14. DKK1, -2 and -4 can bind to LRP5/6 with nanomolar affinity, thereby inhibiting b-catenin mediated WNT signaling (Niehrs, 2006). Crystallization studies have shown that the C-terminal part of DKK1 binds to the b-propeller pair located proximal to the plasma membrane of LRP6, whereas the N-terminal part binds the distal b-propeller pair, the site where most WNT proteins bind to LRP6 (Fig. 6D). An additional function of DKK1 and -2 is their binding to Kremen receptors. The complex formed between LRP5/6, Kremen and DKK1 or -2 can be internalized rapidly, resulting in inhibition of b-catenin mediated WNT signaling (Nakamura et al., 2008).

1.15.6

WNT signaling in stem cells

During development, the proliferation and differentiation of embryonic stem cells into the ectodermal, mesodermal and endodermal lineage is a critical process. However, these processes also play an important role in adult tissues. Many tissues of adult mammals, including the skin, blood and intestines, show a constant renewal of their cellular population. This is achieved by a continuous regeneration of these cells from stem cells, residing in these tissues in so-called niches. On the other hand, several other tissues, including neurons and heart muscle cells, show a very slow turnover (Blankesteijn, 2020). The capacity to regenerate is a convenient asset in case of injury inflicted to the organ or tissue, because the injured cells can be replaced by newly-formed cells that are derived from stem cells. Although many signaling pathways have been implicated in the control of stem cell proliferation and differentiation, WNT signaling has been shown to be essential in this context (Van Camp et al., 2014). One of the settings in which the role of WNT signaling is extensively studied is in the regeneration of intestinal epithelial cells. These cells cover the villi that protrude into the intestinal lumen and migrate from the crypts, located at the base of the villi, towards the tips where they are shed into the luminal content. In seminal work from the Clevers lab, it has been shown that proliferation of

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the stem cells located in the crypts is stimulated by WNT proteins, locally produced by Paneth cells and stromal cells. As these cells start to migrate into the direction of the tips of the villi, the concentration of WNT protein they experience gradually becomes lower, withdrawing them from the cell cycle and allowing their differentiation into mature intestinal epithelial cells. A similar gradient of WNT proteins is observed in the epidermis, although there the WNT protein is produced by the stem cells themselves rather than by neighboring cells. These observations highlight the role of WNT proteins as short-range extracellular signals (Clevers et al., 2014). Nevertheless, the activation of WNT signaling has been shown to induce differentiation of stem cells in other settings. This apparent controversy may be explained by the formation of different complexes between b-catenin and the co-activators CBP and p300 in the nucleus, activating the self-renewal and differentiation of stem cells, respectively (Ring et al., 2014).

1.15.7

WNT signaling in disease

Since its discovery almost 40 years ago, WNT signaling has been implicated in a variety of diseases. Two different modes of activation can be distinguished: (1) a continuous activation of the pathway through an activating mutation, as e.g., frequently observed in cancer and (2) aberrant regulation of components of the pathway induced by the pathological process, leading to an activation of WNT signaling as e.g., frequently present in cardiovascular diseases. In the following paragraphs, some of the conditions where WNT signaling is implicated in the disease process are discussed in more detail.

1.15.7.1

WNT signaling in cancer

The link between WNT signaling and cancer originates from observations in virally-induced breast cancer, where the WNT1 gene was identified as a preferential site of insertion of the viral DNA (Nusse and Varmus, 2012). This was followed by the identification of mutations in the APC gene to be associated with multiple polyps in the colon, a condition frequently leading to colon cancer (Kinzler and Vogelstein, 1996). This has led to the identification of mutations in genes involved in WNT/b-catenin signaling in the vast majority of colorectal tumors (Cancer Genome Atlas Network, 2012). In the meantime, aberrant WNT signaling has been established in a wide array of different malignancies. A common theme in these malignancies is that mutations in the downstream signaling pathway, rather than at the level of the WNT ligand itself, result in an uncontrolled activation of WNT/b-catenin signaling (Aghabozorgi et al., 2020). This, not only leads to uncontrolled cell proliferation but also to resistance to cell death, enhanced immortality, augmented angiogenesis, genome instability and tumor-promoting inflammation, all of these being hallmarks of cancer (Zhong et al., 2020). The effect of WNT signaling on cancer is not limited to uncontrolled activation of WNT/b-catenin signaling, as WNT/PCP signaling also is activated in multiple forms of cancer and may play a role in cell migration and invasive behavior, similar to its role in cell migration in the developing embryo (Humphries and Mlodzik, 2018).

1.15.7.2

WNT signaling in bone metabolism

Bone metabolism is a term describing the constant production and degradation of bone tissue, necessary for its maintenance and repair. The production and degradation of bone are the result of the activity of osteoblasts and osteoclasts, respectively. A disturbance of this balance can lead to excessive bone degradation (osteoporosis), but also to an increased bone density (sclerosing bone disorders). Several mutations in components of WNT signaling have been associated with aberrant bone metabolism, as will be discussed below. Osteroporosis-pseudoglioma syndrome is an inherited disorder characterized by osteoporosis and blindness. It is caused by a loss-of-function mutation in LRP5, attenuating the WNT/b-catenin signaling (Gong et al., 2001). On the other hand, a gainof-function mutation in LRP5 was demonstrated to result in hyperostosis, a sclerosing bone disorder characterized by an increased bone density (Van Wesenbeeck et al., 2003). The osteoporosis phenotype could be reproduced in mice lacking LRP5 and could be aggravated by the simultaneous heterozygous inactivation of LRP6. These studies have shown that LRP5 and -6 are involved in the activation of WNT/b-catenin signaling to stimulate bone formation by osteoblast activation (Maeda et al., 2019). The importance of WNT/b-catenin signaling in bone metabolism is further underscored by the association between mutations in the WNT1 gene and a disease named osteogenesis imperfecta, as well as different forms of bone fragility (Keupp et al., 2013; Laine et al., 2013). Parallel to the discovery of the role of LRP5 in the regulation of bone density, the increased bone density in sclerosteosis was linked to a loss-of-function mutation in the SOST gene, which encodes sclerostin. Sclerostin is a protein capable of reducing bone formation by inhibiting WNT/b-catenin signaling. Its site of interaction is the outer b-propeller domain of LRP5/6, inhibiting the binding of WNT proteins to this co-receptor. Native sclerostin therefore acts as a negative regulator of WNT/b-catenin mediated bone formation, and inactive mutants lead to activation of this signaling pathway resulting in excessive bone formation (Balemans et al., 2001). Along similar lines, a deletion in the downstream non-coding region of the SOST gene results in the van Buchem disease, characterized by endosteal hyperostosis (Balemans et al., 2002). The increased bone mass could be mimicked in mice by knocking out the SOST gene (Li et al., 2008).

386

The Pharmacology of WNT Signaling

1.15.7.3

WNT signaling in Alzheimer’s disease

Alzheimer’s disease (AD) is a very common cause of dementia, for which disease-modifying therapies are currently lacking. A better understanding of the causative factors would help to develop therapeutic strategies to stop, or even reverse the disease process. Histological examination of brain tissue from AD patients invariably shows b-amyloid protein-containing deposits in extracellular plaques and intracellular neurofibrillary tangles containing hyperphosphorylated tau protein. This is accompanied by an impaired number of synapses, a defective blood brain barrier (BBB) and activation of the microglia, the immune cells of the brain. WNT signaling appears to be involved in many of these processes. Examples are an increased expression of GSK3b due to a polymorphism in its promoter, a single nucleotide polymorphism in the LRP6 gene and the increased expression of the DKK1 gene, all leading to reduced WNT/b-catenin signaling (Aghaizu et al., 2020). Synaptic dysfunction, an early event in the development of Alzheimer’s disease, was shown to be associated with an attenuated WNT/b-catenin signaling, and lower levels of LRP6 mRNA were detected in brains from Alzheimer’s disease patients. Moreover, neuronal LRP6 deficiency could be linked to synaptic dysfunction in aging mice (Liu et al., 2014). Inhibition of WNT/b-catenin signaling has been shown to promote the deposition of b-amyloid protein in the extracellular plaques and GSK3b is one of the major kinases involved in the hyperphosphorylation of the tau protein, promoting its aggregation in the neurofibrillary tangles. Moreover, the increased DKK1 expression is associated with synapse disassembly (Aghaizu et al., 2020). Inhibition of GSK3b by lithium, stimulating WNT/b-catenin signaling, was shown to attenuate cognitive decline in AD patients in a meta-analysis of three clinical trials, including a total of 232 patients (Matsunaga et al., 2015). These results suggest that activation of WNT/b-catenin signaling can be a novel therapeutic approach in the treatment of AD, although care has to be taken that this intervention does not stimulate malignant transformation in other parts of the body (please refer to Section 1.15.7.1 for details).

1.15.7.4

WNT signaling in cardiovascular diseases

Following the description of the involvement of WNT signaling in cancer and bone metabolism, mainly based on loss of control of the activity of the pathway due to mutations in key genes, its involvement in cardiovascular diseases has increasingly been recognized. A fundamental difference, however, is that in the vast majority of cardiovascular patients the dysregulation of the pathway is not caused by mutations but by an up- or downregulation of the expression of key components due to the disease process. In a rapidly growing number of studies, the involvement of WNT signaling in the development and regulation of cardiovascular diseases was demonstrated (Foulquier et al., 2018). In the following paragraphs, we will summarize some of the main findings of these studies.

1.15.7.4.1

WNT signaling in atherosclerosis

Atherosclerosis is a chronic inflammatory condition, characterized by lesions of the arterial wall. Its pathophysiology involves dysfunction of the vascular endothelium, allowing the migration of monocytes into the intima. These monocytes differentiate into macrophages, which take up cholesterol to form foam cells. The macrophages release multiple cytokines and growth factors, attracting smooth muscle cells from the media into the intimal space. These smooth muscle cells dedifferentiate towards a synthetic phenotype and eventually form the fibrous cap that covers the atherosclerotic lesion. Inside the lesion, necrosis of foam cells and smooth muscle cells results in the formation of the necrotic core. Rupture of the fibrous cap results in the release of material from the necrotic core into the blood stream, causing acute thrombosis. This condition is typically referred to as an infarct and frequently occurs in the heart and the brain, although it can also happen in other organs and tissues (Yahagi et al., 2016). A link between atherosclerosis and WNT signaling was first established by the identification of a mutation in the LRP6 gene in a family with autosomal dominant coronary artery disease. Later, this LRP6 variant was presented as an independent risk factor for carotid artery stenosis in high blood pressure patients (Sarzani et al., 2011). This was followed by the observation of a positive relation between serum DKK1 levels and future cardiovascular events in patients admitted to the hospital with acute coronary syndrome (Ueland et al., 2019). Elevated serum levels of WNT5a in atherosclerotic patients have been reported as well and the WNT5a expression was found to be increased in more advanced stages of the disease, suggesting a role for WNT5a signaling in atherosclerosis (Malgor et al., 2014). These results evoked more in-depth research on the role of WNT signaling in the different stages of plaque development. Activated WNT/b-catenin signaling in monocytes has been shown to stimulate their adhesion to endothelial cells (Lee et al., 2006), facilitating their entry into the intima. Moreover, an increase of inflammatory gene expression by activation WNT/Ca2þ signaling was demonstrated in endothelial cells (Kim et al., 2010), whereas activation of WNT/b-catenin signaling has been reported to have an anti-inflammatory effect (Borrell-Pages et al., 2015). These results suggest that an imbalance between WNT/b-catenin and non-b-catenin mediated WNT signaling may contribute to the initiation of atherosclerosis. Activation of WNT/b-catenin signaling via LRP5 was suggested to have an atheroprotective effect, as mice lacking this gene develop more severe atherosclerotic lesions (Borrell-Pages et al., 2014). Moreover, LRP5-positive macrophages were found to be of an anti-inflammatory phenotype and to localize deeply in the plaque (Borrell-Pages et al., 2016). In vitro studies have shown that WNT5a activates b-catenin independent WNT signaling and stimulates foam cell formation and thinning of the fibrous cap, characteristics of a vulnerable plaque (Ackers et al., 2020). On the other hand, activation of WNT/b-catenin signaling in smooth muscle cells induces their proliferation (Wang et al., 2002), which is considered beneficial in order to form a stable fibrous cap covering the lesion (Badimon and Borrell-Pages,

The Pharmacology of WNT Signaling

387

2017). Therefore, therapeutic interventions in WNT signaling in atherosclerosis should be aimed at restoring a disturbed balance between WNT/b-catenin and b-catenin independent WNT signaling. An additional feature of atherosclerotic lesions is its calcification, a process with similarities to embryonic bone formation (Demer and Tintut, 2008). The well-known role of WNT signaling in bone metabolism (see Section 1.15.7.2 for details) suggested the involvement of the pathway in vascular calcification, and this indeed turned out to be the case. The vascular smooth muscle cells, recruited to the intima, change from a contractile to a synthetic phenotype, thereby adopting a mesenchymal stem cell-like plasticity. Activation of WNT/Ca2þ signaling has been shown to induce a chondrogenic gene expression program, characterized by the expression of the transcription factors SOX9. During chondrocyte maturation, WNT/b-catenin signaling can induce the transcription of the osteogenic determinant RUNX2, initiating osteoblast differentiation (Reinhold et al., 2020). The correlation between WNT5a levels and vascular calcification in patients with end-stage renal disease lend support to the relevance of this pathway in controlling calcium deposition in atherosclerotic plaques (Liu et al., 2016).

1.15.7.4.2

WNT signaling in cardiac hypertrophy

Cardiomyocytes are terminally differentiated cells with a very low turnover rate (Bergmann et al., 2009). When the heart is exposed to an increased workload or neurohumoral stimulation, the cardiomyocytes respond with a hypertrophic response, leading to a thickening or an elongation of these cells. Although exercise can induce benign cardiomyocyte hypertrophy, the hypertrophy resulting from pathological conditions usually contributes to adverse cardiac remodeling and the development of heart failure (Hunter and Chien, 1999). The involvement of WNT signaling in the hypertrophic response has been the subject of multiple studies. Activation of both WNT/b-catenin signaling (Hagenmueller et al., 2013) and non-b-catenin mediated signaling (Hagenmueller et al., 2014) was reported in hypertrophic cardiomyocytes. Several studies demonstrated that sFRPs have an anti-hypertrophic effect (Askevold et al., 2014; Sklepkiewicz et al., 2015), suggesting that inhibition of WNT signaling attenuates this adaptive response. Similar conclusions could be drawn from experiments on Dvl1 in mice, where overexpression induced cardiac hypertrophy (Malekar et al., 2010) and inactivation of the Dvl1 gene had an anti-hypertrophic effect (van de Schans et al., 2007). More recently, pharmacological inhibition of WNT signaling with the Porcn inhibitor Wnt-C59 was shown to attenuate pressure-overload induced cardiac hypertrophy development in mice (Zhao et al., 2020), supporting therapeutic potential for WNT inhibitors to counter this pathological cardiac adaptation.

1.15.7.4.3

WNT signaling in myocardial infarct healing

Myocardial infarction is the result of an acute occlusion of a coronary artery, interrupting the blood flow to the downstream parts of the heart. In the vast majority of cases, this occlusion is the result of atherosclerosis, and is caused by either the rupture of an unstable plaque or by plaque erosion, the formation of a thrombus on top of a plaque (Quillard et al., 2017). The ischemia, caused by the interrupted blood flow, leads to cell death and evokes a wound healing response in the affected parts of the heart. Different phases in the wound healing response can be distinguished, including an inflammatory phase and the formation of granulation tissue which eventually develops into scar tissue. Many different cell types are involved in the wound healing response, such as polymorphonuclear neutrophils, macrophages, (myo)fibroblasts, and endothelial cells (Cleutjens et al., 1999). The first indication of the involvement of WNT signaling in infarct healing was the observation of increased expression of FZD2 in infarcted hearts. The expression appeared to co-localize with the migration of (myo)fibroblasts into the infarct area during the process of wound healing (Blankesteijn et al., 1997). Over the years, many studies were published in which the effect of an intervention in WNT signaling on the wound healing response after myocardial infarction was tested in animal models. In the majority of these studies, inhibition of WNT signaling had beneficial effects on infarct size and the pump function of the heart. Remarkably, many of these studies showed the involvement of WNT signaling in the regulation of multiple aspects of wound healing, including apoptosis, inflammation, angiogenesis and fibrosis (Daskalopoulos and Blankesteijn, 2021). In an increasing number of studies, effects of interventions in WNT signaling were associated with cardiac regeneration, in which the infarct area is repaired with newly formed heart tissue, rather than by the formation of a scar. This required a paradigm shift, because cardiomyocytes were traditionally viewed as terminally differentiated cells, unable to proliferate (Bergmann et al., 2009). Promoting cardiac regeneration is a promising development because the actual repair of the damaged heart would prevent excessive dilatation and development of heart failure, conditions often exhibited in infarct patients (Hunter and Chien, 1999). Interestingly, cardiac injury in newt and zebrafish is typically repaired by cardiac regeneration, and inhibition of WNT signaling was shown to be vital to steer the wound healing into the direction of regeneration, rather than scar formation (Zhao et al., 2019b). Several studies have confirmed that inhibition of WNT signaling can activate cardiac regeneration in the infarcted heart in mammals as well. It has to be noted, however, that WNT signaling does not operate in isolation in this context, but that there is extensive crosstalk with other pathways such as Notch and Hippo signaling (Blankesteijn, 2020).

1.15.8

Drugs targeting the WNT signaling pathway

As it has been described in the previous parts of this work, the aberrant functioning of the WNT signaling pathway is involved in the establishment and advancement of a wide range of pathological conditions, including cancer and cardiovascular disease. It has become apparent that targeting the WNT signaling at various levels, either upstream, at the ligand or ligand-receptor level, or

388

The Pharmacology of WNT Signaling

more downstream, at the destruction complex level or at the nuclear level, could provide a potentially attractive strategy in the fight against numerous pathologies. During the last two decades, a wide range of compounds (some, more successful than others) have been proposed pre-clinically as therapeutic interventions in WNT signaling. It is noteworthy that during the last years, the literature has been expanding steadily, while on the other hand several clinical trials focusing on WNT signaling have been registered. It is beyond the aims of this article to provide a systematic presentation of all compounds that have been suggested to target the WNT signaling in every possible pathology; for a more comprehensive overview, we refer the reader to these publications (Blagodatski et al., 2020; Foulquier et al., 2018; Shah et al., 2021). In this article we will discuss the most important and promising compounds aiming to target the WNT signaling pathway. An overview of the compounds, discussed in this section, is provided in Table 2, whereas clinical trials on drugs interfering with WNT signaling are listed in Table 3.

1.15.8.1

WNT synthesisdSecretion level

The most upstream part of the WNT signaling pathway is the synthesis and secretion of the WNT ligand, as described in Section 1.15.2. Before the ligands are allowed to bind to FZD and their LRP co-receptors, they undergo extensive lipid modification and glycosylation. Possibly the most important protein taking part in the processing and secretion of WNT ligands is Porcn. Porcn is a highly conserved enzyme that modifies WNTs in the endoplasmic reticulum and has been suggested as a potential target to inhibit the WNT signaling cascade. In Drosophila, the mutation of the Porcn gene leads to an inability of cells to release Wingless (the equivalent of the human WNT) (Mikels and Nusse, 2006). Porcn’s key role in the secretion of the WNT ligands has led to the designing of Porcn-inhibiting compounds, in order to manipulate the WNT signaling. WNT-974 (formerly known as LGK974) was developed by Novartis and is a selective inhibitor of O-acetyltransferase and a potent inhibitor of Porcn. Several pre-clinical studies have shown that the orally active Porcn inhibition by WNT-974 can be used in strategies aiming to block the WNT signaling (Zhang and Lum, 2016). Liu et al. showed that WNT-974 can block WNT1, -2, -3, -3A, -6, -7A and -9A in WNT-dependent reporter screening tests (Liu et al., 2013). Furthermore, when used in vivo (in a MMTV-Wnt1 tumor mouse model), WNT-974 halted the tumor’s growth in a dose-dependent manner. In addition, WNT-974 has been tried in vitro against lymphoma, with impressive effects on apoptosis, cell proliferation and an enhancement of chemosensitivity to the cytotoxic agent doxorubicin, and renal cell carcinoma, where it inhibits migration and invasion and arrests the cell cycle of the tumor cells (Chen et al., 2020b). Very recently, WNT-974 was also shown to have beneficial effects in suppressing the LPS-induced inflammatory response both in vitro (Jang et al., 2017) and in vivo (Jang et al., 2021), while it can protect against cardiac remodeling (Moon et al., 2017) and neuropathic pain (Resham and Sharma, 2019a) in rodents. WNT-974 has also entered a Phase Ib/II clinical trial, in order to assess its clinical activity in combination with other agents in patients with metastatic colorectal cancer, and another one (Phase I, currently recruiting) in patients with various types of neoplasms that are dependent on WNT ligands (https://www. clinicaltrials.gov/ct2/show/NCT01351103?term¼NCT01351103&draw¼2&rank¼1). In 2013, Proffitt et al. were the first to propose the compound C59 as a robust, effective and bioavailable Porcn inhibitor. This study demonstrated that C59 can inhibit all Porcn isoforms in vitro, while it can confer impressive anti-tumor effects in vivo (MMTV-WNT1 transgenic mouse model). Most importantly, the authors proved that C59 demonstrates no signs of cellular or organ toxicity (including intestine, lung, heart, kidney and liver) at the doses used (2.5 mg/kg i.v. or 5 mg/kg p.o.) (Proffitt et al., 2013) and this, in combination to its good bioavailability, could make C59 a very attractive Porcn inhibitor. Similar effects were observed by C59 in suppressing nasopharyngeal neoplasm in the MMTV-WNT1 Tg mouse model, with the Porcn inhibitor appearing to target the cancer stem cells (Cheng et al., 2015). The anti-tumor beneficial effects of C59 (Koo et al., 2015) were confirmed by its therapeutic use in Rnf43;Znrf3-mutation, causing intestinal neoplasm formation, with C59 treatment of mutant mice with 50 mg/kg daily halting tumor growth, without affecting normal intestinal crypts. C59 has also been tested beyond cancer treatment. In 2017, Madan et al. exhibited the robust anti-fibrotic effects of C59 in the unilateral ureteral obstruction mouse model, mediated by an inhibition of inflammatory cytokine activation and a suppression of ECM-depositing fibroblasts expression (Madan et al., 2016). Quite recently, Zhao et al. showed that C59 can inhibit Porcn and lead to an amelioration of pressure overloadmediated cardiac hypertrophy. Indeed, C59 could halt myocyte hypertrophy in vitro and also suppress fibrosis, myocyte apoptosis, hypertrophy and expression of pro-hypertrophic genes in a TAC mouse model (Zhao et al., 2020). Very interestingly, Blyszczuk et al. showed that C59 can prevent cardiac fibrosis, by interfering with the transdifferentiation of cardiac fibroblasts to their activated forms, the myofibroblasts (Blyszczuk et al., 2017), confirming the findings of the Zhao et al. and adding C59 in the list with the promising therapeutic options against heart failure.

1.15.8.2

Extracellular level

The interaction between the ligand (WNT) and its receptor (FZD) is the most crucial step towards the activation of the WNT signaling pathway. Nevertheless, the intricacy of the pathway lies (among others) at the complexity of this very interaction between WNT and FZD. It should be noted that for many years this interaction has beenderroneouslydregarded as a very classical ligandreceptor relationship, ignoring the special characteristics of the various complexes formed, the crucial role of G-proteins and cofactors (like the LRPs), the kinetics and so on. For this reason, maximum caution should be applied in all the above factors, before designing compounds targeting the WNT cascade at this level (Schulte, 2015). Some of the most importantdand promisingdcompounds acting at the extracellular level will be discussed below.

The Pharmacology of WNT Signaling Table 2

Compounds used in basic research as interventions to modulate the WNT signaling pathway and description of their mode of action.

Level of modulation

Proposed mode of action

Effect on WNT signaling References

WNT synthesisdsecretion WNT-974 (aka LGK974)

Porcn inhibition

Inhibition

WNT synthesisdsecretion C59

Porcn inhibition

Inhibition

Extracellular

Foxy-5

FZD5 inhibition

Inhibition

Extracellular Extracellular

Box5 Niclosamide

FZD5 inhibition (?) DVL2 downregulation

Inhibition Inhibition

mAb against DKK1 BHQ880 (mAb against DKK1) DKK1 (vaccine) DKK1

FZD1 internalization LRP6 degradation DKK1 inhibition DKK1 inhibition DKK1 inhibition DKK1 enhancement

Inhibition Inhibition Activation Activation (?) Activation Inhibition

mDKN-01

DKK1 inhibition

Activation

Extracellular

WNT surrogate proteins

FZD-LRP complex enhancement Activation

Intracellular

NSC668036

DVL PDZ domain

Inhibition

Intracellular

XAV939

Axin stabilization

Inhibition

Intracellular

Pyrvinium

CK1a activation

Inhibition

Intracellular

Lithium

GSK3b inhibition

Activation

Intracellular

BIO

GSK3b inhibition

Activation

Extracellular

389

Compound

Chen et al. (2020b) Jang et al. (2017) Jang et al. (2021) Liu et al. (2013) Moon et al. (2017) Resham and Sharma (2019a) Zhang and Lum (2016) Blyszczuk et al. (2017) Cheng et al. (2015) Koo et al. (2015) Madan et al. (2016) Proffitt et al. (2013) Zhao et al. (2020) Canesin et al. (2017) Osman et al. (2019) Safholm et al. (2006) Safholm et al. (2008) Jenei et al. (2009) Chen et al. (2009) Osada et al. (2011) Tomizawa et al. (2013) Chen et al. (2009) Lu et al. (2011) Sato et al. (2010) Fulciniti et al. (2009) Qian et al. (2012) Liang et al. (2019) Wo et al. (2016) Haas et al. (2021) Betella et al. (2020) Chen et al. (2020a) Janda et al. (2017) Miao et al. (2020) Resham and Sharma (2019a) Resham and Sharma (2019b) Shan et al. (2005) Wang et al. (2015) Chen et al. (2014) Huang et al. (2009) Jean LeBlanc et al. (2019) Liao et al. (2020) Pan et al. (2018) Shetti et al. (2019) Song et al. (2021) Stakheev et al. (2019) Wang et al. (2011) Wang et al. (2014) Wu et al. (2016) Thorne et al. (2010) Saraswati et al. (2010) Wang et al. (2020) Dash et al. (2011) Liao et al. (2004) Vallee et al. (2021) Zhu et al. (2011) Fang et al. (2015) Guo et al. (2020) Li et al. (2020) Mathuram et al. (2020) Nicolaou et al. (2012) (Continued)

390

The Pharmacology of WNT Signaling

Table 2

Compounds used in basic research as interventions to modulate the WNT signaling pathway and description of their mode of action.dcont'd

Level of modulation

Compound

Proposed mode of action

Effect on WNT signaling References

Intracellular

CHIR99021

GSK3b inhibition

Activation

Nuclear

iCRT3, iCRT5, iCRT15

CK1g inhibition TCF/LEF inhibition

Activation Inhibition

Nuclear

ICG-001 (aka PRI-724/OP-724) CBP inhibition

Inhibition

Nuclear

ICAT

CBP/p300 & LEF1 inhibition

Inhibition

Nuclear

ICAT-61 miR-30c

CBP/p300 inhibition BCL9 inhibition

Inhibition Inhibition

miR-30a

BCL9 inhibition

Inhibition

miR-1301

BCL9 inhibition

Inhibition

Nikolaou et al. (2019) Sun et al. (2020) Wang et al. (2017) An et al. (2010) Bennett et al. (2002) Jin et al. (2018) Laco et al. (2018) Naujok et al. (2014) Ye et al. (2012) Ye et al. (2021) An et al. (2010) Bilir et al. (2013) Dandekar et al. (2014) Gonsalves et al. (2011) Ji et al. (2020) Mathur et al. (2015) Narayanan et al. (2012) Sogutlu et al. (2019) Akcora et al. (2018) Arensman et al. (2014) Beyer et al. (2013) Cao et al. (2018) Emami et al. (2004) Fatima et al. (2019) Gang et al. (2014) Grigson et al. (2015) Henderson et al. (2010) Koopmans et al. (2016) Rao et al. (2019) Sasaki et al. (2013) Wiese et al. (2017) Zhang et al. (2015a) Daniels and Weis (2002) Kang et al. (2017) Tago et al. (2000) Zhang et al. (2015b) Zhuo et al. (2011) Daniels and Weis (2002) Ling et al. (2016) Zhao et al. (2019a) Jia et al. (2011) Kordass et al. (2018) Liu et al. (2017) Wang et al. (2019a) Yang et al. (2017) Wang et al. (2018)

Abbreviations: BCL9, B-cell CLL/lymphoma 9 protein; BIO, 6-bromoindirubin-30 -oxime; CBP, CREB binding protein; CK, casein kinase; DKK, dickkopf; DVL, disheveled; FZD, frizzled; GSK3b, Glycogen Synthase Kinase-3b; LEF, lymphoid enhancer factor; LRP, low-density lipoprotein receptor; mAb, monoclonal antibody; miR, microRNA; PDZ, initials of the first letters of first 3 proteins discovered to possess the domain in question, i.e., post synaptic density protein, Drosophila disc large tumor suppressor and zonula occludens-1 protein; Porcn, Porcupine; TCF, T-cell factor.

Fifteen years ago, Foxy-5 was suggested as a formylated hexapeptide which acts by mimicking WNT5a. Foxy-5 was demonstrated to mediate its effects in a FZD5-mediated fashion and lead to a suppression of migration of the MDA-MB-468 breast cancer cell line (Safholm et al., 2006). Following these findings, Foxy-5 was shown to confer in vivo anti-tumor effects in mouse models of colon cancer (Osman et al., 2019), prostate cancer (Canesin et al., 2017) and breast cancer (Safholm et al., 2008). Currently, there are 3 clinical trials registered in the US (https://clinicaltrials.gov/ct2/results?recrs¼&cond¼&term¼foxy5&cntry¼&state¼&city¼&dist¼) utilizing Foxy-5, two at phase I/Ib (both focusing on metastatic breast, colon and prostate cancer) and one Phase II (still recruiting), focusing on patients with colon cancer. Unfortunately, no results have been published yet from these clinical trials. It is worth

The Pharmacology of WNT Signaling Table 3

391

Compounds used in clinical trials as interventions to modulate the WNT signaling pathway.

Level of modulation

Compound

Pathology

CT Phase

NCT #

WNT synthesisdsecretion

WNT-974 (aka LGK974)

Metastatic colorectal Ca

Ib/II

NCT02278133 Completed

Pancreatic Ca BRAF mutant colorectal Ca Melanoma Triple negative breast Ca Head & neck squamous cell Ca Carvical squamous cell Ca Esophageal squamous cell Ca Lung squamous cell Ca Head & neck squamous cell Ca

I

NCT01351103 Recruiting

II I

NCT02649530 Withdrawn (no funding) NCT02020291 Completed

Ib

NCT02655952 Completed

II I I

NCT03883802 Recruiting NCT02687009 Terminated NCT03591614 Not yet recruiting

II

NCT03395080 Active/not recruiting

I

NCT02013154 Active/not recruiting

II

NCT03818997 Withdrawn

I/II Iia

NCT03645980 Recruiting NCT04363801 Recruiting

Ib/Iia II

NCT03837353 Recruiting NCT04166721 Recruiting

II I I

NCT04057365 Recruiting NCT01711671 Completed NCT01457417 Completed

I

NCT02375880 Completed

II

NCT01302886 Completed

Ib/II II n/a

NCT00741377 Completed NCT01337752 Completed NCT02601859 Completed

Extracellular

Extracellular Extracellular

Foxy-5

Niclosamide DKK-1 (vaccine) DKN-01 (mAb)

BHQ880 (anti-DKK1 antibody) Intracellular

Lithium

Metastatic breast Ca Colorectal Ca Prostate Ca Metastatic breast Ca Metastatic colorectal Ca Metastatic prostate Ca (WNT5a low) colon Ca Colon Ca Monoclonal gammopathy Smoldering myeloma Myeloma Endometrial Ca Uterine Ca Ovarian Ca Carcinosarcoma Esophageal Ca Adenocarcinoma of the gastroesophageal junction Gastroesophageal Ca Squamous cell carcinoma Gastric adenocarcinoma Esophageal Ca Biliary tract Ca Gastroesophageal Ca Hepatobiliary Ca Hepatocellular Ca Gastric Ca Gastric adenocarcinoma Gastroesophageal Ca Prostate Ca Metastatic esophageal Ca Metastatic gastric Ca Biliary tract Ca Multiple myeloma Multiple myeloma Solid tumors Non-small cell lung Ca Carcinoma of intra/extra-hepatic biliary system Carcinoma of the gallbladder Bile duct Ca Cholangiocarcinoma Smoldering multiple myeloma Multiple myeloma bone disease Multiple myeloma þ renal insufficiency Mild cognitive impairment

Status

(Continued)

392 Table 3

The Pharmacology of WNT Signaling Compounds used in clinical trials as interventions to modulate the WNT signaling pathway.dcont'd

Level of modulation

Compound

Pathology

CT Phase

NCT #

Nuclear

ICG-001 (aka PRI-724/OP724)

Advanced pancreatic Ca

I

NCT01764477 Completed

I I/II

NCT02195440 Completed NCT01606579 Completed

I/IIa

NCT02828254 Completed NCT03620474 Recruiting

I I

NCT04688034 Recruiting NCT04047160 Recruiting

I II

NCT01302405 Terminated NCT02413853 Withdrawn

Metastatic pancreatic Ca Pancreatic adenocarcinoma Hepatitis C virus-infected cirrhosis Acute myeloid leukemia Chronic myeloid leukemia Hepatitis C virus-infected cirrhosis Hepatitis B virus Hepatitis C virus Liver cirrhosis Liver cirrhosis Primary biliary cholangitis Liver cirrhosis Advanced solid tumors Colorectal adenocarcinoma Stage IVA/B colorectal Ca

Status

Abbreviations: BRAF, B-raf protein kinase; Ca, cancer; CT, clinical trial; DKK, Dickkopf; mAb, monoclonal antibody.

mentioning that the same group that developed Foxy-5 also proposed a similar compound called Box5. The two peptides share the same amino acid sequence but their difference lies on the formyl group of Foxy-5 being substituted by a butyloxycarbonyl group in Box5. The authors showed that Box5 can suppress WNT5A-mediated Ca2þ and PKC signaling (and thus antagonize the WNT cascade) and assumed (without proof) that it should also act by blocking FZD5, just like Foxy-5 (Jenei et al., 2009). Niclosamide is an anthelmintic drug, however it has been extensively used against various types of cancer. Its effects on WNT signaling components are really variable: it can downregulate DVL2 and confer anti-proliferative effects in human colon cancer cells (Osada et al., 2011), and suppress cell proliferation in hepatocellular carcinoma (also by downregulating DVL2) (Tomizawa et al., 2013). Furthermore, it has been shown to enhance FZD1 internalization and downregulate DVL2 in the U2OS cell line (human bone osteosarcoma) (Chen et al., 2009), as well as to cause LRP6 degradation in various types of prostate and breast cancer (Lu et al., 2011). Interestingly, 26 different clinical trials have been registered in clinicaltrials.gov (https://clinicaltrials.gov/ct2/ results?cond¼&term¼niclosamide&cntry¼&state¼&city¼&dist¼&Search¼Search) with niclosamide to be tested (the majority of which being at a recruiting/not yet recruiting phase) against a range of pathologies. Nevertheless, only one (NCT02687009) was identified to explicitly mention WNT targeting in its study description. As mentioned earlier, DKK1 is an endogenous inhibitor of the WNT signaling. Thus, its targeting can lead to activation of the cascade and such strategies have been tested against neoplasia. Sato et al. utilized a polyclonal antibody against DKK1 and it exhibited very promising effects on the immune response of mice (Sato et al., 2010). The effects of a DKK1 vaccine were investigated in mouse multiple myeloma model study (Qian et al., 2012). Quite recently, DNK-01 (another antibody against DKK1) was shown to confer beneficial effects against myeloma and breast cancer (Haas et al., 2021), as well as in ovarian cancer (Betella et al., 2020), by acting in the tumor microenvironment. A large number of clinical trials was identified to utilize the DKN-01 as therapy against a wide range of cancer types, with 5 trials currently recruiting, 3 completed and 1 withdrawn. One more clinical trial has been registered (also not recruiting yet) aiming to study the safety and efficacy of a dendritic cell DKK1 vaccine against myeloma (https:// clinicaltrials.gov/ct2/show/NCT03591614). A humanized monoclonal antibody against DKK1 (named BHQ880) has also shown promising results in a mouse myeloma model, that it can suppress tumor cell growth and invasion in vitro, as well as the tumor growth in micedwith a safe profile as regards to toxicity (Fulciniti et al., 2009). Nevertheless, the authors report that the beneficial effects of BHQ880 are likely not to be mediated via a modulation of WNT signaling. On the other hand, DKK1 has also been tested as a therapeutic agent in non-cancer therapies. In a seminal paper in Circulation in 2016, Wo et al. demonstrated that DKK1 injections into the ischemic heart can lead to LRP5/6 downregulation with devastating effects on fibrosis and hypertrophy (Wo et al., 2016). while Liang et al. showed similar deleterious effects in doxorubicin-mediated cardiotoxicity (Liang et al., 2019). Endogenous WNTs that have undergone palmitoylation possess a fatty acyl group, which makes them highly hydrophobic. Due to this property, so far it has been very challenging for scientists to solubilize and purify WNTs and thus manufacture them at a great scale in order to use them in therapeutics (Chen et al., 2020a). In addition, WNTs are not selective for individual FZD receptors and this makes our understanding about this cascade even more perplexing. In 2017, Janda et al. published a very interesting study, with potential to overcome these challenges: they developed a strategy in order to design hydrophilic WNT surrogate proteins that can activate WNT/b-catenin cascade in a FZD-selective mode (Janda et al., 2017). The result was engineered WNT agonists that can heterodimerize with hydrophilic FZD-LRP5/6 complexes of FZD5/8 and FZD1/2/5/7/8 binding domains. Moving a step forward, Chen

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et al. developed a strategy in order to produce more potent and selective WNT surrogates, which could offer the possibility of maximally activating the WNT signaling cascade (Chen et al., 2020a). The authors proved that the surrogate WNTs can form complexes with various combinations of FZDs and LRPs, for example two FZDs of the same isoform with two LRPs of the same isoform, or two different FZDs with two different LRPs, or two different FZDs with two LRPs of the same isoform, or two FZDs of the same isoform with two different LRPs. Other types of surrogates (which the authors called “next generation surrogates” [NGS]), not only can recognize specific FZD subtypes but also induce specific WNT responses in distinct organs. Indeed, following adenoviral administration of NGS WNT to mice via adenoviral delivery, it was shown that FZD8 subtype NGS WNT conferred intestine-specific effects; on the other hand, FZD4, FZD7 or FZD7/8 combinational adenoviral treatment led to liver-specific effects (Miao et al., 2020).

1.15.8.3

Intracellular level

Once WNT and FZD interact with each other, the focus is shifted intracellularly, where the destruction complex has a prominent role. The PDZ domain of DVL binds to the C-terminus of FZD, this leads to the activation of the co-receptors LRP5/6 and the activation of the destruction complex, which consists of axin, CK1, APC and GSK3 (Wong et al., 2003). Several components can thus be targeted in order to mediate the WNT cascade. Various compounds have been designed to specifically target DVL, axin, CK1 or GSK3 and lead to an activation or inactivation of the destruction complex. Shan et al. used computational methods and were the first to identify the organic molecule NSC668036 (Shan et al., 2005). This was shown to bind to the DVL PDZ domain, antagonize WNT3A in Xenopus embryos and act as an antagonist of the canonical WNT pathway. Not only is this compound specific for the DVL PDZ domain (due to a unique binding mode), but also very stable against proteases and highly soluble, making it an attractive compound for further investigation. Indeed, NSC668036 was shown to confer anti-fibrotic effects in bleomycin-mediated pulmonary fibrosis, both in vitro and in vivo (Wang et al., 2015). Furthermore, two studies published in the same year, suggested NSC668036 as a potential therapeutic strategy against peripheral neuropathy mediated by diabetes (Resham and Sharma, 2019a), or by anti-cancer agents such as paclitaxel, in rats (Resham and Sharma, 2019b). Tankyrases (TNKs) are a class of nuclear proteins that regulate the scaffolding protein axin. As mentioned earlier, axin is the ratelimiting factor in the destruction complex. Thus, any manipulation of axin by the TNKs can have profound effects in the integrity of the destruction complex and, as a consequence, in the destruction or stability of b-catenin (Lehtio et al., 2013). Lately, a wide range of TNKs has been discovered, with various applications in cancer and other fields of study. About a decade ago, Huang et al. published a seminal paper, where they proposed a small molecule named XAV939 as a WNT signaling inhibitor. The molecule was shown to inhibit the enzymes TNK1 and -2 and, thus, stabilize axin. XAV939 suppressed the ubiquitination of axin 2 and induced the protein levels of axin, leading to an enhanced formation of the axin-GSK3b complex and a subsequent degradation of b-catenin (Huang et al., 2009). Since then, a huge number of publications has investigated the effects of XAV939 on the WNT signaling and several applications have been proposed against cancer or in the cardiovascular system. XAV939 has been shown to confer beneficial effects against various tumor types, such as colon (Wu et al., 2016), lung (Pan et al., 2018), breast (Shetti et al., 2019), hepatic (Liao et al., 2020) and prostate (Stakheev et al., 2019) cancer. Furthermore, XAV939 is suggested to be able to enhance the differentiation of bone marrow-derived mesenchymal stem cells and to suppress proliferation and myofibroblast trans-differentiation of NIH/3T3 fibroblasts, and so, halt lung fibrosis and injury (Wang et al., 2014). XAV939 might also be useful in cardiovascular system applications, as XAV939 can induce cardiomyogenesis (Wang et al., 2011) and exhibit an inhibitory effect on endothelial cell differentiation in mouse embryonic stem cells. It can also be neuroprotective in a model of neonatal hypoxic/ischemic injury by halting apoptosis and loss of oligodendrocytes (Chen et al., 2014). On the other hand, one should be very careful in extrapolating such a finding in ischemic stroke (Song et al., 2021), as XAV939 was shown to increase edema and exacerbate rupturing of microvessels in a mouse ischemic stroke model (Jean LeBlanc et al., 2019). Thorne et al. (2010) were the first to suggest that pyrviniumda drug molecule that was previously used as an anthelminthicdcould inhibit WNT signaling via allosterically activating CK1a. Interestingly, the group also showed that pyrvinium can bind all CK1 isoforms (a, g, d, 3), but it only activates CK1a. In the same year, Saraswati et al. provided in vitro evidence to support that pyrvinium acts via CK1a and showed that the compound inhibits WNT signaling and produces cardioprotective effects following ischemia in a mouse MI model (Saraswati et al., 2010). Nonetheless, the treatment with pyrvinium was associated with a very mortality (sudden death was a whopping  59%) of mice that received an intracardial injection and this leaves a gray area on the toxicological profile of the compound. In contrast to all this, others have challenged the CK1a theory and have suggested that pyrvinium’s effects are mediated via PI3K/Akt (Venerando et al., 2013), while Harada et al. indicated a STAT3-mediated mitochondrial respiration mechanism (Harada et al., 2012). Recently, a Chinese consortium suggested pyrvinium targeting the CK1a/chromobox homolog 4 (CBX4) in osteosarcoma metastatic patients (Wang et al., 2020). GSK3b is a key regulator of a wide range of roles in many cellular processesdincluding gene expression, protein and fatty acid metabolism, cell cycle progression, apoptosis, glycogen synthesis etc.dand a hub for multiple signaling pathways, including WNT, MAPK, PI3K/mTOR, Hedgehog, BDNF, PPAR etc. (Phukan et al., 2010). It has been known for more than two decades that GSK3b (but not other kinases) is a target of lithium. This is a well-known drug agent used routinely in psychiatry (Klein and Melton, 1996) and is regarded as a classical activator of the WNT cascade (e.g., being used as a positive control in various assays testing WNT activation). Kim et al. in vitro work showed that lithium can protect cells from endoplasmic reticulum stress-induced lipid accumulation. In addition, lithium confers beneficial effects against neuronal loss, and improvement of cognitive function following traumatic brain injury (Dash et al., 2011), even after an ultra-short treatment regimen (lithium injections 30 min after injury and then once daily for 5 days) in a rat brain injury model. Moreover, a recent review paper (Vallee et al., 2021) has compiled

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data indicating that lithium might also be a potentially attractive strategy in combating Parkinson’s disease. Lithium treatment might also have a role in the battle against prostate (among other types) cancer. It has been documented that lithium inhibits the androgen-stimulated PSA (Liao et al., 2004) promoter activity, and halts tumor development and growth of human prostate cancer cells (Zhu et al., 2011) in a mouse xenograft model. No clinical trials were identified with “lithium” and “WNT” in their study descriptions, however we managed to find one trial involving “GSK” in its description (NCT02601859). Lately, lithium and its GSK3 inhibiting properties have been suggested as a potential therapeutic approach against COVID-19, a pandemic which has had an enormous impact globally since early 2019. An “opinion or hypothesis” article from Christopher Rudd proposes the use of GSK3 inhibitors (such as lithium, which is widely used in the clinical setting in the area of psychiatry) in order to halt the viral replication of SARS-COV-2 and boost the CD8 þ T-cell and Natural Killer cell response (Rudd, 2020). In support of this theory, Harrison et al. had shown back in 2007 that lithium can suppress the viral activity of other types of coronaviruses by inhibiting RNA polymerases (Harrison et al., 2007). Nevertheless, whether (i) lithium possesses any clinical effectiveness against COVID-19 and (ii) this potential effect is mediated via WNT-related GSK3 mechanisms, remains to be investigated. 6-Bromoindirubin-30 -oxime (BIO) is a synthetic, selective and competitive inhibitor of GSK3b, and a quite impressive amount of literature exists describing its effects in various pathologies. By inhibiting GSK, BIO activates the WNT cascade and has shown very promising effects in halting cisplatin-induced nephrotoxicity (Sun et al., 2020) in a mouse model of ovarian cancer, by suppressing oxidative stress, reducing apoptosis and renal suppressing injury. In contrast to the findings of this study, very recently it was shown that BIO has robust pro-apoptotic capabilities in lung cancer cells (Mathuram et al., 2020), in cholangiocarcinoma (Li et al., 2020) and breast cancer cells (Nicolaou et al., 2012), possibly via caspase-dependent programmed death. BIO has also shown very promising effects in the cardiovascular pathologies area. Nikolaou et al. showed that GSK3-inhibition by BIO can suppress infarct size (Nikolaou et al., 2019) following ischemia-reperfusion injury in mice, while it can inhibit oxidative stress and enhance autophagy (Guo et al., 2020), leading to cardioprotective effects in the aging mouse heart. The combination of a hydrogel scaffold with growth factors has been previously suggested by our group (Daskalopoulos et al., 2015) as a potentially attractive strategy to confer antiremodeling effects following cardiac ischemia. In agreement with that, the combinational treatment of BIO and insulin-like growth factor 1 (IGF-1) via a hydrogel scaffold (Fang et al., 2015) allowed for improved proliferation of myocytes and enhanced neovascularization in a rat ischemia model, although the authors did not demonstrate any direct effects on GSK or the WNT cascade, in order to prove that the effects were mediated via the actions of BIO on GSK. Furthermore, BIO has also exhibited beneficial effects following cerebral injury: it suppresses inflammation following cerebral hemorrhage (even though treatment starts 3 days after the stroke), while it can improve recovery and enhance neurogenesis following a cerebral event (Wang et al., 2017). CHIR99021 is a small aminopyrimidine derivative that inhibits both GSK3 isoforms, by competing for their ATP-binding sites. It is regarded as a golden standard for GSK3 inhibition, although it is not very selective and it can actually inhibit very successfully CK (isoforms 1g1 and 1g3) and other kinases as well (An et al., 2010). Interestingly, not only CHIR99021 has important advantages compared to other GSK3 inhibitors as regards to potency, but it also appears to be of low toxicity, making it a very attractive compound for wider use (Naujok et al., 2014). About two decades ago, it was proven that CHIR99021 inhibits GSK3b and, thus, leads to an induction of b-catenin stabilization which, as a consequence regulates adipogenesis in 3 T3-L1 preadipocytes (Bennett et al., 2002). In addition, this stabilization of b-catenin following CHIR99021 treatment, is essential for the maintenance of self-renewal of embryonic stem cells (Ye et al., 2012), and adult progenitor cells (Jin et al., 2018), as well as the reprogramming of pluripotent stem cells in combination with other crucial factors (Oct4, Klf4) (Jin et al., 2018). The stem cell reprogramming property of CHIR99021dand more specifically its effects on cell cycledwas very recently suggested by Laco et al., (2018) to play an important modulatory role in cardiac differentiation. Lastly, Ye et al., proposed the use of CHIR99021 in order to enhance cardiomyocyte expansion from iPSCs (Ye et al., 2021), which could offer many capabilities for clinical use in cardiovascular disease.

1.15.8.4

Nuclear level

The translocation of b-catenin into the nucleus is a crucial step of the WNT cascade. Once b-catenin is in the nucleus, it forms a complex (termed “enhanceosome”) with various TCF/LEF transcription factors and co-activators (Groucho/TLE, Pygopus, BCL9, CBP/p300 and others) (Anthony et al., 2020). This interaction drives the transcription of a wide range of b-catenin target genes that control essential cellular functions, such as differentiation, proliferation, migration, angiogenesis, survival, stem cell fate etc. (Herbst et al., 2014). Hence, agents targeting the b-catenin transcriptional complex are of great pharmacological interest. The formation of the b-catenin-TCF/LEF complex is of paramount importance for switching on the WNT target genes, thus targeting the TCF/LEF is an attractive approach in order to turn off an aberrantly activated cascade, without acting too upstream (and so, minimizing the risk of off-target effects). Gonsalves et al. conducted a high-throughput screening of 14,977 compounds from compound libraries of the Institute of Chemistry and Cellular Biology in Harvard (Gonsalves et al., 2011) and these compounds were tested for their inhibitory effect on a dTF12-luciferase reporter gene. The most potent ones were found to be of the oxazole class (iCRT3), the thiazoles (iCRT5) and the thiazolidinediones (iCRT14). These compounds were shown to interfere with the b-catenin/ TCF4 interaction and inhibit the transcription of WNT target genes like Axin 2, Cyc-D1, c-myc and WISP-1. The anti-tumor effects of the blockade of b-catenin/TCF4 interaction by iCRTs have been confirmed in models of acute lymphoblastic leukemia (iCRT14) (Dandekar et al., 2014), head and neck cancer (iCRT3) (Sogutlu et al., 2019), lymphoma (iCRT14) (Mathur et al., 2015), breast cancer (iCRT3) (Bilir et al., 2013), gastric cancer (iCRT14) (Ji et al., 2020), multiple myeloma (iCRT3 and iCRT5) (Narayanan et al., 2012) and colon cancer (iCRT3, iCRT5 and iCRT14) (Gonsalves et al., 2011), among others.

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The interaction between b-catenin and CBP has also been extensively investigated. ICG-001 (also PRI-724 or OP-724), is a small compound which acts by inhibiting the b-catenin/CBP coupling. ICG-001 acts as a WNT pathway antagonist and robustly prevents fibrosis or reverses its development via suppression of myofibroblast (Cao et al., 2018) differentiation in various pathologies, such as pulmonary fibrosis (Henderson et al., 2010), kidney interstitial fibrosis (Rao et al., 2019), skin fibrosis (Beyer et al., 2013), fibrotic response in a MI rat model (Sasaki et al., 2013), urological fibrosis (Zhang et al., 2015a), hepatic fibrosis (Akcora et al., 2018), and lung fibrosis (Koopmans et al., 2016). ICG-001 has also been investigated for potential anti-tumor effects and indeed, its WNT-inhibiting effects were demonstrated to act against acute lymphoblastic leukemia (Gang et al., 2014), pancreatic adenocarcinoma (Arensman et al., 2014), multiple myeloma (Grigson et al., 2015), pediatric glioma (Wiese et al., 2017), breast cancer (Fatima et al., 2019), colon cancer (Emami et al., 2004), and others. Very interestingly, ICG-001 has also been used in various clinical trials, where it demonstrated a safe profile from a toxicological point of view (El-Khoueiry et al., 2013). Currently, there are clinical trials that are recruiting patients (on hepatitis B/C, liver cirrhosis and colon cancer), and four more (on pancreatic cancer, one on myeloid leukemia and two on Hepatitis C cirrhosis) (https://clinicaltrials.gov/ct2/results?recrs¼&cond¼&term¼icg001&cntry¼&state¼&city¼&dist¼) that have already been completed. In 2014, an in silico screening of about 3.5 million compounds tested for compounds with a structural similarity to ICG-001 and suggested PMED-1 and PMED-2 as promising hits. These sister compounds are not only effective in vitro against a hepatic tumor, but also demonstrate low toxicity for (Delgado et al., 2014). It is well-documented that the transcriptional co-activator p300 mediates the activation of target genes by b-catenin and also binds with TCF4 and acts in a synergistic way to activate TCF reporters (Hecht and Stemmler, 2003; Hecht et al., 2000). Interestingly, back in 2000, Tago et al., identified a novel protein called ICAT, which was proven to co-localize with b-catenin in the cytoplasm, as well as in the nucleus of colon cancer cells. There, it inhibits the b-catenin/TCF4 complex (Tago et al., 2000). Hence, targeting ICAT could be a promising strategy to manipulate the WNT cascade at the level of p300. Daniels & Weis experimented a bit with various proteins and concluded that the full-length ICAT peptide (81 amino acids long) can dismantle the complex b-catenin/Lef1/p300 by disrupting the CH3 domain (CBP/p300) and Lef1, while its helical form (called ICAT-61) can itself disrupt the CBP/p300, but not Lef1 (Daniels and Weis, 2002). This proves that ICAT possesses two inhibitory regions with two separate functions. Genetic manipulation of ICAT is shown to confer anti-tumor effects, by suppressing glioblastoma cells proliferation and invasion, while arresting the cell cycle and enhancing apoptosis and halting tumor growth (Zhang et al., 2015b). In the same direction, the indirect targeting of ICAT by inhibition of Phospholipase D1 can suppress colorectal cancer growth (Kang et al., 2017). Nevertheless, further investigations are required in order to acquire a more in-depth understanding of ICAT’s mechanism of action, based on the findings of Zhuo et al. This group showed that ICAT and ICAT-61 can actually enhance the androgen receptor transcription, something that could have noxious repercussions in androgen-sensitive tumors like prostate cancer (Zhuo et al., 2011). Lastly, the BCL9 and BCL9-like (BCL9L) transcriptional co-activators have been proven to play crucial roles in the aforementioned “WNT enhanceosome,” in combination with TCF/LEF and Pygo (Mieszczanek et al., 2019), driving the WNT target genes expression. The aberrant functioning of BCL9/BCL9L has been directly associated with colorectal (Moor et al., 2015) and hepatocellular (Moghe and Monga, 2020) cancer (among others), although the role of BCL9/BCL9L might not be exclusively explained by actions via the canonical b-catenin pathway. MicroRNAs (miRNAs) have been shown to be crucial modulators of the WNT cascade, especially in cancer. Interestingly, a large amount of literature has been published in the last 3–4 years on microRNAs that are used to target the 3’-UTR of BCL9, in order to halt the activation of the WNT cascade downstream (Peng et al., 2017). More specifically, it was demonstrated that miR-30c can target BCL9 to halt prostate cancer (Ling et al., 2016), colorectal cancer (Zhao et al., 2019a), ovarian cancer (Jia et al., 2011) and melanoma (Kordass et al., 2018) growth, while miR-30a might have a suppressive role in gastric (Liu et al., 2017) and ovarian (Wang et al., 2019a) cancer. Moreover, miR-1301 has been shown by Yang et al. to target BCL9 and confer a profoundly robust anti-tumor effect (by suppressing the tumor itself and the metastases, as well as angiogenesis) in hepatic carcinoma (Yang et al., 2017), while another team proved that this miRNA could inhibit proliferation, invasion and migration of osteosarcoma cells (Wang et al., 2018).

1.15.8.5

Challenges in developing WNT-based compound strategies

The WNT cascade has been under the spotlight for over 40 years now, in the hunt for therapies against a wide range of pathologies. Various compounds (both natural and synthetic) have been proposed and some have been used in in vitro and in vivo studies with quite promising results. Nevertheless, no therapies have completed the trip from bench to bedside thus far. The reasons for this are multifactorial. As we described above, WNT ligands are highly hydrophobic and are not selective for the FZD receptors and it is thus, very challenging to purify them and use them as therapeutic agentsdsomething that could be potentially solved by the discovery of WNT surrogates. Furthermore, the WNT cascade is part of a very complex network of cross-talking signaling mechanisms, that control development, growth, the immune system tumorigenesis etc. (Morris and Huang, 2016). Deregulating the WNT cascade could have catastrophic repercussions to other signaling pathways and systems, so one should be very careful when targeting distinct components of the cascade. In this context, in order to develop therapies for the clinic, it is crucial to first gain a deeper understanding of the different parts of the cascade, the complex way in which they interact with each other and the intricate ways in which the WNT pathway communicates with other signaling pathways.

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Wang, L., Zhao, S., Yu, M., 2019a. Mechanism of Low expression of miR-30a-5p on epithelial-mesenchymal transition and metastasis in ovarian cancer. DNA and Cell Biology 38 (4), 341–351. Wang, M., Marco, P., Capra, V., Kibar, Z., 2019b. Update on the role of the non-canonical Wnt/planar cell polarity pathway in neural tube defects. Cell 8 (10). Wang, X., Qin, G., Liang, X., Wang, W., Wang, Z., Liao, D., Zhong, L., Zhang, R., Zeng, Y.X., Wu, Y., Kang, T., 2020. Targeting the CK1alpha/CBX4 axis for metastasis in osteosarcoma. Nature Communications 11 (1), 1141. Wiese, M., Walther, N., Diederichs, C., Schill, F., Monecke, S., Salinas, G., Sturm, D., Pfister, S.M., Dressel, R., Johnsen, S.A., Kramm, C.M., 2017. The beta-catenin/CBPantagonist ICG-001 inhibits pediatric glioma tumorigenicity in a Wnt-independent manner. Oncotarget 8 (16), 27300–27313. Wo, D., Peng, J., Ren, D.N., Qiu, L., Chen, J., Zhu, Y., Yan, Y., Yan, H., Wu, J., Ma, E., Zhong, T.P., Chen, Y., Liu, Z., Liu, S., Ao, L., Liu, Z., Jiang, C., Peng, J., Zou, Y., Qian, Q., Zhu, W., 2016. Opposing roles of Wnt inhibitors IGFBP-4 and Dkk1 in cardiac ischemia by differential targeting of LRP5/6 and beta-catenin. Circulation 134 (24), 1991–2007. Wolf, J., Palmby, T.R., Gavard, J., Williams, B.O., Gutkind, J.S., 2008. Multiple PPPS/TP motifs act in a combinatorial fashion to transduce Wnt signaling through LRP6. FEBS Letters 582 (2), 255–261. Wong, H.C., Bourdelas, A., Krauss, A., Lee, H.J., Shao, Y., Wu, D., Mlodzik, M., Shi, D.L., Zheng, J., 2003. Direct binding of the PDZ domain of dishevelled to a conserved internal sequence in the C-terminal region of frizzled. Molecular Cell 12 (5), 1251–1260. Wu, X., Luo, F., Li, J., Zhong, X., Liu, K., 2016. Tankyrase 1 inhibitior XAV939 increases chemosensitivity in colon cancer cell lines via inhibition of the Wnt signaling pathway. International Journal of Oncology 48 (4), 1333–1340. Wynshaw-Boris, A., 2012. Dishevelled: In vivo roles of a multifunctional gene family during development. Current Topics in Developmental Biology 101, 213–235. Yahagi, K., Kolodgie, F.D., Otsuka, F., Finn, A.V., Davis, H.R., Joner, M., Virmani, R., 2016. Pathophysiology of native coronary, vein graft, and in-stent atherosclerosis. Nature Reviews Cardiology 13 (2), 79–98. Yang, Y., Mlodzik, M., 2015. Wnt-frizzled/planar cell polarity signaling: Cellular orientation by facing the wind (Wnt). Annual Review of Cell and Developmental Biology 31, 623–646. Yang, C., Xu, Y., Cheng, F., Hu, Y., Yang, S., Rao, J., Wang, X., 2017. miR-1301 inhibits hepatocellular carcinoma cell migration, invasion, and angiogenesis by decreasing Wnt/ beta-catenin signaling through targeting BCL9. Cell Death & Disease 8 (8), e2999. Ye, S., Tan, L., Yang, R., Fang, B., Qu, S., Schulze, E.N., Song, H., Ying, Q., Li, P., 2012. Pleiotropy of glycogen synthase kinase-3 inhibition by CHIR99021 promotes self-renewal of embryonic stem cells from refractory mouse strains. PLoS One 7 (4), e35892. Ye, S., Wan, X., Su, J., Patel, A., Justis, B., Deschenes, I., Zhao, M.T., 2021. Generation and expansion of human cardiomyocytes from patient peripheral blood mononuclear cells. Journal of Visualized Experiments (168), e62206. https://doi.org/10.3791/62206. Zhang, L.S., Lum, L., 2016. Delivery of the porcupine inhibitor WNT974 in mice. Methods in Molecular Biology 1481, 111–117. Zhang, K., Guo, X., Zhao, W., Niu, G., Mo, X., Fu, Q., 2015a. Application of Wnt pathway inhibitor delivering scaffold for inhibiting fibrosis in urethra strictures: In vitro and in vivo study. International Journal of Molecular Sciences 16 (11), 27659–27676.

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Zhang, K., Zhu, S., Liu, Y., Dong, X., Shi, Z., Zhang, A., Liu, C., Chen, L., Wei, J., Pu, P., Zhang, J., Jiang, T., Han, L., Kang, C., 2015b. ICAT inhibits glioblastoma cell proliferation by suppressing Wnt/beta-catenin activity. Cancer Letters 357 (1), 404–411. Zhang, X., MacDonald, B.T., Gao, H., Shamashkin, M., Coyle, A.J., Martinez, R.V., He, X., 2016. Characterization of Tiki, a new family of Wnt-specific metalloproteases. The Journal of Biological Chemistry 291 (5), 2435–2443. Zhao, D.W., Li, M.M., Han, J.P., Wang, Y., Jiang, L.X., Chang, H.L., 2019a. MiR-30c exerts tumor suppressive functions in colorectal carcinoma by directly targeting BCL9. European Review for Medical and Pharmacological Sciences 23 (8), 3335–3343. Zhao, L., Ben-Yair, R., Burns, C.E., Burns, C.G., 2019b. Endocardial notch signaling promotes cardiomyocyte proliferation in the regenerating zebrafish heart through Wnt pathway antagonism. Cell Reports 26 (3), 546–554.e545. Zhao, Z., Liu, H., Li, Y., Tian, J., Deng, S., 2020. Wnt-C59 attenuates pressure overload-induced cardiac hypertrophy via interruption of Wnt pathway. Medical Science Monitor 26, e923025. Zhong, Z., Yu, J., Virshup, D.M., Madan, B., 2020. Wnts and the hallmarks of cancer. Cancer Metastasis Reviews 39 (3), 625–645. Zhu, Q., Yang, J., Han, S., Liu, J., Holzbeierlein, J., Thrasher, J.B., Li, B., 2011. Suppression of glycogen synthase kinase 3 activity reduces tumor growth of prostate cancer in vivo. Prostate 71 (8), 835–845. Zhuo, M., Zhu, C., Sun, J., Weis, W.I., Sun, Z., 2011. The beta-catenin binding protein ICAT modulates androgen receptor activity. Molecular Endocrinology 25 (10), 1677–1688.

1.16

Pharmacokinetics: Overview

David S. Riddick, Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada © 2022 Elsevier Inc. All rights reserved.

Glossary ADME An acronym denoting the four phases of the disposition of a foreign chemical in a living organism, namely absorption, distribution, metabolism, and excretion. Clearance A pharmacokinetic parameter characterizing the efficiency of drug elimination, measured as the volume of plasma from which a drug is removed per unit of time. Pharmacokinetics The branch of pharmacology concerned with the study of the time-course of a drug’s movement into, through, and out of the body, i.e., “what the body does to the drug.” Physiologically based pharmacokinetic modeling A mathematical modeling approach in which physiological values and the pharmacokinetic properties of drugs are integrated to simulate the time-course of drug concentrations in plasma and tissues. Steady-state A dynamic equilibrium in which drug concentration is maintained within the therapeutic range because the rate of drug administration is balanced by the rate of drug elimination. Volume of distribution The apparent volume that would be necessary to contain the total administered drug dose at the same concentration observed in blood plasma.

A central goal of pharmacotherapy is to achieve and maintain a desired drug concentration at the target site of action for an adequate period of time so as to produce a beneficial therapeutic outcome with minimal adverse effects. Accomplishing this goal requires a solid understanding of the two main branches of pharmacology, those being pharmacodynamics and pharmacokinetics. The topic of pharmacodynamics, sometimes described simply as “what the drug does to the body,” was covered in detail in the preceding section of “Comprehensive Pharmacology.” Among other fundamental principles, pharmacodynamics encompasses the realms of receptors, signal transduction pathways, and dose-response curves. In this second section of “Comprehensive Pharmacology,” we now turn our attention to the topic of pharmacokinetics, often captured in simple terms as “what the body does to the drug.” Fleshed out in a bit more detail, pharmacokinetics (commonly abbreviated as PK) can be defined as the study of the timecourse of a drug’s movement into, through, and out of the body. It is our hope that academic, industrial, and regulatory scientists will use this section of “Comprehensive Pharmacology” to enhance their understanding of the rational design of dosing regimens and to clarify the roles played by several drug and patient factors in ultimately achieving and maintaining a desired drug concentration at its site of action. A logical place to begin our study of pharmacokinetics is by breaking down the four-letter acronym of “ADME” into its four component parts, describing a drug’s absorption, distribution, metabolism, and excretion. The opening chapter is entitled Oral Drug Delivery, Absorption and Bioavailability (Chapter 1.17, David Dahlgren & Hans Lennernäs), with an appropriate emphasis on oral drug delivery as the most common route of administration. The chapter features detailed discussions of the physicochemical, pharmaceutical, and physiological factors that impact drug absorption and bioavailability in relation to the gastrointestinal tract. This is followed by a chapter entitled PK Interpretation of Drug Distribution: General Concepts and Applications to Special Populations (Chapter 1.18, Shinya Ito). Taking a rigorous mathematical approach, this chapter begins with the two primary pharmacokinetic parameters (volume of distribution and clearance) and illustrates how pharmacokinetic modeling analyses can be instrumental in understanding the changes in drug distribution and other pharmacokinetic processes that occur in pregnancy and during the growth and development of neonates, infants, and children. Drug distribution across the placenta, into human milk, and across the blood-brain barrier is also considered. The “M” of “ADME” is expanded beyond metabolism to also encompass drug transport, and these topics are the subject matter of the next five chapters. Drug metabolism, also known as biotransformation, involves the conversion of a xenobiotic from one chemical form into another in a living system, commonly via an enzyme-catalyzed reaction. The phase I or functionalization reactions typically involve the introduction or exposure of a chemical functional group on a drug molecule. In terms of chemical transformations, phase I processes commonly include oxidation, reduction, and hydrolysis reactions. The first metabolism chapter is entitled Drug Metabolism: Cytochrome P450 (Chapter 1.19, F. Peter Guengerich). The cytochromes P450, constituting a large superfamily of hemoproteins, play key roles in the oxidative metabolism of both foreign chemicals and endogenous substances. This chapter places a strong focus on the subset of human P450s with particularly important roles in the metabolism of clinically important therapeutic agents. The detailed consideration of mechanistic and kinetic aspects of P450 catalysis and inhibition helps to illustrate how the behavior of enzymes influences whole-body drug disposition and pharmacokinetics. Of course, the P450s are not the only phase I enzymes, and this theme is developed in the next chapter entitled Drug Metabolism: Other Phase I Enzymes (Chapter 1.20, Gianluca Catucci et al.). From among the multitude of oxidative, reductive, and hydrolytic enzymes that could be considered, this chapter focuses on the flavin-containing monooxygenases, aldehyde

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oxidases, aldehyde dehydrogenases, alcohol dehydrogenases, and carboxylesterases. Emphasis is placed on the catalytic mechanisms and the kinetic data for the metabolism of specific drugs by these classes of enzymes. The next chapter entitled Drug Metabolism: Phase II Enzymes (Chapter 1.21, Margaret O. James) considers the phase II reactions of drug metabolism, also known as the synthetic or conjugative reactions. The phase II enzymes catalyze reactions involving the combination of a small endogenous substance (the co-substrate) with a drug or drug metabolite, resulting in a conjugate possessing properties that often facilitate elimination from the body. This chapter focuses on the processes of glucuronidation, sulfonation, glutathione conjugation, acetylation, amino acid conjugation, and methylation. Closely related to drug metabolism and also critical in the whole-body disposition of drugs and metabolites are the carrier-mediated transport processes that move xenobiotics across biological membranes. The next chapter entitled Drug TransportdUptake (Chapter 1.22, Philip Sandoval & Bruno Hagenbuch) considers members of the solute carrier superfamily that mediate the cellular uptake of drugs into the liver and kidney, thereby playing important roles in drug distribution and elimination. The emphasis in this chapter is placed on drug uptake transporters recognized by major American, European, and Japanese regulatory agencies as contributing significantly to the clearance of drugs. These include the organic anion transporting polypeptides OATP1B1 and OATP1B3, and the organic cation transporter OCT1 in the liver, as well as OCT2, and the organic anion transporters OAT1 and OAT3 in the kidney. The next chapter entitled Drug Transporters: Efflux (Chapter 1.23, Eliza R. McColl et al.) focuses mostly on members of the ATP-binding cassette superfamily that use active transport to pump their substrates out of cells or cellular compartments. The emphasis is placed on the substrates, expression, function, and role in drug disposition of the key efflux transporters highlighted by the International Transporter Consortium. These include, but are not limited to, P-glycoprotein, breast cancer resistance protein, multidrug resistance-associated proteins, and bile salt export pump. Multidrug and toxic compound extrusion transporters, important proton-coupled efflux transporters from the solute carrier superfamily, are also detailed in this chapter. Fittingly rounding out the collection of “ADME” chapters is the contribution entitled Drug Excretion (Chapter 1.24, Erin F. Barreto et al.). As the final step in the “ADME” process, drug excretion includes pathways that remove an administered drug and/or its metabolites from the body. This chapter provides a detailed analysis of factors involved in drug elimination at the primary sites of excretion, the kidney (urine) and liver (feces), as well as other routes of excretion such as breast milk, saliva, hair, sweat, and respiration. The chapter illustrates how a solid understanding of the factors that influence drug excretion at these various sites can impact drug development, the design of drug dosage regimens, and insights into patient variability in drug response. Much of what we have addressed so far could be described as considerations of the biochemical and physiological determinants of drug disposition. The next chapter entitled Mathematical Aspects of Clinical Pharmacokinetics (Chapter 1.25, Rommel G. Tirona) turns the attention to the quantitative description and prediction of the time-course of drug concentrations in the blood. Building from fundamental pharmacokinetic concepts including bioavailability, volume of distribution, clearance, and half-life, the chapter outlines the theory and application of the mathematical approaches needed for the design of dosage regimens in three scenarios commonly used in routine drug therapy: continuous intravenous infusion, intravenous loading dose, and intermittent dose administration. The remaining chapters in this section of “Comprehensive Pharmacology” deal with a series of interesting and important specialized topic in pharmacokinetics. First, we deal with genetic determinants of inter-individual differences in drug disposition and drug response in a chapter entitled Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters (Chapter 1.26, Mariamena Arbitrio et al.). With an aim of using pharmacogenetics research to ultimately achieve the vision of personalized or precision medicine, the chapter provides detailed analyses of the importance of genetic variation in specific cytochrome P450 enzymes, dihydropyrimidine dehydrogenase, several phase II enzymes, as well as efflux transporters from the ATP-binding cassette superfamily and uptake transporters from the solute carrier superfamily. Another important source of variability in drug response involves drug-drug interactions, whereby administration of one drug (the so-called “perpetrator”) alters the pharmacological actions of a second drug (the so-called “victim”). Focusing on interactions involving changes in drug disposition, the next chapter is entitled Drug-Drug Interactions with a Pharmacokinetic Basis (Chapter 1.27, Lisa Cheng et al.). Induction and inhibition of drug-metabolizing enzymes and drug transporters are important causes of clinically relevant drug-drug interactions. The expression and activity of several drug-metabolizing enzymes can be induced by prior exposure to chemicals that serve as agonists for ligand-activated transcription factors. For a “victim” that is a pharmacologically active parent compound, exposure to an inducing “perpetrator” commonly accelerates the metabolic inactivation and compromises the therapeutic activity of the “victim”. Conversely, exposure to an inhibitory “perpetrator” commonly impairs the metabolic inactivation and exaggerates the activity and/or toxicity of the “victim”. Similarly, induction and inhibition of uptake and efflux drug transporters can also result in significant drug-drug interactions involving altered drug disposition. The study of pharmacokinetics, like other aspects of pharmacology, has featured a traditional focus on small molecule drugs. The shift seen in recent decades to more complex pharmacological targets and the need for novel therapeutic modalities is captured in the next chapter entitled ADME of Biologicals and New Therapeutic Modalities (Chapter 1.28, Robert S. Foti). This chapter focuses on the rapidly evolving understanding of the “ADME” properties of a wide range of biologicals and novel therapeutic modalities including: peptides, hybrid/fusion proteins, antibody drug conjugates, bispecific antibodies, antisense oligonucleotides and siRNA therapeutics, adoptive cellular therapy, oncolytic viruses, and proteolysis targeting chimeras. The next specialized topic is covered in the chapter entitled Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development (Chapter 1.29, Samuel L.M. Arnold & Nina Isoherranen). The “ADME” properties of drug candidates are considered at various stages of drug discovery and development, with contributions from in vitro methods, animal models, clinical investigations, and modeling approaches. Using rigorous mathematical approaches, an important focus of this chapter is a thorough characterization of how physiologically based pharmacokinetic (PBPK) modeling can support both preclinical and clinical stages of drug

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development. Using the PBPK modeling approach, physiological values and the “ADME” characteristics of drugs can be integrated to simulate the time-course of drug concentrations in plasma and tissues; such modeling approaches have seen expanded regulatory acceptance in recent years. Determinations of the human enzymes involved in the biotransformation of a new chemical entity and the sites of metabolism within the substrate’s structure are important aspects of the drug development process. Computational predictive tools can complement empirical evidence derived from traditional experimentation, and such tools are the focus of the final chapter entitled Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools (Chapter 1.30, Jonathan D. Tyzack). With a focus on the cytochrome P450 enzymes, this chapter discusses both ligand-based and enzyme structure-based in silico models for several aspects of P450 metabolism prediction, including site of metabolism within potential substrates, P450 isoform selectivity, P450 inhibition, and metabolite structure. These models employ approaches ranging from rule-based expert methods, machine learning, data mining, docking, molecular interaction fields, molecular dynamics, and quantum mechanics simulations. Taken together, the chapters that make up this section of “Comprehensive Pharmacology” cover the full breadth of essential topics in pharmacokinetics, providing depth that ranges from fundamental principles to current sophisticated research applications. With relevance and applicability to all categories of therapeutic agents, these chapters provide expert guidance on the complex interplay of multiple drug and patient factors as determinants of the time-course of xenobiotic movement into, through, and out of the body. Since achieving and maintaining a desired drug concentration at the site of action is key to successful pharmacotherapy, these chapters build from fundamental concepts the scientific basis for the rational design of drug dosing regimens.

1.17

Oral Drug Delivery, Absorption and Bioavailability

David Dahlgren and Hans Lennerna¨s, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden © 2022 Elsevier Inc. All rights reserved.

1.17.1 1.17.2 1.17.2.1 1.17.2.2 1.17.2.3 1.17.2.4 1.17.2.4.1 1.17.2.4.2 1.17.2.5 1.17.3 1.17.3.1 1.17.3.2 1.17.4 1.17.4.1 1.17.4.1.1 1.17.4.1.2 1.17.4.2 1.17.4.2.1 1.17.4.2.2 1.17.4.2.3 1.17.4.2.4 1.17.4.2.5 1.17.4.3 1.17.4.3.1 1.17.4.3.2 1.17.4.4 1.17.4.4.1 1.17.4.4.2 1.17.4.4.3 1.17.4.5 References

Introduction Physiology and function of the gastrointestinal tract and related organs Morphology Physiological function and nutrient absorption Regional differences Gastrointestinal transit and the impact of prandial state Physiological regulation of GI transit GI transit of different oral dosage forms The hepatobiliary system Pharmacokinetics and pharmacodynamics; processes to consider in oral dosage form research and development Absorption, distribution, metabolism, excretion, and pharmacodynamics Bioavailability and steady-state plasma concentration Fundamental biopharmaceutical parameters for intestinal absorption and bioavailability: Solubility and dissolution, membrane permeability, and first-pass extraction Drug solubility and dissolution Solubility Dissolution Membrane transport mechanisms and intestinal permeability Biological membrane barriers Membrane transport mechanisms Permeability Modified-release formulations and regional intestinal permeability Oral peptide delivery and permeation enhancers Gut-wall and hepatic extraction Gut-wall extraction Hepatic extraction Preclinical intestinal absorption models Solubility and dissolution models Permeability models Gut-wall and hepatic metabolism models Bioequivalence and the biopharmaceutics classification system

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Glossary Bioavailability The fraction of an orally administered drug that reaches the systemic circulation in unchanged form. Fraction absorbed The total mass of an oral drug dose that is being transported from the intestinal lumen across the intestinal epithelial apical cell membrane. Gut-wall extraction The fraction of a drug that is eliminated during its first passage through the intestinal epithelium as it moves from the lumen to the blood. Hepatic extraction The fraction of a drug that is eliminated during its first passage through the liver with the blood. Permeability The intrinsic velocity of a drug molecule that relates its flux (J)dthe mass transfer per area per timedto the concentration gradient across a specific barrier. Pharmacodynamics What the drug does to the body: the onset, intensity and duration of a pharmacological effect of a drug and its relationship to the concentration of the drug at its site of action and the pharmacological response over time. Pharmacokinetics What the body does to the drug: the time-course of a drug and its metabolites within the body, as a consequence of the body’s capacity to take up a drug, distribute, metabolize, and excrete it. Plasma clearance The volume of plasma from which a substance is completely removed per unit time.

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Nomenclature CL Clearance, volume/time CLint Intrinsic clearance, volume/time J Flux, mass/area/time Km Michaelis constant, concentration Papp Apparent permeability, distance/time Peff Effective permeability, distance/time V Apparent volume of distribution, volume Vmax Maximum rate, mass/time

1.17.1

Introduction

A drug that is administered into a blood vessel is directly available and can thus instantly exert its biochemical and/or physiological effect(s) in the target effect compartment. However, the majority of systemically acting drugs are dosed by other administration routes, from where the drug must initially be absorbed and come available in the blood (Holford and Sheiner, 1981). The relative frequency of the most common drug administration routes used in today’s drug therapy are displayed in Fig. 1, which is based on an analysis of FDA-approved pharmaceutical products (Zhong et al., 2018).

Fig. 1 The most common drug delivery routes for innovator and generic drug products: oral accounts for about 62% of administered drugs. Injection is the second most common route of administering, with 22.5%, followed by cutaneous administration (8.7%), mucosal administration (5.2%), inhalation (1.2%), and finally other delivery routes (0.3%). This analysis of most common drug delivery routes and their contribution to the total approval of new and generic drugs based on data between 1980 and 2017 (Zhong et al., 2018). Artwork by Febe Jacobsson.

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The majority of drugs, both innovative and generic, are administered orally, accounting for about 62% of drugs. Injection is the second most common route of administering, with 22.5%, followed by cutaneous administration (8.7%), mucosal administration (5.2%), inhalation (1.2%), and finally other delivery routes (0.3%). With the exception of drug administration directly into the blood stream, all these drug administration routes are faced with various types of barriers present in the human body that restrict the transport of dissolved molecules into the systemic circulation, and therefore represents technical hurdles to all drug research and development. The oral administration route is the most common as there is a long tradition of dosing drugs orally, it offers more flexibility in the pharmaceutical development stage, non-invasiveness means that there is a high patient convenience, and it is usually the safest and least expensive. Taken together, this indicates that the oral route of drug administration will prevail also in the future. This chapter will therefore focus on oral drug delivery and the absorption and first-pass extraction processes involved, as the basic principles for drug delivery and absorption are the same for all non-parenteral administration routes. Intestinal absorption is a pharmacokinetic (PK) term defined as the fraction of a drug dose absorbed from the intestinal lumen as well as the rate of this process. It takes into account all processes taking place from the time of oral drug intake to the appearance of drug in the intestinal epithelial cells. Two main functions of the gastrointestinal (GI) tract are to prevent absorption and translocation of potentially harmful luminal constituents into the central circulation, while still allowing an efficient absorption of nutrients, electrolytes and water (Marchiando et al., 2010). There are consequently several limitations to the oral route, as a drug product and its active pharmaceutical ingredient is exposed to the harsh and dynamic GI luminal environment. These hurdles must be overcome for successful systemic drug delivery and treatment. For instance, as a drug molecule needs to be dissolved in the GI lumen in order to be absorbed, this includes processes such as tablet disintegration and drug particle dissolution, but also post dissolution processes such as chemical and metabolic drug degradation in the GI lumen. The drug solubility in the intestinal GI fluids must be high enough to generate a concentration gradient for drug transport across the apical intestinal cell membrane. In addition, once the drug is absorbed and present inside the epithelial cells, it can undergo metabolic degradation before entering the blood stream and transportation to the liver. In the liver, the drug can be eliminated from the blood stream by metabolism and/or biliary excretion. These processes are schematically presented in Fig. 2.

Fig. 2 Schematic presentation of the gastrointestinal and hepatic processes that determine the rate and extent of drug transport into systemic circulation following oral drug administration of a solid dosage form. CM ¼ carrier-mediated, F ¼ bioavailability, fabs ¼ fraction absorbed, EG/ EH ¼ gut-wall/hepatic extraction, PLD/PPD ¼ passive lipoidal/paracellular diffusion. Artwork by Febe Jacobsson.

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Bioavailability (F) is defined as the fraction of an orally administered dose that reaches the systemic circulation unchanged. It is the result of three general biopharmaceutical and PK processes: (i) fraction of the total dose absorbed (fabs); (ii) gut wall first-pass extraction (metabolism; EG); and (iii) hepatic first-pass extraction (metabolism and transport; EH) (Kwan, 1997). The bioavailability is an often used PK parameter, because it is directly related to the clinical efficacy and safety of a systemically acting orally administered drug. Any successful oral drug dosage form needs to possess favorable properties for drug absorption and metabolic stability, which are determined by the same three main factors: physicochemical, pharmaceutical, and physiological. Physicochemical factors of a drug molecule include, for instance, two- and three-dimensional molecular structure, acid/base dissociation constant, oil-water partition coefficient (log P), hydrogen bonding, and polar surface area. These are some of the molecular drug properties that determine its diffusion rate and solubility in aqueous media, its likelihood to cross biological membrane barriers, and its chemical and metabolic stability. Pharmaceutical factors are primarily related to the type of oral formulation (e.g. solution, suspension, tablet, or capsule), which affect the GI absorption processes following dosing. For instance, a drug product can be designed to either increase, or prolong/ delay drug dissolution and release, in order to optimize the absorption rate of the drug. Pharmaceutical factors also relate to features such as choice of excipients, particle size and the crystalline form of the solid state, chemical and pharmaceutical stability, and method of manufacturing. These factors are adjusted in pharmaceutical development to optimize, for instance, the dissolution and solubility of a drug, and the drug exposure time at the site of administration. In a limited number of cases, pharmaceutical excipients also have the potential to affect GI drug transit, luminal and first-pass metabolism, and to increase the drug transport rate across biological barriers (Flanagan, 2019). Physiological factors in the GI tract determining drug absorption are very dynamic and change as a response to water and food intake for an individual, and it also varies substantially between individuals. This includes the biochemical composition and volumes of the GI fluids, luminal pH, motility, hydrodynamic conditions in the lumen, and epithelial surface area and functions of the different epithelial cells. These factors have a strong influence on the GI transit of a drug formulation and the site and time of drug release, which will affect luminal drug dissolution and membrane transport, as well as the regional tissue area available for drug permeation and the chemical and/or metabolic degradation of some drugs. Any full biopharmaceutical prediction of GI drug absorption and first-pass extraction has to take an integrated approach that fully considers the interplay among all relevant physicochemical, pharmaceutical, and physiological factors, and how it influences the rate and extent of oral bioavailability. It encompasses all aspects of in vivo drug absorption, membrane transport and metabolism, as well as the preclinical in silico, in vitro, and in vivo tools used for its prediction in drug development. This chapter will cover the following biopharmaceutical topics that are of relevance for the understanding of oral drug delivery, absorption, and bioavailability:

• • • • • •

Gastrointestinal physiology and biological factors Main pharmacokinetic parameters and definitions Drug dissolution and solubility Membrane drug transport mechanisms and intestinal permeability Drug metabolism and first-pass extraction Common preclinical tools for investigating drug dissolution, membrane transport and metabolism

Oral drugs and delivery systems intended for a local intestinal effect will not be discussed, as the basic definitions of drug absorption and bioavailability do not apply for those.

1.17.2

Physiology and function of the gastrointestinal tract and related organs

1.17.2.1

Morphology

The GI tract is a continuous, hollow muscular tube that stretches from the mouth to the anus. It is divided into relatively specific regions that each have unique morphology, physiology, and function. First are the pharynx and esophagus, followed by the stomach, small intestine, and large intestine. The stomach is composed of four parts, the initial cardia and fundus, the largest middle region called corpus, followed by the most distal antrum. The small intestine is subdivided into the duodenum, jejunum, and ileum, and the large intestine into the ascending, transverse, descending, and sigmoid colon, and rectum. The total length of the human intestines during normal muscle tonus is about five meters, but it can double in length post mortem. The GI tract is the largest endocrine organ in the body and the endocrine cells within it are collectively referred to as the enteric endocrine system. The GI tract is also connected with several accessory glands and organs that secrete into the GI tube, such as the liver, pancreas, and gallbladder. Combined, these organs and the intestines make up the digestive system (Fig. 3) (Leung, 2014). The characteristics, morphology and functions of the intestinal barrier vary between regions, but it has a common histology (Fig. 4). Between the lumen and the outside of the intestines, the mucosal wall is divided into four distinct layers: the mucosa (composed of epithelium, lamina propria, and muscularis mucosae); the submucosa; the muscle layer (composed of circular muscle, myenteric nerve plexus, and longitudinal muscle); and the serosa. The primary barrier between lumen and blood is the mucosal epithelium, which is comprised of columnar polarized epithelial cells where the apical membrane is considered to be the rate-limiting barrier for at least passive transmembrane diffusion. These intestinal epithelial cells form a protective barrier as

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Fig. 3 Gross anatomy of the human digestive and absorptive systems, including the gastrointestinal regions, liver, pancreas and gall bladder. Artwork by Febe Jacobsson.

Fig. 4 General structure of the intestinal barrier, including the four layers: mucosa, submucosa, muscularis, and serosa. It should be noted that villus is only present in the small intestine. Artwork by Febe Jacobsson.

they are tightly connected by intercellular tight junctions and covered by a protective mucus layer (Johansson et al., 2011; Van Itallie and Anderson, 2004). The underlying mucosal layer, the lamina propria, contains blood vessels, nerve fibers, lymphatic tissue, smooth muscle that regulates blood flow and villi movement, and immune cells such as neutrophils, T-regulatory cells,

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411

macrophages and mast cells. The submucosa contains connective tissue with major blood and lymphatic vessels. The muscle layer contains circular and longitudinal muscles that control GI movement. The serosa is mainly composed of connective tissue that supports the GI tract in the abdominal cavity. The 400–600 million neurons and their nerve fibers in the GI tract are jointly called the enteric nervous system, which is partly autonomous from the central nervous system (Furness, 2012). The enteric nervous system constitutes a variety of sensory neurons, interneurons, motor neurons, and secretory neurons, all of which are involved in regulation of peristalsis, secretion, digestion and absorption. Intestinal biota is also sometimes regarded as a part of the GI system, where it is part of a harmonious ecosystem together with the host. Luminal bacteria and mucosal immune cells show region-related distribution, and together they have synergetic roles in maintaining intestinal homeostasis as well as in dysregulation associated with intestinal inflammation.

1.17.2.2

Physiological function and nutrient absorption

All members of the animal kingdom, ranging from single-cell organisms to mammals, need to extract energy from their environment to grow and live. Digestion and absorption of nutrients are therefore functions present in all these species. To accommodate this, many species have developed some sort of body cavity or hollow tube. One such tube is the GI tract, which is believed to be one of the first body organs formed in evolution sometime during the Cambrian explosion more that 540 million years ago (Vannier et al., 2014). For humans, the primary function of the GI tract is to create a selective barrier that enables luminal digestion and subsequent absorption of nutrients, water, and electrolytes, while at the same time restricting transport of larger, harmful luminal contents, such as viruses, bacteria, toxins, and large peptides and proteins (Marchiando et al., 2010). The GI tract is a highly specialized chemosensory organ, with the capacity to sense nutrients from the luminal side to optimize and coordinate digestion, metabolism, and absorption of the diet following oral ingestion of food and fluids. The ingestion of a meal starts neural and hormonal signaling from the GI tract in response to gastric distension and the chemical presence of nutrients in the GI lumen. Interaction between the enteroendocrine cells located throughout the GI tract and nutrients stimulates the release of peptides that act locally, centrally or peripherally to influence appetite regulation, GI transit and digestion. Nutrient GI absorption starts with mechanical digestion of food by chewing in the mouth, later followed by contractions in the stomach and intestines. Chewed food is at the same time chemically digested by enzymes secreted in the mouth, stomach and duodenum. The stomach accommodates ingested food, efficiently mixes it with local gastric secretions, grinds the content, and empties it at a certain size range into the proximal small intestine. The gastric diffusion barrier protects the stomach epithelium from digestion by the acid-activated protease, pepsin, by several mechanisms: (1) low apical membrane permeability of acid, (2) very narrow high-resistance tight junctions, (3) mucus thickness of 50–200 mm, and (4) a bicarbonate containing microclimate adjacent to the epithelial surface. The small intestine is the primary absorptive organ for nutrients, vitamins, electrolytes, and water. The major meal-derived nutritional components need to be luminally digested into monosaccharides, peptides (di- and tripeptides) and amino acids, and free fatty acids prior to absorption. The intestinal permeation of all these nutrients is rapid and typically completed already in the jejunum (Borgström et al., 1957). The uptake of monosaccharides, peptides and amino acids is mediated by transporter proteins, as they are water soluble nutrients and have a low passive membrane permeability. For instance, the uptake of glucose is mediated primarily by Naþ-coupled SGLT1 across the apical membrane of the enterocytes, and by facilitated diffusion by GLUT transporters across the basolateral membranes (Kellett, 2001). All di- and tripeptides are absorbed by the same transport protein (PepT1), whereas a range of more or less specialized proteins facilitate the uptake of amino acids (there are seven transporters in the SLC gene superfamily) (Hediger et al., 2004; Leibach, 1985). Dietary fats on the other hand are absorbed through a more complex mechanism in the GI lumen, where fat digestion starts with triacylglycerols being hydrolyzed by gastric and pancreatic lipases to two free fatty acids and one monoacylglycerol. These lipolytic products are transported across the apical enterocyte membrane by passive and active mechanisms and are then directed to the endoplasmic reticulum in the enterocyte, where they are converted back to triacylglycerol to be packed into chylomicrons for subsequent transport into the body (Niot et al., 2009). In addition, free fatty acids from the diet are acting through a number of receptors, including G protein-coupled receptors and potassium channels and may have a role in mediating dietary fat preference and total intake (Piomelli, 2013). Unabsorbed water in the small intestines is absorbed in the colon, which leads to concentration of the feces that contains undigested material and commensal bacteria. The peristaltic GI movement continuously transports ingested food distally, from the stomach to the end of the rectum, which also reduces the spread and overgrowth of bacteria in the intestines.

1.17.2.3

Regional differences

The various regional intestinal differences related to the luminal contents and to the mucosal barrier have potential implications for drug absorption. Regional intestinal differences in luminal water volume, pH, osmolarity, length and surface area of the intestinal epithelium, are summarized in Table 1. The small intestine epithelial surface area is increased compared to a smooth tube by a factor of 1.6 because of circular folds, and by a factor of 6 because of the finger-like protrusions called villi (Helander and Fändriks, 2014; Wilson, 1967). In addition, the epithelial cells in both the small and large intestines are covered by microvilli that further increase the surface area by a factor of about 10.

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Table 1

Some regional intestinal anatomical and physiological factors that can have implications for drug absorption and bioavailability (Diakidou et al., 2009; Helander and Fändriks, 2014; Kalantzi et al., 2006; Mudie et al., 2014; Reppas et al., 2015; Schiller et al., 2005; Wang et al., 2015). Osmolarity (mOsm/L)

GI regions

Segments

Fasted water content (mean  SD) Fasted state pH (mean  SD) Fasted Fed

Stomach – 35  7 mL Small intestine Duodenum 43  14 mL Jejunum Ileum Large intestine Cecum 13  12 mL Colon Rectum

1.9  1.3 6.3  0.5 6.8  0.5 7.4  0.5 6.0  0.8 7.0  0.8 7.3  0.8

120 178 60 81

Length (cm) Mucosal surface area (m2)

550 400 30 150 250 150 150 224

0.5 30 1.9

The intestinal water content in the fasted state is low, and resides in distinct pockets. This was determined using magnetic resonance imaging that only monitor free water, and it should therefore be mentioned that total water content (bound and free) may be higher (Mudie et al., 2014; Schiller et al., 2005). The pH in the stomach is about 1.9 in the fasted state and up to 5 in the fed state, and it varies between pH 6.0 and 7.4 in the small and large intestine regardless of prandial state (Wang et al., 2015). It is important to recognize that also the fasted gastric pH may vary between 1.5 and 4.5 between and within individuals. For an extended discussion of the relationship between pH and drug permeability (see Section 1.17.4.2.4). Luminal osmolarity is typically higher in the upper intestinal tract, and it varies between 80 and 180 mOsm/L in all intestinal segments in the fasted state, and between 220 and 550 mOsm/L in the fed state. In addition to the crucial physiological factors displayed in Table 1, the thickness of the intestinal mucus layer varies between segments of the intestine. It is thicker and more firmly attached in the stomach and large intestine (200 mm thick), while it is thinner and loosely attached in the small intestine (15 mm) (Ensign et al., 2012). Digestive enzyme concentrations and their activities are higher in the upper GI tract, where they are secreted as a response to food intake, as well as permanently expressed in the brush border membrane. These enzymes may also be involved in drug degradation, and so may the large bulk of commensal bacteria that resides primarily in the lower GI tract. Finally, the abundance of transporters involved in absorption and efflux of molecules (e.g. nutrients, drugs and other xenobiotics) differ between intestinal segments. There are conflicting data about the regional abundance of intestinal transporters, but absorptive transporters are found primarily in the small intestine, while efflux transporters may be distributed in all parts of the intestinal tract (Estudante et al., 2012).

1.17.2.4 1.17.2.4.1

Gastrointestinal transit and the impact of prandial state Physiological regulation of GI transit

In the fasted state, gastric and small intestinal motility, transit, and secretion is regulated by a cyclic pattern of electrical activity called the interdigestive migrating motor complex (MMC). The MMC regulates the peristaltic movement in the stomach and small intestine between meals. The function is to act as an interdigestive housekeeper, where it empties the stomach and small intestine from undigested food and sloughed enterocytes, and contributes to a reduced upper intestinal bacterial overgrowth. The MMC consists of four consecutive phases appearing in cycles of 120 min (range 50–200 min). Phase I is the silent phase where no contractions occur (z 50% of time); Phase II is composed of random smooth muscle contractions (z40%); Phase III is composed of a few minutes of burst of contractions where material in the stomach and small intestine is propelled forward; and Phase IV is characterized by a rapid decrease of contractions that merges with Phase I (Deloose et al., 2012). The physiological regulation of the MMC is complex, and a range of hormones, such as motilin, erythromycin, ghrelin, and somatostatin, as well as vagus nerve stimulation, are involved. Stomach distension interrupts MMC activity in the upper parts of the GI tract, while small intestinal fluid and nutrients interrupt MMC activity in the whole intestine (Code and Marlett, 1975). The MMC motility system is integrated with the secretory program that starts with acid secretion in the corpus of the stomach and is then propelled distally to antrum where it meets secreted bicarbonate and duodenal back flux. It increases secretion of gastric acid, duodenal secretion of bicarbonate, as well as bile acids and pancreatic juice at the end of each cycle, and consequently affect the gastric pH over time (Dalenback et al., 1996). Another example of how food impacts transit through the GI tract is illustrated by the ileal brake, an endocrine inhibition of upper GI functions elicited by the presence of unabsorbed nutrients in the ileum. It is initiated when food components are locally digested and absorbed in the distal small intestine. This causes a feedback response to the stomach and proximal small intestine leading to reduced gastric emptying and transit rate of luminal chyme. It is considered that the ileal brake is mediated by two peptides: glucagon-like peptide 1 (GLP-1) and peptide tyrosine tyrosine (PYY). These peptides are released when primarily free fatty acids (as little as 3 g perfused into the ileum) and glucose bind to chemosensors on intestinal L-cells in the ileum (Holst, 2007). The activation of these receptors and subsequent release of GLP-1 and PYY can therefore have a pronounced effect on the GI transit of various dosage forms in the fasted and fed state.

Oral Drug Delivery, Absorption and Bioavailability Table 2

413

Gastrointestinal transit time of solutions, pellets and solids at fasted and fed state conditions (Abrahamsson et al., 1996; Davis et al., 1986). Transit Fasted state

1.17.2.4.2

Fed state

Segment

Liquid

Pellets

Capsule

Liquid

Pellets

Capsule

Stomach Small intestine Large intestine

t½ 15 min 3–4 h 8–28 h

0–3 h

0–2 h

0–3 h

3–9 h

6–48 h

4–26 h

t½ 30 min 3–4 h 8–28 h

6–48 h

4–26 h

GI transit of different oral dosage forms

The residence time in the stomach and the transit through the GI tract of different drug delivery formulations is important to consider to optimize the development of a new drug product. The GI transit time of different drug formulations in the fasted and fed state is presented in Table 2. Gastric emptying has a particularly large effect on rate and/or extent of intestinal drug absorption, as dissolved drug molecules are mainly absorbed once they have been emptied into the proximal small intestine (duodenum). The gastric emptying rate and its effect on rate and/or extent of drug absorption and the shape of the plasma concentration-time profile of the drug depends on factors such as biopharmaceutical classification system (BCS) class for the drug, the dose given, type of dosage form, and the meal size and composition. The gastric emptying rate is mainly affected by prandial state, type of dosage form, and disease state. In general the emptying half-life in fasted subjects is shorter for liquids than for solid dosage forms that disintegrate in the stomach (Abrahamsson et al., 1996; Davis et al., 1986). The gastric emptying also depends on the strong contractions during Phase III of the MMC, as well as on the volume of liquid in the stomach; the gastric emptying half-life is fast in phase II and III regardless of water volume. On the other hand, small water volumes (50 mL) administered in the “silent” Phase I are retained for a long time in the intestine, whereas larger water volumes (200 mL) are not (Fig. 5) (Oberle et al., 1990). This shows that the fasted GI motility state at time of dosing is a crucial factor that can lead to variability on systemic drug exposure that is unrelated to the oral dosage form, but rather connected to the time of dosing. For large solid non-disintegrated single-unit dosage forms, such as enteric coated tablets, there is a substantial difference in gastric emptying rate between fasted and fed state stomach. The size of any drug delivery unit needs to be smaller than 700 mm to be emptied uniformly from the stomach during the post-prandial phase. This is because the stomach restricts passage of any particles larger than this when there is food related energy and especially fat present. At the fasted state, the stomach is finally completely emptied of all its contents regardless of size at regular time intervals (Phase III of the MMC, as discussed above). The action of Phase III makes it important for transit of different drug formulations. It is especially so for large, solid non-disintegrated dosage forms (such as enteric coated dosage forms), which can stay in the stomach for many days given that the patient does not enter the fasted state. Elderly patients with repeated oral dosing and with a less functioning pyloric sphincter may be at high risk of dose dumping of such non-disintegration oral dosage forms. This is the reason why such oral dosage forms are not accepted by regulatory agencies.

Fig. 5 The gastric emptying half-life (t1/2) of water in humans following oral administration of 50 or 200 mL of water dosed at different phases of the interdigestive migrating motor complex (MMC). MMC I ¼ silent phase, MMC II ¼ random contractions, MMC III ¼ powerful forward moving burst contractions (Oberle et al., 1990).

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Oral Drug Delivery, Absorption and Bioavailability

In contrast to gastric emptying, there are only minor differences in small intestinal transit time between any oral dosage forms, regardless of prandial state. The small intestinal transit time is also very consistent at 3–5 h, compared to the more variable gastric emptying and colonic transit. Colonic transit may vary between a few hours to a few days, regardless of drug formulations and prandial state. However, there is a tendency for a more rapid and variable colonic transit of solid dosage forms compared to multiple unit based formulations.

1.17.2.5

The hepatobiliary system

The liver is the largest visceral organ in the body and is a dynamic organ involved in the metabolism and transport of endogenous (such as glucose, amino acids, lipids and hormones) and exogenous (drugs) compounds. The liver is an important endocrine organ and is also a part of the digestion of food as it is responsible for storage of nutrients, production of bile acids, and excretion of bile. The gall bladder is situated in conjunction with the liver and the bile ducts are dispersed throughout the liver in a tree-like network. The hepatocytes are arranged in subunits called the sinusoids. The arrangement of the sinusoids enables a high exchange of molecules between the perfusing blood (1350 mL/min) and the hepatocytes. The total liver blood flow originates from the hepatic artery ( 300 mL/min) and the portal vein (1050 mL/min). The portal vein carries blood containing the digested nutrients from the entire GI tract, and also from the spleen and pancreas to the liver. The hepatocytes are polarized cells, i.e. the cells have a sinusoidal/basolateral membrane facing the blood and a canalicular/apical membrane facing the bile canaliculus. These two membranes of the hepatocytes differ in their lipid composition and are separated by tight junctions, and the flow of blood and bile in the sinusoid are divergent. The bile produced by the hepatocytes empties into the bile canaliculus and is transported to the bile duct, which is formed of cholangiocytes. The bile ducts further merge into large ducts and finally merge into the common hepatic duct (Roberts et al., 2002). A schematic illustration of the liver and its subunits are shown in Fig. 6. In the fasted state, hepatic bile can either be stored in the gall bladder or empty into the duodenum and the proportions of these two pathways are highly variable. For instance, the average fraction of an intravenously administered 99mTc-HIDA (radiotracer used in cholescintigraphy) that was stored in the gall bladder in healthy volunteers ranged from 13% to 97% (Shaffer et al., 1980). This shows that even though bile primarily acts as an emulsifier of ingested fat, it empties into the duodenum also in the fasted state (Phase III of the MMC). The human body is efficiently designed to recycle its pool of bile acids ( 3 g) about 4–12 times per day, with approximately 95% being reabsorbed. Following the emulsification of ingested and digested fat in the small intestinal lumen, the bile acids are absorbed by ileal sodium/bile acid cotransporter, also known as apical sodium–bile acid transporter (ASBT), as well as by the ileal bile acid transporter (IBAT) that is a bile acid:sodium symporter protein encoded by the SLC10A2 gene in humans. The ileal transporter takes bile acids into the portal blood, which will transport them back to the liver (and to a much lesser extent via the hepatic artery). There is an efficient and high first pass hepatic uptake transporter proteins, such as a sodium-dependent sodium/taurocholate co-transporting polypeptide (NTCP) and a family of sodium-independent multispecific organic anion transporters (OATPs/ SLCs). The transporters involved in the disposition of bile acids are regulated to maintain bile acid homeostasis. Once at the liver, when bile acids have reentered the bile canaliculi from the hepatocytes, another round of enterohepatic circulation has begun. The enterohepatic circulation can be utilized by drugs and might be one reason for high volume of distribution and that multiple peaks appear in their plasma concentration-time profiles. Ezetimibe, rosuvastatin, and pravastatin are examples of drugs transported via the enterohepatic circulation.

(B)

(A)

7

2

8 1 4

6

5

3

hepatic/central vein hepatic artery/arteriole portal vein/venule

hepatocyte

bile duct

Fig. 6 (A) The liver and its eight segments. (B) The microarchitecture of a hexagonal hepatic lobule. Blood from hepatic arterioles and portal venules empties in the central vein. Bile is collected in the bile canaliculi on the apical side of the hepatocytes and empties in the bile duct outside of the lobule. The portal vein, hepatic arteriole and bile duct are collectively called the portal triad. Adapted with permission from fig. 1 in Dubbelboer, I.R., 2017. Biopharmaceutical Investigations of Doxorubicin Formulations Used in Liver Cancer Treatment. Acta Universitatis Upsaliensis, Uppsala, 2017.

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1.17.3 Pharmacokinetics and pharmacodynamics; processes to consider in oral dosage form research and development 1.17.3.1

Absorption, distribution, metabolism, excretion, and pharmacodynamics

Pharmacokinetics (PK) describes the time-course of a drug and its metabolites within the body, as a consequence of the body’s capacity to take up drugs, distribute, and eliminate them. It describes the processes and rates of drug movement from the site of administration into the blood (i.e. absorption and bioavailability), distribution into various tissues, and elimination by metabolism and/or excretion (Teorell, 1937; Wagner, 1981). Pharmacodynamics (PD) describes the onset, intensity and duration of effect of a drug and its relationship to the concentration of the drug at its site of action and the pharmacological response over time (Fig. 7) (Holford and Sheiner, 1981). Pharmacological response is more or less readily measured, and it can be observed in many different ways following drug treatment. For instance, biochemical markers such as blood cholesterol and glucose levels can be monitored. Examples of physiological and psychological responses are pain reduction and mood-stabilization with analgesics and antidepressants, respectively. Long term effects, such as overall survival rates in oncology, can also be followed in larger patient groups. PK-PD is the relationship between drug concentrations at the site of action and drug effects, and factors influencing this relationship. Regardless of the pharmacological response of a drug, it is related to the drug concentration at the target site, which may be intra- or extracellularly located, or in a specific region such as a tumor or the brain. Knowledge of drug concentrations at the anatomical, cellular, and molecular sites of action is particularly valuable, which is considered to be improved based on the invention and development of new analytical tools, imaging techniques, and pharmacological assays (Longuespée et al., 2020). However, for practical reasons, quantification of drug concentrations is typically not done at the site of action. Rather, it is measured in the peripheral circulatory system, based on a general assumption in pharmacology stating that there is an equilibrium between the free drug concentration in plasma/blood and any target site. This assumption, that the unbound plasma concentration is in equilibrium with the corresponding unbound tissue concentration, is not always valid, but it works as a guide for the relationship between PK-PD. By extension, the pharmacological effect as well as side-effect of a drug are often directly related to its rate of appearance and disappearance in blood, that is, plasma concentration-time curve, also called systemic drug exposure. As the concentration of the drug increases, its pharmacological effect also gradually increases until all the receptors are occupied (the maximum effect, Emax). The magnitude of the effect is determined by the drug concentration at the site of action and the receptor sensitivity of the

Fig. 7 Pharmacokinetics (i.e. absorption, distribution, and elimination) of a drug and its relation to the pharmacodynamics is crucial to understand for the development of drug products and to be able to establish the dose with the best clinical utility. On the bottom right and left are the definitions of potency and efficacy briefly illustrated, as these pharmacodynamics properties are important to consider in the design and development of any oral drug product. Artwork by Febe Jacobsson.

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Oral Drug Delivery, Absorption and Bioavailability

drug. In the field of drug delivery it is important to consider the graded drug concentration-response relationship, which is determined by potency and efficacy as illustrated in Fig. 7. Potency described the concentrations needed to elicit a defined effect and is governed by drug affinity to receptor and receptor abundance, whereas efficacy describes the dose-unrelated maximum effect of a drug. Generally, the field of drug delivery and absorption only covers the processes involved drug appearance, but it also needs a proper understanding of all four fundamental PK processes determining the fate of a drug in the body: absorption, distribution, metabolism, and excretion (ADME). Absorption after oral drug administration is the process of drug movement from the intestinal lumen into (or around) the enterocytes. It encompasses all molecular, dosage form, and physiological dependent parameters involved in this processes, such as tablet disintegration, particle diffusion, drug solubility and dissolution, and transport across intestinal membrane barriers. The fraction dose absorbed (fabs) describes the intestinal absorption of a drug compound; this is regulatory and scientifically defined as the total mass of a dose that is being transported from the intestinal lumen across the intestinal epithelial apical cell membrane at any time (Eq. 1) (Amidon et al., 1995; Wu and Benet, 2005) Z t Massðt Þ 1 ¼ fabs ¼ A  Peff  Clumen dAdt (1) dose dose 0 where A is the area of the intestinal mucosa, Peff is the effective permeability across the intestinal membrane, and Clumen is the free luminal drug concentration at the intestinal site of absorption. From Eq. (1) it can then be derived that the driving forces for absorption are Peff and Clumen, which will be described in detail later in this chapter. Distribution is a parameter that reflects tissue binding of the parent drug and directly describes the average relative proportion of all available drug in the different body tissues, such as blood, brain, lungs, muscle, and fat. Distribution represents the movement of drug from the vascular compartment to these tissue compartments and together with the tissue binding is described as volume of distribution at steady state (V). It is an apparent volume in the body in which the available drug is distributed at any given time (t), i.e. the relation between the amount (A) of drug in the body and the concentration (C) in the vascular compartment (Eq. 2). V ðt Þ ¼

Aðt Þ Cðt Þ

(2)

This equation can be used to estimate the apparent total volume of body fluids needed to distribute the amount of available drug, based on the measured parent drug concentration in plasma after an intravenous dose, which by definition is completely bioavailable. The extent of drug distribution at steady state reflects the affinity to other tissue, and tends to be high (sometimes much higher than the total body volume) for lipophilic cations residing in fatty tissue and bound to negative charge polar head groups on cell membranes, and for compounds that bind strongly to intracellular components (such as mitochondrial and nucleus DNA). The rate of tissue distribution depends on the unbound (cells and proteins) drug concentration in plasma, as it is only unbound drug that can leave plasma (vascular compartment). It also depends on the rate of dispersion into tissue, which may be limited by the transport from plasma into the tissue (permeability limited), or by the blood perfusion rate to the tissue (perfusion limited). Metabolism is the biotransformation of the available drug by the body and describes the elimination of the parent drug. Preabsorption metabolism by luminal enzymes and intestinal bacteria is by definition part of the absorption process, and will be discussed later in this chapter. The primary metabolic organ is the liver, but other organs such as the intestines, lungs, and kidneys can be involved in the metabolism of drugs and of formed metabolites. A more extensive discussion of drug metabolism in the gut wall and liver is presented later in this chapter, but in short, it is performed by more or less specialized enzymes that convert the drug to a more hydrophilic metabolite. Biotransformation is competitive, and typically several different enzymes are involved to various extents. Depending of the type of reaction involved, metabolic enzymes are referred to as Phase 1 (oxidative, reductive, and hydrolytic) and Phase 2 reactions (conjugation), and these often, but not always, occur in sequence. Phase 1 oxidation and reduction enzymes in the endoplasmic reticulum and mitochondria (and ubiquitously expressed hydrolysis enzymes) add a new functional group to the drug molecule. Oxidation and reduction conversions are dominated by the superfamily of cytochrome P450 (CYP) enzymes, with members of family 1, 2 and 3 being particularly important for drug metabolism. Specific CYP enzymes may have high or low substrate specificity, and their relative importance for drug metabolism varies. For instance, about 75% of all CYP-mediated drug metabolism is performed by CYP3A4 and CYP2D6. Especially CYP3A4 is considered to have a broad substrate specificity and is a major CYP enzyme located in both the gut and liver. It should be mentioned that a newly formed metabolite can be more potent and/or toxic than the original drug molecule. If it is only the formed metabolite that is pharmacologically active, the inactive parent substance is called a prodrug, and there are many examples of such drugs on the market. Phase 2 conjugation enzymes are broad-specificity transferases localized mainly in the cytoplasm and endoplasmic reticulum that attach an endogenous molecule to a functional group of a drug (or primary metabolite). Examples of endogenous conjugation molecules are glucuronic acid, sulfate, and glutathione, which are catalyzed by UDP–glucuronosyltransferases, sulfotransferases, and glutathione S-transferases, respectively. These metabolic steps detoxify the drug and often make it more hydrophilic to enable metabolite excretion (Gibson and Skett, 2013). Excretion is the elimination of the unchanged drug from the body, and it is primarily performed by the kidneys (urine) and liver (biliary). Biliary excreted material enters the intestine via the bile duct into the duodenum. The material is considered excreted once

Oral Drug Delivery, Absorption and Bioavailability

417

it leaves the body with the feces, but not if it is reabsorbed in the intestine, that is, enterohepatic recirculated (as discussed earlier). The carrier-mediated (CM) transport of drug into bile is performed by transporter proteins located both at the sinusoidal and canalicular membranes of the hepatocytes. In common with drug-metabolizing enzymes, drug transporter proteins often have wide, overlapping substrate specificities and variable, inducible expression in several tissues involved in key pharmacokinetic processes. Renally excreted material leaves the body with the urine. The primary pathway of renal excretion is by passive filtration of plasma, but CM transport of drug to the urine also takes place. A drug must be sufficiently water soluble for biliary and renal excretion, as a lipophilic drug tends to be reabsorbed in the intestine and renal tubules, respectively. Therefore, drugs are commonly made more water soluble by metabolic processes before they can leave the body by these two pathways. Elimination describes the irreversible loss of drug from the body, which can be exerted both by metabolism and/or excretion in several body organs. Elimination is often expressed as clearance (CL), which is the volume of blood (or plasma) that is being cleared of drug per min (mL/min). CL can be calculated by relating the rate of drug elimination (mg/min) to the plasma concentration (mg/ mL) as shown in Eq. (3): CL ¼

rate of elimination plasma concentration

(3)

As the plasma concentration is related to the total amount of drug in the body and to the volume of distribution, this means that the plasma terminal half-life (t1/2) of a drug decreases with an increased CL, and increases with an increased V, as shown in Eq. (4): t1=2 ¼ ln2 

V CL

(4)

CL and V are called primary PK parameters as they are used to calculate terminal t1/2 (and many other secondary PK parameters). The third primary PK parameter is bioavailability (F), which is described in the next section. Disposition describes both the distribution and elimination of a drug from the central circulation. Accordingly, it encompasses all processes affecting the central and peripheral concentrations of a drug after it has been dosed, absorbed, and become systemically available.

1.17.3.2

Bioavailability and steady-state plasma concentration

Bioavailability (F) is the most important PK parameter for characterizing the fraction of an administered dose that reaches the central circulation in its unchanged form. Following oral administration, F describes three serial processes: the fraction dose absorbed (fabs) in the intestines; and the first pass extraction ratio of the drug in the gut wall (EG) and liver (EH), as an absorbed drug must pass through these two organs before reaching the systemic circulation (Eq. 5). F ¼ fabs  ð1  EG Þ  ð1  EH Þ

(5)

By definition, the F is 1 (i.e. 100%) for a drug that is administered directly into the central circulation (intravenous). The bioavailability following oral administration of one or several different drug formulations can thus be calculated by comparing the area under the plasma concentration-time curve (AUC) of a single extravascular dose to that following a single intravenous (i.v.) administration of the same drug, related to the doses given (Eq. 6): F¼

AUCoral  Doseiv AUCiv  Doseoral

(6)

An assumption in the calculation of F is that the CL of the drug is unaffected between the two dosing events. CL may differ between the oral and i.v. dosing event when the major elimination processes are active, by definition, concentration dependent. This may result in saturation of drug metabolism and CM membrane transport processes involved in drug elimination, leading to a change in these processes with dose and plasma concentration. For instance, for a drug with non-linear CL, a slow appearance rate in plasma after any extravascular dose will result in lower maximum concentration compared to the i.v. dose. This may result in different CL values for different dosing events and will affect F. However, this may be resolved by administering the two formulations simultaneously but with different molecular labels (stable isotope), or by administering microdoses of the drug on different occasions (Browne et al., 1992). For most drug treatments, it is important to relate the dose and dosing interval to the drug concentration in plasma, which ultimately determines drug effect and toxicity. The plasma concentration at steady state (Css) during an intravenous infusion can be calculated by relating CL to the infusion rate (Eq. 7): Css ¼

infusion rate CL

(7)

For an extravascular periodic drug administration, the average Css can be calculated using the same equation, with some necessary modifications. The only difference is that the input rate is calculated from the F, dose, and dosing interval (T) (Eq. 8): Css average ¼

F  Dose T  CL

(8)

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Oral Drug Delivery, Absorption and Bioavailability

The time to reach steady state is determined only by the terminal t1/2 of the drug, regardless of the infusion/input rate. This is because at steady state, the input rate is in equilibrium with the elimination rate, which is expected at 3.3 t1/2 (90% of Css).

1.17.4 Fundamental biopharmaceutical parameters for intestinal absorption and bioavailability: Solubility and dissolution, membrane permeability, and first-pass extraction Following oral administration, the drug must dissolve and pass across one or several cell membrane barriers via a combination of passive diffusion and CM transport, in both directions, prior to reaching the systemic circulation. These biopharmaceutical processes are described by the established BCS, which incorporates the main parameters affecting intestinal drug absorption, that is, solubility, dissolution and intestinal permeability (Amidon et al., 1995). The rate of transport of a specific drug, across a specific barrier, is defined as its permeability. Common for all biological membrane barriers is that they only allow passage of dissolved drug, and not of drug particles under normal physiological conditions. This means that the amount of dissolved drug (C) at the membrane wall, and the membrane permeability (P) of that drug, determines the maximum possible mass transfer (mass/area/time) across the intestinal barrier, which is defined as membrane flux (J). This fundamental relationship is described in Eq. (9): J ¼ PC

(9)

In the case of oral drug delivery, any absorbed drug must also pass through (or between) the enterocytes in the gut wall. After this, the drug molecules are transported with the blood, through the liver, to the central circulation. Both the enterocytes and the liver have the potential to metabolically transform the parent drug compound, and it is only the fraction of drug molecules absorbed and also evading metabolic transformation that becomes systemically bioavailable, as stated in Eq. (5). General molecular, pharmaceutical, and physiological parameters determining the solubility and permeability of a drug are discussed below. Also covered are the different transport mechanisms of a dissolved drug across biological barriers, as well as the metabolic processes taking place in the gut wall and liver. At the end of this section, there follows a description of common preclinical methods and models for evaluating drug solubility, permeability, and metabolism.

1.17.4.1

Drug solubility and dissolution

The definitions of drug solubility and the concept of drug dissolution are discussed in this section. Pharmaceutical methods for increasing drug solubility and dissolution rate are also briefly reviewed.

1.17.4.1.1

Solubility

Intrinsic solubility (S0) is the equilibrium solubility of an un-ionized solute in water at room temperature, and it is considered to reflect the amount of a substance that will dissolve from a saturated solution. This property is determined by the intermolecular interactions of the crystal lattice in the solid state, and by the solvation energy released when a molecule is transferred from the solid state into the surrounding water. The saturation solubility (SS) of a solute on the other hand is determined under distinct conditions, and it is defined as the concentration of a saturated solution in equilibrium with the solid substance (i.e. suspension). The SS of a drug is therefore influenced by a range of factors, such as the solvent, solid-state properties, stability in solution, and ionization. For oral drug delivery, the solvent is typically water and the temperature 37  C, but the pH can vary substantially within and between administration sites (and conditions). This will affect the solubility of ionizable weak acids and bases at different pH, which can be approximated using the Henderson-Hasselbalch equation (shown here for an acid) (Eq. 10) pKa ¼ pH–log

Ss  S0 S0

(10)

where pKa is the negative log of the acid dissociation constant (that is the pH where 50% of the drug is charged). This is a simplified approximation that implies that the solubility of a drug increases by a factor of 10 for each pH unit beyond pKa. The solid-state properties are also important biopharmaceutical parameters for poorly water-soluble drug molecules. The crystal structure can be modified to temporarily increase the solubility of a drug, where the metastable solubility of a material is referred to as its apparent solubility; given enough time an equilibrium solubility will be reached that is unaffected by the initial solid-state properties. Examples of crystal modifications include the use of metastable polymorphs of the crystal structure, different salt forms of an acid or base, inclusion of pH modifiers (which temporarily change the local pH conditions), or amorphous solids. Theoretically, reducing the drug particle size down to very low diameters ( 1000 nm) may also temporarily increase drug solubility, according to the Kelvin effect that is derived from the study of vapor pressure over a droplet in a gas; a reduced particle curvature increases the dissolution pressure. The solubility can also be increased by including a complexing agent in an enabling drug formulation, such as cyclodextrin, which is a hydrophilic molecule with a hydrophobic cavity where the drug molecule can bind, thereby increasing its apparent solubility. Other options are to include excipients that aid in the solubilization of the drug molecules, such as micelle-forming surfactants or lipid based formulations, also in combination with nano-sized active pharmaceutical ingredients.

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Oral Drug Delivery, Absorption and Bioavailability 1.17.4.1.2

Dissolution

The dissolution process of a drug particle can be described in two steps. First, the drug molecules are released from the surface of the particle to the surrounding dissolution media, which creates a saturated, stagnant layer adjacent to the solid surface of the particle. Thereafter, the released drug diffuses into the bulk of the solvent from regions of high, to regions of low, drug concentration (schematically displayed in Fig. 8). The dissolution rate (dM/dt) of a drug substance from its solid state is described by the Noyes-Whitney/Nernst-Brunner equation (Eq. 11) (Brunner, 1904) dM D  A  ðSs  CÞ ¼ dt h  Vm

(11)

The main factors are the effective surface area of the drug particles (A), molecular diffusivity (D) of the drug monomers, diffusion layer thickness (h), saturation concentration (SS), intestinal bulk concentration (C), and dissolution media volume (Vm). The close relationship between dissolution rate and solubility means that dissolution often is the rate-limiting step in the intestinal absorption of poorly water-soluble drugs, when a high dose is necessary (Lipinski, 2002). Methods to increase the saturation solubility were briefly discussed in the previous section, but there are other formulation strategies that can be implemented to increase the dissolution rate of these compounds. These are typically focused on reducing the particle size, which increases the surface area of the solid state available for dissolution. In addition to increasing total surface area, particle size reduction will also affect (to a lesser extent) the diffusion layer thickness surrounding each particle, as described by the Prandtl equation (Eq. 12) (Junyaprasert and Morakul, 2015) pffiffiffi  L ffiffiffiffi h ¼ k p (12) 3 V where h is the diffusion layer thickness, k is a constant, L is the length of the particle surface in the flow direction, and V is the relative velocity of the liquid surrounding the particle. Particle size reduction down to about 2–3 mm can be performed by mechanical grinding. Particles with a diameter below 1 mm are called nanoparticles and they can be manufactured using, for example, wet-milling with beads or controlled precipitation or supercritical fluid technology. In addition to affecting dissolution rate, nanoparticles are suggested to affect other aspects of drug absorption, such as drug solubility according to the previously mentioned Kelvin effect, as well as the free drug concentration at the biological barrier, which is the driving force for permeation (Roos et al., 2017). It should be noted that reduction of the particle

Fig. 8 Schematic description of the dissolution of drug molecules from a solid drug particle as described by the Noyes-Whitney/Nernst-Brunner equation (Eq. 11). Artwork by Febe Jacobsson.

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Table 3

Overview of the pharmaceutical, physicochemical and the physiological properties that influence intestinal drug dissolution. Affected by:

Properties

Physicochemical parameters

Drug particle surface area (A) Drug diffusion (D) Molecular size Diffusion layer thickness (h) Lipophilicity, pKa, melting Drug solubility (SS) point Bulk concentration (C)

Pharmaceutical parameters

Physiological parameters

Wettability, particle size, aggregation, surfactants Surfactants in gastric juice and intestinal fluid (from bile) cause particle deaggregation Viscosity of GI luminal contents Particle size Motility patterns and luminal flow rate Crystal form, solubilization, pharmaceutical excipients, impurities Solubilization, complexation

Luminal pH, buffer capacity, bile and food composition Intestinal absorption and degradation

size generally increases the cohesive forces between the particles, and formulation strategies to reduce particle aggregation are usually needed. Molecular diffusivity and dissolution media volume are constants for a given drug and administration route, and are not affected by formulation strategies. Some physicochemical, pharmaceutical and physiological factors that determine intestinal drug dissolution rate in accordance with the Noyes-Whitney/Nernst-Brunner equation are presented in Table 3.

1.17.4.2

Membrane transport mechanisms and intestinal permeability

The permeability of biological membranes is one of the most important determinants of the pharmacokinetic properties of a drug and its metabolites. Definition and the mechanisms of drug transport across biological membrane barriers, and the concept of intestinal permeability, are discussed in this section. The impact on intestinal drug permeability of the following major physiological factors will be discussed: absorptive surface area, pH gradient, membrane composition, and the thickness of the aqueous boundary layer/mucus lining the mucosal barrier. The regional differences along the intestine regarding these factors will also be discussed, as they have a substantial implication for oral modified-release dosage forms. Pharmaceutical excipients, such as permeation enhancers, altering drug permeability and the potential for intestinal peptide absorption are also covered in brief.

1.17.4.2.1

Biological membrane barriers

The human body is composed many different semi-permeable biological barriers, ranging from the obvious physical barrier of the skin to the more subtle connective tissue surrounding nerve fibers. Cells in the body are surrounded by membranes, which define their body space and the boundary between the intracellular and extracellular environment. These dynamic membranes are composed of several types of lipids and proteins. The capacity of the lipophilic moieties of lipids to self-associate, and the tendency of the hydrophilic moieties to interact with aqueous environments and with each other, drives the formation of the lipid bilayer of cell membranes (Fig. 9). A tissue barrier is most often composed of interconnected cells. These can be living, as that of the intestinal epithelium, or nonliving, as the outer layer of the skin. The tightness between cells in a tissue barrier is important, and strongly affects its overall permeability and especially the leakiness for hydrophilic solutes. This is exemplified by the blood-brain-barrier, which is composed of a tight endothelium that is substantially less permeable than other blood capillaries in the body. The cornea is an example of a water rich barrier, and the skin one with low water content. Further, the individual cell membranes are mainly composed of phospholipidsdbut also of proteinsdorganized as a bilayer with a lipophilic core. The membrane composition and fluidity also differ, affecting the viscosity of the lipid bilayer. For instance, the higher cholesterol content in the colon compared to the small intestine, results in a reduced fluidity that probably contributes to a lower passive membrane permeation for some drugs (BCS class III and IV drugs). The colon also has a thicker, and more firmly adherent, mucus layer than the small intestine. This viscous polymeric layer is secreted by specialized cells and acts as a barrier to bacterial and solute transport. Lastly, the diversity of biological membrane barriers is illustrated by the difference and regulation in expression of transporter proteins/channels.

1.17.4.2.2

Membrane transport mechanisms

The permeation of a dissolved drug molecule across semi-permeable biological barriers is dependent on the molecular properties of the drug, transport mechanism(s), drug concentration, and the nature and conditions of the barrier. The transport mechanisms for a drug molecule may include passive lipoidal and paracellular diffusion, and/or carrier-mediated transport in both the absorptive and excretive directions (Smith et al., 2014). Vesicular transport (endocytosis and exocytosis) is another mechanism of lesser quantitative importance for drug transport, which will be briefly discussed. These different coexisting transport mechanisms across the apical intestinal membrane are shown in Fig. 10. Passive lipoidal diffusion is an electrochemical gradient-driven mass transport from one side of a cellular phospholipid bilayer membrane to the other. As this membrane has no specific binding sites, transport is not saturable, subject to inhibition, and it is insensitive to stereospecific molecular structures. Substantial passive transport of solutes across lipoidal membranes is attributed

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Fig. 9 Schematic illustration of the dynamic lipid membrane bilayer and associated proteins. It is important to consider that different membranes, and different membrane-regions in the same cell, may have unique compositions that will affect its function in different ways. Artwork by Febe Jacobsson.

Lumen

1

DRUG

2

DRUG

3

4

DRUG

5

DRUG DRUG

Blood Fig. 10 The transport mechanisms from the intestinal lumen across the intestinal epithelium, which determine the net permeability of a luminally dissolved drug molecule. (1) Passive lipoidal diffusion, (2) absorptive and (3) efflux carrier-mediated (CM) transport, (4) passive paracellular diffusion, and (5) vesicular transport.

mainly to small, desolvated, uncharged, lipophilic molecules (Lipinski et al., 1997). However, other potential transport mechanisms over the lipoidal membrane are also proposed, based on molecular simulations and membrane experiments, which account for the transport of also charged and hydrated molecules. These rely on bilayer instability giving rise to transient water and lipid head-group pores that facilitate transport of compounds with normally unfavorable properties for passive lipoidal diffusion. Lipoidal transport of such drugs may also be mediated by transmembrane proteins that facilitates diffusion along its exterior. In general, a drug that has a high passive lipoidal transport over one type of biological or artificial membrane, is typically rapidly transported across any cell barrier, as the basic nature of the phospholipid bilayer is common to all cell types. Passive paracellular diffusion is an electrochemical gradient-driven mass transport process where a compound diffuses across a biological barrier in the water-filled paracellular space between cells. The dynamic paracellular space of all epithelia and

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endothelia are sealed by tight junction proteins, such as claudins and occludin, where they control solute movement (Van Itallie and Anderson, 2004). The relative tightness of different barriers is highly variable and can show both charge and size selectivity, depending on the expression of intercellular adhesion molecules. The tightness of the same tissue can also change over time, as tight junction proteins are under physiological regulation and can change their selectivity/tightness and expression as a response to various stimuli. Generally, quantitatively important passive intestinal paracellular diffusion and convection of drugs are associated with small (MM < 300 Da), hydrophilic molecules (log D6.5 <  2). However, also larger, hydrophilic molecules, such as peptides and smaller proteins may diffuse to some extent across the paracellular space of more leaky regions of the GI barrier, such as in the intestinal crypts (Taverner et al., 2015). Carrier-mediated (CM) transport is the process whereby a compound is transported into (influx) or out of (efflux) a cell across the lipoidal membrane bilayer using different protein transporters. Currently, there are over 400 carrier proteins identified, and these belong to two superfamilies of membrane transporters: the solute carrier (SLC) or ATP-binding cassette (ABC) superfamilies. Of these, about 20–30 are regarded to be of clinical relevance for drug absorption, bioavailability and disposition (Sugano et al., 2010). CM transport can be either primary active (ATP-dependent), secondary active (cotransport), or facilitate passive diffusion (Estudante et al., 2012). Active CM transport uses energy to create a concentration gradient across membranes. It is primary active transport if the energy comes from ATP hydrolysis (e.g. ABC transporters), and secondary active transport if the energy comes from a previously generated ion gradient (e.g. SLC transporters), such as the high extracellular Naþ concentration generated by the Naþ/Kþ ATPase. Facilitated transport is an energy-independent process whereby a solute is transported across the cell membrane using a protein transporter (e.g. SLC transporters) downstream of the electrochemical gradient. Membrane transporters can have a crucial role in determining the disposition and cellular concentrations of drugs and metabolites and may have an impact on drug pharmacology and side effects. These drug transporters are also a part of drug elimination and include several members from the SLC and ABC superfamilies such as: P-glycoprotein (P-gp, ABCB1), breast cancer resistance protein (BCRP, ABCG2), organic anion transporting polypeptide (OATP)1B1 (SLCO1B1), OATP1B3 (SLCO1B3), organic anion transporter (OAT)1 (SLC22A6), OAT3 (SLC22A8), organic cation transporter (OCT)1 (SLC22A1), OCT2 (SLC22A2), and multidrug and toxic compound extrusion pumps (MATE)1 (SLC47A1) and MATE2-K (SLC47A2). Unlike passive lipoidal or paracellular diffusion, active or facilitated CM transport is saturable, can be inhibited and demonstrate various substrate specificities. It is also enantioselective, as the transporter proteins are built out of chiral amino acids. Saturation occurs when the number of protein carrier binding sites is lower than the molecules available for transport, that is, when the drug concentration exceeds the Michaelis constant (Km). Inhibition of carrier proteins occurs when a compound interacts with the membrane transporter to reduce its capacity. This can be either competitive, or non-competitive, which results in an increased Km, or reduced maximum transport rate (Vmax), respectively. Similarly to paracellular junctions, the expression of transporters is highly tissue specific reflecting the respective physiological need and function(s). This is exemplified by the glucose transporter in the GI tract, which is highly expressed in the small intestine that is exposed to luminal digested carbohydrates in the ingested food, but all but absent in the lower intestinal tract. In addition, expression and activity of transporters can be up- or downregulated in a specific cell/tissue, which may result in a change in drug absorption and/or disposition over time. For instance, the renal CL of cyclosporine A is increased over time as an effect of an increased expression of the efflux transporter P-gp by inducers such rifampin and St. John’s wort (Elmeliegy et al., 2020). CM transporters are primarily important for the absorptive transport of water soluble nutrients, such as glucose, vitamins, and amino acids, where it enables uptake from, for instance, the intestine to the blood, as well as from the blood, over the BBB, and into the brain. However, this transport mechanism can also be important for drug compounds. For instance, the herpes simplex prodrug, valacyclovir, is specifically designed to be a substrate for the peptide transporter 1 in the intestine to increase its absorption; valacyclovir is transformed into its active form (acyclovir) after absorption (Han et al., 1998). Recently, the CM transport route has been proposed to be the universal transport mechanism, with no impact from passive lipoidal diffusion (Kell et al., 2013). However, we believe that the experimental evidence for this transporter-only theory is weak, and the opposing viewdthat there is a coexistence between CM and passive transport processesdis more widely accepted (Di et al., 2020; Smith et al., 2014). Vesicular transport is the process where a molecule is transported into and across a cell in a vesicle. This is an important physiological transport mechanism for getting larger proteins across biological membrane barriers, such as movement of immunoglobulins and insulin from the blood into the brain. In a physiological setting, this transport is typically receptor or adsorption mediated, and drugs conjugated with a specific ligand, or including a certain amino acid sequence, can thus utilize this transport pathway. In addition, minuscule amount of drug may use the vesicular transport pathway by being randomly inserted in a vesicle and transcytosed across a cell barrier. However, both receptor/adsorption mediated and random transcytosis are generally considered quantitatively unimportant for drug transport across the intestinal mucosal barrier. It should be noted that for the intestine, a dissolved drug molecule must diffuse across a water layer with limited convection covering the cell membrane before it can be transported across the epithelium, regardless of transport mechanism. This layer is called the aqueous boundary layer. The relevance of this layer for absorption of dissolved drug molecules has been thoroughly investigated, and is generally considered not to be the rate-limiting step in drug absorption in vivo. However, for drugs with a very high permeability, it can restrict drug transport kinetics (Levitt et al., 1992). The mucus layer covering the intestinal epithelium may also contribute to the aqueous boundary layer and drug transport to the epithelial membrane. This effect is probably minor for small molecules, exemplified by the observation that the jejunal and colonic

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permeability of ketoprofen is very high in both humans (> 3.4  10 4 cm/s) and rats (> 1.5  10 4 cm/s), despite the fact that the colonic mucus is more strongly adherent and substantially thicker than the jejunal mucus (human 200 vs 15 mm; rat 800 vs 200 mm) (Atuma et al., 2001; Dahlgren et al., 2016). However, physical (i.e. hydrophobic or ionic interactions) or steric (i.e. size and molecular weight) mucus interaction may affect the permeation of drugs that interact with it physically. These interactions between smaller solutes and mucus are generally small, but an increased drug lipophilicity correlates slightly with a reduction in transmucus diffusion rate. Complete steric inhibition is also observed for larger proteins (> 12 kDa) and solid particles with a diameter of about 200 nm (Bernkop-Schnürch and Fragner, 1996).

1.17.4.2.3

Permeability

Permeability (cm/s) is the intrinsic parameter of a solute that relates flux (J)dthe mass transfer per area per timedto the concentration gradient across a specific biological barrier, regardless of transport mechanism (Eq. 13) J ¼ C  ðPP  PCM Þ ¼ PP  C þ

Vmax  C Km þ C

(13)

where C is the dissolved concentration at the membrane barrier, PP is passive permeability (paracellular and/or lipoidal diffusion), PCM is CM permeability, Vmax is the maximum rate of CM transport, and Km is the Michaelis constant that describes the concentration at Vmax/2. Eq. (13) will be valid during the drug absorption phase when sink conditions exist: the free drug concentration at the site of administration must be at least 10 times higher than on the other side of the barrier, such as the cell cytosol or the blood. During in vivo conditions, the free concentration in the blood is typically substantially lower than at the site of administration. For instance, during oral drug administration to human and pig, the free blood concentration is 10% of the luminal concentration in the intestine for a wide range of drugs, regardless of permeability class and transport mechanism (Dahlgren et al., 2014). This illustrates that in most instances, drugs already present in the blood have insignificant effect on the permeation process in drug absorption and delivery, because a pronounced concentration gradient is established. The transport of a low-permeability drug can be increased by increasing the passive transcellular permeability, which is typically done by increasing its lipophilicity and/or reducing its size, or by modifying its molecular structure to become a substrate for an influx protein transporter. However, changes to the molecular structure to improve intestinal absorption properties cannot be performed without affecting the pharmacology. Increased absorption without affecting pharmacology might be possible by drug formulation strategies, such as co-administering with a permeation enhancing excipient that temporarily reduces the integrity of the barrier to increase drug transport. This is a common approach for transdermal delivery systems, but there is currently only one approved oral product using this technology. The few pharmaceutical methods that can be used for increasing intestinal drug permeability is in stark contrast to the multitude of strategies that can be used for temporarily increasing drug solubility and/or dissolution of low solubility drugs (see Section 1.17.4.1).

1.17.4.2.4

Modified-release formulations and regional intestinal permeability

For most immediate-release dosage forms, the small intestine is the primary intestinal region for drug absorption. However, colonic drug absorption can be quantitatively important for drugs incompletely absorbed in the small intestine (i.e. BCS class II, III and IV), or for BCS class I drugs formulated into oral modified-release (MR) dosage forms. An oral MR dosage form is used to optimize pharmacokinetics, pharmacodynamics, and dosage regimens, which can reduce side effects, improve therapeutic effect, enable once per day drug administration, and increase patient compliance. Given that the solubility and dissolution are sufficient, development of a MR dosage form is feasible, but only as long as the drug is absorbed in all parts of the intestines. This is because drug release needs to be substantially longer than the typical human small intestinal transit time of 3–5 h. Sufficient absorption in all intestinal segments is commonly observed for high permeability drugs (BCS class I and II drugs), illustrated by metoprolol that has an absorption time as MR product up to 20 h after oral dosing (Abrahamsson et al., 1990). Conversely, the low-permeability compound atenolol has a substantially lower permeability in the colon compared to the jejunum in humans (Dahlgren et al., 2016). Of importance is also drug metabolism by colonic microflora in the lumen. This may sometimes reduce the fraction absorbed in colon of drugs formulated into MR oral dosage forms, particularly for drugs that are substrates for hydrolytic and other reductive reactions. The primary physiological parameters that impact drug permeability in different intestinal segments are mucosal surface area, luminal composition, and pH. As described in the intestinal physiology section (see Section 1.17.2.1), the total surface area available for absorption is 30 m2 in the small intestine compared to 2 m2 in the colon. The reason for this is that the small intestinal epithelial surface area is increased compared to the colonic surface area because of circular folds and villi. The only surface enhancement present in colon is microvilli, which is also present in the jejunum. This surface enhancement is of great importance for the absorption of low-permeability drugs that utilize the whole surface area for membrane transport, whereas high permeation compounds are rapidly absorbed already at the tip of the villus (Winne, 1978). In addition, there is a regional difference in the size of the paracellular space between individual enterocytes, where the small intestine is regarded to be substantially more “leaky” than the colon. Luminal differences in pH are of importance for regional intestinal permeability, where pH may vary by a factor of 2 between segments in the small and large intestine (Table 1). The relationship between pH and drug permeability is partly described by the pH partitioning theory that states that the charged species of a weak acid or base do not contribute to passive lipoidal diffusion across the cell lipid bilayer, as they do not partition into octanol. The permeation of these molecules is hence highly dependent

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on the pH at the surface of the lipid cell membrane and the pKa of the drug. This is illustrated in a range of in vitro membrane permeability systems, such as in the Caco-2 permeability assay and in the Ussing chamber, where the transport of weak acidic and basic drugs is inversely correlated to the ionized fraction (i.e. the pH) (Zheng et al., 2015). The concept of the pH partitioning theory for predicting passive membrane transport of drugs and other xenobiotics is, however, not that straightforward. For instance, the permeation of charged anions and quaternary ammonium compounds through the lipoidal membrane can be many times more rapid than expected at all in vivo relevant pHs (Thomae et al., 2005). Further, two extensively permeating compounds, ketoprofen and metoprolol, are rapidly absorbed across the human small and large intestine; at the pH values in these intestinal segments less than 1% of these drugs are in the neutral form (Dahlgren et al., 2016). This is in contrast to the pH-dependent decrease in the permeability of ketoprofen observed at increasing pHs in a parallel artificial membrane permeability assay (Sugano et al., 2004). Finally, drug permeability may also be reduced upon drug solubilization in luminal micellar structures. Food components or pharmaceutical excipients may thus increase the solubility of low solubility compounds. This may, however, be accompanied by a lower driving force for partitioning into the enterocyte cell membrane, leading to a reduced intestinal permeability and absorption. Consequently, this permeability-solubility interplay can be of great importance for regional intestinal permeability, as the luminal compositions may be substantially different. The above discussions illustrate that there is a gap in the understanding as to which molecular characteristics determine regional intestinal absorption rate, regardless of the compound’s BCS classification. For instance, in an evaluation in 2007, it was reported that the development of MR dosage forms of new drugs was considered challenging (6%), or very difficult (59%), because of low colonic absorption (Connor et al., 2007).

1.17.4.2.5

Oral peptide delivery and permeation enhancers

There is a great interest in the oral administration route for peptides and other biologicals from the pharma industries. This is because these types of drugs are often superior to small molecules in their selectivity, potency, and safety. For instance, peptide drugs can replace physiological peptide hormones that are lacking in certain disorders, such as insulin in diabetes mellitus and synthetic encephalin analogue in pain management. However, many peptide drugs have a low stability in the intestinal lumen and a low permeability across biological barriers and cell membranes, which prevent them from being absorbed as well as reaching and recognizing their intracellular targets. Ideally, drug molecules with poor intestinal absorption should be identified in preclinical evaluations so that their development can be discontinued. Nonetheless, further development of oral formulations containing peptides with low intestinal stability and/or low intestinal permeability, is sometimes warranted, if the drug product can be designed to mitigate the impact of these unfavorable biopharmaceutical properties. The generally low intestinal stability of peptides can be related to denaturation in the low stomach pH, gastric and pancreatic peptidases and proteinases, and the high peptidase activity in the brush border membrane of the enterocytes. These issues can be partly circumvented by the formulation approaches. For instance, (1) enteric coating can prevent gastric chemical instability and peptide degradation, (2) proteinase/peptidase inhibitors in the formulation can increase the local luminal stability of the drug, (3) pH modifying agents can change the microclimate pH around the formulation, which affects the activity of luminal enzymes, and (4) drug release may be targeted to the large intestine where peptidase activity tends to be lower than in the small intestine. The low intestinal permeability of most peptides is related to their large size, low lipophilicity, and extensive hydrogen binding, all of which are physicochemical properties that predict low passive membrane transport. A strategy to circumvent this is to include a permeation enhancer in the drug product that increases the permeation rate of dissolved drug molecules over the intestinal membrane, and thereby increases the absorption of low permeability drugs. Even if there are safety concerns and regulatory hurdles, in recent years there have been several attempts to develop oral formulations containing pharmaceutical excipients with transient effect that has the purpose of increasing the intestinal transport of low-permeability peptide and protein drugs. With the exception of oral semaglutide (glucagon-like-peptide 1 analogue for glycemic control in type 2 diabetes mellitus, molar mass: 4113.6 Da) and octreotide (long-acting cyclic octapeptide for maintenance treatment of acromegaly, molar mass: 1019.2 Da), these attempts have been unsuccessful, which is most likely attributed to a low in vivo effect of the permeation enhancers, a high variability in the drug permeation enhancement, and safety issues (Abramson et al., 2019; Dahlgren et al., 2019). Nevertheless, there is a recent growing interest in these types of technologies, as the cost of developing and manufacturing therapeutic peptides has plummeted. Advocates of transient and safe intestinal absorption enhancing technologies are hopeful that novel oral formulation strategies will change this (Muheem et al., 2016). For permeation enhancers to effectively increase intestinal drug absorption, it must render the intestinal barrier more permeable. This can be achieved by altering the fluidity of the intestinal epithelial cell lipid bilayer; and/or by modulating the tight junction proteins to increase the size of the paracellular space; and/or by degrading or reducing the viscosity of the intestinal protective mucus (Dahlgren et al., 2019; Maher et al., 2016). Membrane fluidity is primarily affected by surface active agents when incorporated into the lipid bilayer. Tight junction modulators can act either directly on the tight junction structure, or indirectly, by affecting the intracellular signaling mechanism(s) and/or cytoskeletons involved in regulation of tight junctions. In addition, the mucus barrier can be reduced by administering a mucolytic agent. The in vivo ability of a permeation enhancer to increase intestinal drug absorption is, however, not only about its ability to affect the intestinal barrier. The effect on the barrier must be rapid, and large enough, to allow sufficient drug absorption before the drug has been transported from the luminal area with the compromised intestinal barrier. At the same time, the effect must be rapidly

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reversible, and not too potent, so that translocation of harmful luminal contents over the mucosa is still restricted. The impact of the prandial state (fed/fasted) must also be taken into consideration, as this can have a substantial impact on the effect of a permeation enhancer on the mucosal membrane (Roos et al., 2019).

1.17.4.3

Gut-wall and hepatic extraction

The basic general PK concepts of the different metabolic and excretion pathways are already described (see Section 1.17.3.1). In this section, the focus is on first-pass extraction of drugs in the gut wall and liver (see Eq. 5), as all intestinally absorbed drug molecules must pass through these two metabolic barriers before they reach the systemic circulation in unchanged form (i.e. become bioavailable).

1.17.4.3.1

Gut-wall extraction

Clinical trials have clearly shown that the gut mucosa contributes to the overall first-pass metabolism of many drugs. In particular the intestinal first-pass metabolism of CYP3A4 substrates is often reported to be high (Sandstrom et al., 1999; Watkins et al., 1987). The most direct evidence for a significant contribution of intestinal metabolism in humans came from studies during the anhepatic phase in patients undergoing liver transplantation, in which drug and metabolite concentrations were determined in portal venous blood after oral dosing of the parent compound (Kolars et al., 1991; Paine et al., 1996). These studies reported substantial gut-wall extraction (EG) of cyclosporine and midazolam (CYP3A4 substrates), where about 50% of the intraintestinal doses were metabolized when they reached the portal vein. An additional clinical example of the role of the small intestine to EG has been shown in obese patients that have undergone a bariatric surgery. In patients where the entire jejunum is bypassed it was shown that the bioavailability of atorvastatin (CYP3A4 substrate) was significantly increased (Skottheim et al., 2010). These results emphasize the importance of the proximal small intestine to the metabolic first-pass effect. After crossing the apical membrane barrier, the extraction of drugs in the gut wall is primarily attributed to metabolizing enzymes intracellularly. As in the liver, the most abundant enzyme in the intestine is the CYP3A subfamily, accounting for 82% of the total intestinal P450 content (Paine et al., 2006). The small intestinal enterocyte concentrations of CYP3A4 were estimated to be about 100 pmol/mg of microsomal protein, which is comparable to the higher content in the liver (350 pmol/mg) (De Waziers et al., 1990). However, as CYP3A enzymes are expressed only in the enterocytes which account for only a very small fraction of the total intestinal cell population, the entire small intestinal CYP3A content is only approximately 1% of that in the liver (Paine et al., 1997). The second most abundant P450 in the human intestine is CYP2C9 (15%), followed by CYP2C19 (2.9%), CYP2J2 (1.4%), and CYP2D6 (1%) (Paine et al., 2006). The role of these enzymes in gut wall drug extraction is only minor in most cases, as all these enzymes have a low total content relative to the liver. The distribution of CYP enzymes is inconsistent along the length of small intestine. Enteric microsomal CYP3A content, as well as associated catalytic activity, is generally highest in the proximal region and then declines toward the distal ileum and colon. Local intestinal dosing in the small and large intestine of budesonide, with or without a concomitant CYP3A inhibitor (low dose of 16 mg ketoconazole), showed a substantial activity of this CYP isoform in the small intestine but none in the colon (Fig. 11) (Seidegård et al., 2008). Furthermore, the distribution of CYP enzymes is also inconsistent along the crypt-villus axis. The mature columnar absorptive epithelial cells of the outer villi exhibit the strongest activity, whereas no activity was detectable in the epithelial cells in the crypts (Thelen and Dressman, 2009). Also in common with hepatic CYP expression, considerable interindividual variation exists in enteric CYP expression (Paine et al., 2006).

Fig. 11 The bioavailability (F) of budesonide in humans following local single dose administration to the jejunum and colon, with or without concomitant intestinal dosing of a CYP3A4 inhibitor, ketoconazole, at a low dose (16 mg) that does not influence hepatic metabolism (Seidegård et al., 2008). Both the study drug and the inhibitor were given through a 5 m long specialized tube with 99mTc-DTPA to localize the position along the intestine.

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The overlap in the wide substrate selectivity and intestinal co-localization of CYP3A and the efflux transporter, P-gp, has led to the hypothesis that this transporter and enzyme pair act as a coordinated absorption barrier against xenobiotics (Benet and Cummins, 2001). The concept of the efflux-metabolism alliance is described as P-gp controlling the repetitive access of the drug to the enzyme (CYP3A), giving the enzyme multiple opportunities to prevent the intact drug from entering the bloodstream. The intestinal metabolism of a drug could therefore be changed as a function of P-gp activity without either inhibiting or inducing CYP3A enzymes.

1.17.4.3.2

Hepatic extraction

All blood circulating the GI tract (not counting the distal colon and rectum), pancreas, spleen and gall bladder is transported through the liver via the portal vein. Once in the liver, drug molecules can be metabolized in the hepatocytes, where the uptake and efflux processes for drug molecules into and out of the hepatocytes are governed by a combination of passive and CM transport processes. The extraction of drugs in the gut wall is attributed to metabolism alone, while extraction in the liver also includes uptake transporters and biliary secretion of the parent compound, as discussed previously. Non-metabolized drugs and metabolites can be eliminated by biliary clearance and/or re-enter the blood stream by passive diffusion or efflux transport. Hepatic bioconversion of drugs is performed by metabolizing enzymes, such as CYPs and UGTs, where the former is the most common and accounts for about 60% of all hepatic extraction. The major CYP enzymes in the liver vary between different individuals and subpopulations, but the following were recently reported in Caucasians as the most abundant ones: 2E1 (37%) > 2C9 (26%) > 3A4 (10%) > 1A2 (9%) > 2C8 (8%) > 2D6 (5%) > 2B6 (3%) (Drozdzik et al., 2018). However, the abundance of CYP enzymes does not always reflect their contribution to drug metabolism. For instance, 3A4 is responsible for the CYP metabolism of about 30% of clinically used drugs, followed by 2D6 (20%), 2C9 (13%), and 1A2 (9%) (Zanger and Schwab, 2013). The variability in activity of most CYP enzymes is multifactorial and includes both genetic and non-genetic factors. Examples of non-genetic factors are age, sex, and disease. Sex differences may be related both to different expression profiles and to physiological factors such as body weight and blood flow. Drug metabolizing capacity is typically lower for infants and for elderly, which also in this case can be related both to enzyme expression and physiological factors. An example of a disease that heavily impacts the capacity of the liver to metabolize drugs is liver cirrhosis. It changes the architecture of the liver and is associated with a reduced blood flow and loss of function of hepatocytes. Genetic polymorphism of CYP enzymes can result in either an increase or reduction in activity. An increased activity may result from an increased number of gene copies, promotor variants, and amino acid variants that result in increased substrate turnover. Less common are polymorphisms that affect CYP induction or increase substrate specificity. Reduced activity is most often related to splicing and expression of CYP enzymes, whereas SNPs resulting in amino acid changes can also be involved. A CYP enzyme important for hepatic extraction of several drugs (e.g. amitriptyline, metoprolol, and tramadol) that displays substantial polymorphism is 2D6. Based on drug metabolism pattern in different subjects, they are categorized as either poor, intermediate, extensive, and ultrarapid 2D6 metabolizers. Poor metabolizers lack 2D6 function, intermediate ones carry only one normal allele, extensive refers to the normal population, and ultrarapid refers to different genetic variants resulting in increased activity. The impact of CYP polymorphism on plasma exposure is illustrated in Fig. 12. It compares the variability in plasma concentration-time curve of two completely absorbed high permeability compounds, metoprolol and ketoprofen, following intraintestinal exposure to the human jejunum (i.e. no effect of gastric emptying); the variability is substantially lower for ketoprofen as its metabolism is largely unaffected by metabolic polymorphism (70% glucuronide conjugation) (Dahlgren et al., 2016).

Fig. 12 Plasma-concentration time curves of metoprolol and ketoprofen following intraintestinal dosing to the jejunum of healthy human volunteers (Dahlgren et al., 2016). The intraintestinal dosing means that gastric emptying does not explain the high interindividual variability in plasma exposure of metoprolol. The higher interindividual variability in bioavailability for metoprolol is due to a higher first pass-extraction in the liver mediated by highly variable CYP2D6 polymorphic metabolism.

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In addition to polymorphism, a CYP enzyme may also change its activity over time as a result of induction. This effect takes place in hours or days in vivo, and is a result of a direct interaction between a regulatory protein (receptor/transcription factor) and inducing ligands (such as some drugs and St. John’s wort). For instance, CYP3A4 is induced by a wide variety of ligands that interact with the nuclear pregnane X receptor. The activated pregnane X receptor complex forms a heterodimer with the retinoid X receptor, which binds to the XREM region of the CYP3A4 gene. This causes a cooperative interaction with the gene, resulting in increased transcription and expression of CYP3A4. Induction leads to an increased clearance and reduced bioavailability of the drug itself. It can also result in increased toxicity of a drug or coadministered drug as a result of increased formation of reactive intermediates (Hewitt et al., 2007). It should also be mentioned that in addition to CYP enzymes, also drug transporters are affected by polymorphism and induction. This can in some instances have a clinically important effect on intracellular drug exposure, and hepatic extraction, as discussed below. A majority of metabolic processes are mediated by intracellularly expressed and located enzymes in the liver. The rate of metabolism can often be described by saturable Michaelis-Menten kinetics (Eq. 14) v¼

Vmax  C Km þ C

(14)

where Vmax is the maximum rate of metabolism, and Km is the Michaelis constant that describes the concentration at Vmax/2. As the metabolic transformation occurs intracellularly, the rate of metabolism may also be governed by the rate of membrane transport into the hepatocytes, as this can be the rate limiting step for drugs with low passive membrane permeability, or for drugs that are actively effluxed out of the cells. This means that for high permeability compounds, Eq. (14) is unchanged, whereas for permeability limited compounds, v equals the membrane transport rate. (However, low permeability compounds are typically not eliminated by metabolism; Benet et al., 2008.) Given that Km is substantially higher than C, the intrinsic clearance (CLint) can be calculated using Eq. (15) CLint ¼

v Vmax ¼ C Km

(15)

CLint describes the inherent ability (i.e. absence of other limitations) of a hepatocyte to convert, or biliary excrete, an unbound drug compound, and it incorporates all enzymes and transporters involved in these processes. If we assume that all drug molecules that enter the liver with the blood are instantly and evenly distributed throughout the whole liver (i.e. perfusion limited distribution with no diffusion delay), the hepatic drug clearance can be described using Eq. (16) (the well-stirred model of hepatic extraction) (Pang and Rowland, 1977a) CLH ¼ QH 

fu  CLint Q þ fu  CLint

(16)

where fu is the fraction unbound in blood, and QH is the blood flow to the liver. This means that the relationship between fu  CLint and hepatic extraction (EH) can be described using Eq. (17). EH ¼

CLH fu  CLint ¼ Q Q þ fu  CLint

(17)

If we disregard the effect of the differences between plasma and blood concentration, Eq. (17) shows that the CLH cannot exceed the QH (Yang et al., 2007). By plotting EH against fu  CLint, Fig. 13 shows that most compounds can be classified as either high (> 70%) or low (< 30%) extraction, as the interval that falls between these EH values is quite narrow. By extension, this means that for a high extraction drug, minor differences in CLint and fu will have an insignificant effect on EH, as it will only be dependent on liver blood flow (EH z 1). Conversely, for a low extraction drug, blood flow has no effect on EH, instead it is determined by changes in CLint and fu (E H z fu  CLint/QH). These correlations described by the well-stirred model show that the plasma clearance of a high extraction drug is unaffected by changes in CLint and fu, whereas CLint and fu determines plasma clearance for low extraction drugs. However, the opposite is true for the effect of changes in CLint and fu on the oral bioavailability (F) of high and low extraction drugs. For a low extraction drug almost all drug molecules that are absorbed from the intestine will avoid first pass metabolism, regardless if changes in CLint and fu occurs. For high extraction drugs on the other hand, even a small shift in CL int, fu and/or QH may cause substantial change in F because of the first-pass effect (F ¼ 1  EH). This is illustrated by the F of the high extraction drug metoprolol, which can vary up to sixfold between subjects as a result of CYP2D6 polymorphism (Fig. 12) (Silas et al., 1985). Further, enzyme induction by repeated oral administration of St John’s wort significantly decreased the bioavailability of verapamil (high liver extraction drug) by induction of CLint. At the same time was the terminal half-life of verapamil unaffected, as its CL is not influenced (Tannergren et al., 2004).

1.17.4.4

Preclinical intestinal absorption models

In the drug discovery process, candidate drug molecules are selected based on physicochemical properties, the affinity for the pharmacological target, animal pharmacodynamics, membrane transport properties, chemical and metabolic stability, and their safety/ toxicity profile (Hughes et al., 2011). These properties are evaluated by applying various preclinical tools and models with varying

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Fig. 13 The relationship between intrinsic clearance (CLint) and fraction unbound (fu), to hepatic extraction (EH), as described by the well-stirred model (see Eq. 17). Drugs with an EH above 0.7 are classified as high extraction drugs, and those with an EH below 0.3 as low extraction drugs (denoted by the dashed lines).

degrees of complexity. These range from simple screening assays with thousands of molecules, to complex preclinical in vivo animal models that evaluate only a few selected molecules. The aim of these models is to select a drug candidate suitable for testing in human clinical studies. There are three phases of clinical studies (I–III) with an ascending number of subjects. Phase I establishes pharmacokinetics and safety in healthy volunteers at different dose levels, phase II assesses efficacy and side effects at different doses in patients, and phase III assesses effectiveness and safety in patients. The clinical part of drug development is estimated to account for about half of the total costs. This is related to the high number of subjects involved and the required resources and a high attrition rate in drug development, where only about 1 in 10 clinically tested drugs reach the market (DiMasi et al., 2015). The high cost and attrition rate at the later stage of the drugdevelopment process emphasizes the importance of selecting the candidate drugs most likely to succeed; this requires robust and accurate preclinical tools. As discussed earlier, drug solubility, dissolution, permeability, and first-pass extraction are the primary biopharmaceutical and pharmacokinetic variables that influence intestinal drug absorption and bioavailability. These variables are consequently thoroughly investigated in the development of novel drugs and oral drug delivery systems. For this purpose, a variety of models are used in the drug discovery and preclinical development process for predicting human intestinal drug absorption and bioavailability. Models vary in complexity, and choice of a model depends on the question to be investigated and the stage of preclinical drug development. They range from simple simulations and in vitro systems with high-throughput capacity, which are typically used in early drug development, to more complex animal and in silico models that are used in a later stage of non-clinical, or early clinical phase, of drug development, when more in vivo relevant predictions are needed. This section covers some of the principal in silico, in vitro, in situ, and in vivo models used for determination of solubility, dissolution, permeability, and gut-wall and hepatic metabolism/ extraction in drug development.

1.17.4.4.1

Solubility and dissolution models

1.17.4.4.1.1 In silico models Statistical and mathematical based methods can be used to predict drug solubility even if it is not offering fundamental understanding. This includes for example the correlation of drug solubility to experimentally derived parameters (e.g. partitioning coefficient, melting point, entropy of fusion) that incorporates both molecular and crystalline properties. A limitation to this strategy is that it cannot be used to approximate solubility for compounds with unknown parameters. Another statistical methoddquantitative structure–activity relationship (QSAR)dcan be used to approximate solubility of unsynthesized/uninvestigated compounds, as it relies only on molecular descriptors of a compound and machine learning. However, the accuracy of the model is dependent on the statistical approach, choice of molecular descriptors, and the experimental conditions in the lab where the solubility measurements were performed (Ratkova et al., 2017). Still, the QSAR models based on large training sets can be accurate for predicting water solubility. There have also been attempts to use the QSAR method to predict drug solvation in more complex media than water. Other approaches, such as computing the total free energy change of bringing a molecule from the amorphous phase into water has been shown to be successful (Lüder et al., 2009). Computer models predicting drug product performance and drug dissolution in the complex luminal environment requires substantially more complex in silico systems than the statistical approaches. Such advanced in silico models can predict the potential for advanced formulation strategies to enable oral drug delivery of low solubility compounds, as well as their dissolution behavior in biorelevant GI media. These more advanced computer models are based on molecular dynamics simulations that can predict drug solubility even if there is no experimental data available. It relates the chemical

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potential of a solute in solution to the energy in its solid state. Molecular dynamics simulations can be extended to incorporate, for instance, lipids and their digestion in the intestinal tract, and the impact of pH on aggregation and solubilization (Bergström and Larsson, 2018). 1.17.4.4.1.2 In vitro models The pharmacopeia includes a range of in vitro systems that can be used to describe drug product disintegration and dissolution, where the aim is to monitor drug product and compound specific parameters ranging from drug product quality to in vivo relevant drug release and dissolution. These pharmacopeia tests are performed under defined conditions including temperature, pH, fluid volumes, stirring and hydrodynamics (Kostewicz et al., 2014b). Examples of systems that are primarily used for quality control include the basket (USP1) and paddle (USP2) apparatus. They rely on the drug dissolution/release in a beaker with continuous stirring or basket rotation. The beaker contains 500–1000 mL, which is a necessary volume to uphold sink conditions (a prerequisite in quality control testing). Obviously, these volumes have little relevance for the in vivo situation, where gastric volumes in fasted state are in the range of 30–60 mL at normal conditions. There is also no possibility to change the media composition in the paddle or basket method. Thus, these simpler pharmacopeia methods typically have limited relevance for in vivo prediction of drug release and dissolution, which often are affected strongly by dynamic GI physiology. However, there is an abundance of disintegration and dissolution methods described that aim to better predict intestinal drug release and dissolution following oral administration. A common approach to achieve this includes the use of biorelevant dissolution media. These types of media are developed based on chemical characterization of human GI intestinal fluids. A few slightly different simulated intestinal and gastric fluids mimicking the fed and fasted state have been developed. These biorelevant dissolution experiments can be performed in conventional USP apparatus as mentioned before, but that still leaves the problem with non-physiological volumes, hydrodynamics, and absence of dynamic changes including the permeation step. In order to better predict the dynamic in vivo dissolution, multi-compartment models can be used that mimic aspects of the dynamic GI environments. The multi-compartment models may be composed of two or more beakers, where biorelevant dissolution media in the different compartments are transferred and/or changed over time to reflect the changing intestinal conditions. Such methods may capture the dynamic dissolution behavior of weak acids and bases as they are transported from the gastric compartment with low pH (2.0–3.0) to the neutral pH (6–0–6.5) in the proximal small intestine. This way the kinetics of complex luminal solubility effects such as drug precipitation and supersaturation might be predicted. To further increase the in vivo relevance of these in vitro models an absorption sink can be added. An absorption step in a dissolution experiment may increase dissolution rate and reduce precipitation (increase supersaturation time in the system) of a high permeation drug that continuously enters the absorptive sink (Carlert et al., 2010). The combination of invasive and non-invasive techniques for monitoring the direct effect of intestinal motility on drug product disintegration and drug release has spurred the development of in vitro models that mimic the hydrodynamics and/or physical forces in the intestinal lumen. No single apparatus can capture all intraluminal events, but combining different models may increase the understanding of the impact of the heterogeneous behavior in the GI lumen, that range from resting periods to strong pressure forces. For instance, the dissolution-stress test model can simulate the risk for unwanted dose dumping of MR formulations at high pressure events, such as passage through the ileocecal valve or gastric emptying. Other models, such as TIM1 and 2, aim to mimic the mechanical forces taking place during the passage from the stomach through the small intestine and colon. Digestion can also affect drug products containing digestible components, such as the effect of luminal lipase on lipid based formulations. Dissolution models aiming at predicting this typically use lipases from microbes or animals such as dog or pig, and the specificity is not always comparable to human, which needs to be taken into consideration. Many of these models are one-compartment and try to capture the effect in the stomach or small intestine, while others incorporate many compartments, such as TIM-1. There are also descriptions of models that incorporate both dissolution, digestion, and permeation, which allows the study of luminal fat digestion on drug dissolution and absorption of lipid based formulations (Keemink et al., 2019). 1.17.4.4.1.3 In vivo models In vivo models to capture intraluminal drug dissolution and drug product behavior can be either invasive or non-invasive (Lennernas, 1998; Wilding et al., 2001). Invasive techniques include aspiration of luminal intestinal fluids. This can be used to study the interplay between drug release, dissolution, supersaturation/precipitation, and absorption in human (or animals) subjects after drug product administration. Intestinal aspirates are analyzed for dissolved and particulate drug concentration-time profiles. Aspiration can be performed with single or multilumen tubes, where the former allows aspiration in only one luminal compartment, while the other enables simultaneous study in a range of compartments. The aspiration models can be coupled to other invasive (gastric and small intestinal motility recording) or non-invasive (magnetic resonance imaging) techniques that monitor GI movement. The investigations can also be performed under different conditions, including prandial states, degree of gastric acidity, and physical activity level. Combined, this allows for study of the impact of drug product performance on various physiological variables simultaneously (Augustijns et al., 2020). Non-invasive techniques are traditionally used to study indirectly luminal drug release/dissolution by monitoring plasma drug appearance following oral administration. Obviously, this method can only be used to investigate a drug formulation given that dissolution/solubility/drug release is the rate-limiting step in the absorption process, and not drug permeability. The method is based on deconvolution of plasma concentration-time profiles, where elimination and distribution is accounted for by the use

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of an intravenous reference dose. This way the intestinal absorption rate over time can be calculated. If permeability is not rate limiting, this means that the appearance rate in plasma of a drug is directly related to the drug release from the formulation. The deconvolution method can thus be used to study drug transport from the stomach to the small intestine, as no drug absorption takes place over the gastric mucosa. It can also be used to compare the in vivo release pattern from different MR formulations. By extension, this method can also be used to gain some insight also into release and absorption behavior in the large intestine (Margolskee et al., 2016).

1.17.4.4.2

Permeability models

The GI barrier is essential in human health and constitutes the interface between the outside (lumen) and the systemic blood and all organs. A functional intestinal barrier allows efficient absorption of nutrients and fluids but simultaneously prevents harmful toxins and bacteria from crossing the intestinal epithelium and reaching the systemic circulation. 1.17.4.4.2.1 In silico models An in silico method to predict intestinal permeability relies on the application of QSAR models, which relates molecular structure and/or physicochemical properties (e.g. lipophilicity, pKa, PSA, MM) of the drug molecule to experimentally determined permeability values from in vitro or in vivo systems (Gozalbes et al., 2011). This is a computational approach to predict membrane permeability and is ideal in the early high-throughput drug discovery phase, because it requires limited new experimental data. For example, the human in vivo effective jejunal permeability of a set of 22 structurally diverse compounds (see Section 1.17.4.5) has been correlated with both experimentally derived lipophilicity values and calculated molecular descriptors. Based on a multivariate data analysis the best in vivo predictive permeability model used the variables HBD (number of hydrogen bond donors), PSA (polar surface area), and either log D5.5 or log D6.5 (Winiwarter et al., 1998). However, the accuracy of the model is dependent on the statistical approach, choice of molecular descriptors, and the quality of the experimental permeability data. The QSAR approach is consequently of limited used in the drug-development process, and is therefore primarily used for excluding molecules with obvious permeability limitations, or when there is no alternative (Lipinski et al., 2001). However, due to the increase in computer power, studies of drug permeation can be performed using complex molecular simulations. These models can simulate the interaction between a molecule and a biological membrane, and thereby increase the mechanistic molecular understanding of membrane transport (Lee et al., 2016). More complex in silico simulations can be used to estimate intestinal absorption following oral administration of a drug or its formulation. These simulations depend on mathematical equations that describe drug compound biopharmaceutical parameters (e.g. solubility, log D, Peff) and drug product parameters (e.g. disintegration and dissolution rate), physiological parameters (e.g. GI pH, transit times, and regional mucosal surface area), and the drug disposition in vivo (Kostewicz et al., 2014a). Computer simulations can be performed solely on the chemical structure, means of administration route, and understanding of the human physiology and its dynamic processes, but high-quality experimental in vitro and in vivo data are still necessary for simulating accurate plasma exposure profiles (Sugano, 2009). There are several, physiologically-based pharmacokinetic software programs commercially available that aim to predict fa, F and plasma exposure profiles following oral drug administration (Kostewicz et al., 2014a). Currently, the accuracy of these predictive models is too low to compete with conventional in vitro and in vivo experimental studies in drug development, even if they are based on well-characterized physicochemical and biopharmaceutical factors (Sjögren et al., 2016). For instance, International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) began efforts to provide recommendations to harmonize guidance for BCS-based biowaivers (https://www. ema.europa.eu/en/ich-m9-biopharmaceutics-classification-system-based-biowaivers). It was clear that the in silico models were not considered sufficiently robust to replace experimental in vitro data for a biowaiver at this stage. However, a validated in silico model can be useful for evaluating, for instance, the impact of changes in drug formulation and drug–drug interactions, which can help guide the design of both preclinical and clinical studies (Jones et al., 2015). 1.17.4.4.2.2 In vitro models One of the most common in vitro models for studying membrane permeability is the investigation of flux across a barrier that separates two chambers. The barrier can consist of an artificial membrane (e.g. parallel artificial membrane permeability assay), a single layer of grown cells (e.g. Caco-2), or an excised intestinal tissue sample (Ussing chamber) (Artursson, 1990; Hidalgo et al., 1989). An apparent permeability (Papp) of a molecule can be calculated by relating the mass appearing in the receiver chamber at multiple time points (dM/dt) to the area of the barrier (A), and concentration in the donor chamber (Cdonor), using Eq. (18) Papp ¼

dM 1  dt A  Cdonor

(18)

Papp is the intrinsic constant of a molecule that relates flux and concentration gradient, and can therefore be used to predict the transport over any type of biological cell membrane barrier by adjusting for parameters such as, area, hydrodynamics, and solution media pH. In addition, the controlled conditions in a cell-based in vitro system make it useful for mechanistic transport investigations, but require a validation of its transport protein expression and activity. For instance, CM transport can be investigated by comparing the transport rate between the two chambersdin both directionsdby determining the Papp at different substrate concentrations, or before and after addition of a transport inhibitor (Nielsen et al., 2001). The more complex Ussing chamber system enables experimental investigation of gut-wall metabolism and regional intestinal permeability (Sjöberg et al., 2013). Limitations

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associated with these models are the high inter- and intra-laboratory variability, and sensitivity of the cell/tissue to preparation setup and chamber media. It is crucial to validate the expression of transport proteins and enzymes as they always are different when tissues and cells are removed from the host and their natural dynamic biological environment. For permeability investigations in drug discovery, it is therefore recommended by the recent ICH BCS guideline that relative Papp values compared to a set of drugs that are used as reference standards be used, instead of absolute Papp values (https://www.ema.europa.eu/en/ich-m9biopharmaceutics-classification-system-based-biowaivers). 1.17.4.4.2.3 In situ and in vivo models In situ intestinal permeability models in animals and humans can have different technical setup, but they are often based on the drug disappearing from a defined regional intestinal segment. This segment can be continuously perfused, as in the single-pass intestinal perfusion (SPIP) model, or be a closed-off segment, as in the closed-loop Doluisio model (Lozoya-Agullo et al., 2018). Peff is calculated in different ways depending on the luminal hydrodynamics in the specific model. For instance, the SPIP model calculates the Peff using Eq. (19), as there is an exponential reduction in drug concentration along the perfused segment (Amidon et al., 1980)    Qin Cout Peff ¼   ln (19) A Cin where Qin is the perfusion rate, Cin and Cout are the concentrations entering and leaving the perfused segment corrected for fluid transport, and A is the surface area of the perfused segment. Peff is consequently luminal CL per surface area (volume/time/area). The animal SPIP model is generally used in a later stage of preclinical drug development, when more in vivo relevant data are needed. One major advantage is that SPIP enables mechanistic evaluations of drug transport and its physiological regulation, as intestinal morphology and blood flow are kept intact and are combined with the influence and feedback control from the controlled luminal conditions. In humans, there exists different intestinal SPIP techniques that have been developed and used during the last 70 years. The most common approach to determine intestinal Peff for different drugs has been by applying the double-balloon system in the most absorptive region, i.e. proximal small intestine (Lennernäs et al., 1992). One of the advantages with this GI tube is that the occlusion of the test segment between two intraluminal balloons minimizes contamination with luminal fluids both proximally and distally into the perfused segment. In addition, the leakage out from the segment over the balloons is small, so the recovery of the nonabsorbable marker is almost complete. The human jejunal Peff values have been a cornerstone in the development and establishment of the BCS (see Section 1.17.4.5). These human Peff values predict the fraction dose absorbed for drugs when given as solution in vivo in humans (non-tube human studies) as shown in Fig. 14. These jejunal Peff data have also formed the basis in several in silico software applications, correlation to cell monolayers and animals for predictions of intestinal absorption and bioavailability (Dahlgren et al., 2014). Classical in vivo PK models in which drug solutions or formulations are dosed orally, or directly into the stomach or intestine of humans or animals as single dose or controlled infusion, can also be used to determine as well as investigate permeability (Sjögren et al., 2015). The absolute (or relative) F, or fabs (or appearance rate) of a drug, is then determined and compared to an i.v. reference or other drugs/formulations. Such models are the most clinically relevant ones because physiological feedback factors, such as gastric emptying time, gastrointestinal secretions, luminal fluid composition and drug degradation, and post-absorption firstpass metabolism, are expected to affect the determined parameters to various degrees. These full in vivo models are hence less useful for mechanistic studies of intestinal absorption, as the relative impact of the different factors can be difficult to investigate. Their great value in drug development is that they provide quantitative in vivo relevant results for the drug delivery issue investigated.

Fig. 14 Human in vivo effective permeability values (Peff) determined by jejunal perfusion correlated to fraction absorbed following oral administration of 30 drug compounds with a high intestinal solubility (i.e. permeability rate limiting absorption) (Lennernas, 2007).

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1.17.4.4.3

Gut-wall and hepatic metabolism models

1.17.4.4.3.1 In silico models As with drug dissolution/solubility and permeability, machine learning and statistical methods can be used to relate molecular descriptors to drug biotransformation, under the assumption that similar structures potentially exhibit similar chemical and biological activities. Current metabolic QSAR models are constructed for CYP enzymes in the liver, as this is the most important and abundant transformation pathway for drugs. These models can be used to predict the potential for interaction with a certain metabolic enzyme, as well as the metabolic fate of the compound. As with the other mentioned QSAR approaches, the quality of experimental data must be taken into consideration. Advanced machine learning techniques have also increased the usefulness and accuracy of drug biotransformation predictions (Kazmi et al., 2019). 1.17.4.4.3.2 In vitro models In vitro metabolic models aim at identifying the metabolic enzymes involved in biotransformation, the rate of enzymatic conversion, and the tissue(s) and cellular compartments that are involved. Many metabolic questions can be answered by studying sub-cellular fractions derived from drug metabolizing tissues, such as the liver and intestine, including the cytosol, S9, and microsomal fractions. The simplest cellular sub-fraction of the three is the cytosol, isolated from the supernatant of the S9 fraction. It contains soluble drug-metabolizing enzymes and is typically involved in mechanistic studies identifying metabolic pathways. The most frequently used enzyme system relies on the microsomal fraction. It is cheap, easy to use, and contains membrane-bound CYPs and primary conjugation enzymes such as UGTs, which together are involved in the biotransformation of over 90% of approved drug compounds. Thus, microsomal assays are default for metabolism and DDI studies in drug development. For instance, it can be used to determine intrinsic clearance, Vmax, Km and inhibition constant (Ki) of a drug compound and to establish in vitro correlations between different species. Multi-organ metabolism and whole body clearance can also be predicted by investigating clearance from various tissues. Inhibition studies can also readily be performed to determine for instance IC50 values using the microsomal fraction. The S9 fraction contains both microsomes and cytosol, and therefore incorporates almost all drug metabolizing enzymes. Compared to cell incubations that also contain all enzymes, S9 is cheaper and it is easier to handle experimentally. By scaling weight of S9 per weight of tissue, whole body in vivo clearance can be predicted, as well as species differences. Still, the high amount of enzymes in S9 can lead to a dilution of a specific enzyme of interest. On a general note it is also important to take into consideration the contribution of co-factors in sub-cellular fractions, which are involved in a range of biotransformations such as oxidative metabolism and conjugations (Zhang et al., 2012). Enterocytes and hepatocytes are examples of whole cell systems that can be used to predict tissue specific drug metabolism. These cells constitute enzymes and membrane transport proteins, and when freshly prepared in a relevant physiological environment with all co-factors and nutrients, they may predict in vivo clearance. A major advantage of the whole cell models compared to sub-cellular fractions is that they take into consideration also uptake and efflux proteins. These transporters can be involved in determining intracellular concentrations, and thus the metabolic transformation rate of drugs that are targets of these transporters; studies using sub-cellular fractions may consequently over or under-predict drug metabolism. By using cryopreserved human (or animal) tissue, drug metabolism can be performed over time from the same batch (individual), and results can be compared between labs. Drug metabolism in specific subpopulations can also be performed, such as on individuals that lack a certain metabolic enzyme or is a fast metabolizer for another. As cells contain also the full genome, these systems can be used to investigate CYP induction over time. Ex vivo experiments can also be performed on tissue turned into cell systems. The animal from which the tissue comes can be exposed to a repeated high dose of a drug in order to monitor changes in the expression and/or activity of transporters and metabolic enzymes over time. 1.17.4.4.3.3 Ex vivo, in situ and in vivo models Perfusion of the liver and intestines of animals is a close representation of the in vivo situation, where it can be used to study drug extraction under the influence of both membrane transporters and metabolic enzymes. One advantage compared to cellular models of the same tissue is that it also takes into consideration the structure of the organ of interest, such as the effect of different cell populations in a tissue. This can have a profound effect on drug bioconversion as well as on other feedback mechanisms relevant for drug extraction. Whole tissue models also enable studies of local biotransformation and disposition. For instance, during hepatic perfusion, drug and metabolite appearance in bile can be quantified, as can concentrations in cells and in the perfusate leaving the tissue. These experiments can also be coupled to toxicity and pharmacology studies. The whole liver model has also enabled investigation of the impact of blood flow, plasma binding, and intrinsic clearance on biotransformation of drug with different extractions. The system has thus been instrumental in the validation of the well-stirred model for hepatic extraction, which describes the impact of hepatic clearance on bioavailability (see Section 1.17.4.3.2) (Pang and Rowland, 1977b). Tissue experiments can also be performed ex vivo based on organs from animals (and sometimes humans). These experiments can be performed on tissue kept alive by continuously perfusing it with a blood/nutrient mix. Finally, pharmacokinetic studies on preclinical animals are used to investigate drug transformation and disposition following different administration routes, including intestinal and portal, and sampling sites, including portal vein and systemic circulation. This way gut-wall metabolism can be separated from hepatic metabolism. These studies can also be performed on animals modified

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Pe r m e a bilit y ( hum a n e x t e nt of a bsor pt ion)

Biopharmaceutics Classification System Class I

Class II

High Solubility Low Solubility High Permeability High Permeability Class III

Class IV

Low Solubility High Solubility Low Permeability Low Permeability Solubility: Volume of water required to dissolve the highest dose strength over pH 1.2 -6.8.

Fig. 15 The biopharmaceutics classification system that classifies a drug molecule based on its intestinal solubility and permeability (I: high solubility high permeability, II: low solubility high permeability, III: high solubility low permeability, IV low solubility low permeability). Solubility is high if the highest dose of a drug is soluble in 250 mL of water at all physiologically relevant intestinal pH (1.2–6.8). Permeability is high if the fraction absorbed of an orally administered dose is over 80%.

to lack, or overexpress, certain enzymes or transporters. The impact of dose, dosing schedule, food, and drug–drug interactions, on bioavailability and clearance can also be investigated. The obvious general downside with the in vivo models is the high cost and labor intensive experimental setup.

1.17.4.5

Bioequivalence and the biopharmaceutics classification system

Bioequivalence (BE) is an essential drug product standard for both innovator and generic pharmaceutical products. For innovator products, BE is used to establish therapeutic equivalence between the commercialized, marketed product and the clinical-scale product that underwent Phase III safety and efficacy testing. The pivotal Phase III studies that establish the evidence for the label indication(s), use(s), and dosing require that the clinically tested Phase III product report the bioavailability. All subsequent products that contain the same drug and label, must show BE to that clinically tested product (reference). For instance, additional BE testing on the innovator product is needed when the product undergoes various scale-up changes and post approval changes. Thus, BE is an essential continuing standard for ensuring the therapeutic interchangeability and efficacy of pharmaceutical products. The demonstration of BE is often based on plasma exposure data and is an essential standard for ensuring that the patient receives a product that performs as indicated by the label. The biopharmaceutical classification system (BCS) is based on the classification of solubility and intestinal permeability of a drug compound to judge the likely impact of in vitro drug product dissolution properties on in vivo absorption and bioavailability for oral solid immediate-release products (Fig. 15) (Amidon et al., 1995). The aim of the BCS is to provide a regulatory tool for replacing certain BE studies by accurate in vitro dissolution tests. It should be noted that permeability and solubility are substance-specific properties whereas dissolution is product specific. Additional factors to consider when using the BCS system to demonstrate BE are pH, chemical and enzymatic stability in the GI lumen, membrane transport mechanism and therapeutic index. For future refinements and potential extended use of BCS it is necessary to consider the complex relationships between drug and drug product dissolution in vivo, the effects of excipients, intestinal permeation and GI transit, absorption, metabolism, excretion, and systemic availability, and how it can be demonstrated by various in vitro models. All of these issues are important to the ongoing development of BE testing both in vivo and in vitro. In 2000, the FDA issued a guidance for the industry on what was required to obtain a waiver of a human BE study. Several global regulatory agencies have adopted guidances on the use of BCS-based biowaivers. BCS-based biowaivers may be approved to BCS Class I and III drugs, but BCS-based biowaivers for these two classes are not recognized worldwide. This means that pharmaceutical companies have to follow different approaches in the different regions. In 2016, the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use began efforts to provide recommendations to harmonize guidances for BCS-based biowaivers, and the updated guidance was issued July 30, 2020 (https://www.ema.europa.eu/en/ich-m9-biopharmaceutics-classification-system-based-biowaivers). This guideline will provide recommendations to support the biopharmaceutics classification of medicinal products and will provide recommendations to support the waiver of BE studies. This will result in the harmonization of current regional guidelines/guidance and support streamlined global drug development.

See Also: 1.18: PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations; 1.19: Drug Metabolism: Cytochrome P450; 1.21: Drug Metabolism: Phase II Enzymes; 1.22: Drug Transport—Uptake; 1.23: Drug Transporters: Efflux; 1.25: Mathematical Aspects of Clinical Pharmacokinetics; 1.29: Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

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1.18 PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations Shinya Ito, Division of Clinical Pharmacology and Toxicology, Department of Pediatrics, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada © 2022 Elsevier Inc. All rights reserved.

1.18.1 1.18.2 1.18.2.1 1.18.2.1.1 1.18.2.1.2 1.18.2.1.3 1.18.2.2 1.18.2.3 1.18.2.3.1 1.18.2.3.2 1.18.2.4 1.18.2.4.1 1.18.2.4.2 1.18.2.4.3 1.18.2.4.4 1.18.2.4.5 1.18.2.5 1.18.2.5.1 1.18.2.5.2 1.18.2.5.3 1.18.2.5.4 1.18.2.5.5 1.18.2.5.6 1.18.2.6 1.18.2.7 1.18.3 1.18.3.1 1.18.3.2 1.18.3.3 1.18.3.4 1.18.3.4.1 1.18.3.4.2 1.18.3.5 1.18.3.5.1 1.18.3.5.2 1.18.3.5.3 1.18.3.5.4 1.18.3.6 1.18.4 1.18.4.1 1.18.4.2 1.18.4.2.1 1.18.4.2.2 1.18.4.2.3 1.18.4.3 1.18.5 1.18.5.1 1.18.5.1.1 1.18.5.1.2 1.18.5.2 1.18.5.2.1 1.18.5.2.2

438

Introduction General concept of drug distribution in pharmacokinetics (PK) Overview of PK analyses Concentration-time profile First order process Pharmacokinetic modeling analyses Compartment models One-compartment model: Structure and mathematical expression Two-compartment model: Structure Two-compartment model: Mathematical expression Basic concepts of PK parameters Steady-state Clearance (CL) Apparent volume of distribution (Vd) Elimination rate constant Relationship among CL, Vd and k Apparent volume of distribution in a two-compartment model Central compartment: VC Peripheral/tissue compartment: VT Area-based distribution volume: Varea Volume of distribution at steady state: Vss Relationship among parameters of distribution volume Effects of changes in distribution volume on concentration profile Obesity Transporters and tissue distribution of drug PK characteristics in pregnancy Physiological transformation Changes in volume of distribution Clearance changes of drugs eliminated by kidney Clearance changes of drugs metabolized by liver Extraction ratio, hepatic blood flow and intrinsic clearance Bioavailability and pre-systemic elimination PK-based interpretation of changes in concentration-time curves in pregnancy High ER drugs Low ER drugs Oral clearance Clinical impact Distribution of drugs to the fetus across placenta Drug distribution into human milk Influences of lactation on maternal PK parameters Pharmacokinetics of drug distribution into milk: Infant exposure Measured relative infant dose (RID or %RID) Predicted %RID from PK parameters Exposure index Interpretation of %RID Neonates, infants and children Relative growth and allometry Organ size growth Allometry of PK parameters Volume of distribution Fluid and fat compartment sizes Parameter changes

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations 1.18.5.3 1.18.5.4 1.18.5.4.1 1.18.5.4.2 1.18.5.4.3 1.18.5.5 1.18.5.6 1.18.5.7 References

Protein binding Clearance Renal clearance Hepatic clearance Developmental pattern of clearance Units in developmental PK analyses Standardization of dose Drug distribution into brain: Development of blood-brain barrier (BBB)

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Glossary Allometry The scaling relationship between biological traits such as organ mass and body weight, which may show nonproportional correlation. Exposure Index (%) The amount of drug secreted into breast milk ingested by the breastfed infant per time, which is expressed as % of the infant therapeutic dose. It is similar to %RID. Growth and Development Two distinct concepts describing the maturation process of a child. Growth is defined as agedependent mass increase, and development denotes a process of functional maturation. Mean Residence Time (MRT) A mean duration of time that drug molecules spend in the system. MP ratio Milk-to-(maternal) plasma ratio of drug concentrations or AUC. Relative Infant Dose (RID) or %RID The amount of drug secreted into breast milk which the breastfed infant ingests per time. It is expressed as % of the maternal dose per time, and 2 kinds of the parameter exist: measured and predicted %RID.

1.18.1

Introduction

Drug distribution in the body is a combination of complex processes in parallel and sequence. While investigation of drug distribution into target sites at the cellular and molecular levels provides deep mechanistic insight into pharmacodynamics, pharmacokinetics (PK) of drug distribution and elimination processes provides a quantitative description of drug disposition at a systems level, allowing us to interpret and predict system behavior: profiles of drug amount and concentrations over time. The drug distribution process is addressed typically as a PK parameter known as volume of distribution, which is a simple concept but easily misunderstood. In order to correctly understand drug distribution in the body as a PK phenomenon, one must appreciate other basic PK concepts and their underlying assumptions and rationale, including compartment models. Nowadays, the field of PK is expanding into various modeling approaches that are used in drug development to enrich prior knowledge of drug disposition, becoming one of the key fields within a new discipline of pharmacometrics. In clinical settings, PK concepts are routinely used in therapeutic drug monitoring to predict drug concentrationdtime curves and facilitate decision making on dosing schedules of drugs with narrow therapeutic windows. While various diseases affect PK profiles, the two physiological conditions, pregnant women and developing infants, are distinct both in the magnitude of the impact and in the underlying mechanisms of their PK alterations, including distribution volume changes. It is noteworthy that PK model-based simulations and population PK approaches are widely recognized as important tools to deepen our understanding of PK particularly in these populations, in which conventional PK studies are difficult to perform. In this article, two themes are discussed. First, PK concepts of distribution processes are described on the basis of their mechanistic and pharmacological underpinnings. Because the PK concepts of distribution and volume are deeply embedded in PK modeling and other key PK concepts such as clearance, basics of general PK concepts are described as well. Although mathematical definitions are used, an emphasis is placed on the pharmacological meaning of the concepts. Second, two special populations, pregnant women and growing children, are addressed for their PK characteristics including the process of drug distribution, because of their distinct physiological differences from an adult population norm, resulting in vastly altered fundamental PK parameters. Drug distribution into milk is also explained with a focus on its PK principles.

1.18.2

General concept of drug distribution in pharmacokinetics (PK)

Pharmacokinetics (PK) of drug distribution processes is crystalized in the concept of volume of distribution. It is one of the two fundamental PK parameters (the other is clearance). Because volume of distribution has connotations of everyday phenomena,

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it is often difficult to comprehend its PK-focused definition, meaning and significance. Furthermore, how such distribution parameters are defined and applied requires basic understanding of PK modeling and related parameters.

1.18.2.1

Overview of PK analyses

PK parameters including distribution volume can be defined in different ways. In order to address their pharmacological background, basics of PK analyses are briefly discussed.

1.18.2.1.1

Concentration-time profile

Drug concentration-time profiles in body fluid are quantitatively analyzed and interpreted using PK concepts, which guide drug development at a population level and pharmacological management of individual patients. In other words, PK analyses of concentrationdtime curves provide information necessary to define the dose-concentration-effect relationship, establishing appropriate dosing schedules. The body fluid for drug concentration measurement is usually plasma, serum or blood (in this chapter, drug concentrations are assumed to be measured in plasma, unless stated otherwise). Although the time course and mode of drug administration influences the concentration-time curves, a common pattern emerges after sufficient time has elapsed from the last dose. For example, after intravenous bolus injection of a drug at a therapeutic dose, plasma drug concentration starts decreasing from the initial peak, following a pattern of a curve that can be best described by a single exponential decay function with the base e in a form of e
a∙x, or a combination of them. This observed pattern of decay is a process common to many phenomena in nature, where a quantity of a substance is shrinking at a declining rate proportional to its present quantity as seen in radioactivity decay. A general form of such a function as a plasma drug concentrationdtime curve is given by: C¼

n X

Ai ∙e
li ∙t

(1)

i¼1

A variable C is a plasma drug concentration at time t, and
li is a constant that defines a log-linear slope of each exponential function as
l/Ln10 or
l/2.303 , and has a unit of reciprocal time (e.g., min
1). Ai corresponds to the Y-intercept of each exponential term. A mono-exponential decay (n ¼ 1 in Eq. 1) is the simplest form: C ¼ A∙e
l∙t

(2)

In Eq. (1) and Eq. (2) (as a special case of Eq. 1) describing drug concentrationdtime profiles, the coefficient and the exponent are defined as mathematical parameters in relation to the curve. As seen below, however, a more physiologic interpretation is also possible. Assume a mono-exponential decay curve (Eq. 2) for simplicity. The derivative of a mono-exponential decay function (Eq. 2) provides the velocity or the rate of concentration change as follows: dC ¼
l∙C dt

(3)

The minus sign before l indicates that the change direction is quantity reduction as time elapses. In Eq. (3), the proportionality constant l determines the reduction rate of the concentration (i.e., dC/dt), which is proportional to C at a given time. It is evident from Eq. (3) that the rate or the velocity of concentration decay steadily slows down as time passes because C is declining. Note that Eqs. (1)–(3) are based on the assumption that the system volume remains unchanged. They can be rewritten as amount-based formulae. For example, Eq. (3) can be expressed as follows because C ¼ X ∙ (volume)
1 : dX ¼
k∙X dt

(4)

where X is the amount of drug in the system at time t, and k is called an elimination rate constant, indicating its defining role for the rate of drug elimination dX/dt. Although an elimination rate constant k in Eq. (4) and a concentration decay rate l in Eq. (3) are symbolized differently, they are equivalent.

1.18.2.1.2

First order process

This decay process is called “first order” because the left side of Eqs. (3) and (4) are a first order derivative, showing that the function C or X is differentiated once to derive the rate function that is linear. Decay curves of such a first-order process are log-linear (i.e., a straight line when plotted on logarithmic Y-axis). The most important pharmacological characteristics of a first-order process is a linearity of the relation between the rate of elimination (i.e., removal rate from the system) and the amount of drug in the system as shown in Eq. (4). When drug is administered regularly or continuously, drug concentrations/amount eventually reach a plateau (i.e., steady state: see also Section 1.18.2.4.1 below), showing no overall changes. At steady state, the rate of elimination and the rate of administration (i.e., dosing rate) are equal, maintaining a constant concentration and amount in the system. The first-order process gives rise to

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a situation that the relation between dose and concentration is proportional and linear: the higher the dosing rate, the higher the concentration, and vice versa. In contrast, when the plasma concentration decay rate is a constant (instead of being proportional to the existing concentration or amount shown in Eqs. 3 and 4), no differentiation is involved to derive the decay rate (because it is a constant), and it is considered the zeroth derivative; therefore, called zero order. Such a concentrationdtime profile gives a concave-shaped decay curve on logarithmic Y-axis, but a straight line when expressed on an ordinary Y-axis. In this article, we will only consider first-order processes.

1.18.2.1.3

Pharmacokinetic modeling analyses

Besides being a subject of philosophical debate, a model in science may be defined as a simplified version of a system in question (e.g., human body), which is amenable to quantitative treatment to assess, interpret and predict its behavior. PK analyses rely on this approach of data modeling, which has been proven very effective. Indeed, one of the early seminal works on time courses of drug concentration profiles is based on physiologically-based organ models with permeable boundaries (Teorell, 1937), similar to a compartment model. In practice, a series of discrete data points of drug concentrations over a range of time is subjected to a statistical process of curve fitting to construct a model of the concentrationdtime data. As described above, most drug concentrationdtime curves are described best with a mono or multi-exponential function (Eqs. 1 and 2). With this prior knowledge, such a function is fitted to the drug concentrationdtime data to select one with best fit characteristics. In other words, an exponential function is a mathematical model for the data of drug concentrationdtime curves for most cases in therapeutic context. Importantly, such an exponential equation is also a mathematical expression of substance decay time courses in a system consisting of one or more inter-connected compartments, implying that drug concentrationdtime profiles can be theorized using a compartment model. Fig. 1 shows the conceptual links among human body (physiology), drug concentration data, mathematical models and pictorial compartment models. This model-dependent approach may require an iterative process of model refinement and data fitting until a suitable model is identified for a particular concentrationdtime profile. Instead of identifying the best model structure through data fitting, a mathematical model of a pre-defined structure (e.g., bi-exponential function, instead of mono- or tri-exponential function) may be used to describe a whole dataset of drug concentrationdtime curves of a study population. In fact, this is often the way PK data are analyzed. In contrast to the model-dependent approach, the area under the concentration-time profile (AUC) can be calculated without assuming a model, and therefore clearance can be also estimated without being heavily dependent on a compartment model. This model-independent approach may be used to estimate some other PK parameters, which are described briefly later.

1.18.2.2

Compartment models

A compartment model in PK analyses may be seen as a concept linking physiological/anatomical reality to an observed drug concentrationdtime curve. As described above, under an assumption that substance elimination from the system and transfer between compartments is a first-order process, exponential functions as seen in Eq. (1) provide mathematical descriptions of a compartment model. Drug input to the system is an independent variable, which varies ranging from an instantaneous single input (e.g., a single

Fig. 1 PK analyses. Observed drug concentration data may be used to directly derive AUC by trapezoidal methods and then Clearance . and then the function is used to estimate PK parameters . The data may be also fit to a function such as a bi-exponential decay Alternatively, the curve function may be pre-defined from a model or the best model may be identified from the data . The model-dependent analyses also generate model PK parameters .

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(A)

(B)

Fig. 2 Compartment models. (A): A 1-compartment model is mathematically expressed as a single exponential term: C is drug concentration at time t, and C0 is the concentration at time 0 immediately after intravenous injection. k is an elimination rate constant. (B): Three different 2-compartment models are shown, which have different modes of drug elimination: from the central compartment (1), the tissue compartment (2), or both. All three 2compartment models are expressed using a bi-exponential function, and therefore a plasma concentration profile alone cannot distinguish these models. Note that the coefficients and exponents of the equation are hybrid constants of the micro-rate constants (e.g., k10, k12, and k21).

intravenous bolus dose) to continuous infusion or multiple repetitive doses at a fixed dosing interval. In this chapter, we assume either a single intravenous bolus dose, multiple regular doses or constant infusion at a fixed rate. While exponential functions represent compartment models, pictorial expressions provide qualitative characteristics of a model, which consists of one or more than one inter-connected compartments (Fig. 2). Each compartment is filled with plasma or blood as a matrix, reflecting the fact that the drug concentration data to be modeled are based on plasma or blood concentrations. A drug concentration curve is a time-dependent variable of a main compartment (also called a central compartment), and a process of drug elimination from the system is usually assigned to this main compartment. Key assumptions of a compartment model are: (1) homogeneous and instantaneous distribution of drug within a compartment; and (2) each compartment consists of the same body fluid (e.g., plasma). As the last assumption indicates, these pictorial compartment models in PK analyses are not necessarily a faithful copy of the anatomical and physiological entity. Below, a one-compartment model will be explained first to facilitate understanding of PK parameter concepts. A twocompartment model will be also discussed as a basic form of multi-compartment models.

1.18.2.3

One-compartment model: Structure and mathematical expression

This is the simplest of all compartment models. In this model (Fig. 2A), the entire system is a single compartment. The volume of the compartment is known as apparent volume of distribution (Vd), and drug is assumed to distribute evenly throughout the compartment. It is an “apparent” volume due to its model-based imaginary nature. In other words, it is a volume of a space in a model, which is defined independently of physiological space volume. In order to distinguish it from a real volume of physiological spaces such as extracellular fluid space, the term “apparent” is used as a prefix. However, pathophysiological changes in fluid sizes in the body may influence this model volume parameter. Note that model volume parameters in this chapter are depicted without a term “apparent” for simplicity. Also, the term Vd is used to discuss the concept in general and also distribution volume in a 1-compartment model, which has a single volume parameter. The mathematical expression of a 1-compartment model is also simple. Eq. (2) is the general equation of a concentration decay curve of a one-compartment model, when a drug is given as an instant single dose such as intravenous bolus injection. This can be rewritten below in Eq. (5) by depicting the coefficient and the exponent as PK-specific symbols: Ct ¼ C0 ∙e
kt

(5)

where Ct and C0are the drug concentrations in the system at time t and time 0, respectively. Therefore, C0is a Y-intercept of the curve. In Eq. (5), k is an elimination rate constant shown in the pictorial model (Fig. 2A), equivalent to l in Eqs. (2), (3) and (4) as well, defining the declining log-linear slope of the decay curve. Although the exact shape of the entire concentrationdtime profile depends on a mode of drug administration, Eq. (5) defines a decay phase of the concentration profile.

1.18.2.3.1

Two-compartment model: Structure

Multi-compartment models for PK purposes usually have a central compartment connected to multiple peripheral compartments that are independent of each other. This pattern of compartment connection is known as a mammillary structure by analogy with

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443

a configuration of the multiple mammary lobules draining individually into a lactiferous duct toward the nipple. In a twocompartment model, a peripheral compartment and the central compartment are connected by micro-rate constants of intercompartment transfer processes that are assumed to be first order as well. Two-compartment models (Fig. 2B) are discussed here as a representative of multi-compartment models. In this pictorial model of a two-compartment structure, the central and the peripheral compartments are connected by two intercompartmental transfer rate constants (k12 and k21), and the model-based elimination rate constant from the central compartment is k10. Inter-compartmental transfer rate constants (e.g., k12)and model elimination rate constants (e.g., k20 and k10) in these pictorial models are also known collectively as micro-rate constants to distinguish them from system rate constants (for example, a system elimination rate constant b defined in the terminal log-linear portion of a drug concentration-time profile). As discussed before, in a one-compartment model, a model-based elimination micro-rate constant k (or k10) is equivalent to an exponent of a monoexponential concentration decay curve. However, in a two-compartment model (Fig. 2B), a model-based elimination rate constant k10 or k20 is not the same as an exponent b of a terminal elimination phase derived from a bi-exponential decay function of the concentration curve. As described later, b is a hybrid constant consisting of micro-rate constants. This holds true for all other multi-compartment models. Typically, a central compartment is assumed to bear the function of drug elimination. Major drug eliminating organs, liver and kidney, are perfused well, and once the drug is in the systemic circulation, it is likely to be distributed into these organs quickly. Although they are anatomically distinct from intravascular fluid space (i.e., whole blood or plasma), distribution of drug into these organs is rapid enough to consider them as part of the central compartment in a PK model. If there exists a rate limiting step of distribution into liver and/or kidney, these organs cannot be viewed as a central compartment. For example, ICG (indocyanine green) and BSP (bromosulfophthalein), which are used as markers of hepatic blood flow, undergo biliary secretion but they are also substrates of hepatic uptake transporters. Apparently, hepatic uptake is the rate limiting step before elimination into the bile, and therefore, their distribution into the drug eliminating organ is not instantaneous. This warrants the drug eliminating mechanism as an attribute of a peripheral compartment, but not in the central compartment. Similarly, one may assume a two-compartment model with drug eliminating capability from both a central and peripheral compartment. However, as described below in the section of a mathematical expression of twocompartment models, the drug concentrationdtime curve alone does not uniquely identify each model. In other words, those three 2-compartment models with different elimination pathways may give rise to the same curve with the same mathematical model structure. As discussed above in one-compartment models, volume parameters in a two-compartment model are also “apparent.” Because there are two compartments, each compartment has a volume parameter. In addition, at least two other volume parameters exist representing a combined volume of the central and tissue compartments, all of which are described in detail below.

1.18.2.3.2

Two-compartment model: Mathematical expression

Decay profiles of drug concentrations in the central compartment of different two-compartment models in Fig. 2B can be all described by a bi-exponential function, indicating that a drug concentration decay profile in plasma defines the number of compartments but does not inform detailed model structures. Assuming that the drug is given intravenously into the central compartment (i.e., blood) as a bolus, a bi-exponential equation is as follows: Ct ¼ A∙e
at þ B∙e
bt

(6)

where coefficient A and B are Y-intercepts of each of the two exponential terms at time 0, respectively, and exponents a and b indicate a log-linear slope of the decay curve of each exponential function in a form of
a ∙ ln 10
1 and
b ∙ ln 10
1 . In PK analyses, it is customary to define a > b, so that the first exponential term represents an initial rapid decay phase of the curve, combining processes of tissue distribution and elimination. Also, a peak concentration immediately after the rapid injection is a sum of A and B. Because the initial decline of the drug concentrations in the central compartment is dependent on not only out-of-system elimination but also within-system transfer of the drug from the central to peripheral compartment, it is called a distribution phase (or a -phase). The second exponential term shows the terminal phase (or b -phase) of the decay curve. Drug concentrations in a terminal phase are declining in both the central and peripheral/tissue compartments. This parallel relation justifies comparing plasma drug concentrations during this phase with a reference value to infer target site concentrations of the drug in the tissue compartment. Importantly, the exponent a and b are hybrid parameters of the micro rate constants (Fig. 2B). For example, the relation between the model-based micro-rate constants and the exponents of the curve in a commonly used model of central elimination (Fig. 2B, left) is described as follows: ( a þ b ¼ k12 þ k21 þ k10 (7) a∙b ¼ k21 ∙k10 This is in contrast with the equivalence between an exponent k of a mono-exponential decay function (Eq. 5) and a model elimination rate constant k in a one compartment model.

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1.18.2.4

Basic concepts of PK parameters

The distribution process influences drug concentration-time profiles and must be interpreted in the context of the other key PK processes. So far, the abstract nature of compartment models has been emphasized; for example, volume of distribution (Vd) is a model parameter, but not an actual volume of a physiological space. However, the lack of 1:1 relation between a value of Vd and a volume of physiological fluid space does not mean that Vd is completely independent of the fluid spaces in the body. In fact, alterations of physiological space volumes actually influence a model parameter Vd by affecting the drug concentration curve. Similarly, the significance of clearance (CL), which is deeply rooted in the physiological function of drug eliminating organs, can be appreciated also as a parameter of a compartment model. The following sections describe a systems view of PK parameters. Details of PK parameter descriptions are provided in other chapters of this reference work.

1.18.2.4.1

Steady-state

The concept of steady state is essential in capturing the significance of PK phenomena including distribution processes. In clinical settings, steady state is also important because this is often a target state to achieve therapeutic effects. Assume that a central compartment receives a drug at a constant rate (i.e., continuous infusion). As shown in Fig. 3, drug concentrations in the system will increase as the drug accumulates, and eventually reach a dynamic equilibrium state, known as steady state, where drug concentration maintains a plateau and remains stable (steady-state concentration: [C]ss), and the amount of drug coming in per time is the same as that coming out. In the case of repeated doses at a fixed interval, the concentrationdtime profile of a dose at steady state becomes virtually identical to that of the next dose, and AUCs (area under the curve during the dosing interval s at steady state) of one dosing interval is the same as that of the next. In addition, an average concentration at steady state (equivalent to [C]ss defined above) can be defined in the regular intermittent dosing. At steady state, dosing rate (e.g., 1 mg/h) becomes identical to an elimination rate, and there is no overall increase or decrease of the amount of drug in the system. When a drug is administered at a constant rate (i.e., continuous infusion or dosing at a regular interval), time to reach a steady state is approximately four times of an elimination half-life of the drug. This may be explained using dosing at a regular interval equivalent to drug half-life as an example. Assume that regular dosing started. At Dose 5, time of 4 half-life elapses from the start of the therapy, and drug molecules from Dose 1 are almost gone (because 4 half-lives later about 93% of drug is eliminated), leaving Dose 2–5. At Dose 6, molecules from Dose 2 are gone as well, leaving Dose 3–6, which is the same pattern as the previous situation of Dose 5. This pattern repeats itself causing a dynamic equilibrium, a steady-state.

1.18.2.4.2

Clearance (CL)

Distribution volume in PK analyses cannot be fully defined without a concept of clearance. From a physiological viewpoint, clearance is a volume of plasma or blood from which the drug is removed per time (its unit is volume ∙ time
1), representing an efficiency parameter of the drug eliminating mechanism for the particular drug in question. It is a common observation that the rate of elimination (i.e., amount of drug removed from the system per time) is proportional to drug concentration in the system at that moment for most drugs at therapeutic doses. CL has a PK characteristic as a proportionality constant relating drug concentration at time t (Ct) to the rate of elimination at time t: Elimination rate ¼ CL  Ct

(A)

(8)

(C)

(B)

Fig. 3 Steady state. (Panel A): When drug is administered at a regular rate D, initially drug concentrations C are low, and the removal rates R are lower than D, causing drug accumulation in the system. (Panel B): Drug concentration increases as the drug accumulates and eventually reach a dynamic equilibrium state, known as steady state. At steady state, drug concentration maintains a plateau and remains stable (steady-state concentration: Css), and D (the amount of drug coming in per time) is the same as that of coming out Rss. (Panel C): Schematic representation of the time course of drug accumulation toward steady state is shown.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

445

As shown in Eqs. (3) and (4) before, an elimination rate at time t (i.e., dC/dt as a concentration decay rate in Eq. (3) or dX/dt as an amount elimination rate in Eq. 4) is proportional to the respective quantity of drug in the body at the present time t. Eq. (8) can be seen as a modified form of Eqs. (3) and (4), representing a first-order relationship between the drug concentrations and the elimination rate. The concept of CL shown in Eq. (8), which is defined with a moment of time t, can be expanded as follows. First, in a single dose intravenous administration, an integral of the left-hand side of Eq. (8) is a total eliminated amount that is the same as the dose given. An integral of the right-hand side gives: CL ∙ !N 0 Ctdt ¼ CL ∙ AUC. From this, a common definition of CL for a single dose intravenous injection is obtained as: CL ¼

Dose AUC

(9)

For extra-vascular administration such as oral dosing, the numerator becomes Bioavailability  Dose. Second, at steady state, elimination rate and dosing rate are dynamically equilibrated and identical, and there is no net increase or decrease of drug amount in the system over time. Eq. (8) can be, therefore, converted to the following: Elimination rate ¼ Dosing rate ¼ CL  ½Css

(10)

Because [C]ss multiplied by a dosing interval s gives AUCs, Eq. (10) can be further rearranged to the defining formula of CL based on AUC (Eq. 9). As seen in these definitions, CL can be determined without assuming a model because AUC itself can be measured directly. Systemic clearance is the sum of all clearance pathways in the system. Because liver and kidney are major drug eliminating organs for most drugs, CL often represents hepatic and/or renal clearance. Between-individual differences in function of these organs, such as expression levels of drug metabolizing enzymes, introduce inter-individual variations in CL values of a drug. Similarly, betweendrug differences in factors including enzyme/transporter affinity and protein binding become sources for the wide spectrum of CL values among different drugs. Importantly, CL is defined without a reference to Vd, implying that they are independent of each other. In other words, CL changes do not influence Vd, or vice versa, although they are related mathematically via an elimination rate constant k as described below.

1.18.2.4.3

Apparent volume of distribution (Vd)

Apparent volume of distribution (Vd), or simply volume of distribution, indicates an extent of a distribution process. Vd has a dimension of volume, which is in contrast to CL. Because a drug with a mono-exponential decay curve consistent with a 1compartment model has only one Vd parameter, such a drug is assumed here to explain the concept of Vd. Multiple Vd parameters in a 2-compartment model are discussed later. While Vd is an imaginary volume of the system, there exist determinants of Vd including physiological factors as well as physicochemical characteristics of the drug itself. Extracellular fluid space is one of the host factors influencing Vd. Pregnant women and infants are well-known examples of substantial increase in extracellular fluid spaces per body weight, which is reflected in Vd increase (per body weight) of many hydrophilic drugs including antimicrobials such as aminoglycosides and beta-lactams. Defined as volume of whole-blood or plasma (depending on the matrix of drug concentration measurement) filling the space(s), Vd provides a snapshot at time t, relating an amount to a concentration (Ct): Amount in the system ¼ Vd  Ct

(11)

Drug concentration at time 0 immediately after a bolus injection into the system can be estimated by dividing the dose by Vd. Alternatively, if a target concentration is known, a dose required to reach the target concentration can be estimated by multiplying the target concentration by Vd. This is a theoretical base for a loading dose estimation for a drug with relatively long half-life to reach a therapeutic concentration range as quickly as possible, which is otherwise achieved 4 half-life later at a regular dosing rate. Confusions may arise about Vd if the following points are not clearly understood. First, it is not a volume of a physical space. Vd represents a volume of an imagined compartment, but not a physiological fluid space itself. A simple example is a drug that quickly distributes into extracellular fluid space and binds heavily to extra-vascular tissues. Only a fraction remains in plasma, and a drug concentration in plasma is relatively low. Because a model assumes single drug concentration across compartments, this low plasma concentration must be explained by an extremely large volume of the compartment, greatly exceeding the size of the extracellular fluid space that is the real space for the drug to distribute. Second, the central compartment is sometimes called the plasma compartment to distinguish it from a tissue space. This may be confusing because plasma has its own physiological volume, independent of the PK of each drug. In addition, the central compartment usually includes highly perfused extravascular tissues and organs. The term “plasma or blood compartment” by itself does not distinguish a model-based concept (i.e., a central compartment) from a physiological space, and therefore, caution should be exercised. Thirdly, despite its imaginary nature, the Vd value of a drug may become similar to the value of the physiological fluid volume. For example, if the drug is hydrophilic without appreciable binding to tissue proteins or cellular uptake, and if it has a property as a probe marker for an extracellular fluid, then the model derived Vd of this drug equals the volume of the extracellular fluid space.

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

While the models are abstract forms of physical reality as described above, it is important to note that certain levels of relation exist between model parameters and specific physiological factors. In other words, alterations in physiological factors, including organ function and blood flow, fluid volume and protein binding capacity, may be reflected in model parameter changes.

1.18.2.4.4

Elimination rate constant

Drug is eliminated from the compartment at a rate (i.e., amount per time) proportional to the amount of the drug in the system at that time point (Eq. 4). As the amount of drug in the system changes, the elimination rate also changes proportionally as time goes by. The proportionality constant is the rate constant k, which has a dimension of reciprocal time. In a 1-compartment model, k can be estimated directly from a mono-exponential decay curve as an exponent (Eq. 5), a log-linear slope of the curve. In a 2-compartment model with a bi-exponential decay curve (Eq. 6), an elimination half-life is measured in the terminal phase (b-phase), and the exponent b characterizes the log-linear slope (
b/ ln 10). Therefore, the elimination rate constant k in a one-compartment model and the exponent b in a bi-exponential function for a 2-compartment model play a similar role in drug elimination. However, both a and b in a bi-exponential function are hybrids of the model-based micro-rate constants as described before in Eq. (7). In contrast to CL and Vd, a physiological base unique only to k does not exist. In fact, physiological determinants specific to CL or Vd (such as enzyme function for CL and extracellular fluid space for Vd) also influence the elimination rate constant, implying that k is a composite parameter of the two underlying elements, CL and Vd. Physiologically speaking, k is determined by the two independent causative elements, CL and Vd.

1.18.2.4.5

Relationship among CL, Vd and k

From the physiological perspective, the causal relationship among these parameters may be explained as follows. CL of a drug represents a volume of the system, of which the drug is cleared per time (i.e., the dimension is volume/time). The system itself has a volume of Vd. CL is an efficiency parameter of the drug eliminating organs, such as enzyme expression levels; and Vd is determined mainly by fluid space and drug-specific physicochemical characteristics. These two parameters are clearly independent of each other because one parameter can be changed without affecting the other. While these two parameters (Vd and CL) are independent of each other, they both have an impact on the processing time required to remove the drug. For example, in a system with a volume of Vd, CL may be seen as a mechanism with capability of cleaning a certain volume of the space per time by removing drug molecules from the system. The lower the CL (smaller volume cleaned per time, and so less efficient), the longer the elimination processing time. Also, the larger the Vd of the system, the longer the elimination process, because larger spaces need to be cleared of drug. This implies that CL is positively related to the velocity of the cleaning time (i.e., elimination speed), and Vd is inversely related to the velocity. Under the assumption of a first-order process, changes of elimination velocity or rate are proportional to the amount of drug in the body at that time point (Eq. 4), indicating that the amount removed per time is not constant but rather it changes with concentrations of the drug, a feature characteristic to the first-order process. The relation shown in Eq. (4) as a differential equation can be conceptualized as: Elimination rate at time t ¼ k  Amount at time t

(12)

Because elimination rate is also expressed as CL multiplied by Ct (Eq. 8), (Eq. 12) becomes: CL  Ct ¼ k  Amount at time t

(13)

Given that Ct ¼ (Amount at time t)/Vd, Eq. (13) can be rearranged as follows: CL ¼k Vd

(14)

An elimination rate constant k is a ratio of the two independent parameters, CL and Vd, and therefore, it should be considered a secondary parameter dependent on the two primary parameters. From an observed elimination half-life or k, one may calculate Vd by dividing CL by k (Eq. 14), if CL is known. Similarly, if Vd is known, CL can be estimated. However, this mathematical relationship cannot be taken as evidence of a physiological causality between CL and Vd because there is no mechanistic inter-dependence between Vd and CL. So far, the discussion on PK parameters including distribution volume is based on a mono-exponential decay curve and a 1compartment model to facilitate the understanding. Because a 2-compartment model with a bi-exponential function is known to describe disposition of many drugs in therapeutic settings, distribution volume in a 2-compartment model is addressed next.

1.18.2.5

Apparent volume of distribution in a two-compartment model

As described above, there is a single volume parameter in a one-compartment model, and the concept is relatively simple. However, multi-compartment models have at least three PK parameters describing distribution volume. In other words, a one-compartment model is a special case, in which different PK volume parameters all collapse into a single distribution volume. In this section, these three PK parameters depicting different Vd are described with a two-compartment model (with elimination from the central compartment) as a representative of a multi-compartment model. This is also because a two-compartment model is probably

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

447

the most common model for many drugs. Because there are more than one volume terms for drugs with a multi-exponential decay curve, each volume term will be denoted with specific subscript and the term V without a subscript will be used to describe a general concept and a total distribution volume of the body. Two approaches exist to define distribution volume: (1) model-based; and (2) concentration profile-based. These two frameworks represent two different methods to derive and describe a distribution volume. In the following sections, each of these distribution parameters is discussed using these two frameworks in a two-compartment model with an elimination path from the central compartment as a representative of multi-compartment (exponential) models.

1.18.2.5.1

Central compartment: VC

A two-compartment model usually consists of a central compartment (shown as Compartment 1 in Fig. 2B), from where a drug is removed irreversibly, and a peripheral/tissue compartment. Blood and highly perfused organs are included in the central compartment, and a drug elimination mechanism is assumed to exist in the central compartment. While a physio-anatomical model can be constructed with an assumption that the tissue and the central compartment have different drug concentrations, a PK-based model further simplifies the reality, describing equal drug concentrations in all compartments. Distribution volume of the central compartment Vc is synonymous to an initial dilution volume (V1). This model parameter is estimated from an observed peak concentration immediately after intravascular (intravenous or arterial) injection, and the dose given as follows: Vc ¼

Dose AþB

(15)

(A þ B) is a peak concentration immediately after injection, and each represents a coefficient of one of the exponential terms in a bi-exponential function (Eq. 6). Vc is useful in determining a dose to achieve a target peak concentration in blood. An example is aminoglycoside antimicrobials such as gentamicin. Therapeutic target sites of gentamicin are plasma and highly perfused organs (and urine), where pathogenic bacteria reside. Therefore, achieving certain target plasma concentrations are crucial for its therapeutic effects. On the other hand, gentamicin is toxic to cells once it is taken up by cells, manifesting as renal toxicity and/or ototoxicity. Because intracellular space for gentamicin behaves as a peripheral compartment, and distribution into cells is relatively slow compared to elimination from the central compartment, a dosing regimen is designed to give the drug intermittently (instead of continuously), so that a trough concentration can be kept minimal to avoid tissue/cellular accumulation while a target peak concentration can be attained.

1.18.2.5.2

Peripheral/tissue compartment: VT

Assume a simplified physio-anatomical model that has a system volume of V* (an asterisk symbol indicates that this is based on a physio-anatomical model) consisting of two compartments: a central compartment with a volume VC* (including plasma) and a peripheral/tissue compartment with VT*. V  ¼ VC þ VT

(16)

It is important to distinguish this model concept from that of a PK model described next, although pictorial expressions of the two models are similar except for definitions of drug concentrations in the tissue compartment. If we describe drug disposition in the body qualitatively in this physio-anatomical model, the following picture emerges. After an intravenous injection into the central compartment, the drug immediately fills the central compartment and starts exiting from the system while also distributing into a peripheral/tissue compartment within the system. While a concentration in the central compartment keeps declining due to elimination and distribution, drug concentration in the peripheral/tissue compartment is rising due to distribution from the central compartment. As time progresses, a concentration gradient between the central to the peripheral compartment is diminishing, and the rise of drug concentration in the peripheral/tissue compartment becomes increasingly slower and eventually stops. From that point on, a concentration gradient is reversed, and a net movement of drug molecules is from the peripheral/tissue to the central compartment, and concentrations in these two compartments decline in parallel, which is known as a terminal elimination phase (also known as b-phase in a bi-exponential function). If a drug is given as a constant infusion intravenously, plasma concentration reaches a plateau after sufficient time (i.e., 4–5 half-life later). This is a steady state as the amount of drug in the entire system (i.e., body) remains steady while the same amount of drug enters and exits the system continually. Also, there is no net movement of drug across the compartmental boundary. Note that in order to describe model behavior the above explanation simplifies the tissue compartment by regarding it as a single space with a boundary allowing diffusion. In the model above, because of its physio-anatomical basis resembling a real body, volumes of these compartments including the peripheral/tissue compartment are obviously indifferent to time after dosing (i.e., fixed). As described below, this premise changes when the model is converted to a PK-based model that characterizes both the central and peripheral/tissue compartments with the same drug concentrations (i.e., plasma or blood concentration) instead of using often unmeasurable tissue concentrations of drug for the tissue compartment. This modification from a physical volume to an apparent volume in a PK space increases versatility of the model but alters the definition of the distribution volume of the tissue compartment (volume of the central compartment remains the same). The conversion process is explained below. The amount of drug in the peripheral/tissue compartment in the physio-anatomical model is expressed as: CT  VT*, where CT is drug concentration in the tissue compartment, and VT* is the volume of the physio-anatomical tissue compartment.

448

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

Because a PK model assumes drug concentrations are equal across the compartments, volume of distribution of the tissue compartment (VT) in a PK model is defined with plasma drug concentration (CP): CT  VT*h CP  VT. Therefore, VT is expressed as: VT ¼

CT  ∙V CP T

(17)

Using VT, apparent volume of distribution of the body (V) in a PK-based model is defined as: V ¼ VC þ VT

(18)

Note that VC* and VC are equal. Because CT/CP changes over time as described narratively above, VT (and V) in a PK model is dependent on post-dose time as seen in Eq. (17) and (18). Although it is counter-intuitive to assume that a volume of a tissue compartment depends on time after dosing, this is one of the consequences of the definition of “apparent” volume of distribution in a PK-based model. Although VT is an apparent parameter and does not guarantee one-on-one conformity with physiological fluid space, it is evident that physiological fluid volume, which influences VT*, is one of the determinants of VT and V. If a target site of drug for therapeutic and/or adverse effects is located in a peripheral/tissue compartment, then it is of clinical importance to assess VC þ VT, which influences an elimination half-life and therefore a time to steady state, a target state of therapeutic effects. Although the model concepts and resultant concentration profiles described above are relatively elementary, quantitative estimation of a model volume parameter VC þ VT (or VT, assuming that VC is known) from an observed bi-exponential function is not that simple. As described below, there are two distinct approaches to estimate V (hVC þ VT). One is known as Varea or Vb, which is based on an observed terminal elimination phase of a concentration curve, and the other is “volume of distribution at steady state: VSS”, which is derived using a concept of Mean Residence Time and AUC.

1.18.2.5.3

Area-based distribution volume: Varea

Distribution volume of drug can be estimated from the concentration decay curve, using AUC and the exponent of the terminal elimination phase (b), which is known as Vareaor Vb. As described before, a bi-exponential concentration decay curve after intravenous bolus injection, consistent with a two-compartment model, has an initial segment of rapid decline known as a distribution phase or a-phase, and a terminal portion of slower decay known as an elimination phase or b-phase. A distribution phase reflects disappearance of drug from the central to the peripheral/tissue compartment (i.e., distribution) as well as from the central compartment to outside of the body (i.e., elimination). During the terminal elimination phase, a drug concentration curve in plasma is expressed as the second term of Eq. (6) as follows: B ∙ e
bt. This observed terminal portion of the curve may be viewed as manifestation of drug disposition after the end of the net drug distribution into the physio-anatomical peripheral/tissue compartment VT* (Eq. 16). In this phase, drug concentrations in the central and peripheral compartments decline in parallel, behaving as if there is one amalgamated compartment. Therefore, one may also assume that the mono-exponential decay function B ∙ e
bt represents a concentration profile in the combined single compartment from time 0, if the distribution equilibrium is achieved at time ¼ 0. In other words, a parameter of apparent volume of distribution derived from the terminal b phase is a reasonable approximation of VC þ VT. This term is known as Varea or Vb. There are different ways to define it, but the simplest is to use a general definition of an elimination rate constant (Eq. 14). Because the concept of Varea is based on a drug concentration decay curve with a mono-exponential function, which is equivalent to a one-compartment model, b can be defined as follows: CL ¼b Varea

(19)

Because CL is Dose/AUC, the following equation is obtained: Varea ¼

CL Dose ¼ b b  AUC

(20)

Varea is coined as a result of the use of AUC in its definition. Varea can be also expressed in model terms without using AUC. In a two-compartment model, the central compartment is characterized with clearance (CL), a distribution volume (VC) and an elimination rate constant (k10): CL ¼ k10 VC

(21)

Because Varea ¼ CL/b, Eq. (21) above can be rearranged as:   k10 Varea or Vb ¼ VC  b

(22)

In this case, the parameter of volume of distribution is described as Vb. These distribution terms Varea and Vb are equivalent and used interchangeably.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations 1.18.2.5.4

449

Volume of distribution at steady state: Vss

The other parameter of distribution volume can be defined at steady state, which is known as “volume of distribution at steady state: Vss ”. As a PK concept, this is arguably the most intuitive distribution parameter although its practical value in clinical settings may be limited. Two approaches exist to define Vss: a model-dependent approach; and a method based on a statistical moment theory. In a two-compartment model (with elimination from the central compartment) at steady state under continuous/repeated dosing, the rate of drug distribution into a peripheral compartment and the rate of drug returning from the peripheral compartment are equal, maintaining a dynamic equilibrium with no net movement of drug across the plasma-tissue boundary. The plasma drug concentration is also steady with equal input and output (elimination) of drug per time in the entire system. A rate of drug distribution is expressed as: A1 x k12, where A1 is an amount of drug at steady state in the central compartment, and k12 is a micro-rate constant for drug movement from the central to peripheral compartment. Similarly, a rate of drug re-distributing into the central compartment is: A2 x k21, where A2 is an amount of drug in the peripheral compartment at steady state. At steady state, therefore, A1 x k12 ¼ A2 x k21, and A2/A1 ¼ k12/k21 . As described above in the definition of V, concentrations of the central and peripheral compartments in PK models are equal: A1/VC ¼ A2/VT. Therefore, VT ¼ VC  (A2/A1) . Rearranging these relationships, one obtains Vss based on model-derived micro-rate constants:   k21 þ k12 (23) Vss ¼ VC þ VT ¼ VC  k21 The other approach to estimate Vss was originally devised using a statistical moment theory. Here, rather than going into the details of the theory, its pharmacological interpretation is presented. This concept is indifferent to a compartment model, and therefore applicable to any pattern of exponential decay curves. However, the model-based definition above (Eq. 23) and the modelindependent definition described next are equivalent and interchangeable. Again, we assume that a drug is given intravenously as a bolus unless otherwise stated. Mean Residence Time (MRT) is used to define Vss without assuming a model. MRT can be qualitatively described as a mean duration of time drug molecules spend in the system. MRT is different from an elimination half-life as MRT is equivalent to time for a drug concentration to be 37% (i.e., 1/e) of the starting concentration, rather than 50%. This is evident for drug disposition characterized by a mono-exponential decay, but occasional deviation from this rule may be seen for drugs with bi-exponential decay characteristics (Sobol and Bialer, 2004). A pharmacological base to derive MRT can be explained as follows. Simply put, if one knows the amount of drug removed from the system for a very short period Dt around time t1 (i.e., a removed amount at time t1), it may be multiplied by the time t1to generate a sum of “residence time” of a group of drug molecules that have a residence time of t1 in the system. This process may be repeated as integration, which gives a total sum of the residence time for all drug molecules. By dividing this total sum of the residence time by the total number of the drug molecules, MRT can be derived. As shown in Eq. (8), the following relationship exists between CL and removal rate: CL  Ct ¼ Removal rate at time t. By multiplying both sides with a very short period of time Dt, one obtains: CL  Ct  Dt ¼ Amount removed around time t

(24)

The righthand side of Eq. (24) is equivalent to the amount of drug molecules with residence time t. By further multiplying both sides of Eq. (24) by time t and then integrating it, a total sum of residence time (total residence time) can be expressed as: Z N CL  t∙Ct dt ¼ Total Residence Time (25) 0

N

In Eq. (25), !0 t ∙ Ctdt is known as Area Under the first Moment Curve (AUMC). Because a mean of Total Residence Time, which is MRT, can be calculated by dividing Total Residence Time with a total sum of drug molecules, which is the dose given, MRT is expressed as: MRT ¼

CL  AUMC Dose

(26)

Because CL is Dose divided by AUC, Eq. (26) can be re-arranged as: MRT ¼

AUMC AUC

(27)

Although the definition of MRT is not dependent on an assumption of a model (i.e., model-independent), it can be converted to a form expressed with coefficients of the concentration decay curve. For example, if a drug follows a bi-exponential decay (A,e–a þ B∙e–b), AUMC and AUC can be expressed using coefficients and exponents of the decay curve as follows (Eq. 28): AUMC a2 þ b2 ¼ A B AUC aþb A

MRT ¼

B

(28)

Because these coefficients and exponents are hybrid parameters of micro-rate constants, they can be expressed with the modeldependent micro-rate constants as well. Note that the above description applies to drug disposition following bolus injection. If

450

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

(A)

(B)

Fig. 4 Mean residence time (MRT). (Panel A): Once the continuous infusion is stopped at steady state, drug concentration starts decreasing. The amount of drug remaining in the system at the end of infusion is expressed using drug concentration at steady state (Css) as follows: Css  Vss. This amount is identical to that eliminated during the post-dose phase: CL  AUCpost-dose. Therefore, CL  AUCpost-dose ¼ Css  Vss. Because Css  MRT (shaded area) is AUCpost-dose, CL  MRT ¼ Vss. (Panel B): If drug is instantly distributed to VSS after a bolus injection of a dose (D), D/VSS is the concentration at time 0. Because (D/VSS)  MRT (shaded area) becomes AUC which is D/CL, one obtains CL  MRT ¼ VSS.

drug administration is not by bolus injection, the MRT calculation needs to be modified to account for residence time of drugs during the administration processes. Using MRT, Vss may be derived as follows (Fig. 4). First, assume a steady state during continuous infusion of a drug. Once the infusion is stopped, drug concentration starts decreasing. Note that, although this process may follow mono-, bi- or other multiexponential decay functions, MRT-based definition of Vss is not dependent on these specific decay patterns. At the end of the continuous infusion, the amount of drug remaining in the system can be expressed using drug concentration at steady state (Css) as follows: Amount in body at steady state

¼ Css  Vss

(29)

This amount that is remaining in the system at steady state when the continuous infusion stopped will be completely removed from the system, and this process is seen as a post-infusion decay curve. CL multiplied by AUC of this curve from post-infusion time 0 to infinity (AUCpost
dose) represents the amount of drug in the system at the beginning of the decay: CL  AUCpost
dose ¼ CSS  VSS

(30)

AUCpost
dose can be transformed to a rectangle consisting of post-dose MRT on the time axis (x-axis) and CSS on the concentration axis (y-axis) (Fig. 4A). In other words, this is equivalent to a situation where all drug molecules spend the same duration of time identical to post-dose MRT in the system. Therefore, its concentration-time profile is a rectangular area on the concentration-time dimension. This area should be identical to that derived from an actual concentration-time profile, AUCpost
dose. Therefore, Eq. (30) can be rearranged as follows: CL  ðMRT  CSS Þ ¼ CSS  VSS

(31)

CL  MRT ¼ VSS

(32)

Similar concepts can be used to derive VSS from a single bolus injection (Fig. 4B) instead of a continuous infusion mode at steady state described above. In order to transform AUC to an MRT-based rectangle in the concentration-time dimension, an estimate of a concentration at time 0 is needed. This concentration is different from a conventional C0 because C0 is defined as a function of a dose and distribution volume of the central compartment VC; but not VSS, which is VC þ VT at steady state. If drug is instantly distributed into the whole system (i.e., a volume equivalent to VC þ VT at steady state), VSS-based concentration at time 0 is Dose/VSS. Therefore, (Dose/VSS)  MRT ¼ AUC. Because Dose/AUC is CL, the same expression as Eq. (32) is obtained.

1.18.2.5.5

Relationship among parameters of distribution volume

Three distinct PK parameters of distribution volume have been described: Vc ¼ Varea ¼

CL Dose ¼ k10 A þ B

(15, 21)

CL Dose ¼ b b  AUC

(20, 22)

 VSS ¼ CL  MRT ¼ VC 

k21 þ k12 k21

 (23, 32)

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

451

Because Varea and VSS are estimates of VC þ VT, it is natural that Varea and VSS are larger than Vc. Also, under most circumstances, Varea is larger than VSS. This may be understood intuitively using a concentrationdtime profile with bi-exponential decay as follows. While Varea is based on the b slope of the terminal phase, ignoring a-phase, VSS is based on (1/MRT), a log-linear slope of a re-shaped mono-exponential decay curve of the original bi-exponential curve with same AUC, which is likely to take a value between a and b (i.e., a > 1/MRT > b). From Eqs. (20) and (32), Varea is considered larger than VSS. A key point of these distribution PK parameters is the fact that they are proportionality constants relating CL to a rate of elimination that can be measured and/or defined in different ways. Because drug concentrations in a PK space are assumed to be all equal to drug concentration in plasma/serum/blood, distribution volume often exceeds that of a physio-anatomical space. However, a change in volume of fluid space in the body is one of the determinants of these distribution parameters.

1.18.2.5.6

Effects of changes in distribution volume on concentration profile

Given that CL and V are primary parameters defining secondary parameters including AUC, an elimination rate constant and halflife, it is useful to recognize overall influences of these parameter changes on drug concentration-time profiles. In Table 1, effects of isolated changes in volume of distribution on PK parameters are shown. At intermittent dosing at a regular interval, peak concentrations (Cmax) in a dosing interval are affected by volume of distribution particularly that of the central compartment; the larger the distribution volume, the lower the peak concentration. As an elimination half-life (ln2/k) is a function of the ratio between V and CL (CL/V ¼ k: Eq. 14), an increase in V prolongs elimination half-life. In contrast, V does not affect AUC because AUC is determined by a combination of bioavailability, dose and CL (Eq. 9). As described before, CL and V are independent primary parameters that are directly influenced by changes in pathophysiological factors, defining various rate constants. It is important to recognize that mathematical relationships among these parameters do not specify the direction of causation.

1.18.2.6

Obesity

The prevalence of obesity (BMI over 30 kg/m2) has been growing globally. Obesity may alter physiological and hemodynamic parameters and is variably associated with dysfunction of liver and/or kidney. PK parameter changes may also occur as a result. For example, volume of distribution of diazepam, a relatively lipophilic benzodiazepine, in obese subjects is significantly increased in both weight-standardized and non-weight-adjusted values (Abernethy et al., 1983), which is seemingly in accordance with the increase in the fat volume. Although it is tempting to assume that fluid compartment volume remains unchanged in obese subjects, both extracellular and intracellular fluid volumes are, in fact, expanded in obese subjects (Waki et al., 1991). This is consistent with increased cardiac output and additional adipose tissue in obese subjects, which contains extra- and intra-cellular water as well. Consequently, volume of distribution of hydrophilic drugs such as vancomycin is also increased in obese patients almost proportional to weight increase (Adane et al., 2015). This suggests that a body-weight standardized dose of vancomycin in obese patients is in a similar range to that of non-obese patients to achieve a desired therapeutic peak concentration. Dosing intervals of vancomycin need to reflect clearance values, which may be variably affected in obese patients due to co-morbidity.

1.18.2.7

Transporters and tissue distribution of drug

Internal boundaries between systemic circulation and tissues constitute functional gates controlling tissue transfer of drug molecules by various transporters and channels. Membrane proteins for this role are often designated as drug transporters. It is also customary to add a prefix indicating functional direction of substrate movement, such as efflux or uptake transporter in reference to the tissue of expression. Function of these transporters may influence drug concentration profiles in the tissue target site. Examples include OATP1B1 uptake transporter in hepatocytes controlling statin entry into the liver for HMG-CoA reductase inhibition and drug elimination, and ABCB1 or P-glycoprotein efflux transporter limiting brain penetration of opioids. Inhibition of these transporters increases or decreases tissue penetration of drugs. Although these effects give rise to significant pharmacodynamic consequences, their impact on systemic PK parameters of volume of distribution varies. Table 1

Effects of isolated changes in volume of distributiona on PK parameters and concentration profiles. Changes in volume of distribution

PK parameter

Increase

Decrease

Systemic clearance AUC Elimination rate constant: k Elimination half-life Cmax

No change No change Decrease Prolonged Decrease

No change No change Increase Shortened Increase

a

VC and VT.

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

Although there is no prevailing theoretical framework of mechanistic interpretation of effects of transporter function changes on systemic volume of distribution, inhibition of hepatic uptake transporters tends to decrease volume of distribution in the entire system. This may be a result of the relatively large volume of liver tissue, to which a substrate drug loses its access due to the uptake transporter inhibition. On the other hand, inhibition of renal tubular uptake transporters does not consistently show decrease in volume of distribution. It is speculated that distribution changes in the renal tubular cells have little impact on the systemic volume parameters due to the relatively small distribution volume of the tubular cells compared to the liver. While reduced volume of distribution associated with inhibited uptake transporters in the liver may be explained mechanistically, no unified explanation exists for effects of inhibited efflux transporters such as ABCG2 (BCRP) and ABCB1 (P-glycoprotein) on systemic parameters of volume of distribution.

1.18.3

PK characteristics in pregnancy

While certain pathological conditions affect PK parameters in a disease-specific manner, the two physiological attributes, pregnancy, and young age, have significant impact on PK parameters. In this section, pregnancy-associated PK changes are discussed.

1.18.3.1

Physiological transformation

Body-weight standardization of PK parameters is not used in this section unless stated otherwise, which is in contrast to the discussion of drug disposition in infants and children. The reason is because the 15–20% body weight increase associated with pregnancy is fundamentally different from growth and development-associated increase of body size of > 20-fold over years in infants and children. Pregnancy causes considerable changes in physiological and hemodynamic factors. Table 2 lists differences in major indexes between pregnant and non-pregnant states. Significant expansion of fluid spaces and vasodilation are observed during pregnancy, so is increased blood flow to drug eliminating organs (i.e., liver and kidney). Total Body Water (TBW) consisting of Extra-Cellular Water (ECF) and Intra-Cellular Water (ICF) increases by about 20%, which is mainly due to ECF volume expansion of greater than 30% (Van Loan et al., 1995). Blood/plasma volume, heart rate and stroke volume are all increased as well, leading to about a 40% increase in cardiac output (Robson et al., 1989). Time profiles of the transformation are not necessarily identical among these factors. For example, expansion of TBW and ECF volumes begins in the first trimester, which continues further into the third trimester (Van Loan et al., 1995). On the other

Table 2

Pregnancy-associated alterations in physiological parameters. Pregnancy-associated changes Non-pregnancy

Pregnancy

Unit

% Change

Body weight

65

75

kg

þ15%

Blood volume

4

6

L

þ50%

Cardiac output

5

7

L/min

þ40%

Liver blood flow

2

3

L/min

þ50%

Renal plasma flow

0.6

0.9

L/min

þ50%

GFRa

120

180

mL/min

þ50%

Total Body Water

32

39

L

þ20%

ECFb

16

21

L

þ30%

a

Glomerular Filtration Rate. Extra-Cellular Fluid. Based on Van Loan MD, Kopp LE, King JC, Wong WW and Mayclin PL (1995) Fluid changes during pregnancy: Use of bioimpedance spectroscopy. Journal of Applied Physiology 78(3): 1037–1042; Robson SC, Hunter S, Boys RJ and Dunlop W (1989) Serial study of factors influencing changes in cardiac output during human pregnancy. American Journal of Physiology 256: H1060–H1065; Nakai A, Sekiya I, Oya A, Koshino T and Araki T (2002) Assessment of the hepatic arterial and portal venous blood flows during pregnancy with Doppler ultrasonography. Archives of Gynecology and Obstetrics. 266(1): 25–29. b

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hand, nearly 75% of the pregnancy-associated increase in cardiac output is achieved by the end of the first trimester, followed by an additional increase toward the third trimester (Capeless and Clapp, 1991). The substantial GFR increase is also evident by the end of the first trimester, which is maintained until 4–6 weeks into the post-partum period while elevation of renal plasma flow reaches a peak in the second trimester, declining gradually toward the pre-pregnancy level at an early postpartum period (Cheung and Lafayette, 2013). The discrepancy of GFR and renal plasma flow increase during pregnancy implies that pregnancy-associated elevation of GFR is not entirely attributable to increased renal circulation. Time course of recovery of these changes after delivery is not well characterized although restoration of pre-pregnancy levels is achieved by 3–6 months after delivery. As circulating blood volume increases, liver blood flow combining portal vein and hepatic arterial flow increases during pregnancy. This increase reaches þ 50% of the non-pregnancy state and is mainly due to an increase in portal venous flow (Nakai et al., 2002). Studies in the past were contradicting this view, showing that clearance of a marker of liver blood flow such as indocyanine green (ICG) or bromsulfthalein (BSP) is not elevated in pregnancy (Robson et al., 1990; Munnell and Taylor, 1947). This apparent discrepancy is likely a result of reduced function of uptake transporters for ICG/BSP (Hata et al., 2003; de Graaf et al., 2011) during pregnancy as suggested by animal experiments (Shuster et al., 2013). Progesterone metabolites in pregnancy may also interfere with these transporters. While globulin levels are higher during pregnancy (Notarianni, 1990), plasma concentrations of albumin and alpha1-acid glycoprotein, the two major drug binding proteins in plasma, become variably lower. This results in a higher fraction of unbound drug (fu), although the magnitude of the change is variable among drugs (Wu et al., 1983; Krauer et al., 1984; Chen et al., 1982). The reduction of plasma albumin concentrations during pregnancy has been interpreted as a consequence of blood volume expansion with an analogy to “dilutional hypoalbuminemia.” However, it is important to realize that the phenomenon of dilutional hypoalbuminemia is an acute event such as fluid overload during the treatment of patients with burns. This is in sharp contrast to pregnancy-associated fluid expansion, which is a phenomenon observed over months. Assuming that a semi-steady state has been achieved, plasma concentrations of albumin are dependent on the rate of production and their clearance. Contribution of expanded distribution volume to decreased plasma albumin concentrations in pregnancy is likely to be minor, if any (Frederiksen, 2001).

1.18.3.2

Changes in volume of distribution

Pregnancy-associated expansion of fluid volume is reflected in increased volume of distribution for many drugs. The feto-placental unit contributes to the water volume expansion. As ECF is increased by about 30%, volume of distribution of drugs tends to increase by approximately 20–30% during pregnancy. The increase is significant even if the volume of distribution parameter is normalized with body weight. Although the increase in distribution volume, particularly that of the central compartment, necessitates higher doses to achieve a target peak concentration for drugs such as aminoglycosides, this has not been addressed fully in pregnant patients.

1.18.3.3

Clearance changes of drugs eliminated by kidney

Clearance of renally eliminated drugs depends mostly on GFR with some exceptions such as metformin, which shows significant net tubular secretion. Because of the increase in GFR from the early stage of pregnancy, those GFR-dependent drugs show significantly higher clearance during pregnancy, resulting in smaller AUC. Coupled with the Vd increase, Cmax tends to be lower in pregnancy. This is exemplified in the pregnancy-associated increase in CL of hydrophilic beta-lactam antibacterial drugs (Pariente et al., 2016).

1.18.3.4

Clearance changes of drugs metabolized by liver

For drugs metabolized by the liver, pregnancy-associated hyper-perfusion of the liver and decrease in protein binding have variable influences on plasma clearance of drugs. In addition, expression levels and affinity of some drug metabolizing enzymes change during pregnancy, further altering systemic clearance of drug. The following sections address basic PK concepts of hepatic drug metabolism, using the well-stirred model (Pang et al., 2019), so that the PK impact of pregnancy-associated changes in various physiologic and pharmacological factors can be interpreted. The well-stirred model of the liver in the PK analyses, along with the other simple models such as the parallel tube model and the dispersion model, is based on chemical engineering concepts. The well-stirred model itself was originally derived without the knowledge of hepatic transporters (i.e., perfusion-limited), but the model continues to evolve with recent modifications incorporating drug transporters. In the following sections, the simple well-stirred model without hepatic transporters is used for the purpose of basic conceptual understanding.

1.18.3.4.1

Extraction ratio, hepatic blood flow and intrinsic clearance

In this section, for simplicity and clarity, it is assumed that drug distribution into blood cells (i.e., red blood cells, which constitute the main portion of the non-plasma fraction of whole blood) is negligible, if any. This assumption makes the conversion between the plasma-based and the whole blood-based parameters relatively simple.

454

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

The extraction ratio (ER) of a drug, ranging from 0 to 1, provides a scale of efficiency of drug clearance through the liver. In order to highlight the extreme ends of the ER spectrum, an ER of greater than 0.7 and lower than 0.3 are commonly defined as high and low ER, respectively. The definition of ER is given by: ER ¼

Removal rate of drug from liver Input rate of drug into liver

(33)

The above relationship can be explained using the general concept of “clearance,” which stipulates that “clearance” relates a concentration of drug in the system at a moment to a removal rate from the system at the same moment (Fig. 5). For drugs metabolized by the liver, “removal rate of drug from liver” is the same as “removal rate of drug from the body,” which is CLB  [C]B, where [C]B is the whole-blood concentration of drug in the systemic circulation; and CLB is whole-blood clearance of drug. Because the input rate into liver, the denominator of Eq. (33) above, is QB  [C]B,ER can be expressed as: ER ¼

CLB QB

(34)

Under the assumption of no drug distribution into the red blood cells, ER based on whole-blood and that on plasma become identical. This formula (Eq. 34) makes intuitive sense as CL of drug based on whole-blood concentrations does not exceed hepatic blood flow rate. It is worth noting, however, that plasma-based ER may become higher than blood-based ER, depending on the magnitude of drug distribution into blood cells, which further results in higher plasma clearance than hepatic blood flow when drug distribution into red blood cells is substantial. In the following explanation of hepatic clearance, liver flow rate and drug concentrations are based on whole blood because the well-stirred model of the liver is based on the concept of whole-blood concentrations of drug. The whole blood-based model and the plasma-based model are inter-convertible for quantitation purposes such as detailed modeling analyses of in vitrodin vivo extrapolation, which requires careful quantitative treatment to predict plasma clearance (CL) for dosing schedule development. Despite the quantitative differences, their qualitative characteristics are the same. Applying the concept of systemic CL to the liver as a subsystem, one can define “removal rate of drug from liver” as follows (Fig. 5):   iCLB  fuB ∙½CLIVER (35) where iCLB is intrinsic CL relating unbound drug concentrations measured in whole blood to the removal rate, fuBis unbound fraction of the drug in blood, and [C]LIVER is whole blood concentration of total drug (i.e., bound þ unbound) within the liver. As systemic CL relates drug concentrations in the systemic circulation to the removal rate of drug from the body, intrinsic CL (iCLB) relates unbound drug concentrations in the liver (fuB ∙[C]LIVER) to the removal rate. Unbound drug concentrations are used here because we may assume that bound drug molecules are not readily available to metabolizing enzymes in hepatocytes. Note also that [C]LIVER is different from [C]B because [C]B is defined in the systemic circulation, which represents a combined pool of all subsystem circulations. Also, [C]LIVER is likely to be lower than [C]B due to on-going drug elimination from the liver. The denominator of ER (Eq. 33) is the input rate to the liver. When ER was defined above, [C]B in the systemic circulation was the base of the concept. Here, the focus is the liver and its unbound drug concentration in blood fuB ∙ [C]LIVER, but not [C]B in the

Fig. 5 Extraction ratio (ER), organ blood flow (QB) and intrinsic clearance (iCL). Panel A: From a perspective of systemic circulation, drug input rate to the liver is flow  systemic drug concentration: QB  [C]B . In this case, the liver is a black box. Because the removal rate of drug from the system is CLB  [C]B, ER ¼ CLB  QB. Panel B: From a perspective of the liver itself, assuming that unbound drug undergoes metabolism, removal rate is iCL  unbound concentration (i.e., fu ∙[C]Liver), where fu is unbound fraction and [C]Liver is an average drug concentration in the liver blood. Because drug input to the liver is Exit rate þ Removal rate, ER can be expressed as shown.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

455

systemic circulation, and we aim to express the input rate using fuB ∙ [C]LIVER. This is relatively straightforward if one realizes that the input rate to the liver is a sum of the removal rate fuB ∙ [C]LIVER and the drug exit rate from the liver into the systemic circulation, which is: QB  [C]LIVER. Therefore, ER can be written as:   iCLB  fuB ∙½CLIVER fuB ∙iCLB  ¼ ER ¼ (36) QB þ fuB ∙iCLB QB  ½CLIVER þ iCLB  fuB ∙½CLIVER Because CL ¼ Q  ER, the following relationship can be obtained: CLB ¼

QB  fuB ∙iCLB QB þ fuB ∙iCLB

(37)

From Eq. (37) above, the two extreme ends of the ER spectrum can be characterized. If QB [ fuB ∙ iCLB, ER becomes very low approaching 0 and the denominator of Eq. (36) also approaches QB, and therefore, CLB y fuB ∙ iCLB (Eq. 37). This category is known as low ER drugs (commonly defined as ER < 0.3), which is characterized by its fuB ∙ iCLB-sensitive CLB, but changes in hepatic blood flow (e.g., pregnancy-associated hyperperfusion, or reduction in the cirrhotic liver) have little impact. In contrast, if QB  fuB ∙ iCLB, ER approaches 1, and CLB y QB (Eq. 37). This is the property of high ER drugs (i.e., ER > 0.7), which have QBdsensitive but fuB ∙ iCLBdinsensitive CLB. Also, it is worth noting that CL is generally defined with drug concentration in a respective matrix such as whole blood and plasma. Thus, the above derivation of CLB is based on whole blood as a matrix of drug measurement. Under the assumption of fuB  [C]B ¼ fuPL  [C]PL (namely, unbound drug concentrations are no different between whole-blood and plasma: fuPL, unbound fraction in plasma; and [C]PL, drug concentration in plasma), and also because removed amount of drug per time is the same irrespective of the matrix of concentration measurement (CLB  [C]B ¼ CLPL  [C]PL), Eq. (37) above can be rearranged to express CLPL as follows: CLPL ¼

QB  fuPL ∙iCLPL QB þ ðfuPL ∙iCLPL Þ 

½CPL ½CB

(38)

This conversion represents a general form, showing that CLPL of a high ER drug with substantial drug partition into red blood cells (i.e., [C]PL/[C]B < 1) becomes higher than hepatic blood flow rate QB. Further, QB ¼ QPL/(1
Hct), where Hct is hematocrit, a fractional volume of blood cells in reference to whole-blood volume. Therefore, if [C]PL/[C]B < 1/(1
Hct), then CLPL becomes higher than QPL as well. As a special case, if there is no appreciable drug distribution into blood cells, [C]PL/[C]B becomes 1/(1
Hct), and Eq. (38) can be re-written as follows, which is identical to the equation of CLB in Eq. (37), although the matrix is different: CLPL ¼

QPL  fuPL ∙iCLPL QPL þ fuPL ∙iCLPL

(39)

The majority of drug assays are performed in plasma or serum partly due to its technical practicality. Consequently, PK parameters are commonly defined based on plasma or serum, including clearance. This complex relationship between CLB and CLPL as a result of drug partition into red blood cells is critical in quantitative interpretation of PK parameters including modeling analyses to predict CLPL-based design of dosing schedules. However, qualitative assessment of PK profiles to capture physiological attributes in inter- and intra-individual variations can be facilitated by the use of the well-stirred model. While the model is used further to explain oral clearance below, one needs to keep in mind that CLB can be converted to CLPL as CLB ¼ ([C]PL/[C]B)  CLPL, and therefore, the qualitative argument based on CLB is valid for CLPL as well.

1.18.3.4.2

Bioavailability and pre-systemic elimination

ER characteristics also influence effects of the changes in the three key parameters (QB, fuB and iCLB) on oral bioavailability and presystemic elimination (i.e., first-pass effect). Note that this argument is based on an assumption that absorption into the portal circulation is relatively quick and complete. When a drug is administered orally and completely absorbed, drug molecules will emerge in the portal circulation and move on to the liver. In this simplified view, we assume that metabolism by the intestinal cells may happen but to a quantitatively limited degree to influence systemic PK profiles. Because ER is the fraction of drug removed from the liver, (1
ER) represents the fraction of the drug exiting intact from the liver into the systemic circulation; thus, (1
ER) represents oral bioavailability (F). Therefore, ER defined in Eq. (36) can be used to derive F as follows: F ¼ 1
ER ¼

QB QB þ fuB ∙iCLB

(40)

From this relationship, one may infer that low ER drugs have high F, approaching 1 (if absorption is complete), and their F values are insensitive to modest changes in QB or fuB ∙ iCLB. On the other hand, F values of high ER drugs are relatively low, and proportional to the ratio between QB and fuB ∙ iCLB. From Eq. (40) above, and Eq. (37) for CLB, it is evident that CLB estimated from oral administration (CLpo) is expressed as: CLpo ¼

CLB ¼ fuB  iCLB F

(41)

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

Oral clearance (CLpo) is equivalent to fuB ∙ iCLB, and changes in CLpo reflect alterations of fuB ∙ iCLB, regardless of the ER status of the drug. Consequently, AUC after oral administration (AUCpo) increases if fuB ∙ iCLB is decreased, and vice versa, for all livermetabolized drugs, because AUCpo is inversely proportional to CLpo. However, the shapes of drug concentration-time curves change according to the ER status of the drug. Specifically, while relative changes in AUCpo are the inverse of the relative changes in fuB ∙ iCLB for all liver-metabolized drugs (e.g., AUCpo becomes half when fuB ∙ iCLB is increased by twofold), changes in the slope of the elimination phase (i.e., elimination half-life), and Cmax to some extent, differ among drugs according to their ER status. This is easy to comprehend if one realizes that CL of low ER drugs changes as fuB ∙ iCLB changes, affecting the elimination half-life, while CL and elimination half-life of high ER drugs are unaffected by modest alterations in fuB ∙ iCLB. In contrast, changes in hepatic blood flow QB do not affect CLpo or AUCpo of any drug but influence the shape of concentrationtime curves of high ER drugs. Because CLpo of liver-metabolized drugs is dependent on fuB ∙ iCLB, but not on hepatic blood flow QB, modest changes in QB do not affect CLpo of any drug, and therefore AUCpo remains the same irrespective of the ER status of the drug. While changes in QB do not affect AUCpo, altered QB changes CL of high ER drugs, which emerges as altered elimination half-life.

1.18.3.5

PK-based interpretation of changes in concentration-time curves in pregnancy

As described before, pregnancy is associated with hyper-perfusion of the liver, with the magnitude of change being approximately þ 50% compared to a non-pregnant state (Table 2). In addition, plasma protein binding tends to decrease due to reduction of plasma concentrations of albumin and alpha 1 acid glycoprotein, which has variable impact on the relative change of unbound fraction depending on the magnitude of protein binding (i.e., the same absolute reduction of protein binding [e.g., 2%] in highly protein bound drugs [from 97% to 95%] has a higher relative change in the unbound fraction [from 3% to 5%: > 50% increase] than low protein binding drugs [for example, from 50% to 48%: 4% increase in unbound fraction]). Although there is no universal trend in the changes of drug metabolism pathways during pregnancy, using the basic PK concepts based on the well-stirred model of the liver described above, one may interpret the observed pregnancy-associated changes in concentration curves and derived PK parameters. To this end, it is crucial to describe PK phenomena with clearly defined PK terms and physiological parameters. Existing literature may be confusing at times because plasma concentration changes of liver-metabolized drugs are often attributed directly to activity of drug metabolizing enzymes without clearly defining what the activity means. For example, if it simply means systemic clearance, it may be misleading if the ER of the drug is not low enough and if the drug was administered intravenously because systemic clearance of high ER drugs is flow-limited rather than dependent on intrinsic clearance. The following sections address changes in concentration curves and derived PK parameters because of pregnancy-associated increases in hepatic blood flow, decreased protein binding and variably altered intrinsic clearance of drug. Because it is rather rare for a drug to be eliminated through a single pathway, an intrinsic clearance iCL in the following discussion should be considered to represent a hybrid parameter of multiple enzymatic pathways.

1.18.3.5.1

High ER drugs

The pregnancy-associated increase in liver blood flow elevates systemic CL in whole blood or in plasma of high ER drugs, causing AUC reduction after intravenous drug administration. Shortening of the elimination half-life also occurs if the relative increase of Vd is less than that of systemic CL. Consider that concentration-time curves were obtained and AUC and systemic CL were estimated following intravenous administration of a drug. Because systemic CL of high ER drugs is sensitive to changes in liver blood flow, a pregnancy-associated increase in liver blood flow alone elevates their systemic CL by 30–50%, and reduces AUC accordingly, no matter what their responsible enzymatic pathways are. Increased systemic CL shortens elimination half-life, although the magnitude depends on the concomitant Vd enlargement. This is a PK-based mechanism of the enhanced systemic CL (and resultant reduction of AUC) of high ER drugs in pregnancy after intravenous administration. Note that changes in concentration curves and PK parameters of high ER drugs are not influenced by modest changes in expression levels and/or affinity of the enzyme (i.e., fuB ∙ iCLB), if any. But if fuB ∙ iCLB becomes below 50% of the non-pregnant state (e.g., reduction of the expression level by half, or > twofold increase in Km due to endogenous competing substances), a pregnancy-associated increase in systemic CL of high ER drugs may not be obvious anymore. Similarly, if fuB ∙ iCLB increases by > threefold compared to the non-pregnancy state, a mild increase in systemic CL by 50–70% may be observed.

1.18.3.5.2

Low ER drugs

Systemic CL of low ER drugs is insensitive to pregnancy-associated increases in liver blood flow but are responsive to changes in fuB ∙ iCLB. If there is no change in systemic CL and AUC of low ER drugs after intravenous administration compared to the nonpregnant state, one may conclude no appreciable alteration in fuB ∙ iCLB. On the other hand, if a significant decrease in AUC and shortening of the elimination half-life was observed, indicating increased systemic CL, it is likely that fuB ∙ iCLB was significantly increased during pregnancy, although measurement or estimation of fuB is required to accurately assess the changes in iCL.

1.18.3.5.3

Oral clearance

A pregnancy-associated increase in liver blood flow itself does not affect clearance after oral administration (CLPO) and resultant AUCpo,no matter what the ER status of the drug is, but it alters the shape of concentration-time curves of high ER drugs. As described above (Eq. 41), systemic clearance after oral administration of drug (CLpo) and observed AUCPO are not dependent on liver blood

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

457

flow, but rather dependent on fuB ∙ iCLB. Assume that a drug is administered orally, and PK parameters were estimated in pregnant women. Here we assume complete absorption, but there may be pre-systemic elimination, and as a result bioavailability (F) may be less than unity. Despite the fact that blood flow rate to the liver (QB) is increased by about 50% in pregnancy, oral clearance CLpo (Eq. 41), and therefore AUCPO do not change, irrespective of the ER of the drug. While AUCPO remains unchanged, the slope of the elimination phase of the concentration-time curves becomes steeper particularly in high ER drugs. This is because systemic CL of high ER drugs is increased during pregnancy because of the QB increase, which shortens the elimination half-life, although this may be counterbalanced to some extent by a modest enlargement of Vd. In addition to increased liver blood flow, superimposing changes in fuB ∙ iCLB during pregnancy have variable effects on CLpo, AUCPO and concentration curves after oral administration, some of which are dependent on the ER status of the drug. Although precise simulations of resultant changes in PK parameters are beyond the scope of this section, a qualitative description of overall changes may be helpful. Table 3 summarizes the trends of PK parameter changes during pregnancy under various scenarios. Given that liver blood flow QB is increased during pregnancy (by þ 50%), there are three combined states in QB and fuB ∙ iCLB in pregnancy, compared to nonpregnant state: Scenario (1) both QB and fuB ∙ iCLB are increased; Scenario (2) increased QB and unchanged fuB ∙ iCLB; and Scenario (3) increased QB and decreased fu  iCL. Before analyzing each scenario, it should be noted that CLpo is expressed as fuB ∙ iCLB as described before (Eq. 41). For Scenario 1, we assume that the increases in QB and fuB ∙ iCLB are 50% each. The resultant PK pictures are similar among drugs regardless of their ER status: increase in both CLpo and CLB, causing lower AUCPO with shorter elimination half-life, while no appreciable change in F. This can be explained by outlining the consequences of changes in QB and fuB ∙ iCLB separately as follows. For high ER drugs, a pregnancy-associated increase in QB causes higher CLB (by þ 50%) leading to shorter half-life. The magnitude of the shortening depends on the concomitant change in Vd. The QB increase also causes F to increase (Eq. 40) because the absorption process under the increased liver blood flow carries more fraction of intact drug into the systemic circulation while the removal rate remains the same. On the other hand, an increase in fuB ∙ iCLB alone by þ 50% has no impact on systemic CLB, but F decreases (Eq. 40) because a higher fraction is processed after oral administration by the liver due to increased fuB ∙ iCLB. The consequence of the combination of the two events (i.e., increase in both QB and fuB ∙ iCLB by þ 50%) is a CLB increase but no change in F, thereby an increase in CLpo and reduced AUCPO. F remains unchanged due to the offsetting direction of the changes caused by increases in QB and fuB ∙ iCLB under the assumption that both are increased by þ 50%. If fuB ∙ iCLB is increased further, the combined picture is dominated by the increased fuB ∙ iCLB, showing a reduction of F and a substantial increase in CLpo (with further reduction of AUCPO). Examples of this scenario include indinavir among high ER drugs, which is metabolized by CYP3A4. For low ER drugs, CLB is not flow-dependent, and the 50% liver blood flow increase has no impact. Because the F of low ER drugs is near the upper limit of 1, the relative increase in F due to a QB increase is marginal. The increase in fuB ∙ iCLB alone by þ 50%, however, causes an increase in CLB by þ 50%, shortening the elimination half-life. Although F decreases as a higher fraction is presystemically eliminated, the relative change is also marginal. Therefore, the combined effects of increases in both QB and fuB ∙ iCLB by 50% for low ER drugs are the same as for high ER drugs: increases in CL and CLpo, no change in F, shortened elimination half-life, and reduced AUCPO. A further increase in fuB ∙ iCLB also has the same impact as high ER drugs. Carbamazepine, a CYP3A4 substrate, is one of the low ER drugs showing this picture, although the reduction of plasma protein binding needs to be also taken into account. In Scenario 2, the influences of QB increase described in Scenario 1 above applies: for high ER drugs, the consequences are increased systemic CLB with shorter half-life, increased F, unchanged CLpo and no change in AUCPO; for low ER drugs, unchanged CLB with somewhat prolonged elimination half-life due to concomitant Vd increase, no change in F, CLpo and AUCPO. Scenario 3 includes a situation of decreased intrinsic clearance due to reduced enzyme expression levels (Vmax decrease) and/or decreased affinity (Km increase: e.g., presence of endogenous competing substances such as progesterone). A reduction in fuB ∙ iCLB leads to a CLpo decrease and a resultant increase in AUCPO for any drug. While this causes a reduction of systemic CLB in low ER drugs due to the fuB ∙ iCLB dependence of their CL, high ER drugs have flow-limited CL. However, the increase in systemic CL of high ER drugs due to the QB increase is dampened because reduction in fuB ∙ iCLB lowers their ER, depending on the magnitude of the fuB ∙ iCLB reduction. Consequently, their CLB does not show the full increase expected from increased liver blood flow alone. Reduction in oral caffeine clearance, mainly mediated by CYP1A2, is an example of this scenario in low ER drugs. Proguanil metabolized by CYP2C19 may be seen as one of the examples in the high ER drug group.

Table 3

PK impact of changes in liver blood flow and intrinsic clearance during pregnancy. Low ER drug

High ER drug

Liver blood flow

Intrinsic CLB  unbound fraction

CLB

CLPO

F

CLB

CLPO

F

þ50%

Increase No change Decrease

[ / Y

[ / Y

/ / /

[ [ /

[ / Y

/ [ [[

ER, Extraction Ratio in non-pregnant state; CLB, systemic clearance in blood; CLPO, Oral clearance; F bioavailability.

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

1.18.3.5.4

Clinical impact

The impact of these PK changes on clinical outcomes is difficult to evaluate because disease course itself may change during pregnancy. However, some drugs including lithium (GFR dependent) and lamotrigine (low ER drug metabolized by UGT1A4, which is apparently upregulated during pregnancy) may require close therapeutic drug monitoring to avoid therapeutic failure. Also, use of anti-HIV medications combined with cobicistat, which is a CYP3A4 inhibitor known as a PK enhancer for CYP3A4 substrate drugs, is discouraged by the United States Food and Drug Administration for pregnant patients with HIV because therapeutic concentrations of these drugs are hardly achieved during pregnancy because of increased clearance.

1.18.3.6

Distribution of drugs to the fetus across placenta

The placenta is an embryonic/fetal organ providing an exchange mechanism of various substances including oxygen and nutrients between the maternal and fetal blood. Maternal blood is drained into the confined placental space that is bounded by the villi protruded from the fetal body. The villi contain fetal capillaries, which are connected to the two umbilical cord arteries and a vein. In humans, the outer surface of the villi (facing maternal blood) is lined by a multi-nucleated single cell layer known as the syncytiotrophoblast that separates the fetal from the maternal circulation. Macro- and microscopic structural details of the placenta vary among mammalian species; for example, rodent’s placenta has two layers of syncytiotrophoblast. In humans, placental substance exchange between maternal blood and villi interstitial tissues (which contain fetal capillaries) occurs across the microvillous luminal membrane and the basal membrane of the syncytiotrophoblast through multiple mechanisms including diffusion and carrier-mediated processes. Passive diffusion is a significant mechanism of placental drug transfer particularly for small molecule drugs of < 500–600 Da. Large molecules such as insulin, heparin and antibodies do not cross placenta by diffusion, although some of them are endogenous compounds in the fetal body as well. Natural and therapeutic antibodies may cross the placenta through receptor-mediated endocytosis/transcytosis processes as described later. The pH of fetal blood is around 7.3 on average, which is lower than the average maternal blood pH of around 7.4; the relatively acidic environment of the fetus ionizes weakly basic drugs, thereby creating an ion trapping situation for these drugs as lipid bilayers of the syncytiotrophoblast membranes prevent charged molecules from diffusing cross. However, because other drug transfer mechanisms may be also in play, the impact of this phenomenon depends on each drug. In addition to passive diffusion, various carrier-mediated mechanisms operate in the transfer of drugs across the placenta although details of their expression profiles and function remain to be fully elucidated. Some notable examples of active transporters for drugs and endogenous compounds in the placenta include MDR1 (P-glycoprotein: ABCB1), BCRP (ABCG2) and MRP1–5 (ABCC1–5). MDR1 and BCRP, well-characterized primary active ABC transporters, are expressed mainly on the luminal membrane of the syncytiotrophoblast, pumping their substrates from the fetal side into the maternal blood. MDR1 expression tends to decrease as pregnancy advances. In contrast, placental expression of BCRP is relatively stable throughout pregnancy with variations, but inconsistent observation exists. Information on MRP expression is scarce. The Solute Carrier (SLC) group of transporters are also expressed in the placenta. They operate via facilitative diffusion along concentration gradients or as secondary active transporters against the gradients (i.e., coupled with another solute transport). Substrates of these SLC transporters range from conjugates of endogenous steroid hormones and endogenous amines to vitamins and nucleosides. An integrated picture of these transporters in placental transfer of each drug remains to be revealed in detail. Receptor-mediated endocytosis (e.g., at the luminal membrane) followed by intracellular traffic and exocytosis at the basal membrane is involved with placental transfer of some key substances including vitamins and IgG-antibodies from mother to fetus. For example, folate receptor alpha, a cell membrane-bound receptor for folate, carries 5-methyltetrahydrofolate to the fetus by the transcytosis process. Similarly, neonatal Fc receptor is increasingly expressed on the luminal side of the syncytiotrophoblast in the third trimester and binds maternal IgG, transporting IgG molecules across the placenta. This provides passive immunity to the fetus preparing for an extra-uterine life after birth. Because of this mechanism, antibody-based medications including TNF-alpha inhibitors which have the Fc region of IgG are transported to the fetus in late pregnancy.

1.18.4

Drug distribution into human milk

A drug taken by a lactating woman is distributed into milk; the rate of which ranges widely among drugs. Diffusion and active transport constitute the mechanism of drug excretion into milk (Anderson and Sauberan, 2016). The pH of milk is more acidic than maternal plasma, which makes cationic compounds to be more ionized and trapped in the milk compartment because charged molecules are not able to freely diffuse through lipid bilayers. This is the physicochemical base of relatively high concentrations of cationic drugs in milk, compared to those in maternal plasma. To describe the overall picture of milk distribution of drugs, a parameter known as milk-to-(maternal) plasma concentration ratio (MP ratio) is used. The MP ratio is the AUC of drug in milk, expressed as a fraction of the AUC in maternal plasma. Although concentration-time profiles in milk may not parallel those of maternal plasma, the MP ratio is often derived using single data points. As described above, ionization characteristics of drugs are a major determinant of the MP ratio. In addition, high lipophilicity, low protein binding and small molecular sizes of drug are other factors contributing to a high MP ratio.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

459

The MP ratio of drugs ranges from very low (< 0.1) to high (> 2). However, most drugs have MP ratios of around 1–2 or lower. Importantly, as described later, caution should be exercised when the MP ratio is used to assess infant exposure levels to drugs in milk because infant exposure levels (i.e., amount of drug ingested per time by the infant relative to a reference dose) are a function of the MP ratio and a reciprocal of Clearance.

1.18.4.1

Influences of lactation on maternal PK parameters

Although the milk compartment may resemble urine in the bladder, there is a fundamental difference. While little reabsorption is expected from urinary bladder, drug concentration-time profiles in the milk compartment follow those of maternal plasma, if not identical or parallel, causing no appreciable accumulation even if milk expression does not occur frequently. This implies that the drug removal process through milk from the maternal body is not a major component of clearance. Indeed, it is estimated that the amount of drug eliminated through milk is about 1% or less of the maternal dose of most drugs, which implies that its contribution to overall plasma clearance is 1% or less, having no appreciable impact on maternal PK parameters. Although the impact of lactation on volume of distribution in the mother has not been well characterized, a significant influence is unlikely given the balance between expressed milk volume ( 1 L/day) and ECF volume ( 30 L).

1.18.4.2

Pharmacokinetics of drug distribution into milk: Infant exposure

As described in the definition of the MP ratio above, drug concentrations in milk can be expressed as AUC, which is related to plasma AUC in the mother by the MP ratio. Because AUC in milk can be converted to mean drug concentrations in milk, and because a product of milk intake per time and the mean drug concentrations in milk is considered an estimated drug dose of the infant through milk, one should be able to express the infant drug intake via milk as a function of plasma AUC in the mother and the MP ratio. Furthermore, because AUC in the mother is a function of dose and maternal clearance, infant drug intake may be related to these PK parameters. The following section describes the derivation of this relationship.

1.18.4.2.1

Measured relative infant dose (RID or %RID)

An estimated dose of drug the infant would ingest via milk per time is more informative if it is expressed as a fraction or a percentage of a reference dose. For this purpose, Relative Infant Dose (RID) or %RID is defined using a maternal dose as follows (Ito, 2000):   Infant Dose via milk   per kg per time measured   100 ¼  (42) %RID Mother 0 s Dose per kg per time

A %RID of 5–10%, measured or predicted, is used as a safety margin in risk assessment to justify closer observation than routine care, although this margin of 5–10% is not a toxicity threshold. Because therapeutic doses of many drugs are not defined in the neonatal period, maternal dose is used. %RID provides an average picture of infant exposure levels to the drug in milk. This parameter can be obtained in each case using measured milk concentrations, as a surrogate of an average concentration. Briefly, milk concentration of drug is multiplied by an average milk intake of 150 mL/kg/day to derive the numerator of Eq. (42) (Anderson and Sauberan, 2016). From cases reported in the literature, a population average of %RID may be also calculated for a specific drug.

1.18.4.2.2

Predicted %RID from PK parameters

The above definition of measured %RID is based on measurement in individual mother-infant pairs. Importantly, %RID can be shown at a population level as a function of MP ratio and maternal CL as a PK concept at steady state as follows. Because an average concentration in milk at steady state is related to an average concentration in maternal plasma (CmPL) at steady state multiplied by the MP ratio, predicted %RID can be expressed as:   predicted MP ratio  CmPL  0:1 ¼  100 (43) %RID Mother 0 s dosing rate where an average milk intake of 0.1 mL/kg/min (i.e., 150 mL/kg/day) is assumed; because this is a % index (multiplied by 100), CmPL and Mother’s dosing rate must be expressed in appropriate units. Also, maternal CL is related to CmPL as follows: CmPL ¼

F  Mother 0 s dosing rate maternal CL

Therefore, under the assumption of bioavailability (F) to be 1, the following relationship is obtained:   predicted MP ratio ¼  10 %RID maternal CL

(44)

(45)

where the unit of CL is mL/kg/min. This equation for predicted %RID conceptualizes differential impacts of the two independent PK determinants of %RID. It is worthwhile noting that Eq. (45) above assumes a standard individual with all parameters in a range

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of population averages, which were the bases of the therapeutic dose determination. This equation is useful to understand the concepts and assess determinants of %RID among different drugs. It may also enrich prior knowledge of drug therapy during breastfeeding by estimating %RID from reported CL values and MP ratio prediction using physicochemical characteristics when there is no literature case report of milk level measurement.

1.18.4.2.3

Exposure index

%RID is estimated from measured drug concentrations in milk as measured %RID (Eq. 42), or conceptually related to maternal CL and MP ratio as predicted %RID (Eq. 45). While %RID is based on maternal CL, it is logical to link infant CL with infant drug exposure levels. In fact, this conceptual framework was known as the Exposure Index, which is a function of infant CL under the assumption of no difference in the target therapeutic plasma concentrations at steady state between mother and infant, as follows (Ito, 2000): Exposure Index ð%Þ ¼

MP ratio  10 infant CL

(46)

where the unit of CL is mL/kg/min. Exposure Index (Eq. 46) and predicted %RID (Eq. 45) are equivalent because Eq. (43) can be C

infPL rewritten by replacing Mother 0Cs mPL dosing rate with Infant 0 s therapeutic dosing rate , assuming that the target therapeutic plasma concentrations are

the same between mother (CmPL) and infant (CinfPL). The pharmacodynamic equivalence between infant and adult remains unproven mostly but the formalization of infant CL into the equation of infant exposure level highlights the PK significance of infant clearance in determining the infant exposure level. If average infant CL for a drug is significantly different from that of an average adult woman on a weight basis, the equation of Exposure Index takes this into account.

1.18.4.3

Interpretation of %RID

Fig. 6 shows the relationship among predicted %RID, maternal CL and MP ratio. Some aspects of the relationship warrant discussion. First, most drugs belong to the space shown in a shaded area of maternal CL of 1 mL/kg/min or higher (Verstegen et al., 2020; Ito and Koren, 1994) and MP ratio of 2 or lower, implying that predicted %RID remains in a relatively low exposure range (< 10%) for most drugs. Second, impact of individual differences of MP ratio or between-drug differences of MP ratio on predicted %RID depends on clearance of the drugs in question. Specifically, the lower the clearance, the higher the impact of variations of MP ratio on predicted %RID. In other words, between-drug comparison of MP ratio as a determinant of predicted %RID is only valid if the two drugs have similar plasma CL values. This is the reason why a focus on MP ratio alone in infant risk assessment without referring to CL values of drug is of little value or even misleading. %RID is based on mother’s dose on a weight basis. Therefore, if infant CL per body weight is significantly lower than adult values, then measured or predicted %RID needs to be interpreted with caution. In this context, Exposure Index with estimated infant CL rationalizes the conceptual relationship between infant CL and the level of exposure. %RID provides a dose-independent value. For those drugs with a wide range of therapeutic doses, applying %RID to an individual case for risk assessment requires caution because an actual dose the infant would ingest may differ widely at the same %

Fig. 6 Predicted %RID. The shaded area indicates a space where most cases fall into (i.e., maternal clearance of 1 mL/kg/min, and MP ratio 2). Predicted %RID in the Y-axis is defined in reference to maternal clearance (CL). Drugs which have relatively low maternal clearance ( 70 kg of some adolescents. As described later, these two independent aspects of maturation (i.e., Growth and Development) shape variations in PK parameters in infants and children, compared to adult. Although parameters describing the maturation processes of the body are continuous variables, the subgrouping of the pediatric population based on the age categories is commonly used (Table 4). In order to address size variations among individuals, it is customary and practical to normalize the dose by body weight. This is useful when dealing with mature adults with various sizes because body mass and required doses in adults are often proportionally related. This linear relationship between body weight (i.e., mass) and optimal doses of drug is a base for a dose or dose ranges with body weight standardization. As discussed before, a state of pregnancy represents a trait which is not related to growth but causes a significant shift in this relationship because of physiological changes. On the other hand, for the pediatric age group, age-related growth adds another layer of complexity. In contrast to weight variations in the post-growth adult population, variations in body weight in individuals of different ages within the pediatric population stem from their different growth stages, which are characterized by different relationships between body weight and optimal doses. This implies that body weight-standardized doses differ among children depending on their growth and development stages. Also, comparing children and adults, it is a common observation that infants and children require higher per-kg doses than adults to achieve the same plasma concentration and therapeutic effects. Drug dosing standardized by body surface area is also used particularly for cancer chemotherapy, but it is less common due to the cumbersome nature of estimating body surface area from height and weight in most clinical settings. In this section, the term “infants and children” is used to describe the pediatric population in general, and each age category is addressed separately when necessary. In addition, because of the body weight-based dose standardization practice, although the standardized doses may differ among children with different ages, PK parameter and physiological differences in children and adults are described as body weight standardized values, unless stated otherwise.

1.18.5.1

Relative growth and allometry

The size of the body as a whole and each organ often grow at different paces, showing different growth-time profiles, which is known as allometry. This term was initially coined by Huxley and Tessier (1936) to describe differences in growth rates between a part of the body (e.g., an organ) and the whole body. Since then, the concept has been broadly interpreted and now used to describe scaling relationships between any biological trait ranging from weight to function. Assume that body weight is plotted on a horizontal axis among individuals over a wide age range from birth to adulthood, and their organ mass volumes (e.g., liver) on a vertical axis. Clearly, the body weight spectrum on an X-axis represents growth-related differences as well as variations in individuals at the same growth stage. By taking an average organ mass at each age, the graph is now showing an ontogenic or developmental scaling relationship between the organ size and body weight over a wide range of ages. The plot between organ mass or size (y) and body weight (x) shows the following empirical scaling relationship: logy ¼ alogx þ logb

(47)

y ¼ bxa

(48)

This can be rewritten as: where x is body weight, y is an organ mass, a is known as the allometric exponent, and b is the allometric coefficient. Loglinearization of the relationship in Eq. (47) characterizes logb as the y-intercept and a as the slope of the best-fit line. When a is Table 4

Pediatric age categories.

Category

Age

Neonate Infant Child (toddler)a Child (young child)a Child (older child) Adolescent

< 4 weeks 4 weeks–12 months 1–2 years 2–4 years 5–10 years 11–18 years

a

The category of young child may include toddler.

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PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

1, the log-linear relationship indicates the organ and the body weight grow at the same rate, which is called isometry. Otherwise, the relationship is called either hyper-allometry (a > 1) or hypo-allometry (a < 1). Because the term allometry is broadly defined, it could be developmental or ontogenic allometry if the focus is on the developmental relationship as described above, static allometry among individuals in the same developmental/growth stage, evolutionary allometry among different species, or anything depending on the context of comparison. Because of this context sensitive nature, even the relationship of the same two traits may have very different characteristics including the allometric exponent values.

1.18.5.1.1

Organ size growth

During growth of an individual, sizes of some organs such as brain, liver and kidney show hypo-allometric growth trajectory after birth. In other words, the ratio of the organ size to the body weight is the highest at birth, declining to an adult level as age progresses, because the pace of organ mass growth is slower than body weight increase. Using liver as an example, this is expressed as follows:   child BW 0:75 ½child LM ¼ ½adult LM  (49) adult BW where LM is liver mass, and BW is body weight. “Child” denotes a developing individual, and “adult” means a fully matured individual. Biological events and parameters tend to scale as quarter power of body mass, and as shown in the above example, the exponent is often observed to be around 3/4 or 0.75. This fact is explained by a theory that mature and bigger individuals or organisms possess more energy-sparing, metabolically less active building blocks such as bones per body weight, creating a relatively low metabolic state in reference to body weight, compared to smaller and developing individuals (West et al., 1997).

1.18.5.1.2

Allometry of PK parameters

Once the maturation process is complete (i.e., functional unit per organ mass is at the same level as a mature individual), functional parameters such as basal metabolic rate and clearance show the same allometric relationship to body mass with the exponent of 0.75 as organ sizes of the liver and kidney. On the other hand, volume of distribution shows an almost linear relationship (i.e., isometric with a ¼ 1) with body mass, suggesting its nature as a structural component of the body.   child BW 0:75 child CL ¼ adult CL  (50) adult BW  child Vd ¼ adult Vd 

child BW adult BW



1:0

(51)

Because an elimination half-life (t1/2) is proportional to Vd/CL , the scaling relationship of t1/2 to body weight has an allometric exponent of 1/4 or 0.25:   child BW 1:0   adult BW adult Vd adult Vd child BW 0:25    child t1=2 f ¼ (52) adult CL adult CL adult BW child BW 0:75 adult BW

It is also common, particularly in population PK modeling analyses, to express key PK parameters of an individual including infants and children as a value allometrically standardized to a 70 kg mature adult for comparison purposes, using the power model. For example, clearance of a child (CLC) with body weight BWC is allometrically scaled to a standard adult body weight (70 kg) as follows: CLC ! BW C 70kg

¼ 0:75

CLC allometrically standardized to a 70kg adult

(53)

The unit of the allometrically standardized CLC with the 0.75 power model is expressed usually as: Volume ∙ Time
1 ∙ 70kg
1. When plotted against age, the standardized infant clearance defined above produces a near horizontal-slope line after an age when the maturation of intrinsic clearance per liver mass is completed (Fig. 7). Systemic clearance is often the base for dose selection due to its role as a determinant of AUC and average concentration at steady state. Therefore, dose requirement also shows a hypo-allometric relationship to body mass with the exponent of 0.75. This means that the clearance and the required dose in children to achieve a target concentration at steady state are proportional to a reference 0:75  BW child . In other words, infants and children require a higher kg-based dose value of an adult clearance or dose multiplied by BW adult than that predicted from an adult dose normalized to an average adult body weight (i.e., linear isometric relationship with the exponent a ¼ 1). The above explanation on allometric development of organ function is valid once the function of the organ (e.g., clearance through liver and/or kidney) is matured and fully developed to an adult level per organ mass. If the function is still developing in the neonatal and early infantile period, this factor must be taken into account as explained later.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations

463

Fig. 7 Development of clearance standardized by weight using the 0.75 allometric power model. The maturation process (dashed line) such as increase in enzyme expression per organ mass toward an adult level dominates the first part of the developmental trajectory. Once the process is near complete (in this example, around 2–3 years of age), the allometrically weight-standardized clearance reaches a plateau.

1.18.5.2 1.18.5.2.1

Volume of distribution Fluid and fat compartment sizes

On a weight basis, younger individuals have larger fluid compartments. This is mainly due to a large volume of extra-cellular fluid (ECF) rather than intra-cellular fluid (ICF). For example, ECF of a term neonate is 40% of the body weight, which is even higher in premature infants in the 60% range. As a consequence, total body water (ECF þ ICF) is nearly 80% of the body weight in the neonatal period, compared to 50–60% in adults. ECF volume per body weight quickly decreases in 3–4 months, and then gradually decreases further to an adult level of 30% of the body weight by 1–2 years of age. Therefore, when plotted against age, ECF volume per body weight is nearly flat around the 30% range after 1–2 years of age, indicating a linear isometric relationship between ECF and body weight, which is in a sharp contrast to clearance development. On the other hand, fat volume per body weight is relatively low in the neonatal period, increasing to around 20–30% of body weight on average in infants and children, which then decreases below 20% in young adults.

1.18.5.2.2

Parameter changes

Expanded sizes of fluid volume per body weight in neonates result in larger Vd per body weight for hydrophilic drugs than in older children and adults. For example, an aminoglycoside antibiotic tobramycin, which is highly hydrophilic, shows agedependent changes in Vd per body weight, ranging from 0.5 to 1.0 L/kg in neonates (Paap and Nahata, 1990) to 0.4–0.3 L/ kg in older children and adults (Touw et al., 2007). In order to achieve an anti-bacterial therapeutic peak concentration, neonates will require a higher dose per body weight than older children and adults. In addition, aminoglycosides including tobramycin require a trough concentration low enough to avoid toxicity such as renal and auditory cell damage. However, because of the large Vd and low CL (GFR is the main CL pathway for aminoglycosides), elimination half-life of aminoglycosides is prolonged in neonates, and a dosing interval of aminoglycoside needs to be longer in neonates than in older children and adults. From the concept of allometric scaling, Vd and body weight are linearly related and isometric once the transition from the fetal to post-natal life is complete for fluid distribution. Once a child grows into the infantile period, Vd is roughly proportional to body weight.

1.18.5.3

Protein binding

The two major serum proteins, albumin and a1-acid glycoprotein, show age-dependent concentration patterns. In the neonatal period, serum concentrations of albumin and a1-acid glycoprotein are approximately 70–80% and 50% of those of young adults, respectively (Colantonio et al., 2012; Lerman et al., 1989). The concentrations of the serum proteins increase to the adult ranges by 1–5 years of age. This profile has been described for lidocaine, which is bound to a1-acid glycoprotein: unbound fraction on average is higher in the neonatal period (50%) than that after 1 year of age (25%). Albumin-binding drugs also show similar patterns. For example, in ex vivo studies of binding to albumin, the phenobarbital unbound fraction is about 50% in adults and about 60% in neonates, which further increases to 70% in neonatal plasma with endogenous bilirubin as a result of competition at drug binding sites of albumin. Serum bilirubin is increased physiologically in the early neonatal period, causing neonatal jaundice, but serum albumin binds toxic bilirubin preventing it from penetrating into brain. Competitive binding between bilirubin and some drugs such as salicylates following mid- to high-dose therapy (it is rare these days to use relatively high doses of aspirin/salicylate for anti-inflammatory purposes) results in an increase in the unbound fraction of bilirubin.

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1.18.5.4

Clearance

Plasma clearance (CL) and dose (or dosing rate) are the determinants of AUC and average plasma concentrations at steady state (CSS). Because it is customary in most settings of pediatric patient care to use a dose standardized by body weight, CL is also expressed often as a weight-adjusted parameter (e.g., mL/kg/min).

1.18.5.4.1

Renal clearance

Renal handling of drugs is mediated by glomerular filtration and a tubular drug elimination process that is usually designated as net tubular secretion (i.e., secretion minus reabsorption). Plasma clearance of most drugs eliminated through the kidney depends on glomerular filtration rate (GFR), although there are exceptions such as metformin, which shows substantial net tubular secretion. While maturation of net tubular secretion has not been well characterized, the developmental profile of glomerular filtration rate (GFR) is well established. GFR is close to none until a gestational age of 34 weeks. Immediately after birth, a full-term baby shows immature GFR on a body weight basis, which is around 25% of the adult level (Fig. 8) (Schwartz and Work, 2009). Within 3 months, GFR per body weight reaches an adult level, surpassing it significantly throughout childhood. The peak is at around 5 years of age. This childhood “overshoot” phenomenon of GFR per body weight implies that a scaling relationship of the sizes between kidney and the body during growth is not linear as described before. Kidney sizes per body weight at birth are the biggest of all ages, and as an individual grows, the kidney itself grows more slowly than the body size, creating a hypo-allometric relationship. Therefore, plotted against age, kidney mass per body weight shows a negative slope until it reaches an adult level at around an adolescent age (Fig. 9A) (Valentine, 2002). The density of functional nephrons per renal tissue is increasing during the childhood period, and once it reaches the adult level around 4–5 years of age, the large kidney size per body weight makes the GFR per body weight higher in the pediatric population.

Fig. 8 Development of Glomerular Filtration Rate (GFR). At birth, GFR is approximately 25% of adult level per body weight, which increases quickly to an adult level in a few months. Based on Schwartz GJ and Work DF (2009) Measurement and estimation of GFR in children and adolescents. Clinical Journal of the American Society of Nephrology 4(11): 1832–1843.

(A)

(B)

Fig. 9 Growth of kidney and liver mass per body weight. (Panel A): Kidney (both sides). (Panel B): Liver. Both kidney and liver show similar growth patterns of mass per body weight. Because increase in body weight is more than growth rates of kidney and liver, the organ volume or organ weight standardized simply by body weight (not the allometric model) shows decline toward an adult level. Modified from Valentin J (2002) Basic anatomical and physiological data for use in radiological protection: Reference values: ICRP Publication 89. Annals of the ICRP 32(3–4): 1–277; Murry DJ, Crom WR, Reddick WE, Bhargava R and Evans WE (1995) Liver volume as a determinant of drug clearance in children and adolescents. Drug Metabolism and Disposition 23(10): 1110–1116.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations 1.18.5.4.2

465

Hepatic clearance

The developmental trajectory of hepatic drug clearance may be explained as a maturation process of extraction ratio (ER) and liver size growth, which defines liver blood flow. As described before, ER is a dimensionless parameter ranging from 0 to 1, and systemic clearance based on whole-blood drug concentration (CLB) can be written as a product of hepatic blood flow (QB) and ER (Eq. 34). Assuming that liver blood flow QB (25–30% from hepatic artery and 70–75% from portal vein) per liver mass is similar across age groups, QB per body weight (QB per BW) of an infant as a fraction of an adult average is approximately equal to an infant liver mass per body weight as a fraction of the adult value. infant QB per BW infant LMper BW y adult QB per BW adult LMper BW

(54)

where LM is liver mass and BW is body weight. Because CLB ¼ QB  ER (Eq. 34), infant CLB per BW as a fraction of adult CLB per BW becomes:       infant CLB per BW infant LMper BW infant ER (55) ¼  adult ER adult CLB per BW adult LMper BW Similar to the kidney mass growth on a body weight basis, liver mass on a body weight basis in neonates and young infants is nearly twofold as large as the average adult value, which gradually decreases to the adult level around the adolescent age (Fig. 9B) (Murry et al., 1995). This implies that if the liver mass per body weight (as a surrogate of QB) at 3 months of age is 200% of the adult level (i.e., a fraction of 2), an infant ER of 50% of the adult level (i.e., a fraction of 0.5) due to immature enzyme expression may give rise to nearly an adult level CLB per body weight (2  0.5 ¼ 1: Eq. 55). In contrast to the development of QB per body weight, which is drug-independent, the time course of ER maturation is influenced by a balance between QB and fuB ∙ iCLB of each drug. Expression of many drug metabolizing enzymes, which affects fuB ∙ iCLB, starts low in the neonatal period. The time trajectory of the development to reach near adult level per liver mass is enzyme-specific, ranging from < 1 month for CYP2C8 and CYP2C9 to 1 year for CYP3A4 and CYP2C19, and even later for CYP1A2. ER is expressed as

fuB ∙iCLB QB þfuB ∙iCLB

(Eq. 36). If intrinsic clearance is low (i.e., QB [ fuB ∙ iCLB), ER is reduced to fuB ∙ iCLB/QB,

a distinctive feature of low ER drugs. Low ER drugs in a mature adult remain in the low ER range in infants as well. Given that blood flow QB per liver mass is assumed equal across the age groups, for low ER drugs, ER in infants as a fraction of ER in adult becomes:     infant ER infant fuB ∙iCLB ¼ (56) adult ER adult fuB ∙iCLB Assuming that fuB is similar between infants and adults, this is further reduced to a ratio of iCLB between infant and adult, which is considered equivalent to a ratio of enzyme expression levels per liver mass (enzEXPper LM) between infant and adult:       infant enzEXPper LM infant ER infant iCLB ¼ ¼ (57) adult ER adult iCLB adult enzEXP per LM From Eqs. (55) and (57), fractional CLB of infant per body weight for low ER drugs becomes:       infant CLB per BW infant LMper BW infant enzEXPper LM ¼  adult CLB per BW adult LMper BW adult enzEXP per LM

(58)

Roughly speaking, when an enzyme expression level per liver mass for a low ER drug in a developing infant reaches 50% of an adult average, the ER of the drug is about 50% of the adult level as well. If this expression level has been achieved at the age of 6 months of age when the liver size per body weight is about 150% of the adult level, CLB per body weight of the infant becomes as high as 75% of an adult level (i.e., 1.5  0.5 ¼ 0.75). For a high ER drug in adult (assume that it is metabolized by the same enzymatic system as the example of a low ER drug above), an absolute value of iCLB per liver mass is likely to be much higher at most developmental stages than the low ER drug. Even if enzyme expression and resultant iCLB in a 2-month-old infant are about 20% of the adult level per liver mass for a high ER drug in adult (e.g., ER of 0.8 in adult), this may still cause ER of 0.5. For example, if fuB ∙ iCLB per liver mass in the infant is 20% of adult level (with the value for QB staying the same) then the ER may become 0.5: 60% of the adult level of about 0.8. If fractional liver mass per body weight is 1.8 at 2 months of age, clearance per body weight in this 2-month-old is almost at an adult value (i.e., 1.8  0.6 y 1: Eq. 55). This indicates that CLB per body weight reaches and then exceeds the adult average, and the upward trajectory of development in CLB per body weight in the infantile period is steeper in high ER drugs than in low ER drugs even if they are metabolized by the same enzyme pathway. As described before, systemic clearance of orally administered liver-metabolized drug (CLPO) is equivalent to fuB  iCLB (Eq. 41) regardless of the ER status of drug. Because data on enzyme expression levels are usually expressed as per liver protein, assuming that liver microconstituents and the composition are similar across age groups, the fractional enzyme amounts per liver mass relative to an adult average can be used as a surrogate of infant iCLB per liver mass expressed as a fraction of the adult level. Then, this can be further multiplied with liver mass per body weight as a fractional value in reference to an adult average to obtain infant CLPO per

466

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations Table 5

Phenytoin doses per age (oral or intravenous dose divided into 2–3 doses).

Age category

Phenytoin daily dose (mg/kg/day)

1 month 16 years

10–20 8–10 7–9 6–7 4–6

Modified from Al Za’abi M, Lanner A, Xiaonian X, Donovan T and Charles B (2006) Application of routine monitoring data for determination of the population pharmacokinetics and enteral bioavailability of phenytoin in neonates and infants with seizures. Therapeutic Drug Monitoring. 28(6): 793–799; Lau E (2020) SickKids Drug Handbook and Formulary. Lexicomp: Hudson.

body weight as a fraction of an adult average (fuB may be also taken into account if it is known to be significantly different between infants and adults):       infant CLPO per BW infant LMper BW infant enzEXP per LM ¼  (59) adult CLPO per BW adult LMper BW adult enzEXPper LM Eq. (59) above and Eq. (58) described before imply that intravenous CLB and oral CLPOare identical for low ER drugs such as caffeine and phenytoin. CLPOof caffeine in infants of 1–4 months of age is about 0.5 mL/kg/min, 33% of an adult level, which increases to 1.7 mL/kg/min at around 1 year of age, similar to an adult level of 1.5 mL/kg/min. CYP1A2, a main metabolic pathway for caffeine, reaches 50% of adult expression levels per liver mass at around 1 year of age. Given that liver mass per body weight at 1 year of age is about 170% of an adult level, it is anticipated that CLPO per body weight approaches 90% of an adult level by 1 year of age, and then increases further as enzyme expression levels per liver mass increase. Phenytoin is a CYP2C9 substrate antiepileptic. Its therapeutic concentrations are similar in infants and adults, but its maintenance dose per body weight in neonates and infants is the highest of all age groups (Table 5) (Al Za’abi et al., 2006; Lau, 2020), which decreases gradually in older age groups. CYP2C9 expression levels per liver mass reaches an adult level as early as 1 month of age, which suggests that phenytoin CLPO per body weight for an infant is almost double an adult level because liver mass per body weight in a 1-month-old infant is nearly twofold higher than the adult level. Due to its relatively high protein binding (about 90% to albumin), increased unbound fraction in the neonatal period due to relatively low albumin concentrations may further elevate CLPO per body weight in this age group. The maintenance dosing schedule in Table 5 is consistent with this developmental trajectory of phenytoin CLPO.

1.18.5.4.3

Developmental pattern of clearance

Fig. 10 summarizes the concept of developmental trajectories of weight-adjusted hepatic clearance in reference to the discussion above. Liver mass of younger individuals on a body weight basis is larger than that of a standard adult, and its growth (i.e., size increase) is slower than body weight. As a result, body weight-adjusted liver mass is gradually declining to an adult level around adolescent ages. On the other hand, developmental processes of functional aspects including expression of drug metabolizing enzymes in the liver require time to complete, which defines the developmental increase of ER (extraction ratio) toward the adult level. Depending on the speed of the maturation process, the time for body weight-adjusted clearance to exceed an adult level varies and the time to reach a peak level will differ as well. The examples in Fig. 10 are all low ER drugs. As discussed above, high ER drugs metabolized through the same pathways are likely to show a left shift of the initial surge. A developmental pattern of oral clearance CLPO may be analogous to this illustration of the weight-adjusted CL, but CLPO development patterns are not affected by the ER status of the drug.

1.18.5.5

Units in developmental PK analyses

Because of the allometric nature of various traits during growth and development, it is crucial to pay attention to the units they are expressed with. For example, development of drug metabolizing enzymes in the liver is measured and usually expressed as amounts of enzyme per liver protein or per liver mass, and they are compared between children and adults, providing a fractional value in reference to an adult expression level. On the other hand, systemic clearance, which is a basis for the design of dosing schedules, is usually expressed as per body weight. As described above, organ size growth including liver and kidney shows allometric relationships with body weight, increasing at different rates and profiles throughout human growth (i.e., their relative sizes against body weight are larger at birth, declining gradually throughout childhood to an adult level). As a result of this relationship of the ontogenic hypo-allometry between liver/kidney mass and body weight, younger children tend to have larger liver and kidney sizes per body weight. This makes CL and CLPO per body weight higher than an adult average at the same expression levels of drug metabolizing enzymes or functional nephrons per organ mass (i.e., the same intrinsic clearance per liver/kidney). On the other hand, the same kg-based values of CL and CLPO between young infants and adults indicate that the maturation process of drug metabolizing enzymes in the liver of the infant remains to be completed.

PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations 1.18.5.6

467

Standardization of dose

Doses of drug are commonly standardized with body weight for practicality and convenience. As discussed above, the hypoallometric relationship exists in growth rate between liver or kidney mass and body weight with an exponent of 0.75, which shows a decline curve of body weight-standardized liver or kidney mass plotted against age until early adulthood (Fig. 9). As a result, when plotted against age, the systemic clearance of drug standardized by body weight shows an initial surge in infancy (Fig. 10) due to maturation processes of liver and kidney function. Once liver enzyme expression and/or GFR per organ mass matures and reaches an adult level per organ mass, kg-based systemic clearance is peaking as well, followed by a decline along with the decreasing relative mass of the liver and/or kidney per body weight. The age of the peak kg-based systemic clearance may vary among drugs, but it is usually around 2–5 years of age. The allometric scaling of dose in a child (DC) in reference to adult dose (DA) is shown below, which is equivalent to the relationship between clearance and body weight as described before in Fig. 7 and Eq. (50):   child BW 0:75 DC ¼ DA  (60) adult BW Body Surface Area (BSA) is also empirically used to calculate a required dose of certain drugs. As a 2-dimensional descriptor of an area (i.e., length2), BSA is scaled to body weight (BW), which is a 3-dimensional parameter (i.e., length3), as follows: 2

BSAfBW 3

(61)

Similar to the 0.75 power model, BSA-normalized clearance (and body weight-adjusted dose) also shows relatively consistent values after infancy. However, in contrast to the allometric power model with an exponent of 0.75, the BSA-based model lacks strong mechanistic rationale as basal metabolic rate in human is related to body weight with a scaling exponent of around 0.75 (or 3/4), rather than 2/3.

1.18.5.7

Drug distribution into brain: Development of blood-brain barrier (BBB)

Drug distribution into the brain is an important element of therapeutic efficacy of drugs acting in the central nervous system, although the overall impact on volume of distribution in the host system may be small. The functional boundary between the systemic circulation and the brain parenchyma is the endothelial cells of brain capillaries, forming a physiological unit known as the blood-brain barrier (BBB). The other domain of the boundary is the blood-CSF (cerebrospinal fluid) barrier in the choroid plexus, which produces CSF. These barrier mechanisms separate blood from brain parenchyma or from CSF that is immersing the brain. In both barriers, the brain capillary endothelial cells are attached together with tight junctions, which starts at early stages of fetal development. Therefore, immaturities of the barrier functions come mainly from developmental processes of membranebound proteins with substance transporting function.

Fig. 10 Development of body weight-adjusted hepatic clearance. A solid line indicates liver mass standardized by body weight, expressed as % of an adult average, showing that the body weight-adjusted liver mass is the highest in the youngest. Thick dashed lines (a–c) show example patterns of development of hepatic clearance per body weight over the childhood ages. The initial upward curves indicate maturation processes of extraction ratio (ER) toward an adult level. This may be conceptually categorized as rapid (a): phenytoin through (CYP2C9), intermediate (b): carbamazepine through (CYP3A4) and slow (c): caffeine through (CYP1A2). High ER drugs using the same pathways show left-shift of the initial surge. Thin dashed line: an adult level.

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While passive diffusion is a major mechanism of brain/CSF penetration of blood-borne substances, carrier-mediated systems in the brain capillaries add an additional pathway of drug passage into and out of the brain, and maturation processes of their expression and function have an impact on drug distribution in the developing brain. Some of these systems are characterized by a facilitative diffusion function (e.g., influx into the brain along the concentration gradient: the Solute Carrier (SLC) group of transporters), and others may operate as efflux transporters (i.e., pumping substances out of the brain compartment into the blood with relatively broad substrate specificity: primary active ABC transporters). Although details of developmental changes of these transporters remain to be elucidated in humans, animal data suggest that expression of MDR1 (P-glycoprotein: ABCB1) efflux transporter in the BBB is limited in infancy compared to adults. In contrast, BCRP (ABCG2) transporter is expressed already at a relatively high level in human neonates but not in rodents, which may represent one of the species differences of transporter development.

See Also: 1.23: Drug Transporters: Efflux; 1.24: Drug Excretion; 1.25: Mathematical Aspects of Clinical Pharmacokinetics; 1.29: Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development; 2.15: Pharmacogenomics of Anti-Cancer Drugs

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Chen, S.S., Perucca, E., Lee, J.N., Richens, A., 1982. Serum protein binding and free concentration of phenytoin and phenobarbitone in pregnancy. British Journal of Clinical Pharmacology 13, 547–552. Cheung, K.L., Lafayette, R.A., 2013. Renal physiology of pregnancy. Advances in Chronic Kidney Disease 20 (3), 209–214. Colantonio, D.A., Kyriakopoulou, L., Chan, M.K., Daly, C.H., Brinc, D., Venner, A.A., Pasic, M.D., Armbruster, D., Adeli, K., 2012. Closing the gaps in pediatric laboratory reference intervals: A CALIPER database of 40 biochemical markers in a healthy and multiethnic population of children. Clinical Chemistry 58 (5), 854–868. de Graaf, W., Häusler, S., Heger, M., van Ginhoven, T.M., van Cappellen, G., Bennink, R.J., Kullak-Ublick, G.A., Hesselmann, R., van Gulik, T.M., Stieger, B., 2011. Transporters involved in the hepatic uptake of 99mTc-mebrofenin and indocyanine green. Journal of Hepatology 54 (4), 738–745. Frederiksen, M.C., 2001. Physiologic changes in pregnancy and their effect on drug disposition. In: Seminars in Perinatology, vol. 25. WB Saunders, pp. 120–123. Hata, S., Wang, P., Eftychiou, N., Ananthanarayanan, M., Batta, A., Salen, G., Pang, K.S., Wolkoff, A.W., 2003. Substrate specificities of rat oatp1 and ntcp: Implications for hepatic organic anion uptake. American Journal of Physiology. Gastrointestinal and Liver Physiology 285 (5), G829–G839. Huxley, J.S., Tessier, G., 1936. Terminology of relative growth. Nature 137, 780–781. Ito, S., 2000. Drug therapy for breast-feeding women. The New England Journal of Medicine 343, 118–126. Ito, S., Koren, G., 1994. A novel index for expressing exposure of the infant to drugs in breast milk. British Journal of Clinical Pharmacology 38, 99–102. Krauer, B., Dayer, P., Anner, R., 1984. Changes in serum albumin and alpha 1-acid glycoprotein concentrations during pregnancy: An analysis of fetal-maternal pairs. British Journal of Obstetrics and Gynaecology 91, 875–881. Lau, E. (Ed.), 2020. 2020 SickKids Drug Handbook and Formulary. Lexicomp, Hudson. Lerman, J., Strong, H.A., LeDez, K.M., Swartz, J., Rieder, M.J., Burrows, F.A., 1989. Effects of age on the serum concentration of a1-acid glycoprotein and the binding of lidocaine in pediatric patients. Clinical Pharmacology and Therapeutics 46 (2), 219–225. Munnell, E.W., Taylor, H.C., 1947. Liver blood flow in pregnancydHepatic vein catheterization. The Journal of Clinical Investigation 26 (5), 952–956. Murry, D.J., Crom, W.R., Reddick, W.E., Bhargava, R., Evans, W.E., 1995. Liver volume as a determinant of drug clearance in children and adolescents. Drug Metabolism and Disposition 23 (10), 1110–1116. Nakai, A., Sekiya, I., Oya, A., Koshino, T., Araki, T., 2002. Assessment of the hepatic arterial and portal venous blood flows during pregnancy with Doppler ultrasonography. Archives of Gynecology and Obstetrics 266 (1), 25–29. Notarianni, L.J., 1990. Plasma protein binding of drugs in pregnancy and in neonates. Clinical Pharmacokinetics 18, 20–36. Paap, C.M., Nahata, M.C., 1990. Clinical pharmacokinetics of antibacterial drugs in neonates. Clinical Pharmacokinetics 19 (4), 280–318. Pang, K.S., Han, Y.R., Noh, K., Lee, P.I., Rowland, M., 2019. Hepatic clearance concepts and misconceptions: Why the well-stirred model is still used even though it is not physiologic reality? Biochemical Pharmacology 169, 113596. Pariente, G., Leibson, T., Carls, A., Adams-Webber, T., Ito, S., Koren, G., 2016. Pregnancy-associated changes in pharmacokinetics: A systematic review. PLoS Medicine 13 (11), e1002160. Robson, S.C., Hunter, S., Boys, R.J., Dunlop, W., 1989. Serial study of factors influencing changes in cardiac output during human pregnancy. The American Journal of Physiology 256, H1060–H1065. Robson, S.C., Mutch, E., Boys, R.J., Woodhouse, K.W., 1990. Apparent liver blood flow during pregnancy: A serial study using indocyanine green clearance. BJOG : An International Journal of Obstetrics and Gynaecology 97 (8), 720–724. Schwartz, G.J., Work, D.F., 2009. Measurement and estimation of GFR in children and adolescents. Clinical Journal of the American Society of Nephrology 4 (11), 1832–1843. Shuster, D.L., Bammler, T.K., Beyer, R.P., MacDonald, J.W., Tsai, J.M., Farin, F.M., Hebert, M.F., Thummel, K.E., Mao, Q., 2013. Gestational age-dependent changes in gene expression of metabolic enzymes and transporters in pregnant mice. Drug Metabolism and Disposition 41 (2), 332–342. Sobol, E., Bialer, M., 2004. The relationships between half-life (t1/2) and mean residence time (MRT) in the two-compartment open body model. Biopharmaceutics & Drug Disposition 25 (4), 157–162. Teorell, T., 1937. Kinetics of distribution of substances administered to the body, II: The intravascular modes of administration. Archives Internationales de Pharmacodynamie et de Thérapie 57, 226–240. Touw, D.J., Knox, A.J., Smyth, A., 2007. Population pharmacokinetics of tobramycin administered thrice daily and once daily in children and adults with cystic fibrosis. Journal of Cystic Fibrosis 6 (5), 327–333.

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Valentin, J., 2002. Basic anatomical and physiological data for use in radiological protection: Reference values: ICRP publication 89. Annals of the ICRP 32 (3–4), 1–277. Van Loan, M.D., Kopp, L.E., King, J.C., Wong, W.W., Mayclin, P.L., 1995. Fluid changes during pregnancy: Use of bioimpedance spectroscopy. Journal of Applied Physiology 78 (3), 1037–1042. Verstegen, R.H., Anderson, P.O., Ito, S., et al., 2020. British Journal of Clinical Pharmacology. https://doi.org/10.1111/bcp.14538. Waki, M.A., Kral, J.G., Mazariegos, M.A., Wang, J.A., Pierson Jr., R.N., Heymsfield, S.B., 1991. Relative expansion of extracellular fluid in obese vs. nonobese women. American Journal of Physiology. Endocrinology and Metabolism 261 (2), E199–E203. West, G.B., Brown, J.H., Enquist, B.J., 1997. A general model for the origin of allometric scaling laws in biology. Science 276 (5309), 122–126. Wu, P.Y., Udani, V., Chan, L., Miller, F.C., Henneman, C.E., 1983. Colloid osmotic pressure: Variations in normal pregnancy. Journal of Perinatal Medicine 11, 193–199.

1.19

Drug Metabolism: Cytochrome P450

F. Peter Guengerich, Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, United States © 2022 Elsevier Inc. All rights reserved.

1.19.1 1.19.2 1.19.3 1.19.4 1.19.5 1.19.6 1.19.7 1.19.7.1 1.19.7.2 1.19.7.3 1.19.8 1.19.8.1 1.19.8.1.1 1.19.8.1.2 1.19.8.1.3 1.19.8.1.4 1.19.8.2 1.19.9 1.19.10 1.19.10.1 1.19.10.1.1 1.19.10.1.2 1.19.10.1.3 1.19.10.1.4 1.19.10.1.5 1.19.10.2 1.19.10.2.1 1.19.10.2.2 1.19.10.2.3 1.19.10.2.4 1.19.10.2.5 1.19.10.2.6 1.19.10.2.7 1.19.10.3 1.19.10.3.1 1.19.10.3.2 1.19.10.3.3 1.19.11 1.19.11.1 1.19.11.2 1.19.11.3 1.19.11.4 1.19.11.5 1.19.11.6 1.19.11.7 Acknowledgments References

1.19.1

History Significance Regulation of P450s Structures Catalytic mechanism Conformational changes and catalytic selectivity Kinetics Basic kinetics Rate-limiting steps Kinetic (deuterium) isotope effects (KIE) Inhibition Types of inhibition Reversible inhibition Metabolite complex inhibition Irreversible inhibition of P450s The example of intestinal P450 3A4 and grapefruit juice Approaches to inhibition screening and modeling P450 and reaction oxygen species (ROS) P450 enzymes involved in drug metabolism Major human P450s involved in drug metabolism P450 1A2 P450 2C9 P450 2C19 P450 2D6 P450 3A4 Other P450 enzymes that can have significant contributions to drug metabolism P450 1A1 P450 2A6 P450 2B6 P450 2C8 P450 2E1 P450 2J2 P450 3A5 and 3A7 Some other P450s that can be involved in drug metabolism P450 4F2 P450 11A1 P450 46A1 P450s as targets for drugs P450s 1A1, 1A2, 1B1 P450 2A6 P450 3A4 P450 11B2 P450 17A1 P450 19A1 P450 51A1

470 472 473 474 475 477 479 479 479 480 481 481 482 482 484 484 484 486 489 489 489 489 495 495 496 496 496 497 497 497 498 499 499 499 499 499 499 500 500 500 501 501 501 501 501 501 502

History

The history of cytochrome P450 (P450) began with studies on the metabolism of drugs, steroids, and chemical carcinogens in the 1930s and 1940s (Mueller and Miller, 1948; Williams, 1947; Gillette et al., 1957; Ryan, 1959). In 1962 Ryo Sato and his graduate

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Fig. 1 Classical Fe2þ$CO vs. Fe2þ difference spectrum, of a P450 (Omura and Sato, 1964). The P450 sample (P450BM-3) was split into two cuvettes. The sample cuvette was saturated with CO gas and solid sodium dithionite was added. Sodium dithionite was also added to the reference cuvette, and the difference spectrum was recorded in a split-beam spectrophotometer. In this example, the P450 concentration was 1.25 mM (DA450 A490/0.091) and the calculation for P420 {(DA420-A490)  0.041[P450]/0.110} showed none present (Guengerich and Fekry, 2020). Reprinted with permission from Guengerich FP, Fekry MI (2020) Methylene oxidation of alkyl sulfates by cytochrome P450BM-3 and a role for conformational selection in substrate recognition. ACS Catalysis 10, 5008–5022. Copyright 2020 American Chemical Society.

student Tsuneo Omura reported an unusual spectrum of a heme protein in liver microsomes, in which the ferrous-carbon monoxide complex displayed a Soret absorption band near 450 nm (Fig. 1) (Omura and Sato, 1962). The name cytochrome P450 (“P” for pigment) developed. The acronym “CYP” developed later in the context of conventions for gene searching (Nebert et al., 1987). P450 was defined as the “terminal oxidase” (i.e., the protein that actually does the oxidations) in microsomal electron transport systems in the 1960s (Cooper et al., 1965). The three components of the microsomal systemdP450, NADPH-P450 reductase, and phospholipiddwere separated and reconstituted by Minor Coon and Anthony Lu in 1968 (Lu and Coon, 1968). Since then a number of animal and human P450s were purified from liver microsomes (Ryan et al., 1982; Guengerich et al., 1982) and shown to be involved in the oxidation of various drugs and other chemicals (Guengerich, 2015). In the early work on these enzymes, it was not clear how many P450s there were. Ultimately the answer came with the completion of the human genome project (and genome projects with other species). Humans have 57 P450 (CYP) genes, although one of these leads to multiple proteins (4F3A, 4F3B, due to exon skipping) and whether some of the genes are expressed is not clear (e.g., 2A7). These are classified into Families (1, 2, 3.) and Subfamilies (A, B, C.) based on sequence identity (Nebert et al., 1987). In general (with some exceptions), sequences
40% identical are placed in the same Family and with
60% identity are in the same Subfamily. The sequence similarity may or may not be relevant to catalytic function (Nebert et al., 1991). Although the microsomal P450s will be emphasized here, in the context of pharmacology, the point should be made that seven of the 57 human P450s are mitochondrial; i.e. P450s 11A1, 11B1, 11B2, 24A1, 27A1, 27B1, 27C1 (Table 1). These are coded for by Table 1

Classification of human P450s based on major substrate class.

Sterols

Xenobiotics

1B1 7A1a 7B1 8B1 11A1a 11B1a 11B2a 17A1a 19A1a 21A2a 27A1 39A1 46A1a 51A1a

1A1 1A2a 2A6a 2A13a 2B6a 2C8a 2C9a 2C18 2C19a 2D6a 2E1a 2F1 2 W1 3A4a 3A5a 3A7 3A43

a

a

Fatty acids 2 J2 2S1 2 U1 4A11 4A22 4B1b 4F11 4F12 4F22 4 V2 4Z1

Eicosanoids 2 U1 4F2 4F3 4F8 5A1 8A1a

Vitamins a

2R1 24A1 26B1 26C1 27B1 27C1

Unknown 2A7 41 20A1

This classification is somewhat arbitrary in some cases, e.g., P450s 1B1 and 27A1 could be grouped in either of two different categories. a Crystal structure available. b Crystal structure of animal orthologue available.

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nuclear genes and imported into mitochondria, where they use the flavoprotein NADPH-adrenodoxin reductase and the iron-sulfur protein adrenodoxin as their sources of electrons. Although these seven mitochondrial P450s are primarily involved in the metabolism of steroids and vitamins (Guengerich, 2015), some can also be involved in drug oxidations (e.g., P450 11A1 (Zhang et al., 2012), P450 46A1 (Mast et al., 2003)). Some of the microsomal P450s can also undergo modification and subsequent import into mitochondria, where they utilize the adrenodoxin electron transfer pathway (Sangar et al., 2010b).

1.19.2

Significance

P450 enzymes are involved in  95% of the known oxidation-reduction reactions (Rendic and Guengerich, 2015) due to their vast repertoire of substrates including drugs, industrial chemicals, and natural products (Guengerich, 2020). Regarding small-molecule drugs, P450s are involved in  75% of the metabolic transformations, followed next by UDP-glucuronyl transferases (Williams et al., 2004a). Of the P450s, about five (1A2, 2C9, 2C19, 2D6, and 3A4) dominate, with nearly 90% of drugs being oxidized by these enzymes (although at least 38 of the 57 P450s can do some drug metabolism reactions) (Guengerich, 2015). This domination of the drug metabolism “scene” by a relatively small number of P450s has been fortuitous in expediting drug development, in that most of the issues with a new chemical entity can be addressed quickly with available reagents and a strong background of experience. As discussed in more detail later in this chapter and in other chapters in this volume, P450 activity varies among individuals due to genetics, induction, and the presence of inhibitors. Thus, P450s play major roles in inter-individual differences in drug efficacy and drug-drug interactions, two major problems in clinical practice. Understanding P450s has led to practical means of addressing many of these problems. The history and significance of P450s in drug metabolism and safety assessment are exemplified in the example of terfenadine (Fig. 2) (Guengerich, 2014). Developed and introduced in the 1980s, terfenadine (SeldaneÒ) was the first non-sedating antihistamine to reach the market. More than 100 million prescriptions were written world-wide and the drug became popular, but in the early 1990s a number of deaths due to arrhythmias were reported (the exact number of deaths due to this problem is unknown but has been estimated as
170). At that time, no information was available about individual P450s involved in the oxidation of terfenadine. Terfenadine was shown to be oxidized to both of its major products by P450 3A4 (Fig. 2) (Yun et al., 1993). One of these is inactive (azacyclonol) but the tert-butyl alcohol is further oxidized to the carboxylic acid, fexofenadine, which also has activity in blocking the histamine receptor. In most individuals terfenadine is completely transformed and not found in the bloodstream, circulating as fexofenadine. Terfenadine is a strong inhibitor of the hERG channel, a phenomenon leading to torsades des pointes and the fatal arrhythmias. Individuals with low levels of P450 3A4 or, more commonly, who concomitantly used the P450 inhibitors ketoconazole or erythromycin, blocked the P450 3A4 oxidations and accumulated terfenadine, leading to the hERG problems. The United States Food and Drug Administration (FDA) required a contraindication warning for the use of terfenadine and then later withdrew terfenadine from the market. Subsequently fexofenadine was marketed, which did not have the hERG interaction

Fig. 2 Oxidations of the antihistamine terfenadine catalyzed by P450 3A4. The oxidation of terfenadine was attenuated in individuals who have inherently low levels of P450 3A4 (Yang et al., 2010) or used P450 3A4 inhibitors (e.g., erthyromycin, ketoconazole) concomitantly with terfenadine (Yun et al., 1993; Guengerich, 2014).

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problem (nor was it a P450 3A4 substrate). This drug (AllegraÒ) had a successful lifetime and is still marketed as a generic (Guengerich, 2014). The terfenadine issue led to increased activity in this area at the U.S. FDA. Today an IND application (Investigational New Drug, required for administration of a new chemical entity to humans) is expected to include in vitro metabolism information about all human enzymes involved, which shows how much the field has changed in the past 30 years. The P450 (and other enzyme) information can be used to guide in vivo considerations about anticipated drug-drug interaction and the identification of sensitive populations. This chapter is about P450s and their relevance in pharmacology, but P450s are abundant in almost all forms of life and are of wide interest because of that. This chapter is focused on human pharmacology, but all of the considerations about drugs also apply in veterinary medicine, e.g. interindividual variations and drug-drug interactions. Only recently have many of the P450 studies been applied in cattle, horses, dogs, cats, etc. (Knych et al., 2019). Another issue relevant to both human and veterinary medicine is the roles of microbial P450s in the production of drugs. For instance, Actinomyces species (e.g., Streptomyces) have P450s that play critical roles in the biosynthesis of drugs (Guengerich, 2020). As early as 1952, a Rhizobus (fungal) culture was used by Upjohn to introduce an 11a-hydroxyl group in cortisol synthesis, achieving a selective reaction that was difficult with methods of classic organic synthesis (Peterson, 1952). P450s are critical in the biosynthesis of the important antibiotic vancomycin (Cryle et al., 2010), as well as many other drugs. One strategy being explored today is the use of natural and engineered P450s to tailor natural products to produce better drugs (Guengerich, 2002a).

1.19.3

Regulation of P450s

The main issues involved are genetics (discussed elsewhere in this volume), induction, and inhibition (discussed later in this chapter). P450 induction was really discovered in the 1950s in the work of James and Elizabeth Miller and their student Allan Conney (Conney et al., 1956). At the same time, the phenomenon was observed clinically by Herbert Remmer (Remmer, 1957), with the barbiturate-induced enhancement of drug metabolism. Enzyme induction is important in two respects. Remmer first noted that individuals who were using barbiturates cleared other drugs more rapidly (Remmer, 1957). P450 induction is problematic when a drug induces its own metabolism, in that drug metabolism increases with the time that the patient is using the drug. This, of course, can lead to non-linear pharmacokinetics, i.e. decreasing Cmax (maximum plasma concentration) and AUC (“area-under-the-curve”) as a result of treatment with the drug. Another pharmacokinetic problem arises when one drug induces a P450 that catalyzes the metabolism of a second one. A classic case is the induction of P450 3A4 (by barbiturates, rifampicin, or St. John’s wort) and the associated increased clearance of oral contraceptives, resulting in pregnancy (Janz and Schmidt, 1974; Murphy et al., 2005). The second issue with P450 induction is more subtle but has been problematic in drug development. Many P450 inducers also cause rodent liver cancer, namely AhR (aryl hydrocarbon receptor) and PPARa (peroxisome proliferator-activated receptor-a) ligands and those that involve CAR (constitutive androstane receptor) (e.g. barbiturates). This was a major concern with the FDA several decades ago. However, many epileptics have been on barbiturates for most of their lives without increased cancer incidence (Olsen et al., 1989), and the levels of PPARa receptor in humans are much lower than in rodents (Palmer et al., 1998). Although the development of liver tumors in rodent bioassays may not be very relevant to humans, it is still an unwanted result in the preparation of an NDA (New Drug Application) application and must be explained to the FDA or other agency. Most of the mechanisms of P450 induction involve enhanced gene transcription. The general model is depicted in Fig. 3. A cytosolic receptor binds a ligand that then causes binding to another protein to form a heterodimer, which is then translocated into the nucleus, binds to a cognate sequence 50 to the transcription start site, recruits co-activator protein(s) (which are often tissue specific), and then changes the gene/chromosome structure to allow more access for RNA polymerase. More mRNA is produced leading to more protein and enzymatic activity. There are several major systems involved. The AhR system uses the partner ARNT (aryl hydrocarbon receptor nuclear translocator) and induces mainly P450s 1A1, 1A2, and 1B1. Many of the ligands are polycyclic aromatic hydrocarbons, heterocyclic amines, several endogenous indoles, and a few drugs. The PXR (pregnane X receptor) system is involved in the induction of P450 3A enzymes (and to some extent P450 2C genes). PXR binds certain steroids and drugs, heterodimerizes with retinoid-activated RXR (retinoid X receptor), and induces its target genes. The CAR system is more complex. A few compounds can bind directly to CAR and cause heterodimerization with activated RXR to induce mainly Subfamily P450 2B and 2C genes (and 3A). The more common phenomenon is that drugs, especially barbiturates, bind to the epidermal growth factor (EGF) receptor, which then phosphorylates CAR. CAR then binds RXR, enters the nucleus, and proceeds to enhance transcription. The binding site is about 2000 bases upstream of the transcription start site (Park et al., 1996). The PPARa system is similar. PPARa binds a fatty acid or a drug, dimerizes with an RXR, enters the nucleus, and enhances the transcription of P450 Subfamily 4A enzymes, as well as several others involved in peroxisomal proliferation. In addition to transcriptional regulation, some other mechanisms can be operative. With P450 2E1, there is evidence of protein stabilization by a ligand (ethanol) (Gonzalez, 2007). With several P450 genes there is evidence for modulation by deoxycytidine methylation, e.g. P450s 2A13, 2E1, 2R1, 8A1, 19A1, 24A1, 27A1, 27B1, 2 W1. In several cases there is evidence for roles of micro

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Drug Metabolism: Cytochrome P450

Fig. 3 General scheme for transcriptional regulation of P450s. L: ligand, R: receptor, R0 -heterodimeric partner, Coactiv: co-activator protein (e.g., hepatic nuclear factor (HNF) a in the case of P450 3A4), RNA pol: RNA polymerase.

RNAs regulating transcript stability and translation, e.g. 1B1, 2E1, 3A4, 24A1, and histone acetylation (P450s 2A13, 2E1, 46A1) (Guengerich, 2015). P450s are generally down-regulated during infections and inflammatory responses, probably through mechanisms involving interferons (Lee et al., 2009). This phenomenon has practical consequences in that people have long been known to have attenuated drug metabolism while having colds/flu or following administration of vaccines (Renton, 1981). Another phenomenon that has been documented in rodents is the down-regulation of certain P450s by chemicals that induce others, e.g. barbiturates and polycyclic aromatic hydrocarbons (Guengerich et al., 1982). The level of a particular P450 can decrease by an order of magnitude (Dannan et al., 1983) and has a transcriptional basis (Riddick et al., 2004), but the mechanism has not been elucidated and the relevance to humans has not been investigated. Rodents show major sex differences in some P450s, and the mechanisms involve steroids, growth hormone pulsatile patterns, and JAK-STAT signaling (Waxman and Holloway, 2009). However, in humans any sex differences in P450s are consistently modest if seen at all (Yang et al., 2010), although a German study has reported a P450 3A4 difference not seen elsewhere (Wolbold et al., 2003). Pharmacokinetic differences can sometimes be observed between men and women but attributed to differences in body fat and drug deposition, not P450s themselves. The sex differences in rodents are important, however, in that dramatic gender differences seen in drug metabolism (or in cancer susceptibility) in these animals are often evidence that the rodent patterns of drug metabolism are unique and unrelated to the human situation.

1.19.4

Structures

All P450s (> 380,000) are defined by the signature peptide sequence FXXGXbXXCXG, the cysteine (C) of which binds the heme iron as the proximal ligand (and Xb is a basic residue). To date at least 981 P450 structures are known, including 207 structures of 23 of the human P450s, plus two close orthologues from animals (Table 1). The overall structures of all P450s are similar (Fig. 4). P450 structures are not static, and these proteins utilize their flexibility to achieve their broad substrate specificity. In general, the structures of P450s are “open” in the absence of ligand and “closed” in the presence of ligand. A key conformational change occurs, with the F and G helices moving in the transition. However, this description is too simplistic, and the roles of structural changes in achieving catalytic selectivity will be discussed later in this chapter. The size of the canonical active site varies considerably, at least from 190 Å3 in P450 2E1 (Porubsky et al., 2008) to 1385 Å3 in P450 3A4 and 1438 Å3 in P450 2C8 (Yano et al., 2004; Schoch et al., 2008). Not only the size but also the shape of the active site is an issue. For instance, the P450 2C8 active site is “L” shaped but the P450 3A4 active site is “wide open.” Although multiple ligand occupancy was proposed as a mechanism for sigmoidal kinetic and related heterotropic phenomena observed with P450 3A4 (Shou et al., 1994), evidence was not available for many years. However, there are now multiple pieces of evidence for dual occupancy of P450 active sites by ligands (Dabrowski et al., 2002; Ekroos and Sjögren, 2006). However, in several cases in which X-ray crystal structures have clearly shown multiple occupancy (Fig. 5), there is no evidence for cooperativity to

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Fig. 4 A structure of P450 3A4 (Protein Data Bank (PDB) 1TQN), with major helices labeled (Yano et al., 2004). The heme prosthetic group is shown in gray.

Fig. 5 Binding of two ligands in P450 21A2 (Zhao et al., 2012; Pallan et al., 2015). A human P450 21A2•progesterone complex (green, PDB 4Y8W) and bovine P450 21A2•17a-hydroxyprogesterone complex (pink, PDB 3QZ1) are overlaid. The ligands are shown in gray, with the active site ligand in the same position and the distal ligand (17a-hydroxyprogesterone) also shown in the case of the bovine enzyme. (In the case of the human P450 21A2•progesterone complex, the electron density was very weak in the distal ligand region (Pallan et al., 2015).) (A) Overall views. The N- and C-terminals of the protein are indicated. (B) Close-up view of the superimposed distal ligand site regions from Part A. The electron density of the substrate is indicated with netting. Selected helices and amino acids are labeled. Note the differences in the F0 and G helices of the human and bovine proteins. For the structure of the human P450 21A2•17a-hydroxyprogesterone complex see (Wang et al., 2017).

accompany this (Schoch et al., 2008; Zhao et al., 2012). Thus, it appears that multiple occupancy can be a reasonable basis for homotropic cooperativity (Hosea et al., 2000; Sohl et al., 2008) but may not necessarily produce this phenomenon.

1.19.5

Catalytic mechanism

The classic P450 cycle is shown in Fig. 6 and is discussed briefly. More extensive reviews of the involved chemistry have been published elsewhere (Guengerich, 2018; Guengerich and Yoshimoto, 2018; Ortiz de Montellano, 2015).

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Fig. 6 P450 catalytic cycle. The nine labeled steps show sequential (1) substrate binding, (2) 1-electron reduction, (3) oxygen binding, (4) second 1-electron reduction, (5) protonation of “Compound 0”, (6) loss of water to form “Compound I”, (7) hydrogen atom abstraction by Compound I, (8) oxygen rebound to form product, and (9) product dissociation. As indicated, ferrous P450 can also bind substrate (Yun et al., 2005). In some cases, cytochrome b5 can provide the electron in step 2 or 4. In some sequential reactions, step 9 does not occur and a second oxidation of the initial product is observed (Gonzalez and Guengerich, 2017; Reddish and Guengerich, 2019).

The cycle generally begins with P450 without substrate with the iron in the ferric (Fe3þ) statedthe iron usually has water bound at the sixth ligand position. In step 1, substrate is bound near the iron but not directly to it. If the water is displaced, the iron atom shifts from the low-spin to high-spin configuration, involving a rearrangement of the d (outer shell) electrons. The extent of this change depends upon the P450 and the substrate. In step 2, the flavoprotein NADPH-P450 reductase (POR) (which uses two flavins, FAD and FMN) donates an electron to the P450 iron. In some casesdbut not alldthe presence of the substrate facilitates the reduction step (Guengerich and Johnson, 1997). The presence of substrate may or may not change the oxidation-reduction (redox) potential (Em,7) of the P450, depending on the P450 enzyme (Guengerich, 1983). It should be pointed out that the substrate is in equilibrium between the free and ferrous P450bound forms (Yun et al., 2005), and under some cellular conditions a significant fraction of the P450 can exist in the ferrous resting state in the absence of an exogenous substrate (Johnston et al., 2011). Ferrous P450 iron binds molecular oxygen (O2) in step 3 (ferric iron does not bind O2.) This complex is electronically equivalent to oxyhemoglobin but is much less stable and can break down to ferric P450 and superoxide anion (O2 d), which dismutes to yield H2O2. This complex can be written as Fe2þO2 or with a bond as Fe3þ-O2. This is an inherently unstable complex, with a t1/2 varying from 0.005 to 140 s at 23  C (Denisov et al., 2006). Step 4 involves the transfer of another electron from NADPH-P450 reductase to the Fe2þO2 complex, yielding a species that can be written as Fe3þ-O2. In some cases this electron transfer can come from cytochrome b5 (b5), another microsomal hemoprotein. However, in several cases b5 can stimulate P450 oxidation without actually transferring an electron (Yamazaki et al., 1996, 2001, 2002; Auchus et al., 1998; Guengerich et al., 2019a). The Fe3þ-O2 complex (termed “Compound 0”) has been difficult to detect, and only in a few cases has anyone claimed to have detected it as a reaction intermediate (Mak et al., 2015). Steps 5–8 are very elusive and much of what is known has been inferred from indirect experiments. The process consists of protonation of the Fe3þ-O2complex, which renders the OeO bond labile and leads to the formation of “Compound I,” usually written as a formal FeO3þ complex (one of the positive charges is thought to reside in the porphyrin ring). In step 6 the FeO3þ species abstracts a hydrogen atom from a substrate to yield FeOH3þ, known as Compound II (electronically equivalent to l

FeO2þ if a proton is lost). The complex of Compound II and the substrate radical (R• in Fig. 6) is tight and is poised to undergo a rapid “oxygen rebound,” or recombination process, to generate the product in step 8. Step 9 involves the release of the product. This appears to be fast in most cases, when it has been measured. In some cases it is slow, and the first reaction cycle is much faster than what is observed in the steady-state system (i.e., “burst kinetics”) (Bell and Guengerich, 1997). In some cases, the product may stay on the enzyme and be a substrate for the next cycle, skipping steps 9 and 1 (Gonzalez and Guengerich, 2017; Reddish and Guengerich, 2019). Several points should be made before concluding this section. P450s catalyze not only hydroxylation reactions but also many others, including heteroatom cleavage (e.g., N-dealkylation), heteroatom oxygenation (e.g., formation of N- and S-oxides),

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477

Fig. 7 Some of the reactions known to be catalyzed by P450 enzymes (Guengerich, 2001). See the cited review for more. Most of the reactions are indicated generically, except for the reactions with pulegone (McClanahan et al., 1989), norharman/aniline (Totsuka et al., 1998), and 4hydroxytamoxifen (Williams et al., 1994; Crewe et al., 2000).

epoxidation, and oxidative group migrations (Fig. 7) (Guengerich, 2001; Guengerich and Macdonald, 1984). Some of the most complex of these are reactions in which CeC bonds are broken and created (Guengerich and Yoshimoto, 2018). Some of this diversity is due to the abilities of reaction intermediates to rearrange in P450 active sites before step 8 is completed. An individual P450 cannot generally be classified as participating in only a single type of reaction. That is, a single P450 can, presented with the right substrates, participate in many of the reactions mentioned above because the inherent chemistry (Fig. 6) is the same. There does not appear to be a single “rate-limiting-step” (in Fig. 6) that applies to all P450s. Rate-limiting steps vary (Guengerich, 2002b). Although CeH bond breaking (Step 7) is partially rate-limiting in many cases (Guengerich, 2017), the degree to which steps 1, 2, 4, and 9 also limit reaction rates varies considerably.

1.19.6

Conformational changes and catalytic selectivity

As already discussed, a small number of P450 enzymes is responsible for the oxidations of a large number of drugs, which vary in details of their structures. As indicated earlier, X-ray crystal structures are now available for
40% of the human P450s (Table 1). An obvious question is how a single enzyme can catalyze reactions with so many substrates. For instance, P450 3A4 probably has thousands of substrates, varying in size from acetaminophen (FW 151) to cyclosporin A (FW 1203) (the general rule of 18 Å3/non-H atom gives an estimated molecular volume of  1350 Å3, consistent with the estimate of the site of the crystal active site, vide supra). A major impetus for the structural work with P450s has been the ability to predict sites of metabolism, in the context of docking. Such rational design has had some success in the design of new chemical entities (Brodney et al., 2015). However, many of the P450s have now been found to exist in different conformations when free of ligand and also when bound to different substrates (Ekroos and Sjögren, 2006; Bart and Scott, 2018). Thus, it is still difficult to predict exactly how a putative substrate will dock into a P450. It may be possible to group structures of a given P450 into a finite number of possibilities to improve predictability. Alternate substrate site prediction conformations include reliance on ease of chemical oxidation at individual sites (e.g., allylic carbons for hydroxylation) or utilizing knowledge of known reactions to predict new ones, in the absence of knowledge about enzyme structures or chemical potential. The question arises as to how P450s “change” shapes to accommodate different ligands. Although a classical lock-and-key model of enzyme-substrate complementarity is still often presented in introductory biochemistry courses, the situation has been known to be more complex for some time. Koshland and his associates presented the induced fit model in the 1960s, in which initial binding of a substrate to an enzyme triggers conformational changes that poise the enzyme-substrate complex for productive catalysis (Koshland et al., 1966). However, an alternate explanation is that an enzyme exists in multiple conformations, in equilibrium, that one (or more) of these binds the substrate in a productive way (Gianni et al., 2014; Vogt and Di Cera, 2012). The basic possibilities are compared in Fig. 8. The overall free energy change in going from E to E’S (Fig. 8) is the same regardless of the route taken, so thermodynamic analyses cannot resolve the kinetic question. Further, the structures above cannot resolve it in that (i) they are static and (ii) solving one structure cannot prove that others do not exist. Kinetic approaches to address the possibilities in Fig. 8 have been applied to several human P450s (Guengerich et al., 2019a, b) and bacterial P450s (Guengerich, 2020). The possibilities in Fig. 8 can be addressed by measurement of rates of binding as

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Fig. 8

Drug Metabolism: Cytochrome P450

Hypotheses to explain complex substrate recognition data (Gianni et al., 2014; Vogt and Di Cera, 2012).

a function of concentrations of enzyme and ligand (Gianni et al., 2014; Vogt and Di Cera, 2012). With P450s 2C8, 2D6, 3A4, 4A11, 17A1, and 21A2, the evidence is in favor of a conformational selection mechanism (Guengerich et al., 2019a, b). For P450 2E1, a clear preference has not been distinguished (Guengerich et al., 2019b). Elements of both conformational selection and induced fit are probably both operative, however. The binding of the nonsteroidal inhibitors orteronel and seviteronel to P450 17A1 is best described by the initial rapid formation of a high-spin complex that converts at a slower rate ( 1 s 1) to a Fe-N (low-spin) complex (Child and Guengerich, 2020). Apparently both of these complexes are inhibitory. Thus, P450 17A1 exists in at least two conformational states in the absence of ligand (Guengerich et al., 2019b) and in at least two conformational states in the presence of the inhibitors (Child and Guengerich, 2020). Multiple forms of orteronel-bound P450 17A1 yield distinct X-ray crystal structures of P450 17A1 bound to the (R) and (S)-enantiomers of orteronel (Petrunak et al., 2017). Thus, a picture emerges of P450s as a landscape, in the context of current general views in enzymology (Benkovic et al., 2008). P450s exist in multiple conformations in both the absence and presence of substrates (and inhibitors). The kinetic analyses described earlier (Guengerich, 2020; Guengerich et al., 2019a, b; Child and Guengerich, 2020) were done with two conformations but these do not rule out the presence of more (adding more possible forms in kinetic models is problematic in that the more complex models cannot be proven) (Johnson, 2019a). In the context of the treatment of Benkovic et al. (2008), the catalytic scheme of Fig. 6 probably has multiple conformations at each electronic state, best represented overall as a free energy landscape rather than a cycle. The discussion of conformations can be extended to substrates as well. Perhaps the clearest example comes from a reaction with the steroidogenic P450 11B2 (Fig. 9), which catalyzes the 3-step conversion of 11-deoxycorticosterone to aldosterone. Thus, corticosterone and 18-hydroxycorticosterone are both products and substrates. If 18-hydroxycorticosterone is added to the enzyme, it is not efficiently converted to aldosterone, whereas the final product is formed better from 11-deoxycorticosterone or corticosterone (Reddish and Guengerich, 2019). The explanation is that 11-deoxycorticosterone, free of the enzyme, exists mainly in a hemiketal (lactol) form (> 98%), and only the open form (aldehyde) is a good substrate for P450 11B2 (Reddish and Guengerich, 2019). Thus, multiple conformations of a substrate can also be an issue in P450 reactions, as well as multiple conformations of the enzyme.

Fig. 9

P450 11B2 oxidation of 11-deoxycorticosterone to aldosterone (Reddish and Guengerich, 2019).

Drug Metabolism: Cytochrome P450

1.19.7

Kinetics

1.19.7.1

Basic kinetics

479

There are a number of misunderstandings that abound in biochemistry and pharmacology about enzyme kinetics, and a brief primer is in order. It is important to remember that individual reaction steps in an enzyme cycle are either first-order (only a single entity changing, units of s 1 or min 1) or second-order (two entities coming together, units of M 1 s 1 or M 1 min 1). Rate constants are values that describe a basic reaction, and rates are what are observed under specified conditions (e.g., concentrations of individual reactants.) “Rates” (usually “v”) are measured but “rate constants” (k) are descriptive of individual steps. Second-order chemical and biochemical reactions are diffusion-controlled and should have rate constants of  106 M 1 s 1 or higher. If a rate much lower is reported, there is probably another (slower) step involved (e.g., a conformational change, Fig. 8). Some definitions are in order. kcat is the first-order rate constant for the (most) rate-limiting step in the turnover of an enzyme in one catalytic cycle, i.e. ES / E þ P (where E is the enzyme, S is the substrate, P is the product, and I is an inhibitor). It refers to the properties of E$S, E$P, or E$I. Sometimes this is called “turnover number,” although turnover numbers are often reported for only a single substrate concentration, not extrapolated to infinite substrate concentration. We will use the parameter kcat here, which is for a single enzyme. Vmax is generally used for crude enzyme mixtures (e.g., microsomes), in that the concentrations of individual P450s are usually not established. Ks is the substrate “binding constant,” often presented as Kd, the substrate dissociation constant (in biochemistry and other biological science, the general convention is to use dissociation constants instead of association constants). kcat/Km is the second-order rate constant describing the efficiency of the combination of enzyme and substrate and its conversion to product, i.e. E þ S / E þ P. Thus, it really refers to the properties of free E and S. This is sometimes termed the catalytic efficiency but is most properly called the specificity constant (Johnson, 2019a). In the equations that follow, Ks ¼ k 1/k1, or Ks ¼ koff/kon. In principle, rate constants for kon are diffusion-limited (vide supra) and Ks values (dissociation constants) for enzymes are dominated by the koff rates, i.e. how slowly the complexes break down (Johnson, 2019a; Fersht, 1999). For the simplest systems k1 k2 E þ S % E$S / E þ P k1 and if k2 [k1 then kcat/Km y k1. or, in a more complex model, k1 k3 k2 E þ S % E$S / E$P % E þ P k1 k3 so if k2 [ k3 then kcat y k3 (k1 and k 3 are diffusion controlled). The catalytic specificity constant is kcat/Km, which is k1/k 1 multiplied by the efficiency of the forward reaction. In the above considerations Km ¼ (k 1 þ k2)/k1 so only under the condition where k 1 [ k2 is Km ¼ k 1/k1 y Ks. This is the exception rather than the rule. If k 1 y k2 then Km ¼ (k2 þ k 1)/k3 and Km > Ks. If k2 [ k 1 then Km y k2/k1 and this has the consequence that kcat/Km y k2/(k2/k1) ¼ k1. More complex reactions have more than two steps, as is the case with P450s (Fig. 6). Comparing the specificity constants with two substrates informs one what the specificity of the enzyme is among alternate substrates. Northrop (1998) and Johnson (2019a, b) have both made the point that the two most important kinetic parameters are kcat (first-order rate at high substrate concentrations) and kcat/Km (second-order rate at low concentrations), not Km itself. kcat/Km is most relevant for extrapolation of in vitro results to in vivo considerations, and modern physiologically-based pharmacokinetic (PBPK) modeling uses this as a basis (Bois et al., 2010). kcat/Km is most related to the important pharmacokinetic parameter clearance (Cl). Sometimes pharmacologists refer to kcat/Km as “in vitro clearance,” although in vivo clearance is more complex and the author discourages the use of this term for in vitro work. So what is Km? Km can be derived from dividing kcat by kcat/Km (and there are numerical reasons to handle it in this way to reduce error (Johnson, 2019b)). Km is simply the concentration of substrate required for half-maximal velocity. That is all. It is not the substrate binding constant. In principle, Km is the apparent dissociation constant for all enzyme-bound species (Km ¼ [E][S]/ ([ES] þ [E$P] þ [E$I] þ .)) (Johnson, 2019a) but measuring all of these (Fig. 6) is not possible. Pharmacologists sometimes refer to changes in Vmax (kcat) as indicative of “capacity” and Km as “affinity,” but neither of these terms is used appropriately.

1.19.7.2

Rate-limiting steps

The “rate-limiting step” in a reaction cycle is the slowest step, and the overall rate of a reaction cannot exceed this rate. However, more than one step can contribute to the overall reaction rate, so the concept of a single rate-limiting step is often not valid. During

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the last 50 years there has been considerable discussion of rate-limiting steps in P450 reactions (Gander and Mannering, 1980; Ullrich, 1969; Guengerich et al., 2002; Guengerich, 2017). In summarizing the vast amount of literature on kinetics of P450 reactions, the conclusion is that this can vary. There can be variations regarding rate-limiting steps for even a single P450 in terms of different reactions. Steps 1, 2, 4, 7, and 9 (Fig. 6) have all been shown to be rate-limiting in various situations (Gonzalez and Guengerich, 2017; Guengerich and Johnson, 1997; Bell and Guengerich, 1997; Isin and Guengerich, 2006; Kim et al., 2014; Guengerich, 2013). The evidence for step 4 is very indirect in that some P450 reactions are stimulated upon electron transfer from cytochrome b5 (Bell and Guengerich, 1997). Even in well-studied bacterial P450s there has been difficulty in defining a single rate-limiting step, and in Bacillus megaterium P450BM-3 the hypothesis that the second electron transfer step (step 4 in Fig. 6) is rate-limiting is difficult to address (Guengerich, 2020). It should be emphasized again that Fig. 6 shows only electronic steps in the catalytic cycle and that there are changes in protein structure occurring throughout the reaction cycle. How these changes affect productive substrate binding is discussed elsewhere in this chapter (Fig. 8).

1.19.7.3

Kinetic (deuterium) isotope effects (KIE)

Step 7 in the P450 catalytic cycle (Fig. 6) involves the breaking of a CeH bond in many cases (but not all, Fig. 7). Deuterium (and tritium) bonds are stronger than protium, and the availability of deuterated substrates has allowed for experiments on the extent to which step 7 (Fig. 6) is rate-limiting. Although such experiments have been ongoing since at least 1961 (Elison et al., 1961), there is often confusion about the meaning of KIEs in the literature because several types of KIE experiments are possible with P450 reactions (Fig. 10). The meaning of certain KIE results varies (Fig. 10). The schemes in Parts A–C of Fig. 10 yield results that are interesting in terms of several steps of the reaction cycle, but the scheme in Part D provides an indication of the contribution of the rate of CeH bond breaking to the overall reaction rate (Guengerich, 2017). The non-competitive intramolecular experiment (Fig. 10A) works well with hydroxylations of methyl groups by P450 enzymes, which is a fairly common reaction that generates a primary alcohol.

Fig. 10 Types of kinetic deuterium isotope effect experiments used with P450 reactions (Guengerich, 2017). In all cases R-CH3 and derivatives are hydroxylated to form R-CH2OH. The convention used is DV ¼ kcat(H)/kcat (D) and D(V/K) ¼ (kcat(H)/Km(H))/(kcat (D)/Km(D)), where H and D indicate protiated and deuterated substrates, respectively (Northrop, 1981, 1982).

Drug Metabolism: Cytochrome P450

481

The reaction is non-competitive because a single isotopologue is being analyzed, and the deuterium atoms to be removed are equivalent with each other. The comparison of CeH bonds being broken is intramolecular. The KIE should be independent of issues of substrate concentration and only a single (mass spectrometry) measurement is required. In principle, the KIE in this approach is an estimate of the intrinsic KIE (designated Dk), i.e., the KIE for the actual CeH bond-breaking step, as in a purely chemical reaction. In a competitive intermolecular experiment (Fig. 10B), there is competition between two kinds of molecules, the protiated and the deuterium-labeled. In this reaction, two different substrate isotopologues are used that compete for enzyme binding and the reaction is considered intermolecular relative to case Fig. 10A. The resulting KIE is not a direct reflection of the rate-limiting nature of the CeH bond-breaking step. A high value, based on this argument, would indicate that the chemical step is at least partially ratelimiting and also that the enzyme has fast exchange of substrates. The experiment depicted in Fig. 10C can only be done with substrates that have equivalent moieties within them, e.g. N,N-dimethyl groups. The experiments cannot be done if pro-chirality is an issue. As in Fig. 10A and B, the analysis is done using mass spectrometry and yields an estimate of D(V/K). The results are a measure of the ability of the substrate to rearrange (“tumble”) in the active site. The experiment described in Fig. 10D requires more effort than any of the three others (Fig. 10A–C), in that assays must be done at multiple substrate concentrations with both protiated and deuterated substrates. The experiments yield both DV and D(V/K), and non-competitive experiments are the only method to determine DV. The value obtained for a KIE in Fig. 10D should be compared to the intrinsic KIE, which can be estimated from (i) an experiment of the type described in Fig. 10A, (ii) Northrop’s approach (Northrop, 1981, 1982, 1977) in which deuterium and tritium KIEs are compared, or (iii) analysis of isotope-sensitive metabolic switching, if there are alternate reaction products (Jones et al., 1986). In considering many non-competitive intermolecular KIE experiments in the literature, the values can go from 1 (unity) to > 10 (Guengerich, 2017). The higher values are greater than the theoretical limit of  7 for classical quantum mechanical considerations and are suggestive of quantum mechanical tunneling, although some of the requisite experiments for proving this (Kohen and Klinman, 1999) have never been done with P450s, for a number of technical reasons. The magnitude of the KIE is not related to the rate of a P450 reaction (Guengerich, 2020, 2017; Shinkyo and Guengerich, 2011a). Another point to be made is that almost all P450 reactions show some non-competitive KIE (Guengerich, 2017), although the contribution of secondary KIEs (due to deuterium substitution at CeH bonds not being broken, values generally  1.2 but multiplicative (Matsson and Westaway, 1998) cannot be ruled out. Aside from the issue of secondary KIEs, the significance of the KIEs greater than unity (1), implies that in many P450 reactions the rate of the CeH bond breaking step is at least partially rate-limiting. KIEs with P450s (and some other enzymes, e.g. aldehyde oxidase, monoamine oxidase) are of potential interest in that in principle deuterium substitution could be used to attenuate drug metabolism in vivo to improve pharmacokinetics. This concept first originated in 1961 (Elison et al., 1961) and has been adapted by several new companies over the years (Halford, 2016; Yarnell, 2009). In some cases it may be effective. A legal issue regarding patents is the obviousness of the approach in light of the available literature (Guengerich, 2017, 2013; Elison et al., 1961).

1.19.8

Inhibition

Inhibition of P450 reactions is extremely important because of the significance of drug-drug interactions, which are a major issue in medicine and a significant cause of death. Although much has been learned about P450s in the past 60 þ years, adverse interactions with P450 3A4 are still a leading cause of drug-drug interactions (Yu et al., 2018). Another reason to understand P450 inhibition is in the context of inhibiting P450s as drug targets, as discussed later (e.g., 3A4, 2A6, 19A1, 17A1, 11B2, 51A1) (vide infra). As in the case of the basic kinetics, some definition of parameters is in order. An IC50 value is simply the concentration of inhibitor needed to reduce the activity of the enzyme by 50%. Because the substrate and the inhibitor often compete for the same site, this value may be influenced by the substrate concentration. In order to correct for this, the inhibitor and substrate concentrations are both varied in order to calculate an inhibition constant, Ki. In the case of irreversible inactivation, another parameter of interest is kinactivation, the maximal rate of inactivation, extrapolated to infinite inhibitor concentration. This parameter is also independent of substrate concentration. In principle the Ki should be a direct measure of the interaction of the inhibitor with the enzyme (a dissociation constant for the enzyme-inhibitor complex) (unlike Km).

1.19.8.1

Types of inhibition

P450 inhibition can be divided into three categories: Reversible inhibition is dependent upon the presence of both the drug substrate and the inhibitor. Upon removal of the inhibitor, full activity is restored. Metabolite complex inhibition is a phenomenon that has been seen with many P450s. The inhibition is the result of very tight binding of a reaction product to the heme iron. This can, at least in principle, be reversed, although slowly, by oxidation of the iron atom. Irreversible inhibition is a process in which an unstable enzyme-substrate complex reacts to form a covalent bond between the enzyme and the intermediate. In some cases the P450 heme group is modified instead of (or in addition to) a nucleophilic moiety on the protein. Unless the bond formed is unstable, the complex is truly irreversible and the P450 is not reactivated.

482

Drug Metabolism: Cytochrome P450

1.19.8.1.1

Reversible inhibition

Reversible inhibition is generally one of four basic types: (i) Competitive inhibition. The substrate S and the inhibitor I compete for the same binding site on the enzyme E. E þ S % ES / EP % E þ P EþI%EI This is by far the most common type of inhibition. kcat (Vmax) is unaffected but Km changes (Fig. 11A). (ii) Noncompetitive inhibition. The inhibitor I can interact with either E or ES, generally at a site distinct from where S binds. Km is unaffected but kcat (Vmax) is reduced (Fig. 11B). (iii) Uncompetitive inhibition. In this mode, which is rather uncommon in practice, I only binds to ES and not E. kcat and Km both change but kcat/Km remains the same (Fig. 11C). (iv) Mixed inhibition. In this case, kcat, Km, and kcat/Km all change. In practice, this information tells one very little about the mechanism of inhibition (Fig. 11D). (v) Other modes. A number of other possibilities exist, especially in cases where more than one ligand can fit into the enzyme active site. For an extensive review, see Segel (Segel, 1975). One can consider the effect of non-productive binding related to conformational changes and complex binding phenomena (Fig. 8). In terms of the effect of non-productive substrate binding, the equations (Fig. 12) resemble those seen with uncompetitive inhibition (Fig. 11C). In practice, this is harder to detect because no inhibitor is added. There are approaches, such as measurement of burst kinetics and modeling, that can be applied (Johnson, 2019a; Furge and Guengerich, 1999). As an example of an alternate mode of inhibition that was not immediately obvious, cholesterol is an inhibitor of the oxidation of nifedipine or quinidine by P450 3A4. It is also a substrate for P450 3A4 (4b-hydroxylation). Steady-state kinetic analysis yields plots diagnostic of non-competitive inhibition (Fig. 13) (Shinkyo and Guengerich, 2011b). A viable explanation is that cholesterol can fit into the large active site of P450 3A4 in two ways, one of which blocks the access of nifedipine or quinidine to the heme iron and another in which the C-4 atom of cholesterol is positioned near the heme iron for oxidation (Fig. 14) (Shinkyo and Guengerich, 2011b).

1.19.8.1.2

Metabolite complex inhibition

In metabolite complex inhibition a reaction product (from a drug) complexes with the ferrous heme iron, resulting in inhibition. The enzyme is inactive until the complex dissociates, which may take hours or days (Mansuy et al., 1978b). Dissociation can be enhanced by oxidation of the iron in some cases (e.g., using ferricyanide under laboratory conditions). The most common examples of the phenomenon involve C-nitroso compounds (eC]O, derived from primary amines) and carbenes (with C: character, derived from methylenedioxyphenyl-containing compounds) (Mansuy et al., 1978a). The insecticide “booster” piperonyl butoxide is a classic example here, a methylenedioxyphenyl that is activated and prevents the P450s in insects from metabolizing other insecticides (Casida, 1970). A classic drug involved in such a phenomenon is the amine troleandomycin, and this inhibitory behavior is observed in vivo (Delaforge et al., 1984). These complexes are associated with their characteristic Soret absorption spectra with bands near 455 nm (Paulsen-Sörman et al., 1984).

Fig. 11 Qualitative analysis of enzyme inhibition. Basic schemes, resulting Lineweaver-Burk plots, and relevant equations are shown. (A) Competitive inhibition; (B) non-competitive inhibition; (C) uncompetitive inhibition; (D) mixed-type inhibition. Note that the double-reciprocal Lineweaver-Burk plots are used for illustrative purposes only. For estimation of actual parameters (e.g., kcat), only non-linear regression methods should be used.

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Fig. 12 Mathematical analysis of non-productive substrate binding to an enzyme. (A) General scheme depicting non-productive binding. (B) Double reciprocal plot showing the difference between the expected theoretical productive binding and what is observed due to a mixture of productive plus non-productive binding. Compare the plot to Fig. 11C. The relevant equations are shown. (C) and (D) In these models, there is equilibrium between the non-productive and productive forms of the enzyme; compare with Fig. 8. (C) Conformational selection model. (D) Induced fit model.

Fig. 13 Inhibition of nifedipine oxidation by cholesterol in human liver microsomes: C, 1.5 mM; n, 6.6 mM; :, 11.7 mM; B, 16.8 mM cholesterol. This research was originally published in The Journal of Biological Chemistry. Shinkyo R, Guengerich FP (2011) Inhibition of Human Cytochrome P450 3A4 by Cholesterol. The Journal of Biological Chemistry 286: 18426–18433. © The American Society for Biochemistry and Molecular Biology.

Fig. 14 Proposed explanation for mechanism of inhibition of oxidation of the drug nifedipine (s) by the inhibitor/substrate cholesterol (I) (Segel, 1975). This research was originally published in The Journal of Biological Chemistry. Shinkyo R, Guengerich FP (2011) Inhibition of Human Cytochrome P450 3A4 by Cholesterol. The Journal of Biological Chemistry 286: 18426–18433. © The American Society for Biochemistry and Molecular Biology.

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Drug Metabolism: Cytochrome P450

1.19.8.1.3

Irreversible inhibition of P450s

Irreversible (or mechanism-based, or “suicidal”) inactivation of P450 is rather common, probably because of the high energy nature of enzyme intermediates (Fig. 6). Some of the moieties that give rise to this are olefins, acetylenes, thiophenes, and cyclopropylamines. The mechanism with an olefin is somewhat representative for other molecules (Correia and Hollenberg, 2015). Partitioning of the reactive intermediate between covalent binding and production of a stable product is observed, with an associated partition ratio. A reaction can occur with either the P450 protein or the heme, depending upon the situation. kinactivation/Ki ratios are predictive of the in vivo inhibitory efficiency of drugs and other chemicals. Mechanism-based inhibition is distinct from a reaction in which a reactive product is produced that can, at least in principle, leave the enzyme (and re-bind). The differences are that such a reactive product can be quenched with a scavenger, e.g. glutathione, which is ineffective in true mechanism-based inhibition. Also, a reactive product has the potential to react with other proteins. Irreversible inhibition of P450s is problematic in that complex pharmacokinetics can result if the inhibition is irreversible. The pharmacokinetics must include rates of enzyme resynthesis. Mechanism-based inhibition reactions that destroy the heme are also problematic in that porphyrin products can disrupt normal heme synthesis and cause porphyrias, where colored products are excreted in urine (Ortiz de Montellano et al., 1981). In principle, designing a mechanism-based inhibitor has appeal in achieving drug selectivity in medicinal chemistry, in that both substrate recognition and catalysis are required for inhibition. However, there are few examples with the known P450 drug targets (vide infra). One issue with chemically-modified P450s has been the production of autoantibodies in the body. In the cases of tienilic acid (P450 2C9) and dihydralazine (P450 1A2) there is extensive covalent binding of the drug to the P450, production of antibodies that selectively recognize the unmodified and modified P450, and clinical liver damage (Beaune et al., 1987; Bourdi et al., 1990). However, it has not been possible to clearly show a causal effect of the autoantibodies in liver pathology in humans or experimental animals.

1.19.8.1.4

The example of intestinal P450 3A4 and grapefruit juice

An interesting practical example of a P450 inhibitor is grapefruit juice. The discovery of this phenomenon is interesting. Grapefruit juice was used as an additive in an ethanol interaction study for felodipine metabolism, as a means of disguising treatment in volunteers. The ethanol/grapefruit juice mixture produced a dramatic inhibition of clearance of felodipine (now recognized to be a P450 3A4 substrate (Guengerich et al., 1991a). More experiments revealed that the grapefruit juice was the inhibitory factor, not the ethanol (Bailey et al., 1990). Orange juice did not have this effect, although subsequent work has shown that some other citrus fruit juices do (e.g., starfruit, Seville orange). Grapefruit has a number of specific compounds present. One is naringenin (and its glycoside, naringin), but these proved to be relatively weak inhibitors of P450 3A4 (Bailey et al., 1993; Guengerich and Kim, 1990). However, the furanocoumarin bergamottin was identified as a better suspect (Bailey et al., 2000; Goosen et al., 2004). Hollenberg and his associates were able to demonstrate in vitro inactivation of P450 3A4 by bergamottin (He et al., 1998), including showing modification of the protein. In vivo studies supported a role for the concentration of bergamottin in grapefruit juice contributed to the effects on felodipine Cmax and AUC (oral dose) (Goosen et al., 2004). Ultimately, the major modification on P450 3A4 was identified as Gln-273 (Fig. 15). An X-ray crystal structure of the complex has been published (Sevrioukova, 2019). Both bergamottin and 60 ,70 -dihydroxybergamottin, an oxidation product of bergamottin (Fig. 16) are inhibitors. The inhibitory effect of bergamottin is also seen with P450s 3A5 and 2B6, although the kinactivation/Ki ratios are not as high as for P450 3A4 (He et al., 1998; Lin et al., 2005). Another important point is that the primary effect of grapefruit juice is on intestinal P450 3A4 not hepatic P450 3A4 (Huang et al., 2004). Accordingly, the effects may be more dramatic with low-dose P450 3A4 substrates. The amount of P450 3A4 in the intestine (Paine et al., 1997, 2006) is only a  1% of the amount in liver (roughly 1500 nmol) (Guengerich, 1990), and accordingly only drugs (orally) administered at low doses can have a large fraction of their metabolism done in the intestine, before reaching the liver (via the portal vein). P450 2B6, unlike P450 3A4 and 3A5, is not expressed in the intestine (vide infra). Today many drugs that are P450 3A4 substrates have warnings about concomitant use of grapefruit in their labeling. However, no reports of deaths due to an interaction have been noted (Huang et al., 2004). A number of questions remain about grapefruit and inhibition of drug metabolism. Can bergamottin (and 60 ,70 -dihydroxybergamottin) explain all of the effects, or are there still other components (Goosen et al., 2004)? Is the heme modified, in addition to the apoprotein? P450 3A4 (and other P450s) lost heme during in vitro incubations but no products were identified (Lin et al., 2005). The amide nitrogen of glutamine is not a very good nucleophile, so why is this residue reacting (Lin et al., 2012)? Finally, the discovery of this interaction was a result of serendipity, and there are probably more food-drug interactions that are still unknown.

1.19.8.2

Approaches to inhibition screening and modeling

In practice, screening for P450 inhibition can be done in several ways, depending on how many molecules need to be screened and extent of information derived. A somewhat typical cascade of this sort is not unusual in the pharmaceutical industry. In cases i–iii the reaction time is fixed and the product P is measured: (i) Single concentration of the substrate S and the inhibitor I. (ii) Single concentration of S, vary I to determine IC50.

Drug Metabolism: Cytochrome P450

Fig. 15

485

Mechanism of bergamottin inhibition of P450 3A4 underlying the grapefruit juice phenomenon (Lin et al., 2012).

(iii) Vary both S and I to determine Ki. (iv) If possible, use biophysical or spectroscopic methods to measure the affinity of E and I. For instance, many azole and other heterocyclic nitrogen-containing drugs produce “Type II” spectral complexes (shift of heme Soret band to  430 nm). If the binding is very tight (i.e., Kd < 100 nM), the accuracy of UV–visible spectral estimates may be difficult, in that most of the ligand I will be complexed and even tight-binding (quadratic) equations may be difficult to apply. Sometimes fluorescence measurements are more useful for tighter complexes. (v) Vary the reaction time after adding I to ES, in order to determine if mechanism-based inactivation is present. (vi) Vary I and the reaction time to determine Ki and kinactivation for mechanism-based inactivation. Some examples of modeling P450 inhibition reactions have been presented elsewhere, where KinTek ExplorerÒ software was used (Guengerich, 2019). Phenomena such as very tight binding and consumption of an inhibitor during a reaction can be modeled and were shown to dramatically alter the apparent Ki values. Using such modeling, it was possible to distinguish between direct inhibition of P450 17A1 by orteronel and a phenomenon termed slow, tight-binding inhibition (Fig. 19) of P450 17A1 by the drug orteronel. This phenomenon, which to our knowledge has not been clearly demonstrated for inhibition of a P450, can be characterized readily in kinetic analysis (Child and Guengerich, 2020). It has many of the features of mechanism-based inactivation but is reversed by adding higher concentrations of substrate, and individual rate constants can be determined. The usual simple linearized steady-state plots are useful in identifying types of inhibition (Fig. 11). However, computer modeling programs are far more effective in understanding modes of inhibition, and pre-steady-state kinetic analysis (Figs. 16– 19) can be critical.

Fig. 16 Effect of long reaction time and substrate depletion on steady-state kinetic parameters (Guengerich, 2019). (A) No lack of linearity at substrate concentrations. (B) Plots of product formation after 360 s, for P only (C) and also including EP complex (P þ EP, :). The Km, apparent values were 64 mM for P only and 34 mM for E þ P; compare to 11.5 mM in Fig. 17A (vide infra).

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Drug Metabolism: Cytochrome P450

Fig. 17 Effect of inhibitors on modeled steady-state kinetics (Guengerich, 2019). The kinetic scheme was the same as in Fig. 16, but the enzyme concentration was decreased 20-fold, and the substrate concentrations were the same (no inhibitor added). A, C; The basic system was set up with the following model: E þ S%ES k1 106 M1 s1 ; k1 10 s1 ES/EP k2 0:1 s1 EP%E þ P k3 2 s1 ; k3 106 M1 s1 E þ I%EI

k4 106 M1 s1 ; k4 0:1 s1

with [E] ¼ 2.0 mM and [S] ¼ 1, 2, 3, 4, 6, 7, 10, 15, 20, 30, 50, 80, 100, 150, and 200 mM, yielding the results shown in Parts A and C in the absence of inhibitor. kcat 0.0097 s 1, Km 11.5 mM, kcat/Km 840 M 1 s 1. B, D; I ¼ 2 mM (added 0.1 s after reaction started). kcat 0.0098 s 1, Km 190 mM, kcat/Km 52 M 1 s 1, Ki,app 0.064 mM (compare with 0.10 mM for k 4/k4).

1.19.9

P450 and reaction oxygen species (ROS)

In the P450 catalytic cycle (Fig. 6), the “coupling” of the system to form products from drug or other substrates is not very tight, as long known from the work of Gillette, LaDu, and Brodie (Gillette et al., 1957) and others. The abortive breakdown of any of the intermediates following steps 2–5 (Fig. 6) can result in the formation of O2 or H2O2, two major ROS that can be released from the enzyme or damage the enzyme itself. There are two issues here: the production of ROS and the effect of ROS on P450s (Albertolle and Guengerich, 2018). Much has been written about P450s, ROS, and oxidative stress. It should be emphasized that H2O2 has been recognized as an important secondary signaling molecule, and several laboratories have characterized redox sensitive enzymes (Groitl and Jakob, 2014; Gupta et al., 2017). However, ROS and oxidative stress have been suggested to be involved in many diseases (Valko et al., 2006, 2007). Much of this work is all in vitro or, even if in vivo, done with what are often considered l

Drug Metabolism: Cytochrome P450

Fig. 18 Effect of consumption of inhibitor by an enzyme (Guengerich, 2019). The basic scheme of Fig. 17A was used, except that a step EI / E þ X (inactivation of inhibitor I) was added. (A) and (B) Plots of product formation vs. times at varying concentrations of S (as in Fig. 17A). (C) and (D) Plot as in Part A but with [I] ¼ 2 mM in premix solution and no consumption of I. Ki,app 0.13 mM. (E) and (F) A rate constant of 0.1 s 1 was added for EI / E þ X. Ki, apparent 0.70 mM. (G) Time courses of P (red), (I) (green), and EI (blue) during the reaction, as a function of substrate concentration.

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Drug Metabolism: Cytochrome P450

(A)

(B)

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488

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Fig. 19 Kinetics of slow, tight-binding enzyme inhibition. The model system was set up as follows with the enzyme E, substrate S, product P, and inhibitor I. FI is a modified form of the enzyme inhibitor complex EI. E þ S%ES k1 107 M1 s1 ; k1 5 s1 ES/EP k2 0:5 s1 EP%E þ P k3 10 s1 ; k3 107 M1 s1 E þ I%EI EI%FI

k4 107 M1 s1 ; k4 100 s1 k5 1 s1 ; k5 0:001 s1

(A) The reaction began with 0.1 mM E and 20 mM S. After 20 s, an equal volume of 0, 0.6, 2, 6, 20 or 60 mM I was added to give the final I concentrations indicated. Note the curvilinear traces, especially with 1, 3, 10, and 30 mM final concentrations of I. (B) After 20 s, an equal volume of 0.2, 0.6, 2, 6, or 20 mM S was added to 0.1 mM EI to give the final concentrations of S indicated. Note the curvilinear nature of the plots, especially with 0.3, 1, 3, and 10 mM concentrations of S.

inappropriate biomarkers (Kalyanaraman et al., 2012). One of the best established markers of oxidative stress is the formation of isoprostanes (Morrow and Roberts II, 1996; Kadiiska et al., 2005a, b). These can be measured in vivo, even in humans. When isoprostanes were measured in rats treated with classic P450 inducers, levels of isoprostanes were only increased by phenobarbital treatment (Dostalek et al., 2007). The same limited ROS response was seen with mice treated with inducers, and deletion of P450 2e1 had no effect on total isoprostanes (Dostalek et al., 2008). Moreover, the phenobarbital effect was linked to a change in pyridine nucleotide metabolism, not P450 induction (Dostalek et al., 2008). Although global ROS changes were not associated with changes in P450s in rodents, localized changes cannot be ruled out. Trafficking of P450 2E1 into mitochondria, related to N-terminal sequence changes, modified the electron transport chain (switching to the use of adrenodoxin as the electron source) and was shown to enhance ROS production (as verified by in vitro isoprostane analysis) (Bansal et al., 2010). Thus local ROS production may be associated with P450s but not global ROS in vivo. Another aspect of ROS is the effects on P450s. The heme of P450, particularly ferrous P450, can be destroyed by H2O2, at least in in vitro settings. This phenomenon is often seen in longer incubations with purified P450s, due to the production of H2O2, and is blocked by adding catalase (and sometimes superoxide dismutase as well). This is not an issue with microsomes, which are usually contaminated with (peroxisomal) catalase (Guengerich, 1978). The extent to which this is observed in vivo is unknown. In the course of studying the stimulatory effect of mild reductants (e.g., thiols) on P450 4A11 catalytic activity we demonstrated that the cysteine thiol bound to heme was oxidized to a sulfenic acid (-SOH) (Albertolle et al., 2017). The ligation to the iron atom is lost, but the sulfenic acid is reduced back to Cys with the reductant, restoring activity (and the classic P450$CO spectrum (Fig. 1). In addition, further oxidation of various cysteines in P450s has been observed, to the oxidation states of sulfoxides and sulfonic acids, which are irreversible (Albertolle et al., 2018). A detailed mass spectral analysis showed that the Cys-457 sulfenic acid was present to the extent of 40% of the total in liver and kidney microsomes, prepared from transgenic mice expressing human P450 4A11 (Albertolle et al., 2017). Subsequent work showed the presence of heme-peptide Cys sulfenic acid in a number of P450s in human liver and kidney samples, and sulfenic acids were present in a number of other drug-metabolizing enzymes as well (e.g.., UDP-glucuronyl transferase, flavin-containing monooxygenase) (Albertolle et al., 2018).

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Another aspect of ROS and the related field of reactive nitrogen species is the modification of P450s by nitric oxide (Wink et al., 1993; Park et al., 2018). Nitric oxide was reported by Morgan’s group to regulate P450 2J2 posttranslationally and the modified CYP2J2 was shown to undergo ubiquitin-independent proteasomal degradation (Park et al., 2018).

1.19.10 P450 enzymes involved in drug metabolism Of the 57 human P450 enzymes (Table 1), at least 38 (2/3) have been shown to be able to catalyze the oxidation of drugs. However, in many cases the number of drugs involved is small (e.g., P450 2S1 (Nishida et al., 2010; Wang and Guengerich, 2012)). The P450 reactions with drugs have been divided into three groups in this chapter: (i) the five major P450s involved in drug metabolism, (ii) six P450s less involved in drug metabolism, plus P450s 3A5 and 3A7, and (iii) three more P450s that are primarily involved in pathways with endogenous compounds but have been shown to also be involved in some important reactions with drugs. Most of the information about the P450s in the first two categories is presented in Tables 2–7, in terms of the individual drugs that are substrates, inhibitors, and inducers. As mentioned earlier,  75% of drug metabolism (of small molecules) is done via P450 reactions (with UDP-glucuronyl transferase often, but not always, following) (Williams et al., 2004a). Most of the reactions with drugs are attributed to P450s 1A2, 2C9, 2C19, 2D6, and 3A4 (Fig. 20). The literature is in general agreement with P450s 1A2, 3A4, and the Subfamily 2C members being most abundant (mass basis) (Fig. 21). However, the fractions attributed to some of the other P450s have been variable, even in comparing proteomic measurements, e.g. 2A6, 2D6, 2C19 (Fig. 21). In comparing Figs. 20 and 21, it is apparent that some of the less plentiful P450s have contributions to drug metabolism much greater than might be expected for their abundance, e.g., P450s 2C19 and 2D6. Recently a case has been made for a role of P450 1A1 in hepatic drug metabolism, with LC-MS data showing a level of 3% (of total P450) in one liver sample (Lang et al., 2019), although generally lower. The enzyme is expressed in human lung (Kim et al., 2004) and could contribute to the metabolism of drugs administered by inhalation.

1.19.10.1 Major human P450s involved in drug metabolism 1.19.10.1.1

P450 1A2

This enzyme is almost exclusively hepatic and is involved in the metabolism of a number of drugs (Table 2). It is the main catalyst involved in the metabolism of caffeine, and this activity provides a safe and useful biomarker for the enzyme (Butler et al., 1989). The enzyme is inducible by a number of AhR agonists (Fig. 3), including some drugs, smoking, and ingestion of charbroiled food (Table 4). Regulation of transcription also involves HNF4a, explaining the hepatic localization. A number of drugs are inhibitors of P450 1A2 and can give rise to serious drug-drug interactions (Table 3). Another important aspect of P450 1A2 is its role in the bioactivation of many chemical carcinogens, particularly arylamines and heterocyclic amines (Kim and Guengerich, 2005). At least 41 single nucleotide variations are known in the CYP1A2 gene (https://www.pharmvar.org/ gene/CYP1A2), plus five or more others in which the haplotype has not been determined. These genetic variations and the inducibility underlie the
40-fold variation in P450 1A2 expression and activity levels in humans (Yang et al., 2010; Butler et al., 1989). Genetic variation in CYP1A2 is a main driver in tolerance to coffee consumption (Sulem et al., 2011). An X-ray crystal structure of human P450 1A2 is available, with the inhibitor a-naphthoflavone bound (Sansen et al., 2007b). As predicted from structure-activity relationships of known substrates, the formal active site is medium-sized but large enough to possibly bind two pyrene or benzo[a]pyrene molecules (Sohl et al., 2008). a-Naphthoflavone is also a substrate for 5,6epoxidation by P450 1A2 (Bauer et al., 1995), and the only available crystal structure (PDB 2HI4) cannot be catalytically competent. Accordingly, an alternate conformation must be formed for catalysis to occur.

1.19.10.1.2

P450 2C9

The CYP2C9 gene is expressed mainly in the liver but also in the gastrointestinal tract (https://www.proteinatlas.org/ ENSG00000138109-CYP2C9/tissue). It is one of the major P450s found in liver (Fig. 21) and, due to its localization and ability to oxidize many substrates (Table 2), often involved in first-pass drug metabolism in both the small intestine and the liver. A number of drugs can be used for in vivo phenotyping, of which tolbutamide and (S)-warfarin are currently most recommended (Center for Drug Evaluation and Research, Food and Drug Administration, 2020). Transcription of P450 2C9 is complex with the promoter containing several regulatory elements, including two HNF-4a sites, a PXR site, a CAR site, and a glucocorticoid response site. CAR has a second, distal binding site (Ferguson et al., 2002). CAR has a mechanism distinct from many of the other steroid nuclear receptors in that it is constitutively active in the absence of a ligand (but can be activated by some ligands) (Kobayashi et al., 2015). Nuclear translocation is driven by dephosphorylation of Thr-38, which in turn is under the control of the EGF receptor in the mechanism of barbiturate induction (Mutoh et al., 2013). HNF-4a and C/EBPa also influence the hepatic localization. A number of SNVs are known (
61, https://www.pharmvar.org/gene/CYP2C9). Two polymorphic variants in particular have been studied for relevance, *2 and *3. These both have lower frequencies in Asian and African populations. Both, but especially *3, have lower catalytic activity in warfarin metabolism and have clinical implications for this narrow therapeutic dose-window

490 Table 2

Drug Metabolism: Cytochrome P450 Substrates of major P450s.

1A2

2C9

2C19

2D6

3A4

Acetaminophen Amitriptyline Caffeine Clomipramine Clozapine Cyclobenzaprine Doxepin Duloxetine Estradiol Fluvoxamine Haloperidol Imipramine Mexiletine Nabumetone Naproxen Olanzapine Ondansetron Phenacetin Pirfenidone Propranolol Riluzole Ropivacaine Rucaparib Tacrine Theophylline Tizanidine Triamterene Verapamil Warfarin Zileuton Zolmitriptan

Amitriptyline Capecitabine Celecoxib Clopidogrel Diclofenac Doxepin Fluoxetine Fluvastatin Glibenclamide Glimepiride Glipizide Glyburide Ibuprofen Irbesartan Lesinurad Lornoxicam Losartan Meloxicam Nateglinide Phenytoin Piroxicam Rosiglitazone S-Naproxen S-Warfarin Suprofen Tamoxifen Tolbutamide Torsemide Valproic Acid Venlafaxine Voriconazole Zakirlukast

Amitriptyline Atomoxetine Brivaracetam Carisoprodol Chloramphenicol Citalopram Clomipramine Clopidogrel Cyclophosphamide Diazepam Doxepin Escitalopram Esomeprazole Fibanserin Hexobarbitol Imipramine Indomethacin Labetalol Lansoprazole Moclobemide Nelfinavir Nilutamide Omeprazole Pantoprazole Phenobarbital Phenytoin Primidone Proguanil Propranolol R-Mephobarbital R-Warfarin S-Mephenytoin Suvorexant Teniposide Venlafaxine Voriconazole

Alprenolol Amitriptyline Amphetamine Aripiprazole Atomoxetine Brexipiprazole Bufuralol Cariprazine Carvedilol Chlorpheniramine Chlorpromazine Citalopram Clomipramine Clonidine Codeine Codeine Debrisoquine Desipramine Deutetrabenazine Dexfenfluramine Dextromethorphan Donepezil Doxepin Duloxetine Eliglustat Encainide Escitalopram Flecainide Fluoxetine Fluvoxamine Haloperidol Ibrutinib Imipramine Lidocaine Methoxyamphetamine Metoclopramide Mexiletine Minaprine Nebivolov Palonosetron Nortriptyline Ondansetron Oxycodone Paroxetine Perhexiline Perphenazine Phenformin Pimavanserin Promethazine Propafenone Propranolol Risperidone Rucaparib S-Metoprolol Sparteine Tamoxifen Tetrabenzine Thioridazine Timolol Tramadol

Abemaciclib Acalabrutinib Alectinib Alfentanil Alprazolam Amitriptyline Amlodipine Aprepitant Aripiprazole Astemizole Atorvastatin Boceprevir Brexpiprazole Brigatinib Buspirone Cafergot Caffeine Carbamazepine Cariprazine Cerivastatin Chlorpheniramine Cilostazol Cisapride Citalopram Clarithromycin Clopidogrel Cobimetinib Cocaine Codeine Copanlisib Cyclosporine Daclatasvir Dapsone Deflazacort Dexamethasone Dextromethorphan Diazepam Diltiazem Docetaxel Domperidone Doxepin Efavirenz Elbasvir Eliglustat Eplerenone Erythromycin Escitalopram Esomeprazole Estradiol Felodipine Fentanl Finasteride Flibanserin Grazoprevir Haloperidol Hydrocortisone Ibrutinib Idelalisib Imatinb Indinavir

Drug Metabolism: Cytochrome P450 Table 2 1A2

491

Substrates of major P450s.dcont'd 2C9

2C19

2D6

3A4

Valbenazine Venlafazine Zuclopenthixol

Irinotecan Isavuconazonium Ivabradine Lansoprazole Lenvatinib Lercanidipine Levacetylmethadol Lidocaine Lovastatin Methadone Midazolam Naldemedine Naloxegol Nateglinide Nelfinavir Neratinib Netupitant Nevirapine Nifedipine Nisoldipine Nitrendipine Olaparib Omeprazole Ondansetron Osimertinib Palbociclib Panobinostat Primavanserin Pimozide Progesterone Propranolol Quinidine Quinine Regorafenib Ribociclib Risperidone Ritonavir Rolapitant Romidepsin Salmeterol Saquinavir Selexipag Sildenafil Simvastatin Sirolimus Sonidegib Sorafenib Sunitinib Suvorexant Tacrolimus (FK506) Tamoxifen Taxol Telaprevir Telithromycin Terfenadine Testosterone Torisel Tramadol Trazodone Valbenzaine (Continued)

492 Table 2

Drug Metabolism: Cytochrome P450 Substrates of major P450s.dcont'd

1A2

2C9

2C19

2D6

3A4 Velpatasvir Vemurafenib Venetoclax Venlafaxine Verapamil Vincristine Voriconazole Zaleplon Ziprasidone Zolpidem

Modified from Flockhart, D.A. Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine, 2007. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

Table 3

Inhibitors of major P450s.

1A2

2C9

2C19

2D6

3A4

Amiodarone Cimetidine Ciprofloxacin Citalopram Crisaborole Efavirenz Fluoroquinolone Fluvoxamine Furafylline Interferon Methoxsalen Mibefradil Ribociclib Rucaparib Ticlopidine

Amiodarone Capecitabine Clopidogrel Crisaborole Efavirenz Fenofibrate Fluconazole Fluvastatin Fluvoxamine Isoniazid Lovastatin Metronidazole Paroxetine Phenylbutazone Probenicid Rucaparib Sertraline Sulfamethoxazole Sulfaphenazole Teniposide Voriconazole Zafirlukast

Chloramphenicol Cimetidine Citalopram Esomeprazole Felbamate Fluoxetine Fluvoxamine Indomethacin Isoniazid Ketoconazole Lansopraxole Modafinil Omeprazole Oxcarbazepine Pantoprazole Probenicid Rucaparib Ticlopidine Ropiramate Voriconazole

Amiodarone Bupropion Celecoxib Chlorpheniramine Chlorpromazine Cimetidine Cinacalcet Citalopram Clemastine Clomipramine Cocaine Diphenhydramine Doxepin Doxorubicin Duloxetine Escitalopram Fluoxetine Halofantrine Haloperidol Hydroxyzine Levomepromzaine Methadone Metoclopramide Mibefradil Midodrine Moclobemide Palonosetron Panobinostat Paroxetine Perphenazine Promethazine Quinidine Ranitidine Riclopidine Ritonavir Rolapitant Rucaparib Sertraline Terbinafine Tripelennamine

Amiodarone Aprepitant Atomoxetine Boceprevir Chloramphenicol Cimetidine Ciprofloxacin Clarithromycin Delaviridine Diethyldithiocarbamate Diltiazem Erythromycin Esomeprazole Fluconazole Fluvoxamine Gestodene Grapefruit Juice Idelalisib Imatinib Indinavir Itraconazole Ketoconazole Lesinurad Mibefradil Mifepristone Nefazodone Nelfinavir Netupitant Norfloxacin Norfluoxetine Omeprazole Pantoprazole Regorafenib Ribociclib Ritonavir Saquinavir Starfruit Telaprevir Telithromycin Verapamil Voriconazole

Modified from Flockhart DA (2007) Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

Drug Metabolism: Cytochrome P450 Table 4

493

Inducers of major P450s.

1A2

2C9

2C19

2D6

Broccoli Brussels Sprouts Carbamazepine Charbroiled meat Modafinil Nafcillin Omeprazole Rucaparib Teriflunomide Tobacco and tobacco smoke

Carbamazepine Enzalutamide Nevirapine Phenobarbital Rifampin Secobarbital St. John’s wort

Carbamazepine Efavirenz Enzalutamide Norethindrone Enzalutamide Norethindrone Prednisone Rifampicin Ritonavir St. John’s wort

3A4 Barbiturates Brigatinib Carbamazepine Efavirenz Enzalutamide Glucocorticoids Modafinil Nevirapine Oxcarbazepine Phenobarbital Phenytoin Pioglitazone Rifampin St. John’s wort Troglitazone

Modified from Flockhart DA (2007) Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

Table 5

Substrates of other P450s.

1A1

2A6

2B6

2C8

2E1

2 J2

Anetholedithiolethione Axitinib Conivaptan Erlotinib Granisetron Riociquat

(þ)-3,5-Dimethy-2-(3-pyridyl)thiazolidine-4-one Artemisinin Artesunate Efavirenz Letrozole Metronidazole Nicotine Pilocarpaine SM-12502 Tegafur Tyrosol Valproic Acid

Artemisinin Bupropion Cyclophosphamide Efavirenz Ifosphamide Ketamine Meperidine Methadone Nevirapine Propafol Selegiline Sorafenib Tramadol Velpatasvir

Amodiaquine Cerivastatin Paclitaxel Repaglinide Selexipag Sorafenib Torsemide

Acetaminophen Chlorzoxazone Enflurane Ethanol Halothane Isoflurane Methoxyflurane Sevoflurane

Albendazole Amiodarone Apixaban Astemizole Carebastine Danazol Ebastine Eperisone Fenbendazole Hydroxyebastine Mesaridazine Terfenadine Thioridazine Vorapaxar

Modified from Flockhart DA (2007) Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

Table 6

Inhibitors of other P450s.

2B6

2C8

2E1

Clopidogrel Crisaborole Rucaparib Thiotepa Ticlopidine Voriconzole

Gemfibrozil Glitazone Montelukast Quercetin Teriflunomide Trimethoprim

Diethyldithiocarbamate Disulfiram Ribociclib

Modified from Flockhart DA (2007) Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

drug (Rettie et al., 1994). The functional effects of the P450 2C9 (and other) coding region variants can change depending upon the substrate (Maekawa et al., 2009). Even with the X-ray crystal structures of P450 2C9*3 available (i.e., PDB 5X24) (Maekawa et al., 2017), the reasons for the attenuated catalytic activities of the *2 and *3 variants are not obvious. Our own work with the substrate arachidonic acid indicated that the difference can be explained by the rates of reduction of P450 2C9, which appears to be the rate-

494

Drug Metabolism: Cytochrome P450 Inducers of other P450s.

Table 7 2B6

2C8

2E1

Artemisinin Carbamazepine Efavirenz Nevirapine Phenobarbital Phenytoin Rifampin

Rifampin

Ethanol Isoniazid

Flockhart DA (2007) Drug Interactions: Cytochrome P450 Drug Interaction Table. Indiana University School of Medicine. https//drug-interactions.medicine.iu.edu. Accessed 9 March 2020.

Fig. 20 Fractions of total P450-mediated drug metabolism attributed to individual human P450 enzymes. The collective numbers for “drugs,” “marketed drugs,” and “drugs in development” from Rendic and Guengerich (Rendic and Guengerich, 2015) were used to prepare the pie chart. 40

% Total

30

20

10

1A 1 1A 2 2A 6 2B 6 2C 8 2C 9 2C 19 2D 6 2E 1 2J 2 3A 4 3A 5

0

P450 Fig. 21 Percentages of total P450 in human liver samples accounted for by each P450. The data points were compiled from four sets with multiple liver samples (Shimada et al., 1994; Kawakami et al., 2011; Achour et al., 2014) and one with a single liver sample high in P450 1A1 (Lang et al., 2019). The estimates were made immunochemically in one case (Shimada et al., 1994) and by LC-MS proteomic methods in the others (Kawakami et al., 2011; Achour et al., 2014; Lang et al., 2019). The value for P450 1A1 is a mean of measurements of 30 samples (Lang et al., 2019). The individual colors have no meaning but are added to facilitate visualization.

limiting step (Sausville et al., 2018). Interestingly the attenuated activity appeared to provide a protective effect against cancer for patients in that study, which could be replicated experimentally in a mouse model (Sausville et al., 2018). The substrate selectivity of P450 2C9 is rather broad, with many drugs included (Table 2). Thus, the existence of polymorphisms can have considerable effects in pharmacokinetics, even if not totally predictable. Drug-drug interactions can also result from the long list of inhibitors and inducers (Tables 3 and 4).

Drug Metabolism: Cytochrome P450

495

Several X-ray crystal structures of P450 2C9 with ligands are available (Williams et al., 2003; Wester et al., 2004). Many P450 2C9 substrates and other ligands have an anionic charge, and Arg-108 is involved in docking in some cases. Other residues involved in binding interactions include Arg-97, Arg-98, Phe-114, Phe-476, and Leu-208 (Guengerich, 2015).

1.19.10.1.3

P450 2C19

Interest in this enzyme developed from the discovery of the genetic polymorphism in (S)-mephenytoin 4-hydroxylation, which followed on the debrisoquine 10 -hydroxylation polymorphism eventually attributed to P450 2D6 (Küpfer and Preisig, 1984; Wedlund et al., 1984). Although delineating the activities in the P450 2C Subfamily was not trivial and some of the early preparations of P450 2C8 and 2C9 showed some mephenytoin hydroxylation activity (Shimada et al., 1986), heterologous expression work by Goldstein’s group established the 2C19 sequence as the one related to the polymorphism (Goldstein et al., 1994). P450 2C19 is expressed essentially only in the liver and gastrointestinal tract, and its regulation is rather similar to that of P450 2C9 (vide supra). Variations among individuals is due to inducibility (Table 4) and to the genetic polymorphism. In East Asian populations, the incidence of “poor metabolizers” (Küpfer and Preisig, 1984; Wedlund et al., 1984) is  20%, leading to issues in the use of drugs oxidized largely by P450 2C19 in these populations (Nakamura et al., 1985). At least 37 major allelic variants are known, with many subtypes among these (https://www.pharmvar.org/gene/CYP2C19). The major deficient allele (*2) in Caucasians is the result of an mRNA splicing defect. In Caucasians the poor metabolizer frequency is only  2%. At least one X-ray crystal structure of P450 2C19 has been reported (Reynald et al., 2012), and the size of the active site is similar to that of P450 2C9 (vide supra) but much smaller than that of P450 2C8 (vide infra). Mephenytoin itself is not a major drug today, but P450 2C19 has been found to be involved in the oxidation of many other drugs (Table 2). In general, pharmaceutical companies avoid developing drug candidates that show a large fraction of metabolism by a highly polymorphic enzyme such as P450 2C19. Among the important drug substrates for P450 2C19 is the ulcer drug omeprazole (proton pump inhibitor), developed before characterization of the P450 2C19 polymorphism. Poor metabolizers have shown better response to ulcer treatment (Chiba et al., 1993). Another substrate is the anti-coagulant clopidogrel (PlavixÒ), which is converted to its active form in two steps, both catalyzed in large part by P450 2C19 (Kazui et al., 2010). In some medical centers genetic analysis is done prior to treatment, as poor metabolizers do not benefit from it, although the cost effectiveness of the procedure has been debated (Guengerich, 2015).

1.19.10.1.4

P450 2D6

P450 2D6 has a long history in the context of the work by Smith, Lennard, Eichelbaum, and others on the genetic polymorphism observed for debrisoquine and sparteine (Mahgoub et al., 1977; Tucker et al., 1977; Eichelbaum et al., 1979). This was the first P450 reaction, at least regarding drug metabolism, shown to be under monogenic control. This information led to the view that individual human P450s with high substrate/reaction selectivity could be purified and characterized, an approach used in this laboratory (Distlerath et al., 1985). The abundance of P450 2D6 in human liver is relatively low (Fig. 21). The enzyme is also expressed in the gastrointestinal tract (https://www.proteinatlas.org/ENSG00000100197-CYP2D6/tissue) and, although not reported in the Protein Atlas, there are reports of expression in human brain and lung (Siegle et al., 2001; Lo Guidice et al., 1997). Although P450 2D6 is generally considered a microsomal protein, Avadhani and his associates have reported that a N-terminal chimeric signal (residues 23–33) mediates targeting to mitochondria, where the enzyme can utilize electrons from the adrenodoxin system (Sangar et al., 2010a). The mitochondrial localization may be a factor in the activation of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) in Parkinson’s Disease models (Bajpai et al., 2013). Some factors involved in P450 2D6 regulation are HNF-4a and C/EBPa (Corchero et al., 2001; Jover et al., 1998), but P450 2D6 induction by drugs or other xenobiotics has never been demonstrated (Table 4). The enzyme does appear to be elevated in pregnancy (Wadelius et al., 1997), and Krüppel-like factor 9 (KLF9) may be involved (Koh et al., 2014). Other than pregnancy, variation in P450 2D6 activity is understood in the context of genetic variability, and currently at least 139 alleles are known, with a number of further divisions (https://www.pharmvar.org/gene/CYP2D6). Considerable racial differences are seen. In Caucasians,  7% of individuals display a poor metabolizer phenotype (depending on multiple genotypes linked to various ethnic groups within the overall race) and the most common variant (*2) is the result of aberrant mRNA splicing (Gonzalez et al., 1988), as in the case of P450 2C19 (vide supra). It should also be noted that gene deletions, frameshifts, and duplications are also known. In the latter case, present at up to 7% of Caucasians, as many as 13 copies of the CYP2D6 gene are present (*1XN) (Johansson et al., 1993; Dahl et al., 1995). This “Ultra-Metabolizer” phenotype is an issue in drug ineffectiveness, as expected, but was also implicated in the death of a nursing infant due to unexpectedly high rates of oxidation of codeine to morphine by her mother (Koren et al., 2006). X-ray crystal structures of P450 2D6 have been published with ligands present. Many of the substrates for P450 2D6 contain a basic nitrogen (but not all (Guengerich et al., 2002)), and both Asp-301 and Glu-216 can be used in binding via charge interactions (Wang et al., 2015). Many inhibitors of P450 2D6 are known (Table 3), one of the most well-known being quinidine (Otton et al., 1984), which is a substrate for P450 3A4 but not P450 2D6 (Guengerich et al., 1986b). The list of drug substrates of P450 2D6 is extensive (Table 2, Fig. 20), in light of the low abundance of P450 2D6 in human liver (Fig. 21). Pharmaceutical companies have a general aversion to advancing a drug candidate for which a large fraction of its metabolism is due to a single, highly polymorphic enzyme such as P450 2D6. Many of the P450 2D6 substrates in Table 2 were developed before the concerns about polymorphisms. Indeed, the model substrate spirosulfonamide (Guengerich et al., 2002) had been

496

Drug Metabolism: Cytochrome P450

a candidate in the cyclooxygenase-2 inhibitor program but had been dropped because part of the clearance was done by P450 2D6 (the candidate that did reach market was rofecoxib (VioxxÒ), which was subsequently recalled for other reasons). In light of these concerns, the large number of P450 2D6 substrates on the market may seem surprising (Table 2, Fig. 20), considering that 1 of every 14 Caucasians cannot clear drugs by this mechanism. Three possible reasons may be proposed: (i) other pathways of metabolism may make major contributions, (ii) the safety margins may be so great that attenuated clearance is not an issue, and (iii) many of the drugs were on the market before the concerns about P450 2D6 polymorphism (vide supra), related to the first two reasons.

1.19.10.1.5

P450 3A4

This enzyme was first purified as the nifedipine oxidase (P450NF) (Guengerich et al., 1986a). The impetus was a report that this activity showed polymorphic behavior in a population, at a clinical level (Kleinbloesem et al., 1984). Although some of the initial conclusions about polymorphism did not hold up, today
34 variants are known (https://www.pharmvar.org/gene/CYP3A4). Nifedipine oxidation proved to be only one of many catalytic activities of the enzyme, and today P450 3A4 is recognized as one of the most general catalysts in biology (Fig. 20). P450 3A4 is expressed primarily in liver and small intestines (https://www.proteinatlas.org/ENSG00000160868-CYP3A4/ tissue), although there are reports of expression in lung, stomach, colon, brain, and adrenals. P450 3A4 expression has not been reported in kidney, prostate, testis, or thymus although other Subfamily 3A P450s have (Guengerich, 2015). As is the case with other human P450s, there is little or no sex difference in expression (Yang et al., 2010), although a difference has been reported in a European Caucasian population (Wolbold et al., 2003; Schirmer et al., 2007). One liver sample was found to contain as much as 60% of its P450 as P450 3A4, as determined using the selective mechanismbased inhibitor gestodene (Guengerich, 1990). On the average, P450 3A4 accounts for  20% of the total P450, depending upon the study (Fig. 21). In the small intestine, P450 3A4 accounts for  80% of the total P450 (Paine et al., 2006). Most of the population varies in P450 3A4 activity over a range of an order of magnitude (Kleinbloesem et al., 1984). The variation is due to (i) genetic variants; (ii) enzyme induction, and (iii) inhibition by drugs and other chemicals. The regulation of P450 3A4 is mainly at the transcriptional level, dominated by the PXR system. A list of inducing drugs appears in Table 4. Most of these interact with PXR (Fig. 3). CAR can also interact with the CYP3A4 gene at the PXR site (Goodwin et al., 2002). Other factors contributing to P450 regulation are C/EBPa, DBP, and HNF-4a (Guengerich, 2015). The vitamin D receptor can also compete at the PXR site. Degradation of P450 3A4 has been linked to a ubiquitin/proteasome pathway (Murray and Correia, 2001). A number of X-ray crystal structures of P450 3A4 have been published (
68 in PDB), including a ligand free enzyme (Yano et al., 2004) (Fig. 4) and with a variety of substrates and inhibitors. The active site is large (1385 Å3) and open, explaining the multiple occupancy seen with some ligands (Ekroos and Sjögren, 2006; Williams et al., 2004b). Evidence for complex binding of substrates and inhibitors to P450 3A4 has been presented (Guengerich et al., 2019b; Isin and Guengerich, 2006, 2007; Sevrioukova and Poulos, 2010). P450 3A4 has many substrates but also many inhibitors (Table 3). A number of these are problematic in clinical practice (as are the inducers, Table 4). For instance, ketoconazole and erythromycin have long been known to have interactions with several drugs. Moreover, the extent to which individual inhibitors interfere with each drug is not exactly predictable, although current FDA guidelines and those of other agencies allow for in vitro identification of which pairs of drugs should be considered for more extensive in vivo investigation. Some of the known inhibitors operate by mechanisms that are more complex than simple reversible inhibition (vide supra). One phenomenon is formation of “metabolite complexes,” seen with an amine derived from troleandomycin, RNH2 //R–N ¼ O þ Fe2þ  P450/RNð ¼ OÞ$Fe2þ $P450 where the ferrous iron nitroso complex has a long half-life and may persist for hours or days (Paulsen-Sörman et al., 1984). Another type of event is mechanism-based inactivation, or time-dependent inhibition, in which a reactive intermediate reacts with either the apo-enzyme or the P450 heme to inactivate. This phenomenon is seen with many P450s (vide supra) but most often with P450 3A4 (perhaps due to the large active site?). Several moieties on drug molecules are notorious in this regard, particularly terminal olefins and acetylenes, cyclopropylamines, and 4-alkyl-1,4-dihydropyridines (Correia and Hollenberg, 2015). Many of the heme and protein adducts have been characterized now (Correia and Hollenberg, 2015; Ortiz de Montellano and Correia, 1983). This behavior is not only seen with drugs but also natural compounds. Grapefruit juice (and a few other fruits) are contraindicated for use with some drugs cleared by several P450 enzymes but particularly those dealt with by P450 3A4 (Bailey et al., 1990) (vide supra). In general, development of a mechanism-based inactivator is problematic at best and most companies have avoided these (but see the discussion of ritonavir and cobicistat, vide infra).

1.19.10.2 Other P450 enzymes that can have significant contributions to drug metabolism 1.19.10.2.1

P450 1A1

P450 1A1 has a long history as the AhR-inducible aryl hydrocarbon hydroxylase, although that role is now shared with P450 1B1 (Shimada et al., 1999). Expression has generally been considered to be extrahepatic, especially in the lung (https://www. proteinatlas.org/ENSG00000140465-CYP1A1/tissue), although that reference shows numerous tissues, including liver. Lang

Drug Metabolism: Cytochrome P450

497

et al. (Lang et al., 2019) used proteomic analysis to document expression in human liver (Fig. 21). Even in the sample with the highest expression P450 1A1 only accounted for 3% of the total P450 in the liver. Despite the low fraction of total P450, the enzyme had a major role in the metabolism of two drugs, granisetron and riociquat (Lang et al., 2019). A dominant role in metabolism may still be limited to a small number of drugs, and a list of known drug substrates is presented in Table 5. At least 13 different variant alleles are known (https://www.pharmvar.org/gene/CYP1A1), although not all of their phenotypes have been elucidated. The X-ray structure of P450 1A1 with the inhibitor (and substrate) a-naphthoflavone resembles the structure of P450 1A2 (Walsh et al., 2013) and also has the site of oxidation in a catalytically incompetent pose (Bauer et al., 1995). More recently, the Scott laboratory has published P450 1A1 structures with the larger compounds bergamottin, erlotinib, and the kinase inhibitor GDC-0339, which indicate the ability of the enzyme to use a different structure to bind these larger and non-planar molecules (Bart and Scott, 2018; Bart et al., 2020). Interest in inhibitors of P450 1A1 has been largely in chemopreventive agents. Inducers are generally AhR agonists that largely overlap with those of P450 1A2, despite the generally different tissue localization pattern.

1.19.10.2.2

P450 2A6

P450 2A6 is expressed in liver (Fig. 21, https://www.proteinatlas.org/ENSG00000255974-CYP2A6/tissue), as well as in nasal mucosa, trachea, lung, and esophageal mucosa, along with colorectal tumors (Guengerich, 2015). The closely related P450 2A13 is expressed in nasal mucosa, trachea, and lung, as well as some other extrahepatic sites (Guengerich, 2015). Reported levels of expression in human liver have been quite variable (Fig. 21). At least 95 variant alleles have been reported, and there are many subtypes (https://www.pharmvar.org/gene/CYP2A6). There has been interest in the pharmacogenetics in terms of associating the metabolic capability (especially with nicotine metabolism) with genotype in the context of various cancers (Pianezza et al., 1998). Structures of the enzyme are available (Yano et al., 2005, 2006; Sansen et al., 2007a; Devore and Scott, 2012). The active site is one of the smallest ( 260 Å3) and is relatively rigid, although some larger ligands can be accommodated (Table 5). The list of drugs that are metabolized by this enzyme is relatively short (Fig. 20, Table 5).

1.19.10.2.3

P450 2B6

P450 2B6 is essentially only a hepatic enzyme (https://www.proteinatlas.org/ENSG00000197408-CYP2B6/tissue), although one report of expression in the lung has appeared (Hukkanen et al., 2002). The level of expression in liver has generally been reported to be low, with the exception of a single proteomic study (Fig. 21) (Guengerich, 2015; Achour et al., 2014). The regulation of expression of P450 2B6 is dominated by CAR but there is also some “crosstalk” with PXR (Guengerich, 2015; Willson and Kliewer, 2002) and some other factors (Guengerich, 2015; Wang and Negishi, 2003) (Table 7). At least 38 allelic variants are known (https://www. pharmvar.org/gene/CYP2B6), plus a number of subvariants, and some have been reported to be linked to changes in the metabolism of a few drugs (Guengerich, 2015). Several X-ray structures of 2B6 have been published, including those with the substrates amlodipine and an analog of efavirenz (Shah et al., 2012). The protein is malleable and residues move to accommodate different ligands. A number of drugs are oxidized by P450 2B6 (Table 5), although the contribution is much less overall than P450 3A4 (Fig. 20). The most selective drugs in vivo appear to be bupropion and efavirenz. Other substrates are methadone and the important antimalarial artemisinin. A number of inhibitors are also known (Table 6).

1.19.10.2.4

P450 2C8

P450 2C8 is another hepatic member of the P450 2C Subfamily. Although the Protein Databank reports only expression in liver (https://www.proteinatlas.org/ENSG00000138115-CYP2C8/tissue), there have been reports of expression in kidney (Dai et al., 2001) and elsewhere (Guengerich, 2015; Klose et al., 1999). The level of expression in liver is intermediate between P450 2C9 and 2C19 (Fig. 21). At least 14 allelic variants are known but the phenotypes of the coding region variants have not been reported (https://www.pharmvar.org/gene/CYP2C8). Inducibility involves PXR, CAR, HNF-a, and the glucocorticoid receptor, as in the case of other 2C Subfamily P450s. The list of substrates is not as extensive as for P450s 2C9 and 2C19 (Fig. 20, Table 2) but there are some drugs worthy of notedpaclitaxel, troglitazone, pioglitazone, montelukast, amodiaquine, and ceruvastatin (recalled). One of the most interesting features of P450 2C8 is its active site. A structure of P450 2C8 was first reported by the Johnson laboratory in 2004 (Schoch et al., 2004). Two molecules of a substrate, palmitic acid (derived from heterologous expression), were bound to the dimer interface. The size of the formal active site is large (1438 Å3), similar to that of P450 3A4, but is more rigid and of an “L” shape. Structures with montelukast, troglitazone, felodipine, and 9-cis-retinoic acid have been reported. In the case of the latter ligand, a second molecule was located above the proximal ligand and postulated to push the first one for more efficient hydroxylation (Schoch et al., 2008). However, this multiple occupancy was not associated with any apparent cooperative behavior in binding or catalytic assays. The large size of the active site is of relevance not only in consideration of substrates (Table 5) but also inhibitors (Table 6). The fibrate gemfibrozil has an unusual mechanism in that the glucuronide conjugate is oxidized within the large active site of P450 2C8, leading to irreversible inactivation as the result of heme alkylation (Baer et al., 2009). Although this result was considered unusual, the estimated molecular volume of gemfibrozil glucuronide is predicted to be only  540 Å3, well within the size of the active site. This ability of P450 2C8 to bind and oxidize glucuronide conjugates was also found to be of relevance in a problem that had been

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Fig. 22

Oxidation of a desloratadine glucuronide by P450 2C8 (Yumibe et al., 1995; Kazmi et al., 2015).

difficult to solve, i.e. the mechanism of 3-hydroxylation of desloratadine (ClarinexÒ) (Fig. 22) (Kazmi et al., 2015). Although 3hydroxydesloratidine was a prominent product in vivo, its formation could not be demonstrated in standard in vitro assays with microsomes, P450s, or aldehyde oxidase. Here the estimated molecular volume of 3-hydroxydesloratidine glucuronide is  630 Å3, well within the size of the cavity of P450 2C8 (vide supra). Thus, glucuronide conjugates can be substrates for P450 2C8 and are cleaved prior to excretion, one of the many cases in which a “Phase 2” reaction can precede “Phase 1” (Josephy et al., 2005).

1.19.10.2.5

P450 2E1

P450 2E1 was first characterized because of its role in the oxidation of ethanol. The content of P450 2E1 in human liver is relatively high (Fig. 21), and the enzyme is primarily hepatic (https://www.proteinatlas.org/ENSG00000130649-CYP2E1/tissue). A fraction of the P450 2E1 is transported to mitochondria and is relevant there (Bansal et al., 2013). The enzyme is also expressed in a number of extrahepatic sites (Guengerich, 2015), and at least 19 allelic variants are knowndonly three have coding region changes (R76H, V389I, V179I) (https://www.pharmvar.org/gene/CYP2E1). The number of drug substrates is relatively small (Table 5). Several of the drugs are low molecular weight gas anesthetics. One very important drug substrate is acetaminophen (paracetamol, TylenolÒ), which is also a substrate for P450s 1A2 and 3A4). Much of the interest in P450 2E1 is related to its roles in the oxidation of ethanol (particularly at high concentrations if the alcohol dehydrogenase capacity is exceeded) and chemical carcinogens, particularly N-nitrosamines and industrial compounds (e.g., vinyl monomers such as vinyl chloride) (Guengerich et al., 1991b). The best marker of P450 2E1 catalytic activity is probably chlorzoxazone 6-hydroxylation, which can be used in vitro or in vivo (Peter et al., 1990). X-ray crystal structures of P450 2E1 have been published by the Scott laboratory (Porubsky et al., 2010). The structures indicate an extra pocket near the binding site for small molecules, and with different ligands the size of the space available for a substrate can vary from 190 to 470 Å3 (DeVore et al., 2012). Thus, P450 2E1 can be considered a molecular sieve in one sense but is also able to bind (and catalyze the u-1 hydroxylation of) fatty acids. Several of the inhibitors of P450 2E1 (Table 6) are also inhibitors of alcohol and aldehyde dehydrogenases (e.g., disulfiram or AntabuseÒ) so that they have not been of use in discerning the roles of these enzymes in ethanol metabolism in vivo. Two points should be made about P450 2E1. One is that a considerable literature exists on a role of this enzyme in oxidative stress, i.e. production of ROS (Cederbaum, 2006; Ekström and Ingelman-Sundberg, 1989). However, treatment with the P450 2E1 inducer isoniazid did not elevate the level of F2a-isoprostanes (a validated marker of ROS) in rats or mice, (Dostalek et al., 2007, 2008) and Cyp2e1/ mice had the same isoprostane levels as their wild-type counterparts (Dostalek et al., 2008). Although a global elevation of ROS can be ruled out in rodents in these studies, targeting of P450 2E1 to mitochondria did increase ROS in a cellular model (confirmed with isoprostane measurement), so a localized mechanism of ROS damage may be possible. The other unusual aspect of P450 2E1 involves its kinetics, which has a number of uncommon properties. Detailed kinetic analysis of P450 2E1 oxidation of ethanol showed that the immediate product acetaldehyde is converted to acetic acid in a rather processive manner (Bell and Guengerich, 1997; Bell-Parikh and Guengerich, 1999), and this was also the case for the oxidation of the carcinogen N,N-dimethylnitrosamine (Chowdhury et al., 2012). The oxidation of ethanol and acetaldehyde are characterized by “burst kinetics,” i.e. a rate-limiting step following product formation. This situation gives rise to some interesting steady-state kinetics in that Km is much lower than Ks, the substrate dissociation constant (vide supra), and Km is actually a function of kcat (Bell and Guengerich, 1997; Guengerich, 2019; Guengerich et al., 1995). In addition, a deuterium KIE (for CeH bond breaking) is seen in Km not kcat (Bell and Guengerich, 1997; Guengerich, 2017).

Drug Metabolism: Cytochrome P450 1.19.10.2.6

499

P450 2J2

This is an extrahepatic P450, although some expression in liver has been reported (Fig. 21) (Lang et al., 2019). Expression has been reported in heart, as well as skeletal muscle, placenta, small intestine, kidney, lung, pancreas, seminal vesicles, leukocytes, and brain (Guengerich, 2015), at least at the mRNA level (https://www.proteinatlas.org/ENSG00000134716-CYP2J2/tissue). A number of allelic variants exist (https://www.pharmvar.org/gene/CYP2J2) but any role in disease or risk is not yet clear. No structure is available, aside from homology models. The substrates that have been studied most are fatty acids, particularly in the context of epoxidation of arachidonic acid and relevance to hypertension and other diseases. However, a number of drugs have also been identified as substrates (Table 5), although the overall contribution to drug clearance has not been established. However, a contribution to local concentrations of these drugs (e.g., in the heart) may be important in some cases.

1.19.10.2.7

P450 3A5 and 3A7

These two enzymes are closely related to P450 3A4 and have very similar but not identical catalytic specificities (Guengerich, 2015). In general, most activities are lower than seen with P450 3A4, with some important exceptions. The X-ray crystal structures of P450s 3A4 and 3A5 are very similar, as might be expected (Hsu and Johnson, 2019). The list of inducers of P450 3A4 generally applies to P450s 3A5 and 3A7. However, there are several important distinctions among the Subfamily 3A P450s. P450 3A5 is also a liver enzyme but is expressed in small intestine (as is P450 3A4) and in kidney, lung, brain, prostate, adrenal gland, and pituitary (Guengerich, 2015). P450 3A7 is the main fetal P450, expressed in liver and small intestine. Its expression generally decreases rapidly after birth and P450 3A4 replaces it (Hines, 2008). However, there are reports of expression of P450 3A7 in some adults (Guengerich, 2015). Both P450s 3A5 and 3A7 show many allelic variants (https://www.pharmvar.org/gene/CYP3A5, https://www.pharmvar.org/gene/ CYP3A7). Of particular relevance is the lack of expression of P450 3A5 in a large fraction of Caucasians but its expression in Africans (Kuehl et al., 2001; Daly, 2006). This racial difference has clinical implications. Some selective substrates and inhibitors of P450s 3A4 and 3A5 are known and can be used in the laboratory in the context of discerning reactions catalyzed by these two enzymes, e.g. the selective P450 3A4 inhibitor CYP3cide (PF-4981517; 1-methyl-3-[1methyl-5-(4-methylphenyl)-1H-pyrazol-4-yl]-4-[13S]-3-piperidin-1-yl-pyrrolidine-1-yl]-1H-pyrazole[3,4-d]pyrimidine) and the selective P450 3A5 inhibitor clobetasol propionate (Wright et al., 2020). Expression of the other 3A Subfamily member P450 3A43 appears to be very low and this enzyme is not considered to have a significant role.

1.19.10.3 Some other P450s that can be involved in drug metabolism Of the 57 human P450s, several have been shown to be primarily involved in the metabolism of endogenous substrates but also to have interesting roles in the metabolism of some drugs.

1.19.10.3.1

P450 4F2

P450 4F2 is a hepatic P450 that catalyzes u-hydroxylation of a number of lipids, including leukotriene B4, 6-trans-leukotriene B4, arachidonic acid, lipoxin A4, 12-hydroxystearic acid, and 8- and 12-hydroxyeicosatetraenoic acids (Guengerich, 2015). Drugs reported to be oxidized by P450 4F2 are DB289 (2,5-bis[4-amidinophyl]furan-bis-O-methylaldoxime) (Wang et al., 2006) and figolimod (FTY720) (Jin et al., 2011). The latter has an octyl side chain but DB289 bears no resemblance to the eicosanoid substrates. The polymorphism V433M was found to affect the clinical warfarin dose (Caldwell et al., 2008) but this effect was attributed to the oxidation of vitamin K by P450 4F2 and not to warfarin metabolism (McDonald et al., 2009).

1.19.10.3.2

P450 11A1

This mitochondrial enzyme plays a critical role in the 3-step cleavage of the side chain of cholesterol to form the key steroid pregnenolone in steroidogenic tissues (Fig. 23). It had been considered to be highly specific, but recent studies have shown that it uses a variety of related sterols as substrates, e.g., 7-dehydrocholesterol, desmosterol (Acimovic et al., 2016), and does individual hydroxylations of vitamin D (Guryev et al., 2003; Slominski et al., 2014). A Bristol Myers-Squibb drug candidate (“BMS-A”), ((N-(4-1(1Hpyrrolo[2,3[b]pyridine-4-yl)oxy)-3-fluorophenyl)2-oxo-1,2-dihydropyridine-3-carboxamide) was found to cause necrosis in the adrenal cortex of rats and in human adrenal cells, and the cause was attributed to bioactivation of the compound by P450 11A1 (Zhang et al., 2012). Some other drugs are substrates for this enzyme (Zhang et al., 2012).

1.19.10.3.3

P450 46A1

P450 46A1 is a brain P450 enzyme, the physiological function of which is the 24-hydroxylation of cholesterol (Russell et al., 2009). It is also expressed in neurons of the neural retina (Bretillon et al., 2007). The enzyme also uses 7-dehydrocholesterol and desmosterol as substrates (Goyal et al., 2014). Although the specificity constant (kcat/Km) is low ( 150 M 1 s 1), the enzyme is important because a deletion in mice produces a learning defect (Kotti et al., 2006). P450 46A1 also binds and oxidizes a variety of drugs, (Mast et al., 2003, 2010) and as in the cases of P450s 4F2 and 11A1, is a P450 that appears to be specialized for oxidation of endogenous substrates that can also act on xenobiotics. In addition, P450 46A1 activity toward the substrate cholesterol is stimulated by binding to some of these drugs, e.g. the HIV inhibitor efavirenz

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Fig. 23

Some multi-step steroid biosynthetic reactions catalyzed by human P450s. (A) P450 11A1; (B) P450 17A1; (C) P450 19A1.

and by neuroactive compounds (Mast et al., 2017, 2020). In vivo evidence for the relevance of the stimulation has been shown in a mouse model (Mast et al., 2014).

1.19.11 P450s as targets for drugs In several cases there are reasons to attenuate the activities of P450s, in a controlled manner. The list discussed below is not exhaustive but includes a number of reasons for inhibiting P450s.

1.19.11.1 P450s 1A1, 1A2, 1B1 The Family 1 P450s are involved in the bioactivation of a large fraction of chemical carcinogens (Guengerich, 2015; Rendic and Guengerich, 2012). In experimental animal models there has long been considerable evidence that the development of tumors can be modulated by altering the activities of P450s (Guengerich, 1988), and a field termed chemoprevention is focused on such goals (including enhancement of detoxication pathways as well as preventing bioactivation). However, this field has not been as active recently as it once was (Conney, 1982). One of the inherent problems is that the P450s involved in bioactivation (Family 1) are also involved in detoxication (Guengerich, 2015; Rendic and Guengerich, 2015, 2012), and in many situations with experimental animal models it has been shown that attenuating P450s (e.g., NADPH-P450-reductase knockouts) actually elevates levels of biomarkers such as DNA adducts (Reed et al., 2018). At this time the immediate prospects for carcinogenesis intervention in this area are not well developed.

1.19.11.2 P450 2A6 P450 2A6, along with the Subfamily enzyme P450 2A13, converts nicotine to carcinogenic nitrosamines, and there has been interest in the development of drugs that inhibit P450 2A6 as chemopreventive agents (Yano et al., 2006; Stancil et al., 2019). The goal is to use drugs to modify smoking behavior (Howard et al., 2002). 8-Methoxypsoralen effectively decreased tumors in a nitrosaminetreated mouse model (Takeuchi et al., 2003). Genetically slow P450 2A6 metabolism has been associated with the incidence of quitting for adolescent smokers (Chenoweth et al., 2013).

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501

1.19.11.3 P450 3A4 P450 3A4 is the most prominent enzyme involved in drug metabolism (Guengerich, 1999). In the cases of some drugs that are cleared too rapidlydor are very expensivedit may be desirable to attenuate the rate of metabolism, and P450 3A4 has been dealt with in this way. For instance, this has been an issue with some of the protease and viral integrase inhibitors used in AIDS therapy. One approach is to use a known, approved drug such as ritonavir or ketoconazole as a “booster.” However, these drugs have their own effects, and a simpler solution is to recommend the consumption of grapefruit juice, which contains the mechanism-based inactivator bergamottin. There is anecdotal evidence that some physicians have done this to boost drug responses, although this is difficult to control. Some regimens have utilized ritonavir, a strong P450 inhibitor, or cobicistat, a ritonavir-like drug developed as a “booster” (Burger et al., 2020).

1.19.11.4 P450 11B2 P450 11B2 plays a critical role in the production of aldosterone (Fig. 9). However, overproduction of aldosterone is an issue in hypertension, and some major pharmaceutical companies have P450 11B2 inhibitor programs in development. Although P450 11B1 and 11B2 are 94% identical (only differing in 32 residues), it has been possible to develop molecules that have > 500fold selectivity for P450 11B2 (Hu et al., 2014). Like P450 17A1 and 19A1, P450 11B2 is an interesting enzyme in that it catalyzes a multi-step process. As in the case of P450 17A1 (Gonzalez and Guengerich, 2017), in P450 11B2 a fraction of an intermediate product (corticosterone) does not dissociate from the enzyme and goes on to yield the final product (Reddish and Guengerich, 2019).

1.19.11.5 P450 17A1 P450 17A1 converts progesterone to androstenedione and pregnenolone to dehydroepiandrosterone, both in two-step processes (Fig. 23B). As in the case of female cancers dependent on estrogens, prostate cancer is androgen stimulated. Castration-resistant prostate cancer is associated with the production of androgens in the adrenals. Treatment can involve antagonists of the androgen receptor or inhibition of the synthesis of androgens, i.e. P450 17A1. The only approved drug for inhibiting androgen synthesis is abiraterone (administered in the form of abiraterone acetate, a prodrug). A goal is the selective inhibition of only the second step of P450 17A1, the 17,20-lyase reaction (Fig. 23B), in that the 17a-hydroxy intermediate products are needed for the synthesis of glucocorticoids. Currently, abiraterone treatment includes supplementation with prednisone. Because of this, there is interest in the development of drugs that selectively inhibit the lyase reaction (Fig. 23B). This is not a trivial undertaking, in that apparently there is only a single substrate binding site in the enzyme (Petrunak et al., 2017). The ability of drugs to preferentially inhibit the second reaction has been attributed to multiple conformations of P450 17A1 (Guengerich et al., 2019a; Child and Guengerich, 2020).

1.19.11.6 P450 19A1 P450 19A1 catalyzes the 3-step oxidation of androgens (testosterone, androstenedione) to estrogens, a critical reaction (Fig. 23C). P450 19A1, the “steroid aromatase,” is not known to be involved in the metabolism of any drugs. A number of tumors are estrogenstimulated (e.g., breast, ovarian) and one strategy is to block estrogen synthesis (another is to block binding to the estrogen receptor). Several third-generation aromatase inhibitors are used widely, including exemestane, letrozole, and anastrozole. These have nanomolar dissociation constants and can be very effective, at least in estrogen receptor-positive tumors. These are excellent drugs in terms of their affinities, and the side effects are mechanism-based, i.e. disruption of calcium homeostasis due to the inhibition of estrogen-related events.

1.19.11.7 P450 51A1 Interest in P450 51A1 inhibition has been directed toward drugs that inhibit P450 51A enzymes in fungi and yeasts. Most of these drugs are azoles and block the biosynthesis of ergosterol, which is needed for membrane synthesis. Human P450 51A1 could conceivably be a drug target for blocking cholesterol synthesis, although statin hydroxymethylglutarate (HMG) CoA reductase inhibitors are well-established, safe, and difficult to compete with. However, human P450 51A1 has been considered as a target in the treatment of tumors (Friggeri et al., 2019).

Acknowledgments Thanks are to extended to K. Trisler for help in preparation of the manuscript and to Sarah M. Glass and Dr. Stella A. Child for scientific critiques of draft versions.

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See Also: 1.05: An Overview of Steady-State Enzyme Kinetics; 1.26: Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters; 1.27: Drug-Drug Interactions with a Pharmacokinetic Basis; 1.30: Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools

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Wolbold, R., Klein, K., Burk, O., Nussler, A.K., Neuhaus, P., Eichelbaum, M., Schwab, M., Zanger, U.M., 2003. Sex is a major determinant of CYP3A4 expression in human liver. Hepatology 38, 978–988. Wright, W.C., Chenge, J., Wang, J., Girvan, H.M., Yang, L., Chai, S.C., Huber, A.D., Wu, J., Oladimeji, P.O., Munro, A.W., Chen, T., 2020. Clobetasol propionate is a hememediated selective inhibitor of human cytochrome P450 3A5. Journal of Medicinal Chemistry 63, 1415–1433. Yamazaki, H., Johnson, W.W., Ueng, Y.F., Shimada, T., Guengerich, F.P., 1996. Lack of electron transfer from cytochrome b5 in stimulation of catalytic activities of cytochrome P450 3A4. Characterization of a reconstituted cytochrome P450 3A4/NADPH-cytochrome P450 reductase system and studies with Apo-cytochrome b5. The Journal of Biological Chemistry 271, 27438–27444. Yamazaki, H., Shimada, T., Martin, M.V., Guengerich, F.P., 2001. Stimulation of cytochrome P450 reactions by Apo-cytochrome b5: Evidence against transfer of heme from cytochrome P450 3A4 to Apo-cytochrome b5 or heme oxygenase. The Journal of Biological Chemistry 276, 30885–30891. Yamazaki, H., Nakamura, M., Komatsu, T., Ohyama, K., Hatanaka, N., Asahi, S., Shimada, N., Guengerich, F.P., Shimada, T., Nakajima, M., Yokoi, T., 2002. Roles of NADPH-P450 reductase and Apo- and holo-cytochrome b5 on xenobiotic oxidations catalyzed by 12 recombinant human cytochrome P450s expressed in membranes of Escherichia coli. Protein Expression and Purification 24, 329–337. Yang, X., Zhang, B., Molony, C., Chudin, E., Hao, K., Zhu, J., Gaedigk, A., Suver, C., Zhong, H., Leeder, J.S., Guengerich, F.P., Strom, S.C., Schuetz, E., Rushmore, T.H., Ulrich, R.G., Slatter, J.G., Schadt, E.E., Kasarskis, A., Lum, P.Y., 2010. Systematic genetic and genomic analysis of cytochrome P450 enzyme activities in human liver. Genome Research 20, 1020–1036. Yano, J.K., Wester, M.R., Schoch, G.A., Griffin, K.J., Stout, C.D., Johnson, E.F., 2004. The structure of human microsomal cytochrome P450 3A4 determined By X-ray crystallography to 2.05 Å resolution. The Journal of Biological Chemistry 279, 38091–38094. Yano, J.K., Hsu, M.H., Griffin, K.J., Stout, C.D., Johnson, E.F., 2005. Structures of human microsomal cytochrome P450 2A6 complexed with coumarin and methoxsalen. Nature Structural Biology 12, 822–823. Yano, J.K., Denton, T.T., Cerny, M.A., Zhang, X., Johnson, E.F., Cashman, J.R., 2006. Synthetic inhibitors of cytochrome P-450 2A6: Inhibitory activity, difference spectra, mechanism of inhibition, and protein co-crystallization. Journal of Medicinal Chemistry 49, 6987–7001. Yarnell, A.T., 2009. Heavy-hydrogen drugs turn heads, again. Chemical and Engineering News 87, 36–39. Yu, J.J., Zhou, Z., Tay-Sontheimer, J., Levy, R.H., Ragueneau-Majlessi, I., 2018. Risk of clinically relevant pharmacokinetic-based drug-drug interactions with drugs approved by the US food and drug administration between 2013 and 2016. Drug Metabolism and Disposition 46, 835–845. Yumibe, N., Huie, K., Chen, K.J., Clement, R.P., Cayen, M.N., 1995. Identification of human liver cytochrome P450 enzymes that metabolize the nonsedating antihistaminic loratadine. Biochemical Pharmacology 51, 165–172. Yun, C.-H., Okerholm, R.A., Guengerich, F.P., 1993. Oxidation of the antihistaminic drug terfenadine in human liver microsomes. Role of cytochrome P-450 3A(4) in N-dealkylation and C-hydroxylation. Drug Metabolism and Disposition 21, 403–409. Yun, C.-H., Kim, K.H., Calcutt, M.W., Guengerich, F.P., 2005. Kinetic analysis of oxidation of coumarins by human cytochrome P450 2A6. The Journal of Biological Chemistry 280, 12279–12291. Zhang, D., Flint, O., Wang, L., Gupta, A., Westhouse, R.A., Zhao, W., Raghavan, N., Caceres-Cortes, J., Marathe, P., Shen, G., Zhang, Y., Allentoff, A., Josephs, J., Gan, J., Borzilleri, R., Humphreys, W.G., 2012. Cytochrome P450 11A1 bioactivation of a kinase inhibitor in rats: Use of radioprofiling, modulation of metabolism, and adrenocortical cell lines to evaluate adrenal toxicity. Chemical Research in Toxicology 25, 556–571. Zhao, B., Lei, L., Kagawa, N., Sundaramoorthy, M., Banerjee, S., Nagy, L.D., Guengerich, F.P., Waterman, M.R., 2012. Three-dimensional structure of steroid 21-hydroxylase (cytochrome P450 21A2) with two substrates reveals locations of disease-associated variants. The Journal of Biological Chemistry 287, 10613–10622.

1.20

Drug Metabolism: Other Phase I Enzymes

Gianluca Catucci, Gianfranco Gilardi, and Sheila J. Sadeghi, Department of Life Sciences and Systems Biology, University of Torino, Torino, Italy © 2022 Elsevier Inc. All rights reserved.

1.20.1 1.20.2 1.20.2.1 1.20.2.2 1.20.2.3 1.20.2.4 1.20.2.5 1.20.2.6 1.20.2.7 1.20.2.7.1 1.20.2.7.2 1.20.2.7.3 1.20.2.7.4 1.20.2.7.5 1.20.2.8 1.20.3 1.20.4 1.20.5 1.20.6 1.20.7 References

Introduction Flavin-containing monooxygenases Catalytic mechanism Tissue distribution Induction Inhibition Structural features Genetic variants Reactions catalyzed Drugs metabolized exclusively by hFMO1 Drugs metabolized exclusively by hFMO2 Drugs metabolized exclusively by hFMO3 Drugs metabolized exclusively by hFMO5 Drugs metabolized by hFMO1 and hFMO3 Effect of genetic variants on drug metabolism Aldehyde oxidase Aldehyde dehydrogeneases Alcohol dehydrogenases Carboxylesterases Conclusions and future perspectives

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Glossary Adverse drug reaction (ADR) A harmful effect suspected to be caused by a drug. Agonist A drug that binds to and activates the receptor, resulting in a biological response. Allele frequency The allele frequency represents the incidence of a gene variant in a population reflecting the genetic diversity within that population. EC50 The concentration of an agonist that produces 50% of the maximal possible effect of that agonist. Induction A biological process resulting in an increased biosynthesis of an enzyme thereby increasing its apparent activity. Ki The dissociation constant describing the binding affinity between the inhibitor and the enzyme. Prodrug A chemical with little or no pharmacologic activity that undergoes change in the body into a more active material. The change may be a result of biotransformation. Polymorphism Genetic variants of a specific enzyme that may/may not alter its activity and as a consequence the metabolism of a given drug. Single-nucleotide polymorphism (SNP) DNA sequence variations that occur when a single nucleotide (A, T, C, or G) in the genome sequence is altered.

1.20.1

Introduction

In general, the aim of drug metabolism is to eliminate the drug or xenobiotic from the body. For the majority of xenobiotics, this metabolism is carried out in the liver and this is the site where the duration of the action of the drug is controlled. In order to eliminate a drug, several reactions can be carried out to make it more hydrophilic but the predominant pathway of drug metabolism is oxidation. As seen in the previous chapter, the most important family of drug metabolizing enzymes are the hepatic cytochromes P450. However, several other non-P450 enzymes have also been recognized for their contribution to phase I metabolism and these will be the focus of this chapter. The first and most obvious reason for studying the other enzymes is because scientists in the last two decades have designed compounds to limit the action of cytochromes P450 and as a consequence more and more drugs face different biotransformations

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in the human body (Dixit et al., 2017). For example a recent study of a set of 125 orally or intravenously administered smallmolecule drugs approved by the United States Food and Drug Administration (FDA) from 2006 to 2015 demonstrated that non-P450 enzymes contribute to around 30% of the metabolism of the drugs studied (Cerny, 2016). The latter biotransformations are carried out by several different non-P450 enzymes with important roles in phase I drug metabolism and of which five will be discussed in more detail in this chapter: Flavin-containing monooxygenases, Aldehyde oxidases, Aldehyde dehydrogenases, Alcohol Dehydrogenases and Carboxylesterases (Fig. 1).

1.20.2

Flavin-containing monooxygenases

In addition to cytochromes P450, microsomes also contain another monooxygenase system with overlapping substrate spectrum but with a flavin adenine dinucleotide cofactor (FAD) instead of the heme. These human flavin-containing monooxygenases (hFMO, EC 1.14.13.8) were studied originally more than forty years ago by the group of Prof. Ziegler (Ziegler and Mitchell, 1972). In the early days hFMOs were referred to as “mixed-function amine oxidases” due to their ability to perform monooxygenation of a variety of nitrogen-containing substrates (Sofer and Ziegler, 1978). Drug metabolism studies have demonstrated that hFMOs are capable of splitting molecular oxygen and inserting one atom of oxygen into a target substrate that is typically a soft nucleophile, in most cases a nitrogen or sulfur but also phosphorous (Krueger and Williams, 2005). In vitro studies have revealed the ability of some hFMOs to carry out also Baeyer-Villiger reactions leading to carbon oxidation, but limited data are available. Five different functional FMOs are present in humans (Phillips and Shephard, 2017). They are all flavoproteins that contain FAD as prosthetic group and use NADPH as the reducing cofactor (Beaty and Ballou, 1981).

1.20.2.1

Catalytic mechanism

One of most intriguing aspects about hFMOs is how these enzymes are able to perform catalysis using their internal “fixed” FAD cofactor and their external “mobile” NADPH cofactor (Fig. 2). In order to fully understand their functioning, it is necessary to avoid thinking that hFMOs behave like typical enzymes where a substrate actually binds the catalytic pocket and the product is released upon oxidation. The catalytic mechanism of hFMOs is not based purely on substrate binding, but it could be better explained by the term substrate proximity. Indeed, the entire catalytic process is favored by the formation of a highly reactive intermediate species, the C4a-hydroperoxyflavin, an enzyme-bound oxidant (Beaty and Ballou, 1981; Gao et al., 2017), that is able to perform the monooxygenation when the substrate is at a suitable distance for the reaction to occur. At the start of the catalytic cycle the enzyme is in its oxidized state and can be monitored spectroscopically due to the presence of the two typical peaks of FAD cofactor in the visible region at 375 and 450 nm. In the first step of the cycle FAD is fully reduced to FADH2 by NADPH. This electron transfer process involves two electrons that are transferred in a single reaction from the donor NADPH directly to the acceptor FAD. Upon reduction, the oxidized NADPþ cofactor remains bound to the protein (Krueger and Williams, 2005). Biophysical experiments have clearly shown that NADPþ stabilizes hFMOs (Gao et al., 2017; Catucci et al., 2020). The stabilization effect has been detected by different techniques including differential scanning calorimetry (DSC). Indeed, DSC has shown that NADPþ can not only shift the melting temperature (Tm) of hFMO, but it can also strengthen the overall structure by creating new bonds that result also in a higher enthalpy for the NADPþ-bound enzyme (Catucci et al., 2020). Therefore, while the FAD reduction process is fast, the oxidized NADPþ cofactor by remaining in the active site of the enzyme can also have a role in dictating the optimal conformation for substrate binding. In the second step of the catalytic cycle the FADH2 binds molecular oxygen resulting in the formation of the highly reactive C4a-hydroperoxyflavin intermediate. At this point the enzyme is ready to perform catalysis. A crucial role for the stabilization of this reaction intermediate has been assigned to NADPþ and the presence of a conserved ASN residue in the active site of hFMO (Gao et al., 2017). Stopped-flow studies have demonstrated that the recombinant purified form of hFMO3 is only marginally able to stabilize the hydroperoxy intermediate. Stabilization only occurs in the presence of NADPþ and if ASN 61 is

Fig. 1 Pie chart of the contribution of non-P450 phase I enzymes to metabolism of drugs. Percentage values are the estimated contribution of each enzyme to drug metabolism obtained by excluding the drugs that are metabolized by cytochrome P450 enzymes. Alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), aldehyde oxidase (AOX), epoxide hydrolase (EH), esterase (EST), monoamine oxidase (MAO) and flavin-containing monooxygenase (FMO). By authors.

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Fig. 2 FMO catalytic mechanism. Changes to the isoalloxazine ring of the FAD cofactor leading to the conversion of the substrate (S) to an oxidized metabolite (S-OX). Carbon atoms are shown in cyan, oxygen in red, nitrogen in blue and hydrogen atoms in white. Modified from Siddens LK, Krueger SK, Henderson MC, and Williams DE (2014) Mammalian flavin-containing monooxygenase (FMO) as a source of hydrogen peroxide. Biochemical Pharmacology 89: 141–147.

mutated to serine, the stabilization is almost completely lost, justifying also the diminished ability of this mutant to carry out catalysis (Gao et al., 2017). The third step of the catalytic cycle is the monooxygenation reaction. At this point, any suitable soft nucleophile that gains access to the active site can be converted to the oxidized product. Soft nucleophiles are electronegative molecules that are in search of electrophile counterparts. Generally soft nucleophiles differ from hard nucleophiles due to their negative charge being more widely distributed across the entire molecule. Human FMOs have been extensively characterized for their ability to oxidize N- or S-containing substrates, but some isoforms have also demonstrated Baeyer-Villiger activity on C atoms (Phillips and Shephard, 2017; Coleman, 2020). This peculiarity is probably due to an increased ability of stabilizing the hydroperoxy intermediate and forming the so-called criegee intermediate. Nevertheless, more detailed studies are needed to establish and assign singularities to each hFMO isoform. The fourth step of the catalytic cycle leads to the removal of the second oxygen atom from the remaining hydroxyflavin followed by the NADPþ release during the fifth and final step. Therefore, it is clear that the concerted activity of FAD and NADPþ is essential for the catalytic activity of these enzymes. The external cofactor NADPH not only provides the reducing equivalents but the NADPþ participates in the whole catalytic cycle remaining bound close to the active site FAD and hence stabilizing the C4a-hydroperoxy intermediate. When the catalytic cycle is analyzed in more detail, unproductive pathways can also be identified. The electrons that are transferred in a single redox process from NADPH to FAD yielding FADH2, are not always coupled to the formation of a product. This is due to the fact that the activated molecular oxygen bound within the C4a-hydroperoxyflavin can dissociate and form hydrogen peroxide. If the reducing equivalents provided by the NADPH are all directed to the substrate oxidation this results in a fully coupled enzyme. If, however during the catalytic cycle the C4-hydroperoxyflavin is unstable and decays, it can result in the fully oxidized flavin and the release of hydrogen peroxide. Several studies have demonstrated that hFMOs can produce hydrogen peroxide even in the presence of a substrate (Rauckman et al., 1979; Tynes et al., 1986; Siddens et al., 2014; Catucci et al., 2019a,b) and therefore they are uncoupled enzymes. In some instances, data are available for a specific isoform, hFMO3, which are fully in line with the intrinsic instability of the hydroperoxy intermediate in this enzyme, whereas more experiments are needed to establish the uncoupling of other isoforms. In addition to hydrogen peroxide it has also been shown that hFMO3 can produce other reactive oxygen species namely superoxide (Catucci et al., 2019a,b). Also in this case the hydroperoxide intermediate is not used productively and it decays in two consecutive reactions to 2 moles of superoxide per mole of NADPH. When studying NADPH consumption by hFMOs two different phenomena need to be addressed separately: uncoupling due to production of reactive oxygen species and basal NADPH consumption. While uncoupling clearly indicates the inefficient use of reducing equivalents by the enzyme, NADPH consumption in the absence of substrate can only indicate how stable the intermediate is in the presence of oxygen. It is therefore recommended to refer to the latter phenomenon as “leakage” to distinguish it from the actual uncoupling. Both hydrogen peroxide and superoxide are highly reactive oxygen species that can damage membranes, proteins and other cellular components, so further studies are necessary to understand the importance of these phenomena within the cell and their physiological relevance. Finally, hydrogen peroxide is also recognized as a signaling molecule and its production by hFMOs might represent another role for these enzymes.

1.20.2.2

Tissue distribution

The human genome encodes five functional FMO genes, designated FMO1, 2, 3, 4 and 5. The first four of these genes, FMOs 1–4, are located within a 245-kb cluster on chromosome 1, in the region q24.3. Within this cluster an additional FMO gene, FMO6P, has also been identified but due to its inability to produce a correctly processed mRNA it is classified as a pseudogene. A second cluster

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of FMO genes located at 1q24.2 has also been discovered but apparently consists of only pseudogenes (Phillips and Shephard, 2020). Each hFMO isoform has a different level of tissue expression as shown in Fig. 3 (Janmohamed et al., 2004; Cashman and Zhang, 2006; Phillips and Shephard, 2017). Human FMO3 and FMO5 are both present in the liver whereas hFMO1 can only be found during the developmental stages and is silenced in adult individuals but is expressed mainly in kidneys. Human FMO2 is expressed in the lungs, but most individuals are homozygous for a genetic variant that causes a premature truncation of the expressed protein, FMO2*2A, resulting in a shorter inactive form of the enzyme that lacks the last 64 amino acids. Human FMO3 is expressed at high levels in the liver, but it can also be found in the pancreas, in adrenal medulla and cortex. It is the most important FMO isoform in terms of hepatic drug metabolism with expression levels around 60% (Overby et al., 1997) of that of CYP3A4, the most significant cytochrome P450. Menstruation can temporarily affect the levels of expression of hFMO3 in females. Human FMO5 is also expressed at high levels in the liver. It is additionally found in the digestive tract including the stomach, small and large intestine and pancreas. Finally, the least expressed isoform, hFMO4, is found at very low levels in kidneys.

1.20.2.3

Induction

In general, it is thought that hFMOs are not induced by xenobiotics. Nevertheless, two different studies have demonstrated that the expression of these enzymes can be modulated by foreign compounds and therefore inducible. 2,3,7,8-Tetrachlorodibenzo-pdioxin (TCDD) was identified as a polycyclic aromatic compound able to induce FMO2 and FMO3 mRNAs in the liver of mice with wild-type aryl hydrocarbon receptor (AHR) but not in Ahr-null mice (Celius et al., 2008). FMO3 mRNA levels can increase as much as six times upon induction by TCDD. FMO5 mRNA was significantly down-regulated by TCDD in both male and female adult mice in the same study. Despite the increase in the amount of mRNA in both FMO2 and FMO3, the activity was not altered significantly.

Fig. 3

hFMO isoform distribution in human organs: (A) lungs, (B) liver, (C) kidney and (D) intestine. By authors.

Drug Metabolism: Other Phase I Enzymes

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3-Methylcholanthrene (3MC) was also reported as an inducer of FMO3 mRNA in mouse liver (Celius et al., 2010). Benzo[a]pyrene (BaP) and 3MC were both found to be responsible for higher mRNA levels of FMO3 in Hepa-1 cells. Nevertheless, the same study reported that TCDD does not induce FMO3 mRNA in Hepa-1 cells. In the same cells, cycloheximide strongly upregulated FMO3 mRNA. In the case of hFMO4, rifampicin was found to be an inducer in human hepatocytes using cDNA based microarrays (Rae et al., 2001). Synthetic progestin R5020 was found to act as a regulator of hFMO5 expression in a breast cancer cell line that stably expressed progesterone receptor B (YB cells) (Miller et al., 1997). Finally, the effect of St. John’s wort and hyperforin on FMO gene expression levels were assessed using Affymetrix microarray hybridization and real time reverse transcription-PCR in HepG2 cells. The data demonstrated that both compounds increased the mRNA levels of hFMO5 (Krusekopf and Roots, 2005).

1.20.2.4

Inhibition

The simplest way to rule out an enzymatic activity within a complex mixture of different enzymes is to use a specific inhibitor of the target enzyme. Different types of inhibitors are available but they can be broadly divided into two categories: reversible and irreversible inhibitors. Irreversible inhibitors mainly bind covalently or with strong interactions to the active site of the enzyme and as a consequence block any substrate from binding, leading to a complete inactivation of the enzyme. In the case of reversible inhibitors, they can be further subdivided into competitive, noncompetitive or mixed inhibitors. Competitive inhibitors as the name suggests, act by occupying the same binding pocket as that of the substrate and therefore they exert their activity by competing for the enzyme’s active site. Increasing the substrate concentration is sufficient to remove the activity of a competitive inhibitor, so high concentrations of inhibitor are required to inactivate the enzyme and total inhibition is difficult to achieve, especially under physiological conditions. Competitive inhibitors have an effect on the measured Km values. On the other hand, noncompetitive inhibitors bind to a different site on the enzyme than that of the substrate. Therefore, these inhibitors will have no effect on the measured Km but will affect the Vmax values. Finally, in mixed inhibition both Km and Vmax are affected. In the case of hFMOs, in contrast to cytochromes P450, these enzymes do not seem to be readily inhibited by chemicals. There are only two reports of inhibition of hFMOs, one by (N,N-dimethylamino) stilbene carboxylates carried out by in vitro assays with FMO1 and the other inhibition of hFMO3 by dietary indoles present in brassicas including brussels sprouts. The anticarcinogenic properties associated with these vegetables are primarily attributed to the presence of glucosinolates including indole-3ylmethylglucosinolate (I3M-GS), which upon breakdown leads to the formation of indole-3-acetonitrile and indole-3-carbinol. Cashman’s research group (Cashman et al., 1999a,b) demonstrated that acid condensation products of indole-3-carbinol (present in Brussels sprouts) and formed during passage through the gastrointestinal system, were potent competitive inhibitors of hFMO3 with Ki values in the low micromolar range. The hFMO3 activity was measured by determining the levels of urinary trimethylamine and trimethylamine N-oxide in human volunteers. The significance of this study is that daily intake of brussels sprouts, a dietary source of indole-3-carbinol derived compounds, may lead to a decrease in hFMO3 activity, and this may have consequences for metabolism of other xenobiotics or dietary constituents. Cashman’s group also used the in vitro assay of the N-oxygenation of 10-([N,N-dimethylaminopentyl]-2-trifluoromethyl)phenothiazine as a reference reaction for the inhibition study but it is not known whether these compounds can also affect NADPH consumption (Cashman et al., 1999a). More recently, Goji berry, the fruit of Lycium barbarum L. (Solanaceae), also known as wolfberry, has likewise been indicated as a possible inhibitor of hFMO3. It is a well-known traditional Chinese medicine with beneficial effects such as reducing blood glucose and serum lipids, immunomodulation, anticancer, antifatigue and, male fertility enhancement. The data demonstrate a moderate inhibition of FMO3 S-oxygenation activity (30–60%) by both the fresh juice as well as the commercially available juice (Liu et al., 2016). In addition to the above inhibition by chemicals, FMOs are also subject to competitive inhibition by their substrates. The bestknown example is methimazole, a substrate of hFMO with methimazole S-oxide as the product of the enzymatic reaction. Due to its solubility in water and high turnover numbers, it can efficiently compete with other substrates of hFMO for the catalytic pocket of the enzyme. The latter is the basis of a colorimetric assay (Dixit and Roche, 1984) to determine FMO activity in lysates or with purified enzymes (Catucci et al., 2017). Many in vitro studies conducted with liver microsomes or cell lysates have employed methimazole as a selective inhibitor of hFMO activity in order to evaluate the contribution of solely cytochromes P450 in the metabolism of a certain drug or xenobiotic. This practice has a major drawback due to the ability of methimazole to cause a decrease in cytochrome P450 content, i.e., observed loss of cytochrome P450 with 1 or 20 mM methimazole indicating a saturation of FMO at lower concentration. A negative control where FMO was inactivated by heat, confirmed that cytochrome P450 content is affected by methimazole S-oxide (Kedderis and Rickert, 1985). Methimazole was also used in another study to test the inhibitory effects of two compounds, (E)-3-[2-(4-(dimethylamino) phenyl)vinyl]benzoic acid (DS3CO) and (E)-2-[2-(4-(dimethylamino)phenyl)vinyl]benzoic acid (DS2CO), on the activity of hFMOs. The data indicated that both DS3CO and DS2CO act as competitive and noncompetitive inhibitors for the tested substrates with Ki > 0.150 mM. Nevertheless, the calculated Ki values for noncompetitive inhibition were all > 1 mM except for DS2CO against methimazole that showed a Ki of 0.65 mM (Clement et al., 1996). These data are encouraging especially because DS3CO and DS2CO are not able to inhibit cytochrome P450 reductase, so they seem to have a certain degree of specificity for hFMOs. In conclusion, it is apparent that more studies are needed to identify potent and selective inhibitors of hFMOs.

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Drug Metabolism: Other Phase I Enzymes Structural features

Structure/functional studies of hFMOs have been hindered, especially when compared to cytochromes P450, due to technical issues that researchers have faced with the production of recombinant forms of these proteins. To date, there are still no three-dimensional structures available for any of the five hFMOs. However, in the last few years advances have been made in resolving their structures that together with biophysical studies have revealed significant details about the reaction mechanism of these enzymes (Catucci et al., 2012, 2019a,b, 2020; Gao et al., 2017, 2018; Nicoll et al., 2020; Bailleul et al., 2020). Within the cell, hFMOs are bound to the smooth endoplasmic reticulum via a C-terminal anchor of about 25–40 amino acids. Human FMOs tend to form oligomers in solution, due to the partial hydrophobic nature of their structure and purified enzymes are not stable at room temperature because heat induces aggregation. In addition to being membrane-bound, two other factors have also played a role in limiting the in-depth characterization of these enzymes: these enzymes are heat labile and are inactivated within a few minutes at temperatures above 40  C and they do not have many known inhibitors as discussed in the previous section. The latter is an important point to consider for drug metabolism studies carried out with liver microsomes where both cytochromes P450 and FMOs are present. The availability of a selective inhibitor of FMOs could help in excluding the relative contribution of these enzymes to the conversion of a specific drug. Recently, two different publications have revealed the main structural features of FMOs through the successful crystallization of the ancestral homologs of the human enzymes (Nicoll et al., 2020; Bailleul et al., 2020). The ancestral synthetic proteins share above 80% sequence identity with the hFMOs. The structures of Ancestral FMOs (AncFMOs) were solved by X-ray crystallography at a resolution of 2.7–3.0 Å (Nicoll et al., 2020). AncFMO2 was crystallized both in the presence and in the absence of the NADPþ cofactor, while the other isoforms were crystallized only in the presence of the cofactor. Furthermore, AncFMO3-6 is a crystal structure that can be used to model both hFMO3 and hFMO6, while AncFMO1 and AncFMO5 are the best structural models for hFMO1 and hFMO5 (Fig. 4). Two highly conserved FAD and NADPH binding domains compose the overall structures and the C-terminal regions are composed of alpha helices for AncFMO1,2,3-6 whereas AncFMO5 exhibited a highly disordered C-terminus. All AncFMO structures were found to be dimers that are held together by a 2000 Å interface (Nicoll et al., 2020) which is unique to the hFMOs. The human proteins are also larger than other FMOs with an insertion region spanning 80 amino acid residues. This insertion forms a ridge-like triangular fold partially contributing to membrane insertion, supporting monomer-monomer interactions as well as helping navigate the substrate toward the active site (Nicoll et al., 2020). Unexpectedly, AncFMOs reveal a buried active site as opposed to their soluble homologs. Close inspection of the possible substrate access channels of AncFMOs to the active site reveals both similarities and significant differences among the isoforms. All the structures contain a gating residue at the entrance of the access channel, Leu 375, that is most probably responsible for protecting the catalytic environment from the solvent. In general, the substrate is thought to enter the access channels through the subdomain that is unique to hFMOs. Locally, the route toward the active site is guided by the presence of three different loops. Loop 1 forms a large arched fold underneath the NADPH binding pocket in the case of AncFMO2 and AncFMO5, while a coiled alpha helical structure is present in AncFMO3-6 leading to the formation of an open cavity (Nicoll et al., 2020). Loop 2 is a second element of distinction and also in this case

Fig. 4 Homology models for (A) hFMO1 (blue), based on PDB: 7AL4 (B) hFMO2 (green), based on PDB: 6SEM (C) hFMO3 (orange), based on PDB: 6SE3 (D) hFMO5 (magenta) based on PDB: 6SEK. Homology models generated by Swiss-model (https://swissmodel.expasy.org) based on freely available PDB files: 7AL4, 6SEM, 6SE3 and 6SEK.

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AncFMO3-6 shows a different conformation. Finally, Loop 3 distinguishes AncFMO5 from the other isoforms due to its shorter alpha helix, part of the insertion domain, that alters the opening of the cavity. AncFMO1, the most recent structure to be solved (Bailleul et al., 2020), demonstrates a remarkable difference in the binding mode for NADPþ. While two histidines are highly conserved for all the other structures at positions 149 and 150, AncFMO1 possesses a Leu in position 150. This substitution prevents the formation of hydrogen bonds leading to a rotated Lys 373 and an enlarged active site (Bailleul et al., 2020). In AncFMO1 the 20 phosphate of NADPþ occupies a new conformation that is unique to AncFMO1 where this part of the molecule is not extending out to the solvent anymore, but it is inserted more deeply in the NADPH binding domain (Bailleul et al., 2020). The 20 -phosphate is stabilized by hydrogen bonds to the sidechains of Arg 280 and Thr 214 and the carbonyl peptide of Met 192. Although the synthetic AncFMOs are currently the best structure models for the structure-function relationships of hFMOs, their biophysical properties are not the same. These synthetic proteins have higher melting temperatures most probably leading to diminished overall flexibility and therefore might not fully represent the real human enzymes.

1.20.2.6

Genetic variants

As mentioned earlier, hFMOs are not readily induced or inhibited with the exception of hFMO5 which is induced by rifampicin and synthetic progestin R5020 (Rae et al., 2001; Miller et al., 1997). As a result, any interindividual variations in activity of these enzymes is therefore the result of mainly genetic or physiological factors. This offers a great opportunity for personalized medicine and therapies where drugs metabolized by hFMOs are concerned. Genotyping individuals for their FMO variants would lead to the identification of those who might not benefit from taking the drug in question or that might experience adverse reactions. To this end, it is important to compile pharmacokinetic data regarding the conversion of drugs metabolized by the wild type hFMOs as well as their many different polymorphic variants. This section summarizes the different known polymorphic variants of the hFMOs. Furnes and colleagues were the first group to investigate the genetic variability of hFMO1 in individuals of African-American descent (Furnes et al., 2003). They reported four nonsynonymous single nucleotide polymorphisms (SNPs) with allele frequency of < 2%: H97Q, I303V, I303T, R502X (Koukouritaki and Hines, 2005). In a follow up study by the same group, a modest effect of these four variants on the function of hFMO1 was demonstrated (Furnes and Schlenk, 2004). Looking through the SNP database (https://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi) only one of the four reported SNPs, I303V, is present and therefore the other three seem to be rare alleles within the populations studied. In the case of hFMO2 there are essentially two main variants. Most human genomes encode a non-functional truncated form of the enzyme, Q472X, lacking the last 64 amino acid residues resulting in a premature termination (Koukouritaki and Hines, 2005). However, about 50% of individuals living in some regions of sub-Saharan Africa possess a functional hFMO2 (Veeramah et al., 2008). Around 26% of African-Americans also have the full-length hFMO2 polypeptide. An active hFMO2 could result in interindividual differences in drug metabolism especially if the drug’s entry point or target organ is the lungs. Among the different hFMOs, the FMO3 gene is the most polymorphic one where both common and rare variants occur. In all, 15 nonsynonymous variants have been identified: E24D, N61K, D132H, E158K, G180V, R205C, V257M, M260V, V277A, E308G, L360P, E362Q, K416N, I486M, G503R together with 30 rare mutations: E32K, I37T, R51G, A52T, V58I, N61S, K64KfsX2, M66I, M82T, N114S, V143E, G148X, P153L, C197fsX, D198E, I199T, T201K, R223Q, R238P, E305X, E314X, R387L, W388X, K394KfsX11, M405IfsX, M434I, Q470X, G475D, R492W, R500X (Phillips and Shephard, 2020; Fig. 5).

Fig. 5 hFMO homology model monomer, based on PDB: 6SE3, with FAD in yellow, the FAD binding domain in blue, the NADPH binding domain in orange and the insert domain in red. The most common hFMO3 polymorphic variants are labeled and shown in magenta while the all the other variants are in green. Homology model monomer generated by Swiss-model (https://swissmodel.expasy.org) based on freely available PDB file:6SE3.

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There are loss-of-function mutations which give rise to a disease called trimethylaminuria or “fish odor syndrome.” Affected individuals lose the ability to convert the highly volatile tertiary amine, trimethylamine (derived from dietary choline and carnitine) to its N-oxide form (Dolphin et al., 1997; Messenger et al., 2013). The unmetabolized trimethylamine with its strong malodor is excreted in body fluids. There are in excess of 30 rare FMO3 alleles (with less than 1% frequency) that totally abolish the activity of the resulting hFMO3 enzyme and therefore cause the disease (Shephard et al., 2012). A review and full discussion regarding these rare alleles are outside the scope of this chapter but the reader is directed to consult the many review articles on this subject (Mitchell and Smith, 2001; Messenger et al., 2013; Fennema et al., 2016). Moreover, these mutations cannot be the underlying cause of differences in hFMO3 drug metabolism seen in different populations. More interesting from a drug metabolism point of view are the common polymorphic variants of hFMO3 that result in an altered but functional enzyme. These variants include E158K (15167G > A), V257M (18281G> A) and E308G (21443A > G) (Koukouritaki and Hines, 2005). The remaining two isoforms are not highly polymorphic. Three non-synonymous variants have been reported for hFMO4: I37T, T308S, V323A and two variants for hFMO5: P457L and R506S (Phillips and Shephard, 2020). Evidence that any of these will have a marked effect on the activity of the resulting enzymes is lacking. The effect of polymorphisms of the hFMO isoforms on drug metabolism will be discussed in the following sections.

1.20.2.7

Reactions catalyzed

Human FMOs metabolize a large variety of drugs and xenobiotics. The drugs and xenobiotics that can be transformed by these enzymes are essentially N-, S- and to a lesser extent P- and Se-containing molecules. The lack of a proper mechanism of binding makes hFMOs highly promiscuous in terms of substrate recognition and the mechanism that the enzyme uses to perform monooxygenation is often referred to as “loaded gun” or “cocked gun.” Indeed, based on mechanistic studies from the 1980s and more recent publications, FMOs have a strong affinity for the NADP(H) cofactor that changes the redox state of the enzyme and allows oxygen binding and in the specific case of FMO1 a remarkable ability to stabilize the reaction intermediate, but they do not perform mutual recognition of the whole substrate structure. In order to understand how FMOs function, one should imagine that the enzyme spends most of its catalytic cycle in the oxidative half-reaction and that even when the substrate is converted to product, NADPþ is the last to leave the active site before a new cycle can start. With the breakdown of FADH-OH resulting in water and the release of NADPþ being the rate-limiting steps of the cycle, it is easy to understand why the oxidized/reduced cofactor exchange is so relevant for FMOs and how evolution did not focus on substrate recognition. In actual fact, the substrate structure has been shown to have little effect on the catalytic constant (kcat). Therefore, for hFMOs the key determinant of the specificity constant (kcat/ KM) is the measured KM for the substrate. Moreover, in these enzymes the KM is a measure of the ease with which a substrate gains access to the active site rather than the typical affinity of the substrate for the enzyme. While hFMO1, hFMO2 and hFMO3 share a common substrate preference for nucleophiles, hFMO5 was recently reported to be selective for electrophilic molecules. This peculiar behavior is probably associated with a different stabilization mechanism of the C4a-hydroperoxyflavin intermediate during the catalytic cycle, but full characterization of this phenomenon is still lacking. The most evident result of hFMO5 activity that distinguishes this isoform from the others is its ability to perform Baeyer-Villiger reactions that result in C oxidation (Matsumoto et al., 2021). Since hFMO5 is highly expressed in the liver, C oxidation that was historically attributed to mainly cytochromes P450 should be revisited to allow for the contribution of hFMO5. In general, in order to investigate the role of the different isoforms in the metabolism and conversion of drugs and xenobiotics, kinetic parameters are measured. These parameters include KM for substrate affinity, moles of product/mg of enzyme or kcat for activity. Kinetic data present in literature are not homogenous due to the source of enzymes used in the various studies. These include supersomes, human liver microsomes or different recombinant expression systems (bacterial or baculovirus), so careful attention is needed to compare these catalytic parameters. From a purely biochemical perspective the best way to obtain reliable data is to minimize the number of variables that can affect the enzymatic assay, so it would be desirable to use purified enzymes for comparison of calculated kcat/KM values of different substrates; however, as mentioned earlier not only the source of the enzymes is different but also the experimental set up including the pH of the reactions. Nevertheless, in the following section, where possible, an attempt to compare and summarize the published data of the different hFMO isoforms with selected substrates will be made. The published and available hFMO drug substrates, with the exception of hFMO5, can be divided into five main categories: secondary amines, tertiary amines, sulfur-containing compounds, piperazines and pyrrolidines. In addition, there are several other categories of drugs which are represented by only one substrate: piperidines, pyridines, pyrimidines, morpholines, quinolines and for this reason they are grouped together under the “others” category. FMO5 specific substrates are either linear or cyclic ketones and will be dealt with separately in the following section.

1.20.2.7.1

Drugs metabolized exclusively by hFMO1

1.20.2.7.1.1 Tertiary amines Imipramine, 1, is a tricyclic antidepressant (TCA) mainly used in the treatment of depression. It is also effective in treating anxiety and panic disorders. The drug is also used to treat bedwetting. It works by increasing levels of serotonin and norepinephrine and by blocking specific serotonin, adrenergic, histamine and cholinergic receptors. hFMO1 catalyzes the N-oxidation of imipramine resulting in the formation of imipramine N-oxide, 1a (Furnes and Schlenk, 2004). The measured Km for imipramine is 14 mM, whereas the Vmax is 51 nmol min 1 nmol 1 protein (Furnes and Schlenk, 2004).

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1.20.2.7.1.2 Sulfur-containing compounds Hypotaurine, 2, is an organic osmolyte and a cytoprotective agent that acts as an antioxidant to scavenge reactive hydroxyl radicals. hFMO1 is able to convert hypotaurine to taurine through an S-oxidation reaction, 2a, with measured kinetic parameters of Km and kcat of 4 mM and 7.5 min 1, respectively (Veeravalli et al., 2020). Taurine can either be obtained from the diet or formed from cysteic acid or hypotaurine. Interestingly, until recently taurine was thought to be synthetized by a NAD-dependent hypotaurine dehydrogenase but the research group of Shephard (Veeravalli et al., 2020) through two different experiments demonstrated that the enzyme responsible was actually FMO1: (a) Metabolite analysis of the urine of FMO1-null mice by 1HNMR spectroscopy revealed a buildup of hypotaurine and, (b) In vitro assays with baculosomes containing hFMO1 confirmed that indeed this enzyme was responsible for the conversion of hypotaurine to taurine. Methiocarb, 3, is an ester of carbamic acid and is known as carbamate. It is a highly effective pesticide for a variety of crops, agricultural animals and pets. Methiocarb acts as an acetylcholinesterase inhibitor with a reversible mechanism. This pesticide was found to induce oxidative stress in rats. hFMO1 catalyzes the S-oxidation of methiocarb, 3a (Fujino et al., 2016). The chemical structures of three drugs and their corresponding metabolites produced by the enzymatic action of hFMO1 are shown in Fig. 6A.

Fig. 6 hFMO isoform specific substrates. Drugs metabolized exclusively by hFMO1 (A), hFMO3 (B), and hFMO5 (C). The numbered compounds are referred to in the text. By authors using “ChemSketch”. https://www.acdlabs.com/contact/inforequest.php.

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Fig. 6

Drug Metabolism: Other Phase I Enzymes

(continued).

1.20.2.7.2

Drugs metabolized exclusively by hFMO2

While FMO2 homologs from other mammalian species have shown the ability to perform N-oxidation reactions (Geier et al., 2015), hFMO2 substrates are mainly sulfur derived compounds like thioureas or thioetherorganophosphates (Geier et al., 2015). The three main drugs metabolized by hFMO2 are ethionamide, methimazole and thiacetazone (Phillips and Shephard, 2017). However, these drugs are not exclusively metabolized by this isoform and are therefore described in other sections.

Drug Metabolism: Other Phase I Enzymes

Fig. 6

519

(continued).

1.20.2.7.3

Drugs metabolized exclusively by hFMO3

1.20.2.7.3.1 Tertiary amines Almotriptan, 4, (3-(2-dimethylaminoethyl)-5-(1-pyrrodinylsulfonylmethyl)-1H-indole) is a selective 5HT1B/1D agonist for the oral treatment of acute migraine attacks. In studies carried out by Salva and colleagues, almotriptan showed diminished spasmogenic effects on cardiac arteries and therefore an improved vascular profile compared with the reference compound sumatriptan. The same authors demonstrated the conversion of almotriptan to its corresponding N-oxide, 4a, by hFMO3 (Salva et al., 2003). Clomiphene citrate (CC), 5, is a nonsteroidal drug that induces ovulation indirectly (Yilmaz et al., 2018). It is used in the treatment of anovulatory patients and polycystic ovarian syndrome (PCOS). It is also used for male infertility due to spinal cord injury

520

Fig. 6

Drug Metabolism: Other Phase I Enzymes

(continued).

and multiple sclerosis and gynecomastia in both adolescent and pubertal males. hFMO3 catalyzes the formation of the clomiphene N-oxide, 5a, with a measured Km value of 18.3 mM and a kcat of 0.07 min 1 (Catucci et al., 2018). Dasatinib, 6, is a potent protein kinase inhibitor used to treat certain cases of chronic myelogenous leukemia (CML) and acute lymphoblastic leukemia (ALL). It is mainly used in Philadelphia chromosome-positive (Ph þ) cases. hFMO3 converts dasatinib into dasatinib N-oxide, 6a (Wang et al., 2008), but no kinetic parameters are available. Danusertib, 7, is an Aurora kinase inhibitor and in vitro experiments have demonstrated that treatment with this compound can significantly inhibit melanoma cell growth and induce autophagy in several cancer cell lines. Aurora kinases, consisting of Aurora A, Aurora B and Aurora C are a family of serine/threonine kinases with substantial functions in cell division and proliferation that are overexpressed in several types of cancers. In vitro enzyme assays with purified hFMO3 have demonstrated the production of danusertib N-oxide, 7a (Catucci et al., 2012), with the measured Km value of 57.3 mM and a kcat of 0.57 min 1 (Catucci et al., 2013). N,N-Diallyltryptamine, 8, is a synthetic psychoactive tryptamine that belongs to the class of new psychoactive substances (NPS) in addition to synthetic cannabinoids, phenethylamines, synthetic cathinones, and others. These drugs influence the serotonergic

Drug Metabolism: Other Phase I Enzymes

Fig. 6

521

(continued).

system, which includes activation of the 5-HT1A and 5-HT2A receptor subtypes resulting in the increase of serotonin release. hFMO3 has been shown to catalyze the formation of N,N-diallyltryptamine N-oxide, 8a (Wagmann et al., 2016) but without any kinetic parameter determination. The next drug, oxycodone, 9, is a medication used as an analgesic and antitussive. Oxycodone is also employed for severe pain associated with arthritis, degenerative disk disease and cancer. Oxycodone is a mu opioid receptor agonist and exerts its pharmacological activity through this receptor. Recently, the research group of Cashman has demonstrated that hFMO3 catalyzes the formation of oxycodone N-oxide, 9a (Cashman et al., 2020).

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Drug Metabolism: Other Phase I Enzymes

(continued).

Pyrazoloacridine (PZA), 10, is an inhibitor of both topoisomerase I and II that decreases the formation of topoisomerase-DNA adducts. It is an anti-cancer agent that has demonstrated efficacy in multidrug-resistant neuroblastoma, doxorubicin-resistant human colon carcinoma and breast cancer cell lines. Experiments carried out by Reid and colleagues (Reid et al., 2004) demonstrated that PZA N-oxide and N-demethyl-PZA were detected in urine samples of patients after PZA administration. They further investigated the enzymes responsible for these two metabolites and found out that hFMO3 was responsible for catalyzing the

Drug Metabolism: Other Phase I Enzymes

Fig. 6

523

(continued).

formation of PZA N-oxide, 10a (Reid et al., 2004), with measured Km value of 147 mM with a Vmax of 1.32 nmol min 1 mg 1 protein. The second metabolite, N-demethyl-PZA was formed by cytochrome P450 3A4. Ranitidine, 11, is a histamine H2-receptor antagonist that is used to decrease gastric acid secretion and therefore for treating gastrointestinal ulcers. Interestingly, hFMO3 is able to perform both N- and S-oxidation of this drug, 11a, 11b (Overby et al., 1997), with measured Km values of around 2 mM and Vmax of 17 nmol min 1 nmol 1 protein.

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S 16020, 12, is a cytotoxic agent derived from 9-hydroxy olivacine. It interacts with DNA by intercalation and stimulates DNA cleavage mediated by topoisomerase II by stabilizing the covalent enzyme-DNA complex. It has a broad spectrum of antitumor activity in murine and human tumor models. The metabolite produced by hFMO3 is the corresponding N-oxide, 12a (PichardGarcia et al., 2004). 1.20.2.7.3.2 Secondary amines In the case of secondary amines, there are fewer examples of drugs that are converted to their corresponding N-oxide solely by hFMO3 when compared to tertiary amines. One very well-known example of this category is amphetamine, 13, which is also exploited as a recreational substance. It is a central nervous system (CNS) stimulant used in the treatment of attention deficit hyperactivity disorder (ADHD), narcolepsy and obesity. It is also classified as a doping drug in sports and used by athletes as a performance enhancer. The drug is a racemic mixture consisting of a 50:50 combination of the two enantiomers: levoamphetamine and dextroamphetamine. hFMO3 is able to convert both enantiomers to the corresponding hydroxylamines. When the conversion occurs hFMO3 performs two consecutive N-oxidations resulting in two products, 13a and 13b.The second product formed by hFMO3 is unstable and decays into a trans oxime, 13c (Cashman et al., 1999a,b). Oximes seem to have little pharmacological activity, and therefore FMO-mediated N-oxygenation of biogenic amines appears to be a mechanism for inactivation (Krueger and Williams, 2005). The measured Km for amphetamine is 44.2 mM with a Vmax of 6.5 nmol min 1 mg 1 protein for the () product, whereas for the (þ) product a Km of 11 mM and a Vmax of 1.1 nmol min 1 mg 1 protein was obtained (Cashman et al., 1999a,b). 1.20.2.7.3.3 Sulfur-containing compounds Albendazole, 14, [methyl 5-propylthio-1H-benzimidazol-2-yl carbamate; ABZ] is a broad-spectrum anthelmintic agent that has shown activity against gastrointestinal nematodes and the larval stages of cestodes such as Echinococcus granulosus and Taenia solium. ABZ is rapidly metabolized to the sulfoxide form (ABS) and subsequently to a sulphone (ABSO). Only the parent compound is thought to have anthelmintic activity and hFMO3 is involved in the initial biotransformation leading to the N-oxide metabolite, 14a (Rawden et al., 2000). ABZ has two different enantiomers and it is not clear which enantiomer is more active, although previous data suggest that the (þ)-(R)-albendazole sulfoxide product is an active enantiomer against Taenia solium (Paredes et al., 2013). In this case, the measured Km was found to be 9.6 mM with a calculated Vmax value of 1,103 pmol min 1 mg 1 protein (Rawden et al., 2000). The second example of a sulfur-containing compound is teneligliptin, 15, a potent, selective, and long-lasting DPP-4 inhibitor that has antioxidative properties and has shown endothelial protective effects in several non-clinical as well as clinical studies. Teneligliptin is a therapeutic option for the type 2 diabetes mellitus patients, including elderly subjects and those with renal impairment. hFMO3 is postulated to be the physiological FMO isoform able to catalyze the formation of teneligliptin N-oxide, 15a (Ceriello et al., 2019; Nakamaru et al., 2014). The measured Km for teneligliptin is 126 mM, whereas the Vmax is 52.1 nmol min 1 nmol 1 protein (Nakamaru et al., 2014). A more recent example of this category of compounds is tetrahydrothiophene, 16, a metabolite that originates from the metabolism of busulphan. Busulphan is first conjugated to glutathione resulting in the unstable sulphonum ion of glutathione that decays into tetrahydrothiophene. hFMO3 catalyzes the formation of tetrahydrothiophene-1-oxide, 16a (El-Serafi et al., 2017). Busulphan (Bu) is an alkylating agent administered at high doses prior to hematopoietic stem cell transplantation HSCT used as a curative treatment for several malignant and non-malignant disorders including leukemias, aplastic anemia, thalassemia and inborn errors of metabolism. It reacts with DNA to form intra-strand crosslinks via the guanine and adenine nucleotide base pairs. DNA crosslinking leads to cell damage and triggers apoptosis. 1.20.2.7.3.4 Piperazines This category of compounds comprises a wide range of drugs from antidepressants to antihistamines. The connecting property of all these chemicals is the presence of a piperazine functional group consisting of a six-membered ring containing two nitrogen atoms at opposite positions in the ring. The first example is clozapine, 17, an antipsychotic medication. It is mainly used for the management of schizophrenia that does not improve following the use of other antipsychotic medications. hFMO3 converts clozapine to clozapine N-oxide, 17a (Tugnait et al., 1997). The measured Km for clozapine is 324 mM, with a Vmax of 12.8 nmol min 1 nmol 1 protein (Tugnait et al., 1997). K11777, 18 (N-methyl-piperazine-Phe-homoPhe-vinylsulfone-phenyl) is a peptidomimetic acting as a potent, irreversible inhibitor of cysteine proteases such as cathepsin B, L and cruzain. While cathepsin B and L are cancer targets, cruzain is a cysteine protease of Trypanosoma cruzi, a protozoan parasite causing Chagas’ disease. hFMO3 catalyzes the formation of K1177 N-oxide, 18a, with a measured Km of 14 mM and a Vmax of 3460 pmol min 1 mg 1 protein (Jacobsen et al., 2000). Another example of an antipsychotic agent is loxapine, 19, a tricyclic compound used clinically for the management of acute and chronic schizophrenia. Also in this case, hFMO3 catalyzes the formation of the N-oxide, 19a (Luo et al., 2011) with published Km value of 170 mM and a Vmax of 72.5 pmol min 1 mg 1 protein. L-775,606, 20, is a serotonin 5-HT1D/1B receptor agonist, used/tested for the treatment of migraine headache. For this agonist, hFMO3 is capable of producing two different N-oxide products of the same molecule, 20a, 20b (Prueksaritanont et al., 2000). The published kinetic parameters for isomer 1 and isomer 2 are Km values of 115 and 118 mM with Vmax of 729 and 340 nmol min 1 mg 1 protein, respectively (Prueksaritanont et al., 2000).

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Finally, the last two examples of antipsychotic drug substrates of hFMO3 are perazine (PER), 21, and trifluoperazine, 22. PER has an intermediate potency and acts as a dopamine antagonist whereas trifluoperazine works by blocking dopamine D1 and D2 receptors in the mesocortical and mesolimbic pathways. Similar to all other antipyscotics described, hFMO3 is responsible for the formation of the N-oxide product of these two drugs, 21a (Störmer et al., 2000) and 22a (Lomri et al., 1993), respectively. No kinetic parameters are available for these drugs. 1.20.2.7.3.5 Others In this section, compounds that do not fall within the above-mentioned categories are clustered together with a short description of the catalytic activity of hFMO3 toward them. The first compound is DMXAA or vadimezan, 23, a flavone-acetic acid-based drug that is a vascular disrupting agent (VDA) that induces a rapid shutdown of blood flow in tumors but not in normal tissue. Although a promising drug candidate, it did not pass phase III clinical trials probably due to STING receptor variations between mice and humans. However, it is worth mentioning here due to the unusual 6-methylhydroxylation product, 23a, reportedly formed by hFMO3 (Zhou et al., 2002) with the corresponding measured kinetic data of Km of 9.7 mM and Vmax of 0.017 nmol min 1 mg 1 protein (Zhou et al., 2002). The second drug, primaquine, 24, is an anti-malaria drug. The mechanism of its action is unknown but in 2014 Jin and colleagues indicated a role for hFMO3 in the metabolism of this drug (Jin et al., 2014). However, more recent studies have indicated that its activity is actually exerted by the active metabolites produced in the liver by cytochrome P450 2D6 (Camarda et al., 2019). More studies are required to substantiate and clarify the role played by hFMO3 in the metabolism of this anti-malarial drug. Moclobemide, 25, is a reversible inhibitor of monoamine oxidase-A (another Phase I drug metabolizing enzyme) given to patients suffering from depression and social anxiety. This drug was used as a model substrate for the first report on a bacterial system expressing hFMO3 for the synthesis of a human metabolite in a multi-milligram scale. hFMO3 catalyzes the formation of moclobemide N-oxide acting on the morpholine ring, 25a (Hanlon et al., 2012). The chemical structures of the above-mentioned exclusive drug substrates of hFMO3 and their corresponding metabolites (compounds 4–25) are shown in Fig. 6B.

1.20.2.7.4

Drugs metabolized exclusively by hFMO5

1.20.2.7.4.1 Cyclic ketones hFMO5 shares a few substrates with the other isoforms, such as the S-methyl esonamrimod, nomifensine and phospho-sulindac. These overlapping substrates will be discussed in the followings sections dedicated to joint specificities of hFMO isoforms. Nevertheless, the most important feature of hFMO5 is its ability to catalyze Baeyer-Villiger reactions. Two drugs were already identified as potential substrates for the Baeyer-Villiger oxidation by hFMO5, but the role of this isoform might be underestimated. ER-879819, 26, is the oxidation product of E7016 ([10-((4-hydroxypiperidin-1-yl) methyl)chromeno[4,3,2-de]phthalazin-3(2H)-one]) for which an enzyme is yet to be assigned. E7016 is a poly(adenosine diphosphate [ADP]-ribose) polymerase (PARP) inhibitor that is under investigation for cancer treatment. hFMO5 is able to convert ER-879819 to ER-879123 when the reaction mixture is fortified with cytosolic liver cellular fractions. Specifically, hFMO5 converts the ER-879819 ketonic form, 26, to the lactone, 26a, and the cytosolic fraction is needed to form the final product, ER-879123, an opened ring structure probably through the action of a carboxylesterase (Lai et al., 2011). MRX-I, 27, (5S)-5-[(isoxazol-3-ylamino)methyl]-3-[2,3,5-trifluoro-4-(4-oxo-2,3-dihydropyridin-1-yl)phenyl]oxazolidin-2-one, is an oxazolidinone antibiotic where the morpholine heterocycle in linezolid is substituted with the more planar 2,3dihydropyridin-4-one (DHPO) ring, leading to increased antibacterial activity of MRX-I compared with that of linezolid. The principal metabolic pathway proposed in this case involves the Baeyer-Villiger oxidation of the 2,3-dihydropyridin-4-one (DHPO) ring, 27a. The latter compound subsequently undergoes several other transformations including hydrolysis, tautomerization and reduction/oxidation reactions generating MRX459 or MRX445-1 (Meng et al., 2015). E7016 and MRX-1 illustrate specificity for hFMO5 since the other isoforms did not lead to formation of a product and can therefore be used as specific probes for hFMO5 activity. 1.20.2.7.4.2 Linear ketones Nabumetone, 28, is a nonsteroidal anti-inflammatory drug (NSAID). 6-Methoxy-2-naphthylacetic acid is the active metabolite resulting from the metabolism of the nabumetone. 6-Methoxy-2-naphthylacetic acid, inhibits the cyclooxygenase enzyme and preferentially blocks COX-2 activity. The drug is used to treat pain and inflammation. In order to produce the prodrug metabolite nabumetone is first converted to the ester product N-M1 by hFMO5, 28a, hydrolysis leads to formation of an alcohol, which undergoes two rounds of oxidation (by an alcohol dehydrogenase and an aldehyde oxidase, or alternatively aldehyde dehydrogenase; Fiorentini et al., 2017). The second example, pentoxifylline, 29, is a competitive nonselective phosphodiesterase inhibitor which raises intracellular cAMP, activates PKA, inhibits TNF and leukotriene synthesis and, reduces inflammation and innate immunity. It is used to treat muscle pain in people with peripheral artery disease. In this case pentoxifylline is first converted to the ester product P-M1, 29a or 29b, by hFMO5, hydrolysis then leads to formation of an alcohol which undergoes two rounds of oxidation (by an alcohol dehydrogenase and an aldehyde oxidase, or alternatively aldehyde dehydrogenase) (Fiorentini et al., 2017). The chemical structures of the above-mentioned exclusive drug substrates of hFMO5 (compounds 26–29) and their corresponding metabolites are shown in Fig. 6C.

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Drug Metabolism: Other Phase I Enzymes

1.20.2.7.5

Drugs metabolized by hFMO1 and hFMO3

Human FMO1 and FMO3 share many different substrates. In this section the substrates that have been reported in literature to be metabolized by both these enzymes will be discussed. 1.20.2.7.5.1 Tertiary amines ABT-126, 30, is a potent and selective a7 neuronal acetylcholine receptor agonist with high binding affinity in the low nanomolar range (Ki of 12–14 nM). These receptors are acetylcholine-gated cation channels that are localized on the brain regions critical to the synaptic plasticity underlying learning and memory. Several studies have suggested that a7 nAChR plays a significant role in the development of Alzheimer’s Disease (AD) pathology. hFMOs perform the N-oxidation of the parental compound producing ABT-126 N-oxide, 30a (Liu et al., 2018). In the case of FMO1 the reported Km for ABT-126 is 12.5 mM whereas for hFMO3 is 89.4 mM. The corresponding Vmax values are very similar; 2610 and 2870 pmol min 1 mg 1 protein for hFMO1 and hFMO3, respectively (Liu et al., 2018). Benzydamine, 31 (1-benzyl-3-(3-dimethylaminopropoxy)-1H-indazole) is a nonsteroidal anti-inflammatory drug. Its metabolism in rats and humans leads to N-oxidation, hydroxylation of the benzene ring or the benzyl group, elimination of the dimethylaminopropyl group and desmethylation of the amino function (Ubeaud et al., 1999). This drug may be of minor interest from a pharmacological perspective, but it is highly relevant for the characterization of the activity of hFMOs and it has been used as the marker substrate of hFMO3 (Lang and Rettie, 2000). The main reasons for selecting benzydamine are due to its solubility (as high as millimolar range), its detection by reverse-phase HPLC and high turnover number resulting in significant NADPH consumption that can be used to estimate catalytic parameters. hFMOs perform the N-oxidation of benzydamine generating the benzydamine N-oxide, 31a (Lang and Rettie, 2000). In the case of FMO1 the measured Km for benzydamine was reported to be 60 mM with a kcat of 46 min 1 (Lang and Rettie, 2000). In the case of FMO3, the measured Km was 80 mM with a kcat of 36 min 1 (Lang and Rettie, 2000). Deprenyl, 32, is an irreversible inhibitor of monoamine oxidase (MAO) type B. In the human brain dopamine is mainly metabolized by MAO-B, so by inhibiting MAO deprenyl acts as a modulator of the amount of dopamine in the central nervous system (Heinonen and Lammintausta, 1991). hFMOs catalyze the formation of deprenyl N-oxide, 32a. Due to its chemical structure deprenyl can be produced either in its R or its S form. Pharmacological studies have demonstrated that the R isomer of deprenyl, also called selegiline, is a more potent enzyme inhibitor than its antipode (Magyar et al., 2010). Human FMO1 and FMO3 can both metabolize deprenyl isomers with the concomitant production of 1R,NR-/1R,NS-deprenyl-N-oxide, 1S,NR-/1S,NS-deprenyl-Noxide (Szöko et al., 2004). However, hFMO1 demonstrated a more considerable product stereoselectivity resulting in about 6 and 13 times higher amounts of 1R,NS- and 1S,NS-deprenyl-N-oxide formed from R- and S-deprenyl, compared to the 1R,NRand 1S,NR-deprenyl N-oxide isomers (Szöko et al., 2004). In the hFMO3 catalyzed reaction of deprenyl the measured kinetic parameters were 314 mM and 1.4 min 1 for Km and kcat values, respectively (Catucci et al., 2017). GSK5182, 33, (4-[(Z)-1-[4-(2-dimethylaminoethyloxy) phenyl]-hydroxy-2-phenylpent-1-enyl]phenol), is a structural analog of 4-hydroxy-tamoxifen and it has been reported as a highly selective inverse agonist of ERRg that does not interact with other nuclear receptors. Its activity is exerted by restoring impaired insulin signaling via diacylglygerol production. GSK5182 also lowers blood glucose levels by inhibiting hepatic gluconeogenesis. This molecule is currently being evaluated as a new anti-diabetic drug agent for type 2 diabetes mellitus. GSK5182 has two isomers, E and Z (Koh and Park, 2011). Human FMO3 metabolizes both isomers with slightly different selectivity. The product of the reaction is always an N-oxide of GSK5182, 33a (Joo et al., 2015). When hFMO3 catalyzes the reaction with GSK5182 the Km and kcat values of 9.82 mM and 2.2 min 1 have been reported, respectively (Catucci et al., 2018). Itopride, 34, is a gastroprokinetic agent that stimulates gastrointestinal motor activity through synergistic effects of dopamine D2 receptor blockade and acetylcholine esterase inhibition. The primary metabolite of itopride in human is the N-oxide, 34a, generated by hFMOs (Mushiroda et al., 2000). In the case of hFMO1 the measured Vmax has been reported to be 3.64 nmol min 1 nmol 1 protein whereas for hFMO3 the same parameter was reported as 4.10 nmol min 1 nmol 1 protein (Mushiroda et al., 2000). N-(3R)-1-azabicyclo[2.2.2]oct3-ylfuro[2,3-c]pyridine-5-carboxamide, 35, is an a7 nAChR agonist compound. This drug has demonstrated in vivo efficacy in both auditory sensory gating and rat novel object recognition, a preclinical model of cognitive performance (Toyohara and Hashimoto, 2010). FMOs perform the N-oxidation (35a) of the parental compound with a reported Vmax of 1.81 pmol min 1 mg 1 protein and 0.204 pmol min 1 mg 1 protein for FOM1 and FMO3, respectively (Shaffer et al., 2007). Nicotine, 36, which is present in tobacco and tobacco smoke, activates the nicotinic acetylcholine receptors in the brain, resulting in tobacco addiction. It is rapidly metabolized in vivo with a half-life of approximately 2 h. The product of the reaction is nicotine N-oxide, 36a. FMOs perform the stereoselective N0 -oxidation of nicotine (Perez-Paramo et al., 2019). In the case of FMO1, the reported Km for nicotine is 0.75 mM with a Vmax of 11 pmol min 1 mg 1 protein whereas the same kinetic parameters for FMO3 were 0.70 mM and 11 pmol min 1 mg 1 protein, respectively (Perez-Paramo et al., 2019). In addition, FMO2 and FMO5 are also capable of carrying out this same conversion (Perez-Paramo et al., 2019). Olopatadine, 37, is a histamine H1 receptor-selective antagonist used for the treatment of allergic rhinitis, chronic urticaria, eczema, dermatitis and conjunctivitis (Kajita et al., 2002). This drug has shown mast cell stabilization, potent H1-antihistaminic activity and anti-inflammatory effects (Kaliner et al., 2010). It has been demonstrated that the hFMO isoforms are involved in the production of olopatidine N-oxide, 37a.

Drug Metabolism: Other Phase I Enzymes

527

Pargyline, 38, is an irreversible monoamine oxidase (MAO) type B inhibitor that was originally used in the treatment of depression but its use was discontinued due to undesirable side effects. hFMOs catalyze the N-oxide formation, 38a. Pargyline is a chiral molecule and its metabolism by purified porcine hepatic FMO has revealed the stereospecific formation of the N-oxide. The major isomer produced in all the microsomal and purified enzyme systems investigated has been the (þ)-enantiomer (Hadley et al., 1994). The human enzymes demonstrated opposite selectivity with FMO1 forming mainly the (þ)-enantiomer and FMO3 forming the ()-enantiomer (Phillips et al., 1995). Procainamide, 39, is a type I antiarrhythmic agent used to treat several different atrial and ventricular dysrhythmias. This drug is rapidly absorbed in the body and almost half of the administered procainamide dose is eliminated unchanged in the urine. In vitro metabolism has shown that thirteen urinary procainamide metabolites are produced from incubations with cytochromes P450 and FMOs. The N-oxide metabolites, 39a, produced by hFMOs (Li et al., 2012) were indicated as possible causative agents of procainamide-induced systemic lupus erythematosus (SLE). In the case of FMO1 and FMO3 the measured Km values for procainamide were reported to be 342 and 543 mM, respectively (Li et al., 2012). Tamoxifen, 40, is an antiestrogenic currently used extensively for breast cancer therapy and as a prophylactic agent in healthy women who are considered to be at high risk of breast cancer. FMO1 is more potent than FMO3 in catalyzing the N-oxidation of tamoxifen. Previous data have indicated that tamoxifen may undergo oxidation and reduction in the liver in a cyclic fashion: hepatic FMO1 and FMO3 oxidize tamoxifen to tamoxifen N-oxide (40a) and cytochromes P450 reduce it back to tamoxifen (Parte and Kupfer, 2005). The in vitro kinetic parameters measured with purified FMO3 for tamoxifen are reported to be a Km of 6.4 mM, a Vmax value of 29 pmol min 1 mg 1 protein with a kcat of 1.13 min 1 (Bortolussi et al., 2021). 1.20.2.7.5.2 Primary amines Dapsone, 41, is an aniline derivative sulfone and an antimicrobial agent that exerts bacteriostatic action. It inhibits the synthesis of dihydrofolic acid by competing with para-aminobenzoic acid for the active site of dihydropteroate synthetase. This drug has been shown to be N-hydroxylated by hFMO, 41a. The product of the reaction can auto-oxidize to arylnitroso derivatives, which in turn bind to cellular proteins and can act as antigens/immunogens and result in protein haptenation (Vyas et al., 2006). 1.20.2.7.5.3 Sulfur-containing compounds Anethole dithiolethione, 42, (ADT, 5-(p-methoxyphenyl)-3H-1,2-dithiole-3-thione) is a drug used for its choleretic and sialogogic properties. It exhibits chemoprotective effects against cancer and various kinds of toxicity caused by some drugs and xenobiotics mainly due to its antioxidant properties. hFMOs are capable of forming the S-oxides (42a, 43a) of both the parental drug and the demethylated metabolite, 43, which is generated by the action of cytochromes P450 (Dulac et al., 2018). Another sulfur-containing drug is cimetidine, 44, a histamine H2-receptor antagonist used in the treatment of peptic ulcer disease and gastric hypersecretory syndromes. In humans, cimetidine is metabolized principally to cimetidine S-oxide, 44a. FMO1 produces mainly the () cimetidine S-oxide enantiomer whereas FMO3 generates mainly the (þ) cimetidine S-oxide enantiomer (Hai et al., 2009). For FMO1 the measured Km values for cimetidine were reported to be very similar and in the millimolar range, 4.31 and 4.56 mM for the (þ) and () isomer, respectively (Hai et al., 2009). In the case of FMO3 the reported Km for cimetidine is also in the millimolar range, 4 mM, with a Vmax of 15 nmol min 1 nmol 1 protein (Overby et al., 1997). Esonarimod, (KE-298) is an antirheumatic drug that suppresses arthritis in various animal models by inhibiting the production of inflammatory cytokines. Esonarimod initially undergoes metabolic transformation by deacetylation followed by a subsequent methylation reaction. hFMOs are able to convert the methylated form of esonarimod, 45, into the S-oxide, 45a (Ohmi et al., 2003). S-Methyl esonarimod has been reported to be metabolized also by hFMO5 and for this isoform the following kinetic parameters were measured: Km of 2.71 mM with a Vmax of 0.37 nmol min 1 mg 1 protein (Ohmi et al., 2003). Ethionamide, 46, is a pro-drug used for the treatment of tuberculosis. Ethionamide can be converted to the corresponding Soxide, 46a, active metabolite by either Mycobacterium tuberculosis or by hFMO. In the case of the bacterial conversion the S-oxide product can be further metabolized by the endogenous FMO to the toxic sulfonic acid product, whereas in the case of human FMOs the second S-oxidation occurs with much lower rates (Henderson et al., 2008). Ethionamide seems to be a better substrate of FMO1 than FMO3 with lower Km and higher kcat values. The measured kinetic parameters for these two isoforms are Km values of 105 mM, 336 mM with corresponding kcat values of 89.9 and 58.4 min 1, respectively (Henderson et al., 2008). In addition, the other two isoforms, FMO2 and FMO5, are also able to perform this reaction (Henderson et al., 2008). Methimazole, 47, is a drug used in the treatment of hyperthyroidism. Approximately 2% of women and 0.2% of men are affected by hyperthyroidism. Methimazole, like carbimazole and propylthiouracil acts by blocking thyroid hormone synthesis. A secondary action is thought to be the control of thyrotoxicosis by immune suppression (Abraham et al., 2005). Methimazole has been also considered as a good marker substrate of FMO activity. A very well-known spectrophotometric assay, where DTNB and DTT are also employed, allows the determination of FMO activity by monitoring the color disappearance at 412 nm (Dixit and Roche, 1984; Catucci et al., 2017). Methimazole is converted by hFMOs to methimazole S-oxide, 47a (Kim and Ziegler, 2000). For FMO1 and FMO3 reported kinetic parameters are Km of 5 and 12 mM with Vmax or kcat values of 18.09 nmol min 1 mg 1 protein and 59 min 1, respectively (Kim and Ziegler, 2000; Lattard et al., 2003). MK-0767 methyl sulfide, 48, is the metabolite of MK-07067 [()-5-[(2,4-dioxothiazolidin-5-yl)methyl]-2-methoxy-N-[[(4trifluoromethyl) phenyl]methyl]benzamide], a peroxisome proliferator-activated receptor a/g agonist. MK-07067 is first converted to the mercapto form by cytochrome P450 3A4 and subsequently converted to the methyl-mercapto form by two methyltransferases, TMT and thiopurine methyltransferase (TPMT), that are capable of catalyzing the S-methylation of xenobiotic compounds

528

Drug Metabolism: Other Phase I Enzymes

(Karanam et al., 2004). hFMOs have been reported to catalyze the S-oxidation of the mercapto metabolite of MK-0767, 48a (Karanam et al., 2004). Phospho-sulindac, 49, is phospho-version of a non-steroidal anti-inflammatory drug (NSAID) that was originally designed to reduce the gastrointestinal and renal toxicity that chronic use of NSAIDs may induce in patients. hFMOs have been reported to catalyze the S-oxidation, 49a, of phospho-sulindac (Xie et al., 2012). S-Methyl N,N-diethyldithiocarbamate, 50, is a metabolite of disulfiram, a drug used for the treatment of alcohol addiction as its metabolites decrease the metabolism of acetaldehyde, a product of ethanol metabolism. Accumulation of acetaldehyde results in the unpleasant effects of the “disulfiram-ethanol reaction,” which deters patients from consuming alcohol. Disulfiram is rapidly reduced in vivo to N,N-diethyldithiocarbamate and methylated to form S-methyl N,N-diethyldithiocarbamate (Glauser et al., 1993). The latter is a substrate of FMOs that catalyze its conversion to the S-oxide product, 50a, with calculated Km value of 15 mM and Vmax of 7.6 nmol min 1 nmol 1 protein (Pike et al., 2001). Sulfamethoxazole, 51, is a structural analog of para-aminobenzoic acid (PABA). It competes with PABA to bind to dihydropteroate synthetase and inhibits conversion of PABA and dihydropteroate diphosphate to dihydrofolic acid, or dihydrofolate. The inhibitory activity results in altered bacterial synthesis of folic acid (folate), ultimately leading to diminished bacterial growth and replication. The S-oxide product of the hFMO reaction, 51a, can auto-oxidize to arylnitroso derivatives, which in turn bind to cellular proteins and can act as antigens/immunogens and result in protein haptenation (Vyas et al., 2006). Sulindac sulfide, 52, is a metabolite derived from the administration of the drug, sulindac. Sulindac is a NSAID that contains a chiral sulfoxide moiety and it is normally administered clinically as a racemate. Upon administration the drug undergoes reduction in vivo to the active metabolite sulindac sulfide that exerts its activity by inhibiting cyclooxygenases. hFMOs perform stereoselective transformation of sulindac sulfide to R-sulindac sulfoxide, 52a (Hamman et al., 2000). In vitro enzyme assays with purified FMO3 have led to the determination of kinetic parameters for sulindac sulfide which are reported to be a Km of 15.6 mM, kcat of 0.56 min 1 and a Vmax value of 9.6 nmol min 1 mg 1 protein (Bortolussi et al., 2021). Tazarotenic acid, 53, is a metabolite produced by esterases that are able to convert the pro-drug tazarotene. Tazarotenic acid modulates the pathogenic factors of psoriasis by normalizing abnormal keratinocyte differentiation. The drug also has potent anti-hyperproliferative effects in skin and it decreases inflammation. Tazarotenic acid is transformed to the sulfoxide metabolite, 53a, by hFMOs (Attar et al., 2003). Thiacetazone, 54, is a second line drug for the treatment of tuberculosis. It is used for the treatment of patients infected with multidrug-resistant tuberculosis in developing countries. Thiacetazone is associated with gastrointestinal disturbances, hepatotoxicity and dermal hypersensitivity that can result in life-threatening skin reactions. EtaA is a bacterial FMO that oxidizes thiacetazone first to the intermediate sulfenic acid and then to either the sulfinic acid (54a) or the carbodiimide (54b), depending on the pH. The sulfenic acid or the carbodiimide can react with glutathione resulting in the formation of the parent drug or glutathione derivative. Since human FMOs are capable of forming the same metabolites they can contribute to the antitubercular activity and/or toxicity of thiacetazone (Qian and Ortiz de Montellano, 2006). 1.20.2.7.5.4 Piperazines As mentioned earlier, piperazines are compounds that consist of a six-membered ring containing two nitrogen atoms at opposite positions in the ring. Some of these compounds have already been discussed under drugs which are exclusively metabolized by hFMO3. In this section other piperazines are mentioned, which are substrates of both FMO3 as well as FMO1. EST64454, 55 (1-(4-(2-((1-(3,4-difluorophenyl)-1H-pyrazol-3-yl) methoxy) ethyl) piperazine-1-yl) ethanone) is a selective sigma-1 receptor (s1R) antagonist. It is currently being explored for pain management. hFMOs catalyze the N-oxidation of EST64454 yielding EST64454 N-oxide, 55a (Yeste et al., 2020). N-Deacetyl ketoconazole (DAK), 56, is the major metabolite of ketoconazole. Ketoconazole operates as an antifungal agent acting as inhibitor of lanosterol 14a-demethylase and a potent inhibitor of cytochrome P450 3A4. The parent drug, ketoconazole, is extensively used and has been evaluated in the context of hepatotoxicity. The reaction product generated by hFMOs, N-hydroxyDAK (56a), can be further converted by oxidation to a nitrone. These metabolites have also been reported as possible causes of observed hepatotoxicity (Rodriguez and Miranda, 2000). MK-0457 (VX-680, tozasertib), 57, is a pan-Aurora kinase inhibitor that is strongly active against Aurora kinase A with Kiapp of 0.6 nM in a cell-free assay. Its potency is reduced toward Aurora kinase B/Aurora kinase C with Kiapp of 4.6 and 18 nM, respectively (Harrington et al., 2004). Fms-related tyrosine kinase-3 (FLT-3) and BCR-ABL tyrosine kinase are also inhibited by tozasertib and both have a Ki of 30 nM (Cheetham et al., 2007). hFMOs convert tozasertib to tozasertib N-oxide, 57a (Ballard et al., 2007). In the case of FMO1 the reported Vmax is 1832 pmol min 1 mg 1 protein (Ballard et al., 2007). For FMO3 more kinetic parameters have been reported with a Km of 23.8 mM, a kcat of 9.3 min 1 and a Vmax value of 1919 pmol min 1 mg 1 protein (Ballard et al., 2007; Catucci et al., 2013). In addition, there are reports of FMO5 also performing this conversion reaction (Ballard et al., 2007). The last example of this group, olanzapine (OLA), 58, is an antipsychotic used in the treatment of schizophrenia and bipolar disorders that acts on dopamine and serotonin receptors. It works on dopamine D2 receptors in the mesolimbic pathway as an antagonist, blocking dopamine from having a potential action at the post-synaptic receptor. It binds loosely to the receptor and dissociates easily, allowing for normal dopamine neurotransmission. It has been reported to act similarly on serotonin 5-HT2A receptors in the frontal cortex as an antagonist. hFMOs have be demonstrated to catalyze the N-oxidation of olanzapine, 58a (Söderberg et al., 2013).

Drug Metabolism: Other Phase I Enzymes

529

1.20.2.7.5.5 Pyrrolidines The next set of drugs belongs to the group of pyrrolidines and three examples will be covered. In general, these molecules are also known as tetrahydropyrroles and are cyclic secondary amines and as such are converted to N-oxide products by FMOs. The first example of this group is cediranib, 59, (AZD2171) a potent and reversible small molecule vascular endothelial growth factor (VEGF) receptor tyrosine kinase (RTK) inhibitor. The inhibition of VEGF signaling leads to the inhibition of angiogenesis, lymph angiogenesis, neovascular survival and vascular permeability. Its use is currently being explored as an effective drug in patients with tumors exploiting the inhibition of angiogenesis and the normalization of tumor vasculature (Schulz-Utermoehl et al., 2010; Tang et al., 2017). hFMOs perform the N-oxidation of cediranib, resulting in cediranib N-oxide, 59a. In the specific case of FMO1, the following kinetic parameters have been reported; Km of 11.9 mM with a Vmax value of 1919 nmol min 1 mg 1 protein (Schulz-Utermoehl et al., 2010). The second example, methylenedioxypyrovalerone (MDPV), 60, is a pyrrolidine derivative of pyrovalerone, originally used for the management of chronic fatigue and lethargy but subsequently withdrawn from the market due to its associated abuse and dependency problems. It is a potent monoamine transporter blocker that increases the extracellular levels of monoamine neurotransmitters, impacting dopamine and norepinephrine facilitating the extracellular release and reuptake inhibition. Currently it is used as a recreational substance due to its psychostimulant properties. hFMOs convert MDPV to the corresponding N-oxide, 60a (Kim et al., 2016). The last example of this group of drugs is TG100435, 61, a Src kinase inhibitor. Src is involved in tumor progression and metastases and consequently Src inhibitors have a potential value in the treatment of various kinds of cancers. Both TG100435 and its major N-oxide metabolite have demonstrated anti-cancer activity in preclinical studies. Interestingly, the N-oxide metabolite (TG100855) (61a) produced by FMO can be reduced back to TG100435 by human cytochrome P450 reductase (Kousba et al., 2007). Kinetic parameters reported for the FMO3 reaction with this drug are Km of 30 mM with a Vmax of 154 pmol min 1 mg 1 protein (Kousba et al., 2007). 1.20.2.7.5.6 Others As mentioned previously, some of the drugs could not be grouped together under the different selected categories but due to their importance have been collected together under this section. The first of this group, nomifensine (8-amino-2-methyl-4-phenyl-1,2,3,4-tetrahydroisoquinoline maleate), 62, is an antidepressant that has been withdrawn from the market due to its major side effects including hemolytic anemia and liver toxicity. Nevertheless, the N-oxide metabolite formed by hFMO, 62a, does not lead to reactive metabolites that react with GSH and result in unwanted bioactivation (Yu et al., 2010). Vandetanib, 63, (Caprelsa, N-(4-bromo-2-fluorophenyl)-6-methoxy-7-[(1-methylpiperidin-4-yl)methoxy]quinazolin-4-amine) is an anticancer drug that acts as a tyrosine kinase inhibitor. Its administration reduces tumor cell-induced angiogenesis, tumor vessel permeability, and inhibits tumor growth and metastasis in mouse models of cancer. hFMOs have been shown to be responsible for the N-oxidation of vandenatib, 63a (Indra et al., 2020). The next compound within this group is L-selenomethionine (SeMet), 64, an amino acid used in cancer chemoprevention and as a possible anticancer agent. Cytochromes P450 cannot metabolize SeMet to the corresponding oxide, indeed this reaction is only carried out by hFMOs. The product of the oxidative reaction, selenomethionine selenoxide (SeOMet), 64a, is highly reactive and gluthathione can reduce it back to SeMet (Hai et al., 2010). This mechanism involves only SeOMet and has not been reported for methionine or methionine sulfoxide and could support the cancer preventive activity of SeMet. Another example is voriconazole, 65, a second-generation triazole with potent antifungal activity against a broad spectrum of fungal pathogens. hFMOs perform the N-oxidation of voriconazole, 65a (Yanni et al., 2008). In the case of FMO1 the measured Km was reported to be 3 mM with a Vmax of 0.025 pmol min 1 mg 1 protein (Yanni et al., 2008). The same kinetic parameters reported for FMO3 were Km value of 3.44 mM with a Vmax of 0.044 pmol min 1 mg 1 protein (Yanni et al., 2008). Another drug, xanomeline, 66, is a selective M1-muscarinic agonist that was developed for the symptomatic treatment of Alzheimer’s disease. It has been shown to be extensively metabolized to the N-oxide form, 66a (Ring et al., 1999). However, further drug development of this compound was discontinued due to its gastrointestinal side effects in clinical trials. The penultimate example, F18, 67, is an analog of calanolide A (a plant derived non-nucleoside reverse transcriptase inhibitor) that has demonstrated a half-maximal effective concentration (EC50) of 7.4 nM. This compound is a promising antiretroviral candidate for the treatment of HIV-1-infected patients given that it has demonstrated a high potency against the Y181C single mutation of HIV-1. Its oxidative dehalogenation was demonstrated only with human liver microsomes (HLM) because experiments carried out with recombinant FMO1, FMO2 or FMO5 failed in producing the reaction product. Conversely, the reduction of the carbonyl group, resulting in metabolites M3-1 (67a) or M3-2 (67b), was found to be carried out by FMO1, FMO3, FMO5. Interestingly for this substrate FMO is believed to perform a reduction or an oxidative dehalogenation (Wu et al., 2017), not a typical FMO reaction. Compound 1 N1-substituted-6-arylthiouracil, 68, is an irreversible inhibitor of the hemoprotein myeloperoxidase with high selectivity also for thyroid peroxidase (TPO) and cytochrome P450 enzymes. They form covalent adducts to the heme prosthetic group through an oxidized sulfur species (presumably a thiol radical). hFMOs are able to perform the oxidative desulfurization of the parental compound in a postulated 3-step mechanism leading to the final product, 68a (Eng et al., 2016).

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Drug Metabolism: Other Phase I Enzymes

All the chemical structures of the common substrates and the corresponding metabolites produced by hFMO1 and hFMO3 within this section are shown in Fig. 7 with the kinetic parameters summarized in Table 1.

1.20.2.8

Effect of genetic variants on drug metabolism

In an earlier section on genetic variants of FMO, the different polymorphic variants of the five hFMO isoforms were introduced but their effect on drug metabolism was not addressed. As FMO3 is the major hepatic drug metabolizing enzyme of this family, the

Fig. 7 Chemical structures of the common hFMO1 and hFMO3 substrates. The numbered compounds are referred to in the text. By authors using “ChemSketch”. https://www.acdlabs.com/contact/inforequest.php.

Drug Metabolism: Other Phase I Enzymes

Fig. 7

531

(continued).

single nucleotide polymorphisms relevant in the biotransformations carried out by this isoform will be discussed in more detail. However, in addition to drugs, this isoform is also important due to its role in the conversion of trimethylamine to trimethylamine N-oxide. This substrate is not a drug, but high levels of trimethylamine in body fluids are symptomatic of a rare disease called trimethylaminuria. Individuals affected by trimethylaminuria typically carry a non-common SNP in the FMO3 gene that causes a dramatic decrease in the activity of the enzyme. The clinical manifestation of this disease is the malodor typical of rotten fish that is perceived in the urine and sweat of the patients. Interestingly elevated plasma concentrations of the metabolite, trimethylamine N-oxide (TMAO), has been recently associated with cardiovascular disease and diabetes but the cell biology of the FMO3-

532

Fig. 7

Drug Metabolism: Other Phase I Enzymes

(continued).

TMAO axis is outside the scope of this present chapter. On the contrary, from a drug metabolism perspective an increasing number of molecules are being observed in connection with the common single nucleotide polymorphisms of hFMO3. The effect of polymorphic variants of FMOs on drug metabolism has been reviewed several years ago by Koukouritaki and Hines (2005). More recent studies from our group and others have focused more on the effect of the common polymorphic variants of hFMO3 including V257M, E158K and E308G. In one report, clomiphene was studied using the recombinant forms of wild type protein and the latter three common polymorphic variants (Catucci et al., 2018). The data obtained clearly indicated that V257M and E308G have higher turnover numbers compared to the WT enzyme. Moreover, when kcat/Km ratios were compared,

Drug Metabolism: Other Phase I Enzymes

Fig. 7

533

(continued).

these two variants exhibited higher catalytic efficiencies resulting in higher overall clearance (Catucci et al., 2018). The same study also evaluated the metabolism of GSK5182 and tamoxifen. Only in the case of tamoxifen was an altered metabolism observed. V257M showed a diminished clearance for tamoxifen whereas a marked increase was recorded for E158K (Catucci et al., 2018). In another report, danusertib and tozasertib were studied using the recombinant forms and comparing the catalytic activity of WT and the V257M hFMO3 variant. The conversion of tozasertib and danusertib to their corresponding metabolites demonstrated

534

Fig. 7

Drug Metabolism: Other Phase I Enzymes

(continued).

significant differences. In the case of tozasertib, the V257M variant showed a catalytic efficiency similar to that of the wild-type, whereas for danusertib, V257M showed a 3.4  decrease in catalytic efficiency (Catucci et al., 2013). In the case of sulindac, this drug was found to be metabolized at a slower rate by the double-variant E158K-E308G (Hamman et al., 2000). The marked reduction of activity against sulindac was shown to be responsible for the higher and more persistent concentrations of the active drug that are linked to positive clinical outcomes and therefore highly beneficial. The same variant has been reported as associated to a decreased N-oxygenation of ranitidine (Park et al., 2002), itopride (Zhou et al., 2014), fenthion (Furnes and Schlenk, 2004) and olanzapine (Cashman et al., 2008).

Drug Metabolism: Other Phase I Enzymes

Fig. 7

535

(continued).

Finally, the effect of polymorphic variants of FMO3 and FMO1 on the metabolism of several selected drugs and xenobiotics are summarized in Table 2.

1.20.3

Aldehyde oxidase

Aldehyde oxidase (AOX) is a cytosolic enzyme that is present in several tissues in mammals. It works with FAD as cofactor and the molybdenum cofactor, molybdopterin, also known as Moco (Fig. 8). Due to its broad substrate specificity, AOX catalyzes the oxidation of numerous substrates that are not necessarily aldehydes. Humans contain only one active isoenzyme that is expressed at high levels in the liver. As in the case of hFMOs very little is known about the physiological function of this enzyme, but a key role in drug metabolism has emerged in recent years (Cheshmazar et al., 2019). The crystal structure of human AOX1 has been resolved in complex with both a substrate (phthalazine) and an inhibitor (thioridazine) (Coelho et al., 2015) demonstrating different sites

536

Fig. 7

Drug Metabolism: Other Phase I Enzymes

(continued).

of interaction between them. Overall AOX1 is a homodimeric protein made up of two 150 kDa subunits. The enzyme can be divided into three different domains containing: (1) two iron-sulfur (2Fe-2S) redox centers, (2) the FAD binding domain, and (3) the Moco cofactor binding site and the substrate binding site. Human AOX1 Moco is composed by three sulfurs and two oxygens (Coelho et al., 2015). The two 2Fe-2S clusters are located in two different parts of the protein structure; the FeSI is proximal to Moco, FeSII is proximal to FAD. As a consequence, the two iron centers mediate the electron transfer process that leads to oxygen reduction leading to superoxide anions and finally to hydrogen peroxide formation.

Drug Metabolism: Other Phase I Enzymes

Fig. 7

(continued).

537

538

Drug Metabolism: Other Phase I Enzymes

Table 1

Kinetic data for human FMO drug metabolizing activity.

Substrate

Isoform Km

kcat

Vmax

References

ABT-126 ABT-126 Albendazole Amphetamine

FMO1 FMO3 FMO3 FMO3

12.5 mM 89.4 mM 9.6 mM 44.2 mM () product 11 mM (þ) product FMO1 60 mM FMO3 80 mM FMO1 11.9 mM

n/a n/a n/a n/a

Liu et al. (2018) Liu et al. (2018) Rawden et al. (2000) Cashman et al. (1999b)

46 min 1 36 min 1 n/a

2610 pmol min 1 mg 1 protein 2870 pmol min 1 mg 1 protein 1103 pmol min 1 mg 1 protein 6.5 nmol min 1 mg 1 protein () product 1.1 nmol min 1 mg 1 protein (þ) product n/a n/a 8.2 nmol Eq/(min  mg) protein

n/a

n/a

n/a

n/a

Hai et al. (2009)

Cimetidine Clomiphene Clozapine Danusertib Deprenyl Disulfiram metabolite DMXAA Ethionamide Ethionamide Ethionamide Fenthion

FMO1 (þ) isomer 4.31 mM () isomer 4.56 mM FMO3 4 mM FMO3 18.3 mM FMO3 324 mM FMO3 57.3 mM FMO3 314 mM FMO1 15 mM FMO3 9.7 mM FMO1 105 mM FMO2 261 mM FMO3 336 mM FMO1 340 mM

Lang and Rettie (2000) Lang and Rettie (2000) Schulz-Utermoehl et al. (2010) Hai et al. (2009)

n/a 0.07 min 1 n/a 9.9 min 1 1.4 min 1 n/a n/a 89.9 min 1 48.3 min 1 58.4 min 1 n/a

15 nmol product min 1 nmol 1 n/a 12.8 nmol min 1 nmol 1 protein 0.57 nmol min 1 mg 1 protein n/a 7.6 nmol min 1 nmol 1 protein 0.017 nmol min 1 mg 1 protein n/a n/a n/a 93 nmol min 1 nmol 1 protein

Fenthion

FMO3 145 mM

n/a

11 nmol min 1 nmol 1 protein

GSK5182 Hypotaurine Itopride Itopride K11777 L-775,606

FMO3 FMO1 FMO1 FMO3 FMO3 FMO3

9.82 mM 4.1 mM n/a n/a 14 mM 115 mM (isomer 1) 118 mM (isomer 2) FMO3 170 mM FMO1 5 mM

2.22 min 1 55 min 1 n/a n/a n/a n/a

Methimazole MK-0457 MK-0457 MK-0457 MK-0767 N-(3R)-1-azabicyclo[2.2.2]oct-3-ylfuro[2,3-c] pyridine-5-carboxamide N-(3R)-1-azabicyclo[2.2.2]oct-3-ylfuro[2,3-c] pyridine-5-carboxamide Nicotine

FMO3 FMO1 FMO3 FMO5 FMO1 FMO1

59 min 1 n/a n/a n/a n/a n/a

n/a 7.5 mM min 1 3.64 nmol min 1 mg 1 protein 4.10 nmol min 1 mg 1 protein 3460 pmol min 1 mg 1 protein 729 nmol min 1 mg 1 protein (isomer 1) 340 nmol min 1 mg 1 protein (isomer 2) 72.5 pmol min 1 mg 1 protein 18.09 nmol substrate oxidized/ min mg 1 n/a 1832 pmol min 1 mg 1 protein 1919 pmol min 1 mg 1 protein 110 pmol min 1 mg 1 protein 99% enantiomeric excess 1.81 pmol min 1 mg 1 protein

Overby et al. (1997) Catucci et al. (2018) Tugnait et al. (1997) Catucci et al. (2013) Catucci et al. (2017) Pike et al. (2001) Zhou et al. (2002) Henderson et al. (2008) Henderson et al. (2008) Henderson et al. (2008) Furnes and Schlenk (2004) Furnes and Schlenk (2004) Catucci et al. (2018) Veeravalli et al. (2020) Mushiroda et al. (2000) Mushiroda et al. (2000) Jacobsen et al. (2000) Prueksaritanont et al. (2000)

FMO3 n/a

n/a

0.204 pmol min 1 mg 1 protein

Shaffer et al. (2007)

FMO1 0.75 mM

n/a

11 pmol min 1 mg 1 protein

Nicotine

FMO2 4.5 mM

n/a

6.5 pmol min 1 mg 1 protein

Nicotine

FMO3 0.70 mM

n/a

11 pmol min 1 mg 1 protein

Nicotine

FMO5 >5 mM

n/a

n/a

Procainamide Procainamide

FMO1 342 mM FMO3 543 mM

n/a n/a

n/a n/a

Perez-Paramo et (2019) Perez-Paramo et (2019) Perez-Paramo et (2019) Perez-Paramo et (2019) Li et al. (2012) Li et al. (2012)

Benzydamine Benzydamine Cediranib Cimetidine Cimetidine

Loxapine Methimazole

12 mM n/a n/a n/a n/a n/a

n/a n/a

Luo et al. (2011) Kim and Ziegler (2000) Lattard et al. (2003) Ballard et al. (2007) Ballard et al. (2007) Ballard et al. (2007) Karanam et al. (2004) Shaffer et al. (2007)

al. al. al. al.

Drug Metabolism: Other Phase I Enzymes Table 1

539

Kinetic data for human FMO drug metabolizing activity.dcont'd

Substrate

Isoform Km

kcat

Vmax

References

Pyrazoloacridine Ranitidine S-Methyl esonarimod Sulindac sulfide Tamoxifen Teneligliptin Teneligliptin TG100435 Voriconazole Voriconazole

FMO3 FMO3 FMO5 FMO3 FMO3 FMO1 FMO3 FMO3 FMO1 FMO3

n/a n/a n/a 0.56 min 1 1.13 min 1 n/a n/a n/a n/a n/a

1.32 nmol min 1 mg 1 protein 17 nmol min 1 nmol 1 protein 0.37 nmol min 1 mg 1 protein 9.6 nmol min 1 mg 1 protein 29 nmol min 1 mg 1 protein 57.6 nmol min 1 nmol 1 protein 52.1 nmol min 1 nmol 1 protein 154 pmol min 1 mg 1 protein 0.025 pmol min 1 pmol 1 protein 0.044 pmol min 1 pmol 1 protein

Reid et al. (2004) Overby et al. (1997) Ohmi et al. (2003) Bortolussi et al. (2021) Bortolussi et al. (2021) Nakamaru et al. (2014) Nakamaru et al. (2014) Kousba et al. (2007) Yanni et al. (2008) Yanni et al. (2008)

147 mM 2 mM 2.71 mM 15.6 mM 6.40 mM 221.5 126.1 30 mM 3 mM 3.4 mM

By authors.

These enzymes are also capable of oxidizing nitro/nitroso compounds and aromatic N-heterocycles (Pryde et al., 2010). The main external source of aldehydes arises from the Phase-I drug metabolism exerted by cytochrome P450 enzymes. Indeed, AOXs can readily convert aldehyde intermediates generated by drug-metabolizing enzymes from alcohols or amines. They can also catalyze the reduction of exogenous and endogenous compounds such as molecules containing N-oxides, sulfoxides or nitro groups (Coelho et al., 2015). In vitro data have demonstrated that AOXs can be efficiently inhibited by many different drugs and compounds that are used in the treatment of different pathologies. For example, well-known anti-depressants such as amitriptyline and clomipramine inhibit AOX (Barr and Jones, 2011; Cheshmazar et al., 2019). Moreover, calcium channel blockers such as felodipine and amlodipine are also known to inhibit AOX (Obach et al., 2004). Since many other inhibitors of AOX are available, with varying inhibitory activity, drug-drug interactions should be carefully evaluated especially with regard to the type of inhibition that is obtained. Also, in the case of this class of enzymes their contribution to drug metabolism might be underestimated because most studies are carried out using microsomal fractions that do not contain the soluble cytosolic fraction that contains AOX. This enzyme is unstable and enzyme preparations can yield significant changes in terms of activity if they are not fresh or if they are not properly handled. Hydrophobic substrates are often solubilized in methanol solutions and this has been also shown to lead to enzyme inhibition. Detailed enzymatic characterization (Fig. 8) is also hampered by limitations due to bacterial production of the recombinant form of the enzyme that could lack optimal holoprotein content leading to diminished measured activity. In terms of drug metabolism by this enzyme, a selected group of drugs is discussed. The first drug of interest is 6mercaptorpurine, an antimetabolite antineoplastic agent with immunosuppressant properties. It works by interfering with nucleic acid synthesis by inhibiting purine metabolism and is used, usually in combination with other drugs, in the treatment of or in remission maintenance programs for leukemia. It is converted first to 6-thioxanthine by xanthine oxidase, 69, and then to 6thiurinc acid by xanthine oxidase and AOX, 69a (Rashidi et al., 2007). The next drug, acyclovir, is a nucleotide antiviral analog used in the treatment of herpes simplex, Varicella zoster, herpes zoster, herpes labialis and acute herpetic keratitis. It has been demonstrated that 6-deoxyacyclovir, 70, a potential prodrug of acyclovir, is oxidized by AOX resulting in 8-hydroxy-6-deoxyacyclovir, 70a. A second oxidation step is required to transform the compound to acyclovir, 70b, by AOX (Krenitsky et al., 1984). Azapetine is a benzazepine derivative that works as an a-1 adrenoceptor antagonist. It is a potent arterial vasodilator used in the treatment of peripheral vascular diseases. The drug is converted first to the iminium ion by cytochrome P450, 71, and then to a lactam metabolite by AOX, 71a (Kitamura et al., 2006). Brimonidine, 72, an a-adrenergic agonist and 2-imidazoline derivative, is an a-2 adrenergic receptor agonist and it binds primarily at a-2 adrenoceptors over a-1 receptors. Ophthalmically, brimonidine is used to lower intraocular pressure by reducing aqueous humor production and increasing uveoscleral outflow. AOX has been shown to convert brimonidine to 2-(72a), 3-(72b) and 2,3-brimonidine (72c) (Ni et al., 2007). Caffeine, 73, is a central nervous system stimulant of the methylxanthine class of drugs and widely used in tea and coffee. As a drug it is used in certain respiratory conditions of the premature newborn, pain relief, and to combat drowsiness. Many years ago, Relling and coworkers demonstrated that it can be oxidized by AOX, 73a (Relling et al., 1992). Citalopram belongs to a class of antidepressant agents known as selective serotonin-reuptake inhibitors (SSRIs) and is widely used to treat the symptoms of depression. The drug is N-demethylated by cytochrome P450 yielding the substrate of AOX, 74, which subsequently performs the oxidation of this molecule, 74a (Rochat et al., 1998). Cyclobenzaprine is a 5-HT2 receptor antagonist used for muscle spasms from musculoskeletal conditions of sudden onset. It acts by relieving muscle spasm through actions on the central nervous system, i.e., a centrally acting muscle relaxant. AOX is able to reduce the cyclobenzaprine N-oxide metabolite, 75, to the original cyclobenzaprine, 75a (Kitamura and Tatsumi, 1984). In another example, decernotinib, 76, a Janus kinase 3 inhibitor that has been studied in patients with rheumatoid arthritis, has been shown to be hydroxylated by AOX, 76a (Zetterberg et al., 2016).

540

Drug Metabolism: Other Phase I Enzymes

Table 2

Summary of the kinetic parameters of the activity of different human FMO variants.

Substrate

Isoform Variant

Km

kcat

Vmax

References

Clomiphene

FMO3

FMO3

GSK5182

FMO3

Ranitidine

FMO3

Fenthion

FMO1

Fenthion

FMO3

Methimazole

FMO1

Methimazole

FMO3

Imipramine

FMO1

0.07 min 1 0.30 min 1 0.06 min 1 0.25 min 1 9.9 min 1 3.2 min 1 2.22 min 1 5.69 min 1 1.16 min 1 1.2 min 1 27 min 1 13 min 1 13 min 1 3 min 1 93 nmol min 1 nmol 1 protein 125 nmol min 1 nmol 1 protein 99 nmol min 1 nmol 1 protein 149 nmol min 1 nmol 1 protein 51 nmol min 1 nmol 1 protein 11 nmol min 1 nmol 1 protein 10 nmol min 1 nmol 1 protein 64 nmol min 1 nmol 1 protein 104 nmol min 1 nmol 1 protein 71 nmol min 1 nmol 1 protein 113 nmol min 1 nmol 1 protein 59 nmol min 1 nmol 1 protein 27 nmol min 1 nmol 1 protein 48 nmol min 1 nmol 1 protein 15 nmol min 1 nmol 1 protein 11 nmol min 1 nmol 1 protein 51 nmol min 1 nmol 1 protein 66 nmol min 1 nmol 1 protein 48 nmol min 1 nmol 1 protein 71 nmol min 1 nmol 1 protein 25 nmol min 1 nmol 1 protein 0.56 min 1 0.41 min 1 0.25 min 1 0.37 min 1 1.13 min 1 0.59 min 1 0.45 min 1 0.37 min 1 9.3 min 1 4.3 min 1

n/a n/a n/a n/a 0.57 nmol min 1 mg 1 protein 0.18 nmol min 1 mg 1 protein n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 9.6 nmol min 1 mg 1 7.1 nmol min 1 mg 1 4.4 nmol min 1 mg 1 6.4 nmol min 1 mg 1 29 nmol min 1 mg 1 15.4 nmol min 1 mg 1 11.3 nmol min 1 mg 1 9.8 nmol min 1 mg 1 0.54 nmol min 1 mg 1 0.25 nmol min 1 mg 1

Catucci et al. (2018)

Danusertib

18.3 mM 33.2 mM 20.46 mM 44.4 mM 57.3 mM 60.1 mM 9.82 mM 28.5 mM 4.57 mM 5.87 mM 3 mM 1 mM 1 mM 0.7 mM 340 mM 320 mM 351 mM 240 mM 300 mM 145 mM 150 mM 7 mM 14 mM 7 mM 16 mM 12 mM 10 mM 15 mM 17 mM 12 mM 14 mM 15 mM 20 mM 14 mM 16 mM 15.6 mM 14.3 mM 15.1 mM 10.1 mM 6.4 mM 8.1 mM 1.56 mM 2.5 mM 23.8 mM 12.9 mM

Sulindac sulfide FMO3

Tamoxifen

FMO3

Tozasertib

FMO3

WT V257M E308G E158K WT V257M WT V257M E308G E158K WT E158K E308G E158K/E308G WT H79Q I303V I303T R502X WT D132H WT H79Q I303V I303T WT E158K D132H E308G E158K/E308G WT H79Q I303V I303T R502X WT V257M E158K E308G WT V257M E158K E308G WT V257M

Catucci et al. (2013) Catucci et al. (2018)

Park et al. (2002)

Furnes and Schlenk (2004)

Furnes and Schlenk (2004) Furnes and Schlenk (2004)

Lattard et al. (2003)

Furnes and Schlenk (2004)

Bortolussi et al. (2021)

Bortolussi et al. (2021)

Catucci et al. (2013)

By authors.

Famciclovir is a guanine analog used to treat herpes virus infections. It is most commonly used to treat herpes zoster (shingles). Famciclovir is a prodrug of penciclovir with higher oral bioavailability that is initially converted to 6-deoxypenciclovir, 77, by an esterase and finally to penciclovir, 77a, by AOX (Rashidi et al., 1997). Fenthion is an organothiophosphate insecticide, avicide, and acaricide that works by inhibiting choloinesterases. This insecticide can be oxidized by cytochrome P450 or hFMO1 to the sulfoxide form, 78, and AOX is able to convert it back to fenthion, 78a (Kitamura et al., 2003). Another metabolite of hFMO1, imipramine-N-oxide, 79, can be reduced by AOX to the original imipramine parent drug, 79a (Kitamura et al., 2001).

Drug Metabolism: Other Phase I Enzymes

541

Fig. 8 Catalytic mechanism of aldehyde oxidase. Modified from Cheshmazar N, Dastmalchi S, Terao M, Garattini E, and Hamzeh-Mivehroud M (2019) Aldehyde oxidase at the crossroad of metabolism and preclinical screening. Drug Metabolism Reviews 51: 428–452.

Lenvatinib, 80, is a receptor tyrosine kinase (RTK) inhibitor that inhibits the kinase activities of vascular endothelial growth factor (VEGF) receptors VEGFR1 (FLT1), VEGFR2 (KDR), and VEGFR3 (FLT4). It also inhibits other RTKs that have been implicated in pathogenic angiogenesis, tumor growth, and cancer progression in addition to their normal cellular functions, including fibroblast growth factor (FGF) receptors FGFR1, 2, 3, and 4, the platelet derived growth factor receptor a (PDGFRa), KIT, and RET. Lenvatinib is used in the treatment of patients with locally recurrent or metastatic, progressive, radioactive iodine (RAI)-refractory differentiated thyroid cancer. AOX is able to oxidize this compound, 80a, or carry out the oxidation of a metabolite generated by cytochrome P450, 80c, that results in a second oxidation product of AOX, 80d (Inoue et al., 2014). The reported Km for lenvatinib is 164.5 mM with a Vmax value of 73 pmol min 1 mg 1 protein (Inoue et al., 2014). Methotrexate, 81, is a folate derivative that acts by inhibiting several enzymes responsible for nucleotide synthesis. The inhibition activity leads to suppression of inflammation as well as prevention of cell division. This drug is used in the treatment of arthritis and also to control cell division in neoplastic diseases such as breast cancer and non-Hodgkin’s lymphoma. AOX is responsible for the hydroxylation of methotrexate resulting in 7-hydroxymethotrexate, 81a (Choughule et al., 2015) with a reported Km of 2792 mM and a Vmax of 40 pmol min 1 mg 1 protein (Benowitz et al., 2009). Nicotine (also a substrate of FMOs) is a chiral alkaloid that is naturally produced in plants and is widely used recreationally as a stimulant and anxiolytic. As a pharmaceutical drug, it is used for smoking cessation to relieve withdrawal symptoms. This drug is initially converted to the iminium ion by cytochrome P450, 82, and subsequently oxidized by AOX to cotinine, 82a (Benowitz et al., 2009). Prolintane is an amphetamine-related central nervous system stimulant and norepinephrine-dopamine reuptake inhibitor. It is used for the treatment of narcolepsy and attention deficit hyperactivity disorder. Again in this case, the drug is transformed by cytochrome P450 to the iminium ion, 83, and then converted to oxoprolintane by AOX, 83a (Whittlesea and Gorrod, 1993). Sulindac, 84, also a substrate of FMOs, can be reduced by AOX to the prodrug sulindac sulfide, 84a (Kitamura et al., 2001). Tolbutamide is an oral antihyperglycemic agent used in the treatment of non-insulin-dependent diabetes mellitus (NIDDM). Its structure resembles acetohexamide, chlorpropamide and tolazamide, and belongs to the sulfonylurea class of insulin secretagogues, which act by stimulating b cells of the pancreas to release insulin. The action of sulfonylureas is exerted by increasing both basal insulin secretion and meal-stimulated insulin release. The substrate of AOX is the aldehyde intermediate, 85, generated by two sequential biotransformations of tolbutamide mediated by cytochrome P450 or alcohol dehydrogenase. The product of the reaction carried out by AOX is the oxidized carboxytolbutamide, 85a (McDaniel, 1969). Zaleplon, 86, is a sedative/hypnotic drug, mainly used for the management of insomnia. It exerts its action by interacting with the GABA receptor complex and shares some of the pharmacological properties of the benzodiazepines. It is oxidized by AOX to 5oxo-zaleplon, 86a (Lake et al., 2002) with a reported Km in the range of 33–144 mM and a Vmax of 57–117 pmol min 1 mg 1 protein (Lake et al., 2002). Zebularine, 87, is a cytidine analog, lacking the amino group normally found at the 4-position of the cytosine base, that displays anti-tumor properties. It acts as a transition state analog inhibitor of cytidine deaminase by binding to the active site as covalent hydrates. It was also found to inhibit DNA methylation and tumor growth both in vitro and in vivo. It is oxidized by AOX to uridine, 87a, with measured kinetic parameters Km of 7.3 mM (male) or 8.4 mM (female) with the Vmax values of 2.1 nmol min 1 mg 1 protein (male) or 2.7 nmol min 1 mg 1 protein (female) (Klecker et al., 2006).

542

Drug Metabolism: Other Phase I Enzymes

Ziprasidone, 88, is an atypical antipsychotic used in the management of schizophrenia and bipolar disorder. It is a dopamine and 5-HT2A receptor antagonist and AOX is involved in one of the three main routes of its metabolism where it is transformed into dihydroziprasidone, 88a, via a reduction reaction (Beedham et al., 2003). Zonisamide, 89, is an antiseizure sulfonamide drug and is structurally unrelated to other antiseizure agents. The mechanism of its action is unknown but it is thought to lead to the suppression of neuronal hypersynchronization. AOX has been reported to carry out the reduction of zonisamide to 2-sulfamoylacetyl phenol, 89a (Kitamura et al., 2001). The chemical structures of the drug substrates of AOX and their corresponding metabolites are shown in Fig. 9. In addition, the very few kinetic parameters available for some of the above-mentioned drugs are summarized in Table 3.

1.20.4

Aldehyde dehydrogeneases

Aldehyde dehydrogenases (ALDHs) can be found in all forms of life ranging from bacteria to humans (Ahmed Laskar and Younus, 2019). The catalytic mechanism of these enzymes (Fig. 10) can be described according to the two different reactions that they carry out. For the NADþ/NADPþ-dependent dehydrogenase activity the following steps are necessary: (1) a basic glutamate activates the catalytic cysteine through water-mediated proton abstraction; (2) the activated cysteine performs a nucleophilic attack on the carbonyl carbon of the aldehyde; (3) a thiohemiacetal intermediate is formed with concomitant hydride transfer to NAD(P)þ; (4) The thioester intermediate hydrolyzes releasing carboxylic acid and the reduced cofactor; and finally (5) oxidized cofactor regenerates the system (Mukerjee and Pietruszko, 1992; Koppaka et al., 2012). In the case of esterase activity of these enzymes, the same amino acids are responsible for catalysis but in the absence of an external cofactor. Water-mediated abstraction of a proton by a glutamate residue activates the cysteine residue leading to the formation of the thioloate ion, that subsequently attacks the carbonyl carbon of the ester resulting in an oxyanion intermediate and thioacyl-enzyme complex formation and the release of the alcoholic/phenolic group. The next step is a nucleophilic attack by the glutamate-activated water molecule at the carbonyl carbon in the thioacyl-enzyme complex leading to the formation of a tetrahedral intermediate. Finally, the rearrangement of the intermediate leads to the release of carboxylic acid and the free enzyme. ADLHs are classified into different families as well as classes depending on either their sequence composition or their affinity toward acetaldehyde as substrate. Human ALDH genes are sub-divided into 11 families: ALDH 1–9, ALDH 16 and ALDH 18 (Ahmed Laskar and Younus, 2019). Class 1 ALDHs (ALDH1) are cytosolic homotetrameric enzymes made up of 54 kDa subunits and exhibit a low Km for acetaldehyde (30 mM). They are expressed in many different tissues including liver, retina, brain and stomach. ALDH1 transforms all-trans-9-cis-retinal into retinoic acid (Yoshida et al., 1992) and it is also involved in the detoxification of aldophosphamide in the liver (Jelski and Szmitkowski, 2008). Class 2 ALDHs (ALDH2) are mitochondrial tetrameric enzymes made up of 54 kDa subunits expressed in many different tissues including kidney, heart, lung, brain with the highest expression level in the liver (Perez-Miller et al., 2010). They have high affinity for acetaldehyde (3 mM) and therefore are the main enzyme involved in the elimination of this toxic compound. Class 3 ALDHs (ALDH3) are dimeric cytosolic enzymes made up of 54 kDa subunits (Agarwal et al., 1993) expressed in many different tissues including lungs, stomach, cornea, esophagus, kidney, brain and others (Ahmed Laskar and Younus, 2019). Since their Km for acetaldehyde is relatively high (83 mM), they are mainly involved in the metabolism and detoxification of long/medium chain aliphatic aldehydes and aromatic aldehydes. Class 4 ALDHs (ALDH4) are mitochondrial and microsomal dimeric enzymes made up of 54 kDa subunits expressed mainly in the liver. Their Km for acetaldehyde is high (2–5 mM), so they are mainly involved in the metabolism and detoxification of aldehydes generated both endogenously and exogenously as in the case of drugs, pollutants, amines and lipid peroxidation products. Aldehydes can readily react with different molecules resulting in the formation of adducts or in the disruption of the structures of biological macromolecules. Moreover, they can cause oxidative stress, cytotoxicity or carcinogenicity (Lindahl, 1992). Therefore the correct functioning of hALDHs is essential to avoid changes to normal metabolism and cellular homeostasis. A higher risk of cancer development was demonstrated to be linked to reduced hALDH activity (Lindahl, 1992); in contrast, higher hALDH activity or expression can be protective against cancer progression (Muzio et al., 2012). Nevertheless, the protection mechanism can also have a negative impact on cancer treatment in the case of drug resistance. Oxazaphosphorine pro-drugs that are commonly used in the treatment of different cancers are metabolized by cytochromes P450 producing aldophosphamides that become spontaneously phosphoramide mustards, toxic acrolein and chloroacetaldehyde through ß-elimination (Ahmed Laskar and Younus, 2019). Phosphoramide mustards are the active metabolites that by cross-linking with DNA are intended to cause cancer cell death. hALDH1A1, hALDH3A1 and hALDH5A1 are involved in the transformation of the aldophosphamide intermediate into carboxyphosphamide or conversion of acrolein into acrylic acid leading to the inactivation of the pro-drugs. Inactivation of ALDH1A1 or ALDH3A1 can in this case lead to sensitization of different cancer cell types to anticancer drugs (Ahmed Laskar and Younus, 2019). A key enzyme involved in cancer is salivary ALDH, hsALDH belonging to Class 3 ALDHs (ALDH3A1). This enzyme uses NAD(P)þ as cofactor for the conversion of medium and long chain aliphatic and aromatic aldehydes to the corresponding carboxylic acids. This phase I drug metabolizing process is carried out in the oral cavity and in the upper aero-digestive tract (UADT). Substrates of hsALDH include: benzaldehyde, cinnamaldehyde, vanillin, 2-naphthaldheyde, 6-methoxy-2-naphthaldehyde, 4hydroxynonenal, malondialdehyde and acrolein (Ahmed Laskar and Younus, 2019). High intake of coffee and broccoli is associated with increased levels of this enzyme in saliva, whereas caffeine is an inhibitor of its activity. Lower activity of salivary ALDH in the oral cavity was reported as a risk factor for cancer development (Giebułtowicz et al., 2013).

Drug Metabolism: Other Phase I Enzymes

543

Fig. 9 Aldehyde oxidase substrates. The numbered compounds are referred to in the text. By authors using “ChemSketch”. https://www.acdlabs. com/contact/inforequest.php.

544

Fig. 9

1.20.5

Drug Metabolism: Other Phase I Enzymes

(continued).

Alcohol dehydrogenases

Alcohol dehydrogenases (ADH) are zinc-containing enzymes that catalyze the oxidation of primary and secondary alcohols to aldehydes and ketones (Di et al., 2021). This reaction is reversible but the forward direction is favored (Edenberg and McClintick, 2018). ADH uses NADþ as cofactor that upon catalysis is converted to the reduced form. ADHs belong to the superfamily of medium-chain dehydrogenases/reductases (MDRs) (Di et al., 2021). The seven human ADHs are classified into five classes. ADHs are dimeric

Drug Metabolism: Other Phase I Enzymes

Fig. 9

(continued).

545

546

Fig. 9

Drug Metabolism: Other Phase I Enzymes

(continued).

proteins organized in two subunits. ADH1A, ADH1B and ADH1C belong to Class 1. Enzymes that are part of this class share 90% sequence identity and can be found in either homo- or hetero-dimeric forms (Di et al., 2021). Each ADH subunit contains two bound Zn2þ ions with only one of the two involved in catalysis (Di et al., 2021). The catalytic Zn2þ is coordinated to one water molecule, one histidine and two cysteine residues. The non-catalytic Zn2þ plays a structural role and forms a tetrahedral complex with four cysteine residues (Di et al., 2021). The catalytic cycle starts with the binding of NADþ. At this point the alcohol can exert the displacement of the water molecule coordinated by the Zn2þ ion. The alcohol is deprotonated to form a zinc alkoxide intermediate and the hydride is transferred to the

Drug Metabolism: Other Phase I Enzymes

Fig. 9

547

(continued).

Table 3

Kinetic data for human AOX drug metabolizing activity.

Substrate Lenvatinib Methotrexate Zaleplon Zebularine

By authors.

Isoform AOX1 AOX1 AOX1

Km 164.5 mM 2792 mM 33-114 mM 7.3 mM male 8.4 mM female

kcat n/a n/a n/a n/a

Vmax

References 1

1

73 pmol min mg protein 40 pmol min 1 mg 1 protein 57-117 pmol min 1 mg 1 protein 2.1 nmol min 1 mg 1 protein male 2.7 nmol min 1 mg 1 protein female

Inoue et al. (2014) Benowitz et al. (2009) Lake et al. (2002) Klecker et al. (2006)

548

Drug Metabolism: Other Phase I Enzymes

Fig. 10 Catalytic mechanism of aldehyde dehydrogenase. Modified from Oppaka V, Thompson DC, Chen Y, Ellermann M, Nicolaou KC, Juvonen RO, Petersen D, Deitrich RA, Hurley TD, Vasiliou V (2012) Aldehyde dehydrogenase inhibitors: A comprehensive review of the pharmacology, mechanism of action, substrate specificity, and clinical application. Pharmacological Reviews 64: 520–539.

NADþ forming the aldehyde product and NADH. Water ends the cycle by displacing the product molecule regenerating the catalytic cycle. The initiation of the catalytic cycle (Fig. 11) is based on a major conformational change of ADH that causes the approaching of the catalytic triad and the NADþ binding domain upon NADþ binding (Eklund et al., 1981; Auld, 2001). In the closed conformation the alcohol substrate can displace the water molecule to create a more hydrophobic environment for the Zn2þ substrate complex. The conformational changes of the enzyme also allow the correct orientation of the Zn2þ-bound substrate for hydride transfer from the alcohol to the NADþ. The binding of NADþ or NADH occurs before substrate binding for catalysis to occur. The substrate binding pocket of ADH is a better fit for primary rather than secondary alcohols (Di et al., 2021). This is mainly due to the size of the pocket that can be schematized by a cylinder of 7–10  15 Å. Single ADH isoforms have unique substrate specificities. In general, ADH class I enzymes metabolize primary alcohols effectively but ADH1A oxidizes secondary alcohols more efficiently than ADH1B and ADH1C. In terms of enantioselectivity, ADH1B and ADH1C prefer S-enantiomeric forms as opposed to ADH1A that prefers the R-forms. ADH1C is the only human isoform that can metabolize steroid substrates. For ADH the longer the carbon chain of the substrate the higher the activity, indicating a favorable hydrophobic interaction between the substrate and the enzyme. Human ADHs are mostly present in the liver with exception of ADH5 that is present in most tissues and ADH7 that is found mainly in the gastrointestinal tract. ADH1B is the most abundant ADH in the liver and it can also be found in the adipose tissue, in blood vessels and in the brain. Two different polymorphic variants of ADH are known for exhibiting higher activity toward ethanol: ADH2B*2 and ADH2B*3 (Bosron and Li, 1987; Ehlers et al., 2001). ADH2B*2 is common in people from China, Japan and Korea and rare in individuals of European or African descent. ALDH2B*3 is found almost entirely in individuals of African descent (Ehlers et al., 2001). In terms of specific drug substrates of this class of enzymes, some examples will be discussed. 1,4-Butanediol, 90, is an organic solvent that upon ingestion is rapidly converted to g-hydroxybutyrate. The sodium salt of ghydroxybutyrate is used in the treatment of narcolepsy and cataplexy. ADH metabolizes 1,4-butanediol to 4-hydroxybutyraldehyde, 90a, that is further converted to g-hydroxybutyrric acid by ALDH (Liakoni et al., 2019). Abacavir, 91, is a nucleoside reverse transcriptase inhibitor that is used in the treatment of HIV. In order to function, the prodrug must be converted to the pharmacologically active metabolite, carbovir-triphosphate, that is responsible for the inhibition of viral replication. ADH converts abacavir into the carboxylic acid form, 91a (Walsh et al., 2002).

Drug Metabolism: Other Phase I Enzymes

549

Fig. 11 Catalytic mechanism of alcohol dehydrogenase. Modified from Di L, Balesano A, Jordan S, and Shi SM (2021) The role of alcohol dehydrogenase in drug metabolism: Beyond ethanol oxidation. The AAPS Journal 23: 20.

Celecoxib is a selective cyclooxygenase-2 (COX-2) inhibitor used in the treatment of rheumatoid arthritis, osteoarthritis, ankylosing spondylitis and acute pain. This drug is extensively metabolized by cytochrome P450 2C9 and one of its main metabolites, the primary alcohol, 92, is transformed to the carboxylic acid by ADH, 92a (Sandberg et al., 2002). Cyclophosphamide is an anticancer and immunosuppressive agent. The prodrug is metabolized to both active and inactive metabolites. Cytochrome P450 converts cyclophosphamide to 4-hydroxycyclophosphamide. The activity of ADH leads to the formation of the inactive metabolites. Indeed ADH transforms 4-hydroxycyclophosphamide, 93, to 4-keto cyclophosphamide, 93a, that is further converted by ALDH to carboxyphosphamide (de Jonge et al., 2005). Ethambutol, 94, is bacteriostatic against actively growing tuberculosis bacilli. It exerts its activity by obstructing the formation of the cell wall. This drug is used in the treatment of tuberculosis and usually given in combination with other tuberculosis medications, such as isoniazid, rifampicin and pyrazinamide. ADH converts the drug to the aldehyde, 94a, that is subsequently further converted by ALDH to the carboxylic acid (Di et al., 2021). Ethanol, 95, is metabolized in humans by cytochrome P450 2E1, catalase and ADH. ADH converts ethanol to acetaldehyde, 95a (Edenberg and McClintick, 2018). The affinity of ethanol depends on the ADH isoform involved and it can vary from 0.013 to 1000 mM. Felbamate is an anticonvulsant used in the treatment of epilepsy with the precise mechanism of action currently unknown. The metabolism of this drug is exerted by an esterase-mediated hydrolysis leading to a primary alcohol, 96, that is further converted to the aldehyde by ADH, 96a (Foti and Dalvie, 2016). The aldehyde can cause toxicity by spontaneous decomposition into CO2, NH3 and the reactive atropaldehyde that can covalently bind to proteins resulting in adduct formation. Fluvoxamine is a serotonin (5-HT) reuptake inhibitor for the treatment of patients with obsessive compulsive disorder. Cytochrome P450 2E1 is mainly involved in the formation of the alcohol metabolite of fluvoxamine, 97, that subsequently undergoes conversion by ADH/ALDH to the acid metabolite, 97a (Miura and Ohkubo, 2007). Hydroxyzine, 98, is an anti-histamine that has a high affinity for the histaminic receptor in the brain resulting in anxiolytic effects. ADH and ALDH extensively convert hydroxyzine to cetirizine, 98a (El-Haj and Ahmed, 2020). The latter is an active metabolite with selective inhibition properties for the H1 receptor. JWH-018 is a synthetic cannabinoid that was among the first molecules to appear on the illicit market. This class of molecules is active on CB1 and CB2 cannabinoid receptors. Several derivatives of this scaffold are available. Cytochrome P450 converts JWH-018 to the hydroxylated form, 99, and ADH further converts it to the aldehyde, 99a, and the latter is further transformed to the carboxylic acid by ALDH (Holm et al., 2016). Luseogliflozin is sodium-glucose cotransporter 2 (SGLT2) inhibitor used in the treatment of type 2 diabetes mellitus. It is metabolized by cytochrome P450 to a number of different metabolites and in this pathway a hydroxy metabolite, 100, is further converted to the carboxylic acid, 100a, through the ADH/ALDH enzymes (Miyata et al., 2017).

550

Drug Metabolism: Other Phase I Enzymes

Tolbutamide is eliminated through oxidative metabolism by cytochrome P450 2C9 to form the alcohol metabolite, 101, that is then converted to a carboxylic acid by ADH/ALDH, 101a (Thomas and Ikeda, 1966). ADH can be efficiently inhibited by several different molecules such as: fomepizole, cimetidine, and formamides. The chemical structures of the selected substrates of alcohol dehydrogenases are shown in Fig. 12.

1.20.6

Carboxylesterases

Mammalian carboxylesterases (CES) are part of the serine hydrolase superfamily (Wang et al., 2018). They are localized in the lumen of the endoplasmic reticulum (Sanghani et al., 2009; Satoh and Hosokawa, 1998, 2006). CES is involved in the transformation of many ester- or amide-containing substrates into the corresponding alcohol and carboxylic acid. CES can act on thioester, ester, carbamate or amide bonds that are present in many different xenobiotics. Humans possess three different CES enzymes: CES1, CES2 and CES3. CES1 and CES2 are involved in the metabolism of xenobiotics and drugs, but they may also act on endogenous esters (Wang et al., 2018). The fold of CES belongs to the a/b hydrolase family of proteins. They are localized in the endoplasmic reticulum (i.e., the microsomal fraction) where they are delivered due to the HXEL C-terminal motif. Cleavage of the C-terminal signal peptide causes the release of these proteins from the membrane making them fully soluble proteins residing in the ER lumen. The structure of CES1 is composed of a central catalytic domain, an a/b domain and a regulatory domain containing the low-affinity surface ligand-binding Z-site (Wang et al., 2018). CES1 can exist as a monomer, trimer or hexamer. Substrate binding changes the equilibrium favoring the formation of a homooligomeric structure. The structures of CES2 and CES3 are not known, but they are thought to exist as monomers (Wang et al., 2018). Both CES1 and CES2 contain a catalytic triad (Ser, Glu, His) that in CES1 is at the interface of the three domains. They also contain a highly conserved HGGG motif. The active site capacity of human CES1 is large and is composed mainly of hydrophobic residues. Two pockets can be found in the active site, a rigid and a flexible one. The rigid pocket is selective for the substrates with small acyl groups, whereas the flexible pocket makes it promiscuous for a large number of esters with different acyl groups (Bencharit et al., 2003). These structural features allow CES1 to be able to interact with a variety of substrates. CES hydrolyzes substrates using a base-catalyzed mechanism (Fig. 13) including a reaction that consists of two steps that are common in serine hydrolases. In the first step the carbonyl carbon of the substrate receives a nucleophilic attack of the baseactivated serine oxygen. At this stage, an acyl-enzyme intermediate is formed and the alcohol/thiol/amine product is released. The acyl-enzyme intermediate is attacked by water resulting in the release of the carboxylic acid metabolite regenerating the original state of CES. Mammalian CES can perform transesterification reactions using alcohol instead of water in the replacement of the acylenzyme intermediate generating an ethyl ester product, as in the case of formation of cocaethylene due to the combined abuse of alcohol and cocaine. CES1 can also catalyze the creation of cholesteryl esters from cholesterol and fatty acids and fatty acid ethyl esters from fatty acid-CoA. CES1 and CES2 share 47% sequence identity, but they are expressed in different tissues and also differ in their substrate specificity (Hosokawa, 2008; Imai, 2006; Wang et al., 2018). Both CES1 and CES2 are expressed in the epithelia, liver, intestine and kidney. Nevertheless, CES1 is abundant in liver and adipocytes, whereas CES2 is abundant in small intestine and colon. In general CES1 is selective for substrates that contain a small alcohol group and a bulky acyl group. On the contrary, CES2 is more selective for large alcohol groups and small acyl groups. A number of single nucleotide polymorphisms of CES1 and CES2 are known that affect the enzyme turnover. A very interesting point regarding the activity of CES is the ability to interact with well-known pharmaceutical excipients, such as sodium laurylsulfate and Tween 20. Finally, CES can interact with a large number of ligands that can inhibit the activity of the enzyme. These compounds can either be: benzyls, alkyl-1,2-diones, isatines, 1,2 quinones, benzene sulfonamides or triterpenoids or other natural compounds. A selected group of drug substrates of these enzymes will be briefly discussed. Clopidogrel, 102, is an antiplatelet medication used to reduce the risk of heart disease and stroke in those at high risk. It is a prodrug which is activated in two steps by cytochromes P450. The active metabolite then specifically and irreversibly inhibits a subtype of ADP receptor, which is important in activation of platelets and eventual cross-linking by the protein fibrin. However,  85% of the absorbed prodrug is rapidly hydrolyzed to the inactive metabolite, clopidogrel carboxylic acid, 102a. This reaction is catalyzed by hepatic CES1 with measured Km value of 67.2 mM and a Vmax of 3558 pmol min 1 mg 1 protein (Zhu et al., 2013). Cocaine, 103, is a local anesthetic indicated for the introduction of local (topical) anesthesia of accessible mucous membranes of the oral, laryngeal and nasal cavities. It acts by inhibiting the reuptake of serotonin, norepinephrine, and dopamine. CES catalyzes the hydrolysis of cocaine to form benzoylecgonine, 103a (Brzezinski et al., 1997). For CES1 the reported Km for cocaine is 116 mM (Brzezinski et al., 1997) whereas for CES2 the measured Km is 390 mM with a kcat of 7.2 min 1. Enalapril, 104, is a prodrug belonging to the angiotensin-converting enzyme (ACE) inhibitor drug class that acts on the reninangiotensin-aldosterone system, which is responsible for the regulation of blood pressure and fluid and electrolyte homeostasis. It is rapidly biotransformed into its active metabolite, enalaprilat, 104a, by CES with a reported Km of 1721 mM and a Vmax of 34 nmol min 1 mg 1 protein (Thomsen et al., 2014). Flutamide, 105, is a nonsteroidal antiandrogen with potent effects that exerts its action by inhibiting androgen uptake and/or by inhibiting nuclear binding of androgen in target tissues. It is therefore used in the treatment of prostate cancer. Flutamide is hydrolyzed by CES, 105a (Kobayashi et al., 2012). Kinetic parameters reported for CES2 are a Km of 300 mM and a Vmax of 1113 pmol min 1 mg 1 protein (Kobayashi et al., 2012).

Drug Metabolism: Other Phase I Enzymes

Fig. 12 Alcohol dehydrogenase substrates. The numbered compounds are referred to in the text. By authors using “ChemSketch”. https://www. acdlabs.com/contact/inforequest.php.

551

552

Drug Metabolism: Other Phase I Enzymes

Fig. 12

(continued).

Irinotecan, 106, is an antineoplastic agent primarily used in the treatment of colorectal cancer. It is a derivative of camptothecin that inhibits the action of topoisomerase I. Irinotecan prevents religation of the DNA strand by binding to the topoisomerase I-DNA complex, and causes double-strand DNA breakage and cell death. CES converts irinotecan into its active metabolite 7-ethyl-10hydroxycamptothecin (SN-38), 106a (Humerickhouse et al., 2000). In the case of CES1 the measured Km for irinotecan was

Drug Metabolism: Other Phase I Enzymes

Fig. 12

553

(continued).

Fig 13 Catalytic mechanism of carboxylesterases. Modified from Wang D, Zou L, Jin Q, Hou J, Ge G, and Yang L (2018) Human carboxylesterases: A comprehensive review. Acta Pharmaceutica Sinica B 8: 699–712.

554

Drug Metabolism: Other Phase I Enzymes

Fig. 14 Carboxylesterases substrates. The numbered compounds are referred to in the text. By authors using “ChemSketch”. https://www.acdlabs. com/contact/inforequest.php.

reported to be 42 mM with a Vmax of 530 pmol min 1 mg 1 protein. For CES2 the same parameters were reported as a Km of 3.4 mM with a Vmax of 2500 pmol min 1 mg 1 protein (Humerickhouse et al., 2000). Meperidine, 107, is a narcotic analgesic that can be used for the relief of most types of moderate to severe pain. CES catalyzes the hydrolysis of this apolar drug into a more soluble acid product, 107a (Zhang et al., 1999). For CES1 conversion of meperidine the reported Km value is 1.9 mM with a Vmax of 11 nmol min 1 mg 1 protein and the kcat is 0.67 min 1 (Zhang et al., 1999). Methylphenidate, 108, is a stimulant medication used to treat attention deficit hyperactivity disorder (ADHD) and narcolepsy. It acts as a norepinephrine-dopamine reuptake inhibitor (NDRI). CES1A1 is the major enzyme responsible for the first-pass, stereoselective metabolism of methylphenidate, 108a (Sun et al., 2004). For CES1 the measured Km for methylphenidate is 40.1–47.1 mM

Drug Metabolism: Other Phase I Enzymes

Fig. 14

555

(continued).

(L-form) or 110.4–126.2 mM (D-form) with the corresponding kcat values of 0.31–0.36 min 1 (L-form) or 0.16–0.17 min 1 (Dform) (Sun et al., 2004). Oseltamivir, 109, is used in the treatment and prophylaxis of both influenza virus A and B infections. It is an ester prodrug and in general it is readily converted to its active form oseltamivir carboxylate, 109a, mediated by hepatic CES1 with a measured Km of 1.38 mM and a Vmax of 145 nmol min 1 mg 1 protein (Zhu and Markowitz, 2009).

556

Drug Metabolism: Other Phase I Enzymes

Fig. 14

(continued).

Table 4

Kinetic data for human carboxylesterase activity.

Substrate Clopidogrel Cocaine Enalapril Flutamide Irinocatecan Meperidine Methylphenidate Oseltamivir Prasugrel Sacubitril

Isoform CES1 CES1 CES2 CES1 CES2 CES1 CES2 CES1 CES1 CES1 CES1 CES2 CES1

Km 62.7 mM 116 mM 390 mM 1721 mM 300 mM 42 mM 3.4 mM 1.9 mM 40.1-47.1 mM L-form 110.4-126.2 mM D-form 1.38 mM 9.25 mM KS ¼ 11.1 mM 767 mM

kcat n/a n/a 7.2 min 1 n/a n/a n/a n/a 0.67 min 1 0.31–0.36 min 1 L-form 0.16–0.17 min 1 D-form n/a n/a n/a n/a

Vmax

References 1

1

3558 pmol min mg protein n/a n/a 34 nmol min 1 mg 1 protein 1113 pmol min 1 mg 1 protein 530 pmol min 1 mg 1 protein 2500 pmol min 1 mg 1 protein 11 nmol min 1 mg 1 protein n/a

Zhu et al. (2013) Brzezinski et al. (1997) Zhang et al. (1999) Thomsen et al. (2014) Kobayashi et al. (2012) Humerickhouse et al. (2000)

145 nmol min 1 mg 1 protein 0.725 nmol min 1 mg 1 protein 19.0 nmol min 1 mg 1 protein 557 nmol min 1 mg 1 protein

Zhu and Markowitz (2009) Williams et al. (2008)

Zhang et al. (1999) Sun et al. (2004)

Shi et al. (2016)

By authors.

Prasugrel, 110, is a medication used to prevent the formation of blood clots. It is a platelet inhibitor and an irreversible antagonist of specific ADP receptors and belongs to the thienopyridine class of drugs. It is a prodrug and is rapidly metabolized by CES to thiolactone, 110a, which is subsequently converted by cytochromes P450 to a pharmacologically active metabolite (R-138727) (Williams et al., 2008). Prasugrel is a substrate of both CES1 and CES2 with measured Km values of 9.25 mM and 11.1 mM with corresponding Vmax values of 0.725 and 19 nmol min 1 mg 1 protein, respectively (Williams et al., 2008). Finally, sacubitril, 111, is a prodrug neprilysin inhibitor used in combination with valsartan to reduce the risk of cardiovascular events in patients with chronic heart failure. lt is an inactive ester prodrug that is activated in vivo by the action of CES to its metabolite sacubitrilat (LBQ657), 111a, a potent neprilysin inhibitor. The latter is responsible for its intended pharmacological effects (Shi et al., 2016). For CES1 the measured Km for sacubitril was reported to be 767 mM with a Vmax value of 557 nmol min 1 mg 1 protein (Shi et al., 2016). The chemical structures of the selected substrates and the corresponding products of carboxylesterases are shown in Fig. 14 with the kinetic data summarized in Table 4.

Drug Metabolism: Other Phase I Enzymes

1.20.7

557

Conclusions and future perspectives

Although it is recognized that a major role in drug metabolism is played by the cytochromes P450, this chapter provides the reader with the increasing evidence of the importance also of several other phase I enzymes, primarily FMOs, but also aldehyde oxidase, aldehyde and alcohol dehydrogenases, and carboxylesterases. Through specific drug substrates of each of these enzymes and their measured kinetic parameters, we have revealed the significant contributions that they make to the metabolism and consequently elimination of xenobiotics. The many examples covered demonstrate that a wide array of drug metabolizing enzymes should be examined during the design and evaluation of new chemical entities in drug discovery and development. Understanding the structure of these enzymes, their catalytic cycles and mechanisms will allow for the rational design of better drug molecules with improved pharmacokinetic profiles. In addition, in the last decade important advances have been made in terms of personalized medicine and the effect of genetic variants of these non-P450 enzymes, in some instances even resulting in beneficial health outcomes in the ethnic populations carrying specific allelic variants with higher incidences. This review chapter is one of the first stepping stones on this subject and it clearly shows that despite the increased body of evidence, there is still much to learn about non-P450 phase I drug metabolizing enzymes with important implications for drug design and development.

See Also: 1.19: Drug Metabolism: Cytochrome P450; 1.26: Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters; 1.27: Drug-Drug Interactions with a Pharmacokinetic Basis

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Drug Metabolism: Phase II Enzymes

Margaret O. James, Department of Medicinal Chemistry, University of Florida Academic Health Center, Gainesville, FL, United States © 2022 Elsevier Inc. All rights reserved.

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Introduction UDP-glucuronosyltransferases, UGTs The co-substrate for glucuronidation, UDPGA Drug substrates Expression in tissues and regulation by drug exposure or age Enterohepatic cycling of drug-glucuronides Polymorphisms PAPS-sulfotransferases The co-substrate PAPS Inhibition of sulfonation Drug substrates and competition between sulfonation and glucuronidation Expression in tissues, regulation and age-related changes Polymorphisms Glutathione transferases, GSTs The co-substrate, GSH Drug substrates for GSTs Expression in tissues, regulation by drugs, and age-dependent changes Polymorphisms N-acetyltransferases, NATs The co-substrate acetyl-CoA Drug substrates for acetylation Expression in tissues and age-dependent changes Polymorphisms Amino acid conjugation enzymes and drug-acyl-CoA pathways Co-substrates for amino acid conjugation Drug substrates for amino acid conjugation or acyl-CoA formation Expression in tissues, regulation and age-dependent changes Polymorphisms in the enzymes for amino acid conjugation Other pathways taken by drug acyl-CoA derivatives Methyltransferases, MTs Co-substrate for methylation Drug substrates for methylation Expression in tissues and age-dependent change Polymorphisms Conclusions

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Glossary Co-substrate In a two-substrate enzyme-catalyzed reaction, the co-substrate is the endogenous molecule that forms a covalent bond with the drug substrate as a result of the enzyme-catalyzed reaction. Phase II enzymes Also termed conjugative enzymes, these enzymes are classified as a group because they catalyze the combination of an endogenous small molecule with a drug or drug metabolite. This results in a conjugate of the drug or drug metabolite with the endogenous molecule.

Nomenclature AAconj Amino acid conjugation Acetyl-CoA Acetyl-coenzyme A Acyl-CoA Carboxylic acid drug coenzyme A thioester

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CoASH Coenzyme A GSH Glutathione, i.e., the tripeptide gamma-glutamyl-cysteinyl-glycine GST Glutathione transferase MT Methyltransferase NAT N-acetyltransferase PAPS 30 -phosphoadenosine-50 -phosphosulfate SAM S-adenosyl-methionine SULT Sulfotransferase UDPGA Uridine diphospho-a-D-glucuronic acid UGT Uridine diphospho-glucuronosyltransferase

1.21.1

Introduction

The enzymes that catalyze drug metabolism have been grouped according to their general function into phase I enzymes, also known as functionalization enzymes and phase II enzymes, also termed conjugative enzymes (David Josephy et al., 2005; Williams, 1959). Most orally effective drugs are lipophilic molecules, a property that aids their absorption and distribution to the site of action in the body. For such drugs, formation of water-soluble conjugates in one or more enzyme-catalyzed steps is required for the drug to be eliminated from the body in a timely and predictable manner. The enzymes that catalyze drug metabolism also recognize other xenobiotics, such as pesticides, food additives, combustion products and chemicals in personal care products as substrates, with the drug metabolism pathway taken dependent upon the molecular structure of the xenobiotic. Historically, phase II metabolites were the first identified products of drug metabolism, described in the 1800s, as these are often the final excreted metabolites of drugs and other xenobiotics (Conti and Bickel, 1977). The phase II enzymes catalyze the combination of a small endogenous molecule with a functional group in a drug molecule. The endogenous molecule is usually a polar, water-soluble substance and often participates in the phase II enzyme-catalyzed reaction as an activated derivative, such as uridine diphospho-glucuronic acid (UDPGA), the source of glucuronic acid in the resulting glucuronide metabolites. The endogenous molecule is termed a co-substrate of the phase II reaction. The products of some of the major phase II reactions, including glucuronidation, sulfonation and amino acid conjugation are anionic at physiological pH. This property not only makes the phase II metabolite more water soluble, but also facilitates recognition of the conjugate by certain efflux transporters and subsequent excretion from the body in urine or feces (Jetter and Kullak-Ublick, 2020). The major phase II enzymes are UDP-glucuronosyltransferase (UGT), PAPS-sulfotransferase (SULT), glutathione transferase (GST), N-acetyltransferase (NAT), amino acid conjugating enzymes and methyltransferases (MT). All of these enzymes are present in multiple forms, usually as part of an enzyme superfamily of related individual enzyme proteins. A particular enzyme superfamily may include enzymes that metabolize both drugs and endogenous substrates. For example, some SULT enzymes transfer the sulfonic group from the co-substrate 30 -phosphoadenosine-50 -phosphosulfate (PAPS) to physiologically important steroids. Typically, for phase II enzymes, the binding site of the endogenous co-substrate is highly conserved within the superfamily, both in the primary amino acid sequence of the enzyme protein and in tertiary structure, while the amino acid sequence and topology of the xenobiotic drug substrate binding site is more variable. The variable properties of the drug substrate binding site play an important role in determining which drug substrates will readily bind to a particular enzyme and therefore control the substrate selectivity for the preferred biotransformation enzyme. In phase II enzymes, as with most drug-metabolizing enzymes, the substrate binding site is flexible and is likely to exhibit relatively low affinity for its drug substrates. This is in comparison to the properties of enzymes

UGT SULT GST NAT MT AAconj Other Fig. 1 The major phase II (conjugation) enzymes and their overall contribution to drug metabolism. Adapted from fig. 2 in Evans WE and Relling MV (1999) Pharmacogenomics: Translating functional genomics into rational therapeutics. Science 286: 487–491.

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important in intermediary metabolism. This means that it is often the case for phase II enzymes, as with phase I enzymes, that a particular drug can be a substrate for more than one member of a particular enzyme superfamily. As shown in Fig. 1 pie chart, glucuronidation is quantitatively the most important biotransformation pathway for drug molecules or their phase I metabolites (Evans and Relling, 1999). Although this paper was published a number of years ago, pharmaceutical scientists agree that the relative importance of each enzyme in drug metabolism is likely to apply to currently used drugs. UGT enzymes can transfer glucuronic acid from UDPGA to a nucleophilic site such as the hydroxyl group, a carboxylate group, a primary, secondary or tertiary amine or a thiol group. Many drugs or their phase I metabolites contain such functional groups. With the exception of carboxylate groups, SULTs recognize the same nucleophilic functional groups as potential substrates, however sulfonation is quantitatively less common than glucuronidation. This is in part because the cosubstrate for glucuronidation, UDPGA, is present in tissues in higher concentrations than the co-substrate for sulfonation, PAPS (Cappiello et al., 1990, 1991; Klaassen and Boles, 1997). Furthermore, UDPGA is readily biosynthesized in liver and other tissues from UDP-glucose, whereas several energy dependent steps are needed to biosynthesize PAPS, as discussed in greater detail below. Glutathione conjugation, catalyzed by GSTs with the co-substrate glutathione, GSH, is the major pathway for elimination of drugs or their metabolites that contain electrophilic centers. Other than chemotherapeutic drugs, it is unusual for a drug to contain a reactive electrophilic center, however, electrophilic centers may be introduced by cytochrome P450-dependent metabolism. The glutathione conjugate formed is not eliminated intact, but is further processed to the Nacetyl-cysteine conjugate (mercapturic acid), the final excreted metabolite. The other pathways shown in Fig. 1 such as N-acetyltransferase (NAT), methyltransferase (MT) and amino acid conjugation (AAconj) recognize specific functional groups, so are limited in scope to those drugs that possess those groups. NAT enzymes catalyze the acetylation of primary amines and hydrazines, MT enzymes catalyze the methylation of catechols and thiols, and AAconj enzymes catalyze formation of amino acid conjugates of carboxylic acids. Most phase II pathways are expressed at the highest level in the liver, the major organ of drug biotransformation, however expression is not limited to the liver. Most phase II enzymes are also expressed in the intestine and kidney and some in the lung, skin, brain and other tissues (Gundert-Remy et al., 2014). Drugs taken orally will first encounter enzymes in the intestine then the liver, and are likely to undergo pre-systemic biotransformation, also known as first pass metabolism, in these tissues. Thus, expression of phase II enzymes in intestine as well as liver presents a greater chance of such pre-systemic biotransformation. The kidney is a major organ of excretion and formation of phase II metabolites in kidney will be likely to facilitate elimination of the drug. A few phase II enzymes, notably certain GSTs are found in lung and low expression of SULTs has been found in steroidogenic tissues (ovary, breast, placenta, adrenals) however these sites are less important than liver, intestine and kidney for phase II drug biotransformation. Another factor that influences rates of phase II drug metabolism is genetic polymorphism of the drug-metabolizing enzyme (Evans and Relling, 1999). It is now widely recognized that individuals respond differently to a particular therapeutic drug, and one component of the response is the rate of drug metabolism. The genes for most if not all of the phase II enzymes have been shown to exhibit single nucleotide polymorphisms (SNPs). Depending on the location of the SNP in the gene, it may affect the level of enzyme protein expression, i.e., the amount in the liver or other tissue, or it may give rise to a change in a single amino acid in the protein sequence. In some cases, changes in amino acid sequence have little or no effect on enzyme stability or activity with drug substrates, while in other examples the stability, activity or both properties of the drug-metabolizing enzyme are affected.

1.21.2

UDP-glucuronosyltransferases, UGTs

As noted in the introduction, glucuronidation, catalyzed by UGT enzymes, is quantitatively the phase II pathway most utilized by drugs. UGTs are members of a protein superfamily, the UDP-glycosyltransferases, that catalyze the transfer of a sugar moiety to a substrate. Glycosylation is a physiologically important regulatory pathway for several classes of endogenous molecules as well as drugs (Hu et al., 2019). This section will focus on glucuronidation of drugs. Recent papers have described the structural characteristics of the UGTs and their roles in drug glucuronidation and drug resistance in the treatment of cancer (Allain et al., 2020; Hu et al., 2019; Radominska-Pandya et al., 2010). UGTs are membrane-bound enzymes present in the endoplasmic reticulum of the liver cell. When tissues such as liver and intestinal mucosa are subjected to homogenization and differential centrifugation to prepare sub-cellular fractions, the UGTs are located in the microsomal fraction, on the lumenal side (inside) of the microsomal particle. Fig. 2 shows the proposed topology of the UGT enzyme and related proteins in the liver cell (Liu and Coughtrie, 2017). The UGT enzyme is a transmembrane protein with the binding sites of the substrate, i.e., the drug, and the co-substrate, UDP-a-D-glucuronic acid (UDPGA) on the inside lumenal side of the membrane. Following completion of the enzymatic reaction, the drug-b-D-glucuronide is transported out of the lumen with the assistance of a transporter protein. Because the drug and UDPGA binding sites of the UGT enzymes are inside the lumen, it is necessary to disrupt the microsomal membrane to ensure maximal activity when conducting studies of glucuronidation in vitro. Several members of the UGT superfamily catalyze drug glucuronidation. These are in the UGT1 and UGT2 families and are expressed in liver as well as intestine, kidney and sometimes lung (Meech et al., 2019). Within the UGT1 family, the portion of the protein that comprises the UDPGA binding site is identical in each family member, and only the portion of each protein that forms the substrate-binding site differs in amino acid sequence. It has been found

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Fig. 2 Architecture of glucuronidation in the endoplasmic reticulum (ER). The drug substrate, X-OH, enters the lumen by passive diffusion (A) and UDPGA is transported by the UDPGA/UDP-N-acetylglucosamine (UDP-GlcNAc) antiporter (B). The UDPGA-binding site and the substrate binding site in the UGT enzyme face the lumenal side. The resulting drug-glucuronide conjugate (X-O-GA) is transported out of the ER by a glucuronide transporter (C). Reproduced from Liu Y and Coughtrie MWH (2017) Revisiting the latency of uridine diphosphate-glucuronosyltransferases (UGTs)how does the endoplasmic reticulum membrane influence their function?. Pharmaceutics 9.

from extensive studies that glucuronidation of many drugs can be catalyzed by more than one UGT enzyme, i.e., the UGT enzymes have overlapping substrate selectivities (Meech et al., 2019). For example, the over-the-counter drug acetaminophen is a substrate for UGT1A1, UGT1A6 and UGT1A9, though the enzymes differ in their affinity for acetaminophen, as measured by their apparent Michaelis constant, KM, values for acetaminophen and their apparent maximal velocity for glucuronidation in the presence of saturating concentrations of UDPGA, measured as Vmax (Court et al., 2001). In this example, the major UGT enzyme catalyzing acetaminophen glucuronidation in a person who takes the drug will depend upon the dose of acetaminophen ingested and thus the liver concentration.

1.21.2.1

The co-substrate for glucuronidation, UDPGA

In considering human drug metabolism, the most common sugar to be transferred to nucleophilic groups in drug substrates is glucuronic acid. The glucuronic acid is supplied by UDP-a-D-glucuronic acid (UDPGA) which is formed in the liver prior to interaction with the UGT enzyme. UDPGA is synthesized through the following steps. Glucose-1-phosphate is converted to UDP-a-D-glucose by the action of UDP-glucose pyrophosphorylase then the UDP-a-D-glucose is oxidized to UDPGA by the NADþ-dependent enzyme, UDP-glucose dehydrogenase (Adeva-Andany et al., 2016; Bhagavan, 2001). The concentration of UDP-glucose in human liver of young and elderly volunteers was measured by whole body 31P-magnetic resonance spectroscopy and found to be 0.74  0.17 mM, mean  S.D., n ¼ 5 in young lean volunteers and 0.81  0.34 mM, n ¼ 7 in elderly lean volunteers (Pfleger et al., 2019), similar to a previous report from human liver samples that found UDP-glucose concentrations ranged from 0.8 to 1.5 mM (Ercan-Fang et al., 2002). The concentration of UDPGA in human liver samples was reported to be 0.20–0.35 mM (Cappiello et al., 1991). Where this has been studied, the apparent KM of UDPGA for drug glucuronidation ranges from 0.06 to 1.3 mM, depending on the aglycone substrate (Cappiello et al., 1991; Court et al., 2001; Manevski et al., 2011). Most studies of glucuronidation in vitro in liver microsomes or with expressed UGT enzymes use UDPGA concentrations of at least 1 mM and up to 20 mM, to ensure that maximal rates of drug glucuronidation are measured (Burchell et al., 2005). As the liver concentration of UDPGA is 0.2–0.35 mM, rates measured in vitro with non-physiological concentrations of UDPGA may not be directly translatable to the in vivo situation. Nevertheless, the relatively high concentration of UDPGA in liver compared to that of the sulfonation cosubstrate, PAPS (see below) and the ease of biosynthesis of UDPGA in one step from UDP-glucose means that glucuronidation is generally a high capacity pathway of drug metabolism.

Drug Metabolism: Phase II Enzymes 1.21.2.2

567

Drug substrates

The reaction catalyzed by UGTs is shown in Fig. 3. The reaction is a nucleophilic substitution reaction with inversion of configuration of the sugar linkage resulting in formation of a drug-b-glucuronide. Drugs that are substrates for glucuronidation, sometimes termed aglycones, have in common the presence of at least one nucleophilic functional group. As illustrated in Fig. 3, this could be hydroxyl, carboxyl, thiol, a primary, secondary or tertiary amine or an acidic carbon. Examples of drug or other xenobiotic substrates in each of these classes are shown in Fig. 4. A recent review lists many drugs that form glucuronide conjugates and the specific UGTs that catalyze their glucuronidation (Meech et al., 2019). As well as parent (administered) drugs with nucleophilic functional groups, glucuronidation can be a major pathway for functional groups introduced by phase I biotransformation. For example, drugs are often substrates for cytochrome P450 enzymes,

Fig. 3 red.

The glucuronidation reaction. RXH is the drug substrate and UGT is the enzyme. The part of glucuronic acid that is transferred is shown in

Fig. 4

Examples of xenobiotics that are glucuronidated. The possible sites of glucuronidation are indicated by arrows.

568

Drug Metabolism: Phase II Enzymes

which introduce aromatic or aliphatic hydroxyl groups into the drug structure that are subsequently glucuronidated. Esterase and amidase hydrolytic enzymes convert ester and amide drugs to metabolites that contain carboxyl, hydroxyl or amine groups. Glucuronidation is frequently a secondary pathway of biotransformation of these drugs. Although most glucuronide conjugates are non-toxic, readily excreted metabolites, formation of glucuronide conjugates of carboxylic acids, termed acyl-glucuronides, is sometimes associated with formation of protein adducts because of the chemical reactivity of the acyl-glucuronide (Baba and Yoshioka, 2009). Recently, in attempts to avoid rapid biotransformation to inactive metabolites, medicinal chemists have designed drugs that are resistant to cytochrome P450-dependent drug monooxygenation. Many of these new drugs have heterocyclic rings that contain nitrogen and are potential or demonstrated substrates for glucuronidation. This has led to an increased recognition of the importance of glucuronidation in drug metabolism (Busse et al., 2020).

1.21.2.3

Expression in tissues and regulation by drug exposure or age

Liver, kidney and small intestine have the highest concentrations of UGT enzymes in people, both enzyme protein and mRNA. The mRNAs that code for UGTs have been found in other tissues, including salivary gland, esophagus, stomach, colon, pancreas, trachea, lung, kidney, heart, bone marrow, spleen, thymus, adrenal gland, thyroid, ovary, uterus, cervix, placenta, breast, testis, prostate, adipose, skeletal muscle, brain, nasal, and bladder (Meech et al., 2019). It is not clear if all these sites express active UGT enzyme protein in addition to the mRNA. It is not surprising that tissues involved in drug absorption (intestine) and elimination (liver, kidney) express the highest concentrations of UGTs. Studies have revealed notable inter-individual variability between adults in capacity for glucuronidation that is related to differences in expression of one or more UGT isoforms in the major drug-metabolizing tissues. It has been shown that tissue-specific transcription factors and nuclear receptors control expression of UGTs in families 1 and 2 (Mackenzie et al., 2010). Several UGT isoforms are inducible by exposure to drugs that interact with nuclear receptors leading to increased expression of those isoforms in people taking such drugs (Rowland et al., 2013). The regulation of UGT expression by drugs is an ongoing area of study. Age-related changes in UGTs in the liver have been of interest for many years, as it is well-known that some drugs that are cleared by glucuronidation such as morphine, propofol and acetaminophen are very slowly metabolized in infants, particularly newborns (Allegaert et al., 2009). If there is a clinical indication for treatment of infants with these drugs, the effective dose is very low due to the slow glucuronidation in addition to small body size. Studies have shown that several UGT isoforms are expressed at very low levels in neonates and expression increases slowly during maturation to adulthood (Court, 2010; Rowland et al., 2013). This is true for UGTs in families 1 and 2, but especially those in the UGT1 family. Some studies show a slight decline in expression and activity at advanced age, but considerable interindividual variability is observed and the significance for drug metabolism is less clear-cut for the elderly than for the very young.

1.21.2.3.1

Enterohepatic cycling of drug-glucuronides

Orally administered drugs can be converted to glucuronide conjugates in the intestine or the liver. Drug-glucuronides formed in the liver are often excreted in bile, which is secreted into the intestine. This is also true for glucuronides of the phase I metabolites of drugs that are produced in the liver, for example by cytochrome P450 or esterases. As they pass down the intestinal tract to the large intestine and colon, glucuronide conjugates of drugs or drug-phase I metabolites will encounter b-glucuronidases both in the intestinal microflora and in the mucosa (Lindop et al., 1985). These hydrolytic enzymes can convert the drug-glucuronide back to the parent drug (or phase I metabolite), which can then be re-absorbed into the body. This process is termed entero-hepatic cycling, and can delay the elimination of drugs such as morphine that are excreted in bile as glucuronides (Hasselström and Säwe, 1993).

1.21.2.4

Polymorphisms

Genetic variants have been described for several UGT genes (Guillemette et al., 2014). Two well-known polymorphisms in the UGT1A1 gene that led to reduced (Gilbert’s syndrome) or no glucuronidation (Crigler-Najjar syndrome) of the physiologically important molecule, bilirubin, were described many years ago through clinical observation of jaundice (a yellowing of the skin and whites of the eyes due to build-up of bilirubin). If not detoxified by glucuronidation, bilirubin transfers into the brain and causes brain damage. Left untreated, Crigler-Najjar syndrome, where there is no active UGT1A1 enzyme in the liver, is fatal. Patients with Gilbert’s syndrome either express low amounts of UGT1A1 or express a variant with lower activity. This syndrome rarely produces adverse effects, unless the person is given drugs that are metabolized mainly by UGT1A1. For example, the active metabolite of the anticancer drug irinotecan, SN-38, is inactivated by glucuronidation catalyzed by UGT1A1, UGT1A7 and UGT1A9. It has been shown that patients with Gilbert’s syndrome are more likely to suffer adverse effects following irinotecan treatment than other patients, because they form the glucuronide conjugate of SN-38 more slowly (de Man et al., 2018).

1.21.3

PAPS-sulfotransferases

Another phase II pathway taken by drugs or drug metabolites that contain nucleophilic functional groups is sulfonation. Substrates for sulfonation are usually hydroxyl groups, aliphatic or aromatic, and amines, but not carboxylic acids. This pathway is catalyzed

Drug Metabolism: Phase II Enzymes

Fig. 5

569

The sulfotransferase reaction. The transferred sulfonic group is shown in red.

by sulfotransferase (SULT) enzymes that require 30 -phospho-adenosine-50 -phosphosulfate (PAPS) as a co-substrate. The SULT enzymes that catalyze formation of drug sulfates or sulfamates are cytosolic enzymes that function catalytically as dimers. SULTs in the SULT1 and SULT2 families are important in human drug metabolism. The SULT enzyme contains a large binding pocket that accommodates PAPS and the drug substrate. Once PAPS enters the binding pocket a flexible loop of the protein with about 30 amino acid residues becomes more ordered and forms a cap over this binding pocket (Leyh et al., 2013; Tibbs et al., 2015). Drugs can bind to the substrate-binding pocket of the enzyme in the presence or absence of PAPS, however if PAPS binds first, the size of the substrate-binding pocket is reduced and the entry of larger size drugs into the active site may be affected, as discussed in a review article (Tibbs et al., 2015). Conserved lysine, serine and histidine amino acids found in human cytosolic SULTs assist in stabilizing the PAPS (lysine, serine) and the drug substrate (histidine) to facilitate the reaction. Drug substrates bind in such a way that the nucleophilic group is within 3 Å of a nitrogen of the conserved histidine residue and forms a hydrogen bond that aids in deprotonation of the hydroxyl or amine substrate. The sulfonic group of PAPS forms a bond with the deprotonated oxygen or nitrogen of the substrate, giving a sulfate or sulfamate conjugate (Fig. 5). The conjugate leaves the active site followed by 30 -phosphoadenosine-50 -phosphate (PAP).

1.21.3.1

The co-substrate PAPS

The required co-substrate for sulfonation, PAPS, is produced in human liver by a bifunctional enzyme, PAPS synthase (PAPSS) that biosynthesizes PAPS in two sequential steps. In the first step, adenosine-50 -phosphosulfate is formed from ATP and inorganic sulfate and in the second step, a second molecule of ATP is utilized to produce the PAPS (Venkatachalam et al., 1998). Two forms of PAPSS are present in people, and contribute to PAPS synthesis, though their respective roles are incompletely understood (Gunal et al., 2019). The measured concentration of PAPS in human liver is reported as 23 mM (Cappiello et al., 1989), about one-tenth that of UDPGA. Where studied with drugs or steroid substrates, apparent KM values for the PAPS co-substrate are typically around 1 mM (King et al., 2006; Tibbs et al., 2015).

1.21.3.2

Inhibition of sulfonation

Sulfonation reactions are often found to be subject to inhibition, either from another drug or xenobiotic, or by increased concentrations of the drug substrate. Drugs and other xenobiotics that compete with each other for entry into or occupancy in the substratebinding pocket can inhibit SULT reactions, and there are numerous examples where natural products, pollutant chemicals and other drugs inhibit sulfonation, especially with SULTs in the SULT1 family (James and Ambadapadi, 2013; Wang and James, 2006). Studies in vitro of the enzyme kinetics of drug sulfonation reactions often find that the reaction is inhibited as the substrate concentration increases. The enzyme kinetics sometimes follow typical substrate inhibition kinetics, where increases in substrate concentration drive the reaction rate down to zero, and sometimes follow partial substrate inhibition kinetics where increasing the substrate concentration reduces but does not prevent sulfonation (James, 2021). Partial substrate inhibition is thought to be due in part to the large size of the substrate-binding pocket, which has been shown to be capable of accommodating two molecules of certain drug substrates as well as PAPS. The presence of the two molecules of drug substrate slows the release of the sulfonated product and leads to the observed inhibition. Another proposed mechanism of partial substrate inhibition is that after transfer of the sulfonic group, the PAP may be retained in the substrate binding pocket together with a molecule of substrate and slow down the binding of another molecule of PAPS to support sulfonation. Both mechanisms have support from crystallographic studies of SULT1A1 (Gamage et al., 2006).

1.21.3.3

Drug substrates and competition between sulfonation and glucuronidation

Drugs that contain hydroxyl or amine groups are potential substrates for glucuronidation, sulfonation or both phase II pathways. In several examples it has been found that both pathways are taken, however the predominant pathway depends on the drug dose. At

570 Table 1

Drug Metabolism: Phase II Enzymes Factors influencing drug glucuronidation versus sulfonation.

Property

Sulfonation

Glucuronidation

KM for drug substrate Vmax Co-substrate availability

High affinity, i.e., low KM Sometimes low Poor, PAPS liver concentration 0.02 mM. Difficult to biosynthesize Low capacity

Low affinity, i.e., high KM Usually high Good, UDPGA liver concentration 0.2–0.35 mM. Easy to biosynthesize High capacity

Overall

low doses of drug, which result in low mM drug concentrations in liver, sulfonation usually predominates, whereas at higher drug doses that result in high mM or low mM drug concentrations in liver, glucuronidation predominates. As well as the difference in cosubstrate concentrations in the liver, it is often the case that SULT enzymes have higher affinities for a drug substrate than UGT enzymes. Thus, two factors influence the major phase II pathway for drugs that have hydroxyl groups, the KM of the drug for UGT versus SULT and the availability of the co-substrate, UDPGA versus PAPS (Table 1). The commonly used analgesic, acetaminophen, forms both sulfate and glucuronide conjugates, with acetaminophen glucuronide the dominant metabolite at the usual therapeutic dose (Critchley et al., 1986). If an overdose of acetaminophen is taken, the sulfonation and glucuronidation pathways are both overwhelmed, in part because of the limited availability of PAPS and rapid depletion of UDPGA. Under this circumstance, cytochrome P450-dependent formation of a reactive acetaminophen metabolite, N-acetyl-p-benzoquinone-imine, occurs and if this is not inactivated by glutathione conjugation (see below), fatal damage to the liver and kidney can result (Bessems and Vermeulen, 2001).

1.21.3.4

Expression in tissues, regulation and age-related changes

SULTs in families 1 and 2 are expressed in sites important for drug disposition such as liver, intestine, kidney and lung. Some isoforms, particularly those important in steroid sulfonation, such as SULT1E1 and SULT2A1 are also found in other tissues (Coughtrie, 2016). The expression of SULT enzymes in families 1 and 2 can be up- and down-regulated by interaction of ligands with nuclear receptors that affect other drug-metabolizing enzymes, however the regulation of SULTs is incompletely understood (Runge-Morris et al., 2013). Age-related changes in the mRNA and protein expression of several SULT enzymes have been studied in human liver. The expression of the major drug-metabolizing SULT proteins, i.e., SULT1A1, SULT1A2, SULT1B1, SULT1E1 and SULT2A1 was relatively stable during development from the fetal period through 18 years of age (Dubaisi et al., 2019). This differed from results for expression of UGT enzymes and provides an explanation for clinical observations that in very young infants given acetaminophen, more acetaminophen sulfate was formed than acetaminophen glucuronide (Cook et al., 2016).

1.21.3.5

Polymorphisms

Several polymorphic variants of drug-metabolizing SULT enzymes have been found in the SULT1A subfamily. SULT1A1*1 was found to be more stable than SULT1A1*2 and to have higher activity with common substrates (Nagar et al., 2006). Several investigators have tested the hypothesis that polymorphic variants of SULT1A1 are associated with greater susceptibility to diseases such as breast and colon cancer, perhaps through alterations in metabolism of carcinogens and steroids. Associations have been found, however results suggest additional factors may influence disease incidence and outcome (Xiao et al., 2014). SULT1A2 was found to have several polymorphic variants, however the impact on function with drug substrates is unclear (Lee et al., 2013). Study of the drug-metabolizing and other consequences of polymorphic variants of the SULTs is an ongoing area of study.

1.21.4

Glutathione transferases, GSTs

GST enzymes catalyze the conjugation of an electrophilic site in a drug or phase I metabolite with the thiol group of the tripeptide glutathione, g-glutamyl-cysteinyl-glycine (GSH). For most drug substrates, this results in formation of a GSH conjugate, which is then further metabolized by g-glutamyl-transpeptidase, cysteinyl glycinase and cysteine-S-conjugate N-acetyltransferase (Fig. 6). The final product is an N-acetylcysteine conjugate also known as a mercapturic acid (Hanna and Anders, 2019). This pathway is illustrated in Fig. 6 with the drug ethacrynic acid, which contains an a,b-unsaturated ketone electrophilic functionality, as an example. There are some instances where a GST does not catalyze the formation of a stable GSH conjugate. For example, the experimental drug dichloroacetate is a substrate for GSTZ1-1 (see below for an explanation of the enzyme name) and GSH is required in the reaction but the product is not a stable GSH conjugate, instead the product is glyoxylate (James et al., 2017). The GSTs involved in human drug metabolism belong to a superfamily of cytosolic enzymes that exist as homodimers in the cell. In this superfamily are seven families or classes of GSTs, termed alpha (A), mu (M), pi (P), theta (T), zeta (Z), omega (O) and sigma (S), however the omega and sigma GSTs are not involved in drug metabolism. Within these GST classes are individual enzymes that

Drug Metabolism: Phase II Enzymes

571

Fig. 6 Glutathione conjugation and the mercapturic acid pathway, illustrated with ethacrynic acid. The glutathione molecule and its enzymatically processed products that form the excreted N-acetylcysteine metabolite are shown in red for contrast.

are named with numerals, for example GSTA1–1. Some GST classes, e.g., A and M have more than one member whereas others, e.g., Z and P have just one individual enzyme member. Some GSTs have a dual location in both cytosol and mitochondria (Li et al., 2011; Raza, 2011). In the mitochondria, GSTs are found in the mitochondrial matrix compartment.

1.21.4.1

The co-substrate, GSH

The tripeptide GSH is synthesized from its component amino acids, glutamic acid, cysteine and glycine, by the action of two ATPdependent enzymes, gamma-glutamyl-cysteine synthase and glutathione synthetase (Lu, 2013). In well-nourished individuals GSH is generally present at 2–10 mM concentrations in tissues where drug metabolism occurs such as liver, kidney, intestine and lung (Lu, 2020). These concentrations are adequate for detoxication of low levels of electrophilic drugs, but in cases of overdose, where available GSH is used up, there may be insufficient GSH until more is biosynthesized. In most tissues, including liver, a small fraction of the GSH is present as oxidized GSH, i.e., GSSG, which has two molecules of GSH joined through a thiol bond. GSSG can be reduced to GSH through the action of glutathione reductase.

1.21.4.2

Drug substrates for GSTs

Other than drugs used in chemotherapy, where the objective of the therapy is to design a drug that will bind covalently to a critical target in the cancer cells, then cause cell death, few currently approved drugs have electrophilic centers. The example shown in Fig. 6 is a diuretic drug that was approved in 1967, however in recent years it is rarely prescribed to patients. In general, for non-cancer indications, medicinal chemists avoid drugs with electrophilic centers because the likelihood of off-target toxicity is greater with such drugs (Baillie, 2020). This attitude has changed somewhat recently, if there is a very well-defined protein target for a weakly electrophilic drug; however, this approach is still being developed because of the risk of off-target toxicity (Baillie, 2021; Halford, 2020). Electrophilic centers are often introduced into drugs through phase I metabolism, typically by cytochrome P450. For example, double bonds can be converted to oxiranes, also called epoxides, and drugs with aromatic rings can be oxidized to quinones or quinone-imines. A well-known example is the over-the-counter drug acetaminophen (paracetamol). A minor pathway of metabolism of acetaminophen is cytochrome P450-dependent oxidation to N-acetyl-p-benzoquinone-imine, a reactive, electrophilic metabolite known to cause liver and kidney damage and the reason why an overdose of acetaminophen can be lethal (Ghanem et al., 2016). The N-acetyl-p-benzoquinone-imine metabolite can be detoxified by glutathione conjugation (Fig. 7), however if large amounts of this reactive metabolite are produced, such as if an overdose of acetaminophen is ingested, the tissue stores of GSH

572

Drug Metabolism: Phase II Enzymes

Fig. 7 Acetaminophen metabolism, showing formation of the active metabolite that is detoxified by glutathione conjugation. The major pathway at therapeutic doses is glucuronidation.

can be depleted more rapidly than they can be re-synthesized. Then, the reactive electrophile will instead bind to nucleophilic sites in proteins and cause potentially lethal damage to tissues through several mechanisms (Ghanem et al., 2016). Another drug in which quinone-imine reactive metabolites are thought to be formed, and potentially detoxified by GSH conjugation, is the non-steroidal anti-inflammatory drug diclofenac (Lazarska et al., 2018). Diclofenac is a widely used drug, however a small number of people experience potentially fatal hepatic injury after a few months of taking this drug, for reasons that remain incompletely understood. Extensive investigation of its metabolism has led to clues about reactive metabolites, which include quinone-imines and acyl-glucuronides.

1.21.4.3

Expression in tissues, regulation by drugs, and age-dependent changes

GST enzymes from several classes are found in liver and many extrahepatic tissues, including kidney, intestine, lung, placenta, brain, skin and blood. Members of the GSTA class, especially GSTA1-1, are highly expressed proteins in human and laboratory animal liver cytosol (Hayes and Pulford, 1995). GSTM1-1 is expressed in lung and is thought to play a role in protecting against carcinogenesis by certain tobacco carcinogens. GSTP1-1 was initially discovered in placenta, however this enzyme is also found in other tissues. In liver, GSTP1-1 expression is increased in liver tumors and it is thought to be a tumor marker (Sakai and Muramatsu, 2007). Compared with GSTA1-1, GSTZ1-1 is a low expression enzyme in liver; however, examining GSTZ1-1 expression in various tissues, the liver is relatively the tissue with highest GSTZ1-1 expression. Although GSTZ1-1 can be measured in kidney, brain and heart, the protein expression is very low (Squirewell et al., 2020). GSTT1-1 has been measured in several human tissues, with highest expression in human liver, kidney and small intestine, and lowest expression in lung and testes (Sherratt et al., 1997). The expression levels in tissues of several GSTs in the A, M, P and T classes are upregulated by various xenobiotics and proinflammatory agents that interact with nuclear receptors (Higgins and Hayes, 2011). The nuclear receptors involved include the constitutive androstane receptor (CAR), the pregnane X receptor (PXR), the aryl hydrocarbon receptor (AhR), the peroxisome proliferator activated-receptor alpha (PPARa) and the nuclear factor-erythroid 2-related factor 2 (Nrf2) acting via the anti-oxidant response element (ARE). Different ligands interact with these above-named receptors and initiate synthesis of certain GSTs as well as other drug-metabolizing enzymes. The ligands may be drugs; e.g., phenobarbital leads to CAR activation, rifampin binds to PXR, clofibrate binds PPARa; environmental chemicals, e.g., polycyclic aromatic hydrocarbons bind the AhR; or natural products, e.g., sulforaphane activates Nrf2 and promotes binding to the ARE (Kensler and Wakabayashi, 2010). The GSTs induced through these pathways protect cells against potentially toxic electrophilic drugs and drug metabolites through formation of non-toxic GSH conjugates or through reversible or irreversible binding of the chemicals, thus upregulation is a protective response. There is no evidence that GSTZ1-1 expression is inducible by drugs or other chemicals, however exposure to one of its substrates, dichloroacetate, results in formation of adducts of a reactive metabolite of dichloroacetate to the GSTZ1-1 enzyme protein that trigger its destruction. Loss of GSTZ1-1 protein following exposure to dichloroacetate has been documented in people and animals, and results in slower elimination of dichloroacetate as well as accumulation of its endogenous substrates, the tyrosine catabolites maleylacetoacetate and maleylacetone (James et al., 2017). Like other drug-metabolizing enzyme super-families, GSTs exhibit age-dependent changes in protein expression. This has been studied in humans and lab animals, with a focus on expression in liver. Hines has classified developmental patterns of expression of proteins into three classes (Hines, 2013). Those in class 1 exhibit the highest expression in the first trimester of pregnancy and declines after birth. Those in class 2 exhibit relatively stable expression during gestation and into adulthood. The class 3 proteins are typically not expressed, or are expressed at very low levels in the fetal period and begin to rise after birth. Some enzymes considered to belong to class 3 do not reach full expression until puberty, whereas others reach mature levels within a few weeks or months after birth. In human liver, GSTP1–1 was found to be in class 1, i.e., the protein was expressed in the fetal period then declined to very low levels in normal liver samples from adults (Strange et al., 1989). The GSTs that exhibit high expression in adults, GSTA1-1 and GSTA1-2 fell into class 2, expressed at similar levels throughout the life span (Strange et al., 1989). GSTM1-1 was expressed at

Drug Metabolism: Phase II Enzymes

573

a very low level during gestation then rose after birth, typical of class 3 enzymes (Strange et al., 1989). For another class 3 enzyme, GSTZ1-1, protein expression was undetectable prenatally and in the early post-natal period, then rose during early life to reach adult levels by mid-childhood (Li et al., 2012; Zhong et al., 2018). There is no information in the literature about developmental patterns of GSTT1-1.

1.21.4.4

Polymorphisms

Most human GST genes exhibit polymorphic variants with varying incidences in human populations, as discussed in a comprehensive review (Board and Menon, 2013). If the genetic change is in the open reading frame of the gene, the resulting polymorphic variant proteins will often have an alteration in an amino acid in the enzyme protein. Depending on the location of the change relative to the substrate- or co-substrate-binding site, this can result in changes in the activity of the enzyme, either relating to the affinity of the enzyme for its substrate or co-substrate, or changes in reaction rate. If there are genetic changes in the promoter region of the gene, an outcome can be changes in the expression level of the target GST. Examples of the changes described have been observed in the human GST genes. Another genetic change, reported for two GST families, is gene deletion, where the affected individual does not have the gene for a particular GST. As may be expected with genetic changes, the incidence of each polymorphism in individuals varies with population or ethnic group. Some of these genetic variants are linked with susceptibility to cancer or changes in drug pharmacokinetics, and research into this is ongoing as personalized approaches to medicine develop that consider the influences of individual genetic differences. Polymorphic variants have been discovered in the GSTA class, which makes up a large proportion of the human liver GST enzymes. A promoter region polymorphism is linked to higher expression of the GSTA1 enzyme protein in liver. A variant in the coding region is a synonymous SNP, therefore there is no resultant amino acid change in the GSTA1 protein (Board and Menon, 2013). A well-studied example of gene deletion is the GSTM1 gene, which is deleted in up to 75% of certain populations, such that many people do not express the GSTM1-1 protein. This deletion has been linked with increased susceptibility to lung cancer as well as other forms of cancer, presumably due to lack of detoxifying activity with electrophilic chemical carcinogens such as epoxides (Klusek et al., 2018; Seidegard et al., 1986). There are well-documented polymorphisms in the GSTT1 and GSTT2 genes, including gene deletion, which is observed frequently in people, with varying incidences by population (Board and Menon, 2013). The GSTT1 gene deletion has also been associated with adverse health effects, including cancer progression. The GSTP1 gene has been found to have several polymorphic variants that result in a change to a single amino acid in the enzyme protein. One of these has been associated with altered stability and activity of the enzyme, but there are contradictory reports on the properties of the variants (Board and Menon, 2013). There are three common and a few rare SNPs in the GSTZ1 gene that result in amino acid changes in the enzyme protein and give rise to five common haplotypes (James and Stacpoole, 2016). These impact the pharmacokinetics of single and repeated doses of dichloroacetate (James and Stacpoole, 2016; James et al., 2017; Tian et al., 2019). Interestingly, the rate of inactivation of GSTZ1-1 by dichloroacetate is sensitive to the presence of chloride, and varies by haplotype. GSTZ1*A is more rapidly inactivated by dichloroacetate than GSTZ1*C in the presence of physiological concentrations of chloride (Zhong et al., 2014). In people given dichloroacetate, the first dose showed rapid clearance in an individual who was homozygous for the GSTZ1*A variant, similar to persons who were homozygous for the GSTZ1*C variant, but after multiple doses, the GSTZ1*A homozygote showed a much greater reduction in clearance, (Shroads et al., 2012) suggesting rapid inactivation and providing some confirmation of the in vitro findings of the effect of chloride.

1.21.5

N-acetyltransferases, NATs

Drugs that have a primary amine, hydrazine or N-hydroxy functional group in their structure are potential substrates for N-acetylation. A limited number of drugs possess these functional groups, thus drug acetylation is quantitatively a minor pathway. The two drug-metabolizing NATs are NAT1 and NAT2 and the acetyl group is provided by the co-substrate acetyl-coenzyme A (acetyl-CoA). NAT1 and NAT2 are cytosolic enzymes that exist as monomers (Sim et al., 2008). NAT1 and NAT2 contain the catalytic triad of cysteine, histidine and aspartic acid, which exist in proximity to each other in the active enzyme. For NAT1 and NAT2, the cysteine residue is acetylated by acetyl-CoA, then the acetyl group is transferred to the amine or hydrazine group of the drug substrate (Sim et al., 2014; Weber and Cohen, 1967). Fig. 8 shows the acetylation of isoniazid catalyzed by NAT2.

1.21.5.1

The co-substrate acetyl-CoA

The co-substrate for acetylation is acetyl-CoA, a substance that also plays many roles in physiological processes, where it is an acetyl group donor in protein, lipid and carbohydrate metabolism. Acetyl-CoA concentrations in rat liver were reported to be 0.1–0.2 mM (Perry et al., 2017). Acetyl-CoA can be synthesized in several ways, both in the mitochondria and the cytosol of the cell, due to its importance in intermediary metabolism as well as drug acetylation (Pietrocola et al., 2015). The concentration of acetyl-CoA has not been reported to be a limiting factor in the rate of acetylation of drug substrates.

574

Fig. 8

Drug Metabolism: Phase II Enzymes

Acetylation of a hydrazine, isoniazid. The transferred acetyl group is shown in red.

1.21.5.2

Drug substrates for acetylation

As noted above, potential substrates for acetylation by NAT1 or NAT2 contain free amine or hydrazine groups with a free amine. Examples of drug substrates with a free primary amine include 4-aminosalicylic acid, 5-aminosalicyclic acid, 4-aminobenzoic acid, and amphetamine. Reduction of nitro groups can give rise to amines, for example, a minor pathway of chloramphenicol metabolism is reduction of the nitro group to an amino group. Although hydrazine groups are sometimes present in drug molecules as linkers, these drugs do not have free amines so are not substrates for acetylation. Isoniazid, hydralazine and phenelzine are examples of drugs with the free hydrazine functionality. Overall, however, there are relatively few drug substrates that can undergo acetylation.

1.21.5.3

Expression in tissues and age-dependent changes

The two main drug-metabolizing NATs exhibit different expression in tissues. NAT1 protein is expressed in many tissues, including bladder, prostate, intestine, breast and other sites, whereas NAT2 is predominantly expressed in liver and intestine (Sim et al., 2008). Because of its widespread tissue expression, it has been suggested that there could be physiologically important substrates for NAT1. There is evidence that NAT1 is involved in folate catabolism (Sim et al., 2014; Zheng and Cantley, 2019). This is not the case for NAT2. Although there is some overlap in the drug substrates for each isoform, compounds have been identified that are preferentially or solely acetylated with human NAT1 or human NAT2. A well-known NAT2 substrate that is not a substrate for NAT1 is isoniazid, while a NAT1 substrate that is a very poor substrate for NAT2 is p-aminobenzoic acid (Sim et al., 2014). There have been few studies of the expression of NAT1 and NAT2 enzymes in human development. One study examined acetylation of p-aminobenzoic acid, a NAT1 substrate, in liver, placenta and other tissues from human fetuses as well as adult liver and other tissues, and reported that activity was readily measured in all tested samples, providing evidence that NAT1 is expressed through life (Pacifici et al., 1986). Later studies reported that NAT2 was not detectable in human fetal tissues, summarized in a review (Sim et al., 2014).

1.21.5.4

Polymorphisms

One of the very first reports of individual differences in drug metabolism that were subsequently demonstrated to be due to single nucleotide polymorphisms in the gene for a drug-metabolizing enzyme was for the enzyme NAT2. The drug isoniazid was developed as a treatment for tuberculosis and approved for use in the USA in the early 1950s. Very soon after this drug was introduced, it became apparent that patients differed in their rates of conversion of isoniazid to N-acetyl-isoniazid, and that this was a stable property of an individual (Hughes et al., 1954; Peters et al., 1965). These differences affected the patient’s therapeutic response: those who were slow acetylators were likely to experience adverse side effects, while therapeutic efficacy was lower in rapid acetylators. Through studies of the urinary excretion of isoniazid and N-acetyl-isoniazid, it was proven that it was the metabolic formation of the inactive metabolite N-acetyl-isoniazid from isoniazid, which differed between the individuals, and not the excretion of the N-acetyl-isoniazid that explained these observed differences between individuals (Peters et al., 1965). Different populations around the world exhibited differences in the proportion of rapid and slow acetylators (Gross et al., 1999). Subsequent advances

Drug Metabolism: Phase II Enzymes

575

in techniques and knowledge about molecular biology and genetics led to the discovery that the individual differences in acetylation rates were the result of polymorphic variants in the gene for NAT2 that led to expression of enzymes with differing stability and kinetic properties (Hein, 2006, 2009). Experience with isoniazid was the beginning of the concept of personalized medicine that considers individual differences in drug disposition and action. Some researchers now include a third group, intermediate acetylators in the NAT2 classification (Singh et al., 2009). The NAT1 enzyme also has several polymorphic variants, but studies so far have not shown association of any of these variants with slow drug acetylation. There is, however, some evidence that folate catabolism differs in people with low expression variants of NAT1 (Sim et al., 2014).

1.21.6

Amino acid conjugation enzymes and drug-acyl-CoA pathways

Substrates for amino acid conjugation enzymes are drugs that contain a free carboxylic acid group in their structure. A glycine conjugate of benzoic acid, benzoyl-glycine, commonly known as hippuric acid, was the first drug metabolite to be identified, almost 200 years ago, as described in a review (Conti and Bickel, 1977). When details of the enzymology of this reaction were elucidated (Moldave and Meister, 1957; Schachter and Taggart, 1953), it was found to be unusual among drug metabolism pathways for several reasons. Firstly, it is a mitochondrial pathway, found in the matrix of renal and hepatic mitochondria, and very few drug-metabolizing enzymes are solely located in the mitochondria. Secondly, the drug is first activated to a more reactive derivative, rather than the endogenous co-substrate being activated as is the case for glucuronidation, sulfonation, acetylation and methylation. Thus, amino acid conjugation is a three-step pathway. In the first step, the carboxylic acid drug is converted to the adenylate monophosphate ester (Horng and Benet, 2013; van der Sluis, 2018). In the second step, the adenylate monophosphate ester forms a coenzyme A thioester. Frequently, the first two steps are shown as one step to form the acyl-CoA derivative of the carboxylic acid drug, as the same acyl-CoA synthase (also called acyl-CoA ligase) enzyme catalyzes both steps (Grillo and Lohr, 2009). This enzyme has been shown to be a medium chain acyl-CoA synthase (also called medium chain acyl-CoA ligase, MACS or ACS) (Vessey et al., 1999). In the final step, leading to the excreted amino acid conjugate, an acyl-CoA amino acid N-acyl transferase enzyme uses the drug acyl-CoA thioester as a substrate and catalyzes formation of the amide bond with an amino acid. In humans, this amino acid can be glycine, glutamine or taurine. If the enzyme uses glycine as a substrate, the gene name is GLYAT, and the protein expressed by this gene is the most studied member of this family of enzymes. The reactions described are shown with the example of acetylsalicylic acid (aspirin) as substrate in Fig. 9, part A. In part B examples of drug substrates for taurine and glutamine conjugation are shown (Baldacci et al., 2004; Shirley et al., 1994). It should be noted that other acyl-CoA synthase/ligase enzymes are present in the microsomes and cytosol fractions of the cell, and these normally utilize endogenous compounds including bile acids and long-chain fatty acids as substrates. There is evidence, discussed below, that microsomal or cytosolic acyl-CoA synthases can utilize some carboxylic acid drugs as substrates to form the acyl-CoA derivatives (Darnell et al., 2015). This is important, because in addition to being an intermediate in the formation of an amino acid conjugate, the drug acyl-CoA thioester can take one of several other pathways, some of which do not result in readily excreted metabolites and may cause toxicity (Grillo, 2011; Grillo et al., 2012; Knights, 1998). The other pathways are shown in Fig. 10. These pathways have not been studied extensively as yet.

1.21.6.1

Co-substrates for amino acid conjugation

As described above and shown in Fig. 9, the amino acid conjugation process involves three steps and the final step requires the amino acid co-substrate. In the first two steps, ATP and CoASH are required to form the drug acyl-CoA derivative. These substances are required for many pathways of intermediary metabolism that are critical to life and health, so it may be assumed that they are present in adequate amounts through tightly-regulated feedback mechanisms. In well-nourished people, the availability of glycine, taurine and glutamine to form amino acid conjugates is not limiting. It has, however, been demonstrated that in cases of proteincalorie malnutrition, amino acid conjugation is reduced (Thabrew et al., 1982, 1980).

1.21.6.2

Drug substrates for amino acid conjugation or acyl-CoA formation

Only drugs that contain carboxylic acid groups are potential substrates for formation of acyl-CoA derivatives and only drugs that form acyl-CoA derivatives can subsequently form amino acid conjugates. Many non-steroidal anti-inflammatory drugs such as acetylsalicyclic acid, ibuprofen, ketoprofen and others contain the carboxylic acid functionality and are potential substrates for this pathway (Darnell et al., 2015; Grillo et al., 2012; Hashizume et al., 2021). One study showed that diclofenac and mefenamic acid did not form CoA derivatives in human liver (Hashizume et al., 2021); however, mefenamic acid did form a CoA derivative in rat liver (Grillo et al., 2012; Horng and Benet, 2013), and diclofenac was metabolized to the taurine conjugate in mice, implying the diclofenac-acylCoA was formed in the mouse (Pickup et al., 2012). Similarly the anticonvulsant drug valproate was shown to form small amounts of valproyl glutamine and valproyl glycine in patients taking this drug, thus humans can form the valproyl-CoA intermediate (Gopaul et al., 2003). Valproyl-CoA may be involved in the toxicity of sodium valproate to the liver that is observed in some patients (Silva et al., 2008). It should be remembered that carboxylic acid drugs are also potential substrates for glucuronidation or for elimination in urine as the unchanged drug.

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Drug Metabolism: Phase II Enzymes

Fig. 9 Amino acid conjugation. Part (A) shows all steps in the formation of the glycine conjugate of acetylsalicylic acid (aspirin). CoASH is coenzyme A. The first two steps lead to the thioester of Coenzyme A with the carboxylic acid of the drug substrate. The acyl-CoA thioester forms an amino acid conjugate. (B) Examples of taurine and glutamine conjugation of drugs. The amino acids that form the conjugates are shown in red.

Drug Metabolism: Phase II Enzymes

Fig. 10

1.21.6.3

577

Summary of pathways known to be taken by drug-acyl-CoA intermediates.

Expression in tissues, regulation and age-dependent changes

As described above, the amino acid conjugation pathway requires two enzymes: the acyl-CoA ligase (or synthase) enzyme to form the drug acyl-CoA derivative and the amino acid N-acyltransferase enzyme to form the conjugate. The mitochondrial acyl-CoA ligase enzyme is expressed in the liver and kidney, but not other tissues (van der Sluis, 2018; Vessey et al., 1999; Watanabe et al., 2020). The human ontogeny of this enzyme has not been reported, but in mice the enzyme was not detectable until a few days after birth then rose rapidly to reach stable levels by 1 month of age (Watanabe et al., 2020). There are no reports of the ontogeny of glutamine or taurine N-acyltransferase in humans or animals, however the age-related development of glycine N-acyltransferase (GLYAT) was reported in rats, rabbits and humans (James and Bend, 1978a; Mawal et al., 1997). The enzyme was present in rat liver in late gestation and reached levels similar to those found in adult rats shortly after birth. In rabbits, activity was low in liver and kidney at birth and increased gradually to reach adult levels by 4 weeks of age for liver and 12 weeks of age in kidney: the activity in kidney was about 16-fold higher than in liver (James and Bend, 1978a). In human liver mitochondria, activity was very low in the weeks following birth and increased to adult levels by 18 months of age (Mawal et al., 1997). In mice, the mRNA for GLYAT was not detected prenatally, but rose rapidly after birth to reach adult levels by 20 days (Lu et al., 2013). There have been few studies of the effect on the amino acid conjugation pathway of treatment with compounds that affect expression of other drug-metabolizing enzymes. Early studies suggested that treatment with salicylic acid, acetylsalicylic acid or benzoic acid increased glycine N-acyltransferase activity modestly, but no mechanistic studies were done (James and Bend, 1978a; Wan and Riegelman, 1972). More recently it was shown that treatment of mice with the CAR ligand TCPOBOP (1,4-bis-[2-(3,5dichloropyridyloxy)] benzene) caused a three-fold increase in the mRNA for GLYAT (Li et al., 2016).

1.21.6.4

Polymorphisms in the enzymes for amino acid conjugation

It is likely that there are SNPs that affect expression or protein sequence of the enzymes that form amino acid conjugates, namely acyl-CoA ligase (synthase) and amino acid N-acyltransferases (see Fig. 9); however, this has received little attention by drug metabolism scientists. There have been studies of the genetic diversity of the medium chain acyl-CoA ligases (synthases) in the 1000 genomes Ensembl dataset reported to include data for 2504 individuals from 26 populations as well as in South African Afrikaner volunteers, and five genetic variants that gave rise to amino acid changes in the ACSM2A and 2B enzymes were identified (van der Sluis, 2018). As yet, the effects of these polymorphisms on activity with drugs that contain carboxylic acid functionalities have not been determined. Studies of GLYAT genetic polymorphism have been undertaken in 95 Japanese individuals (Yamamoto et al., 2009) and 55 French Caucasians (Lino Cardenas et al., 2010). Several SNPs were identified in the Japanese and French subjects, some of which were in exons 2 and 5 that were hypothesized to affect metabolic activity, however studies of activities were not conducted. Further research will be needed to determine if variability in either the acyl-CoA synthase genes or amino acid Nacyltransferase genes cause marked effects on the detoxication of carboxylic acid-containing drugs.

1.21.6.5

Other pathways taken by drug acyl-CoA derivatives

As shown in Fig. 10, acyl-CoA derivatives of drugs do not always go on to form amino acid conjugates (pathway 1). The drug-acylCoA derivatives can be substrates for other pathways, often those involved in metabolism of physiologically important acyl-CoA

578

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derivatives, where the drug-acyl-CoA is apparently similar enough to one of the normal endogenous substrates to enter this pathway. Additionally, the drug-acyl-CoA is a chemically reactive substance, which drives some of the pathways shown. Recent studies have shown that acyl-CoA derivatives of several drugs, including zomepirac, diclofenac, tolmetin, flunoxaprofen, ibuprofen and mefenamic acid react with the cysteine thiol of glutathione to form thioester-linked glutathione adducts as shown in pathway 2 (Grillo, 2011; Grillo et al., 2012). These acyl glutathione adducts are generally short-lived, but can potentially react with protein nucleophiles (Wagner et al., 2017), and have been speculated to be linked with adverse effects such as liver injury (Boelsterli, 2002). It has also been postulated that acyl-CoA derivatives can form protein adducts, resulting in toxicity (Darnell and Weidolf, 2013). The interesting phenomenon of chiral inversion of carboxylate drugs with a chiral center adjacent to the carboxylate group, as found in 2-arylpropanoic acids, is listed as pathway 3 in Fig. 10. The pathway has been investigated with the non-steroidal antiinflammatory drug ibuprofen. It was shown that R-ibuprofen converted to S-ibuprofen in people and animals and that the conversion occurs through formation of the acyl-CoA of R-ibuprofen, which then epimerized to the S-derivative and the acyl-CoA derivative of S-ibuprofen was hydrolyzed back to S-ibuprofen (Reichel et al., 1995; Shieh and Chen, 1993; Woodman et al., 2011). With ibuprofen this is therapeutically beneficial, as the S-ibuprofen is a more effective drug than the R-enantiomer. Pathway 4 indicates that drug-acyl CoA derivatives can interact with lipid metabolism to form mixed triglycerides where one of the glycerol hydroxy groups forms an ester with the drug carboxylate group. This phenomenon was observed many years ago, but has received little attention by scientists who study drug metabolism (Crayford and Hutson, 1980; Moorhouse et al., 1991). It clearly differs from most phase II pathways in that the product, the mixed triglyceride, is highly lipophilic and likely to be retained in fatty tissues rather than excreted, as is the case for other phase II metabolites. It has been difficult to determine how often mixed triglycerides of carboxylate drugs are formed, because the products are not present in blood plasma, urine and feces that are normally analyzed for drug metabolites. There may be small amounts in blood lipids, but these are not normally analyzed in pharmacokinetics studies. It is thought that when such mixed triglycerides are formed they will eventually be hydrolyzed and the drug carboxylate released and excreted. Theoretically, this could prolong the duration of action of a drug that forms esters with triglycerides, but the amount involved is probably too small to exert a therapeutic effect. Pathway 5 is another place where some drug-acyl-CoA derivatives enter pathways important in intermediary metabolism. The carnitine pathway is physiologically important in fatty acid metabolism, because the coenzyme A derivatives of fatty acids acylate Lcarnitine and the resulting fatty-acyl-carnitines are taken up across the mitochondrial membranes to enter the mitochondrial matrix, where enzymes of beta-oxidation are located. There is good evidence that valproyl-CoA, arising from the anticonvulsant drug valproic acid, can form valproyl-carnitine in people as well as laboratory animals, as can pivaloyl-CoA arising from pivaloic acid, often used as an ester in prodrugs (McCann et al., 2021; Melegh et al., 1990). Both these examples are small aliphatic carboxylic acids. Pathway 6 is hydrolysis of the drug acyl-CoA back to the parent drug and may be catalyzed by acyl-CoA thioesterases that are present in many tissues, including liver, and whose main role is hydrolyzing fatty acid CoA thioesters of varying chain lengths (Hunt and Alexson, 2002). Rapid hydrolysis of phenylacetyl-CoA was found in rabbit lung, intestine and placenta (James and Bend, 1978a,b). Pathways 7 and 8 are highly related to normal pathways of fatty acid metabolism but have occasionally been observed with drug carboxylic acids. Chain elongation by addition of a two-carbon fragment adjacent to the carboxylate group was observed in studying a potential drug candidate, 5-(40 -chloro-n-butyl)picolinic acid (Miyazaki et al., 1976) and the pathway was later demonstrated with benzoic acid (Marsh et al., 1982). The mechanism has not been studied, however it has been pointed out that similar reactions occur during synthesis of fatty acids (Caldwell and Marsh, 1983). Chain shortening was discovered during studies of the metabolism of the local anesthetic drug sameridine, which has an n-hexyl side chain. It was found that the terminal carbon of this side chain was oxidized to the hydroxylated n-hexanol metabolite and subsequently to the hexanoic acid: the hexanoic acid then lost two carbons and was converted to the n-butanoic derivative (Sohlenius-Sternbeck et al., 2000). The authors suggested, but did not demonstrate, that the acyl-CoA of the hexanoic acid metabolite entered the beta-oxidation pathway, consistent with the loss of two carbons. There are only a few examples in the literature of several of the additional pathways that can be taken by drug-acyl-CoA derivatives. Some of these pathways may result in toxicity, thus should be kept in mind when considering the metabolism of new carboxylate-containing drugs.

1.21.7

Methyltransferases, MTs

Methylation is another relatively minor phase II pathway of drug metabolism, because a limited number of drugs are substrates. It differs from most phase II pathways in that the metabolite formed is less polar and more lipophilic than the starting drug or drug metabolite, thus it is not readily excreted. Functional groups in drugs that are potentially substrates for one of the methyltransferase (MT) enzymes include catechols and thiols. The methylation reaction can be reversed by cytochrome P450, to re-form the catechol or thiol drug through N-dealkylation. The source of the methyl group is S-adenosylmethionine and one or more of a family of methyltransferase enzymes catalyzes the reaction. As well as being a drug-metabolizing enzyme for a rather small number of drug substrates, various MT enzymes have very important physiological functions. For example, catechol O-methyltransferase, COMT, metabolizes dopamine and other physiologically important bioamines, and is a drug target in treatment of patients with Parkinson’s disease. COMT also metabolizes catechol estrogens, which are proposed as carcinogenic metabolites of estrogens

Drug Metabolism: Phase II Enzymes

579

Fig. 11 Methylation reactions. The upper panel shows methylation of the catechol metabolite of a drug, propranolol and the lower panel shows methylation of 6-mercaptopurine. The methyl group that is transferred from S-adenosylmethionine is shown in red.

(Cavalieri et al., 1997), to non-carcinogenic metabolites. Thiopurine methyltransferase (TPMT) is the major enzyme that metabolizes thiopurine drugs used in chemotherapy and in treatment of Crohn’s disease, and has several polymorphic variants that affect activity. Although not directly relevant to drug metabolism, methylation of target groups in DNA is an epigenetic mechanism that regulates gene expression and methylation of bases in RNA is a post-transcriptional modification (Moore et al., 2013; Zhou et al., 2020). There are numerous methyltransferase enzymes involved in DNA and RNA methylation. Separate methyltransferases catalyze small molecule methylation. Fig. 11 shows the methylation reaction with example catechol and thiopurine drugs.

1.21.7.1

Co-substrate for methylation

The methyl donor in all methylation reactions is provided by S-adenosyl-methionine (SAM). S-adenosyl-methionine is often abbreviated AdoMet. The methyl-thio group of methionine bonded to adenosine renders the methyl group reactive and ready to participate in the enzyme-catalyzed methylation reaction. The concentration of SAM in hepatocytes from control mice was found to be 42  19 mM (Caudill et al., 2001) and to be reduced to 13  4 mM in mice fed a methionine-folate-choline deficient diet. In humans the liver concentration is reported to be 50–80 mM (Finkelstein, 1990). It has been reported that SAM synthesis in the liver is reduced in various disease states, especially liver diseases (Lu and Mato, 2012). The KM values for SAM in catalyzing methylation of various physiologically important catechols such as dopamine by expressed human COMT, soluble and microsomal forms, ranged from 20 to 50 mM (Lotta et al., 1995). As the concentration of SAM in healthy people is similar to the KM, rates of methylation of catechols could be reduced in disease states. The SAM KM for the human liver TPMT enzyme has been reported to be 2.7 mM, thus SAM concentration should not be rate-limiting for this enzyme (Szumlanski et al., 1992).

1.21.7.2

Drug substrates for methylation

Several drugs that are inhibitors of COMT contain catechol groups in their structures, including carbidopa, entacapone and tolcapone; as well as being inhibitors, these drugs are substrates for methylation of the catechol group (Jorga et al., 1999; Vickers et al., 1975). The best known substrate of TPMT is 6-mercaptopurine, which is also an active metabolite of the thiopurine pro-drug, azathioprine (Kapiowitz, 1976; Stocco et al., 2007). In addition, several ortho-, meta- and para-substituted thiophenols were found to be substrates of TPMT (Ames et al., 1986).

1.21.7.3

Expression in tissues and age-dependent change

The COMT enzyme has microsomal and cytosolic forms that are products of the same gene and are widely expressed in many tissues of the body, including brain, as well as in red blood cells, with highest expression in liver and kidney (Myöhänen et al., 2010; Weinshilboum et al., 1999). The microsomal form has an extra 50-amino-acid peptide at the N-terminus of the protein but is otherwise identical to the cytosolic form. One study of the age-related development of COMT in humans was published in 1971 (Agathopoulos et al., 1971). This study reported COMT activity was measurable in pre-term infants, rose steadily after birth, reached adult levels between age 6 and 18, then declined somewhat after age 60. A more recent study in mice examined mRNA expression in liver and showed COMT was present prenatally then rose rapidly after birth to reach adult levels by 30 days of age (Lu et al., 2013).

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Drug Metabolism: Phase II Enzymes

TPMT is a cytosolic enzyme (Szumlanski et al., 1992) that, like COMT, is expressed in many tissues including red blood cells. Its highest expression is in liver and kidney (Pacifici et al., 1991). TPMT was shown to be present in human fetal liver and kidney with measured enzyme activity about one-third of that in adults (Pacifici et al., 1991). In mice, mRNA levels for TPMT were present prenatally, increased rapidly to a peak at day 20 after birth, then decreased somewhat to a steady level up to 60 days of age, the oldest studied (Lu et al., 2013).

1.21.7.4

Polymorphisms

As is the case for many drug-metabolizing enzymes, the methyltransferase enzymes, including COMT and TPMT, exhibit variations in protein structure and properties due to SNPs in the genes for these enzymes (Appell et al., 2013; Hall et al., 2019; Weinshilboum et al., 1999). The most widely studied polymorphism of the COMT gene is a change from valine to methionine in the protein, which causes lower activity and lower protein stability in the form that has methionine (Syvänen et al., 1997). Because of the important role of COMT in metabolism of neurotransmitters, including dopamine, the influence of this polymorphism on neuropsychiatric disorders has been studied, as discussed in a recent review (Hall et al., 2019). Researchers have also investigated the possibility of a link between the low activity variant of COMT and increased risk of breast cancer, due to reduced metabolism of catechol estrogens, however results are not clear-cut and it was suggested other factors are important also (Sak, 2017). There has been extensive research into the polymorphic variants of TPMT, as in this case the phenotype will determine whether or not the drugs 6-mercaptopurine and azathioprine can be used safely in a patient with cancer or Crohn’s disease (Weinshilboum et al., 1999). Clinical research has demonstrated that knowing a patient’s phenotype or genotype for this enzyme is very important for predicting the safe and effective use of azathioprine, 6-mercaptopurine and 6-thioguanine in cancer chemotherapy and other therapeutic applications (Krynetski and Evans, 2003). Numerous variants of the TPMT gene have been discovered, several of which affect expression, activity or both properties of the TPMT protein (Appell et al., 2013). Tests to measure activity (phenotype) were first introduced in the 1990s, however it is now more common to analyze the genetic variants (genotype) prior to therapy with these drugs to determine the safe and effective dose (Relling et al., 2011).

1.21.8

Conclusions

This chapter has described the drug metabolism enzyme activities and characteristics of the major phase II enzymes, classified by reaction type. Usually, the phase II conjugative reaction is the last enzymatic step before eliminating a drug or a phase I drug metabolite from the body, with or without the assistance of transporter proteins. Like the phase I drug-metabolizing enzymes, there are superfamilies of related phase II enzymes that can catalyze a particular phase II reaction. Each family member catalyzes the same overall reaction pertinent to a particular functional group, but typically, different family members have distinct properties that govern preference for a preferred drug substrate. A distinguishing feature of the phase II enzymes is that they require adequate concentrations of the endogenous co-substrate for the reaction for optimal activity with a given concentration of the drug substrate. For several phase II pathways of drug metabolism, the endogenous concentration of the co-substrate in the liver or other tissue may be less than that needed for maximal activity with a given drug concentration, for various reasons. As in vitro studies of drug metabolism typically employ conditions in which the maximal activity is measured, these should be extrapolated with caution to the in vivo situation. Although phase II pathways were the first drug metabolism pathways to be discovered, we still have much to learn about the importance of these pathways in safe and effective drug therapy.

See Also: 1.23: Drug Transporters: Efflux; 1.26: Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters; 1.27: Drug-Drug Interactions with a Pharmacokinetic Basis

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Molecular Genetics and Genomics 293, 1279–1292. Venkatachalam, K.V., Akita, H., Strott, C.A., 1998. Molecular cloning, expression, and characterization of human bifunctional 30 -phosphoadenosine 50 -phosphosulfate synthase and its functional domains. The Journal of Biological Chemistry 273, 19311–19320. Vessey, D.A., Kelley, M., Warren, R.S., 1999. Characterization of the CoA ligases of human liver mitochondria catalyzing the activation of short- and medium-chain fatty acids and xenobiotic carboxylic acids. Biochimica et Biophysica Acta 1428, 455–462. Vickers, S., Stuart, E.K., Hucker, H.B., Vandenheuvel, W.J.A., 1975. Metabolism of carbidopa, (-)-L-.alpha.-hydrazino-3,4-dihydroxy-.alpha.-methylbenzenepropanoic acid monohydrate, in the human, rhesus monkey, dog, and rat. Journal of Medicinal Chemistry 18, 134–138. Wagner, G.R., Bhatt, D.P., O’Connell, T.M., Thompson, J.W., Dubois, L.G., Backos, D.S., Yang, H., Mitchell, G.A., Ilkayeva, O.R., Stevens, R.D., Grimsrud, P.A., Hirschey, M.D., 2017. A class of reactive acyl-CoA species reveals the non-enzymatic origins of protein acylation. Cell Metabolism 25, 823-837.e8. Wan, S.H., Riegelman, S., 1972. Renal contribution to overall metabolism of drugs. II. Biotransformation of salicylic acid to salicyluric acid. Journal of Pharmaceutical Sciences 61, 1284–1287. Wang, L.Q., James, M.O., 2006. Inhibition of sulfotransferases by xenobiotics. Current Drug Metabolism 7, 83–104. Watanabe, H., Paxton, R.L., Tolerico, M.R., Nagalakshmi, V.K., Tanaka, S., Okusa, M.D., Goto, S., Narita, I., Watanabe, S., Sequeira-Lopez, M.L.S., Gomez, R.A., 2020. Expression of Acsm2, a kidney-specific gene, parallels the function and maturation of proximal tubular cells. American Journal of Physiology. Renal Physiology 319, F603–f611. Weber, W.W., Cohen, S.N., 1967. N-acetylation of drugs: Isolation and properties of an N-acetyltransferase from rabbit liver. Molecular Pharmacology 3, 266–273. Weinshilboum, R.M., Otterness, D.M., Szumlanski, C.L., 1999. Methylation pharmacogenetics: Catechol O-methyltransferase, thiopurine methyltransferase, and histamine Nmethyltransferase. Annual Review of Pharmacology and Toxicology 39, 19–52. Williams, R.T., 1959. Detoxication Mechanisms The Metabolism and Detoxication of Drugs, Toxic Substances and Other Organic Compounds. Chapman and Hall, Ltd., London. Woodman, T.J., Wood, P.J., Thompson, A.S., Hutchings, T.J., Steel, G.R., Jiao, P., Threadgill, M.D., Lloyd, M.D., 2011. Chiral inversion of 2-arylpropionyl-CoA esters by human a-methylacyl-CoA racemase 1A (P504S)dA potential mechanism for the anti-cancer effects of ibuprofen. Chemical Communications 47, 7332–7334. Xiao, J., Zheng, Y., Zhou, Y., Zhang, P., Wang, J., Shen, F., Fan, L., Kolluri, V.K., Wang, W., Yan, X., Wang, M., 2014. Sulfotransferase SULT1A1 Arg213His polymorphism with cancer risk: A meta-analysis of 53 case-control studies. PLoS One 9, e106774. Yamamoto, A., Nonen, S., Fukuda, T., Yamazaki, H., Azuma, J., 2009. Genetic polymorphisms of glycine N-acyltransferase in Japanese individuals. Drug Metabolism and Pharmacokinetics 24, 114–117. Zheng, Y., Cantley, L.C., 2019. Toward a better understanding of folate metabolism in health and disease. The Journal of Experimental Medicine 216, 253–266. Zhong, G., Li, W., Gu, Y., Langaee, T., Stacpoole, P.W., James, M.O., 2014. Chloride and other anions inhibit dichloroacetate-induced inactivation of human liver GSTZ1 in a haplotype-dependent manner. Chemico-Biological Interactions 215C, 33–39. Zhong, G., James, M.O., Smeltz, M.G., Jahn, S.C., Langaee, T., Simpson, P., Stacpoole, P.W., 2018. Age-related changes in expression and activity of human hepatic mitochondrial glutathione transferase zeta1. Drug Metabolism and Disposition 46, 1118–1128. 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1.22

Drug TransportdUptake

Philip Sandovala and Bruno Hagenbuchb, a Global Drug Metabolism and Pharmacokinetics, Takeda Pharmaceutical Company Limited, Cambridge, MA, United States; and b Department of Pharmacology, Toxicology and Therapeutics, The University of Kansas Medical Center, Kansas City, KS, United States © 2022 Elsevier Inc. All rights reserved.

1.22.1 1.22.2 1.22.2.1 1.22.2.2 1.22.3 1.22.3.1 1.22.3.2 1.22.3.2.1 1.22.3.2.2 1.22.3.2.3 1.22.3.2.4 1.22.3.2.5 1.22.3.3 1.22.3.3.1 1.22.3.3.2 1.22.3.3.3 1.22.3.3.4 1.22.3.3.5 1.22.3.4 1.22.3.4.1 1.22.3.4.2 1.22.3.4.3 1.22.3.4.4 1.22.3.4.5 1.22.3.5 1.22.3.6 1.22.3.6.1 1.22.3.6.2 1.22.3.6.3 1.22.3.6.4 1.22.3.6.5 1.22.3.7 1.22.3.7.1 1.22.3.7.2 1.22.3.7.3 1.22.3.7.4 1.22.3.7.5 1.22.3.8 1.22.3.8.1 1.22.3.8.2 1.22.3.8.3 1.22.3.8.4 1.22.3.8.5 1.22.4 1.22.5 References

Introduction Basic principles of transport Transporters as drug targets Transporters as targets for drug-drug interactions Main drug uptake transporters Introduction to liver transporters OATP1B1 (SLCO1B1) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions OATP1B3 (SLCO1B3) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions OCT1 (SLC22A1) (EMA considerations) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions Introduction to kidney transporters OCT2 (SLC22A2) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions OAT1 (SLC22A6) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions OAT3 (SLC22A8) Expression and function Animal models Regulation Polymorphisms Drug-drug interactions Summary and conclusions Disclosure of interest

Comprehensive Pharmacology, Volume 1

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Glossary Ki or IC50 Concentration of inhibitor that reduces transport by 50%.

1.22.1

Introduction

During the past 10–20 years interest in drug transporter identification and characterizations has increased both in academia and more recently also in the pharmaceutical industry. The first drug uptake transporters were cloned in the mid-to-late 1990s (Kullak-Ublick et al., 1996; Hsiang et al., 1999; Konig et al., 2000a,b; Abe et al., 1999; Tamai et al., 2000; Gorboulev et al., 1997; Reid et al., 1998; Race et al., 1999). After the sequencing of the human genome it was estimated that about 10% of all human genes are transporter related. Among them are co-transporters, exchangers and passive transporters that are classified as Solute Carriers (SLC) (https://www.bioparadigms.org/slc/intro.htm). In contrast to these SLC transporters there is the superfamily of the ATP-binding cassette (ABC) transporters, most of which are efflux transporters and are covered elsewhere in this reference work. It recently became clear that many of the available and newly discovered drugs relied at least in part on the function of transporters to gain entry into the body (absorption), to reach their respective targets within the cells of different organs (distribution), and to be excreted by the kidneys and the liver (elimination). After the realization that in addition to drug-drug interactions at drug metabolizing enzymes, adverse drug effects could be due to drug-drug interactions at drug transporters, regulatory agencies like the U. S. Food and Drug Administration (FDA), the European Medicines Agency (EMA) and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) began to require the pharmaceutical industry to test certain drug transporters for such drug-drug interactions. These requirements were developed based on recommendations published in whitepapers by the International Transporter Consortium (Giacomini et al., 2010; Hillgren et al., 2013; Zamek-Gliszczynski et al., 2018b). In this chapter we will focus on six uptake transporters that FDA, EMA and PMDA require or are considering to require to be tested when new drugs are developed. These transporters are classified in two solute carrier families, the SLC22 and the SLCO families. More specifically, they are the organic cation transporter 1 (OCT1; SLC22A1), OCT2 (SLC22A2), the organic anion transporter 1 (OAT1; SLC22A6), OAT3 (SLC22A8), the organic anion transporting polypeptide 1B1 (OATP1B1; SLCO1B1) and OATP1B3 (SLCO1B3).

1.22.2

Basic principles of transport

Unlike in the early days of drug development when the major pathway of drug absorption was considered to be simple diffusion (passive), it is quite well-established nowadays that most drugs need transport proteins to cross the cell membrane (carrier mediated) and that simple diffusion through the phospholipid bilayer is negligible (Dobson et al., 2009). Carrier mediated transport can occur passively along the electrochemical gradient, or actively against the electrochemical gradient (Fig. 1). Active transport requires energy. Depending on the way the energy is supplied, active transport can be classified into primary, secondary or tertiary active transport (Fig. 1). Primary active transporters are proteins that hydrolyze ATP to pump their substrates against the electrochemical gradient of the substrate. Examples are the efflux transporters discussed elsewhere in this reference work and the Naþ/Kþ-ATPase that establishes the out-to-in sodium and in-to-out potassium gradient. Secondary active transporters are uptake transporters that use the sodium gradient that has been established by the Naþ/Kþ-ATPase to drive their substrates into the cell against the electrochemical gradient of the substrate. Examples of secondary active transporters are the small intestinal Naþ/glucose cotransporter (SGLT1; SLC5A1), the liver Naþ/taurocholate cotransporting polypeptide (NTCP; SLC10A1) or the apical sodium-dependent bile acid transporter (ASBT; SLC10A2). Examples of tertiary active transporters are the renal organic anion transporter 1 (OAT1; SLC22A6) and OAT3 (SLC22A8) (Burckhardt and Burckhardt, 2011). These OATs use the in-to-out gradient of a-ketoglutarate established by the secondary active sodium-dependent dicarboxylate transporter (NaDC3; SLC13A3) to drive uptake of organic anions across the basolateral membrane into renal proximal tubule cells (Fig. 1). In order to characterize drug uptake, transporters are generally expressed in a cell line that does not express any significant amount of the respective transporter and thus these “wild-type” cells can be used as negative controls. Examples of cell lines that are frequently used for drug uptake studies are human embryonic kidney (HEK293) and Chinese hamster ovary (CHO) cells. These two cell lines are fibroblast like and not polarized but grow well on plastic and are normally used as adherent cells. If polarized cells are required, Madin-Darby Canine Kidney (MDCK) cells, pig kidney epithelial cells (LLC-PK1) or Caco-2 cells, derived from a human colorectal adenocarcinoma are used. These three cell lines exhibit clear apical and basolateral membranes and grow in a polarized fashion on plastic or on filters. In most cases, transporters that are found on the basolateral membrane in an organ are also expressed at the basolateral membrane in these polarized epithelial cells. Similarly, transporters normally expressed at the apical membrane are expressed at the apical membrane of these cells. These features allow the generation of double,

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Fig. 1 Mechanisms of substrate transport. Substrate transport can occur passively (left side) along an electrochemical gradient or actively (right side) against an electrochemical gradient. Active transport is further divided into three categories: primary active if the transporter directly hydrolyzes ATP, secondary active if a gradient established by a primary active transporter is used, and tertiary active if a concentration gradient established by a secondary active transporter is used to drive uphill substrate transport. a-KG: a-ketoglutarate; OA: organic anion.

triple and even quadruple transfected cell lines that can be used to mimic transepithelial transport of drug substrates by a combination of uptake and efflux transporters (Kopplow et al., 2005; Fahrmayr et al., 2012; Hirouchi et al., 2009). In all of these transfected cell lines the non-transfected “wild-type” cells are used as negative controls and uptake values obtained in these negative controls are subtracted from the values of the transfected cells to obtain transporter-mediated uptake. If primary isolated cells like hepatocytes, enterocytes or renal proximal tubular cells are used, the negative controls are frequently obtained by measuring uptake at 4  C, arguing that any carrier-mediated transport is essentially zero under this condition, and any substrate uptake would correspond to simple diffusion. However, it should be kept in mind that the fluidity of the cell membrane changes as a function of temperature and a better negative control would be to incubate the cells in the presence and absence of a transporter inhibitor at the same temperature (normally 37  C).

1.22.2.1

Transporters as drug targets

Transporters can be targeted either by an inhibitor to prevent drug uptake or by a substrate for tissue- or cell-selective uptake. Examples of transporter inhibitors are loop diuretics like furosemide or bumetanide that are direct inhibitors of the Naþ/Kþ/ 2 Cl-cotransporter (NKCC2; SLC12A1) located in the thick ascending limb of the loop of Henle. Similarly, thiazide diuretics directly inhibit the NaCl-cotransporter (NCC; SLC12A3) in the distal convoluted tubules. Both of these drug classes lead to increased excretion of sodium and are used in the treatment of heart failure, edema and high blood pressure. Canagliflozin, dapagliflozin, and empagliflozin are inhibitors of the sodium glucose cotransporter 2 (SGLT2; SLC5A2) expressed in the kidney (Vasquez-Rios and Nadkarni, 2020). These medications reduce blood glucose levels by inhibiting glucose reabsorption in the proximal tubule and are used to treat type 2 diabetes. There are also examples of transporters that allow drugs to reach their respective intracellular target. Statins are used to treat hypercholesterolemia and work by inhibiting HMG-CoA reductase, the rate limiting enzyme in hepatic cholesterol synthesis. Thus, statins must enter hepatocytes in order to reach their target enzyme. The first statin was approved in 1987 by the FDA, well before the involved drug uptake transporters were identified and characterized. Today we know that statins are mainly taken up into hepatocytes with the help of OATP1B1 and OATP1B3, transporters almost exclusively expressed in human hepatocytes. Another example is metformin, one of the preferred drugs to treat type 2 diabetes. It is a substrate of OCT1, the hepatocellular organic cation transporter expressed at the basolateral membrane. Metformin works by decreasing hepatic glucose production and thus needs to be taken up into hepatocytes where it inhibits the mitochondrial respiratory chain. This leads to activation of AMPK which in turn enhances insulin sensitivity (Rena et al., 2017). These two examples of drugs that act in the liver demonstrate that selective uptake by transporters expressed in the target organ or cell can make certain drugs relatively selective and efficient. Along these lines researchers are actively attempting to target certain transporters (e.g., expressed at the blood-brain barrier) to increase drug concentrations in the brain.

588 1.22.2.2

Drug TransportdUptake Transporters as targets for drug-drug interactions

Numerous pharmacokinetic studies have demonstrated that besides drug metabolizing enzymes, transporters are also important for drug absorption and elimination. Like drug-drug interactions at drug metabolizing enzymes, drug-drug interactions can also occur when transporters are inhibited (see above). All of the important drug uptake transporters have a broad substrate specificity, meaning that many different drugs are transported by the same transport proteins, and this can lead to drug-drug interactions at the transporter level. Such drug-drug interactions can be positive or negative. An example of a positive drug-drug interaction would be the selective inhibition of OCT2-mediated cisplatin uptake in the kidneys of cancer patients that are treated with cisplatin, preventing cisplatin induced nephrotoxicity. Another example of a positive drug-drug interaction that is actually used in the clinics is the use of probenecid to inhibit URAT1-mediated reabsorption of uric acid to treat hyperuricemia. Probenecid also inhibits renal OAT1 and OAT3, transporters that help with the secretion of penicillin. Inhibition of these transporters increases serum levels of penicillin and thus lower doses of penicillin can be given. An example of a negative drug-drug interaction is the inhibition of OATP1B1 and OATP1B3 by the immunosuppressant cyclosporin A. Cyclosporin A is a potent inhibitor of these two OATPs and will result in increased statin plasma levels due to inhibition of statin uptake into hepatocytes (Patel et al., 2016; Niemi et al., 2011). Similar effects can be seen with rifampicin, gemfibrozil and HIV protease inhibitors. Another example of a negative drug-drug interaction is the inhibition of OCT1 by verapamil which leads to decreased efficacy of metformin due to decreased uptake into hepatocytes. Probenecid can also lead to decreased renal clearance of drugs that are substrates of OAT1 and or OAT3, like acyclovir, cidofovir and furosemide. Thus, whether drug-drug interactions are positive or negative depends on the exact circumstances. Because some of these drug-drug interactions at the transporter levels can result in serious adverse effects, e.g., rhabdomyolysis due to increased statin plasma concentrations, the International Transporter Consortium held a workshop in 2008 to discuss transporters that are important for absorption, distribution and elimination of drugs and discussed methods to test these transporters in vitro for potential drug-drug interactions. The workshop resulted in the first whitepaper in 2010 that later on was the basis for the FDA guidance to the pharmaceutical industry for the approval of new drugs (Giacomini et al., 2010). Since 2010, the International Transporter Consortium has met several times and updated their recommendations in additional whitepapers (Hillgren et al., 2013; Brouwer et al., 2013; Zamek-Gliszczynski et al., 2018b) that eventually resulted in the latest FDA Guidance for Industry (FDA, 2020). Based on the recommendations of the International Transporter Consortium, the regulatory agencies in the United States of America (FDA), in Europe (EMA) and in Japan (PMDA) consider the following uptake transporters relevant for drug disposition and recommend or require them to be tested for drug-drug interactions when new drugs are developed: the liver transporters OATP1B1, OATP1B3, OCT1 (only considered by EMA and PMDA), and the renal transporters OCT2, OAT1, and OAT3. In the following, we will summarize the current knowledge that is available for these transporters and briefly outline their physiological and pharmacological roles.

1.22.3

Main drug uptake transporters

1.22.3.1

Introduction to liver transporters

The liver is one of the major organs for drug metabolism and elimination. In particular, large (> 400 Da) and hydrophobic endoand xenobiotics, including bile acids and numerous drugs, are transported into hepatocytes by several different transporters (Fig. 2).

Fig. 2 Transport systems in human hepatocytes. Transport systems for bile acids (BA, green), organic anions (OA, black) and organic cations (OCþ, red) are depicted. BCRP: breast cancer resistance protein; BSEP: bile salt export pump; NTCP: Naþ/taurocholate cotransporting polypeptide; MATE1: multidrug and toxin extrusion 1; MDR: multidrug resistance protein; MRP: multidrug resistance-associated protein; OAT2: organic anion transporter 2; OATP: organic anion transporting polypeptide; OCT1: organic cation transporter 1. For details please see the text.

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These compounds can then be excreted unchanged mainly by ATP-binding cassette (ABC) transporters including breast cancer resistance protein (BCRP), bile salt export pump (BSEP), multi-drug resistance protein (MDR1), or multi-drug resistance associated proteins (MRPs) (Fig. 2). Some are modified by phase I drug metabolizing enzymes of the cytochrome P450 superfamily and then conjugated to more polar and thus more water-soluble compounds by transferases. Once metabolized, these compounds are transported into bile for fecal excretion or secreted back into the blood stream for eventual excretion by the kidneys. To cross the sinusoidal or basolateral membrane of hepatocytes the above mentioned three liver transporters OATP1B1, OATP1B3 and OCT1 are especially important (Fig. 2).

1.22.3.2 1.22.3.2.1

OATP1B1 (SLCO1B1) Expression and function

The organic anion transporting polypeptide 1B1 (OATP1B1) was cloned independently by three different groups and the results were published in 1999 and 2000. It was initially called OATP2 (Hsiang et al., 1999), LST-1 (liver-specific transporter 1) (Abe et al., 1999), and OATP2 or OATP-C (Konig et al., 2000b). Since the introduction of a phylogenetic classification for the OATPs, it is known as OATP1B1 with the gene symbol SLCO1B1 (Hagenbuch and Meier, 2004). OATP1B1 is a glycoprotein with an apparent molecular weight of 84 kDa encoded by 691 amino acids. It is evenly expressed throughout the liver lobule at the sinusoidal membrane of human hepatocytes (Abe et al., 2001; Cui et al., 2003) where it mediates the uptake of various endo- and xenobiotics. Endogenous substrates include bile acids, bilirubin and its glucuronides, conjugated and unconjugated hormones, leukotrienes and prostaglandins (Hagenbuch and Stieger, 2013). Besides these endogenous substrates, OATP1B1 transports a wide variety of structurally unrelated organic compounds that include several drug classes like the cholesterol lowering statins, the antihypertensive sartans, antibiotics, antivirals, and many anticancer drugs (Roth et al., 2012; Schulte and Ho, 2019). Frequently used model substrates to characterize OATP1B1 function in vitro are estrone-3-sulfate, estradiol-17b-glucuronide, bromosulfophthalein, and pravastatin mainly because they are readily available in radioactive form or because they are of clinical importance (statins) (Hsiang et al., 1999; Konig et al., 2000b; Gui et al., 2008; Kindla et al., 2011; Tirona et al., 2001). The main recombinant expression systems used in the characterization of OATP1B1 and in experiments aimed at investigating potential drug-drug interactions are HeLa (Tirona et al., 2003) and HEK293 (Wagner et al., 2020) cells transiently expressing the protein or stable cell lines such as CHO (Gui et al., 2008), HEK293 and MDCKII (Konig et al., 2000b) cells. The mechanism of OATP1B1 mediated uptake into hepatocytes has not been fully elucidated yet. However, experiments demonstrating trans-stimulation suggest that OATPs function as exchangers and that physiologically bicarbonate could be the counterion (Satlin et al., 1997; Sugiyama et al., 2003; Schafer et al., 2018; Leuthold et al., 2009).

1.22.3.2.2

Animal models

Frequently the importance of enzymes or transporters can be evaluated in animal models, in particular using knockout mice. This is not as straight forward for OATPs as it is for other proteins. Besides OATP1B1, humans also express OATP1B3 (see below) which has 80% amino acid identity to OATP1B1, at the basolateral membrane of hepatocytes (Hagenbuch and Meier, 2004). In contrast, mice and rats only have a single OATP1B family member expressed, OATP1B2 (Slco1b2). Thus, the OATP1B2-null mouse (Lu et al., 2008; Zaher et al., 2008) and the OATP1B2 knockout rat (Ma et al., 2020) do not allow distinction between the functions of the two human OATP1B members but resemble the loss of both human OATPs. Because OATP1A members potentially could compensate for the loss of function of OATP1B2, the Schinkel group generated an OATP1A/1B knockout mouse where all the Slco1a genes (Slco1a1, Slco1a4, Slco1a5 and Slco1a6) in addition to the Slco1b2 gene were removed (van de Steeg et al., 2010). These mice confirmed the importance of the OATP1A/1B members for the uptake of bilirubin in addition to the uptake of unconjugated bile acids (van de Steeg et al., 2010). Later on, humanized mice expressing either OATP1B1 and/or OATP1B3 on the OATP1A/ 1B null background were generated. These OATP1A/1B knockout mice helped to explain the human Rotor syndrome which is caused by a combined loss of functional OATP1B1 and OATP1B3 (van de Steeg et al., 2012). Rotor syndrome is a rare benign conjugated hyperbilirubinemia that resembles the Dubin-Johnson syndrome, but patients do not have any mutations in the ABCC2 gene and thus bilirubin glucuronide is transported normally across the canalicular membrane. These animal models confirmed the importance of OATP1B1 and OATP1B3 for the disposition of statins (Salphati et al., 2014; Higgins et al., 2014), and for the handling of antitumor drugs (Durmus et al., 2016). They were also used to investigate drug-drug interactions at OATP1B1 and OATP1B3 in vivo (Durmus et al., 2015). With respect to endogenous substrates, studies with OATP1B2 knockout animals together with studies that investigated the effects of polymorphisms in SLCO1B1 in healthy volunteers indicated that OATP1B1 and OATP1B3 are important for the uptake of unconjugated bile acids (Lu et al., 2008; Zaher et al., 2008; Xiang et al., 2009; Iusuf et al., 2012).

1.22.3.2.3

Regulation

OATP1B1 expression can be regulated at the transcriptional and post-translational levels. At the transcriptional level, several transcription factors have been shown to interact at the promotor of the SLCO1B1 gene: the two hepatocyte nuclear factors HNF1a (Jung et al., 2001) and HNF4a (Kamiyama et al., 2007) seem to be crucial for the liver-specific expression of OATP1B1; while the oxysterol-activated nuclear liver X receptor (LXR) and the bile acid-activated nuclear farnesoid X receptor (FXR) might be responsible for the interindividual expression levels of OATP1B1 (Meyer Zu Schwabedissen et al., 2010). MicroRNAs like miR-206, which can suppress the expression of OATP1B1, act at the post-transcriptional level (Krattinger et al., 2016a; El Saadany et al., 2019).

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Furthermore, epigenetic regulation mechanisms have been suggested to control the tissue selective expression of OATP1B1 (Imai et al., 2013a). A study of OATP1B1 protein expression in pediatric liver samples ranging in age from 9 days to 12 years showed high interindividual variability but no age-dependent changes (Thomson et al., 2016). Besides the transcriptional and the post-transcriptional level, OATP1B1 is also regulated at the post-translational level. As mentioned above, OATP1B1 is a glycoprotein and the fully glycosylated protein was detected at  84 kDa on western blots of basolateral membranes from human liver, which upon treatment with N-glycosidase F was reduced to  58 kDa (Konig et al., 2000b). In order to investigate the potential importance of N-glycosylation on membrane expression and function, and to determine which of the potential asparagine residues would be glycosylated, Yao et al. (2012) used site-directed mutagenesis and expressed the constructs in HEK293 cells. The wildtype protein was detected at  95 kDa and could be deglycosylated to  75 kDa. After mutating all three asparagine residues N134, N503 and N516 together to glutamines, the molecular weight of the resulting construct was not affected anymore by deglycosylation with N-glycosidase F, suggesting that all three residues can be glycosylated. While single mutations were expressed and functioned normally, the triple mutation N134/503/516Q was expressed at greatly reduced levels, was retained within the endoplasmic reticulum and was no longer functional. The authors concluded that under normal conditions, positions N134 and N516 are glycosylated and that when N134 was mutated, also N503 could be glycosylated, potentially to rescue plasma membrane expression and function of the protein (Yao et al., 2012). Certain disease states could affect the glycosylation pattern of OATP1B1 resulting in reduced uptake function which in turn could result in adverse drug reactions. Indeed, in liver samples from patients with non-alcoholic liver disease, impaired N-glycosylation of OATP1B1 was reported (Clarke et al., 2017) and this could potentially affect drug uptake into hepatocytes and result in adverse drug effects. Many membrane proteins are regulated by phosphorylation. Using computer algorithms, several potential phosphorylation sites in intracellular loop 2 and at the C-terminal end of OATPs have been predicted. Hong et al. (2015) treated HEK293 cells expressing OATP1B1 with the protein kinase A activator phorbol 12-myristate 13-acetate (PMA) and demonstrated downregulated transport function and reduced plasma membrane expression of OATP1B1 (Hong et al., 2015). However, so far it is not known which sites in the OATP1B1 protein are indeed phosphorylated and what role phosphorylation might play in any disease states. Ubiquitination is another post-translational modification. If a protein needs to be degraded it can be tagged with several ubiquitin molecules and this ubiquitinated protein is then recognized by the proteasome system (Claessen et al., 2012). Ubiquitination of OATP1B1 was demonstrated by immunoprecipitation experiments in HEK293 cells that were cotransfected with ubiquitin (Alam et al., 2017). These authors also demonstrated that treatment of the cells with the proteasome inhibitor bortezomib increased the detectable amount of OATP1B1 after immunoprecipitation, suggesting that OATP1B1 degradation is inhibited by bortezomib. However, bortezomib treatment did not affect OATP1B1-mediated transport and it is therefore not clear to what extent ubiquitination or its inhibition by bortezomib affect OATP1B1- mediated drug uptake.

1.22.3.2.4

Polymorphisms

Several clinically relevant single nucleotide polymorphisms (SNPs) have been reported for the SLCO1B1 gene (Giacomini et al., 2013; Yee et al., 2018). The best characterized alleles are SLCO1B1*1a, *1b, *5 and *15 (Wilke et al., 2012). The first, *1a, corresponds to the wild type DNA sequence. In rs2306283 which corresponds to the *1b allele, replacement of c.388A > G results in p.N130D but functionally it remains wild type. These two alleles are found at frequencies of 72% (Caucasians) to 100% (Oceanians) in the major ethnic or geographic groups (Ramsey et al., 2014). The other two frequent alleles are *5 (rs4149056), where c.521T > C leads to p.V174A and which is found at frequencies of 1–3% (Caucasians) and 5% (Middle Easterners) (Giacomini et al., 2013; Ramsey et al., 2014), and *15, which is a combination of *1b and *5 and is found at frequencies of 3% (Africans) to 24% (South/Central Americans) (Ramsey et al., 2014). In in vitro studies, both OATP1B1*5 and *15 were shown to have reduced plasma membrane expression (Wagner et al., 2020; Tirona et al., 2001) explaining their reduced transport function, which can result in increased plasma drug levels (Pasanen et al., 2006). The Study of the Effectiveness of Additional Reductions in Cholesterol and Homocysteine (SEARCH) collaborative group found in 2008 in a genome wide association study a strong association of the *5 allele with simvastatin-induced myopathy (Link et al., 2008). Based on these findings and additional evidence for simvastatin-induced myopathy in patients with the rs4149056 SNP, the Clinical Pharmacogenetics Implementation Consortium (CPIC) recommended to adjust the dose of simvastatin according to the genotype or to switch to another statin that is not as affected as simvastatin, such as atorvastatin or pravastatin (Wilke et al., 2012; Ramsey et al., 2014). They considered that TT at position c.521 would lead to a transporter with normal activity, a TC would result in a transporter with intermediate function and a CC would encode a transporter with low activity (Wilke et al., 2012, Ramsey et al., 2014). Besides effects on simvastatin pharmacokinetics, these OATP1B1*5 and OATP1B1*15 variants can also affect the disposition of other drugs, in particular other statins, antibiotics, antihypertensives, antivirals, anticancer agents and ezetimibe (Gong and Kim, 2013; Rajman et al., 2020). It is very likely that with the increased interest in precision medicine and the improved sequencing methods, patients’ drug therapies will be guided by their respective SNPs, not only in the SLCO1B1 gene but in other transporter genes as well (see below).

1.22.3.2.5

Drug-drug interactions

A number of clinically relevant drug-drug interactions have been documented that involve substrates that are transported by OATP1B1 and OATP1B3 (Maeda, 2015). When talking about drug-drug interactions frequently the terms “perpetrator drug” and “victim drug” are used. In this context the perpetrator is the drug that inhibits the uptake of the victim by OATP1B1. The immunosuppressive cyclosporin A is a frequent perpetrator because it is a potent inhibitor of OATP1B1. It has led to increased plasma concentrations of many victim drugs. Plasma concentrations of several statins in kidney and heart transplant patients increased

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(Patel et al., 2016; Niemi et al., 2011) and in healthy volunteers, plasma concentrations of repaglinide, another substrate of OATP1B1, were also increased when cyclosporine A was co-administered (Kajosaari et al., 2005). Another perpetrator is the antibiotic rifampicin, a known substrate of OATP1B1 (Vavricka et al., 2002). Several studies in healthy volunteers documented increased plasma concentrations of fimasartan, pitavastatin, pravastatin, and atorvastatin in the presence of rifampicin (Patel et al., 2016). The fibrate gemfibrozil is another OATP1B1 inhibitor that potentially is added to a statin therapy. In in vitro studies gemfibrozil inhibited OATP1B1 mediated substrate uptake (Noe et al., 2007) and in studies with healthy volunteers gemfibrozil increased the AUC of atorvastatin (Whitfield et al., 2011) or rosuvastatin (Schneck et al., 2004). Based on multiple observations of such drug-drug interactions with OATP1B1 substrates/inhibitors, the International Transporter Consortium recommended that new molecular entities or investigational drugs be tested in vitro to determine whether they are transported by OATP1B1 (Giacomini et al., 2010). For clinical drug-drug interaction studies with OATP1B1, pitavastatin, rosuvastatin or atorvastatin were listed as potential drug substrates, while coproporphyrin I and III, glycocheodeoxycholate-3-O-sulfate, conjugated and unconjugated bilirubin, hexadecanedioate and tetradecanedioate are considered as potential endogenous biomarkers for OATP1B1 inhibition (Chu et al., 2018). Several of the recommendations of the International Transporter Consortium were adopted by the FDA, EMA and PMDA and during the development of novel drugs inhibition or transport studies are required (Giacomini et al., 2010; FDA, 2020).

1.22.3.3 1.22.3.3.1

OATP1B3 (SLCO1B3) Expression and function

The cloning of OATP1B3 was originally reported as OATP8 (Konig et al., 2000a) and as liver-specific organic anion transporter 2 (LST-2) (Abe et al., 2001). It is now known as OATP1B3 with the gene symbol SLCO1B3 (Hagenbuch and Meier, 2004). Like OATP1B1, OATP1B3 is also a glycoprotein but with a higher apparent molecular weight of 120 kDa that is reduced to 65 kDa upon deglycosylation (Konig et al., 2000a). OATP1B3 is encoded by 702 amino acids and has a distinctly different C-terminal end compared to OATP1B1 allowing the generation of OATP1B3-specific antibodies (Konig et al., 2000a; Abe et al., 2001). Using such antibodies OATP1B3 was detected mainly around the central vein with weaker staining around the portal vein; thus, unlike OATP1B1, which is evenly expressed throughout the liver lobule, OATP1B3 is mainly expressed in zones 3 and 2. Besides hepatic expression, OATP1B3 has also been detected in multiple cancers and tumor cell lines and was evaluated as a possible tool to target anticancer drugs to these cancer cells (Abe et al., 2001; Svoboda et al., 2011; Schulte and Ho, 2019; Obaidat et al., 2012). However, it turned out that a splice variant, the cancer-type OATP1B3 (Ct-OATP1B3) is expressed in cancer cells, and this splice variant is missing the amino-terminal 28 amino acids (Thakkar et al., 2013). Further experiments revealed that the Ct-OATP1B3 was hardly expressed at the plasma membrane, was mainly found in the cytoplasm and when expressed in HEK293 cells had strongly reduced function compared to wild type OATP1B3 (Chun et al., 2017). While OATP1B3 indeed transports several anticancer drugs, these results suggest that wild type OATP1B3 expressed in the liver most likely would remove anticancer drugs from the plasma before Ct-OATP1B3 could transport them into the cancer cells. Recently a spliceosome-mediated RNA trans-splicing (SMaRT) approach was suggested to engineer a herpes simplex virus 1 thymidine kinase Ct-OATP1B3 fusion protein that then would render the cancer cells that express Ct-OATP1B3 sensitive to ganciclovir treatment (Sun et al., 2018). However, further studies are needed to demonstrate whether this approach indeed could be effective in humans with cancer. Similar to OATP1B1, wild type OATP1B3 can transport a multitude of endo- and xenobiotics. The substrate specificity of OATP1B3 overlaps with OATP1B1 and similarly includes bile acids, bilirubin and its glucuronides, conjugated and unconjugated hormones, and a wide variety of organic compounds including cholesterol lowering statins, antihypertensive sartans, antibiotics, antivirals, and many anticancer drugs (Hagenbuch and Stieger, 2013; Roth et al., 2012; Schulte and Ho, 2019). Although the two transporters share most of their substrates, there are a few selective OATP1B3 substrates: cholecystokinin octapeptide (CCK-8) (Ismair et al., 2001), ouabain and digoxin (Kullak-Ublick et al., 2001), and telmisartan (Ishiguro et al., 2006). In addition, it seems that OATP1B3 might be the better transporter for several linear and cyclic oligopeptides like deltorphin II, [D-penicillamine2,5]enkephalin (DPDPE) and BQ123 (Kullak-Ublick et al., 2001). In addition, substrates containing fluorescein, like 8-fluorescein-cAMP (Bednarczyk, 2010) or fluorescein-methotrexate (Gui et al., 2010) are in general better substrates of OATP1B3 than OATP1B1. Thus, radiolabeled CCK-8 is frequently used as a model substrate to characterize OATP1B3, in particular in experiments with human hepatocytes or when OATP1B3 is co-expressed with other transporters (Zhang et al., 2017, 2020b). However, estrone-3-sulfate, estradiol-17b-glucuronide, bromosulfophthalein, and pravastatin have also been used. Like for OATP1B1, the main recombinant expression systems to characterize OATP1B3 are HEK293 cells for transient (Zhang et al., 2017) or stable (Kindla et al., 2011) expression, as well as CHO (Gui et al., 2008) and MDCKII (Konig et al., 2000a) cells.

1.22.3.3.2

Animal models

As explained above (Section 1.22.3.2.2.) for OATP1B1, knockout mice have been used to characterize the mouse orthologue of OATP1B1 and OATP1B3. These OATP1B2-null mice (Lu et al., 2008; Zaher et al., 2008) and the OATP1B2 knockout rat (Ma et al., 2020) as well as the OATP1A/1B knockout mouse (van de Steeg et al., 2010) could be used to generate humanized OATP1B1 or OATP1B3 models by expressing the human proteins as transgenes. Such models have been used to investigate the extent to which the individual OATPs contribute to the disposition of drugs such as methotrexate or paclitaxel (van de Steeg et al., 2013).

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1.22.3.3.3

Regulation

Transcriptional as well as post-transcriptional and post-translational regulation of OATP1B3 has not been extensively studied. Early reports demonstrated that the OATP1B3 promoter could be transactivated by HNF-1a (Jung et al., 2001), and that the bile acid chenodeoxycholic acid, a ligand of the bile acid nuclear receptor FXR, could induce OATP1B3 promoter activity (Jung et al., 2002). The later study also reported that activation of the nuclear receptors pregnane X receptor (PXR) and LXR had no effect. After incubation of human liver slices with prototypical activators of PXR, constitutive androstane receptor (CAR) and aryl hydrocarbon receptor (AhR), no clear conclusions could be drawn when measuring mRNA levels of SLCO1B3; however, in 2 of the 4 samples mRNA levels were decreased after incubation with the PXR activator rifampicin and the CAR activator phenobarbital seemed to increase SLCO1B3 mRNA expression, again only in 2 out of 4 samples (Olinga et al., 2008). Studies with primary human hepatocytes indicated that activation of AhR and nuclear factor erythroid 2-related factor 2 (Nrf2) lead to a decrease of SLCO1B3 mRNA expression (Jigorel et al., 2006). Expression of SLCO1B3 mRNA and OATP1B3 protein showed strong correlation with Wnt/b-catenin signaling (Ueno et al., 2014); however, to the best of our knowledge, so far no direct molecular studies have demonstrated direct regulation of SLCO1B3 by Wnt/b-catenin. MicroRNA miR-192 indirectly suppresses the expression of SLCO1B3 mRNA by reducing the expression of FXR (Krattinger et al., 2016b). In addition, abiraterone increased SLCO1B3 mRNA levels via the miRNA hsa-miR-579-3p that binds to the SLCO1B3 3’UTR (Barbier et al., 2021). In cancer cell lines Ct-OATP1B3 expression is regulated by epigenetic mechanisms. Treatment of HepG2 and Caco-2 cells with an inhibitor of DNA methyltransferase resulted in 16- and 32-fold increased mRNA levels in these two cell lines, respectively (Imai et al., 2013b). At the post-translational level OATP1B3 is modified by N-glycosylation. In human liver the fully glycosylated protein has a molecular weight of 120 kDa, which is reduced to about 65 kDa upon deglycosylation (Konig et al., 2000a). A similar molecular weight pattern was also seen in pediatric livers and expression of OATP1B3 was relatively high at birth, decreased during the first month and then increased again. In addition, the glycosylation pattern changed and the highly glycosylated OATP1B3 of 120 kDa increased with increased age (Thomson et al., 2016). So far nothing is known about how many and which asparagine residues are glycosylated in OATP1B3 but the positions that are glycosylated in OATP1B1 are conserved in OATP1B3 and thus it is very likely that N134, N503 and N516 are involved in the glycosylation of OATP1B3. Like OATP1B1, the glycosylation of OATP1B3 was also strongly reduced in samples of patients with non-alcoholic steatohepatitis (NASH). Only the core-glycosylated forms were detectable (Clarke et al., 2017) and thus OATP1B3 could have decreased function, and this could result in decreased drug disposition or uptake into hepatocytes. Regarding the phosphorylation of OATP1B3, not a lot is known to date. Although several amino acid residues are predicted to potentially be phosphorylated, no systematic studies have been published. Activation of protein kinase C by incubation of primary human hepatocytes with PMA decreased the uptake of CCK-8 (Powell et al., 2014). In addition, PMA increased phosphorylation of FLAG-tagged OATP1B3 that was expressed with an adenoviral vector in these human hepatocytes. However, PMA treatment did not affect total or surface protein expression of OATP1B3 when expressed in HEK293 cells (Powell et al., 2014). Thus, drugs that affect phosphorylation have the potential to also affect OATP1B3-mediated drug uptake. Like OATP1B1, OATP1B3 is ubiquitinated when expressed in HEK293 cells that are cotransfected with ubiquitin (Alam et al., 2017). However, in contrast to OATP1B1 where treatment with the proteasome inhibitor bortezomib did not affect transport, OATP1B3-mediated uptake of CCK-8 was decreased, both in HEK293 cells expressing OATP1B3 and in sandwich-cultured human hepatocytes after treatment with bortezomib (Alam et al., 2017). These results indicate that the two OATPs are regulated differently with respect to ubiquitination and suggest that additional studies are required to fully understand the consequences of posttranslational regulation on drug disposition via the two liver-specific OATPs.

1.22.3.3.4

Polymorphisms

Clinically important polymorphisms are less frequent in the SLCO1B3 gene than in SLCO1B1 (Schwarz et al., 2011). The most frequent SNPs are rs4149117 where c.334T > G results in S112A and rs7311358 with c.699G > A resulting in M233I (Smith et al., 2007; Schwarz et al., 2011). There are also less frequent SNPs which are mainly found at an allelic frequency of 0.6% in Asians (c.1559A > C, H520P) and at 3.6% in African Americans (c. 1679T > C, p.V560A). These two infrequent SNPs resulted in reduced protein expression at the plasma membrane and reduced transport function (Schwarz et al., 2011). In a study with renal transplant patients receiving mycophenolate mofetil as an immunosuppressant, AUC of the drug was increased in patients with the SNPs and co-treated with either tacrolimus or sirolimus but not when co-treated with cyclosporine A (Picard et al., 2010). A more recent survey reported that only rs4149117 was associated with adverse outcomes in patients receiving mycophenolic acid (Na Takuathung et al., 2021). Plasma concentrations of the anticancer drugs docetaxel and paclitaxel, both known substrates of OATP1B3, were not affected by the two major SNPs but the small numbers of patients and the possibility that other transporters are involved could explain the negative findings (Schwarz et al., 2011). Furthermore, the International Transporter Consortium concluded that OATP1B3 could be an important polymorphic transporter for current and new drugs (Yee et al., 2018).

1.22.3.3.5

Drug-drug interactions

Given the high amino acid sequence identity between OATP1B1 and OATP1B3, their spectrum of transported substrates is very similar and it requires the expression of the individual transporters and careful kinetic characterization sometimes in combination with results from SNP correlations to delineate whether one or the other or both liver OATPs are involved (Maeda, 2015). Drug-

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drug interaction studies have mainly focused on OATP1B1 but several perpetrator drugs such as cyclosporine and rifampicin also inhibit OATP1B3 (Giacomini et al., 2010). Based on correlation studies with SLCO1B1 SNPs many of these drug-drug interactions indeed seem to be due to inhibition of OATP1B1 rather than OATP1B3 (see e.g., the GWAS results for simvastatin induced myopathy (Link et al., 2008)). However, the FDA guidelines do not distinguish between OATP1B1 and OATP1B3 (FDA, 2020). Based on these guidelines new chemical entities that are taken up into the liver for metabolism or elimination should be tested in vitro as to whether they are substrates of OATP1B1 or OATP1B3. A drug is considered a substrate of OATP1B1 or OATP1B3 if it is taken up more than 2fold above control cells and if known inhibitors like rifampicin or cyclosporine inhibit the uptake of the drug by more than 50% at an inhibitor concentration at least 10-times that of its Ki or IC50. Furthermore, if these studies show that the new drug is an OATP1B1 or OATP1B3 substrate, in vivo studies might be necessary (FDA, 2020). Following these recommendations numerous studies are published that investigate drugs for whether they interact with OATP1B1 and/or OATP1B3 and what the clinical importance could be (e.g., Gopaul et al., 2021; Kayesh et al., 2021; Zurth et al., 2019).

1.22.3.4 1.22.3.4.1

OCT1 (SLC22A1) (EMA considerations) Expression and function

The organic cation transporter 1 (OCT1) is the third liver uptake transporter we cover in this chapter, and it is considered to be important for drug uptake by the European and the Japanese regulatory agencies. Human OCT1 is a glycoprotein of 70 kDa encoded by 554 amino acids. Upon treatment with peptide:N-glycosidase F (PNGaseF), the 70 kDa form was deglycosylated to 50 kDa (Seitz et al., 2015). OCT1 was cloned in Gorboulev et al. (1997) and Zhang et al. (1997) and it is mainly expressed at the basolateral membrane of human hepatocytes but also in cholangiocytes (Nies et al., 2009). In addition, OCT1 protein has been detected in the apical membrane of the renal proximal and distal tubules (Tzvetkov et al., 2009), at the brush-border membrane of human enterocytes (Han et al., 2013), in the lungs, the blood-brain barrier and in several tumor cells (Hyrsova et al., 2016b). Functionally, OCT1 transports a wide variety of endogenous and exogenous organic cations, and the broad substrate specificity has recently been reviewed extensively (Hyrsova et al., 2016b; Koepsell, 2020; Haberkorn et al., 2021). Transport is electrogenic, sodium- and proton-independent, and driven by the electrochemical gradient of its substrates (Koepsell, 2020). In vitro characterization of OCT1 frequently involves its expression in HEK293, MDCK and CHO cells and the most common model substrates used are the fluorescent ASPþ ([4-(4-(dimethylamino)styryl)-N-methylpyridinium]), or radiolabeled MPPþ (1-methyl4-phenylpyridinium), TEAþ (tetraethylammonium) and metformin as a drug substrate (Koepsell, 2020, Haberkorn et al., 2021).

1.22.3.4.2

Animal models

Unlike for OATP1B1 and OATP1B3 (see above), human OCT1 has a single orthologue in mice and rats; however, differences in tissue distribution and function exist (Koepsell, 2020). The first OCT1 knockout mouse was reported in 2001 by the Schinkel group (Jonker et al., 2001). It demonstrated that OCT1 in mice is important for the uptake of TEAþ into the liver and for its direct secretion into the intestine. Liver accumulation of an additional known OCT1 substrate, MPPþ was reduced as well while for other potential OCT1 substrates (cimetidine and choline) no differences between wild type and knockout mice were observed (Jonker et al., 2001). Similarly, metformin uptake into the liver was strongly reduced in the knockout mice but distribution of metformin to the kidney and renal excretion were not affected, suggesting that OCT1 mediates liver uptake but that OCT2 is important for the kidneys (Wang et al., 2002; Shu et al., 2007). Furthermore, experiments with OCT1 knockout mice revealed an important role of OCT1 for thiamine uptake into hepatocytes. In the absence of OCT1, mice had increased total body fat, increased blood glucose levels and increased lipids (Chen et al., 2014; Liang et al., 2018). Expression of human OCT1 as a transgene in OCT1 knockout mice rescued the knockout phenotype, demonstrating that such transgenic animals could be used to study pharmacokinetics of drugs transported by OCT1 (Chen et al., 2014; Hyrsova et al., 2016b).

1.22.3.4.3

Regulation

OCT1 expression is regulated at the transcriptional and post-translational levels. At the transcriptional level, the OCT1 promoter has binding sites for and is transactivated by HNF4a (Saborowski et al., 2006). HNF1a binds within intron 1 and seems to contribute to the liver-selective expression of OCT1 (Kamiyama et al., 2007; O’Brien et al., 2013). Increased methylation of the promoter resulted in lower amounts of SLC22A1 mRNA in hepatocellular carcinoma (Al-Abdulla et al., 2019; Koepsell, 2020). Furthermore, the microRNA hsa-miR-330, which was upregulated in hepatocellular carcinoma, also reduced SLC22A1 mRNA levels (Al-Abdulla et al., 2019). Results for PXR regulation of OCT1 are contradictory. Rifampicin, a PXR ligand, has been shown to induce OCT1 mRNA in peripheral blood cells and OCT1-mediated uptake of metformin into the liver (Cho et al., 2011). In human hepatocytes rifampicin treatment resulted in downregulation of SLC22A1 mRNA but this effect was indirect, via inhibition of HNF4a activation (Hyrsova et al., 2016a). Similarly, the effects of FXR and small heterodimer partner (SHP) seem to be mediated indirectly via inhibition of HNF4a activation (Hyrsova et al., 2016b). Regulation of OCT1 expression at the post-translational level has also been documented and recently reviewed, in particular with respect to regulation via kinases (Ciarimboli, 2020). However, so far potential phosphorylation sites in the human OCT1 protein have only been predicted but not demonstrated (Ciarimboli, 2020). Regarding N-linked glycosylation, human OCT1 contains the potential glycosylation sites in the big extracellular loop but so far they have not been confirmed experimentally. Regarding regulation via protein-protein interactions, a few interacting proteins have been identified but follow-up studies are

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needed to clarify the physiological/pharmacological relevance of these interactions (Snieder et al., 2019). OCT1 has been shown to interact with OATP1B3 both in transiently transfected HEK293 cells as well as in human hepatocytes (Zhang et al., 2020b). This interaction affects the function of OATP1B3 but does not seem to have any effects on OCT1 function. Additional studies are required to better characterize aspects of post-translational regulation, including identification of individual amino acid residues that are glycosylated and phosphorylated, as well as follow-up studies to investigate effects of protein-protein interactions.

1.22.3.4.4

Polymorphisms

Many studies documented the effects of polymorphisms in SLC22A1 on expression and function of OCT1 and many of the available studies have been summarized by the International Transporter Consortium and can be found in (Yee et al., 2018) and in a recent review by Zazuli et al. (2020). The six most frequent polymorphisms are rs12208357 (c.181C > T, p.R61C), rs683369 (c.480C > G, p.F160L), rs2282143 (c.1022C > T, p.P341L), rs34130495 (c.1201G > A, p.G401S), rs628031 (c.1222A > G, p.M408V), and rs202220802 (c.1260-1262delCAT, p.M420del) (Yee et al., 2018). While P341L and M420del show similar expression at the membrane as the reference OCT1, expression of R61C and G401S is reduced (Seitz et al., 2015; Shu et al., 2003, 2007). G401S shows a similar glycosylation pattern as wild type OCT1 but R61C seems to be deglycosylated, potentially explaining the reduced plasma membrane expression (Seitz et al., 2015). All six SNPs resulted in substrate specific reductions in uptake. Like OATP1B1, OCT1 alleles were also defined and their effects were investigated in vitro and in pharmacokinetic and pharmacodynamic studies. The wild type allele is OCT1*1, whereas OCT1*2 contains M420del, OCT1*3 consists of R61C plus P160L, OCT1*4 contains P160F and G401S, OCT1*5 is M420del and G465R, and OCT1*6 is C88R and M420del (Tzvetkov et al., 2009; Seitz et al., 2015). Fenoterol, an anti-asthmatic and tocolytic drug with a narrow therapeutic window because of cardiovascular and metabolic adverse effects, is a substrate of OCT1. Tzvetkov et al. (2018) conducted a study on fenoterol pharmacokinetics in healthy individuals that were genotyped for OCT1 polymorphisms. First, six OCT1 alleles were constructed and expressed in HEK293 cells. OCT1*1 and OCT1*2 were able to transport fenoterol while OCT1*3, OCT1*4, OCT1*5, and OCT1*6 were considered inactive (Tzvetkov et al., 2018). After administrating fenoterol to healthy subjects, individuals with zero active alleles showed 1.9-fold higher AUC of fenoterol as compared to individuals with either one or two active alleles, resulting in increased heart rate and increased blood glucose levels (Tzvetkov et al., 2018). These results suggest that OCT1 inhibitors have the capacity to affect fenoterol disposition and potential drug-drug interactions need to be considered. Metformin, an important drug that is frequently used to treat type 2 diabetes, is transported by OCT1 into hepatocytes but OCT1 does not affect its plasma levels because metformin is cleared by the kidneys (Zamek-Gliszczynski et al., 2018a). In healthy volunteers with OCT1 genotypes that resulted in vitro in reduced transport, metformin AUC and Cmax were both increased (Shu et al., 2008). Furthermore, pharmacokinetics studies in healthy volunteers with the reference OCT1 had lower plasma glucose levels after treatment with metformin as compared to subjects with variants with reduced transport (Shu et al., 2008; Santoro et al., 2018) clearly indicating that the reduced function variants did transport less metformin into hepatocytes. Overall, the International Transporter Consortium suggests that polymorphisms in SLC22A1 should be considered during drug development, in particular for drugs with a narrow therapeutic window (Yee et al., 2018).

1.22.3.4.5

Drug-drug interactions

As indicated above, OCT1 drug-drug interactions might not have an impact on the drug’s pharmacokinetics like in the case of metformin and subjects with inactive OCT1 alleles, but nevertheless might affect pharmacodynamics. This requires different measurements when investigating possible interactions and is supported by a study conducted to test the effect of verapamil, a substrate of OCT1, on metformin pharmacokinetics and pharmacodynamics. In healthy volunteers, verapamil co-administration with metformin did not affect any pharmacokinetic parameters of metformin but clearly affected glucose plasma concentrations during a glucose tolerance test, demonstrating that verapamil prevented metformin uptake into hepatocytes (Cho et al., 2014). Similarly, rifampicin induced SLC22A1 mRNA measured in blood and increased the glucose-lowering effect of metformin with minimal effects on its pharmacokinetics (Cho et al., 2011). Based on clinical evidence, mainly due to the effects seen with polymorphisms but supported by studies like the verapamildmetformin interaction (Cho et al., 2014) the International Transporter Consortium recommends evaluation of new chemical entities as inhibitors and/or substrates of OCT1 following the same procedure as recommended for OATP1B1 and OATP1B3 (Zamek-Gliszczynski et al., 2018b).

1.22.3.5

Introduction to kidney transporters

The kidney, in addition to the liver, is involved in the elimination of drugs from the body where it is predicted to serve as the rate limiting step in the elimination of low molecular weight (< 400 Da) and low permeable cations, anions and zwitterions (Varma et al., 2015). The elimination of drugs by the kidney is the result of glomerular filtration, tubular secretion and tubular reabsorption. Early experiments using stop flow techniques and isolated tubule segments identified the proximal tubules as the primary site for the tubular secretion of drugs and xenobiotics. These experiments also identified two parallel pathways for the secretion of these compounds namely, the organic anion and organic cation secretion pathways (Pritchard and Miller, 1993). The major uptake transporters that have been implicated in the secretion of organic cations and anions by the proximal tubule cells of the kidney and that are considered by the International Transporter Consortium as potential sites for drug-drug interactions that can impact the disposition and elimination of new molecular entities include the organic cation transporter OCT2 and the organic anion transporters OAT1 and OAT3 (Fig. 3). As done with the liver transporters, the physiology and pharmacology of these transporters will be discussed briefly in the following sections.

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H+

Urate

595

H+

Urine

Apical OAT4

URAT1

MRP2

OA¯

ATP

-KG

OAT1

Basolateral

MRP4

OA¯

ATP

MDR1

+ ATP OC

MATE 1

MATE2-K

OC+

OC+

OCT2

OCT3

-KG

OAT3

OAT2

Blood

Fig. 3 Transport systems in the kidney proximal tubule. Transport systems for organic anions (OA, black) and cations (OCþ, red) are depicted. MATE: multidrug and toxin extrusion; MDR: multidrug resistance protein; MRP: multidrug resistance-associated protein; OAT: organic anion transporter; OCT: organic cation transporter; URAT1: urate transporter; a-KG: a-ketoglutarate. For details please see the text.

1.22.3.6 1.22.3.6.1

OCT2 (SLC22A2) Expression and function

OCT2 was first cloned from the kidney of a rat in 1996 and the human orthologue was subsequently cloned in Okuda et al. (1996) and Gorboulev et al. (1997). OCT2 is a 555 amino acid protein and has a molecular weight of 47–85 kDa depending on the glycosylation of the protein (Pelis et al., 2006; Pelis and Wright, 2011). OCT2 is primarily expressed in the kidney and to a lesser extent in the brain, spinal cord, lung, placenta and nasal mucosa (Koepsell, 2020). In the kidney, immunohistochemistry studies determined that OCT2 is localized to the basolateral membrane of proximal tubule cells (Motohashi et al., 2002). OCT2 function has been studied in Xenopus laevis oocytes, in HEK293, CHO, MDCK, and COS-7 cells (Koepsell, 2020; Wright and Dantzler, 2004). Overexpression of OCT2 in these cell lines enhances the uptake of the organic cations TEAþ, MPPþ, ASPþ, and metformin, which are commonly used probe substrates to study the function of the transporter in vitro (Nies et al., 2011). Endogenous substrates of OCT2 include many neurotransmitters and metabolites, and drug classes known to interact with OCT2 include tricyclic antidepressants, antiarrhythmic medications, anesthetics, antivirals and anti-parkinson medications (Nies et al., 2011). Early experiments using isolated basolateral membrane vesicles from proximal tubule cells determined that uptake of organic cations by this membrane was passive and impacted by the membrane potential (Pritchard and Miller, 1993). Manipulation of the membrane potential in these vesicles, such as by altering the potassium concentration on either side of the membrane, affected the accumulation of organic cations. Greater inside negative membrane potentials produced a greater substrate accumulation (Takano et al., 1984). The OCT2-mediated transport of organic cations in transfected cell lines was influenced by the membrane potential. Accumulation of organic cations in oocytes expressing OCT2 was reduced in the presence of valinomycin, a potassium ionophore which dissipates the membrane potential (Okuda et al., 1999). Additionally, measured inwardly directed OCT2 mediated electrical currents, which reflects the uptake of organic cations by OCT2, were increased at greater negative membrane potentials (Budiman et al., 2000). Thus, it is widely accepted that OCT2 mediates the accumulation of organic cations down their electrochemical gradient into proximal tubule cells (Pelis and Wright, 2011; Koepsell, 2020).

1.22.3.6.2

Animal models

Mice express an orthologue of OCT2 in the basolateral membrane of proximal tubule cells. However, unlike in humans, mice also express a significant quantity of OCT1 in the kidney that impacts the tubular secretion of cationic compounds by the proximal tubules in these animals. In mice dosed with TEAþ, knocking out either OCT1 or OCT2 did not significantly impact the renal excretion or plasma exposure of TEAþ in these animals (Jonker et al., 2003). However, double knockout mice for both OCT1 and OCT2 demonstrated reduced clearance and increased plasma exposure of TEAþ. The double knockout mice also had a reduced concentration of TEAþ in the kidney when compared to wild-type mice indicating that OCT1 and OCT2 mediate the entry of organic cations into the proximal tubule cells of these mice (Jonker et al., 2003). These results highlight the overlapping function of OCT1 and OCT2 in the clearance of organic cations in these animals and it is presumed that OCT1 and OCT2 in mice are functionally equivalent to OCT2 in humans (Filipski et al., 2009). The OCT1/OCT2 double knockout mice also have reduced clearance of the drugs metformin and the antipsychotic sulpiride, demonstrating the important role of OCT1/OCT2 in the clearance of drugs by the mouse kidney (Takano et al., 2017; Higgins et al., 2012). Additionally, the OCT1/OCT2 knockout mice experience a reduction in the nephrotoxic effects of cisplatin. Urinary excretion of cisplatin was reduced in the OCT1/OCT2 double knockout mice when compared to wild-type mice. Additionally, the severe tubular necrosis that was observed in cisplatin treated wild type mice was not observed in cisplatin treated OCT1/OCT2 double knockout mice (Filipski et al., 2009). Thus, OCT2 can potentially mediate renal toxicity by permitting the entry of toxins, such as cisplatin, into the proximal tubule cells of the kidney.

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1.22.3.6.3

Regulation

One transcription factor implicated in the transcriptional regulation of OCT2 is the upstream stimulatory factor-1 (USF-1). USF-1 binds to an E-box consensus sequence (CACGTG) in the 50 flanking region of the OCT2 gene and stimulates the promoter activity of OCT2 in a dose dependent manner. Mutation of the E-box sequence results in a reduction in the promoter activity of OCT2 consistent with USF-1 regulating the basal transcription of OCT2 by binding to the E-box (Asaka et al., 2007). Epigenetic factors have been implicated in the tissue specific expression of OCT2. In the liver, the E-box sequence is methylated to a greater extent than in the kidney suggesting that USF-1 is unable to bind to the OCT2 promoter region in the liver, preventing its expression in this tissue (Aoki et al., 2008). E-box methylation has also been observed in renal cell carcinoma (RCC) tissues that have greatly reduced OCT2 expression and is associated with resistance by this cancer to the chemotherapeutic drug oxaliplatin, which is a substrate of OCT2. In RCC cell lines, Liu et al. (2016) found that demethylation of DNA in these cells resulted in a restoration of OCT2 protein expression and improved the uptake and therapeutic efficacy of oxaliplatin. They also determined that OCT2 expression is regulated by binding of the c-MYC protein (MYC) to the E-box which recruits the methylase mixed-lineage leukemia-1 enzyme (MML-1) that catalyzes the methylation of histone-3 associated with active chromatin (Liu et al., 2016). Follow-up studies also implicated miRNAs and histone acetylation in the repression of OCT2 expression in RCC (Zhu et al., 2019; Chen et al., 2019). OCT2 function is also modified by post-translational modifications. OCT2 has three conserved glycosylation sites at asparagine residues 71, 96, and 112 on its extracellular loop between transmembrane domains 1 and 2. Mutation of these asparagine (N) residues to glutamine (Q) disrupts glycosylation at these sites and demonstrates their functional effect on OCT2. The N71Q, N96Q, and N112Q mutants each had an increased affinity for TEAþ. The N71Q and N96Q mutants had normal plasma membrane expression while the N112Q mutant had reduced plasma membrane expression. The turnover rate, which is defined as the number of TEAþ molecules transported across the cell membrane per unit time, was reduced in the N96Q mutant. While individually the N71Q and N96Q mutants did not impact OCT2 plasma membrane expression, a double mutant of these residues reduced the plasma membrane expression of OCT2. These results highlight the importance of glycosylation on the trafficking of OCT2 to the plasma membrane, its affinity for substrate, and its turnover rate (Pelis et al., 2006). Protein kinases can also regulate the function of OCT2. Phosphatidylinositol-3-kinase (PI3K), PKA, and PKC activation result in a reduction of OCT2 activity while activation of the calcium/calmodulin-dependent protein kinase II (CAMKII) increased OCT2 activity. Inhibition of CAMKII resulted in a decrease in the plasma membrane expression of OCT2 and reduced the transporter’s affinity for TEAþ (Cetinkaya et al., 2003; Biermann et al., 2006). It is not currently known if these kinases modulate OCT2 function by phosphorylating the protein directly (Koepsell, 2020). The tyrosine kinase Yes1, which belongs to the Src family of tyrosine kinases, is important for the phosphorylation status of OCT2 and for the regulation of its function. Inhibition of Yes1 or disruption of its physical interaction with OCT2 reduced both the phosphorylation and function of the transporter. The importance of Yes1 in regulating OCT2 function was also demonstrated in vivo as TEAþ dosed Yes1 knockout mice had reduced OCT2 phosphorylation in their kidneys and elevated plasma levels of TEAþ when compared to wild type mice. These results are consistent with Yes1 directly phosphorylating OCT2 to regulate its function (Sprowl et al., 2016).

1.22.3.6.4

Polymorphisms

The most common variant of OCT2 that is found in all ethnicities is the SNP rs316019 (c.808G > T, p.A270S). In vitro and in vivo data disagree on the potential impact of this variant on renal drug clearance (Zazuli et al., 2020). In vitro, the uptake of metformin, MPPþ, lamivudine, and ASPþ by the A270S variant was observed to be decreased (Choi and Song, 2012; Song et al., 2008). In contrast, in another study, the in vitro uptake of metformin was observed to be increased in the A270S variant (Chen et al., 2009). These conflicting results have also been observed in the clinic with the A270S variant being associated with decreased, increased, or normal plasma clearance and exposure of metformin (Koepsell, 2020; Zazuli et al., 2020). Similar conflicting results have been observed with cisplatin nephrotoxicity with the A270S variant being associated with either reduced or increased toxicity (Zazuli et al., 2020). The other common variant is the SNP in the 30 untranslated region of the SLC22A2 mRNA referred to as rs596881 (T > C), but this variant has not been implicated in affecting the renal clearance of drugs. Other variants of OCT2 that have been identified include rs8177516 (c.1198G > T, p.R400S), rs8177517 (c.1294A > G, p.K432Q), rs8177507 (c.495G > A, p.M165I), rs201919874 (c.596C > T p.T199I), and rs145450955 (c.602C > T, p.T201M). These variants are associated with reduced OCT2 function, but each has an allele frequency below 1% across all ethnicities (Zazuli et al., 2020). Thus, it is unlikely that they will have a significant impact on individual variability in the renal clearance of cationic drugs. The current stance from the International Transporter Consortium is that it is currently not clear what the impact of OCT2 polymorphisms is on the clearance of drugs due to contradictory results found in the literature (Yee et al., 2018).

1.22.3.6.5

Drug-drug interactions

Metformin is primarily eliminated by the kidney with tubular secretion by the organic cation pathway of the proximal tubule cells having a significant role in its clearance. Notable perpetrator drugs that inhibit metformin clearance by this pathway include the weak base and antihistamine cimetidine, the anti-parasite medication pyrimethamine, and the anti-viral dolutegravir, each of which can inhibit OCT2 function. Each of these drugs, when co-dosed with metformin, inhibit the renal clearance and increase the plasma exposure of metformin in human subjects (Somogyi et al., 1987; Song et al., 2016; Ito et al., 2012). However, cimetidine and pyrimethamine at therapeutic concentrations do not produce significant inhibition of OCT2 in vitro but they do significantly inhibit the multidrug and toxin extrusion proteins MATE1 and MATE2-K, which are organic cation efflux transporters located on the apical

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membrane of proximal tubule cells (Tsuda et al., 2009; Ito et al., 2012). These data are consistent with cimetidine and pyrimethamine reducing the clearance of metformin by primarily inhibiting its efflux from proximal tubule cells by MATE1 and MATE2-K. Therapeutic concentrations of dolutegravir, however, inhibited OCT2 in vitro and not MATE1/MATE2-K, which is consistent with OCT2 being a significant site of the dolutegravir-metformin interaction (Song et al., 2016). The FDA currently recommends assessing whether a new drug is a substrate of OCT2 if the renal clearance of the drug is greater than or equal to 25% of the systemic clearance. Similar to the liver transporters, a new drug is considered a substrate of OCT2 if the accumulation of the drug in OCT2 expressing cells is 2-fold or greater than in control cells and if a prototypical inhibitor of OCT2, such as cimetidine, inhibits this uptake by 50% or more at a concentration at least 10-times that of its Ki or IC50 (FDA, 2020). Also, if the ratio of the new drug’s unbound Cmax over its IC50 value, which is the concentration of the drug that inhibits the OCT2 mediated uptake of a probe substrate by 50%, is greater than or equal to 0.1, then the drug has the potential to significantly inhibit OCT2 function in vivo. If this is the case it is recommended to assess the inhibitory potential of the new drug against OCT2 in a clinical study (FDA, 2020). The selection of probe substrates for these studies is important because the inhibition of OCT2 has been shown to be dependent on the substrate. MPPþ, while an efficient substrate of OCT2 that produces an excellent signal for in vitro experiments, has been consistently shown to be inhibited to a lesser extent than other probe substrates of OCT2 (Hacker et al., 2015; Belzer et al., 2013; Sandoval et al., 2018; Wright, 2019). IC50 values of OCT2 inhibitors against MPPþ uptake have been demonstrated to be, in two different studies, six- to ninefold higher than the IC50 values of these inhibitors against other OCT2 substrates (Belzer et al., 2013; Sandoval et al., 2018). As a result, using MPPþ to assess the inhibitory potential of a new drug according to FDA guidelines can potentially result in underestimating the ability of the drug to perpetrate drug-drug interactions at OCT2 in vivo (Wright, 2019). In recognition of the potential for substrate dependence at multidrug transporters, the FDA recommends using a probe substrate that is known to produce lower IC50 values (FDA, 2020). In patients dosed with drugs that inhibit OCT2, the plasma exposure of the metabolites creatinine and N1-methylnicotinamide is increased and their renal clearance is decreased indicating that these metabolites are potential biomarkers that can be used to assess a drug-drug interaction risk at OCT2 (Chu et al., 2018; Mathialagan et al., 2020).

1.22.3.7 1.22.3.7.1

OAT1 (SLC22A6) Expression and function

Organic anion transporter 1 (OAT1) was first cloned from mouse and rat in 1997 and the human orthologue was cloned in 1998 (Lopez-Nieto et al., 1997; Sweet et al., 1997; Reid et al., 1998). OAT1 is primarily expressed in the kidney and is localized to the basolateral membrane of proximal tubule cells (Hosoyamada et al., 1999; Motohashi et al., 2002). OAT1 has a broad substrate selectivity and is able to transport and interact with a variety of structurally diverse endogenous and xenobiotic organic anions (Pelis and Wright, 2011). Para-aminohippurate (PAH) is the prototypical substrate used to study OAT1 in numerous in vitro systems including HEK293, Cos-7, HeLa, and CHO cells in addition to X. laevis oocytes (Burckhardt and Burckhardt, 2011; Wright and Dantzler, 2004). OAT1 interacts with many endogenously produced compounds including second messengers (cAMP and cGMP), hormones, and metabolites. Exogenous compounds that interact with OAT1 include prescription drugs from many therapeutic categories including diuretics, NSAIDs, antivirals, antihypertensives, and anti-biotics (Burckhardt and Burckhardt, 2011). Unlike the uptake of organic cations across the basolateral membrane of proximal tubule cells, the uptake of organic anions across this membrane is energetically unfavorable and requires the input of energy to drive the accumulation of these compounds into the cell (Pelis and Wright, 2011). The uptake of organic anions by the basolateral membrane of proximal tubule cells was demonstrated to be indirectly coupled to the sodium gradient and to be a tertiary active transport process that is dependent on both an inwardly directed sodium gradient and an outwardly directed dicarboxylate gradient, such as a-ketoglutarate in order to drive the accumulation of organic anions in the tubule cells (Pritchard and Miller, 1993). These observations, in addition to the observation that OAT1 mediates exchange of dicarboxylates for PAH, are consistent with, as mentioned previously, OAT1 operating as a tertiary active transporter that couples the efflux of dicarboxylates to the influx of organic anions and is dependent on the activity of both NaDC3 (sodium-dependent dicarboxylate transporter) and the Naþ/Kþ-ATPase (Fig. 1) (Aslamkhan et al., 2003; Burckhardt and Burckhardt, 2011).

1.22.3.7.2

Animal models

Like in humans, the mouse orthologue of OAT1 is expressed in the basolateral membrane of proximal tubule cells and OAT1 knockout mice have been valuable tools in understanding the physiological role of the transporter (Bahn et al., 2005). OAT1 knockout mice, while having no abnormalities, demonstrate a marked increase in the plasma concentration of many endogenously produced organic anions (Eraly et al., 2006). Additionally, OAT1 knockout mice show an increased plasma exposure to uremic solutes/toxins, such as indoxyl sulfate, which are elevated during chronic renal disease and associated with inflammation and oxidative stress (Pieniazek et al., 2021; Wu et al., 2017). These animal studies indicate that OAT1 has a role in regulating the plasma concentration of many endogenous metabolites/toxins in order to maintain homeostasis. OAT1 knockout mice have also demonstrated the importance of this transporter in the clearance of drugs and in mediating nephrotoxicity. In OAT1 knockout mice the tubular secretion of PAH is almost completely eliminated while the excretion of estrone 3-sulfate, which is a prototypical substrate of OAT3, is unaffected in these animals (Eraly et al., 2006). The renal excretion of the diuretic furosemide was also greatly reduced, and a greater dose of furosemide was needed to achieve the same diuretic effect in knockout animals that could be achieved at a lower dose in wild-type animals (Eraly et al., 2006). OAT1 knockout mice have

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also been shown to be resilient to kidney injury due to acute exposure to mercury (Torres et al., 2011). Mercury ions form organic conjugates in the body and many of these conjugates are substrates of OAT1 (Bridges and Zalups, 2005; Torres et al., 2011). Thus, OAT1 has a role in mediating the nephrotoxic effects of mercury.

1.22.3.7.3

Regulation

The transcription of OAT1 is regulated by isoforms of the HNF1 transcription factors. The promoter region of the OAT1 gene contains a conserved HNF1 binding motif and HNF1a knockout mice have reduced expression of OAT1 (Maher et al., 2006; Saji et al., 2008). Additionally, the transfection of HEK293 cells with HNF1a increased the expression of OAT1 in these cells. Expression of OAT1 was further stimulated when cells were co-transfected with HNF1a and HNF1b but transfection of these cells with HNF1b alone had no effect on OAT1 expression (Saji et al., 2008). Thus, the activity of HNF1b requires the activity of HNF1a in order to impact the expression of OAT1. HNF4a also binds to the OAT1 promoter region and stimulates OAT1 expression (Ogasawara et al., 2007). In rats, sex hormones have also been implicated in regulating OAT1 expression with testosterone increasing expression and estradiol inhibiting expression (Ljubojevic et al., 2004). However, these sex differences in OAT1 expression is species dependent and does not occur in humans, rabbits, and pigs (Burckhardt and Burckhardt, 2011). Post-translational modification of OAT1 also regulates the function of the transporter. The extracellular loop on OAT1 between transmembrane domains 1 and 2 contains five potential glycosylation sites with 4 out of the 5 sites normally being glycosylated (Tanaka et al., 2004). Disrupting the glycosylation of these sites individually had no impact on the function of OAT1. However, completely disrupting the glycosylation of the extracellular loop significantly decreased the plasma membrane expression of OAT1 demonstrating the importance of glycosylation in the trafficking of OAT1 to the plasma membrane (Tanaka et al., 2004). Interestingly, the mutation of the asparagine residue at position 39 in the protein, while having no effect on the trafficking of OAT1 to the plasma membrane, almost completely abolished OAT1-mediated uptake of PAH in transfected cells (Tanaka et al., 2004). These results indicate that glycosylation at asparagine 39 is critical for substrate transport by OAT1. OAT1 is a dynamic transmembrane protein that is constantly shuttled between the plasma membrane and intracellular vesicles (Zhang et al., 2008). At steady state, the majority of OAT1 is expressed in the plasma membrane but the activation of PKC shifts the distribution of OAT1 from the plasma membrane to the intracellular compartment through an increase in the endocytosis of the protein. While short term PKC activation had no effect on OAT1 expression in transfected cells, prolonged PKC activation is associated with increased protein degradation and a reduction in the overall expression of OAT1 (Xu et al., 2017). Interestingly, PKC does not phosphorylate OAT1 directly (Zhang et al., 2021). Instead, ubiquitination of OAT1 by the E3 ubiquitin ligase Nedd4– 2 has a critical role in the PKC induced reduction of OAT1 function (Xu et al., 2016c, 2017). Disruption of the ubiquitin ligase activity of Nedd4–2 or its ability to interact with OAT1 abolished the reduction of OAT1 function due to PKC activation (Xu et al., 2016c, 2017). Additionally, disruption of the ubiquitination of OAT1 through mutation of ubiquitin recognition sites on OAT1 or through mutation of ubiquitin itself abolished the PKC induced internalization of OAT1 from the plasma membrane (Li et al., 2013; Zhang et al., 2013). Thus, it appears that PKC reduces OAT1 function by phosphorylating and activating Nedd4–2 which then binds to and ubiquitinates OAT1. This then promotes endocytosis of the transporter and results in protein degradation (Xu et al., 2017; Zhang et al., 2021). Angiotensin II has been linked to PKC activation and inhibition of OAT1 through this pathway indicates that it is a potential hormone involved in the regulation of OAT1 (Zhang et al., 2021; Li et al., 2009). In contrast to PKC, the activity of serum and glucocorticoid-inducible kinase 2 (sgk2) enhanced the transport activity of OAT1 in transfected cells. Sgk2 activity increased the plasma membrane expression of OAT1 and reduced the degradation of the protein resulting in an overall increase in OAT1 function. Sgk2 interacts directly with OAT1 and this suggests that sgk2 mediates the increase in OAT1 function through this interaction (Xu et al., 2016a).

1.22.3.7.4

Polymorphisms

Overall, the genetic variability of OAT1 has been found to be low suggesting that the gene is under selective pressure. In a screening of 276 individuals from diverse ethnic backgrounds six non-synonymous SNPs were identified (Fujita et al., 2005). Only two of these mutations, R50H and R293W, were found at an allele frequency of greater than 1% in one ethnic population but had no observed differences in their transport activity of PAH and methotrexate when compared to wild type OAT1. However, in another study, the R50H mutant did have altered transport kinetics of adefovir, cidofovir, and tenofovir uptake (Bleasby et al., 2005). One non-synonymous SNP, R454Q, was determined to be non-functional. However, in a clinical study with family members that were heterozygous for R454Q there was no observed reduction in renal clearance of the OAT1 specific substrate adefovir (Fujita et al., 2005). Polymorphisms in OAT1 have been associated with a reduced effectiveness of diuretics in high blood pressure patients and a greater exposure to mercury conjugates in a population of Tanzanian miners (Engström et al., 2013; Han et al., 2011). Overall, the International Transporter Consortium recognizes that there is little evidence that polymorphisms of OAT1 contribute to individual variability in drug exposure (Yee et al., 2018).

1.22.3.7.5

Drug-drug interactions

Due to their overlap in substrate specificity, OAT1 and OAT3 often both contribute to interactions of anionic drugs in the kidney, however, they do not always contribute equally to these interactions. Co-administration of probenecid, an inhibitor of both OAT1 and OAT3, with other drug classes such as cephalosporin antibiotics, anti-histamines, and diuretics has been associated with an increase in plasma concentrations of these drugs and/or reduced renal elimination (Ivanyuk et al., 2017; Brown, 1993). Perhaps the best-known beneficial drug-drug interaction is the interaction of probenecid with penicillin, which is a b-lactam antibiotic

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that is primarily eliminated by the kidney (Burckhardt and Burckhardt, 2011; Barza and Weinstein, 1976). The co-administration of probenecid with penicillin reduces the renal clearance of penicillin resulting in therapeutic doses of penicillin being retained longer in the body, reducing the amount of doses needed to maintain these therapeutic concentrations, and, as a result, preserving scarce penicillin supplies during World War II (Robbins et al., 2012; Pelis and Wright, 2011). Given that penicillin has a higher affinity for OAT3 than for OAT1, OAT3 is likely the primary site of the probenecid/penicillin interaction (Burckhardt, 2012). Drug-drug interactions that are likely to occur predominantly at OAT1 include the interaction of the antivirals adefovir and cidofovir with probenecid. Both of these antivirals are primarily cleared by the kidney and co-administration of these drugs with probenecid has been shown to reduce their renal elimination (Cundy, 1999; Maeda et al., 2014). Both adefovir and cidofovir are transported by OAT1 and OAT3 but they have a higher affinity for OAT1 and, as a result, their interaction with probenecid is thought to primarily occur at OAT1 (Uwai et al., 2007). This seems to be the case for adefovir in that patients that were co-dosed with the drug and either probenecid or PAH, a more selective OAT1 substrate/inhibitor, had similar pharmacokinetic profiles (Maeda et al., 2014). In the case of cidofovir, co-administration with probenecid is required due to the nephrotoxic effects of the drug and preventing its entry into the proximal tubule cells ameliorates its toxic effects in the kidney (Cundy, 1999). Like other drug transporters, there is an interest in using endogenous biomarkers to assess unwanted drug-drug interactions at OAT1. The renal elimination of both the amino acid taurine and the conjugated bile acid glycochenodeoxycholate sulfate (GCDCAS) was reduced in a dose dependent manner by probenecid. In vitro it was determined that taurine was selective for OAT1 and GCDCA-S was selective for OAT3, supporting the idea that these molecules could serve as specific biomarkers for assessing the drug-drug interaction risk at either OAT1 or OAT3 (Tsuruya et al., 2016). Pyridoxic acid (PDA) and homovanillic acid (HVA) are other potential biomarkers. PDA and HVA are substrates for OAT1 and OAT3 and their serum concentrations are increased in patients dosed with probenecid (Shen et al., 2018, 2019). However, the effect of probenecid on PDA plasma levels was stronger and had less variance between patients than its effect on HVA (Shen et al., 2019). As a result, PDA is likely a more promising candidate as a biomarker to assess drug-drug interactions at OAT1 and OAT3.

1.22.3.8 1.22.3.8.1

OAT3 (SLC22A8) Expression and function

OAT3 was first cloned in 1999 and is primarily expressed in the kidney and to a lesser extent in other tissues of the body including skeletal muscle and brain (Race et al., 1999). The OAT3 gene codes for a 542 amino acid protein that has a molecular weight of 62– 82 kDa depending on the extent of glycosylation (Srimaroeng et al., 2008). Immunohistochemical studies determined that OAT3 is located on the basolateral membrane of proximal tubule cells implicating that, like OAT1, it mediates the entry of endogenous and xenobiotic anions into these cells (Motohashi et al., 2002). Similar to OAT1, OAT3 is capable of interacting with and transporting a wide variety of typically anionic, endogenous and exogenous compounds. While there is overlap in the selectivity between the transporters, OAT3 typically interacts with bulkier and more lipophilic compounds than OAT1 (Burckhardt and Burckhardt, 2011). Uptake of PAH was observed in X. laevis oocytes expressing OAT3, however, PAH was shown to have a much lower affinity for OAT3 than OAT1 (Cha et al., 2001). Some substrates more selective for OAT3 than OAT1 include the statins rosuvastatin and pravastatin (Windass et al., 2007; Nakagomi-Hagihara et al., 2007), benzylpenicillin (Maeda et al., 2014), and the hormone conjugate estrone-3-sulfate, which is the prototypical substrate to study OAT3 in vitro (Burckhardt and Burckhardt, 2011). The uptake of estrone-3-sulfate by oocytes expressing OAT3 was linked to both the dicarboxylate and sodium gradients indicating that OAT3, like OAT1, is a tertiary active transporter (Fig. 1) and relies on both of these chemical gradients to drive the uptake of organic anions into the proximal tubule cells (Sweet et al., 2003).

1.22.3.8.2

Animal models

Mouse OAT3 is also expressed in the basolateral membrane of proximal tubule cells and OAT3 knockout mice have been used to determine the physiological role as well as its role in the disposition and elimination of drugs. While OAT1 knockout mice had no apparent abnormalities, OAT3 knockout mice had reduced blood pressure when compared to wild-type mice highlighting a potential role for OAT3 in the regulation of blood pressure (Vallon et al., 2008). OAT3 knockout animals have also been associated with elevated expression of both phase I and phase II metabolizing enzymes. Additionally, these animals are associated with altered amino acid, fatty acid, and mineral metabolism and have a reduced urinary concentration of Krebs-cycle intermediates, including a-ketoglutarate, when compared to wild-type mice (Wu et al., 2013). OAT3 deficient mice also have elevated serum levels of uremic toxins/solutes such as p-cresol sulfate, indoxyl sulfate and trimethylamine N-oxide (Wu et al., 2017). Together these data implicate OAT3 as having a role in mediating the elimination of endogenous compounds and regulating metabolism to maintain normal physiology. OAT3 knockout mice demonstrated a reduced clearance of both in vitro probe compounds and clinically relevant drugs. Estrone3-sulfate clearance was reduced in OAT3 knockout animals while the clearance of PAH is unaffected. The clearance of penicillin G (benzylpenicillin) was impaired and its plasma exposure was increased in these animals (Vanwert et al., 2007). Additionally, the elimination of the chemotherapeutic agent methotrexate and its accumulation into the kidney was also reduced, highlighting the potentially significant role OAT3 has in the renal clearance of methotrexate.

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1.22.3.8.3

Regulation

At the transcriptional level, OAT3 is regulated by the transcription factors HNF1a, HNF1b and HNF4a. In transfected cells HNF1a and HNF1b were shown to increase OAT3 promoter activity and to bind to the OAT3 promoter region likely as either an HNF1a homodimer or as an HNFa/b heterodimer (Kikuchi et al., 2006). Additionally, inhibition of HNF4a activity results in a reduction of the expression of OAT3 in cultured embryonic rat kidney cells (Martovetsky et al., 2013). Epigenetic factors also are potentially involved in the regulation of OAT3 expression as methylation of the OAT3 promoter in vitro resulted in a reduction in its activity (Kikuchi et al., 2006). OAT3 expression in HEK293 cells is not detectable but, interestingly, demethylating the DNA of these cells and co-transfecting them with HNF1a and HNF1b increases the expression of OAT3. These results suggest that in wild type HEK293 cells the OAT3 gene is repressed by DNA methylation, and this supports the idea that OAT3 is regulated by epigenetic factors (Kikuchi et al., 2006). The transcription factors cAMP response element binding protein 1 (CREB-1) and activating transcription factor-1 (ATF-1) bind to a cAMP response element (CRE) sequence in the promoter region of OAT3 and an increase in the phosphorylation of these proteins by PKA activation is associated with an increase in OAT3 promoter activity. Deletion of the CRE element both reduced the impact of PKA activation on OAT3 expression and the basal promoter activity of OAT3. These results are consistent with CREB-1 and ATF-1 mediating both the basal and inducible expression of OAT3 through their interaction with the CRE sequence in the OAT3 promoter region (Ogasawara et al., 2006). In addition to regulating the promoter activity of OAT3, PKA activation has also been implicated in directly phosphorylating OAT3 and modulating its function. The hormone insulin like growth factor-1 (IGF-1) is secreted from the liver in response to growth hormone. IGF-1 increases both the surface expression and transport activity of OAT3 and inhibition of PKA abolishes IGF-1 induced changes in OAT3 function. Exposure of transfected cells to IGF-1 is associated with an increase in the phosphorylation of OAT3 that is reversed in the presence of PKA inhibitors indicating that IGF-1 alters OAT3 function through direct phosphorylation of the protein by PKA (Zhang et al., 2020a). In addition to modulating OAT3 through direct phosphorylation, exposure of transfected cells to IGF-1 increases the SUMOylation of OAT3. Similar to ubiquitination of proteins, SUMOylation can modify the function of other proteins via the covalent addition of the small ubiquitin-related modifier (SUMO) protein to its targets. IGF-1 increases the SUMOylation of OAT3 and inhibition of PKA inhibits the IGF-1 induced SUMOylation of OAT3. These results indicate that IGF-1 also impacts OAT3 function through the covalent modification of OAT3 by SUMO proteins (Wang et al., 2019). Similar to OAT1, activation of PKC, which can be activated by the hormone angiotensin II (Duan et al., 2010), reduces the transport activity of OAT3 through the internalization of the transporter by endocytosis (Xu et al., 2016b). Prolonged PKC activation results in the degradation of OAT3 protein (Zhang et al., 2012). Inhibition of Nedd4–2 attenuates the PKC induced reduction of OAT3 transport activity and reduces the ubiquitination of OAT3. In contrast to PKC, activation of sgk2 results in the phosphorylation of Nedd-4-2 and a reduction in its physical interaction with OAT3. This leads to a reduction in ubiquitination, an increase in plasma membrane expression and an increase in the transport activity of OAT3 (Wang and You, 2017). Similarly, phosphorylation of Nedd4–2 by the Janus associated kinase 2 (JAK-2) also reduces the interaction of Nedd4–2 with OAT3 reducing ubiquitination of the transporter and increasing OAT3 function (Zhang et al., 2018). Thus, Nedd4–2 acts as a molecular switch and can either enhance or inhibit OAT3 transport activity depending on its phosphorylation status (Zhang et al., 2021).

1.22.3.8.4

Polymorphisms

Like OAT1, the genetic diversity of OAT3 is low and the gene appears to be under selective pressure (Yee et al., 2018; Erdman et al., 2006). Despite this, a number of non-synonymous SNPs have been identified that impact the function of OAT3. One study of DNA samples from 270 individuals from different ethnic backgrounds found 10 non-synonymous SNPs with nine of them resulting in amino acid substitutions and one resulting in a premature stop codon and truncation of the protein. The premature stop codon mutation c.715C > T (p.Q239X) and the single amino acid mutations c.445C > A (p.R149S) and c.779 T > G (p.I260R) resulted in a complete loss of function for OAT3. However, these mutations are not common, having an allele frequency of less than 1% in all ethnic populations tested. The most common mutations found in this study were the c.842 T > C (p.V281A) mutation with a 6% allele frequency in the African American population, the c.913 A > T (p.I305F) with a 3% and 1% allele frequency in the Asian American and European-American populations, respectively, and the c.1343 G > A (p.V448I) mutation with a 1% allele frequency in both the European-American and Mexican-American populations. The V281A and V448I mutations have similar function to wild-type OAT3 while the I305F mutation had reduced function (Erdman et al., 2006). The reduced function of the I305F mutation is the result of impaired trafficking of OAT3 to the plasma membrane and this mutation is associated with a reduction in the clearance of the cephalosporin antibiotic cefotaxime and an increased plasma exposure to the drug (Yee et al., 2013). This is the only clinical example of an OAT3 polymorphism impacting drug clearance and, overall, the International Transporter Consortium recognizes that polymorphisms of OAT3 are not significantly associated with individual variability in drug clearance (Yee et al., 2018).

1.22.3.8.5

Drug-drug interactions

As mentioned previously, the probeneciddpenicillin interaction is thought to occur at OAT3 and has been beneficial in prolonging the therapeutic effect of penicillin in patients. In contrast to this beneficial interaction, NSAIDs and methotrexate have been shown to interact detrimentally, with OAT3 being the potential site for this interaction. Methotrexate is primarily cleared from the body by the kidney and co-administration of methotrexate with NSAIDs, which are inhibitors of OAT3 activity in vitro (Burckhardt and Burckhardt, 2011), can result in increased serum levels of methotrexate and potentially life threatening toxicity (Thyss et al., 1986; Moore et al., 2015). Methotrexate is a substrate of both OAT1 and OAT3 but, like penicillin, has a much higher affinity for OAT3. For this reason, it is thought that OAT3 is the primary site of the NSAIDdmethotrexate interaction at the basolateral

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membrane of proximal tubule cells. However, at higher methotrexate concentrations, OAT1 may become more prominently involved (Burckhardt and Burckhardt, 2011). Another drug interaction that is thought to be primarily mediated by OAT3 is the probeneciddfexofenadine interaction. Fexofenadine is a histamine H1 receptor antagonist that is primarily eliminated by the liver. However, a significant amount is also eliminated by the kidney and individuals co-treated with probenecid and fexofenadine have increased plasma exposure and reduced renal clearance of fexofenadine (Tahara et al., 2006). Fexofenadine was determined to be a substrate of OAT3 in vitro but not a substrate for OAT1 or OCT2, which is consistent with OAT3 mediating the probenecidfexofenadine interaction (Tahara et al., 2006). As mentioned previously, potential biomarkers to assess drug-drug interactions at OAT3 include the specific OAT3 substrate GCDCA-S or the OAT1 and OAT3 substrates PDA and HVA. Assessment of the risk for new molecular entities to perpetrate drug-drug interactions at OAT3 is assessed similarly to OCT2 and OAT1 (FDA, 2020).

1.22.4

Summary and conclusions

Based on the recommendations of the International Transporter Consortium, the regulatory agencies in the United States, in Europe and in Japan require or recommend (for OCT1) that several multi-specific drug uptake transporters expressed in the liver (OATP1B1, OATP1B3 and OCT1) and the kidney (OCT2, OAT1 and OAT3) are tested for potential drug-drug interactions when new drugs are developed. These recommendations are based on the characterization of SNPs and pharmacokinetic studies that resulted in increased drug plasma levels or decreased drug effects (e.g., inhibition of metformin uptake into hepatocytes). Given that several of these transporter polymorphisms result in substrate-dependent effects it is likely that precision medicine in the near future will aim at adjusting drug doses based on the functional expression of the respective drug uptake transporters rather than treating every patient the same. This is particularly important in the elderly because these patients frequently are taking 5– 10 different prescription drugs and the risk for drug-drug interactions increases with each additional drug they are prescribed or take over-the-counter. Thus, continued characterization of the function of these six uptake transporters with respect to novel drugs is crucial to enhance drug therapy effectiveness and safety using the patient’s genetic profiles.

1.22.5

Disclosure of interest

Dr. Philip Sandoval is a full time employee of and holds stocks in Takeda Pharmaceutical Company Limited.

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Complete OATP1B1 and OATP1B3 deficiency causes human rotor syndrome by interrupting conjugated bilirubin reuptake into the liver. The Journal of Clinical Investigation 122, 519–528. van de Steeg, E., van Esch, A., Wagenaar, E., Kenworthy, K.E., Schinkel, A.H., 2013. Influence of human OATP1B1, OATP1B3, and OATP1A2 on the pharmacokinetics of methotrexate and paclitaxel in humanized transgenic mice. Clinical Cancer Research 19, 821–832. Vanwert, A.L., Bailey, R.M., Sweet, D.H., 2007. Organic anion transporter 3 (Oat3/Slc22a8) knockout mice exhibit altered clearance and distribution of penicillin G. American Journal of Physiology. Renal Physiology 293, F1332–F1341. Varma, M.V., Steyn, S.J., Allerton, C., El-Kattan, A.F., 2015. Predicting clearance mechanism in drug discovery: Extended clearance classification system (ECCS). Pharmaceutical Research 32, 3785–3802. Vasquez-Rios, G., Nadkarni, G.N., 2020. SGLT2 inhibitors: Emerging roles in the protection against cardiovascular and kidney disease among diabetic patients. International Journal of Nephrology and Renovascular Disease 13, 281–296. Vavricka, S.R., Van Montfoort, J., Ha, H.R., Meier, P.J., Fattinger, K., 2002. Interactions of rifamycin SV and rifampicin with organic anion uptake systems of human liver. Hepatology 36, 164–172. Wagner, J.B., Ruggiero, M., Leeder, J.S., Hagenbuch, B., 2020. Functional consequences of pravastatin isomerization on OATP1B1-mediated transport. Drug Metabolism and Disposition 48, 1192–1198. Wang, H., You, G., 2017. SGK1/Nedd4-2 signaling pathway regulates the activity of human organic anion transporters 3. Biopharmaceutics & Drug Disposition 38, 449–457. Wang, D.S., Jonker, J.W., Kato, Y., Kusuhara, H., Schinkel, A.H., Sugiyama, Y., 2002. Involvement of organic cation transporter 1 in hepatic and intestinal distribution of metformin. 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The Journal of Pharmacology and Experimental Therapeutics 322, 1221–1227. Wright, S.H., 2019. Molecular and cellular physiology of organic cation transporter 2. American Journal of Physiology. Renal Physiology 317, F1669–f1679. Wright, S.H., Dantzler, W.H., 2004. Molecular and cellular physiology of renal organic cation and anion transport. Physiological Reviews 84, 987–1049. Wu, W., Jamshidi, N., Eraly, S.A., Liu, H.C., Bush, K.T., Palsson, B.O., Nigam, S.K., 2013. Multispecific drug transporter Slc22a8 (Oat3) regulates multiple metabolic and signaling pathways. Drug Metabolism and Disposition 41, 1825–1834. Wu, W., Bush, K.T., Nigam, S.K., 2017. Key role for the organic anion transporters, OAT1 and OAT3, in the in vivo handling of uremic toxins and solutes. Scientific Reports 7, 4939. Xiang, X., Han, Y., Neuvonen, M., Pasanen, M.K., Kalliokoski, A., Backman, J.T., Laitila, J., Neuvonen, P.J., Niemi, M., 2009. Effect of SLCO1B1 polymorphism on the plasma concentrations of bile acids and bile acid synthesis marker in humans. Pharmacogenetics and Genomics 19, 447–457. Xu, D., Huang, H., Toh, M.F., You, G., 2016a. Serum- and glucocorticoid-inducible kinase sgk2 stimulates the transport activity of human organic anion transporters 1 by enhancing the stability of the transporter. International Journal of Biochemistry and Molecular Biology 7, 19–26. Xu, D., Wang, H., You, G., 2016b. An essential role of Nedd4-2 in the ubiquitination, expression, and function of organic anion Transporter-3. Molecular Pharmaceutics 13, 621–630. Xu, D., Wang, H., Zhang, Q., You, G., 2016c. Nedd4-2 but not Nedd4-1 is critical for protein kinase C-regulated ubiquitination, expression, and transport activity of human organic anion transporter 1. American Journal of Physiology. Renal Physiology 310, F821–F831. Xu, D., Zhang, J., Zhang, Q., Fan, Y., Liu, C., You, G., 2017. 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1.23

Drug Transporters: Efflux

Eliza R. McColla, Vessela Vassilevab, and Micheline Piquette-Millera, a Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON, Canada; and b Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom © 2022 Elsevier Inc. All rights reserved.

1.23.1 1.23.2 1.23.2.1 1.23.2.2 1.23.2.3 1.23.2.4 1.23.2.5 1.23.3 1.23.3.1 1.23.3.2 1.23.3.3 1.23.4 1.23.4.1 1.23.4.2 1.23.4.3 1.23.5 1.23.5.1 1.23.5.2 1.23.5.3 1.23.5.4 1.23.5.5 1.23.6 1.23.6.1 1.23.6.2 1.23.6.3 1.23.6.4 1.23.7 References

Introduction Important efflux transporters for pharmacokinetics P-glycoprotein (P-gp) Breast cancer resistance protein (BCRP) Multidrug resistance-associated proteins (MRPs) Bile salt export pump (BSEP) Multidrug and toxic compound extrusion transporters (MATEs) Absorption P-gp BCRP MRPs Distribution Efflux transporters at the blood-brain barrier Efflux transporters at the blood-placental barrier Efflux transporters at the blood-testis barrier Hepatobiliary excretion P-gp BCRP MRPs BSEP MATE1 Renal excretion P-gp BCRP MRPs MATEs Summary

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Glossary Apical membrane The surface of the plasma membrane that faces inwards to the lumen of an internal organ. Area under the curve (AUC) The area obtained from integration of a drug plasma concentration-time curve; reflects systemic exposure to a drug. ATP-binding cassette (ABC) transporters A superfamily of (mainly) efflux transporters that use hydrolysis of ATP to move substrates across biological membranes. Basolateral membrane The part of the plasma membrane in contact with the extracellular matrix that forms its basal and lateral surfaces, distinct from the apical (or lumenal) surface. Canalicular membrane The apical membrane of hepatocytes that interfaces with the bile. Cmax The maximum (or peak) concentration of a drug observed in a biological fluid (typically in blood or plasma) after administration. Enterohepatic reabsorption The process by which substrates that are metabolized in the liver are excreted into the bile, passed into the intestinal lumen, reabsorbed from the intestine, and ultimately returned to the liver. Epithelial membrane A layer of epithelial cells connected by tight junctions and supported by connective tissue; exhibits distinct cell polarity resulting in apical and basolateral plasma membrane domains. Nucleotide binding domain (NBD) The structural domains of ABC transporters that bind and hydrolyze ATP for energy production. Single nucleotide polymorphism (SNP) The change in a single nucleotide base of a DNA sequence within the genome. Sinusoidal The basolateral membrane of hepatocytes that interfaces with the blood.

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Solute carrier (SLC) transporter A superfamily of transporters that use energy coupling mechanisms (i.e. secondary active transport and facilitated transport) to move substrates across biological membranes. Syncytiotrophoblast The multinucleated epithelial layer of the placenta that facilitates transport of waste, gas, nutrients, and xenobiotics. Transmembrane domain/helices The structural domains of transporters that span the membrane and interact with the substrate. Zwitterion A molecule that contains an equal number of positively and negatively-charged functional groups such that the net charge of the entire molecule is zero.

Abbreviations ABC ATP-binding cassette ARV Antiretroviral ATP Adenosine triphosphate AUC Area under the curve BBB Blood-brain barrier BCRP Breast cancer resistance protein BSEP Bile salt export pump BTB Blood-testis barrier Cmax Maximum plasma/serum drug concentration DDI Drug-drug interaction EHBR Eisai hyperbilirubinemic rats FDA Food and drug administration ITC International Transporter Consortium KO Knockout MATE Multidrug and toxic compound extrusion transporter MRP Multidrug resistance-associated protein NBD Nucleotide-binding domain OAT Organic anion transporter OATP Organic anion-transporting polypeptide OCT Organic cation transporter P-gp P-glycoprotein SLC Solute carrier SNP Single nucleotide polymorphism TMD Transmembrane domain

1.23.1

Introduction

Efflux transporters pump substrates across biological membranes out of cells or cellular compartments. In general, efflux transporters belong to the ATP-binding cassette (ABC) transporter superfamily (Wilkens, 2015). The human genome contains 49 ABC transporter genes comprised of seven subfamilies (ABCA to ABCG), while the mouse genome contains 52 ABC transporter genes arranged into eight subfamilies (Vasiliou et al., 2009). The ABC transporters encoded by these genes transport substrates across biological membranes against a concentration gradient, usually in only one direction (Wilkens, 2015). This process requires energy, which is derived from the hydrolysis of ATP, generating the necessary energy to move substrates across membranes against concentration gradients, which is known as active transport. ABC transporters typically have four main domains: two nucleotide-binding domains (NBDs) and two transmembrane domains (TMDs) (Wilkens, 2015). Most ABC transporters have a single subunit that contains all four domains, while some others, known as “half transporters”, require homo- or heterodimerization of two halves in order to be active. The NBD, which binds ATP to power efflux, is highly conserved across all ABC transporters. In contrast, TMDs, which each normally consist of 6 transmembrane a-helices, do not demonstrate this level of sequence conservation. However, TMDs within certain subfamilies tend to share similar topologies. The two TMDs of an ABC transporter form a pore spanning the membrane that is lined with particular amino acid residues to facilitate substrate binding. Upon binding of a substrate at the binding site of the TMDs, each NBD binds an ATP molecule, which is subsequently hydrolyzed, resulting in a conformational change, allowing the substrate to be pumped through the

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membrane to the opposite side. Substrates of ABC transporters include a variety of compounds, such as amino acids, sugars, nucleosides, metal clusters, peptides, hormones, numerous environmental toxins, and clinically relevant drugs (Wilkens, 2015). A few members of the solute carrier (SLC) transporter superfamily also serve as efflux transporters. In contrast to ABC transporters, which actively transport via ATP hydrolysis, SLC transporters mediate movement of substrates across biological membranes using energy coupling mechanisms (Colas et al., 2016). These processes include secondary active transport, involving electrochemical gradients of ions and substrates to provide energy for transport against concentration gradients, and facilitated transport, in which diffusion down a concentration gradient is facilitated via a transporter protein. The human genome has 65 families of SLC transporters consisting of over 400 genes (Bai et al., 2017; Schlessinger et al., 2013). Members of each family tend to have at least 20–25% similarity in amino acid sequence identity, but families are also classified based on substrate similarity and/or biochemical function (Bai et al., 2017; He et al., 2009). Crystal structures for SLC transporters are highly diverse and have been identified for a minority of transporters, mostly from prokaryotic species (Colas et al., 2016; Bai et al., 2017). SLC transporters have a wide range of endogenous substrates including inorganic ions, amino acids, sugars, bile salts, organic anions, neurotransmitters, vitamins, and hormones (He et al., 2009). Exogenous compounds, including many clinically relevant drugs, are also substrates for several of these transporters. The families SLC7, SLC10, SLC15, SLC22, SLC28, and SLC47 are the most relevant for drug transport (Schlessinger et al., 2013). While most SLC transporters mediate the cellular uptake of substrates, the SLC47A subfamily of multidrug and toxic compound extrusion transporters (MATEs) actively facilitate the cellular efflux of organic cation substrates using proton gradients (Damme et al., 2011; Omote et al., 2006). Since the focus of this article is on efflux transporters, the discussion of SLC transporters will be limited to MATEs. Both ABC and SLC drug transporters are ubiquitously expressed throughout the body with particularly high expression in areas typically involved with absorption, metabolism, and excretion of drugs, including the intestines, liver, and kidneys (Giacomini et al., 2010). Transporters are also expressed in a number of other tissues such as the brain, placenta, and testis and serve to regulate substrate distribution in these tissues. The involvement of these transporters in the tissue distribution of compounds, including a wide variety of clinically relevant drugs, positions drug efflux transporters to play a significant role in pharmacokinetics. The expression and function of drug transporters is regulated by a variety of factors, including transcriptional regulators, disease and inflammation, physiological factors such as age and sex, and genetic polymorphisms, all of which can impact drug disposition. For example, single nucleotide polymorphisms (SNPs), which represent changes in one base pair of a gene, can have significant impacts on expression, subcellular localization, and functional activity of drug transporters. The presence of these genetic variants can result in clinically meaningful variations in the pharmacokinetics of drug transporter substrates within populations and between different ethnicities, which often display different prevalences of these SNP variants. A number of important polymorphisms for major efflux transporters including P-glycoprotein, breast cancer resistance protein, and multidrug resistanceassociated proteins have been identified and reviewed (Gradhand and Kim, 2008; Leschziner et al., 2007). Further information regarding the impact of such SNPs on clinical pharmacokinetics and resulting clinical guidelines can be found within resources such as the Pharmacogenomics Knowledge Base (www.pharmgkb.com) (Whirl-Carrillo et al., 2012). Due to their affinity for wide ranges of clinically relevant drugs and their ubiquitous expression in multiple organs and tissues, efflux drug transporters play integral roles in mediating the absorption, distribution, hepatic elimination, and renal excretion of drugs. To this point, the International Transporter Consortium (ITC) was established to solidify an understanding of and address critical issues relevant to transporters that are important in drug disposition and drug-drug interactions (DDIs). Before the establishment of the ITC in 2007, FDA guidelines for conducting DDI studies during drug development were largely restricted to drug metabolizing enzymes (Giacomini et al., 2010). The ITC has since proposed a number of recommendations to guide whether clinical DDI studies with transporter substrates should be conducted during drug development (Giacomini et al., 2010; Hillgren et al., 2013; Zamek-Gliszczynski et al., 2018). Transporters recommended by the ITC for evaluation during drug development include the efflux transporters P-gp, BCRP, MRP2 and 4, MATE1 and 2-K, and BSEP and the influx transporters OCT1 and 2, OAT1 and 3, OATP1B1 and 1B3, and OATP2B1 (Zamek-Gliszczynski et al., 2018). The following sections will describe the substrates, expression, and function of the key efflux drug transporters, as identified by the ITC, and their role in drug disposition.

1.23.2

Important efflux transporters for pharmacokinetics

1.23.2.1

P-glycoprotein (P-gp)

P-glycoprotein, encoded by multidrug resistance gene 1 (MDR1/ABCB1) in humans and by Mdr1a (Abcb1a) and Mdr1b (Abcb1b) in rodents, is a prototypical ABC transporter composed of a single protein with two homologous halves, each consisting of six transmembrane segments and an NBD (Kim and Chen, 2018). P-gp is expressed in the epithelia of numerous tissues including the intestine, liver, kidney, blood-brain barrier, testis, placenta, and lungs (Choudhuri and Klaassen, 2006). In general, P-gp expression at blood-tissue barriers serves to restrict the entry of xenobiotics into the tissue in order to protect against toxicity. P-gp is often described as a “promiscuous transporter” due to its large diversity of substrates. Over 300 compounds have been identified as potential P-gp substrates (Chen et al., 2012), many of which are widely used drugs including a variety of cytotoxic anticancer agents, anti-infectives, antiretroviral protease inhibitors, and cardiovascular drugs (Morrissey et al., 2012). These substrates are generally large, hydrophobic, amphipathic, and positively charged at physiological pH; however, these criteria do not account for all P-gp substrates (Sharom, 1997).

Drug Transporters: Efflux 1.23.2.2

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Breast cancer resistance protein (BCRP)

BCRP is encoded by ABCG2, and received its name based on its ability to confer drug resistance in breast cancer cell lines (Jani et al., 2014). Unlike P-gp, BCRP is a half ABC transporter and contains only one TMD with six a-helices and one NBD. In order to be functional, BCRP must homodimerize. BCRP is ubiquitously expressed in tissues such as the colon, intestine, liver, placenta, mammary gland, lung, bladder, testis, prostate, uterus, pancreas, and kidney. Like P-gp, BCRP largely serves a protective role in many of these tissues. BCRP also displays broad substrate specificity for compounds that are usually hydrophobic and includes many cationic or anionic drugs as well as phase II-conjugated metabolites (Jani et al., 2014). This range in substrate specificity is thought to result from the fact that BCRP contains multiple drug-binding sites with distinct localization and affinity within the binding pocket. BCRP transports a variety of endogenous substrates including sex hormones and their precursors and vitamins, as well as numerous clinically relevant drugs such as statins, antibiotics, antivirals, anticancer agents such as methotrexate, glyburide, sulfasalazine, and cimetidine (Morrissey et al., 2012; Jani et al., 2014).

1.23.2.3

Multidrug resistance-associated proteins (MRPs)

MRPs are encoded by the ABCC subfamily of ABC transporters. Like BCRP, MRPs were named for their role in mediating drug resistance against anticancer drugs. Six MRPs (MRP1-6 encoded by ABCC1-6) have been the main focus of characterization and research efforts so far (Borst et al., 2000). MRPs are full transporters with either two (MRP4 and MRP5) or three (MRP1-3 and MRP6) TMDs and two NBDs, and their expression profiles vary (Choudhuri and Klaassen, 2006). MRP1 and MRP5 are ubiquitously expressed, with MRP1 having lower expression in the liver (Morrissey et al., 2012; Borst et al., 2000). MRP2 is expressed in the liver, kidney, and intestine with low expression in the placenta and brain. MRP3 is expressed in the liver, adrenals, kidney, intestine, brain, and placenta. MRP4 is widely expressed including in the prostate, lung, muscle, pancreas, testis, ovary, bladder, liver, placenta, and brain. MRP6 is largely expressed in the liver and kidney. MRPs primarily transport organic anions with substrates of MRP1 and 2 being the most extensively characterized (Choudhuri and Klaassen, 2006). The substrate binding site of MRP1 consists of two pockets which allows for transport of a broad range of structurally dissimilar substrates including glutathione, organic anion conjugates such as glutathione conjugates, glucuronides, and glutathione disulfide, bilirubin, unconjugated anionic drugs, and amphipathic neutral or basic drugs (Choudhuri and Klaassen, 2006; Johnson and Chen, 2017). Such drugs include multiple anticancer drugs as well as some antidepressants, statins, and antibiotics. Similarly to MRP1, MRP2 transports glutathione conjugates in addition to bilirubin glucuronides, conjugated drug metabolites, and several anticancer drugs (Choudhuri and Klaassen, 2006; Borst et al., 2000).

1.23.2.4

Bile salt export pump (BSEP)

BSEP, encoded by ABCB11, is an ABC transporter of typical structure including two NBDs and two TMDs (each containing six a-helices) (Kubitz et al., 2012). Unlike the other ABC transporters discussed thus far, the expression of BSEP is primarily restricted to hepatocytes where BSEP mediates the secretion of primary and secondary bile acids into the bile for removal from the liver (Choudhuri and Klaassen, 2006; Kubitz et al., 2012). While the substrates of BSEP are largely limited to bile acids, a number of drugs are inhibitors of BSEP which can lead to bile buildup in the liver, resulting in drug-induced liver injury (Kubitz et al., 2012). This will be discussed in further detail later on.

1.23.2.5

Multidrug and toxic compound extrusion transporters (MATEs)

As previously mentioned, MATEs are proton-coupled efflux transporters despite belonging to the SLC superfamily of transporters (Damme et al., 2011). Two members of this family are particularly relevant for drug pharmacokinetics: MATE1, encoded by SLC47A1, and MATE2 and MATE2-K, two functional isoforms encoded by transcript variants of SLC47A2. While crystal structures of any of these proteins have yet to be solved, it is thought that MATEs likely consist of 13 transmembrane helices (Damme et al., 2011). MATE1 expression is highest in the kidney, liver, and skeletal muscle with lower expression in the adrenal gland, cervix, uterus, thyroid gland, and testis. In contrast, MATE2 and MATE2-K are primarily restricted to the kidney. Substrates of MATE1 and MATE2-K are generally cationic, hydrophilic, and have low molecular weights (Damme et al., 2011). Due to their affinity for cationic substrates, MATE1 and MATE2-K exhibit significant substrate overlap with organic cation transporters (OCTs). However, MATEs can also transport some zwitterionic and anionic compounds, extending their substrate specificity beyond that of the OCTs. Such substrates of MATE1 and MATE2-K include endogenous substrates such as thiamine, creatinine, guanidine, and estrone-3-sulfate and drugs such as metformin, cimetidine, oxaliplatin, acyclovir, fexofenadine, nadolol, and cephalexin (Damme et al., 2011; Nies et al., 2016). In contrast, investigation into the substrate selectivity of MATE2 has been limited; currently, the prototypical probe substrate tetraethylammonium (TEA) is the only definitively identified substrate of MATE2 (Nies et al., 2016).

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Together, P-gp, BCRP, MRPs, BSEP, and MATEs transport a large number of clinically relevant drugs in multiple tissues important for drug absorption and elimination such as the intestine, liver, and kidney. As a result, these efflux transporters contribute significantly to the pharmacokinetics of numerous widely used drugs. The following sections will discuss the role of each of these transporters in drug absorption, distribution, hepatobiliary clearance, and renal elimination.

1.23.3

Absorption

In order for orally administered drugs to enter the systemic circulation, they must first be absorbed in the gastrointestinal tract, particularly by intestinal epithelial cells. Assuming adequate drug dissolution, membrane permeability is most frequently the rate-limiting step in drug absorption and is highly dependent on the lipophilicity, molecular size, and charge of the drug (Fan and de Lannoy, 2014). For the most part, small, lipophilic, unionized drugs readily pass through the membrane of intestinal epithelial cells down a concentration gradient via passive diffusion while diffusion, and hence absorption, of larger, highly polar and charged compounds is often limited. Hence, there are several transporters that are highly expressed in the intestine and serve to facilitate or limit drug transfer. Uptake transporters such as OCTNs, OATPs, and PEPT1 increase absorption while efflux transporters such as P-gp, BCRP, and MRP2 decrease intestinal absorption. Fig. 1 depicts the relative localization of these transporters to either membrane of intestinal epithelial cells. As the focus of this article is on efflux transporters, we will discuss the role of P-gp, BCRP, and MRPs in intestinal absorption in further detail below. Given that absorption of numerous orally administered drugs is mediated by intestinal transporters, conditions that result in changes to transporter expression or activity can impact drug absorption and subsequent systemic exposure. Since some drugs have the ability to impact transporter activity or expression, these changes can result in DDIs when co-administered with other drugs that are transporter substrates. As a result, the FDA and ITC both recommend that the potential for DDIs involving the intestinal efflux transporters BCRP and P-gp should be investigated during drug discovery (Zamek-Gliszczynski et al., 2018; FDA, 2020).

1.23.3.1

P-gp

Within the intestine, P-gp is localized to the apical (lumen-facing) membrane of intestinal epithelial cells. The apical localization of P-gp allows it to play a protective role by mediating the efflux of xenobiotics that enter the epithelial cells back into the intestinal lumen to prevent absorption and potential subsequent toxicity (Chan et al., 2004). Thus, intestinal absorbance of P-gp substrates is limited by active efflux back into the intestinal lumen by P-gp. The role of P-gp in preventing intestinal absorption of its substrates has been clearly demonstrated in rodent Mdr1a and Mdr1a/ Mdr1b knockout (KO) models in which the gene(s) that encode for P-gp have been deleted. When intestinal sections from Mdr1a KO mice were used to conduct ex vivo transport studies, basolateral to apical efflux of the P-gp substrates digoxin and paclitaxel was virtually absent in comparison to intestinal sections from wildtype mice (Stephens et al., 2002). As compared to wildtype, the area under the plasma concentration time curve (AUC) of digoxin was found to be 3-fold higher in P-gp KO rats after oral administration while minimal differences were seen after intravenous administration (Suzuki et al., 2014). Likewise, as compared to wildtype, the

Fig. 1 Drug transporters in the intestine. Members of the ABC (blue) and SLC (yellow) transporter superfamilies are expressed on the apical (lumen-facing) and basolateral (blood-facing) membranes of enterocytes lining the intestinal tract. Depending on their localization and direction of transport, these transporters regulate intestinal absorption of a wide range of clinically relevant drugs. Figure created with Biorender.com.

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AUC of orally administered paclitaxel was found to be increased by more than 11-fold in Mdr1a/Mdr1b KOs, while levels were not significantly different after intraperitoneal delivery (Hendrikx et al., 2013). These studies clearly demonstrated the profound role that P-gp can have in limiting the oral absorption of its substrates. Several genetic polymorphisms of ABCB1 that are associated with decreased function of P-gp have been identified in humans and investigated with regard to their role in drug absorption and disposition. The three most studied variants are 1236C > T, 2677G> T/A and 3435C > T. With respect to the 2677G> T/A variant, individuals homozygous for the wildtype G allele have significantly lower plasma concentrations of the P-gp substrate tacrolimus after oral administration and require 40% higher doses to reach target trough concentrations than individuals with variant alleles (Anglicheau et al., 2003). It has also been reported that individuals homozygous for variant alleles of the 3435C > T SNP have significantly lower duodenal expression of P-gp, corresponding to significantly higher Cmax values of digoxin upon oral administration (Hoffmeyer et al., 2000). Nevertheless, research findings from these studies have been inconsistent so it is unclear whether genetic polymorphisms of P-gp substantially contribute to inter-subject differences in drug absorption. Numerous preclinical and clinical DDI studies have also illustrated the importance of P-gp in limiting intestinal drug absorption. Furthermore, as many drugs can act as inhibitors or enhancers of P-gp, these studies have demonstrated a need to consider P-gpmediated DDIs in drug development. This is particularly true for orally administered drugs. For example, when the P-gp inhibitor verapamil is co-administered to rats with cyclosporine or tacrolimus, plasma concentrations of both cyclosporine and tacrolimus increase significantly, implicating P-gp efflux in the absorption of both immunosuppressants (Yigitaslan et al., 2016). Conversely, administering either drug with rifampicin, a P-gp inducer, results in significantly decreased plasma concentrations. A similar effect of P-gp has also been demonstrated for the intestinal absorption of the anti-cancer drug, afatinib, in rats. When co-administered with verapamil, the AUC and Cmax of afatinib are approximately 80% higher than when afatinib is administered alone (Zhang et al., 2018). Similar results have been observed in clinical DDI studies. For example, co-administration of the P-gp substrate digoxin with venetoclax, a chemotherapeutic agent known to inhibit P-gp, resulted in a significant increase in the Cmax of digoxin by 35%, and a 9% increase in AUC (Chiney et al., 2018). Similarly, the Cmax and AUC of naldemedine (a mu-opioid receptor antagonist and P-gp substrate) were increased by 1.5 and 1.8-fold, respectively, when co-administered with cyclosporine. Clinically important DDIs involving P-gp have been increasingly documented, highlighting the need to examine their potential during the drug development process. A few examples of clinical DDIs involving intestinal P-gp include digoxin and quinidine or verapamil, loperamide and quinidine, and digoxin and rifampin (Wessler et al., 2013; Lin, 2003; Akamine et al., 2012). Overall, results of studies investigating pharmacokinetics in individuals with P-gp SNPs as well as preclinical knockout and inhibitor studies demonstrate the role that P-gp plays in limiting intestinal absorption of its substrates.

1.23.3.2

BCRP

Similarly to P-gp, BCRP is localized to the apical membrane of intestinal epithelial cells where it serves to efflux xenobiotics back into the intestinal lumen, thereby minimizing absorption of its substrates (Chan et al., 2004). This has been well illustrated in pharmacokinetic studies with BCRP KO rodents. For example, the absolute oral bioavailability (AUC po/AUCiv) of the BCRP substrate sulfasalazine was over 8-fold greater in BCRP KO as compared to wildtype mice while absolute bioavailability of rosuvastatin was 3-fold higher, demonstrating the importance of BCRP in limiting absorption of its substrates (Karibe et al., 2015). Indeed, others have also reported 130-fold higher AUC values for sulfasalazine in BCRP KO mice (Kawahara et al., 2020). These authors estimated that while the apparent absorption from the GI tract into the portal system in KO mice was almost 100%, it was only about 17% in wildtype mice, further solidifying the role of BCRP in intestinal absorption of sulfasalazine. As with P-gp, investigations involving reduced function genetic polymorphisms for BCRP also illustrate involvement of BCRP in limiting drug absorption in humans. One of the most commonly studied SNP variants, 421C > A, is associated with decreased BCRP expression and activity. The AUC0-48 of sulfasalazine in subjects homozygous for the variant A allele was approximately 3.5-fold higher than those homozygous for the wildtype allele (Yamasaki et al., 2008). Similarly, individuals carrying the A variant allele (both homozygotes and heterozygotes) demonstrate significantly higher AUCs for rosuvastatin after oral administration as compared to wildtype individuals (Zhang et al., 2006). Clinical DDI studies have also demonstrated involvement of BCRP as an important source of DDIs involving drug absorption. Co-administration of rosuvastatin with the BCRP inhibitor fostamatinib results in a respective 96% and 88% increase in the AUC and Cmax of rosuvastatin in healthy subjects (Martin et al., 2016). A similar effect was reported for simvastatin, which experiences increases of 74% for AUC and 83% for Cmax when co-administered with fostamatinib. Overall, these drug-drug and drug-gene interaction studies in individuals carrying the 421C> A variant have clearly demonstrated an important role of BCRP in limiting intestinal absorption of drugs in humans.

1.23.3.3

MRPs

In addition to P-gp and BCRP, members of the MRP family are also expressed in the intestine and thus may impact absorption of orally administered drugs. MRP1, 2, and 3 are all expressed in the intestine to varying extents. In the jejunum, MRP2 is most highly expressed followed by moderate expression of MRP3 and low expression of MRP1 (Harwood et al., 2019). Further

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down the intestine in the ileum, MRP3 expression exceeds that of MRP2. MRP2 is located on the apical membrane whereas MRP1 and MRP3 are located on the basal membrane (Chan et al., 2004). As a result, MRP2 plays a role similar to that of P-gp and BCRP in which it effluxes drugs back into the intestinal lumen. In contrast, MRP3 effluxes drugs that have entered intestinal epithelial cells into the blood. While MRP1 has a similar localization as MRP3, its role in mediating drug efflux is thus far unclear. The role of MRP2 in preventing intestinal absorption of its substrate drugs has been elucidated in studies using either MRP2 KO mice or Eisai hyperbilirubinemic rats (EHBR), in which MRP2 is hereditarily defective. In MRP2 KO mice, plasma concentrations of the immunosuppressant mycophenolic acid were found to be significantly higher than in wildtype mice, demonstrating involvement of MRP2 in limiting its intestinal absorption (Wang et al., 2008). In pharmacokinetic studies in EHBR, the Cmax and AUC values of methotrexate and pravastatin were 1.6 and 4-fold higher, respectively, than in Sprague-Dawley rats, which have functional MRP2 (Naba et al., 2004). Furthermore, ex vivo transport studies conducted in intestinal segments from EHBR demonstrated decreased net basolateral to apical secretion of the fluoroquinolone antibacterial agent grepafloxacin as compared to intestinal segments from Sprague-Dawley rats (Naruhashi et al., 2002). Thus, studies in EHBR and MRP2 KO mice both demonstrate how MRP2 efflux into the intestinal lumen prevents oral absorption of its substrates. In contrast to MRP2, the basal localization of MRP3 allows it to efflux drugs into the blood, thus facilitating, rather than preventing, the absorption of its substrates. MRP3 KO mice exhibit significantly lower plasma concentrations of methotrexate after oral administration compared to wildtype mice, implicating MRP3 in facilitating methotrexate transport from intestinal cells into blood (Kitamura et al., 2008). MRP3 also appears to be involved in facilitating enterohepatic reabsorption of its glucuronide-conjugated substrates. The apical to basolateral transport of the glucuronide metabolite of ezetimibe was significantly lower in intestinal segments isolated from MRP3 KO mice as compared to wildtype mice. Moreover, after duodenal administration portal blood concentrations of ezetimibe glucuronide in MRP3 KO mice were only 7% of that seen in wildtype mice, indicating that the transport of ezetimibe glucuronide from intestinal enterocytes into blood is dependent on MRP3 (De Waart et al., 2009). Thus, MRP transporters are involved in both apical and basolateral efflux in the intestine, thereby impacting the oral absorption of substrate drugs and their conjugated metabolites.

1.23.4

Distribution

Once a drug enters the systemic circulation, it is further distributed throughout the body to sites of efficacy and toxicity. Drug distribution depends on a number of factors including organ/tissue blood flow, plasma and tissue protein binding, and whether transport occurs through passive or active processes (Fan and de Lannoy, 2014). Drug transporters play an integral role in regulating the distribution of a drug into various organs and tissues including the liver and kidney; however, the role of drug transporters in governing distribution is especially highlighted at protective blood-tissue barriers. Since the role of blood-tissue barriers is typically to restrict access of potentially harmful compounds into the organ/tissue they surround, efflux transporters located in these bloodtissue barriers are essential for either allowing or preventing xenobiotics from distributing into these compartments. Some common blood-tissue barriers known to express efflux transporters include the blood-brain barrier, the blood-placental barrier, and the blood-testis barrier.

1.23.4.1

Efflux transporters at the blood-brain barrier

The blood-brain barrier (BBB) is a physical and metabolic barrier between the brain and systemic circulation that serves to protect and maintain homeostasis in the brain (Löscher and Potschka, 2005). In keeping with its protective role, efflux transporters expressed in the BBB largely restrict entry of xenobiotics into the brain to prevent toxicity (Fig. 2); however, this presents a significant pharmacokinetic challenge for drugs whose targets of action lie in the brain. This is particularly true for substrates of P-gp and BCRP. P-gp and BCRP are both expressed in the apical membrane of the brain capillary endothelial cells that compose the BBB (Löscher and Potschka, 2005). As a result, substrates of P-gp or BCRP that attempt to cross the BBB are actively pumped back into the blood, thus preventing access to the brain. For example, the AUCbrain of the P-gp substrate paclitaxel was 11-fold higher in Mdr1a/b KO mice than in wildtype (Kemper et al., 2003). Furthermore, the brain-to-blood ratio of verapamil was approximately 5.5-fold higher in Mdr1a/b KO mice compared to wildtype (Römermann et al., 2013). Similarly, brain concentrations of the BCRP substrate methotrexate (of which only 5% of free drug normally crosses the BBB) were approximately 15% higher in BCRP KO mice (Li et al., 2013a). Thus, P-gp and BCRP both play a vital role in limiting brain exposure to their substrates and thus contribute substantially to the protection of the brain from xenobiotics. As previously discussed, the contribution of efflux transporters to intestinal absorption of drugs in humans can be readily assessed by measuring drug concentrations in blood or plasma. Furthermore, the role of individual transporters can be delineated in studies that utilize specific transporter inhibitors or in subjects possessing genetic variants for transporter function. However, measuring the impact of efflux transporters on drug distribution across blood-tissue barriers in humans is much more difficult given that absolute tissue concentrations cannot be quantified non-invasively. By using radiolabelled drug substrates and examining distribution into tissue compartments with positron emission tomography (PET), researchers can visualize drug disposition across human blood-tissue barriers in vivo in a non-invasive manner. Conducting these studies in the presence or absence

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Fig. 2 Drug transporters at the blood-brain barrier. Drug transporters belonging to the ABC (blue) and SLC (yellow) superfamilies are expressed on the apical (blood-facing) and basolateral (brain-facing) membranes of the microvessel endothelial cells that compose the blood-brain barrier. These transporters regulate penetration of their substrates into the brain depending on their localization and direction of transport. (*) indicates confirmed localization in rats or mice, but not in humans. Figure created with Biorender.com.

of transporter inhibitors has also provided further insight into the role of individual efflux transporters. For example, PET studies using radiolabelled verapamil, a P-gp substrate, show very little penetration of verapamil into the human brain (Eyal et al., 2010). In contrast, when patients concurrently receive cyclosporin A, a P-gp inhibitor, there is a 2-fold increase in brain levels of radiolabelled verapamil, therefore exemplifying the role that P-gp plays in limiting brain penetration of its substrates in humans. In addition to P-gp and BCRP, the blood-brain barrier expresses MRP isoforms. However, in contrast to P-gp and BCRP, the localization and function of MRPs at the BBB is far less well defined and subject to significant inter-species variation. For example, MRP4 and MRP5 have been detected at the protein level at the luminal membrane in humans, rats, and mice, whereas MRP2 protein has been detected at the luminal membrane in rats and mice only (Gomez-Zepeda et al., 2020). Furthermore, beyond the actual presence of MRPs in the BBB, their functionality and physiological roles are also unclear. While MRP1 activity in the BBB appears to be limited or non-existent, MRP2 appears to be active; however, its role in mediating drug penetration across the BBB is controversial. For example, while brain penetration of the anticonvulsant drug phenytoin is significantly enhanced in MRP2-deficient rats (Potschka et al., 2003), brain penetration of two other known MRP2 substrates, pemetrexed and methotrexate, is unaltered in MRP2 KO mice (Li et al., 2013a). Additionally, due to the lack of reported protein expression of MRP2 in the human BBB, it is unlikely that MRP2 plays a clinically significant role in drug transport across the BBB. Thus, while P-gp and BCRP have clearly defined protective roles at the BBB and significantly restrict brain distribution of their substrates, the role(s), if any, that MRPs play in mediating distribution of drugs into the brain requires further investigation.

1.23.4.2

Efflux transporters at the blood-placental barrier

Efflux transporters play a significant role in controlling the transfer of exogenous and endogenous substrates across the bloodplacenta barrier (Fig. 3). The placenta, which develops during pregnancy, forms a mechanical barrier between maternal and fetal circulation to protect the developing fetus from potentially harmful compounds in maternal circulation, including xenobiotics. During pregnancy, mothers may take prescription drugs for various indications including diabetes, epilepsy, HIV, and cardiovascular conditions, in addition to various over-the-counter medications (Behravan and Piquette-Miller, 2007). While maternal medication use is often necessary, fetal exposure to these drugs can result in birth defects. Thus, efflux drug transporters located within the epithelial layer of the placenta, the syncytiotrophoblast, serve a protective role by preventing drug distribution across the placenta into the fetal compartment. The expression of many of these transporters changes during pregnancy, conferring different levels of function and protection at different stages of fetal development. P-gp is expressed on the apical, maternal-facing membrane of the placenta (Behravan and Piquette-Miller, 2007). While expression of P-gp can be detected from first trimester to term, its expression gradually decreases throughout gestation (Vähäkangas and Myllynen, 2009). Consistent with its role in other tissues, placental P-gp plays a significant protective role in the placenta to prevent xenobiotics from entering the fetal compartment. Indeed, fetal exposure to the P-gp substrates digoxin, saquinavir, and paclitaxel is

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Fig. 3 Drug transporters in the placenta. The transporting epithelial layer of the placenta, the syncytiotrophoblast, expresses numerous drug transporters from the ABC (blue) and SLC (yellow) superfamilies. These transporters serve to either facilitate or restrict maternal-to-fetal transfer of their substrates depending on their localization to either the apical (maternal-facing) or basolateral (fetal-facing) membranes of the syncytiotrophoblast. Figure created with Biorender.com.

2.4, 7, and 16-fold higher, respectively, in P-gp KO mice, clearly demonstrating its protective role in the placenta (Smit et al., 1999). The protection conferred by P-gp plays an integral role in preventing xenobiotic-induced birth defects; this has been demonstrated using CF-1 mice, which have a 25% chance of P-gp deficiency. When pregnant CF-1 mice were given L-652,280, a P-gp substrate known to cause cleft palate, 100% of P-gp-deficient fetuses were born with cleft palate and displayed 5-fold higher tissue concentrations of L-652,280 as compared to P-gp-proficient fetuses, which had a 0% incidence of cleft palate (Lankas et al., 1998). In humans, the risk of xenobiotic-induced birth defects is influenced by P-gp SNPs, such as the 3435C > T SNP in which TT variants have decreased P-gp expression. Mothers with the 3435TT genotype who use medication during pregnancy have significantly higher risks of having offspring with cleft lip/palate (Bliek et al., 2009). Thus, P-gp plays a clear role in preventing fetal exposure to xenobiotics in maternal circulation. BCRP is also expressed on the apical membrane of the placenta (Behravan and Piquette-Miller, 2007). Whether BCRP expression changes over the course of gestation remains unclear due to conflicting results (Vähäkangas and Myllynen, 2009); however, its role in fetal protection from xenobiotics is significant, given that placental BCRP expression is 100 times greater than its expression in any other tissue including the brain, testis, intestine, or liver (Behravan and Piquette-Miller, 2007). Indeed, the ratio of fetal glyburide AUC to that in maternal plasma was 2-fold higher in pregnant BCRP KO mice than in wildtype mice, demonstrating increased penetration across the placenta (Zhou et al., 2008). Similarly, treating pregnant P-gp KO mice with the BCRP inhibitor GF120918 results in 3-fold higher fetal accumulation of the BCRP substrate topotecan (Jonker et al., 2000). Thus, BCRP plays an important role in limiting fetal drug exposure. It is possible that genetic variability impacts the protective capacity of BCRP in the placenta. For example, homozygous carriers of the variant (A) allele for the BCRP 421C > A SNP express significantly lower protein levels in the placenta, which could limit BCRP’s ability to prevent transplacental passage of xenobiotics (Kobayashi et al., 2005). Future studies investigating the impact of BCRP SNPs in pregnant mothers on fetal accumulation of xenobiotics will be required to fully elucidate the fetal risk of reduced expression/function BCRP SNPs. In contrast to P-gp and BCRP, the role that MRPs play in transplacental drug disposition has been less well studied. The beststudied MRP isoforms in the placenta are MRP1-3 and MRP5 (Behravan and Piquette-Miller, 2007). The expression of MRP2 increases over gestation (Vähäkangas and Myllynen, 2009), and like BCRP and P-gp, MRP2 exhibits apical membrane localization, consistent with a protective role (Behravan and Piquette-Miller, 2007). Indeed, placental perfusion experiments show that in the presence of the MRP2 inhibitor probenecid, placental permeability of the MRP2 substrate talinolol is significantly increased (May et al., 2008). MRP3 and MRP4 are also expressed apically in the placenta (Dallmann et al., 2019). In contrast, MRP1 and MRP5 show low basolateral expression (Joshi et al., 2016). However, the role that these other MRP isoforms may play in placental distribution of their substrates requires further investigation. Additionally, other efflux transporters including BSEP and MATE1 have been detected in the placenta, but the role, if any, that these transporters play in regulating the maternal-to-fetal transfer of drugs remains unclear (Joshi et al., 2016; Ahmadimoghaddam et al., 2013).

Drug Transporters: Efflux 1.23.4.3

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Efflux transporters at the blood-testis barrier

The blood-testis barrier (BTB) is composed of highly polarized Sertoli cells and divides the seminiferous epithelium (functional unit of the testis) into a basal compartment (outer compartment, in contact with blood and lymph) and apical compartment (inner compartment, protected from blood and lymph) (Su et al., 2011). As one of the tightest blood-tissue barriers in the body, the BTB plays an essential role in protecting the testis and spermatogenesis, which occurs within them. In the event of BTB dysfunction, leakage of toxic substrates across the BTB can result in infertility. Thus, in order to help protect the testis from xenobiotic-induced toxicity, efflux drug transporters in the BTB limit xenobiotic entry and accumulation in the testis (Fig. 4). As with the other tissues discussed thus far, basal localization of P-gp and BCRP in the Sertoli cells composing the BTB plays an integral role in protecting the testis from xenobiotics by exporting them back into the blood (Su et al., 2011; Miller and Cherrington, 2018). For example, compared to wildtype mice, concentrations of vinblastine and ivermectin in the testis increased by 2.5 and 4fold, respectively, in P-gp KO mice (Schinkel et al., 1994). Similarly, concentrations of dantrolene, flavopiridol, and mitoxantrone in the testis are significantly higher in BCRP KO mice than in wildtype mice (Kodaira et al., 2010). In addition to P-gp and BCRP, MRP1 also appears to play a protective role in the basal membrane of the BTB. When the anticancer drug etoposide-phosphate was administered to MRP1 KO mice, investigators found that testis weight was significantly decreased and spermatogenesis was impaired, implying an increased accumulation and resulting toxicity to their testis (Wijnholds et al., 1998). MRP2 and MRP4 are also expressed in human testicular tissues, but their role in regulating drug transport at the BTB remains to be elucidated. Furthermore, species differences in localization of MRP4 exist; while MRP4 is localized to the basal membrane of Sertoli cells in humans and monkeys, rats show apical localization of MRP4 (Miller and Cherrington, 2018). Although expression of P-gp, BCRP, and MRP1 at the BTB confers protection from xenobiotic-induced toxicity, it also poses a pharmacokinetic challenge when distribution of drugs to the testis is desired, such as for antiretroviral (ARV) therapy (Huang et al., 2016). During HIV infection, the testis can act as a viral sanctuary site, allowing for continued viral replication despite therapeutic intervention, thus contributing to persistent infection. Given that many antiretrovirals are drug transporter substrates, it is possible that efflux drug transporters in the BTB limit penetration of ARVs into the testis, resulting in subtherapeutic concentrations and continued viral replication. Indeed, transport studies in mouse Sertoli cells demonstrate that the cellular accumulation of the ARV drug atazanavir, a known P-gp substrate, is significantly enhanced in the presence of P-gp inhibitors such as PSC833 and quinidine (Robillard et al., 2012). This suggests that efflux transporters at the BTB could indeed prevent penetration of ARVs into the testis and thus contribute to the role of the testis as an HIV sanctuary site. In summary, efflux transporters expressed at blood-tissue barriers significantly affect the distribution of drugs into the brain and testis and across the placenta into the fetal compartment. While in most cases the presence of efflux transporters, particularly P-gp and BCRP, constitutes an advantageous level of protection, this can also present a pharmacokinetic challenge when drug penetration into these tissues is desirable, such as for the brain and testis.

Fig. 4 Drug transporters at the blood-testis barrier. Tight junctions formed between Sertoli cells compose the blood-testis barrier which separates the seminiferous tubule lumen, where spermatogenesis occurs, from the blood. ABC (blue) and SLC (yellow) drug transporters localized to either the apical (lumen-facing) or basolateral (blood-facing) membranes of the Sertoli cells regulate penetration of their substrates into the testis. (*) indicates localization in humans and primates; in contrast, MRP4 is localized to the apical membrane in rats. Figure created with Biorender.com.

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1.23.5

Drug Transporters: Efflux

Hepatobiliary excretion

Hepatobiliary excretion is a major route of elimination for a wide range of endogenous and exogenous compounds, and, in combination with hepatic metabolism, accounts for approximately 70% of drug elimination in humans (Patel et al., 2016). Hepatobiliary excretion has historically been categorized into Phase I and Phase II metabolism and Phase III transport clearance processes. Phase I clearance occurs through enzyme-mediated oxidation, reduction, and hydrolysis of the parent compound, often resulting in its conversion to a more polar molecule (Garza et al., 2020). Phase II reactions involve conversion of the parent drug or Phase I metabolite to glucuronide, sulfate, glutathione, acetylated, methylated, or amino acids conjugates, with the required endogenous substances derived from carbohydrate, protein, or other sources. Phase I and II metabolism generally convert drugs to products that are more water-soluble or more-readily excreted into urine or bile. Phase III clearance refers to excretion processes that are mediated via efflux transporters. Phase III results in the elimination of the intact drug and/or metabolite(s) via efflux transporters on the apical and basolateral membranes (Patel et al., 2016). Hepatic drug clearance can be defined as the volume of blood that is cleared of the drug by the liver per unit of time, and can be influenced by various parameters including hepatic blood flow, plasma protein binding, and intrinsic metabolic clearance. Substances are directly taken up by hepatocytes via diffusion or membrane transporters, and are then converted into inactive, active, or sometimes toxic metabolites with subsequent elimination into bile for fecal excretion (Patel et al., 2016). Therefore, transport across hepatocyte membranes is one of the key parameters in hepatic drug disposition and elimination, and usually occurs through different carrier-mediated systems. Hepatic membrane transporters thus influence hepatic drug uptake, metabolism, and excretion thereby impacting hepatic and systemic drug efficacy, toxicity, and exposure. Hepatocytes are highly polarized, with specific transport proteins expressed on the sinusoidal (basolateral) or canalicular (apical) membranes (Fig. 5), which are separated by tight junctions, thus isolating the lumen of the bile canaliculi from the systemic circulation (Patel et al., 2016). While basolateral transport systems are responsible for translocating molecules across the sinusoidal membrane (transport between hepatocytes and systemic circulation), canalicular transport systems play a key role in the biliary excretion of drugs and metabolites. When carrier-mediated transport is the main mode of hepatic uptake, this process can become the rate-limiting step in overall drug elimination. For example, hepatic clearance of statins such as atorvastatin and cerivastatin, which are metabolized by CYP enzymes, is mainly mediated by OATP1B1 hepatic uptake (Patel et al., 2016). Furthermore, some drugs that are transported into the liver may not be metabolized but are instead transported across the canalicular membrane into bile or effluxed across the basolateral membrane back into the systemic circulation. Although biliary excretion of a parent drug is rarely a major route of elimination, accounting for about 5% in humans, transporter-mediated excretion of metabolites from the liver into bile and/or blood is common and an important pathway for metabolite clearance, especially for polar or conjugated metabolites (Patel et al., 2016). As a result, many drug metabolites formed in the liver exhibit transporter-mediated disposition, where hepatic efflux is critical for the elimination of metabolites from the liver into the systemic circulation and bile.

Fig. 5 Drug transporters in the liver. Hepatic uptake and hepatobiliary excretion of various drugs, endogenous substrates, and their metabolites are facilitated by ABC (blue) and SLC (yellow) transporters expressed in hepatocytes. Transporters on the sinusoidal/basolateral membrane interface with the sinusoidal blood coming from the portal vein, whereas those localized to the canalicular/apical membrane interface with the bile canaliculi. As hepatic transporters regulate drug uptake, metabolism, and excretion by the liver, their expression and activity influence systemic drug exposure and subsequent drug efficacy or toxicity. Figure created with Biorender.com.

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As sinusoidal uptake transport proteins were reviewed in a previous article, the focus of this article is to reflect on the role of hepatic efflux transporters. These transporters, which are located in both the sinusoidal and canalicular membrane leaflets, mediate the excretion of compounds from hepatocytes into the systemic circulation and bile, respectively. Key hepatic efflux transporters consist of P-gp, BCRP, MRPs, BSEP, and MATE1. It is important to note that the expression and activity of hepatic transporters can be altered in various physiological and pathological conditions including ontogenesis, carcinogenesis, cholestasis, cellular stress, and in response to drug treatment. Furthermore, given their broad substrate ranges and their integral role in drug elimination, hepatic efflux transporters are significant contributors to DDIs and thus influence systemic pharmacokinetics. Such DDIs may reduce or increase systemic exposure to drugs, thus impacting drug disposition and efficacy and possibly leading to treatment failure or increased adverse events. As a result, the ITC has recommended that potential interactions with hepatic P-gp, BCRP, and MATE1 should be assessed during drug development (Zamek-Gliszczynski et al., 2018). Similarly, recent FDA guidelines indicate that if an investigational drug is a substrate for either P-gp or BCRP and is likely to undergo biliary secretion, clinical DDI studies should be performed (FDA, 2020). Several of the key hepatic efflux transporters are discussed in more detail below.

1.23.5.1

P-gp

P-gp is expressed in the canalicular membrane of hepatocytes where it presumably plays a role in excretion of its substrates into bile (Lecureur et al., 2000). The expression of P-gp at the canalicular membrane of hepatocytes is seven-fold lower than in enterocytes of the small intestine, and high inter-individual differences in P-gp expression have also been reported (Cascorbi, 2011; Schuetz et al., 1995). While numerous studies have clearly demonstrated a critical role of P-gp in oral drug absorption and the transport of drugs across the blood-tissue barriers, the relative contribution of hepatic P-gp to the overall disposition of drugs in humans remains uncertain. This could be due to the fact that many P-gp substrates are primarily eliminated though hepatic metabolism rather than biliary excretion, as most drugs transported by P-gp are also substrates for the cytochrome P450 enzymes including CYP3A4 (Chan et al., 2004).

1.23.5.2

BCRP

BCRP is expressed in the canalicular membrane of hepatocytes and is involved in the biliary elimination of a broad range of endogenous and exogenous compounds, with a substrate specificity that partially overlaps with those of P-gp and MRPs (Mao and Unadkat, 2015; Jetter and Kullak-Ublick, 2020). In addition to its drug substrates, BCRP is implicated in the biliary excretion of sulfated conjugates of steroids and xenobiotics (Jetter and Kullak-Ublick, 2020; Noguchi et al., 2014). The hepatic expression of BCRP can be altered under pathological conditions; for instance, hepatic BCRP expression can be increased in patients with cancer and decreased in patients with type I diabetes, thus influencing the biliary excretion of drugs, which can have an impact on clinical efficacy (Mao and Unadkat, 2015; He et al., 2014). However, it is challenging to determine the impact of hepatic BCRP on overall drug pharmacokinetics, since BCRP is also expressed in the intestines and the kidneys. In BCRP KO mice, a significant reduction in the biliary excretion of the HMG-CoA-reductase inhibitors pitavastatin and rosuvastatin, methotrexate and fluoroquinolones, was observed compared with wildtype mice (Meyer Zu Schwabedissen and Kroemer, 2011). In humans, BCRP inhibitors have been reported to significantly increase systemic exposure to statins, and reduced function polymorphisms in the BCRP gene such as 421C > A have been shown to impact the hepatobiliary clearance of rosuvastatin, atorvastatin, fluvastatin, simvastatin lactone, and gefitinib (Keskitalo et al., 2009a, b; Cusatis et al., 2006). For instance, the systemic exposure to rosuvastatin was increased more than 2-fold in patients homozygous for the 421AA variant compared to those with 421CC BCRP genotypes (Keskitalo et al., 2009b). Therefore, a dosing algorithm for rosuvastatin has been developed to determine safe dosing in humans based on BCRP genotype (DeGorter et al., 2013).

1.23.5.3

MRPs

Several MRPs are expressed on the basolateral and canalicular membranes of hepatocytes and mediate the transport of a variety of endogenous and exogenous organic anions. MRP1 (ABCC1) and MRP3 (ABCC3) are localized on the basolateral membrane, whereas MRP2 (ABCC2) is expressed on the canalicular membrane. MRP1 and MRP2 transport conjugates of bilirubin, glutathione, leukotrienes, heavy metals, and sulfated or glucuronidated divalent bile salts (Lecureur et al., 2000). MRP1 is expressed at low levels in the basolateral membrane in healthy human, rat, and murine hepatocytes, suggesting that MRP1 may not have a major role in hepatic xenobiotic metabolism and detoxification; however, increased MRP1 expression has been observed during liver regeneration and endotoxin- or bile duct ligation-induced cholestasis (Leslie et al., 2005). In humans, MRP1 and P-gp expression has been shown to be induced in severe liver disease, likely as a protective mechanism from xenobioticinduced damage for hepatocyte progenitor cells. Furthermore, MRP1 has been implicated in the transport of chemotherapeutics, and while glutathione is reported to be required for MRP1 transport, it has been suggested that it might not be needed for the transport of conjugated drugs (Leslie et al., 2005; Roelofsen et al., 1997; Gu and Manautou, 2010; Flens et al., 1996; Loe et al., 1998). MRP2 contributes to bile formation by transporting glutathione, a driving force in bile salt-independent flow of bile. MRP2 can also transport tauroursodeoxycholate, a non-conjugated hydrophilic bile salt, which is used for the treatment of cholestatic liver disease (Gerk et al., 2007). MRP2 overexpression has been reported to confer resistance to a wide variety of anticancer drugs in both polarized and unpolarized cell lines, and reduced function MRP2 polymorphisms have been associated with susceptibility

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to herbal and drug hepatotoxicity (Slot et al., 2011). However, certain polymorphisms have been associated with a protective effect when receiving irinotecan, which causes diarrhea in 25% of patients (de Jong et al., 2007). Irinotecan is metabolized by carboxylesterases, UGT1A1 and UGT1A7, with subsequent MRP2-mediated secretion of the glucuronide metabolites into bile. Patients with the ABCC2*2 gene variant secrete less of these metabolites into bile and are protected against diarrhea (de Jong et al., 2007). Mutations in the MRP2 gene lead to reduced bile acid-independent flow and cholestasis, and MRP2-deficient rats present with a defect in hepatobiliary excretion, which is analogous to Dubin-Johnson syndrome in humans (Büchler et al., 1996). DubinJohnson syndrome is associated with conjugated hyperbilirubinemia due to the inability of these individuals to excrete non-bile salt organic anions including bilirubin mono- and diglucuronides, resulting in elevated serum bilirubin, nearly half of which is in its conjugated form (König et al., 1999; Paulusma et al., 1997). As a compensatory mechanism, upregulation of MRP3 increases transport of bilirubin glucuronide across the sinusoidal membrane. Interestingly, reduced MRP2 and hepatobiliary function during cholestasis is associated with upregulation of MRP2 in the kidney, thereby facilitating the renal elimination of organic anions, glucuronide, and glutathione conjugates (Kamisako et al., 2000). MRP2 gene expression is downregulated in animal models of sepsis and inflammation (Karpen and Karpen, 2017), leading to altered drug disposition including toxicity and loss of efficacy. Mice fed diets of cholic acid or ursodeoxycholic acid show increased expression of MRP2 RNA, which is significantly reduced in response to inflammatory cytokines (Fickert et al., 2001). It is possible that inflammation-mediated suppression of MRP2 is associated with sepsis-related increases in serum levels of conjugated bilirubin. MRP3 mediates the hepatic excretion of monovalent and sulfated bile salts, along with other organic anions including estradiol17b-glucuronide, methotrexate, and acetaminophen glucuronide (Hirohashi et al., 1999, 2000; Xiong et al., 2000). The primary function of MRP3 is to act as an overflow pump for bile acids in all forms of cholestasis. When the biliary excretion of bile acids is compromised, MRP3 is upregulated to prevent accumulation of toxic bile acid levels in the liver by enabling their excretion into blood for renal clearance (Hirohashi et al., 2000). MRP3 can also mediate sinusoidal efflux of drugs along with glucuronide and sulfate conjugates. While MRP3 has a lower affinity for glutathione conjugates compared with MRP2, it has been suggested that MRP3 can eliminate anionic conjugates from hepatocytes back into the systemic circulation in conditions where MRP2 function is impaired (Hirohashi et al., 2000). For instance, MRP3 mediates the hepatic basolateral excretion of the antihistamine fexofenadine, which is also a substrate for MRP2 (Tian et al., 2008). In non-alcoholic steatohepatitis (fatty liver disease), higher serum and urine levels of acetaminophen glucuronide have been observed, which has been attributed to MRP3 induction in the livers of these patients. In rats, under normal conditions, hepatic MRP3 expression is very low but can be induced by phenobarbital and cholestatic conditions (Ogawa et al., 2000). MRP3 expression is also induced in humans with hereditary defects in biliary excretion of organic anions and in cholestatic patients with Dubin-Johnson syndrome (König et al., 1999); therefore, upregulation of this transporter is suggested to compensate for the diminished ability to excrete organic anions into bile, thus supporting its role in the efflux of xenobiotics and endogenous compounds from the liver into the blood for renal excretion when MRP2 function is compromised.

1.23.5.4

BSEP

BSEP is located on the canalicular membrane of hepatocytes and transports monoanionic and conjugated bile acids including taurochenodeoxycholate, taurocholate, taurodeoxycholate, tauroursodeoxycholate and glycocholate, from hepatocytes into bile. BSEP is critical in maintaining bile salt homeostasis, and impaired BSEP function can lead to reduced bile acid secretion, enhanced intracellular accumulation of bile acids, cholestasis, and liver injury. Indeed, deficiency in BSEP is associated with several genetic forms of cholestasis including progressive familial intrahepatic cholestasis type 2 (PFIC2) and benign recurrent intrahepatic cholestasis type 2 (BRIC2) (Nicolaou et al., 2012). In patients with PFIC2, biliary bile salt concentrations are < 1% of the normal range due to a mutation in the ABCB11 gene, resulting in BSEP loss on the canalicular membrane of hepatocytes. BSEP might be an important site of drug interactions and has been implicated in drug-induced cholestasis as a result of interactions between endogenous compounds and drugs (Nicolaou et al., 2012). BSEP KO mice are cholestatic and display increased accumulation of taurocholate in the liver with minimal excretion into bile; however, these animals have been shown to excrete a substantial amount of tauromuricholate and tetrahydroxy bile salts into bile, suggesting an alternate bile acid transport system in murine hepatocytes (Wang et al., 2001). However, in humans, BSEP is believed to be the primary bile acid transporter, with no evidence of other transporters capable of fully compensating for bile salt excretion when BSEP function is impaired, thus resulting in intrahepatic cholestasis and liver injury under these conditions (Telbisz and Homolya, 2016). Although BSEP is not considered to play a major role in the hepatic excretion of many drugs, it has been shown to transport a few including pravastatin and fexofenadine in humans and rats, and vinblastine in mice (Stieger, 2011; Hirano et al., 2005; Matsushima et al., 2008). However, BSEP inhibition is believed to play a role in drug-induced hepatotoxicity and several drugs and/or metabolites have been shown to inhibit BSEP activity including cyclosporin A, rifampicin, rifamycin, glibenclamide, and bosentan (Stieger, 2010). For instance, the antidiabetic drug troglitazone was withdrawn from the market due to an elevated incidence of hepatotoxicity, resulting from BSEP inhibition by the main metabolite of the drug, troglitazone sulfate (Funk et al., 2001). For this reason, BSEP inhibition assays and BSEP expression profiling are recommended in drug discovery and screening programs (Cheng et al., 2016; Rodrigues et al., 2014).

Drug Transporters: Efflux 1.23.5.5

621

MATE1

Hepatic MATE1 is localized on the canalicular membrane of hepatocytes and mediates the biliary excretion of cationic drugs and metabolites as well as some zwitterionic and anionic molecules (Otsuka et al., 2005; Nies et al., 2011). Diabetic patients with MATE1 polymorphisms involving the G > A SNP rs2289669 have shown increased pharmacodynamic effects of metformin without altered systemic pharmacokinetics, likely due to enhanced hepatic accumulation of the drug (Becker et al., 2009; Stocker et al., 2013). Furthermore, this polymorphism affects the renal clearance of metformin when it is co-administered with ranitidine, which is an antacid medication and inhibitor of MATE1 (Cho and Chung, 2016). Similarly, MATE1 KO mice have significantly higher liver concentrations and systemic exposure of metformin due to decreased renal clearance, and display signs of lactic acidosis compared with wildtype mice, which is a side effect of the drug (Tsuda et al., 2009).

1.23.6

Renal excretion

Renal clearance is a major elimination pathway for many endogenous and exogenous compounds and their metabolites and serves an essential physiological function in maintaining total body homeostasis of fluids and electrolytes. Renal elimination accounts as the major clearance pathway for more than 30% of the most commonly prescribed drugs (Morrissey et al., 2013). Renal elimination involves three concurrent processes occurring in the nephron, which include glomerular filtration, tubular secretion, and reabsorption. Glomerular filtration is a passive process that removes drugs from the systemic circulation into urine. On the other hand, renal secretion is mediated by a variety of transporters located in the basolateral and luminal membranes of tubular cells, which are involved in substrate uptake from the blood and subsequent efflux into the tubular lumen for excretion in the urine. Following basolateral uptake, compounds are secreted into the urine through the apical brush border by several transporters, the majority of which are members of the SLC and ABC families of transporters including MATE1/2, P-gp, BCRP, and MRP2/4 (Fig. 6) (Morrissey et al., 2013; Yin and Wang, 2016). Tubular secretion is characterized by high clearance capacities, broad substrate specificities, and distinct charge selectivity for organic cations and anions. For instance, the secretion of organic cations is mediated by MATE1/2 and P-gp, whereas the secretion of organic anions is mediated by MRP2/4 and BCRP, although some degree of overlap has been reported (Giacomini et al., 2010; Ahn et al., 2009). Altered expression and inhibition of renal drug transporters can lead to modified pharmacological and toxicological responses at local and systemic levels. For instance, decreased renal clearance as a result of transporter inhibition or functional inactivation can lead to abnormal drug accumulation in renal tubular cells, resulting in drug-induced nephrotoxicity, increased plasma drug concentrations, and subsequent changes in drug pharmacokinetics and systemic toxicities. As a result, regulatory agencies have

Fig. 6 Drug transporters in the kidney. ABC (blue) and SLC (yellow) drug transporters are localized to either the blood-facing (basolateral) or urinefacing (apical) membranes of proximal tubule epithelial cells in the kidneys. Depending on their localization and direction of transport, these transporters facilitate renal secretion or reabsorption of their substrates, thereby influencing systemic drug exposure. Figure created with Biorender. com.

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recommended the routine assessment of the inhibition potential of these transporters on investigational drugs. For example, both the ITC and the FDA recommend evaluating investigational drugs that undergo significant renal excretion for interactions with P-gp, MATE1, and MATE2-K during drug development (Zamek-Gliszczynski et al., 2018; FDA, 2020). Although renal DDIs are typically undesirable as they may result in adverse drug reactions, occasionally inhibitors can be co-administered to intentionally alter renal clearance or reduce nephrotoxicity of another drug (Yin and Wang, 2016; Cundy et al., 1995). For example, probenecid, a drug used for treatment of gout, inhibits renal organic anion uptake transporters (OATs). As a result, when co-administered with other OAT substrates, probenecid can reduce renal secretion of antibiotics such as penicillin to enhance activity, or decrease renal accumulation and nephrotoxicity of some antivirals (Yin and Wang, 2016).

1.23.6.1

P-gp

P-gp is located on the apical surface of the epithelial cells of the proximal tubules and facilitates the renal excretion of its substrates (Zhou, 2008). The broad substrate specificity of P-gp can lead to DDIs as exemplified by decreased renal clearance and increased intestinal absorption of digoxin when co-administered with quinidine, cyclosporine, clarithromycin or ritonavir (Ivanyuk et al., 2017). Furthermore, functional polymorphisms in P-gp are associated with reduced renal clearance of digoxin as well as altered pharmacokinetics, efficacy, and toxicity of many anti-cancer, antiviral, and immunosuppressive agents (Kurata et al., 2002; Marzolini et al., 2004). For instance, increased cyclosporine-related nephrotoxicity has been observed in organ transplant patients with the 3435C > T polymorphism, which is associated with decreased P-gp expression leading to impaired renal secretion of the drug (Hauser et al., 2005).

1.23.6.2

BCRP

Like P-gp, BCRP is localized at the brush-border membrane in the proximal tubules and mediates the transport of its substrates into the urine (Ivanyuk et al., 2017). Genetic variations in BCRP have been associated with delayed elimination of some statins and metabolites of immunosuppressive drugs, such as leflunomide, which is a disease modifying anti-rheumatic drug (Keskitalo et al., 2009b; Kim et al., 2011). Polymorphisms in BCRP have also been associated with increased risk for gout and altered allopurinol response, likely as a result of decreased renal excretion of uric acid leading to higher systemic concentrations and reduced allopurinol efficacy, highlighting the involvement of BCRP in urate homeostasis (Brackman et al., 2019; Matsuo et al., 2011; Cleophas et al., 2017; Takashima et al., 2013). BCRP is functionally important for the urinary excretion of certain sulfated compounds in mice as demonstrated by PET imaging studies examining the elimination of a major sulfated metabolite of the non-steroidal anti-inflammatory drug, celecoxib. These studies demonstrated a significant reduction in the renal elimination of this radiolabelled metabolite in BCRP KO mice (Takashima et al., 2013). However, inter-species differences in the renal expression of BCRP have been reported, showing that renal BCRP expression is lower in humans compared with rodents (Huls et al., 2008). Therefore, it is important to consider this differential pattern of expression between species when using rodent models for the evaluation of drug and metabolite elimination.

1.23.6.3

MRPs

MRP2 and MRP4 are located in the apical membrane of renal proximal tubular cells and are involved in the renal secretion of several compounds including small unconjugated and large conjugated organic anions including glucuronides, glutathione and sulfate conjugates, antibiotics, antivirals, diuretics, NSAIDs and methotrexate (Yin and Wang, 2016). MRP4 is also involved in the transport of cyclic AMP and GMP, and contributes to the elective excretion pathway for cyclic nucleotides in renal epithelial cells (LaunayVacher et al., 2006). Inhibition of MRP2/4 function can reduce the renal clearance of these compounds and lead to increased intracellular accumulation and potential nephrotoxicity. For instance, an inactivating heterozygous mutation in MRP2 was shown to lead to impaired methotrexate elimination and nephrotoxicity in a patient with B-cell lymphoma (Hulot et al., 2005). Furthermore, MRP2 polymorphisms have been associated with altered pharmacokinetics and clearance of several drugs including immunosuppressive medications such as mycophenolic acid and methotrexate and the chemotherapeutic irinotecan. Specifically, the 24C > T polymorphism was shown to increase the steady-state plasma concentrations of mycophenolic acid and the AUC of methotrexate, while the 1019A > G variant was shown to reduce the clearance of irinotecan (de Jong et al., 2007; Naesens et al., 2006; Maeda and Sugiyama, 2008; Rau et al., 2006). MRP4 is important for the renal excretion of several antiviral drugs. Polymorphisms in MRP4 have been associated with altered elimination of some antiviral medications including lamivudine-triphosphate and tenofovir, which are used in the treatment of HIV. For instance, the 4131T> G variant was associated with elevated levels of lamivudine-triphosphate and the 3463A > G variant resulted in increased tenofovir nephrotoxicity (Anderson et al., 2006; Kiser et al., 2008).

1.23.6.4

MATEs

Of the MATE transporters, MATE1, MATE2, and MATE2-K are abundantly expressed on the luminal membrane of renal tubular cells and mediate the efflux of mainly cationic endogenous and exogenous compounds (Damme et al., 2011; Otsuka et al., 2005; Nies et al., 2011). As previously discussed, MATEs use proton-coupled substrate transport. Under physiological conditions, a proton gradient is created across the brush-border membrane of renal proximal tubule cells by sodium/proton exchangers, rendering

Drug Transporters: Efflux

623

the tubular lumen more acidic than the cytosol, thus facilitating MATE-mediated influx of protons coupled with the efflux of organic cations into urine. Since MATEs are responsible for the last step of organic cation secretion, these transporters represent sites for both DDIs and toxicity. For example, the clearance of cisplatin and oxaliplatin is mediated by MATEs. While cisplatin is transported by MATE1, oxaliplatin is cleared by both MATE1 and MATE2. Thus, cisplatin tends to accumulate inside the cell, inducing tubular toxicity to a larger extent than oxaliplatin, which is cleared more readily since it is a substrate for both transporters. Furthermore, functional gene polymorphisms of MATE1 and MATE2/-K have been associated with altered pharmacokinetics and response to metformin in patients with diabetes and present an increased risk factor for metformin-associated lactic acidosis (Toyama et al., 2012). Studies using KO mice have revealed the impact of MATEs on the pharmacokinetics of several drugs including their role in the renal disposition and elimination of metformin and cisplatin. For instance, the renal clearance and tissue distribution of metformin have been shown to be altered in MATE1 KO, but not in MATE1 heterozygous (þ/) mice. In MATE1 KO mice, metformin plasma and renal concentrations were significantly increased, while the urinary excretion of the drug was decreased, suggesting that MATE1 plays a predominant role in the tubular secretion of metformin (Tsuda et al., 2009). Conversely, despite lower transporter levels, the pharmacokinetics of metformin in MATE1 heterozygous mice were found to be similar to wildtype mice, suggesting that that the rate-limiting step in the excretion of metformin is renal plasma flow, since renal clearance is comparable to renal plasma flow, and not MATE1-mediated efflux from tubular cells (Toyama et al., 2010). The role of MATE1 in the renal clearance of cisplatin has been demonstrated in MATE1 KO mice, which are hypersensitive to cisplatin-induced nephrotoxicity and display increased cisplatin plasma and renal concentrations (Nakamura et al., 2010). Furthermore, the MATE inhibitor ondansetron, an anti-nausea and -vomiting medication, has been shown to increase cisplatin nephrotoxicity, warranting caution with the co-administration of this drug and cisplatin-based chemotherapy (Li et al., 2013b). However, it is important to note that in contrast to MATE1, rodent MATE2 is not closely related to human MATE2/-K, and therefore data from rodent-based studies are not readily translatable to humans.

1.23.7

Summary

To conclude, efflux drug transporters play integral roles in the intestinal absorption, distribution, hepatobiliary elimination, and renal excretion of a wide variety of drugs. As a result, changes in the expression or activity of these transporters due to disease, genetic polymorphisms, or other drugs can result in clinically meaningful changes in pharmacokinetics. Such changes can include altered systemic drug exposure or drug disposition, which can ultimately impact drug efficacy or lead to drug toxicity and adverse events. As a result, guidelines put forth by the International Transporter Consortium and other regulatory bodies recommend that potential interactions of new drugs with key efflux transporters be evaluated during drug discovery.

See Also: 1.08: Transporters; 1.17: Oral Drug Delivery, Absorption and Bioavailability; 1.18: PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations; 1.21: Drug Metabolism: Phase II Enzymes; 1.24: Drug Excretion; 1.26: Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters; 1.27: Drug-Drug Interactions with a Pharmacokinetic Basis

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1.24

Drug Excretion

Erin F. Barretoa, Thomas R. Larsonb, and Emily J. Koubekc, a Department of Pharmacy, Mayo Clinic, Rochester, MN, United States; b Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States; and c Department of Oncology Research, Mayo Clinic, Rochester, MN, United States © 2022 Elsevier Inc. All rights reserved.

1.24.1 1.24.2 1.24.2.1 1.24.2.2 1.24.2.2.1 1.24.2.2.2 1.24.2.2.3 1.24.2.3 1.24.2.4 1.24.3 1.24.3.1 1.24.3.2 1.24.3.2.1 1.24.3.2.2 1.24.3.3 1.24.3.4 1.24.4 1.24.4.1 1.24.4.2 1.24.4.2.1 1.24.4.2.2 1.24.4.3 1.24.4.3.1 1.24.4.3.2 1.24.4.4 1.24.5 1.24.5.1 1.24.5.2 1.24.5.2.1 1.24.5.2.2 1.24.5.2.3 1.24.5.2.4 1.24.5.3 1.24.5.3.1 1.24.5.3.2 1.24.5.3.3 1.24.5.3.4 1.24.5.4 1.24.6 References Relevant Websites

Introduction Kidney Overview Mechanism Glomerular filtration Tubular secretion Reabsorption Measuring and modeling Clinical application Liver Overview Mechanism Transporters Enterohepatic recirculation Measuring and modeling Clinical applications Breast milk Overview Mechanism Passive diffusion Carrier-mediated transport Measuring and modeling Lactation studies Modeling Clinical application Saliva, sweat, hair and respiration Overview Mechanism Saliva Sweat Hair Respiration Measuring and modeling Saliva Sweat Hair Respiration Clinical application Conclusion

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Glossary ADME (Absorption, Distribution, Metabolism, and Excretion) Term used in pharmacology to describe the movement and disposition of a drug throughout the body. Biotransformation Metabolic modification of a drug to a typically more water-soluble and polar form to enhance drug excretion. Enterohepatic recirculation The process by which drugs excreted into the bile can be reabsorbed in the intestine instead of being directly eliminated from the body.

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Glomerular filtration The first step in the formation of urine in which kidney nephrons filter fluid and waste from the blood into the kidney tubules for elimination. M/P (milk/plasma) ratio The average concentration of drug in breast milk compared to the average concentration of drug in maternal plasma. Population pharmacokinetics The study of drug pharmacokinetic variability within a patient population. Physiologically-based pharmacokinetics A multi-compartmental mathematical modeling technique that uses known anatomy, physiology, and biochemistry to predict the ADME of a drug in an organism. Relative infant dose Compares the infant’s daily dosage of drug from breast milk to the mother’s daily dosage of drug. Tubular secretion Step in drug handling in the kidney where toxins and endogenous molecules are secreted from the blood in peritubular capillaries to the lumen of the tubule.

1.24.1

Introduction

Drug excretion is the final step in the ADME (Absorption, Distribution, Metabolism, and Excretion) process and consists of a series of pathways that remove an administered drug and/or its metabolites from the body. Excreted drugs are either eliminated in their original, unmetabolized form, or they can be eliminated following metabolic biotransformation, as described in the preceding chapters. The metabolic biotransformation prepares drugs for excretion. Typically, more hydrophobic drugs are transformed into a more polar, water-soluble compound that is readily eliminated. As an example, the anti-epileptic drug phenytoin is a highly lipophilic compound. A series of metabolic transformations in the liver convert phenytoin to several inactive water-soluble metabolites that can then be excreted in the urine. In addition, excretion of a drug is dependent on intrinsic properties of the drug, such as pH and size. For example, weakly acidic drugs display increased excretion in basic urine, while weakly basic drugs are excreted more readily in acidic urine. Ionized drugs with a molecular weight greater than 300 g/mol can be actively secreted by the liver into bile. Genetic variation and underlying acute or chronic comorbidities can also impact drug excretion. Impaired kidney function or hepatic diseases that compromise biotransformation pathways may decrease drug excretion, which can result in drug accumulation and potential toxicity. Total medication clearance is described by CL ¼ CLk þ CLh þ CLother where CLk reflects kidney clearance, CLh reflects hepatic clearance, and CLother integrates all other sources such as extracorporeal clearance as with renal replacement therapy or metabolism by pH-dependent plasma esterases (Lea-Henry et al., 2018). As can be seen in the preceding equation, the main contributors to drug excretion are the kidneys and the liver. Water-soluble compounds are excreted primarily by the kidneys, while larger, more hydrophobic compounds are the responsibility of the liver. Secondary routes of excretion do exist (CLother), such as through breast milk, sweat, saliva, hair, and respiration. However, their contribution tends to be small. The following chapter will describe the various routes of excretion, the methods by which researchers and clinicians measure and model drug excretion, and how this information may be applied clinically.

1.24.2

Kidney

1.24.2.1

Overview

The kidney is a critical organ for drug handling. All components of ADME may be affected by kidney function, but the primary related pharmacokinetic parameter is drug excretion or elimination. Approximately 60% of all medications used are eliminated by the kidney (Taber and Mueller, 2006). The kidney is a direct contributor to not only parent molecule clearance, but also clearance of the metabolites. Morphine, an opiate analgesic, is a prototypical example of the importance of renal-excretion of drug metabolites. The parent molecule morphine undergoes biotransformation in the liver into its primary metabolites morphine-3-glucuronide (inactive) and morphine-6-glucuronide (active). Morphine-6-glucuronide is a potent analgesic and its clearance is dependent on renal-elimination (Davison, 2019). Failure to account for underlying kidney dysfunction can lead to accumulation and an increased risk for drug toxicity, which has been reported in patients with acute and chronic kidney disease. Clinicians and scientists alike need to be aware of factors that influence renal drug excretion to improve the use of medications at the bedside, and rigorously test those in the pipeline. Kidney clearance is the collective result of three major biological processes: filtration, secretion, and reabsorption (Fig. 1). Filtration is a passive flow dependent process. Secretion utilizes energy substrates, electrochemical gradients, or concentration gradients to promote the active movement of drug by transporters in tubular cells (Miners et al., 2017). Passive reabsorption extracts substances from the filtrate in the distal tubules and returns them to systemic circulation. Each of these facets of solute and water handling at the kidney are important to maintain homeostasis and manage toxins (e.g., organic acids). Mathematically, kidney clearance is represented as CLk ¼ (Fu  GFR) þ CLsecretion  CLreabsorption where Fu represents the fraction of unbound drug that can be filtered, GFR is the glomerular filtration rate, and CLsecretion and CLreabsorption are the increase and decrease in clearance through these processes, respectively (Lea-Henry et al., 2018).

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Fig. 1 Drug excretion in the kidney is determined by three processes: filtration, secretion (increase clearance), and reabsorption (decrease clearance). Abbreviations: Alpha ketoglutarate (aKG), multidrug and toxin extrusion proteins (MATE), multidrug resistance-associated proteins (MRP), sodium-dependent dicarboxylate transporter (NaDC3), organic anion (OA) and organic cation (OC) transporters (OATs and OCTs), human urate transporter (URAT). Used with permission of Mayo Foundation for Medical Education and Research, all rights reserved.

1.24.2.2 1.24.2.2.1

Mechanism Glomerular filtration

When modifying medication doses, the primary consideration that clinicians consider is the glomerular filtration rate (GFR) (Crass et al., 2019). Package inserts and aggregated tertiary references provide ordinal thresholds based on GFR for drug dose adjustment. As an example for treatment of herpes simplex encephalitis with the antiviral acyclovir, the standard dose is 10 mg/kg every 8 h, but for individuals with estimated GFRs of 25–50, 10–25, and 0–10 mL/min/1.73 m2 the recommended dose changes to 10 mg/kg every 12 h, 10 mg/kg every 24 h, and 5 mg/kg every 24 h, respectively (GlaxoSmithKline, 2003). Despite the prominence of GFR in drug dose decision-making clinically, it is important to note that glomerular filtration receives a relatively small fraction of the kidney plasma flow (20%) and mediates excretion of only free (unbound) molecules. Peritubular capillaries, on the other hand, receive a relatively high proportion of plasma flow (80%) and protein-bound molecules can be taken up by tubular transporters for excretion by an alternate mechanism (Wang and Kestenbaum, 2018). Based on the “intact nephron hypothesis,” it

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has been proposed that a linear relationship exists between glomerular filtration and overall clearance. This hypothesis posits that as kidney function deteriorates, all kidney functions deteriorate in parallel, in other words, it is loss of a “complete nephron.” This theory has been refuted several times in studies of drug clearance (Pradhan et al., 2019). In fact, certain molecules that are highly renally-cleared may exhibit only a modest correlation between GFR and drug clearance due to a heightened reliance on tubular secretion. In fact, in glomerulopathies and chronic kidney disease, drug clearance can remain relatively stable despite the reduced GFR. Several factors contribute to this discrepancy between renal clearance and GFR, one of which is corresponding changes to tubular drug handling, which may increase or decrease (Chapron et al., 2017). While the distinction among tubular handling mechanisms is especially important in drug development and research settings, at the bedside, estimates of GFR continue to be the primary method for dose adjusting medications as previously mentioned. Clinical decision support built into the electronic health record based on GFR guides medication dosing in patients with renal insufficiency and reduces exposure to potentially inefficacious or toxic medication prescriptions (Chertow et al., 2001).

1.24.2.2.2

Tubular secretion

In addition to filtration, drugs and endogenous molecules can be directly influenced by the excretory properties of tubular transporters (Fig. 1). Several types of transporters relevant for drug excretion exist in the kidney. These may be symporters, antiporters, or ATP-dependent processes. Organic anion and organic cation transporters (OATs and OCTs) from the soluble carrier (SLC) family predominate on the basolateral (blood to kidney) side of proximal tubular cells (Miners et al., 2017; Wang and Kestenbaum, 2018). These transporters take up endogenous molecules and drugs from the interstitial space into the proximal tubular cells. On the apical side of proximal tubular cells (kidney to urine), efflux transporters extrude molecules from the intracellular space into the kidney filtrate. Chief among these transporters involved include those from the ATP binding cassette family, such as multidrug resistanceassociated proteins 2 (MRP-2) and 4 (MRP-4), as well as other transporters including multidrug and toxin extrusion protein 1 (MATE1, a Hþ/organic cation antiporter) and human urate transporter 1 (URAT1) (Wang and Kestenbaum, 2018; Gaowa et al., 2011). Tubular secretion is a site of potential drug-drug interactions and drug excretion changes due to genetic polymorphisms. OATs, OCTs, MRP-2, and p-glycoprotein transporters, use an active, concentration-dependent, saturable process with Michaelis-Menten kinetics. Depending on the site of the transporter, competition may have variable effects. Interactions at the basolateral transporters may lead to decreased cellular uptake of a drug, decreased drug excretion, and increased overall systemic exposure, potentially heightening the clinical or toxic effects. Alternatively, decreased affinity, drug interactions or genetic polymorphisms in the apical (luminal) transporters may ‘trap’ the drug in tubular cells and lead to direct toxicity (Miners et al., 2017). This is one mechanism of nephrotoxicity described with the antineoplastic cisplatin (Yonezawa and Inui, 2011).

1.24.2.2.3

Reabsorption

If the renal clearance of a substrate is less than the calculated clearance by filtration, then the substrate undergoes net reabsorption. Reabsorption is most commonly mediated as a passive back-diffusion process primarily driven by concentration gradients (Masereeuw and Russel, 2001). Reabsorption may occur in both the proximal tubular cells and the distal aspect of the tubules. This is commonly observed with endogenous molecule reabsorption, such as sodium, glucose, and urate. Urine flow rate and urine pH are key determinants of the degree of solute reabsorption. Other mechanisms of reabsorption include energy-mediated active transport and endocytosis (Masereeuw and Russel, 2001).

1.24.2.3

Measuring and modeling

Kidney excretion of a substrate can be challenging to measure and model. Generally, clearance is the volume of plasma cleared per unit time (Levey and Inker, 2017). Kidney clearance may be modeled from plasma or urinary concentrations of the substance of interest. Plasma clearance calculations over 1–2 h may be used, which avoids the complexity of prolonged urine collection. Unfortunately, plasma clearance estimations do have some limitations. Extra-renal drug excretion would be reflected in observed plasma clearance, which may lead to an over-estimation of kidney function. In cases of reduced baseline kidney function, extended intervals for plasma sampling will likely be necessary (e.g., 12–24 h) (Levey and Inker, 2017). Another strategy to quantify clearance of a drug is through urinary clearance, a more direct estimate of kidney excretion. Clearance is calculated as CL ¼ U*V/P where U is the urinary concentration of a substrate, P is the plasma concentration of that same substrate at steady state, each of which is collected during a timed interval where V (volume of urine) is obtained. Unfortunately, this does not allow for differentiation among the kidney process(es) that contribute to the observed net drug excretion. In other words it is unclear whether the observed clearance is attributed to filtration, secretion, or reabsorption. Clinically this is less of an issue, but in drug development this may be a very important limitation. Without a clear sense of the renal handling of a drug, it would be difficult to evaluate interactions or explore modifications to the chemical structure to promote or inhibit renal excretion. Other modeling strategies are required to directly determine the contribution of glomerular filtration, tubular secretion, or reabsorption to drug excretion. A general understanding of the drug’s properties may be suggestive of which aspect of kidney clearance is most contributory. For example, a highly protein bound molecule is unlikely to be cleared by filtration. Thus a first investigative step might be to explore the contribution of secretion. Some have proposed use of a “cocktail” approach for assessment of renal drug handling which involves co-administration of exogenous markers of filtration, tubular anion and cation secretion, and passive reabsorption (Gross et al., 2001).

Drug Excretion

631

Measured GFR is the criterion standard for determining the component of kidney clearance mediated by filtration. To measure GFR, an exogenous compound that is freely filtered and neither secreted nor reabsorbed is administered, and the plasma and/or urinary disappearance of the compound is measured. Examples of such compounds utilized to measure GFR include inulin, iothalamate, iohexol, polyfructose sinistrin, 51Cr-EDTA, and 99mTc-DTPA (Levey and Inker, 2017; Levey et al., 2020; Tett et al., 2003; Udy et al., 2014). Measured GFR can then be correlated with kidney drug clearance and the degree to which clearance is explained by filtration can be calculated. Measuring GFR is technically challenging, requires significant laboratory expertise, is expensive, and only represents a single moment in time. Thus, in both clinical and research settings, GFR is often estimated using endogenous markers instead. The most common endogenous marker of kidney function utilized for drug clearance studies is serum creatinine. Serum creatinine is the terminal byproduct of skeletal muscle catabolism and is excreted primarily by glomerular filtration. A fraction of serum creatinine is also eliminated through active tubular secretion and thus creatinine clearance overestimates the true GFR. As kidney function declines, the proportion of total creatinine clearance attributed to tubular secretion increases (Shemesh et al., 1985). Several creatinine-based equations have been developed to estimate creatinine clearance (eCrCl) or GFR (eGFR). The distinction between eCrCl and eGFR reflects the criterion standard utilized in the derivation studies (either measured clearance of creatinine in the urine for eCrCl or measured GFR using an exogenous marker for eGFR). The first and most extensively studied of these equations for drug excretion was the Cockcroft-Gault eCrCl (Cockcroft and Gault, 1976). This study used regression techniques to model the association between clinical and demographic factors and measured creatinine clearance in a cohort of 249 predominately white male individuals. Since that time, new equations were developed and validated against measured GFR including most notably the MDRD eGFR equation and the CKD EPI eGR equation (Inker et al., 2012; Levey et al., 1999, 2009). These equations were primarily developed for the purpose of detecting kidney function decline in stable community outpatients, but are secondarily utilized to predict renal drug clearance. While their use has been a substantial advancement, numerous limitations of creatinine-based equations may render them insufficiently accurate for use in acutely ill patients (Barreto et al., 2018). Use of other endogenous biomarkers, like cystatin C, to estimate GFR may better predict drug clearance than serum creatinine-based approaches, but require further study (Barreto et al., 2019; Frazee et al., 2014, 2017; Teaford et al., 2020). Measuring and modeling the proportion of kidney clearance attributed to tubular secretion is less straightforward and rarely done in clinical practice. Knockout or over-expression models can be used in vitro to assess the contribution of specific tubular transporters to drug clearance (Eraly et al., 2003). The isolated perfused rat kidney (IPK) model is an ex vivo strategy to evaluate drug disposition in development trials (Ajavon et al., 2010). However, some have suggested that renal clearance in the rat, particularly for secreted molecules, overestimates human renal clearance. Evidence indicates that in these circumstances mouse, rabbit, dog, or monkey models may be more suitable (Jansen et al., 2020). In the clinical or research setting in humans, there is no agreed upon gold standard of tubular secretory clearance. P-aminohippurate (PAH) is an exogenous compound utilized for the evaluation of tubular anion secretion, but it has several limitations (Wang and Kestenbaum, 2018). PAH undergoes glomerular filtration, thus clinically it is primarily used as an overall marker of renal plasma flow. It also exhibits supraphysiologic binding to tubular transporters, which may decrease the generalizability of in vitro models to the in vivo system (Back et al., 1989). Other methods to assess tubular anion secretion are with the antihyperuricemic probenecid or the antihypertensive hydrochlorothiazide, which act at tubular transporters (Tett et al., 2003). Approaches to evaluating tubular cation secretion include measurement of endogenous N1-methylnicotinamide or use of exogenous compounds like the histamine-2 receptor antagonist cimetidine (a potent OCT2 inhibitor) or the beta-blocker pindolol (Tett et al., 2003). In a relatively recent example, clofarabine renal drug clearance was characterized (Ajavon et al., 2010). Clofarabine is a potent antineoplastic utilized for acute lymphoblastic leukemia that is predominately eliminated renally as unchanged drug. Using the IPK model, the authors demonstrated a substantial decrease in clofarabine clearance unrelated to GFR in the presence of cimetidine, suggestive of inhibition of clofarabine tubular secretion mediated through OCT2. Unlike glomerular filtration or tubular secretion, distal tubular reabsorption is far less commonly studied. Fluconazole has been suggested as a possible strategy to assess passive reabsorption (Tett et al., 2003), but it is well known that azole antifungals inhibit P450 enzymes, which may complicate pharmacokinetic evaluations of excretion versus metabolism.

1.24.2.4

Clinical application

When products are newly approved by the Food and Drug Administration, only limited testing needs to be performed to assess medication clearance in kidney dysfunction (FDA Center for Drug Evaluation and Research (CDER), 2010). Early studies in drug development typically include individuals with normal or mildly reduced kidney function. Later studies prior to approval may include individuals with more severe kidney disease or those with dialysis-dependent end-stage kidney disease, but the populations are typically small. When designing a medication regimen based on drug excretion through the kidney, the first dose (often a loading dose) remains largely unaffected. The resultant concentration after a loading dose is more a function of the amount of drug being delivered and the theoretical space into which it distributes, the volume of distribution. The maintenance regimen, on the other hand, is more directly affected by drug clearance. Kidney dysfunction may require a decrease in the maintenance dose administered or changes to the drug dosing interval (Roberts et al., 2018). The choice between these approaches is drug specific and is directly related to the pharmacodynamics of the drug. For example, beta-lactams are considered time-dependent antibiotics where the fraction of time above the minimum inhibitory concentration of the organism (%T > MIC) is the primary pharmacodynamic endpoint associated with

632

Drug Excretion

clinical and microbiologic success. In this case, maintaining a shorter administration interval to preserve the %T > MIC would be more advantageous than manipulating a given maintenance dose. In the case of antimicrobials in the acute care setting, early renal dose adjustments may be ill advised. This is because the regulatory pathway designed to facilitate optimal drug effectiveness and safety in reduced kidney function is based on the stable chronic kidney disease population. In the hospital setting where acute kidney injury is common, use of these drug dose adjustment guidelines early in the course of disease could lead to insufficient drug exposure during a critical window. Observational data suggest that approximately one in five patients admitted with an acute infection presents with acute kidney injury, of which the majority resolve within 48-h (Crass et al., 2019). For wide therapeutic window medications such as beta-lactam antibiotics, it is advisable in these cases to refrain from early dose reductions for antibiotics until the renal trajectory declares itself. Beyond filtration, clinicians should be aware of drugs with significant tubular secretion, as this could be a site of drug interactions. Also, tubular secretion of creatinine is mediated through several transporters, including OCT2 (Wang and Kestenbaum, 2018). Substrates or inhibitors of this transporter may lead to an observed increase in serum creatinine concentrations, independent of any change to actual GFR. An example of where this complicates clinical care is the use of sulfamethoxazole/trimethoprim, a renally-eliminated and nephrotoxic agent. Trimethoprim is an OCT2 inhibitor that early in the course of treatment could increase systemic creatinine concentrations through inhibition of tubular secretion of creatinine. While this mostly reflects the limitations of eCrCl for drug dosing, it may raise concern for drug-associated nephrotoxicity and result in a change to therapy or a dose decrease, neither of which may be indicated (Miners et al., 2017).

1.24.3

Liver

1.24.3.1

Overview

The hepatobiliary system plays an important role in the biotransformation and disposition of endogenous molecules and drugs as outlined in previous chapters. The liver is also directly involved in the excretion of drugs and their metabolites by active secretion from hepatocytes into the bile duct. Due to the reliance of biliary elimination on transporters, biliary excretion and drug metabolism are often correlated. Genetics and drug-drug interactions may affect both metabolism and excretion simultaneously. Many drugs or their metabolites, such as the antineoplastic agent eribulin and the morphine metabolite morphine 3-glucuronide, are eliminated through biliary excretion, but for most it is a minor elimination pathway (Devriese et al., 2012; Ouellet and Pollack, 1995). Several factors may have an impact on the biliary excretion of a drug, which is described by the hepatic extraction ratio. The hepatic extraction ratio reflects the efficiency of hepatocytes to uptake drug from plasma. Factors that influence the hepatic extraction ratio include hepatic blood flow, plasma protein binding, and activity of drug metabolizing enzymes and transporters. The active transport of drug into hepatocytes is thought to be the rate limiting step in biliary elimination. Elimination is not the only ADME component affected by the hepatobiliary system. Bile is also important for the absorption of orally administered drugs. Complications with the biliary system can impact drug absorption.

1.24.3.2

Mechanism

Bile is composed of salts, phospholipids, cholesterol, pigments, water, and bile acids. Bile acids are a diverse group of steroid acids frequently conjugated to taurine or glycine residues to increase solubility. The amphipathic nature of bile salts combined with their high concentration allows for the formation of micelles. These micelles solubilize hydrophobic molecules, such as dietary fats, sterols, and drugs in the gastrointestinal tract. This aids lipase activity and localizes lipids near the brush border of the intestine, resulting in increased absorption of lipophilic compounds (Ghibellini et al., 2006). Bile, along with drugs, is secreted from the liver into bile ducts, a series of tube-like structures that carry bile towards the hepatic duct, which connects with the cystic duct and then the gallbladder (Fig. 2). The gallbladder constantly receives bile and stores it until it is stimulated by cholecystokinin. The gallbladder then contracts and drains its contents through the common bile duct into the duodenum. Compounds in the bile are scavenged in the intestines and reabsorbed, with remaining compounds incorporated into feces. This reabsorption process is highly efficient. Drugs can also have secondary absorption in the intestine, called enterohepatic recirculation, leading to secondary drug peaks and increased drug exposure. Biliary excretion is a complicated process involving several organs, including the liver, gallbladder, pancreas, and the large and small intestine. Active uptake of drug into hepatocytes from the plasma by transporter proteins is thought to be the rate limiting step, with passive transport playing a negligible role. Most drugs eliminated through biliary excretion are believed to be conjugated by phase II enzymes and subsequently transported into the bile (Fagerholm, 2008).

1.24.3.2.1

Transporters

Several organic anion transporters (OAT), along with P-glycoprotein, organic cation transporters (OCT), multidrug resistanceassociated proteins (MRP), and multidrug resistance and toxin extrusion (MATE) proteins, are thought to be the main transporter classes involved with biliary excretion. The rate of biliary elimination from the hepatocytes incorporates active transport of drug bidirectionally from blood into hepatocytes (basolateral or sinusoidal flow) and then transport into bile ducts (apical or canalicular flow). OATP1B1, 1B3, and 2B1, OAT2, and OAT7 transport organic anions while OCT1 and OCT3 transport organic cations into and out of the hepatocytes. Apical transport is driven primarily by ATP binding cassette transporters such as P-glycoprotein (MDR1 or ABCB1), MDR3 (ABCB4), BSEP (ABCB11), MRP-2 (ABCC2), BCRP (ABCG2), as well as MATE1 (Keppler, 2017; Song et al., 2013;

Drug Excretion

633

Fig. 2 Bile salts, metabolic byproducts, drugs, and drug metabolites in the liver are actively transported by several different transporter classes (lower) into the bile ducts and stored in the gallbladder (upper). The gallbladder periodically contracts, releasing its contents into the small intestine, where drugs can be excreted or reabsorbed through a process call enterohepatic recirculation. Abbreviations: Bile salt export pump (BSEP), breast cancer resistance protein (BCRP), mitoxantrone (MX), multidrug resistance-associated proteins (MRP), organic anion (OA), and organic cation (OC), taurocholate (TC) [MX, OA, OC, TC, represent typical substrates]. Used with permission of Mayo Foundation for Medical Education and Research, all rights reserved.

Thakkar et al., 2017). Many of these transporters are polymorphic and have different hepatic uptake efficiency between patients. This variability may change inter-individual drug exposure and require a closer examination to determine the correct drug dose.

1.24.3.2.2

Enterohepatic recirculation

Enterohepatic recirculation is the process by which biliary excreted drug is reabsorbed in the intestine instead of being removed from the body. This can result in a lagging secondary absorption process and increases in drug exposure. Another effect of enterohepatic recirculation is increased gastrointestinal (GI) exposure to drug, which may result in GI side effects, even if the drug is not dosed orally. Numerous factors and comorbidities can impact the reabsorption of a drug, changing the patient’s exposure and degree of drug excretion. Inflammation or damage in the liver, gallbladder, pancreas, or GI tract (e.g., Crohn’s disease), can impact normal bile flow or the ability of the villi to efficiently absorb recirculated drug (Thakkar et al., 2017). Additionally, as the eliminated compounds pass through the GI tract they are exposed to the gut microbiome. These bacteria may have a secondary metabolic effect on xenobiotics in the gut, potentially hydrolyzing metabolic conjugates made in the liver and allowing the reuptake of a parent compound.

634

Drug Excretion

Cytochrome P450s are also present in the small intestine, providing the potential for metabolism and changes in reuptake. The balance of these processes can be changed by disease state, diet, or concomitant medications (Klaassen and Cui, 2015).

1.24.3.3

Measuring and modeling

Accurate quantification of the rate and extent of biliary drug excretion remains difficult in humans. The ideal methods for these studies are very invasive and are typically only performed in patients with liver or gallbladder diseases, where studies of bile flow may be important for clinical care. These methods involve using a temporary bile shunt to divert bile flow coming out of the liver or using a nasobiliary tube to interrupt bile flowing from the gallbladder into the duodenum. These studies provide accurate information, but represent patients with atypical livers or gallbladders, reducing the generalizability of these data to the general population (Ghibellini et al., 2006). Studies in healthy populations compromise on data quality in order to enhance the feasibility of the evaluation. Commonly, fecal recovery and analysis for drug and metabolites is performed. The use of fecal matter results in a highly complex matrix, which reduces the sensitivity of LC-MS/MS methods commonly used for identification and quantification. Primary absorption, enterohepatic recirculation, and biliary excretion processes all contribute to the concentration of drug in feces, making it difficult to untangle the various contributions of these processes. Aspirated duodenal fluid may also be used to sample for these studies. The duodenal fluid has a similar, but diluted, composition to bile in the gallbladder. Occlusion of the duodenum can also be performed to allow more efficient bile collection. However, duodenal collection remains technically sophisticated and is not widely used (Ghibellini et al., 2006; Fagerholm, 2008). Due to the difficulty of collecting human in vivo data, animal models, particularly rodent models, are frequently used. Unfortunately, these models have well characterized differences compared to the human system, which reduces the capacity for extrapolation. Additionally, few drugs have extensive studies completed in both animal models and humans in the target population (Ghibellini et al., 2006; Fagerholm, 2008). In vitro methods have been developed to study parts of the system, but due to the complexity and multi-organ system nature of biliary excretion, none provide comprehensive information about the process. Commonly used methods rely on the culturing of either sandwich-cultured or suspended primary hepatocytes. These methods provide insights into hepatic uptake rates, believed to be the rate limiting step in hepatic drug excretion, and provide the opportunity to assess potential transporter drug-drug interactions. Sandwich-cultured hepatocytes develop functional bile and canalicular networks and can maintain cellular polarity, providing the opportunity to study the basolateral uptake and canalicular efflux. All hepatocyte- based methods suffer from rapid decline in expression of metabolic and transporter proteins in culture, and structural and membrane disruptions caused by collagenase digestion necessary to isolate hepatocytes (Ghibellini et al., 2006; Fagerholm, 2008). Recent advances in microfluidics and 3-D printing of biological substrates has led to liver-on-a-chip systems that hold promise for accurately modeling liver microenviroments and structural characteristics. Several systems have been shown to significantly enhance liver functionality over standard cellculture methods and may provide increased predictive ability for use in early drug discovery (Deng et al., 2019a,b; Lee et al., 2019). Computational modeling of biliary excretion and enterohepatic recirculation provides reasonable capacity for modeling these systems. Population pharmacokinetic modeling is a popular option for investigating the variability of patient enterohepatic recirculation, the impact of genotype and clinical markers on drug excretion, and overall impact on drug exposure. Physiologically-based pharmacokinetic (PBPK) modeling allows for simulations to incorporate the genetics and drug-drug interactions at the transporter level, as well as gastric emptying. As PBPK models incorporate estimations of small intestine length and surface area, it can be a useful tool to explore the impact that procedures such as bowel resections, or transporter and P450 polymorphisms may have on these processes (Berg et al., 2013; Jones and Rowland-Yeo, 2013; Okour and Brundage, 2017).

1.24.3.4

Clinical applications

Clinically, biliary excretion remains difficult to directly measure, however several biomarkers have proven useful. Among these are the endogenous markers bilirubin, albumin, and prothrombin time, and elimination of exogenous substrates including antipyrine, indocyanine green, and others (Figg et al., 1995). While these markers provide an estimate of how well the biliary system is functioning, overall drug pharmacokinetics may be concurrently impacted by kidney dysfunction or overall liver dysfunction that also correlates with the observed disposition of these markers. Changes in kidney and liver function may dwarf any impact biliary excretion has on the overall pharmacokinetic profile of the drug. The FDA suggests that a pharmacokinetic study should be conducted in patients with impaired hepatic function if hepatic metabolism and/or excretion contribute more than 20% to the elimination of a parent drug or any active metabolite (FDA Center for Drug Evaluation and Research (CDER), 2003). When accumulation in the gallbladder occurs, cytotoxic agents used in oncology can cause cholecystitis, inflammation of the gallbladder. A patient receiving sunitinib for renal cell carcinoma developed acute acalculous cholecystitis, which resulted in therapy discontinuation and treatment with antibiotics (Gomez-Abuin et al., 2009). While uncommon, biliary toxicity can occur and should be taken into consideration as these toxicities can further impact excretion, exacerbating the toxic potential. Several drugs are known to cause interactions that may impact biliary excretion. These interactions typically result from competitive inhibition of active transporters. Clinically relevant drugs include, but are not limited to, digoxin, pravastatin, and rosuvastatin (Ho and Kim, 2005). Additional interactions may occur through the inhibition of drug metabolizing enzymes, resulting in either increased active transport or diffusion back to the plasma or reduced basolateral transport into the bile duct (Shitara et al., 2005).

Drug Excretion

635

Many diseases have the potential to impact hepatic excretion. Liver diseases, such as alcoholic liver disease, hepatitis B and C, and primary biliary cirrhosis, may impact liver transporter expression, drug metabolizing enzyme expression, liver structure, or the net amount of drug excreted into the bile. Gallbladder conditions or removal can change the storage of drug incorporated in bile and alter enterohepatic recirculation (Roda et al., 1978). GI diseases, like inflammatory bowel syndrome, can alter gut permeability and microbiome, thus altering gut metabolism and drug reabsorption. The biliary excretion of therapeutic compounds plays an important role in the clearance of many drugs. It is a complex system that often has a strong correlation to overall hepatic function and therefore alone it is difficult to incorporate into dose personalization for patients. Evaluation of drug pharmacokinetics in patients with hepatic impairment requires consideration not just of drug metabolism, but also of biliary excretion and the potential for enterohepatic recirculation.

1.24.4

Breast milk

1.24.4.1

Overview

In addition to renal and hepatic clearance, drugs can also be excreted through breast milk. Not surprisingly, a thorough understanding of this mode of drug excretion is of particular importance in regards to breastfeeding. Breastfeeding has been shown over and over again to be superior to formula for both the infant (cognitive development, disease protection) and the mother (Belfort et al., 2016; Lord et al., 2008; Oddy, 2017; Unar-Munguía et al., 2017). However, the breastfeeding rate for infants at 6 months in the US is only around 57%, with concern over concomitant medication use being a major deterrent (Anderson, 2018). The majority of post-partum women take at least one medication and this can lead to the occurrence of one of two situations. One, new mothers are concerned their medications will negatively affect the infant and they choose to either stop drug therapy or cease breastfeeding early (or never start) (Saha et al., 2015). The second, less common, situation, is when the mother continues to take her medication while breastfeeding and toxicity is observed in the infant (Koren et al., 2006; Schultz et al., 2019). In general, it is believed that the benefit of breastfeeding outweighs the risk of harmful drug transfer to the infant and the majority of women can safely continue taking their medications while breastfeeding (Anderson, 2018; Ilett and Kristensen, 2005). However, transfer of drug from the mother to the infant by way of breast milk and its potential to cause harm should not be completely ignored. In 2005, the death of a breastfeeding infant whose mother was taking codeine (a prodrug of morphine) for postoperative obstetric pain was reported. The morphine serum concentration of the neonate was 70 ng/mL compared to the 0–2.2 ng/mL typically observed in breastfed infants of mothers using codeine (Koren et al., 2006). This triggered a case-control study consisting of breastfeeding mother-infant pairs which indicated that maternal doses and pharmacogenomic variants (CYP 2D6 ultrarapid metabolizer status and uridine 50 -diphosphoglucuronosyltransferase 2B7*2/*2 fast metabolizer genotype status) potentiated exposure of infants to toxic levels of active metabolites (Madadi et al., 2009). Thus, while many drugs appear to be safe for use by a breastfeeding mother, it is still important to use caution and consider the drug dose, the maternal and neonatal metabolizer status, and implications of drug accumulation in the neonate prior to use.

1.24.4.2 1.24.4.2.1

Mechanism Passive diffusion

In the breast, mammary epithelial cells separate maternal plasma from breast milk (Fig. 3). In the colostral phase, which lasts for approximately 3–4 days after delivery, the spaces between the mammary epithelial cells lining the alveoli become wider, which allows larger molecules to easily pass through. Following the colostral phase, these gaps tighten and restrict the passage of molecules from maternal plasma to breast milk to those that are less than 200 Da. Large molecules must now pass through the mammary epithelial cells by passive diffusion down their concentration gradient if they are to be excreted in the milk (Anderson, 2018; Ilett and Kristensen, 2005). There are a number of factors that impact the passive diffusion of drugs from the maternal plasma into milk such as ionization, lipophilicity, protein binding, and molecular weight. Milk is typically more acidic than maternal plasma. Because of this, drugs that are weak bases will be ionized in milk and unable to passively diffuse back to the maternal plasma (Bailey and Briggs, 2004; Meskin and Lien, 1985). Additionally, milk’s fat composition can vary throughout the day and impact the diffusion of lipid-soluble drugs, with an increase in fat composition correlating with an increase in lipid-soluble drug concentration in the milk. Drugs that are more lipid-soluble can also more easily cross the cell membrane (Atkinson and Begg, 1988; Syversen and Ratkje, 1985). A drug’s protein binding characteristics are another strong determinant of their capability to diffuse into breastmilk. Maternal plasma protein concentrations are around 75 g/L, while milk protein concentrations are only 8–9 g/L. Thus, drugs that are highly protein bound tend to remain in the maternal plasma (Atkinson and Begg, 1988; Cesari et al., 2018; Fleishaker et al., 1987).

1.24.4.2.2

Carrier-mediated transport

While it appears that most drugs are excreted into the breast milk by passive diffusion, there are some drugs present in breast milk at higher concentrations than would be expected from passive diffusion alone (Oo et al., 1995). For example, nitrofurantoin, an antibiotic commonly used to treat and prevent urinary tract infections, has a predicted breast milk to maternal plasma ratio of 0.28  0.05. However, the actual milk to plasma ratio is much higher at 6.0  2.7 (Gerk et al., 2001). This led researchers to explore

636

Drug Excretion

Fig. 3 Small molecules ( 0.7). In this circumstance, CLliver for a high extraction ratio drug will be most sensitive to changes in hepatic blood flow (Qliver) such as what may occur in liver cirrhosis or heart failure. Moreover, alterations in metabolic activity (CLint) will have minimal effects on CLliver for highly extracted drugs. In the case of low extraction ratio drugs (e.g., Eliver < 0.3), CLliver is sensitive to changes in hepatic metabolic enzyme activity (CLint) such as in the situation of enzyme inhibition or induction (e.g., pharmacokinetic drug-drug interaction). The CLliver of a low extraction ratio drug will not be sensitive to changes in hepatic blood flow (Qliver). For the reasons stated above, it is useful to categorize drugs as having low, medium or high hepatic extraction as it allows for prediction of potential relevance of changes in enzyme activity and liver blood flow on the clearance and oral bioavailability of drugs. The traditional well-stirred model was originally developed to describe the hepatic clearance of highly membrane permeable drugs that distribute into liver cells from blood by passive diffusion. With the subsequent appreciation for a role of drug transporters in controlling the uptake and efflux of drugs in hepatocytes, the “extended clearance model” was developed (Sirianni and Pang, 1997; Yamazaki, Suzuki and Sugiyama, 1996) (Fig. 5B). In the extended clearance model, the individual processes of membrane transport in hepatocytes are parameterized. Specifically, CLin is a parameter that represents the efficiency of sinusoidal uptake (influx) transporters (e.g., Organic Anion Transporting Polypeptide 1B1) that promote drug entry from blood into liver cells. CLef describes the process of drug efflux from the hepatocytes back to the blood which can occur through the actions of transporters (e.g., Multidrug Resistance Protein 3). Intrinsic clearance (CLint) remains a parameter in the extended clearance model, except that the value now incorporates the sum of all the processes that eliminate drug including metabolism and biliary excretion by transport mechanisms (e.g., P-glycoprotein, Breast Cancer Resistance Protein). The equation describing the extended clearance model for hepatic drug clearance is: CLliver ¼ Qliver 

fuB CLin CLint
Qliver CLef þ CLint þ fuB CLin CLint

(17)

Similar to the approach for in vitro to in vivo scaling for metabolic intrinsic clearance, transport clearance values (e.g., CLin and CLef) can also be estimated from results of in vitro drug transport studies. The extended clearance model provides a way to integrate

(A)

Blood Flow (Q)

(B)

Blood Cell

Unbound Drug

Bound Drug

Blood Flow (Q)

Blood Cell

Unbound Drug

Blood

Blood

CLin Unbound Drug

Bound Drug

Bound Drug

CLef

Unbound Drug

Membrane

Bound Drug

Hepatocyte

CLint Metabolite

Hepatocyte

Bile Bile

Fig. 5 Models of hepatic clearance. (A) The well-stirred model. Drug is delivered to the liver by blood flow (Q). Within the blood, drug can bind or distribute into blood cells (e.g., erythrocytes) and can bind to soluble proteins (e.g., albumin). The unbound drug in blood rapidly mixes with and becomes equal in concentration to that in the hepatocyte intracellular space. The hepatocyte membrane does not impede access of drug from movement between the blood and the hepatocytes. Unbound drug is available for elimination by metabolism or biliary excretion. This is a first-order removal process characterized by the parameter, intrinsic clearance (CLint). The concentration of the drug in the liver is equivalent to the concentration of drug leaving the liver in the hepatic vein. (B) Extended clearance model. This model is an extension of the well-stirred model whereby the permeability of the unbound drug across the hepatocyte membrane is taken into consideration. Here, the efficiency of drug uptake into hepatocytes is a function of the first-order, influx clearance parameter (CLin), while the movement of drug from the hepatocyte intracellular space back to the blood is controlled by the efflux clearance parameter (CLef).

Mathematical Aspects of Clinical Pharmacokinetics

CLint Metabolite

653

654

Mathematical Aspects of Clinical Pharmacokinetics

the complex interplay of metabolism and transport in the liver to control the hepatic clearance of medications, including ratelimiting steps. Importantly, the model can be used to predict both the blood and hepatocyte intracellular concentrations of medications in situations where changes in drug transporter and drug metabolizing enzyme activities occur (e.g., drug-drug interactions, pharmacogenetics).

1.25.5

Half-life

The time required for drug concentrations to fall by 50% of an initial value is termed the half-life (t1/2). For drugs administered intravenously as a bolus with one-compartment distribution, t1/2 is constant throughout drug exposure. The t1/2 may be directly calculated from the line of best fit “slope” by regression analysis or from two points along the log-linear phase of the drug concentration-time profile. In the example when two plasma concentration determinations are used (C1 and C2 which are obtained at times t1 and t2, respectively), one can obtain a value for the “slope” which is equivalent to the negative elimination rate constant (k). k¼

ðlnC2  lnC1 Þ ðt2  t1 Þ

(18)

0:693 k

(19)

The t1/2 can then be calculated as: t1=2 ¼

After intravenous bolus injection of a drug with one-compartment distribution, the time required for essentially all the dose to be eliminated by the body is 3–5 times the t1/2. With drugs that distribute in a multi-compartment fashion, t1/2 is usually considered as that found at the terminal phase of the drug concentration-time profile. Half-life is an important determinant of how often a drug should be taken. For example, the concentrations of a drug with a short half-life will decline rapidly and therefore the duration of drug response may be limited. With the simpler one-compartment distribution model, an important pharmacokinetic relationship exists that relates t1/2 with volume of distribution and clearance (Rowland and Tozer, 1995): t1=2 ¼

0:693  V CLS

(20)

It can be appreciated that the drug half-life is not solely a measure of the elimination process, but it is also equally dependent on the volume of distribution. This relationship is particularly useful since it allows one to predict the pharmacokinetic effect of changes in volume of distribution or clearance on the t1/2 of a drug in a patient. For instance, if a drug’s volume of distribution (V) decreased because of dehydration, the t1/2 would also decrease. On the other hand, if a patient was co-administered a drug that inhibits drug metabolizing enzymes, the CLS would decrease and the t1/2 would increase. A more complicated scenario would be where both volume of distribution and clearance of a drug changes in a patient. Let’s consider the case of medications in pregnancy. Pregnancy is associated with increased total body water and a reduction in the concentrations of plasma proteins such as albumin. These physiological changes would result in increased volume of distribution of drugs, particularly those that are hydrophilic or highly bound in the blood. Concurrently, the clearance of drugs generally increases during pregnancy as there is upregulation of drug metabolizing enzymes in the liver as well as increased glomerular filtration rate in the kidney due to greater free fraction in blood and augmented renal blood flow. Therefore, based on the relationships afforded by Eq. (20), in pregnancy, the combined effects of changes in volume of distribution and clearance may be neutralizing to result in minimal changes in half-life.

1.25.6

Application of pharmacokinetics to routine drug therapy

The primary goal of pharmacotherapy is to achieve and maintain drug efficacy and minimize adverse drug effects. For some drugs, desired responses are observed when plasma drug levels reach a minimum effective concentration. Similarly, side-effect risk may be drug concentration-dependent. For many drugs, there is a wide therapeutic window whereby drug concentrations that elicit adverse effects are significantly greater than those required for therapeutic benefit. But when the therapeutic window is narrow, effective concentrations are close to those that cause adverse effects. For the most part, therapeutic individualization is largely a trial-anderror process where dose adjustments are dictated by whether one achieves the desired outcomes or provokes adverse effects. But for particular drugs with a narrow therapeutic window, drug concentration monitoring can be a useful tool to guide drug dosing in addition to monitoring clinical responses. Regardless of the therapeutic optimization strategy, understanding pharmacokinetic principles is a requirement for rational dose selection, especially for medications with a narrow therapeutic window. Aside from route of administration and drug formulation choices, there are two quantitative variables that need to be considered, namely, the amount or dose to be given at any one time and the dosing interval or time between subsequent doses. For continuous infusions, the variable of concern is dose rate. These dosing decisions will be determined, in part, by the pharmacokinetic properties of the drug in the patient. Clearance will define the dose rate, half-life will determine the dosing interval, volume of distribution will

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play a significant role in dose, loading dose and dosing interval, while bioavailability affects relative dose given by different routes of administration.

1.25.6.1

Continuous intravenous infusion

With drug administration by continuous rate intravenous infusion, blood drug concentrations rise in a hyperbolic manner until a constant concentration is sustained. This steady-state in drug concentrations is achieved because the rate of drug delivery equals the rate of drug elimination. The time required for steady-state to be achieved is 3–5 times the t1/2 of the drug. Importantly, the time to steady-state is independent of the dose rate. So, every time the dose rate is changed in a given patient, drug concentrations will rise or fall to new steady-state values, and the time required to reach each new steady-state remains the same. With inspection of Eq. (10), it can be noted that because CLS generally remains constant in a patient, the steady-state concentration of drug will be directly proportional to the dose rate. That is, if you double the dose rate, you will double the steady-state drug concentrations.

1.25.6.2

Intravenous loading dose

In circumstances where a patient’s condition is acute, it may be necessary to achieve therapeutic drug concentrations as rapidly as possible. In this regard, loading doses are given to a patient to attain desired drug concentrations more quickly than would occur with delivering just a continuous intravenous infusion. The loading dose for a one-compartment distribution drug can be determined with knowledge of the drug’s volume of distribution and target drug concentration (Ctarget): Loading Dose ¼ V  Ctarget

(21)

The intravenous loading dose can be administered as a bolus or as a short infusion (e.g., over 30 min). Loading doses administered as a short infusion are particularly useful for drugs that have multi-compartment distribution kinetics where the central volume VC is relatively small and an intravenous bolus loading dose would generate very high initial drug concentrations that may have harmful effects.

1.25.6.3

Intermittent dose administration

Drugs are often taken regularly to treat short-term or chronic conditions. As such, drugs are administered intermittently, with intervals between doses ranging from hours to days. With intermittent dosing, drug concentrations rise and fall with each consecutive dose, dictated by the dose, dosing interval and the half-life. When the dosing interval is shorter than that required for the drug to be fully eliminated by the body between doses, plasma drug levels accumulate with each successive dose. Eventually, a steady-state will occur where the drug concentration profiles resulting from each dose will become similar (Fig. 6). In many respects, the accumulation of drugs administered in intermittent doses is the same as that observed following a constant-rate intravenous infusion. Recall that the steady-state plasma drug level after constant rate intravenous infusion is dependent on the dose rate and CLS (Eq. 10). For

Drug Concentration

150

Css,avg

100 Intermittent Dosing Single Dose 50

0 0

6

12

18

24

30

36

42

48

Time Fig. 6 Pharmacokinetics of intermittent dosing. An oral medication is administered every six time units and the blood concentrations over time are monitored (light green line). With each successive dose, the concentrations increase and decrease as drug is absorbed and eliminated from the body. Not all drug in the body is removed prior to the administration of each dose. In this scenario, drug concentrations increase and approach a steadystate after approximately the 8th dose. Thereafter, drug concentrations with each dose will be similar and will have an average value throughout the dosing interval of Css,avg. For comparison, the drug concentration time profile of a single dose of the medication is overlaid (dark green line and shaded area).

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intermittent dosing, a similar steady-state will occur where the average blood concentration (Css,avg) that reflects the overall drug concentrations will be dependent on the bioavailability, dose, dosing interval (s) and clearance (Rowland and Tozer, 1995). Css;avg ¼

F  Dose CLS  s

(22)

Therefore, any combination of dose and dosing interval that gives the same dosing rate (Dose/s), will attain the same average steady-state blood concentration. The differences between such dosing regimens will be the degree of difference in the maximum and minimum blood concentrations with each successive dose. Small doses given at short intervals gives rise to smaller plasma level fluctuations than large doses administered at longer intervals. Again, with intermittent dosing, it takes 3–5 half-lives to reach steadystate regardless of the dose or dosing interval. Furthermore, the average steady-state drug concentration will be directly proportional to dosing rate and inversely proportional to CLS.

1.25.7

Conclusions

In this article, the quantitative aspects of pharmacokinetics were reviewed. In this regard, the key concepts and mathematical functions that define drug bioavailability, volume of distribution, clearance and half-life were provided. Moreover, common experimental strategies to determine the values of the pharmacokinetic parameters for drugs in humans were also discussed. These basic pharmacokinetic principles and approaches are routinely applied to clinical pharmacology research, drug discovery and development and in rational drug therapy.

See Also: 1.17: Oral Drug Delivery, Absorption and Bioavailability; 1.18: PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations; 1.24: Drug Excretion; 1.29: Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

References Benet, L.Z., Galeazzi, R.L., 1979. Noncompartmental determination of the steady-state volume of distribution. Journal of Pharmaceutical Sciences 68 (8), 1071–1074. Gillette, J.R., Pang, K.S., 1977. Theoretic aspects of pharmacokinetic drug interactions. Clinical Pharmacology and Therapeutics 22 (5 Pt 2), 623–639. Paine, M.F., Shen, D.D., Kunze, K.L., et al., 1996. Intestinal metabolism of midazolam by the human intestine. Clinical Pharmacology and Therapeutics 60 (1), 14–24. Pang, K.S., Rowland, M., 1977. Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. Journal of Pharmacokinetics and Biopharmaceutics 5 (6), 625–653. Pond, S.M., Tozer, T.N., 1984. First-pass elimination. Basic concepts and clinical consequences. Clinical Pharmacokinetics 9 (1), 1–25. Purves, R.D., 1992. Optimum numerical integration methods for estimation of area-under-the-curve (AUC) and area-under-the-moment-curve (AUMC). Journal of Pharmacokinetics and Biopharmaceutics 20 (3), 211–226. Rowland, M., Tozer, T.N., 1995. Clinical Pharmacokinetics: Concepts and Applications, 3rd edn. Lippincott Williams & Wilkins, Philadelphia, PA. Rowland, M., Benet, L.Z., Graham, G.G., 1973. Clearance concepts in pharmacokinetics. Journal of Pharmacokinetics and Biopharmaceutics 1 (2), 123–136. Sirianni, G.L., Pang, K.S., 1997. Organ clearance concepts: New perspectives on old principles. Journal of Pharmacokinetics and Biopharmaceutics 25 (4), 449–470. Wagner, J.G., Nelson, E., 1964. Kinetic analysis of blood levels and urinary excretion in the absorptive phase after single doses of drug. Journal of Pharmaceutical Sciences 53, 1392–1403. Wilkinson, G.R., 1987. Clearance approaches in pharmacology. Pharmacological Reviews 39 (1), 1–47. Wilkinson, G.R., Shand, D.G., 1975. Commentary: A physiological approach to hepatic drug clearance. Clinical Pharmacology and Therapeutics 18 (4), 377–390. Yamazaki, M., Suzuki, H., Sugiyama, Y., 1996. Recent advances in carrier-mediated hepatic uptake and biliary excretion of xenobiotics. Pharmaceutical Research 13 (4), 497–513. Yeh, K.C., Kwan, K.C., 1978. A comparison of numerical integrating algorithms by trapezoidal, Lagrange, and spline approximation. Journal of Pharmacokinetics and Biopharmaceutics 6 (1), 79–97.

1.26 Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters Mariamena Arbitrioa, Francesca Sciontib, Maria Teresa Di Martinob, Licia Pensabenec, Pierfrancesco Tassoneb, and Pierosandro Tagliaferrib, a Institute of Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Secondary site of Catanzaro, Italy; b Department of Clinical and Experimental Medicine, Magna Graecia University, Catanzaro, Italy; and c Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy © 2022 Elsevier Inc. All rights reserved.

1.26.1 1.26.1.1 1.26.2 1.26.2.1 1.26.2.2 1.26.2.3 1.26.2.4 1.26.2.4.1 1.26.2.4.2 1.26.2.4.3 1.26.2.5 1.26.2.5.1 1.26.2.6 1.26.2.7 1.26.2.8 1.26.2.8.1 1.26.2.8.2 1.26.3 1.26.3.1 1.26.3.2 1.26.3.2.1 1.26.3.2.2 1.26.3.2.3 1.26.3.2.4 1.26.3.2.5 1.26.4 1.26.4.1 1.26.4.1.1 1.26.4.1.2 1.26.4.1.3 1.26.4.2 1.26.4.2.1 1.26.4.2.2 1.26.4.2.3 1.26.4.2.4 1.26.4.2.5 1.26.5 1.26.5.1 1.26.5.2 1.26.5.3 1.26.6 References

Introduction Phase I and phase II metabolic enzymes (or ADME genes) PGx of phase I genes CYP1A2 (P450 family 1, subfamily A, polypeptide 2) CYP2A6 (P450 family 2, subfamily A, polypeptide 6) CYP2B6 (P450 family 2, subfamily B, polypeptide 6) CYP2C subfamily CYP2C8 (P450 family 2, subfamily C, polypeptide 8) CYP2C9 (P450 family 2, subfamily C, polypeptide 9) CYP2C19 (P450 family 2, subfamily C, polypeptide 19) CYP2D subfamily CYP2D6 (P450 family 2, subfamily D, polypeptide 6) CYP2E1 (P450 family 2, subfamily E, polypeptide 1) CYP2J subfamily CYP3A subfamily CYP3A4 (P450 family 3, subfamily A, polypeptide 4) CYP3A5 (P450 family 3, subfamily A, polypeptide 5) PGx of non-P450 enzymes Dihydropyrimidine dehydrogenase (DPYD) PGx of phase II enzymes N-Acetyltransferases (NATs) UDP-glucuronosyltransferases (UGTs) Thiopurine S-methyltransferase (TPMT) Glutathione S-transferases (GSTs) Sulfotransferases (SULTs) PGx of drug transporters Efflux transporters: ABC superfamily ABCB1 or P-glycoprotein (P-gp) ABCC1, ABCC2 and ABCC5 or multidrug resistance-associated proteins MRP1, 2, and 5 ABCG2 or breast cancer resistance protein (BCRP) Uptake transporters: SLC superfamily OATP1B1 OATP2B1 OATP1B3 Organic anion transporters (OATs) Organic cation transporters (OCTs) PGx of other enzymes and antigens Vitamin K epoxide reductase complex 1 (VKORC1) Glucose-6-phosphate dehydrogenase (G6PD) Human leukocyte antigen (HLA) Conclusion

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Glossary ADME genes The acronym refers to genes involved in absorption, distribution, metabolism, and excretion of drugs in vivo, encoding for Phase I and II drug metabolizing enzymes, transporters, and modifiers.

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Genotype and phenotype A genotype is the genetic constitution of an individual while the term phenotype refers to all the observable characteristics of an individual that result from the interaction between its genotype and environment. Haplotype-Map (HapMap) Project A catalog of common genetic variations in all ethnicities born from the collaboration of scientists in six countries. All data are freely available and the project has allowed the comprehension of the correlation between genetic differences and human disease. It lists and describes the locations and the variability of haplotypes. Haplotypte A combination of gene alleles closely linked along a chromosome, located between two sites of high recombination (hotspot) and inherited together. The term can refer to the combinations of human leucocyte antigen (HLA) alleles across loci or to a cluster of single nucleotide polymorphisms (SNPs). It facilitates understanding of the inheritance and regulation of polymorphic traits. Heterozygosity The presence of different alleles at a particular gene locus. Homozygosity The presence of identical alleles at a particular gene locus. Imputation analysis A strategy to increase genome coverage for unmeasured or missing genotypes using haplotypes from dense reference panels. In this way, sample haplotypes are matched to haplotypes in the reference panel such that imputed SNPs, that exhibit large associations, may become candidates for replication studies. Linkage disequilibrium (LD) The non-random association of alleles at two or more loci in a population, coinherited in the same haploblock. LD allows the study of a lower number of SNPs to detect association between a gene and a trait. Polymorphic variant Change in DNA sequence that has a population frequency of at least 1% and can lead to the abnormal expression or structure of the protein. Single nucleotide polymorphism (SNP) and Tag-marker SNP is an inherited DNA variation, characterized by substitution of a single base pair, commonly occurring at a population frequency of at least 1%. It may have a functional role when located in a gene’s functional regions (coding sequences or in regulatory regions). In a genomic block a SNP may be in linkage disequilibrium (LD) with another genomic variant and considered as a tag marker of a specific haplotype (TagSNPs). The selection of a small number of TagSNPs allows the identification of the remaining ones among the thousands of human SNPs in LD.

1.26.1

Introduction

Drug response is characterized by large variability among individuals due to several causes such as age and ancestry, organ function, drug-drug interactions (DDIs), concomitant conditions or disease characteristics with important implications for drug efficacy and toxicity. After the completion of the Human Genome Project (HGP) (Lander et al., 2001; International Human Genome Sequencing, 2004) individual genetic make-up has gained great attention in the study of interindividual variability in drug pharmacokinetics (PK) and pharmacodynamics (PD) (Fig. 1). After administration, a drug is absorbed, distributed to its site of action, interacts with its targets, is metabolized, and finally excreted (Hardman et al., 2001). Genomic variants in genes responsible for absorption, distribution, metabolism, and excretion (ADME) can influence PK as well as PD, which may be conditioned by variations at the drug target level or in downstream signaling cascades (St Sauver et al., 2016). PK may be influenced also by DDIs, especially in the case of polytherapy for concomitant diseases, with important effects on unexpected adverse drug reactions (ADRs) or reduced efficacy (Palleria et al., 2013). Pharmacogenetics and Pharmacogenomics (often used interchangeably and here referred to as PGx) have contributed to the understanding of biological differences underlying the interindividual variability in drug response, allowing improvement in efficacy of therapy in several diseases as well as reducing or avoiding toxic effects in cancer. Personalized medicine might impact the reduction of healthcare costs and improve patient quality of life. In this context, genetic analysis facilitates the selection of a safer and more effective pharmacological management for each patient. Progress in high-throughput technologies allowed the identification of genetic variations as predictive and/or prognostic biomarkers linked to drug PK and PD. The FDA (US Food and Drug Administration) and EMA (European Medicines Agency) have accepted strong genotype-phenotype evidence for more than 250 validated PGx biomarkers for their association to risk of potential toxicity or therapeutic failure, planning standardized recommendations. Single nucleotide polymorphisms (SNPs), insertions/deletions (INDEL), polymorphic short tandem repeats (STR) and copy number variations (CNVs) are the most common germline variations investigated for clinical relevance, ethnic variability, and PGx biomarker identification, which may have a functional role when they occur in a gene’s functional regions (coding sequences or in regulatory regions). When variability in drug response cannot be due to these common variants, low frequency (0.1%
minor allele frequency (MAF) < 5%) and rare (MAF < 0.1%) variants might be responsible for highly gene- and drug-specific functional alterations (Ingelman-Sundberg et al., 2018). SNPs are common inherited variations (MAF > 1%), with a substitution of a nucleotide, in linkage disequilibrium (LD) with other undetected SNPs, which can provide the same information, and co-inherited in the same haplotype block. Therefore, the presence of changes in one of the genes usually coincides with changes in other genes in the haploblock. SNPs in LD can function as a “tag marker” of a specific haplotype (TagSNPs). In this way, by selecting a small number of TagSNPs it is possible to represent the remaining ones among the thousands of human SNPs in LD (Johnson et al., 2001; Sherry et al., 1999). Instead, INDELs are small insertions or deletions of base pairs while CNVs are duplications or deletions of DNA segments (1–3 Kb) with a variable number of copies of a specific segment (Santos et al.,

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Fig. 1

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Factors influencing interindividual variability in drug response.

2018). Another class of variations in DNA sequence, comprising up to 3% of the total genomic DNA, are STRs characterized by tandemly repeated DNA sequence with different length: (1) microsatellites (short tandem repeats of DNA motifs, length 1 to 6 bp, or STR); (2) minisatellites (variable number of tandem repeats VNTR, length 10–100 bp) (de Koning et al., 2011). All genetic variants are identified with a standardized nomenclature according to updated guidelines available at HUGO Gene Nomenclature Committee (HGNC) database (Wain et al., 2002). However, several PGx genetic variants are referenced by a historical convention, naming a variant according to the time of its first characterization and report (Wang et al., 2012), but without providing information on the nature and activity of the genetic variation. According to historical nomenclature, the normal allele of a gene on a chromosome is identified as *1 (e.g., CYP2D6*1, wild type), while the other variant alleles, following their historical discovery, were designated *2, *3, *4, and so on. Moreover, a reference SNP (rs number) identifies uniquely each genetic variant and a SNP database is available at the National Center for Biotechnology Information (NCBI) Single-Nucleotide Polymorphism Database (dbSNP). Another highly polymorphic site is represented by the major histocompatibility complex (MHC) including the human leucocyte antigen (HLA)-DQ/DR, which represent immune response genes encoding cell-surface molecules whose role is to guide presentation of endogenous peptides to immune system cells (Illing et al., 2017). A consequence of interactions between specific drugs and expressed HLA molecules is T cell-mediated immune reactivity with off-target effects, whose underlying mechanism is still under investigation (Illing et al., 2017). Germline genetic variations commonly occur for ADME genes and can affect drug availability at the target site or patient sensitivity to a drug (Fig. 2), which can be influenced also by variation in drug-target protein level, as in the case of acquired somatic mutations. The effects, in any cases, will be relevant for drug response and patient outcome due to (a) higher or lower drug exposure, (b) high level of toxic metabolites, (c) altered effect on drug target, or (d) hypersensitivity due to immune system activation. These effects have an impact also in interpopulation heterogeneity in drug response (Hovelson et al., 2017). Advancement in PGx research has allowed identification of predictive and prognostic biomarkers correlated to interindividual variability in drug response, starting from candidate gene studies to genome wide association studies (GWAS) and next generation sequencing (NGS) strategies (Agapito et al., 2020; Arbitrio et al., 2016a,b, 2018; Di Martino et al., 2016a,b). This association was evaluated starting from big data generated in large cohorts of individuals and in different populations with short turnaround time and competitive costs. In this way, it was possible to discover rare genetic and epigenetic biomarkers potentially correlated to susceptibility for several diseases, including cancer, or to drug resistance. Despite these efforts, the contribution of PGx biomarker implementation in clinical care is still far from reality in daily practice, with few exceptions. In this context significant efforts are needed for overcoming existing barriers (regulatory, ethical, economic, educational, validation and standardization issues) in PGx implementation for precision medicine on a broader scale. The aim of this article is to (1) describe PGx progress in studies among drugs and phase I/II ADME genes and transporters, other rare genomic variants and variations in HLA genes, providing several examples of current use of germline PGx biomarkers, (2) to discuss phenotypes influencing drug efficacy or

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Fig. 2

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Influence of genetics in drug response.

toxicity, with implications for treatment failure or success, and (3) highlight challenges correlated to variability in susceptibility and resistance to different diseases.

1.26.1.1

Phase I and phase II metabolic enzymes (or ADME genes)

Phase I and II genes, together with influx/efflux transporters play a pivotal role in ADME genes of drugs, influencing response to therapy or inducing ADRs. Phase I enzymes catalyze hydrolysis, reduction and oxidation reactions while phase II enzymes catalyze sulfation, acetylation and glucuronidation, indicated as conjugation reactions. The most representative phase I metabolic enzymes are the cytochrome P450 (CYP450) superfamily, with multiple forms that are often highly expressed in liver and highly polymorphic (Zhou et al., 2009a,b). They catalyze hydrolysis, reduction and oxidation reactions and are grouped into 18 families and 44 subfamilies consisting of 57 functional genes and 58 pseudogenes. However, members of CYP1, CYP2, CYP3 and CYP4 family play important roles in metabolism of several drugs and other xenobiotics as well as in DDIs. Ethnic and interindividual genetic variation can alter the function of several P450 enzymes (Fig. 3). P450 enzymes play important roles in chemoprevention, carcinogenesis, cancer therapy, and metastasis (Alzahrani and Rajendran, 2020). The most comprehensive source on CYP alleles is the Pharmacogene Variation Consortium (https://www.pharmvar.org/). Phase II drug-metabolizing enzymes are active in detoxification and excretion of endogenous compounds and xenobiotics acting through conjugation reactions. These reactions tend to increase the hydrophilicity of the substrate, deactivate highly reactive species or inactivate pharmacologically active compounds. Genetic variants of phase II enzymes cause a reduced metabolizing capacity and potential drug toxic effects. Moreover, phase II enzymes can sometimes convert xenobiotics and pro-carcinogens into highly reactive intermediates acting as chemical carcinogens and mutagens by covalent binding to DNA. This class of enzymes includes mostly transferases: UDP-glucuronosyltransferases (UGTs), sulfotransferases (SULTs), N-acetyltransferases (NATs), glutathione S-transferases (GSTs) and various methyltransferases (i.e. thiopurine Smethyl transferase (TPMT) and catechol O-methyl transferase, COMT). Polymorphic genotypes in phase I and II enzymes are responsible for phenotypes characterized by altered metabolic states: “ultra” (UM), “intermediate” (IM) and “poor” (PM) metabolizers differ from wild-type individuals identified as “extensive” (EM) metabolizers. These phenotypes are characterized by an anomalous number of alleles influencing enzyme activity: PM phenotype is associated with lack of functional enzyme due to the presence of defective or deleted alleles (null genotypes), IM phenotype carries either one functional and one defective allele, or two partially defective alleles with reduced metabolism genotypes in any case, while UM phenotype relies on gene variant duplications or amplification responsible for two or multiple copies of the functional allele and higher enzyme activity (Ahmed et al., 2016). The phenotype of EM instead carries two active functional alleles with normal enzymatic activity. Genetic alteration in enzymatic activity may produce important clinical effects both in the case of altered metabolism of parent compounds and active metabolite; the common risks for PM and IM are reduced clearance, increased plasma concentrations and ADRs, whereas the risks for UM are increased clearance, lower drug concentrations and lower efficacy. Instead, in the case of a prodrug the opposite is to be expected (ADRs for UM and lower efficacy for PM) and the pharmacological effect of the metabolite(s) must be considered (Zanger and Schwab, 2013). A critical role for ADME genes is also played by the transporters that mediate the efflux and/or influx of drugs in organs by active transport or facilitated diffusion, accounting for drug uptake, bioavailability, targeting, efficacy, toxicity and clearance. Both influx and efflux transporters are important in regulating plasma drug bioavailability. Among transporters, ATPbinding cassette (ABC) and solute-linked carrier (SLC) proteins are often polymorphic and involved in the transport of a wide

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Fig. 3 Major human CYP genes in which genetic variants affect drug dose (dosage), response (efficacy/toxicity) or metabolism (PK). The size of each pie piece refers to variant–drug–phenotype associations based on PharmGKB (https://www.pharmgkb.org).

spectrum of small inorganic and organic molecules. Many of the most relevant allelic variants involved in drug disposition have been identified in the ADME genes encoding phase I/II enzymes and transporters.

1.26.2

PGx of phase I genes

1.26.2.1

CYP1A2 (P450 family 1, subfamily A, polypeptide 2)

The CYP1A2 is a member of the CYP1 family, consists of seven exons and six introns and is located on chromosome 15. Its localization is hepatic, and this enzyme activates several procarcinogens including polycyclic aromatic hydrocarbons, heterocyclic aromatic amines/amides, mycotoxins such as the aflatoxin B and natural compounds such as aristolochic acids (Wang and Zhou, 2009). CYP1A2 is highly inducible by phenobarbital, cigarette smoking and by a number of xenobiotics (Ghotbi et al., 2007). CYP1A2 metabolizes endogenous compounds such as retinols, melatonin, steroids, uroporphyrinogen and arachidonic acids and plays a major role in procarcinogen activation and DDIs, due to induction and inhibition by several therapeutic drugs. In particular, carbamazepine, antofloxacin, isoniazid, fluvoxamine, abiraterone, rofecoxib, ciprofloxacin, quinidine are strong inhibitors of CYP1A2, while phenobarbital, carbamazepine, rifampicin, dovitinib, rucaparib are important inducers (https:// www.drugbank.ca). So, clinical DDIs are explainable with reversible and irreversible CYP1A2 inhibition/induction. Moreover, it is known that genetic polymorphisms (Ghotbi et al., 2007; Zhou et al., 2009a) and transcriptional factors influence CYP1A2 expression and activity; there is also evidence for post-transcriptional regulation by microRNAs (miRNAs). CYP1A2 activity is related also to susceptibility to a range of diseases. For example, high CYP1A2 activity related to pro-carcinogen activation, in the case of exposure to aromatic and heterocyclic amines, may account for individual susceptibility to colorectal cancer. This correlation remains controversial because data derived from the meta-analysis of He et al. (2014) evaluating the role of CYP1A2*1F (-163C < A, rs762551), CYP1B1 Leu432Val (rs1056836), Asn453Ser (rs180040), and Arg48Gly (rs10012) polymorphisms found no significant association with colorectal cancer risk. However, the correlation of SNPs in CYP1A2 to overall cancer risk needs to be further investigated by high-quality studies because a large meta-analysis by Vukovic et al. (2016) demonstrated no significant effect with the exception of a borderline non-significant OR of 0.84 (95% CI, 0.70–1.01) for bladder cancer in homozygous carriers of mutant CYP1A2*1F. Probably, correlative studies need to be stratified according to the tumor site. In the northern Chinese population, Bai et al. (2017) demonstrated a significant association of the polymorphism in CYP1A2 (rs2470890) with outcome in patients with breast cancer. According to a meta-analysis by Koonrungsesomboon et al. (2018), individual homozygous or heterozygous carriers of the CYP1A2*1F mutation in intron 1 ( 163C< A, rs762551) could have a functional role in promoting CYP1A2 inducibility among regular cigarette smokers. CYP1A2 induction, indicated by increased caffeine metabolism, is widely accepted as the gold standard for measuring in vivo CYP1A2 activity. A correlation between coronary heart disease (CHD) as assessed by myocardial infarction (MI) risk and DNA-damaging cigarette smoke mutagens was evaluated according to CYP1A2 genotype by Cornelis et al. (2006). They found that the low inducibility genotype for CYP1A2 was associated with an increased risk of MI, independently of smoking status, suggesting a role in CHD. On the other hand, for other CYP1A2 substrates, conflicting findings are reported on genotype-phenotype prediction and the functional relevance of increased CYP1A2 inducibility is currently unknown. CYP1A2 mediates the metabolism of several commonly used drugs and therefore the drug-interactions due to its reversible or irreversible inhibition in individuals could be of clinical relevance to identify a specific metabolic phenotype for a drug to avoid treatment failure or toxicity.

662 1.26.2.2

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters CYP2A6 (P450 family 2, subfamily A, polypeptide 6)

Human subfamily CYP2A genes (2A6, 2A7, 2A13 and a split pseudogene CYP2A18P) are members of the CYP2 family that contains 16 full-length genes with hepatic and extrahepatic localization, such as lungs and trachea (Tornio and Backman, 2018). The subfamily is organized in a gene cluster on chromosome 19 also containing genes and pseudogenes of the CYP2B, 2F, 2G, 2S and 2T subfamilies (CYP2ABFGST cluster) (Hoffman et al., 2001). Among CYP2A genes, CYP2A6 and CYP2A13 are functional, although CYP2A7, which encodes a nonfunctional gene not incorporating heme, is located near the CYP2A6 gene and shows 96.5% identity in the coding nucleotide sequence (Fukami et al., 2006). In fact, the CYP2A6*1B allele has a gene conversion with CYP2A7 in the 30 -untranslated region or a novel CYP2A7 variant leading to mis-genotyping as the CYP2A6*4A allele (Fukami et al., 2006). Pregnane X receptor (PXR) and constitutive androstane receptor (CAR) activators such as rifampin and phenobarbital induce transcriptional regulation of CYP2A6 (Itoh et al., 2006). Furthermore, dexamethasone acts through the glucocorticoid receptor to modulate transcription of CYP2A6 (Onica et al., 2008) and estrogen-containing oral contraceptives can increase CYP2A6 (Benowitz et al., 2006) activity with acceleration of nicotine metabolism and clearance of its metabolite cotinine. CYP2A6 has a small active site compared to other P450 enzymes (Yano et al., 2005), showing substrate-selective catalytic activity (Fukami et al., 2005), with a phenotype ranging from PM to UM, and a distribution of variant alleles differing among populations (Zhou et al., 2017). CYP2A6 enzyme activity is correlated to variability in smoking behavior, drug toxicities, and susceptibility to several cancers. The principal substrates are coumarin, letrozole, nicotine, and tegafur, and CYP2A6 also participates in the metabolism of other drugs, such as valproic acid, efavirenz, artemisinin, artesunate, SM-12502, caffeine, tyrosol halothane and pilocarpine. At present, almost 45 star alleles with additional sub-alleles have been described (https://www.pharmvar.org/gene/ CYP2A6). Genomic variants include SNPs (e.g., CYP2A6*2, *6), gene deletions (e.g., CYP2A6*4A-H), gene conversions (e.g., CYP2A6*1B1-17 and *6) and a hybrid gene with CYP2A7 (e.g.,*3, *12A-C) (McDonagh et al., 2012) or breakpoint (CYP2A6*1X2A and CYP2A6*1X2B) (Table 1). Inter-individual variability in metabolism of nicotine to cotinine is due to CYP2A6 variants (Bloom et al., 2011) and associated with reduced nicotine metabolism. This condition seems to be linked with a lower likelihood to become a smoker or for lower cigarette consumption, and with a better chance to quit than normal extensive metabolizers (EMs) (McDonagh et al., 2012). CYP2A6 in humans catalyzes also the conversion of cotinine to 30 -hydroxycotinine, an activity that was demonstrated to be significantly higher in women than in men and linked to higher hepatic CYP2A6 expression and enzyme activity in females and to estrogen induction of its transcription (Benowitz et al., 2006). Moreover, CYP2A6 catalyzes the bioactivation of tegafur to 5-fluorouracil, which is impaired in CYP2A6 PMs, but not all studies provided consistent findings and results may be dependent on the cancer type and combination treatment prescribed (Park et al., 2011). In addition, letrozole metabolism is influenced by decreased-function alleles of CYP2A6 associated with high plasma concentrations (Desta et al., 2011). CYP2A6 also influences the PK of efavirenz and valproic acid, drugs whose metabolism and excretion is influenced also by other enzymes (Tanner and Tyndale, 2017). There is in vitro evidence for the role of nicotine C-oxidation and of coumarin-7-hydroxylation as CYP2A6 marker activities (Hosono et al., 2017), although these reactions are also catalyzed by CYP2A13 (Su et al., 2000). In addition, the metabolism of disulfiram, fadrozole, halothane, osigamone, methoxyflurane, pilocarpine, promazine, and valproic acid is influenced by CYP2A6 variants (Di et al., 2009), which metabolize also the endogenous substrate bilirubin (Abu-Bakar et al., 2012). In particular the expression and/or activity of significant CYP2A6 variants are decreased (include synonymous SNPs: *2, *5, *7, *10, *17, *18, *21, *23, *24, *25, *28, and *35 whereas the *4, *9, *12, and *20 variants are deletion, non-coding, or hybrid alleles). Their MAF is > 1% in one or more populations (Table 1). Hosono et al. additionally characterized properties of these variants on the in vitro activity toward nicotine and coumarin (Hosono et al., 2017). The exception is represented by variants with gene duplications that result from an unequal crossover during recombination (CYP2A6 and the adjacent CYP2A7) such as CYP2A6*1X2A and CYP2A6*1X2B (gene duplications) and CYP2A6*1B alleles, associated with faster nicotine metabolism (Rao et al., 2000). CYP2A6*1B is also in LD with reduced function variants with consequent complication in estimation of its activity. CYP2A6*2, CYP2A6*5, CYP2A6*6, CYP2A6*10, CYP2A6*26, CYP2A6*36, and CYP2A6*37 showed lower or no enzymatic activity and others such as CYP2A6*29, CYP2A6*30, CYP2A6*32, and CYP2A6*33 need further characterization. CYP2A6*3, CYP2A6*4, CYP2A6*12, CYP2A6*20, CYP2A6*27, and CYP2A6*34 carry amino acid substitutions and are known to have lower or absent activity due to frame-shift mutations, gene conversion with the pseudogene CYP2A7, or whole-gene deletion (Tanner and Tyndale, 2017). CYP2A6*9 has reduced transcriptional activity owing to an interruption in the TATA box and is common in all major populations with a MAF ranging from 8% in Africans to 23% in East Asians; compared to subjects with the *1/*1 genotype, *9 and *12 heterozygotes had 80% of normal activity, whereas *4 heterozygotes or carriers of two low-activity alleles had a reduction of 50% in nicotine C-oxidation activity. CYP2A6*17, CYP2A6*23, CYP2A6*25, and CYP2A6*28 are frequent only in Africans, and CYP2A6*7 and CYP2A6*19 in East Asians. In Europeans, CYP2A6*9 and CYP2A6*35 are common variants leading to decreased function (MAFs of 11% and 15%, respectively) (Tornio and Backman, 2018). Besides, several substances can act as inducers or inhibitors on the expression and activity of CYP2A6 including drugs, endogenous substances like estrogen, and dietary constituents. Among inducer drugs are included phenobarbital, dexamethasone, and rifampin (Rae et al., 2001), through increased CYP2A6 transcription mediated by CAR, PXR, peroxisome proliferator-activated receptor-g coactivator 1a (PPARGC-1a), hepatocyte nuclear factor 4a (HNF4a), and the glucocorticoid receptor (Itoh et al., 2006). In the diet, broccoli consumption (500 g for 6 days) determines increased CYP2A6 enzyme activity (Hakooz and Hamdan, 2007). Instead, 8-methoxypsoralen and selegiline (MAO inhibitor) are mechanism-based

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters Table 1

663

Functional CYP2A6 genetic variants.

Allele

Nucleotide changes

Protein effect

Enzyme activity

Frequency

CYP2A6*1B

Gene conversion in 30 flanking region

None

Increased mRNA stability

CYP2A6*1X2A

Breakpoint at intron 8–30 -UTR

CYP2A6 gene duplications

Increased mRNA expression

CYP2A6*1X2B

Breakpoint at 5.2–5.6 kb downstream from stop codon

CYP2A6 gene duplications

Increased mRNA expression

CYP2A6*2

479T>A

L160H

None

CYP2A6*4

CYP2A6 gene deletion

CYP2A6 gene deletion

None

CYP2A6*5

1436G>T; gene conversion in the 30 flanking region

G479V

None

CYP2A6*7

1412T>C; gene conversion in the 30 flanking region

I471T

Decreased

CYP2A6*9

48T>G

TATA box

Decreased

CYP2A6*10

1412T>C; 1454G>T; gene conversion in the 30 flanking region

I471T; R485L

Decreased

CYP2A6*12

Exons 1–2 of CYP2A7 origin; exons 3–9 of CYP2A6 origin

10 aa substitutions

Decreased

CYP2A6*17

1224C> T

V365M

Decreased

CYP2A6*18

1175A> T

Y392F

Decreased

CYP2A6*21

1427A> G

K476R

Decreased

CYP2A6*20

587_588delAA

196Frameshift

Decreased

CYP2A6*23

607C>T

R203C

Decreased

CYP2A6*24

328G>C; 1312A>T

V110L; N438Y

Decreased

CYP2A6*25

352T>C

F118L

Decreased

CYP2A6*28

1252A> G; 1257G>C

N418D; E419D

Decreased

CYP2A6*35

1312A> T

N438Y

Decreased

Caucasian: 28%–35% African: 11%–18% Asia: 26%–57% Caucasian: 0%–1.7% African: 0% Asia: 0%–0.4% Caucasian: 0%–1.7% African: 0% Asia: 0%–0.4% Caucasian: 1.1%–5.3% African: 0%–1.1% Asia: 0% Caucasian: 0.1%–4.2% African: 0.5%–2.7% Asia: 4.9%–24% Caucasian: 0%–0.3% African: 0% Asia: 0%–1.2% Caucasian: 0%–0.3% African: 0% Asia: 2.2%–13% Caucasian: 5.2%–8.0% African: 5.7%–9.6% Asia: 16%–22% Caucasian: 0% African: 0% Asia: 0.4%–4.3% Caucasian: 0%–0.3% African: 0%–0.4% Asia: 0%–0.8% Caucasian: 0% African: 7.1%–11% Asia: 0% Caucasian: 1.1%–2.1% African: 0% Asia: 0%–0.5% Caucasian: 0%–2.3% African: 0%–0.6% Asia: 0%–3.4% Caucasian: 0% African: 1.1%–1.7% Asia: 0% Caucasian: 0% African: 1.1%–2.0% Asia: 0% Caucasian: 0% African: 0.7%–2.3% Asia: 0% Caucasian: 0% African: 0.5%–1.2% Asia: 0% Caucasian: n.a. African: 0.9%–2.4% Asia: n.a. Caucasian: 0% African: 2.5%–2.9% Asia: 0.5%–0.8%

664

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

inhibitors and metabolically activated by CYP2A6, and their metabolic products irreversibly inhibit CYP2A6 (Blobaum, 2006) while tranylcypromine (MAO inhibitor) and anti-fungal ketoconazole act as competitive inhibitors of CYP2A6 in vitro (Zhang et al., 2001) through reversible binding which can be overcome by the use of a high concentration of substrate (Draper et al., 1997). Among endogenous substances, high concentrations of neurotransmitters such as dopamine and serotonin appear to inhibit CYP2A6 activity (Higashi et al., 2007); similarly, dietary constituents such as grapefruit juice, caffeic acid, p-coumaric acid, and quercetin act as inhibitors of CYP2A6 enzyme activity.

1.26.2.3

CYP2B6 (P450 family 2, subfamily B, polypeptide 6)

The human CYP2B6 gene is the principal hepatic isoform of the CYP2B subfamily and constitutes 6% of the total hepatic P450 content. CYP2B6 is also expressed in the brain (Miksys and Tyndale, 2004). The functional CYP2B6 gene and its related nonfunctional pseudogene CYP2B7P are located on chromosome 19 within the CYP2ABFGST cluster (Nelson et al., 2004). It is highly polymorphic, with up to 38 alleles and over 100 SNPs, which result in amino acid replacement with undefined haplotype structure (https://www.pharmvar.org/gene/CYP2B6), with the exception of the rare CYP2B6*28 resulting in protein truncation at arginine 378 and an important loss of function (Rotger et al., 2007). Its developmental maturation occurs at 1 year of age; infants older than 1 year demonstrated greater expression of CYP2B6 compared with those younger than 1 year of age (Tateishi et al., 1997). CYP2B6 plays important roles in the metabolism of clinically important drugs such as efavirenz, nevirapine, cyclophosphamide, carbamazepine, bupropion, ifosfamide, thioTEPA, methadone, meperidine, artemisinin, ketamine, propofol and several statins and also several drugs of abuse, procarcinogens, and numerous environmental chemicals and toxins. Phenobarbital is an important inductor of CYP2B genes acting via CAR (NR1I3), which is the major regulator of CYP2B6 together with PXR (NR1I2) (Li et al., 2010) whose contribution to CYP2B6 induction is through a distal xenobiotic-responsive enhancer module (CYP2B6-XREM) (Wang et al., 2003). Differences in CYP2B6 expression and activity contribute to the large interpatient variability in the PK and response seen with its substrates. Several variants are in LD and located in coding and noncoding regions. Among CYP2B6 genetic polymorphisms there are phenotypically null alleles characterized by partially diminished function (e.g., the *6 allele), noncoding SNPs such as the 15631G > T intronic variant responsible for formation of a non-functional splice variant, CYP2B6*29 with deletion of exons 1e 4, CYP2B6*28 with deletion polymorphisms at 1132C > T (R378stop); CYP2B6*22 has a gain of function with a 82T > C change in the 50 -flanking region, while the *4 allele (K262R) and the *6 allele cause increases in CYP2B6 expression (Zanger et al., 2007). CYP2B6*6, (c.516G > T, c.785A> G) is the most studied allele with reduced activity and a MAF of 16% in South Asians, 6% in Africans, 3% in Europeans, East Asians, and in admixed Americans (Zhou et al., 2017). However, CYP2B6*9 (c.516G > T) is the most common variant allele in all populations with an unclear function. CYP2B6*4 and CYP2B6*22 have increased function and a MAF of < 2% in most major populations with the exception of a MAF of 4% in Africans. The most important substrate of the reduced-function CYP2B6*6 allele (PM) is the anti-HIV reverse transcriptase inhibitor efavirenz, a drug with important central nervous system and hepatic toxicities (Vo and Varghese Gupta, 2016). Compared with other hepatic P450s, CYP2B6 is the main catalyst of efavirenz primary and secondary metabolism, with PMs showing significantly decreased rates of 8-hydroxylation of efavirenz and high plasma levels of the parent drug. In this context also clinical pediatric studies demonstrated that efavirenz clearance increases with age and/or body weight after birth and at 9 months of age is reported a maturation of clearance of 90% compared to adult (Salem et al., 2014). Efavirenz is a candidate for prescription with genotype based dosing guidelines for patients with PM phenotype. CYP2B6*6 has also been reported to modulate the metabolism of the most commonly prescribed opioids (Pergolizzi et al., 2018). For example, patients homozygous for the CYP2B6*6 genotype show increased risk of methadone-induced QTc prolongation due to slow (S)-methadone metabolism and to its arrhythmogenic activity postulated from in vitro studies (Kharasch et al., 2015). Instead, CYP2A6*9 has been associated with increased efavirenz plasma concentrations in CYP2B6 PMs (Haas et al., 2014). Although CYP2B6*6 has been related to reduced metabolism of bupropion, a prodrug activated by CYP2B6, its effects on the efficacy in smoking cessation are inconsistent in several studies (Chenoweth and Tyndale, 2017). Instead, the genotyping for CYP2B6 alleles (CYP2B6*4 (rs2279343), CYP2B6*5 (rs3211371), and CYP2B6*9 (rs3745274)) in breast cancer patients treated with tamoxifen or cyclophosphamide appear of interest but without demonstrated effect on personalized treatment (Fleeman et al., 2011). CYP2B6 also catalyzes the conversion of “ecstasy” (3,4-methylenedioxymethamphetamine, MDMA) to 3,4-methylenedioxyamphetamine (MDA) together with CYP2C19 and CYP1A2 according to in vitro studies (Vizeli et al., 2017) and participates in the metabolism of pesticides and several other environmental chemicals and pollutants (Hodgson and Rose, 2007). Another functionally deficient allele is CYP2B6*18 (21,011T > C [I328T]), distributed predominantly in African subjects with a frequency of 4%–11% (Li et al., 2012). The *18 allele is phenotypically a null allele, along with 12 additional null or low-activity alleles investigated in vitro with some substrates (Honda et al., 2011). These rare alleles showed effects on drug metabolism if present in heterozygous genotypes in combination with *6 or *18 (Rotger et al., 2007). The CYP2B6*22 allele is a gain-of-function variant associated with increased transcription (Zukunft et al., 2005) and activity in vitro (Rotger et al., 2007). It was demonstrated that in the CYP2B6*22 allele the  82T/ C substitution creates a functional CCAAT/ enhancer-binding protein (C/EBP) binding site with enhancement of gene basal expression and alteration of the TATA-box into a functional C/EBP binding site with a consequent increase in transcription from an alternative downstream initiation site (Zukunft et al., 2005). Interestingly, the  82T > C polymorphism is important also in synergistic enhancement of CYP2B6 inducibility by the PXR-mediated mechanism, which may contribute to individual variations and inducibility of CYP2B6 in humans (Li et al., 2010). In 2007, Gatanaga et al. reported about the role of the *26 allele containing 499G for the c.499C > G SNP (rs3826711), and 499G coexisting with 516G > T and 785A> G in the same haplotype containing the 499C> G, 516G > T and 785A > G SNPs (Gatanaga

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

665

et al., 2007). Other variants discovered in unscreened ethnic groups like a Columbian population are represented by CYP2B6/ 2B7P1 duplicated fusion allele (CYP2B6*30) screened for copy number variations (Martis et al., 2013), and CYP2B6*33 to *37 identified after resequencing the CYP2B6 gene in a Rwandese cohort of HIV-1-infected patients (Radloff et al., 2013). Studies conducted for functional characterization and assessment of in silico prediction tools of these variants showed that four variants resulted in complete or almost complete loss of function with bupropion and efavirenz as substrates (Zanger and Klein, 2013).

1.26.2.4

CYP2C subfamily

The CYP2C subfamily is one of the main groups of enzymes involved in metabolism of several endogenous and exogenous substances, including about 20% of clinically used drugs such as paclitaxel, tolbutamide and proton pump inhibitors. CYP2C8, CYP2C9, CYP2C18 and CYP2C19 enzymes belong to this subfamily and their encoding genes are grouped into two consecutive clusters on chromosome 10 and are in high LD. The close proximity of CYP2C8 and CYP2C9 on chromosome 10 and their LD, have offered the opportunity to characterize 17 common haplotypes (haplotypes A through Q) (Daily and Aquilante, 2009). Moreover, the CYP2C subfamily, together with CYP2J2, has an important role in the biosynthesis of epoxyeicosatrienoic acids (EETs), starting from arachidonic acid (AA), which are potent lipid mediators involved in several biological events in the cardiovascular and renal systems (Fan et al., 2015). SNPs linked with the expression or activity of these enzymes could be of relevance to assess interindividual risks in developing hypertension (Larsen et al., 2007). CYP2C enzymes are inducible by ligands of PXR/CAR, glucocorticoid receptor (GR) and vitamin D receptor (VDR), although with different relative inducibility (Chen and Goldstein, 2009).

1.26.2.4.1

CYP2C8 (P450 family 2, subfamily C, polypeptide 8)

CYP2C8 plays a crucial role in the biotransformation of several xenobiotics and endogenous compounds and has high hepatic expression with lower expression in the kidney, adrenal gland, mammary gland, brain, ovary, uterus, and duodenum. CYP2C8 has gained increased attention for PGx relevance. Several SNPs within the coding region of the CYP2C8 gene have been identified (https://www.pharmvar.org/gene/CYP2C8). Thirteen variants in CYP2C8 have been identified (Dai et al., 2001; Soyama et al., 2001) with different frequencies among ethnic groups. CYP2C8*1 (or *1A) is the wild-type or reference allele, while the CYP2C8*1B ( 271C > A, rs7909236) (Bahadur et al., 2002), high-activity allele, is present in about 23% of Caucasians and 10% of Asians but absent in Africans, as well as CYP2C8*1C ( 370G > T, rs1934953), which is present in about 12% of Caucasians, 28%–34% of Asians, and is rare in Africans (https://www.ncbi.nlm.nih.gov/snp/) and whose function needs additional investigation. CYP2C8*1B and *1C (rs7909236 and rs1934953) together with CYP2C9 (rs9332242), and CYP2C19 (rs4244285) are associated with susceptibility to essential hypertension (EH) in a Russian population as reported by Polonikov et al. (2017). In particular the rs7909236 variant was associated with increased risk of EH (P ¼ 0.005) and the combination of the TT genotype in rs7909236 and the GG genotype in rs4244285 was associated with increased EH risk (P ¼ 0.004). CYP2C8*2,*3,*4,*8 and *14 have a reduced function. CYP2C8*2 (c.805A > T, rs11572103) and *3 (two amino acid substitutions in c.416G > A, rs11572080 with p.R139K, and c.1196A> G, rs10509681 with p.K399R) are in high LD and *4 (c.792C> G, rs1058930, p.I264M) was detected in Caucasians with a low allele frequency (Pechandova et al., 2012). These SNPs may lead to a reduced enzyme activity. The *3 and *4 alleles are more frequent in Caucasians while *2 is common in Africans. The rare allele *5 (rs72558196, frame-shift deletion) is common in Japanese while the other rare variants through CYP2C8*14 have been found in less than 1% of the population, mainly Asians (https://www.pharmvar.org/gene/CYP2C8). The functional effects of these variants were demonstrated in vitro and specifically involve a premature stop codon for CYP2C8*5 (rs72558196, c. 475delA, exon 3) at position 177 as a result of a frame shift (Soyama et al., 2002) as well as for CYP2C8*7 (rs72558195, c.556C > T, p.R186X), while CYP2C8*8 (c. 556C> G) involves an Arg to Gly change at codon 186 (Hichiya et al., 2005) responsible for reduced enzymatic activity compared with wild-type. However, these SNPs are rare and ancestry specific, occurring primarily in Asian populations and their role (*5 through*14) in variability in clinical drug response or rare ADRs remains unsolved. Several clinically available drugs are substrate of CYP2C8, including antidiabetic agents (pioglitazone, repaglinide, rosiglitazone, and troglitazone), paclitaxel, amiodarone and the antimalarial agents amodiaquine, chloroquine and dapsone (Zanger and Schwab, 2013). The importance of CYP2C8 for repaglinide PK and clinical relevance is still unclear since one study reported a correlation between heterozygosity for CYP2C8*3 and higher drug clearance with 60% lower plasma levels of repaglinide compared to CYP2C8*1 carriers (RodriguezAntona et al., 2008), while another study, conducted in healthy volunteers, indicated that CYP2C8*3 did not alter PK and PD of repaglinide (Tomalik-Scharte et al., 2011). The CYP2C8*11 loss-of-function variant (c.820G > T, p.E274X, exon 6) was correlated to decreased rosiglitazone hydroxylation. Among all CYP2C8 variants, CYP2C8*2 and CYP2C8*3 showed a reduced activity for paclitaxel and AA metabolism compared with wild-type enzyme (Dai et al., 2001). However, available information is unclear and indicates increased paclitaxel metabolism to its hydroxylated metabolites and consequent increased neurotoxicity. CYP2C8*4 showed reduced activity toward paclitaxel compared to the wild-type enzyme (Leskela et al., 2011), as well as CYP2C8*14 (c. 712G > C, p.A238P) showing decreased paclitaxel binding affinity and decreased intrinsic clearance of amiodarone (Hanioka et al., 2010). Statins are inducers of CYP2C8, the most strongly inducible member of the CYP2C subfamily (Feidt et al., 2010). An overlap between CYP2C8 and CYP2C9 was reported for substrate specificity in the case of ibuprofen or other drugs (Lai et al., 2009), along with the condition of LD seen for these genes. Moreover, CYP2C8 has a major role in metabolizing amodiaquine, chloroquine and dapsone (Kerb et al., 2009).

666

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

1.26.2.4.2

CYP2C9 (P450 family 2, subfamily C, polypeptide 9)

CYP2C9 is an important member of the CYP2C subfamily of enzymes located on chromosome 10 in close proximity to CYP2C8 and CYP2C19. CYP2C9 is expressed in human liver microsomes and in the gastrointestinal tract, contributing to the metabolism of approximately 15% of all drugs handled by P450s; CYP2C9 plays important roles in the metabolic clearance of several drugs with a narrow therapeutic index. CYP2C9 protein expression and activity are important for inter-individual variability in efficacy/toxicity of drug treatment. This enzyme is involved in the oxidation of a large number of drugs (phenytoin, tolbutamide, torsemide, losartan, nonsteroidal anti-inflammatory drugs and warfarin), weakly acidic compounds but also basic drugs (e.g.,

amitriptyline, fluoxetine and zopiclone), and in the metabolism of other xenobiotics (e.g., pyrene, safrole, sulprofos and O-9tetrahydrocannabinol) and several endogenous compounds including arachidonic acid and linoleic acid. Moreover, CYP2C9 may be inhibited by a wide range of drugs such as cimetidine, ketoconazole, sulfaphenazole, fluconazole, voriconazole and amiodarone. These drugs are often prescribed in polytherapy and the risk of clinically significant DDIs is relevant. Nuclear receptors including PXR, CAR, GR, estrogen receptor (ER) and VDR play roles in CYP2C9 induction. The ER seems to be a regulator of CYP2C9 expression involving mediation of an ERa-dependent regulation of CYP2C9 gene transcription and CYP2C9 activity in vivo is inhibited by oral contraceptives. CYP2C9*1 (Arg144/Ile359) represents the wild-type protein whereas the polymorphic variants CYP2C9*2 (c.430C > T, rs1799853) and CYP2C9*3 (c.1075A > C, rs1057910) are the most studied and relatively rare variants (https://www.pharmvar.org/gene/CYP2C9) associated with highly reduced activity among the 62 variants identified to date. Differences in distribution frequencies in the population differ according to ethnic origins with *2 and *3 variants more commonly expressed in individuals of white European descent but very rare in African-Americans. Single amino acid substitutions (p.R144C for *2 and p.I359L for *3) in the coding region result in reduced enzyme activity in homozygous (*2/*2, *3/*3) and heterozygous (*2/*3) genotypes, with *2 accounting for a 30% reduction and *3 accounting for a 80% reduction. These two SNPs are more common in Caucasians (11% and 7%, respectively) than in other ethnicities, while Chinese and Japanese populations feature predominantly the *1/*3 genotype with a frequency of 4% without carriers of the other genotypes (*2/*2, *2/*3, *1/*2, and *3/*3). The most frequent ADRs include hypoglycemia during treatment with hypoglycemic drugs, gastrointestinal bleeding in patients treated with nonsteroidal anti-inflammatory drugs (NSAIDs) or serious bleeding from anticoagulant treatment. CYP2C9 mediates warfarin metabolism. Warfarin exists as a racemic mixture of S-warfarin and R-warfarin, with S-warfarin having a more potent anticoagulant effect than R-warfarin (O’Reilly, 1974) and serving as a substrate of CYP2C9. PGx of CYP2C9 is interesting for warfarin dosing and response in addition to the vitamin K epoxide reductase complex subunit 1 (VKORC1). Rieder et al. (2005) showed that in 539 white patients on steady-state warfarin therapy, interindividual variability in warfarin dose was explained by CYP2C9 variants (9%) and VKORC1 variants (25%), while the variability in maintenance dose of warfarin in Caucasians could be explained for 50 and 60% by combination of CYP2C9 and VKORC1 variants associated to other patient factors (e.g. body size, age). CYP2C9 catalyzes the conversion of warfarin from the active S-enantiomer to an inactive metabolite. There is strong evidence that carriers of the *2 and *3 alleles have problems with warfarin induction therapy, show increased time to achieve stable dosing, have a lower mean-dose requirement and have increased risks of elevated international normalized ratios (INRs) and bleeding. For these, the FDA in 2007 included PGx information related to response for CYP2C9 and VKORC1 genetic variants in the warfarin labeling and this was revised in 2010 to include specific dosing recommendations (Table 2). In particular, a dose reduction of 30% and 47% is recommended for carriers of heterozygous genotypes of CYP2C9*1/*2 and CYP2C9*1/*3, respectively, and up to 80% for patients homozygous for CYP2C9*3/*3. Both SNPs are important for prediction of warfarindose requirement and the consideration of patient genotypes could lead to more accurate dose prediction and lower ADRs. According to ethnic diversity in CYP2C9 and VKORC1 variants, highest doses of warfarin are required in Africans while Asians require the lowest doses. On the other hand, the relevance of other variants of CYP2C9 to the metabolism of warfarin has not yet been clearly understood and several functional characterizations are conducted on in vitro models (Niinuma et al., 2014). Also for the anticoagulants acenocoumarol and phenprocoumon interindividual variability in dosage has been correlated to CYP2C9 genotype. The VKORC1 genotype is discussed in another section of the article. Although the role of CYP2C9 in warfarin metabolism is the most investigated, it influences also the metabolism of sulfonylurea hypoglycemic drugs (e.g., glibenclamide, tolbutamide, glyburide, glimepiride) affecting PK and efficacy. Yang et al. (2018) demonstrated in vitro that glimepiride and gliclazide are substrates of CYP2C9 and OATP1B1; the reduced metabolism of these drugs by the CYP2C9*2 and *3 variants and the reduced transport capacity by the OATP1B1*5 and *15 polymorphic variants suggests the necessity for simultaneous analysis of both OATP1B1 and CYP2C9 mutations in clinical practice for prescription of oral hypoglycemic drugs. Although still controversial, dose-dependent ADRs (i.e. gastrointestinal bleeding), especially in aging populations, were reported for several NSAIDs related to CYP2C9 polymorphisms. CYP2C9*2 and *3 polymorphisms were associated also with risk of atherosclerosis, ischemic vascular disease or death after ischemic heart disease related to the role of these variants in metabolism of arachidonic acid, but no confirmation was provided by three independent studies evaluating more than 52,000 individuals overall. In addition, CYP2C9*3 is significantly associated with phenytoin hypersensitivity together with HLA-B*13:01, HLA-B*15:02, and HLA-B*51 with different phenotypic specificities in Asian populations. In particular, the meta-analysis of Su et al. (2019) showed that an increase in predictive genetic risk to develop phenytoin related severe cutaneous adverse hypersensitivity reactions (SCARs) might be linked to four combined alleles, CYP2C9*3/HLA-B*13:01/HLA-B*15:02/HLA-B*51:01. Other polymorphic variants related to decreased CYP2C9 activity included *5 (rs28371686), *6 (rs9332131), *8 (rs7900194), and *11 (rs28371685). Different ethnic distributions have been shown for CYP2C9*5, *6, *8 and *11, which are present almost exclusively in African Americans. CYP2C9*13 was instead identified only in Chinese individuals. In addition, Zhou et al. (2010) identified the association between a loss-of-function CYP2C9

Table 2

PK and PD biomarkers with pharmacogenomic information found in the drug labeling (“Table of Pharmacogenomic Biomarkers in Drug Labeling”, www.fda.gov). Therapeutic area

Drug

Labeling section including PGx information

CYP1A2 CYP2B6

Oncology Infectious Diseases Gynecology Cardiology Cardiology

Rucaparib Efavirenz Ospemifene Prasugrel Clopidogrel Prasugrel Ticagrelor Dexlansoprazole Esomeprazole Lansoprazole Omeprazole Pantoprazole Rabeprazole Drospirenone and Ethinyl Estradiol Flibanserin Voriconazole Brivaracetam Clobazam Diazepam Lacosamide Phenytoin Citalopram Doxepin Escitalopram Formoterol Carisoprodol Meloxicam Prasugrel Dronabinol Flibanserin Ospemifene Avatrombopag Warfarin Phenytoin Siponimod

Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology, Clinical Studies Boxed Warning, Warnings and Precautions, Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology, Clinical Studies Clinical Pharmacology Drug Interactions, Clinical Pharmacology Drug Interactions, Clinical Pharmacology Drug Interactions, Clinical Pharmacology Drug Interactions, Clinical Pharmacology Clinical Pharmacology Drug Interactions, Clinical Pharmacology Clinical Pharmacology Adverse Reactions, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Warnings, Clinical Pharmacology Clinical Pharmacology Adverse Reactions Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology, Clinical Studies Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Drug Interactions, Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Contraindications, Drug Interactions, Use in Specific Populations, Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Drug Interactions, Clinical Pharmacology Clinical Pharmacology

CYP2C19

Gastroenterology

Gynecology Infectious Diseases Neurology

Psychiatry

CYP2C9

Pulmonary Rheumatology Anesthesiology Cardiology Gastroenterology Gynecology Hematology Neurology Oncology Rheumatology

Erdafitinib Celecoxib Flurbiprofen Lesinurad Piroxicam

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

Gene

(Continued)

667

PK and PD biomarkers with pharmacogenomic information found in the drug labeling (“Table of Pharmacogenomic Biomarkers in Drug Labeling”, www.fda.gov).dcont'd Therapeutic area

Drug

Labeling section including PGx information

CYP2D6

Anesthesiology

Codeine

Boxed Warning, Warnings and Precautions, Use in Specific Populations, Patient Counseling Information Use in Specific Populations Boxed Warning, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology, Patient Counseling Information Drug Interactions, Clinical Pharmacology Drug Interactions, Clinical Pharmacology Dosage and Administration, Clinical Pharmacology Dosage and Administration, Warnings and Precautions, Drug Interactions, Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Indications and Usage, Dosage and Administration, Contraindications, Warnings and Precautions, Drug Interactions, Use in Specific Populations, Clinical Pharmacology, Clinical Studies Drug Interactions Dosage and Administration, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology Warnings and Precautions, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Warnings and Precautions Dosage and Administration, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Warnings and Precautions, Adverse Reactions, Drug Interactions, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Drug Interactions

Lofexidine Tramadol Cardiology

Carvedilol Metoprolol Nebivolol Propafenone

Gynecology Inborn Errors of Metabolism

Propranolol Metoclopramide Ondansetron Palonosetron Flibanserin Eliglustat

Infectious Diseases Neurology

Quinine Sulfate Deutetrabenazine

Gastroenterology

Dextromethorphan and Quinidine Donepezil Galantamine Meclizine Tetrabenazine Valbenazine Oncology Psychiatry

Gefitinib Rucaparib Tamoxifen Amphetamine Aripiprazole Aripiprazole Lauroxil Atomoxetine Brexpiprazole Cariprazine Citalopram Clozapine Desvenlafaxine Doxepin Duloxetine

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

Gene

668

Table 2

Escitalopram Fluoxetine Fluvoxamine Iloperidone

Rheumatology Urology

CYP3A5 DPYD

Cardiology Dermatology Oncology

G6PD

Anesthesiology

Dermatology Endocrinology Gastroenterology

Hematology

Glimepiride Ascorbic Acid, PEG-3350, Potassium Chloride, Sodium Ascorbate, Sodium Chloride, and Sodium Sulfate Metoclopramide Methylene Blue Succimer

Use in Specific Populations, Overdosage Contraindications, Warnings and Precautions Clinical Pharmacology (Continued)

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

Pulmonary

Modafinil Paliperidone Paroxetine Perphenazine Pimozide Pitolisant Risperidone Thioridazine Venlafaxine Vortioxetine Arformoterol Formoterol Umeclidinium Upadacitinib Darifenacin Fesoterodine Mirabegron Tamsulosin Tolterodine Prasugrel Fluorouracil Capecitabine Fluorouracil Articaine and Epinephrine Lidocaine and Prilocaine Lidocaine and Tetracaine Oxymetazoline and Tetracaine Dapsone

Drug Interactions Precautions, Clinical Pharmacology Drug Interactions Dosage and Administration, Warnings and Precautions, Drug Interactions, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Drug Interactions, Clinical Pharmacology Precautions, Clinical Pharmacology Dosage and Administration, Precautions Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Contraindications, Warnings, Precautions Drug Interactions, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Drug Interactions, Clinical Pharmacology Clinical Pharmacology Warnings and Precautions, Adverse Interactions, Clinical Pharmacology Warnings and Precautions, Drug Interactions, Clinical Pharmacology Use in Specific Populations, Clinical Pharmacology, Clinical Studies Contraindications, Warnings Warnings and Precautions, Patient Counseling Information Warnings and Precautions, Patient Counseling Information Warnings and Precautions Warnings and Precautions, Clinical Pharmacology Warnings and Precautions Warnings and Precautions Warnings and Precautions, Use in Specific Populations, Patient Counseling Information Warnings and Precautions, Adverse Reactions Warnings and Precautions

669

Gene

Therapeutic area

Drug

Labeling section including PGx information

Infectious Diseases

Chloroquine Dapsone Hydroxychloroquine Mafenide Nalidixic Acid Nitrofurantoin Primaquine Quinine Sulfate Tafenoquine

Oncology Rheumatology

Dabrafenib Rasburicase Trametinib Pegloticase

Toxicology Oncology Infectious Diseases

Probenecid Sodium Nitrite Ipilimumab Abacavir

Precautions, Adverse Reactions Precautions, Adverse Reactions, Overdosage Precautions, Adverse Reactions Warnings, Adverse Reactions Precautions, Adverse Reactions Warnings, Adverse Reactions Contraindications, Warnings, Precautions, Adverse Reactions, Overdosage Warnings and Precautions Dosage and Administration, Contraindications, Warnings and Precautions, Use in Specific Populations, Patient Counseling Information Warnings and Precautions, Adverse Reactions, Patient Counseling Information Boxed Warning, Contraindications, Warnings and Precautions Adverse Reactions Boxed Warning, Contraindications, Warnings and Precautions, Patient Counseling Information Adverse Reactions Warnings and Precautions Clinical Studies Boxed Warning, Dosage and Administration, Contraindications, Warnings and Precautions Boxed Warning, Warnings, Precautions Warnings and Precautions Warnings and Precautions Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology Dosage and Administration, Use in Specific Populations, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Adverse Reactions Dosage and Administration, Warnings and Precautions, Adverse Reactions, Clinical Pharmacology Dosage and Administration, Warnings, Precautions, Clinical Pharmacology Dosage and Administration, Warnings, Precautions, Drug Interactions, Adverse Reactions, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Warnings and Precautions, Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Clinical Pharmacology Dosage and Administration, Clinical Pharmacology

Neurology

HLA-DQA1 HLA-DRB1 NAT2

SLCO1B1 TPMT

Oncology Oncology Oncology Neurology Endocrinology Gynecology Oncology

Rheumatology UGT1A1

Infectious Diseases Oncology

Pulmonary VKORC1

Hematology

Carbamazepine Fosphenytoin Oxcarbazepine Pazopanib Lapatinib Lapatinib Amifampridine Amifampridine Phosphate Rosuvastatin Elagolix Cisplatin Mercaptopurine Thioguanine Azathioprine Dolutegravir Raltegravir Belinostat Binimetinib Irinotecan Nilotinib Pazopanib Arformoterol Indacaterol Warfarin

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

HLA-A HLA-B

PK and PD biomarkers with pharmacogenomic information found in the drug labeling (“Table of Pharmacogenomic Biomarkers in Drug Labeling”, www.fda.gov).dcont'd

670

Table 2

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

671

(rs7089580) variant in intron 3 and greater response to sulfonylureas and reduced failure of therapy, while the same SNP is related to a higher warfarin dose requirement. Other variants such as CYP2C9*14, CYP2C9*27, and CYP2C9*29 were identified during in vitro studies in bacteria or cell lines and seem to be associated with significant decreases in enzyme activity, while CYP2C9*52 might have a functional mutation of a residue known to contribute to substrate binding (viz. Thr299Ala) in a Chinese population. For the other identified variants including *28, *30, *52, *55, *57,*62 listed in the PharmVar database, no activity data are available (https://www.pharmvar.org/gene/CYP2C9). Recently, it has been reported that CYP2C9 poor metabolizer individuals with *2 and *3 variants show reduced levels of EETs derived from AA metabolism. EETs have pro-angiogenic activity and promote proliferation and migration of human endothelial cells (ECs) regulating tumorigenesis. Therefore, CYP2C9*2 and CYP2C9*3 play an important role in limiting non-small cell lung cancer (NSCLC) growth with outcome improvement by lower production of EETs from endogenous pools of AA (Sausville et al., 2018).

1.26.2.4.3

CYP2C19 (P450 family 2, subfamily C, polypeptide 19)

CYP2C19 is a highly polymorphic gene of the CYP2C subfamily located on chromosome 10 near CYP2C8 and CYP2C9, and in LD with these related genes. Because of its large interindividual variability in enzymatic activity, CYP2C19 is among the PGx genes tested in clinical laboratories and is included in the Table of Pharmacogenomic Biomarkers in Drug Labeling for several FDAapproved drugs (https://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm, 2018) (Table 2). CYP2C19 has been deeply studied for the role in determining ADRs, especially with low-therapeutic-index drugs such as warfarin, phenytoin and tolbutamide, drugs that are also substrates of CYP2C9. Moreover, CYP2C19 can metabolize anxiolytics (diazepam), proton pump inhibitors (omeprazole), anticonvulsants (S-mephenytoin), and antimalarial biguanides (Ahmed et al., 2016). Numerous CYP2C19 variant alleles have been defined (https://www.pharmvar.org/gene/CYP2C19) but the most studied variants are CYP2C19*2 and CYP2C19*3. CYP2C19 contributes to the metabolism of several drugs including the antiulcer drug omeprazole, the antiplatelet drug clopidogrel, the anticonvulsant mephenytoin, the antimalarial drug proguanil, the anxiolytic drug diazepam or antidepressants such as citalopram, imipramine, amitriptyline, and clomipramine (Lee, 2012). According to CYP2C19 genotypes the metabolic phenotypes reported are: EM, IM and PM; for many drugs, CYP2C19 genotyping results suggest a need for adjustment of the drug dose or an alternative drug choice. EMs are identified as individuals carrying at least one functional allele while IMs are those with one functional and one loss-of-function allele. In CYP2C19 PMs, in which there is enzymatic inactivity, the administration of diazepam causes prolonged sedation and unconsciousness (Bertilsson, 1995), while for clopidogrel there has been reported a diminished response. Instead, treatment with omeprazole and lansoprazole revealed a greater cure rate for gastric ulcers with Helicobacter pylori infections in PMs compared to EMs as a consequence of higher plasma concentrations of the parent drugs (Furuta et al., 1998). In the case of IM group in which high interindividual variation is reported, the clinical decision is uncertain due to unclear underlying mechanism for this variation. In this condition it is important to also evaluate clinical factors, environmental factors, and drug response-modulating factors. The allelic variants extensively studied are CYP2C19*2, *3, and *17. In particular, *2 (c.681G > A, rs4244285) and *3 (c.636G > A, rs4986893) are null alleles responsible for an inactive CYP2C19 enzyme and for a PM phenotype with differences in ethnic distribution (Xie et al., 2001), being commonly found in Asians, and more rare in Caucasians and African Americans. The causal mutation of *2 is located in exon 5 and is responsible for aberrant splicing whereas *3 mutation is located in exon 4 and is responsible for a premature stop codon (de Morais et al., 1994a,b). Both *1/*2 and *1/*3 genotypes occur in about 50% of the Chinese population while 24% have the *2/*2, *2/*3, or *3/*3 genotypes (Yamada et al., 2001). The *1/*2 genotypes are seen in 30%–40% of Caucasians. Genotyping CYP2C19*2 and *3 together with the less common CYP2C19*4 (rs28399504) and CYP2C19*6 (rs72552267) might be important for defining PMs by a common genotyping assay. CYP2C19 *5 (rs56337013), *7 (rs72558186), and *8 (rs41291556) are rare variants with a MAF < 1%. The CYP2C19*17 allele (c.-806C > T rs12248560) shows a “gain of function” in the 50 -flanking region of CYP2C19, involving increased gene transcription, high enzyme activity and an EM phenotype (Sim et al., 2006). The prevalence of CYP2C19*17 (18% in Swedes and Ethiopians while 4% in Chinese) showed the *1/*17 and *17/*17 genotypes more frequent in Caucasians and Ethiopians (up to 36%) than Asians (8% of Chinese and 1% of Japanese) (Sim et al., 2006). CYP2C19 is the major enzyme affecting the metabolism of proton pump inhibitors (PPIs) (omeprazole, lansoprazole, pantoprazole, rabeprazole) and of the antiplatelet agent clopidogrel. It participates also in the metabolism of antidepressants (citalopram, sertraline, moclobemide, amitriptyline, clomipramine), the benzodiazepine diazepam, the antifungal drug voriconazole (and hence, its interaction with other drugs), the antimalarial drug proguanil and the anticancer drug cyclophosphamide. CYP2C19 is susceptible to inhibition by drugs such as cimetidine, fluoxetine, and diazepam in a gene dose-dependent manner. In this last case, the highest inhibition is reported in patients with two CYP2C19*17 alleles compared to little to no inhibition for the CYP2C19 PM phenotype. Endogenous substrates of CYP2C19 are melatonin and progesterone (Zanger and Schwab, 2013). Compared to EMs, CYP2C19 PMs needed lower doses of lansoprazole for therapeutic benefit (Furuta et al., 1998). Infact, drug labels for lansoprazole, omeprazole, diazepam and clopidogrel report pharmacogenomic information related to CYP2C19 genotypes (Ahmed et al., 2016). However, the most important substrates of clinical relevance for CYP2C19 are the PPIs and clopidogrel. PM phenotypes showed 5- to 12-fold increases in the area under the curve (AUC) of PPIs resulting in higher Helicobacter Pylori eradication compared to EMs (Furuta et al., 2007), whereas patients homozygous for the CYP2C19*17 showed a 2.1-fold lower AUC than EMs (Baldwin et al., 2008) resulting in subtherapeutic drug exposure. CYP2C19 genotypes influence also the PPI-induced increase in achievable intragastric pH and treatment outcome. According to CYP2C19 effects on the PK of PPIs, several studies in Asian and European populations showed a benefit for PM phenotypes in acid inhibition and intragastric pH levels due to their lower metabolism rate and higher drug levels for a longer

672

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

time (Furuta et al., 2007). The correlation between CYP2C19 variants and PPI metabolism is important also in other diseases treated with this class of drugs such as non-ulcer dyspepsia, reflux oesophagitis, gastroesophageal reflux disease (GERD), and the ZollingerEllison syndrome (Schwab et al., 2005). Otherwise, esomeprazole and rabeprazole metabolism aren’t strictly dependent on CYP2C19 activity (El Rouby et al., 2018). CYP2C19 PM phenotypes interfere with anticoagulant activity of clopidogrel (Plavix) reducing the effects and increasing risks for cardiovascular ADRs (Mega et al., 2009). FDA added a Boxed Warning to the label for Plavix to specify diminished effectiveness in CYP2C19 PMs (https://www.accessdata.fda.gov/drugsatfda_docs/label/2010/ 020839s048lbl.pdf). However, there are conflicting reports about the influence of CYP2C19 genotype on clopidogrel effectiveness; a meta-analysis (Bauer et al., 2011) found no significant or consistent correlation whereas another study (Holmes et al., 2011) reported an association with clopidogrel responsiveness but no correlation of genotypes (*2–*8) with cardiovascular events. A metaanalysis conducted on 16 clinical studies with patients of different ethnic groups confirmed that genotyping CYP2C19 is clinically important for prediction of patient outcomes and risk of cardiovascular events (Jang et al., 2012). Another meta-analysis on healthy volunteers evaluated the interaction between PPIs and clopidogrel related to CYP2C19*2 and showed a decrease in platelet response to a standard maintenance dose of clopidogrel and the need for doubling the dose to revert the effect of the PM phenotype (Hulot et al., 2010). For carriers of the gain-of-function allele CYP2C19*17, an association with poor responsiveness after clopidogrel loading dose was found in patients undergoing percutaneous coronary intervention (Geisler et al., 2008), while Sibbing et al. reported a high risk of bleeding events in patients with coronary stent placement (Sibbing et al., 2010). In the case of UMs with the CYP2C19*17/*17 genotype, a risk for decreased escitalopram effectiveness was reported as a consequence of low serum concentrations of drug (Tai et al., 2002). Moreover, conflicting results are reported on the importance of CYP2C19*17 for the outcome of postmenopausal breast cancer patients undergoing tamoxifen treatment (Li-Wan-Po et al., 2010), while a recent study revealed no evidence supporting a clinically meaningful role of this polymorphic variant (Damkier et al., 2017). Also for other CYP2C19 substrates such as antidepressants and voriconazole (Wang et al., 2009a) the UM phenotype might require higher doses although, despite PK differences, the impact of CYP2C19*17 on therapeutic outcomes needs further evaluations.

1.26.2.5

CYP2D subfamily

The human CYP2D subfamily comprises CYP2D6, CYP2D7, and CYP2D8 in which the first is the most studied enzyme because it catalyzes the metabolism of very commonly prescribed drugs, while the latter two are pseudogenes (Nelson, 2009). They are located on chromosome 22.

1.26.2.5.1

CYP2D6 (P450 family 2, subfamily D, polypeptide 6)

The CYP2D6 gene is a key hepatic enzyme located on chromosome 22 in proximity to the nonfunctional pseudogenes, CYP2D7 and CYP2D8. The CYP2D6 gene is highly polymorphic with almost 139 variants with a designated star allele, including CNVs (https:// www.pharmvar.org/gene/CYP2D6). Hybrid genes due to gene deletions, duplications, multiplications and gene rearrangements between CYP2D6 and CYP2D7 have all been observed. The different variations confer all phenotypes from PMs to UMs, with absent, decreased, normal, or increased activity among individuals and populations. Dextromethorphan, debrisoquine, bufuralol and sparteine are the probe drugs used for in vivo CYP2D6 phenotyping. At present, according to metabolic capability, it is possible to translate CYP2D6 genotype information into predicted qualitative measure of phenotypes through the activity score (AS) system utilizing revised Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations or other bioinformatics tools (Gaedigk et al., 2017, 2008; Dalton et al., 2020). According to the AS system, alleles are grouped for their catalytic functionality with a value assigned to each allele of each diplotype: no-function (0) alleles (*3, *4, *4xN,*5, *6, *7, *8, *11, *12, *36, *40, *42, *56), decreased function (0.5) alleles (*9, *10, *17, *29, *41, *44, *49), normal-function (1) alleles (1*, *2, *35, *43, *45), and increased-function (2) alleles (*1xN, *2xN). Consequently, the CYP2D6 phenotype of PM has an AS ¼ 0, the IM phenotype has an AS ¼ 0.5, the normal or EM phenotype has an AS ¼ 1–2, and the UM phenotype has an AS > 2. However, consensus on the distinction between EM and IM phenotypes is controversial; in some reports, individuals with an AS of 1.0 have been classified as normal-slow metabolizers while individuals with an AS of 1.5–2 have been classified as normal-fast metabolizers (Gaedigk et al., 2017, 2008). CYP2D6 alleles have variable frequencies among different populations. The CYP2D6*2 allele, whose function is normal, and the variant CYP2D6*4 (rs3892097) with no defined function, although causing a splicing defect resulting in an inactive CYP2D6 gene product (PM), are common with allele frequencies of 27%–36% in European, African, and South Asian populations, and 12%–16% in admixed American populations (Zhou et al., 2017). On the contrary, the inactive alleles CYP2D6*3 (4%) and CYP2D6*6 (2%) are frequent only in European ancestry populations, while CYP2D6*10, whose function is reduced, is found only in African, South Asian, and particularly in East Asian populations (59%). CYP2D6*14 is only frequent in East Asian populations (2%), while CYP2D6*17 (20%), CYP2D6*29 (9%), and CYP2D6*43 (2%) are common in Africans. CYP2D6*1xN and CYP2D6*2xN, which are duplicated increased-function alleles, are found in Caucasians and Asians (1%–2%) but are much more common in certain African populations (> 29%), and can be enriched in the Finnish population (7%) (Pietarinen et al., 2016). Moreover CYP2D6*5, characterized by full gene deletion, varies from 1% to 7% in frequency (Gaedigk et al., 2017). The novel haplotype CYP2D6*138 contains R474Q while the novel haplotype containing R365H was designated CYP2D6*139. CYP2D6 metabolizes several substrates, including antiarrhythmics (e.g., propafenone, mexiletine, flecainide), tricyclic and second-generation antidepressants (e.g., amitriptyline, paroxetine, venlafaxine), antipsychotics (aripiprazole, risperidone), b-blockers (bufuralol, metoprolol), as well as anticancer drugs, particularly tamoxifen (selective estrogen receptor modulator), several opioid analgesics including codeine and tramadol, and many others. Moreover, an association between CYP2D6 genotype

Pharmacogenetics/Pharmacogenomics of Drug-Metabolizing Enzymes and Transporters

673

and lung cancer risk was first reported several years ago but still remains controversial (Ayesh et al., 1984; Zeng et al., 2017). Also, associations with other types of neoplasms were studied: individuals carrying two or more functional alleles have increased risk of liver cancer (Agundez et al., 1995) and melanoma (Dolzan et al., 1995), while no relevant association was found in acute leukemia (Sinnett et al., 2000), prostate (Agundez et al., 1998), bladder (Anwar et al., 1996), brain (Elexpurucamiruaga et al., 1995), renal (Gallou et al., 2001) and colorectal cancers (Sachse et al., 2002). CYP2D6 metabolizes codeine to morphine, a more active analgesic (Crews et al., 2014). In CYP2D6 PMs the rate of bioactivation and analgesic efficacy of codeine is markedly decreased while UMs show enhanced morphine production and greater analgesia, although more information from clinical studies is needed (Somogyi et al., 2015). However, the CPIC guideline recommends treatment of PM and UM phenotypes with alternative analgesics. Codeine is contraindicated in known CYP2D6 UMs. In lactating mothers with the UM phenotype, normal codeine doses can lead to fatal morphine concentrations entering the breast milk and infant blood (Gasche et al., 2004). These recommendations are provided for other opioids, including tramadol, hydrocodone, and oxycodone, which are O-demethylated by CYP2D6, for the risk of treatment failure or toxicity (Orliaguet et al., 2015). However, for oxycodone and hydromorphone the effect of CYP2D6 metabolism seems to be smaller due to the variable role of parent drug and metabolites related to opioid effects. In the case of antidepressants, all drugs are metabolized to less active hydroxylated metabolites by CYP2D6 (Hicks et al., 2017) with consequent risk at standard dose of toxicity or failure to produce therapeutic drug concentrations in the systemic circulation in PMs and UMs, respectively (amitriptyline and nortriptyline). The same condition is reported for the SSRIs, fluoxetine, and particularly fluvoxamine and paroxetine. Therefore, selection of an alternative antidepressant is indicated in UMs, whereas the risk of greater drug exposure to fluvoxamine and paroxetine in PMs compared to EMs requires a reduction of dosing of about 30%–50%, if alternative antidepressants cannot be selected. Fluoxetine is converted into R-norfluoxetine by CYP2D6, which is a major active metabolite. Therefore, CYP2D6 PMs have high fluoxetine levels and low norfluoxetine levels with no clinically significant variation among CYP2D6 genotypes. CYP2D6 also metabolizes the antiemetic drugs ondansetron and tropisetron to inactive metabolites (Bell et al., 2017). PK data indicate that the metabolism and clearance of tropisetron are highly dependent on CYP2D6 phenotype, while moderate effects are reported for ondansetron; there is clinical evidence showing a risk of reduced antiemetic response to ondansetron and tropisetron in UMs. For such cases, there is a moderate recommendation in the CPIC guideline for selection of an alternative antiemetic approach. Another important CYP2D6 substrate is tamoxifen, an anti-estrogenic agent used in breast cancer therapy. Studies of tamoxifen and its primary metabolites confirmed the association of CYP2D6 genotype with concentrations of endoxifen, a therapeutically active metabolite (Jin et al., 2005). CYP2D6 converts tamoxifen into 4-hydroxytamoxifen and endoxifen (Goetz et al., 2018). CYP2D6 PMs show lower endoxifen concentrations and a higher risk of cancer recurrence in tamoxifen adjuvant therapy in early breast cancer as demonstrated in retrospective examinations of randomized prospective trials (Goetz et al., 2007) and confirmed also in biobank studies in Germany (Schroth et al., 2007) and in Japan for the CYP2D6*10 variant (Kiyotani et al., 2008). In this setting the CPIC guideline for tamoxifen recommends the use of alternative hormonal therapy as well as for CYP2D6 IMs and EMs with an AS of 1. However, in all cases, if there is contraindication for aromatase inhibitor use, consideration should be given to use of tamoxifen at a higher but approved dose, which could be 40 mg/day for PMs. In tamoxifen-treated postmenopausal breast cancer patients, the CYP2D6*4 allele was a predictor of a higher risk of relapse and a lower incidence of hot flashes together with worse recurrence-free time and disease-free survival, but not overall survival compared to heterozygous and homozygous wild-type patients (Ferraldeschi and Newman, 2010). At present, the routine CYP2D6 testing to predict tamoxifen benefit is not currently recommended. Another important aspect to be considered is CYP2D6 mediated DDIs, which could have clinical implications in patients with differences in metabolic phenotypes due to competitive inhibition of CYP2D6. For example, the consequences could be greater in EMs than in PMs with higher and prolonged hemodynamic responses to metoprolol for DDI via CYP2D6 inhibition (Hamelin et al., 2000).

1.26.2.6

CYP2E1 (P450 family 2, subfamily E, polypeptide 1)

CYP2E1 is the only gene of the CYP2E subfamily located at chromosome 10 and expressed at high level in liver and within the mitochondrion (as well as the expected location in the endoplasmic reticulum), with lower expression levels in brain, nasal mucosa, kidney cortex, testis, ovary, gastrointestinal tract and at somewhat higher levels in cardiac tissue (Joshi and Tyndale, 2006; Ferguson and Tyndale, 2011; Michaud et al., 2010). Although its expression is undetectable in fetal liver, there is evidence that the accumulation of its mRNA is correlated with the methylation status of CpG residues in the 50 -flanking region in the period from 1 month to 1 year, and stabilization of CYP2E1 protein is mediated by ketone bodies (Zanger and Schwab, 2013). HNF1a and b-catenin (transcriptional activators) participate in the regulation of CYP2E1 expression (Gonzalez, 2007). The high interindividual variability of CYP2E1 protein is correlated to catalytic activity (Ohtsuki et al., 2012). In consideration that CYP2E1 transcript levels are not completely correlated to protein, it is hypothesized that post-transcriptional regulation by miR-378 occurs (Mohri et al., 2010). The CYP2E1 enzyme is inducible by acetone, ethanol and isoniazid as well as several hormones by complex mechanisms involving transcriptional, translational and post-translational effects, while clomethiazole, diethyldithiocarbamate and disulfiram are inhibitors (Gonzalez, 2007). In humans induction by ethanol occurs already at moderate alcohol consumption, which was rapidly reversed following alcohol withdrawal (Oneta et al., 2002). Moreover, CYP2E1 is induced under diabetes, obesity, fasting, alcohol and non-alcoholic liver disease where it has been recognized to have a pathophysiological role (Aubert et al., 2011). CYP2E1 substrates are low molecular weight molecules such as ethanol, acetone, halothane, chlorzoxazone and acetaminophen. Industrial chemicals and occupational and environmental toxicants as well as procarcinogens are also CYP2E1 substrates

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(Lu and Cederbaum, 2008). Endogenous substrates of CYP2E1 are lauric acid and acetone, a product of fatty acid oxidation, further oxidized to acetol and then to 1,2-propanediol during gluconeogenesis. Although the most important enzyme for ethanol oxidation to acetaldehyde is alcohol dehydrogenase (ADH), CYP2E1 contributes at elevated ethanol concentrations (Caro and Cederbaum, 2004) with generation of reactive oxygen species (ROS), which contribute to the damage of liver cells. Several studies demonstrated that CYP2E1 plays a causative role in alcoholic liver disease, nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) probably through enhancement of hepatic lipid peroxidation (Aubert et al., 2011). Furthermore, CYP2E1 plays a role in cancer risk as in the case of chronic alcohol consumption and esophageal cancer associated with carcinogenic and genotoxic effects of acetaldehyde and oxidative stress (Wang et al., 2009b; Millonig et al., 2011). CYP2E1 has been associated with altered susceptibility to hepatic cirrhosis induced by ethanol and with increased risk of development of other malignant tumors such as lung cancer (Wang et al., 2010). For CYP2E1, 7 distinct alleles and few variants have been identified (https://www.pharmvar. org/gene/CYP2E1), but functional impacts are not clear for any of these. CYP2E1*5A has a frequency of 5% in Caucasians and up to 38% in Asians, whereas *5B appears to be present only in Asians at  2 to 8% frequency. CYP2E1 haplotype diversity was much higher among African populations.

1.26.2.7

CYP2J subfamily

CYP2J2 is the only gene of the human CYP2J subfamily that encodes a microsomal P450 protein. The CYP2J2 gene comprises 9 exons and 8 introns, located on chromosome 1 and highly expressed in the heart (cardiac myocytes and in endothelial cells) (Delozier et al., 2007), with lower expression in lung, gastrointestinal tract, and pancreas (Zeldin et al., 1996, 1997a,b), in specific brain regions (Dutheil et al., 2009), as well as in some human carcinoma tissues (Jiang et al., 2005). Data are currently unclear regarding hepatic expression of this enzyme (Solanki et al., 2018). The housekeeping CYP2J2 gene promoter lacks a TATA-box and Sp1 regulates its basal activity (King et al., 2002). In silico approaches reported a structural similarity between CYP2J2 and CYP3A4 with homology in their active sites (Lee et al., 2010). However, further examination shows slight differences in structural geometry. CYP2J2 metabolizes both exogenous and endogenous substrates; several drugs, including some antihistamines (terfenadine, ebastine, and astemizole), anticancer agents (doxorubicin and tamoxifen), and immunosuppressants (cyclosporine), have been identified as efficient substrates. Other drugs typically metabolized by CYP3A4 were identified as novel CYP2J2 substrates including amiodarone and cyclosporine (Lee et al., 2010). However, CYP2J2, as well as CYP3A4 (Node et al., 1999) are highly involved in the metabolism of arachidonic acid (AA) into EETs, which modulate the risk of drug-induced cardiotoxicity (Xu et al., 2011). EETs also modulate renal, pulmonary, cardiac and vascular functions, and at the cardiac level, some of the CYP2J2-generated products have anti-inflammatory and vascular-protective effects (Zanger and Schwab, 2013). For example the anti-inflammatory effects of 11,12EET is exerted by inhibiting endothelial nuclear factor-kB while the same anti-inflammatory effects of 8,9- and 11,12-EETs can be mediated by binding as ligands to PPARa (Wray et al., 2009). Moreover, CYP2J2 has a role in cancer as confirmed by its high and selective expression in different human tumor tissues and cancer cell lines. CYP2J2 is polymorphic with 10 identified variants, eight of which carry nonsynonymous SNPs. The variants *2: T143A; *3: R158C; *4: I192N; *6: N404Y showed decreased catalytic activity toward AA in experimental settings without any current evidence for clinical impact in humans (King et al., 2002). Otherwise, CYP2J2*7 is the variant with greatest potential for functional importance and the key SNP rs890293 seems to confer a  50% reduced promoter-activity compared to the wild-type promoter (Spiecker et al., 2004), but further evaluations are still needed.

1.26.2.8

CYP3A subfamily

The CYP3A subfamily of enzymes is highly expressed in the liver and in the small intestine and participates in the metabolic elimination of several currently used drugs and many endogenous and structurally different environmental chemicals. CYP3A enzymes contribute to first-pass and systemic metabolism. The human CYP3A locus on chromosome 7 consists of four functional genes (CYP3A43, CYP3A4, CYP3A7, CYP3A5), with three pseudogenes (CYP3AP1-3). Interindividual differences in enzyme expression are due to: variable homeostatic control mechanisms, altered homeostasis due to disease states, influence of environmental stimuli, and genetic variants. CYP3A4 is the most abundant and participates in the clearance mechanism of the majority of CYP3A substrates in the absence of “null” alleles.

1.26.2.8.1

CYP3A4 (P450 family 3, subfamily A, polypeptide 4)

Current evidence suggests the existence of 34 SNPs within the CYP3A4 gene (https://www.pharmvar.org/gene/CYP3A4) occurring at allele frequencies < 5% and appearing as heterozygous with the wild-type allele. Coding sequence variants may contribute to interindividual differences in CYP3A-dependent clearance or catalytic function. CYP3A4*1A is the wild-type allele while the most common variant is CYP3A4*2, an A-392G transition in the 50 -flanking region with an allele frequency of 0% in Asians, 5% in Caucasians, and 54% in African-Americans (Williams, 2017). CYP3A4 mediates the metabolism of currently used drugs such as macrolide antibiotics, antidepressants, antipsychotics, anxiolytics, calcium channel blockers, immunosuppressants, opiates, and the statins. No correlations of CYP3A4 genetic variants have been reported for drug metabolism but important associations exist with various disease states including prostate cancer, secondary leukemias, and early puberty. Although other rare variant alleles with some functional consequences have been identified, the interindividual variability in populations related to CYP3A4-mediated drug clearance seem to be mostly attributable to environmental exposures (inducer and inhibitor drugs and chemicals). Presently, CYP3A4

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polymorphisms are considered of minor clinical relevance with low variant allele frequencies, small changes in enzyme activity attributed to the variant allele, and CYP3A4 and CYP3A5 overlap of substrate specificity. Several drugs are inducers or inhibitors of CYP3A4 activity in the liver and the gastrointestinal tract, influencing its variable activity. Currently, no uniform agreement is available on metabolizer subgroups for CYP3A4 whose clinical relevance is associated with CYP3A5. Also CYP3A4*22 together with CYP3A5*3 status have been related to improvement of tacrolimus dose prediction (Andreu et al., 2017; Lloberas et al., 2017; de Jonge et al., 2015). In the CPIC guideline are reported recommendations for tacrolimus (Birdwell et al., 2015). On the contrary, no dosage adjustment based on CYP3A4 and CYP3A5 genotypes is necessary for ticagrelor despite a significant effect of CYP3A4*1G (g.20230G> A, rs2242480) and CYP3A5*3 reported in a study on healthy Chinese subjects in which no association on the extent of inhibition of platelet aggregation was demonstrated (Liu et al., 2017a). The *2 and *7 alleles are the more prevalent CYP3A4 polymorphisms common in Caucasians, whereas *16 and *18 are more common in Asians. CYP3A4*22 is absent in both Asian and African populations. CYP3A4*22 was associated with reduced metabolic activity for several CYP3A4 substrates such as cyclosporine, erythromycin, sunitinib, tamoxifen, and quetiapine (Werk and Cascorbi, 2014).

1.26.2.8.2

CYP3A5 (P450 family 3, subfamily A, polypeptide 5)

CYP3A5 metabolizes almost all CYP3A4 substrates in vitro, but it represents the dominant clearance mechanism for very few drugs (Lamba et al., 2012) and is expressed in about 10%–20% of Caucasians, 33% of Japanese and 55% of African-Americans. CYP3A4*1 and CYP3A5*1 are in LD. CYP3A5 is the primary extrahepatic CYP3A isoform and its polymorphic variants are associated with disease risk and the metabolism of endogenous steroids or xenobiotics in several tissues such as lung, kidney, prostate, breast, and leukocytes. CYP3A5*3 involves improper mRNA splicing and reduced translation of a functional protein and its frequency varies from 50% in African-Americans to 90% in Caucasians. Functionally, CYP3A5*3/*3 at the hepatic level showed reduced catalytic activity toward midazolam. Moreover, intronic or exonic mutations (CYP3A5*5, *6, and *7) may alter splicing resulting in premature stop codons or exon deletion. Several CYP3A5 coding variants have been described, but these are rare variants with undefined functional significance probably due to the lack of appropriate phenotyping tools (inhibitors or substrates) that specifically distinguish this enzyme from CYP3A4. CYP3A5 expression in humans exhibits a bimodal distribution, due to the influence of ethnic background on the proportion of CYP3A5 “high expressers” and “low expressers” (Williams, 2017). CYP3A5 contributes to tacrolimus metabolism. Several retrospective studies in organ transplant patients demonstrated that the CYP3A5 genotype is a good predictor of dose requirements and plasma concentrations. In particular, carriers of CYP3A5*1 alleles (CYP3A5 “high expressers”) are associated with relatively higher doses of tacrolimus to achieve target blood concentrations than carriers of the CYP3A5*3 allele in homozygosity (CYP3A5 “low expressers”). Moreover, CYP3A5 genotype influences also sirolimus dose requirements. However, CYP3A5 genetic variation cannot explain all variability related to tacrolimus (Williams, 2017). In contrast to the findings for tacrolimus, no evidence supports a relationship between the CYP3A5 genotype and cyclosporine disposition, dose requirements, or clinical response. CYP3A5 also metabolizes vincristine. Instead, CYP3A7 is the major fetal liver CYP3A enzyme whose expression is down-regulated after birth even though protein and mRNA have been detected in adults (Lamba et al., 2002); in this case, there is the replacement of an approximately 60-bp stretch of the CYP3A7 promoter with a homologous segment from the CYP3A4 promoter sequence (CYP3A7*1C) (Gonzalez, 1988). The fetal/neonatal CYP3A7 showed high catalytic activity for the 16a-hydroxylation of estrone (E1) and dehydroepiandrosterone (DHEA), important endogenous substrates, as well as some exogenous chemicals (Lee et al., 2003; Miller et al., 2004). CYP3A7*1C may play a role in regulating DHEAS levels by enhancing the clearance of DHEA and DHEAS. CYP3A43 was identified in 2000, but its role and function remains unclear.

1.26.3

PGx of non-P450 enzymes

The biotransformation of drugs also involves other metabolic enzymes including non-P450 phase I oxidative enzymes such as flavin-containing monooxygenases (FMOs), monoamine oxidases (MAOs), peroxidases, cyclooxygenase (COX), alcohol dehydrogenase (ADH), and aldehyde dehydrogenase (ALDH) and reductive enzymes (P450 reductase, DT-diaphorase). For example, although MAO and COX enzymes have no functional role in the metabolism of anticancer drugs, there is interest in COX-2, frequently expressed in many types of cancers and studied for its pleiotropic role in carcinogenesis and cancer cell resistance to chemo- and radiotherapy (Goradel et al., 2019). In particular, COX inhibitors seem to be effective in cancer prevention and treatment. In addition, the polymorphic enzyme ADH has been associated with cancer risk (Yokoyama and Omori, 2003), especially in high alcohol consumers as a result of exposure to acetaldehyde, as well as the ALDH and its role in detoxifying aldehydes whose dysfunction may contribute to several diseases including cancer (Zhao et al., 2016). Moreover, three SNPs in the human ADH4 gene (rs1126670, rs1126671, rs2032349) and one in the ADH5 gene (rs2602836) were found to be associated with Fabry disease progression (p < 0.05) in a case-control study of patients either responding or not responding to enzyme replacement therapy (ERT) (Scionti et al., 2017). Other examples of non-P450 phase I oxidation, deamination and hydrolytic enzymes are represented, respectively, by dihydropyrimidine dehydrogenase (DPYD), involved in the detoxification of fluoropyrimidines, cytidine deaminase (CDA), involved in the inactivation of gemcitabine and cytarabine, and bleomycin hydrolase (BLMH), involved in bleomycin metabolism. Otherwise, Phase II conjugative enzymes are represented by UDP-glucuronosyltransferases (UGTs), thiopurine S-methyltransferase (TPMT), sulfotransferases (SULTs), N-acetyltransferases (NATs), and glutathione S-transferases (GSTs). In this article on pharmacogenetics, we focus on DPYD and the phase II conjugative enzymes while the other mentioned enzymes are covered in other articles.

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1.26.3.1

Dihydropyrimidine dehydrogenase (DPYD)

DPYD is a minor phase I metabolizing enzyme, considered the initial and rate-limiting enzyme for the catabolism of pyrimidines such as uracil and thymine. It is also important for the metabolism of 5-fluorouracil (5-FU) and its oral prodrugs capecitabine (Diasio and Harris, 1989) and tegafur. Recently, the CIPC has updated the DPYD guideline recommendation to reduction of the dose of fluoropyrimidines by 25%–50% (from the full standard dose) in DPYD deficiency or even avoid the drug according to the DPYD phenotype (https://cpicpgx.org/guidelines/guideline-for-fluoropyrimidines-and-dpyd). The most common polymorphism associated with DPYD deficiency is IVS14 þ 1G > A (DPYD*2A) due to a defect in processing of the DPYD precursor mRNA with a complete exon skipping variant resulting in a truncated protein with loss of activity (Wei et al., 1996; Meinsma et al., 1995) with allele frequencies between 0.1% and 1.0% in African-American and Caucasian populations, respectively (Henricks et al., 2017; Caudle et al., 2013). Carriers homozygous for the *2A variant allele showed complete DPYD deficiency (van Kuilenburg et al., 2001), while the heterozygous genotype shows 50% of the normal DPYD enzyme activities with prolonged clearance times for 5-FU and extremely high risk of drug toxicity (van Kuilenburg et al., 2012); fluoropyrimidines should be avoided in these individuals. Also the 2846A> T (Asp949Val, rs67376798) polymorphism negatively affects DPYD enzyme activity (Dobritzsch et al., 2001; Blasco et al., 2008; Schwab et al., 2008; Deenen et al., 2011) as well as the rare variant I560S (rs55886062) linked to reduced DPYD activity (Offer et al., 2013a) and 5-FU toxicity (Morel et al., 2006) and a third variant, D949V associated with severe 5-FU toxicity (Schwab et al., 2008). The allele frequencies of c.2846A > T were reported to vary from 0.1% to 1.1% in African-Americans and Caucasians, respectively (Offer et al., 2014; Caudle et al., 2013). Presently, more than 450 polymorphisms are identified for the DPYD gene (https://www.pharmvar.org/gene/DPYD). It is demonstrated that in 3%–5% of the population there is a partial DPYD deficiency, with an increased risk for 5-FU-induced severe toxicity. Several studies demonstrated the association of DPYD*2A and/or 2846A> T with severe, even lethal, toxicity to fluoropyrimidines, although DPYD 2846A> T is slightly less predictive of severe toxicity than DPYD*2A (Boisdron-Celle et al., 2007; van Kuilenburg et al., 2001, 2002; Morel et al., 2006; Deenen et al., 2011; Boige et al., 2010). The rare coexistence of both variants in an individual is lethal shortly after the start of fluoropyrimidine treatment (Boisdron-Celle et al., 2007; Ezzeldin et al., 2003). Other decreased function variants associated with grade 3- and grade 4-toxicities in 5-FU-treated patients include DPYD*13 (rs55886062.1, c.1679T> G, p.I560S, no function), c.11295923C> G (rs75017182), c.1601G> A and c.1236G > A (rs75017182, rs56038477). For some variants data on clinical impact are still inconclusive. Homozygosity of DPYD*13 resulted in a 75% reduction of DPYD enzyme activity (Offer et al., 2013b). The c.1236G > A polymorphism is a synonymous variant in complete LD with c.483þ18G > A, c.680þ139G > A, c.959–51T> G, and c.1129– 5923C > G and termed haplotype B3 (HapB3) with a reduced activity (Amstutz et al., 2009; van Kuilenburg et al., 2010). Other variants such as DPYD*9A (rs1801265) have a normal function (Amstutz et al., 2018).

1.26.3.2 1.26.3.2.1

PGx of phase II enzymes N-Acetyltransferases (NATs)

In humans, two isoenzymes catalyze acetylation reactions, the N-acetyltransferase 1 (NAT1) and 2 (NAT2), whose genes are located on chromosome 8 and share 87% coding sequence homology (Blum et al., 1990). NATs are cytosolic enzymes with different substrate specificities and organ and tissue distribution. NAT2 protein, evident from about 12 months after birth, has mainly hepatic and intestinal distribution (Grant et al., 1990; Hickman et al., 1998) while NAT1 is ubiquitous (Sim et al., 2008) and its activity is present in both adult as well as fetal and neonatal tissue (lungs, kidneys, and adrenal glands) (Pacifici et al., 1986). There is evidence for a role of NAT1 in carcinogenesis through enhanced mutagenesis but may also influence the resistance of some cancers to cytotoxic drugs (Adam et al., 2003). In human, NATs are involved in the acetylation of many arylamine and hydrazine drugs, as well as for carcinogens present in diet, environment, and cigarette smoke; the enzymes can have distinct and overlapping substrates. NAT1 metabolizes p-aminobenzoic acid, p-aminosalicylic acid, 2-aminofluorene, caffeine, and the antibiotics sulfamethoxazole and sulfanilamide (Ginsberg et al., 2009) and also plays a role in folate metabolism (Ginsberg et al., 2009; Minchin, 1995). NAT2 provides detoxification of drugs such as the anti-tuberculosis drug isoniazid, the anti-hypertensives hydralazine and endralazine, several sulphonamides (anti-bacterial drugs), the anti-arrhythmic drug procainamide, the benzodiazepine nitrazepam and the anti-inflammatory drug dapsone (Kawamura et al., 2005). NATs are polymorphic enzymes, resulting in individual differences in the rate of metabolism of drugs such as isoniazide in clinical studies (Evans et al., 1960). Individuals with a faster rate are called rapid acetylators (RA) and individuals with a slower rate are slow acetylators (SA), which resulted in elevated serum concentration and adverse side effects due to an accumulation of unmetabolized drug (Brockton et al., 2000). NAT1 and NAT2 acetylator genotypes are considered to be involved in human cancer susceptibility including head and neck, colorectal, liver, breast, and prostate cancers, as well as in other disease conditions such as birth defects or neurodegenerative and autoimmune diseases (Agundez, 2008). NAT2*4 is reported as the wild-type allele, while a phenotype of SA results from the variants NAT2*5 (rs1801280) carrying the c.341T> C SNP, NAT2*6 (rs1799930) with the c.590G > A SNP as well as NAT2*7 (rs1799931) with the c.857G > A SNP (Hein, 2002). The distribution of SAs varies significantly in different populations: 90% of Arabs, 40%–60% of Caucasians, and 5%–25% of East Asians.

1.26.3.2.2

UDP-glucuronosyltransferases (UGTs)

UDP-Glucuronosyltransferases (UGTs) are a superfamily of enzymes that generally fulfill detoxification roles, catalyzing the glucuronidation of various exogenous (i.e. environmental toxicants, carcinogens, dietary toxins, drugs) as well as endogenous compounds

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(i.e. bilirubin, steroid hormones, fat soluble vitamins (and bile acids) (Kiang et al., 2005) to increase water-solubility and their elimination from the body in urine or bile. UGTs are membrane-bound enzymes localized at the subcellular level in the endoplasmic reticulum (ER) with an active site facing the lumenal side of the ER membrane. The genetic variation in UGT enzymes can modulate its regulation and expression, influencing UGT activity and resulting in pharmacological and physiologic effects (Desai et al., 2003). In particular, UGT genetic variation can impact ADRs and cancer susceptibility. Four UGT families have been characterized in humans: UGT1, UGT2 (consisting of UGT2A and UGT2B subfamilies), UGT3 and UGT8. UGT1 and UGT2 family enzymes have roles in the glucuronidation of exogenous and endogenous compounds, whereas members of the UGT3 and UGT8 families have different roles. Infact, UGT3 family enzymes (UGT3A1 and UGT3A2) are expressed in thymus, testis and kidney, with a potential role in the metabolism of ursodeoxycholic acid during treatment of cholestasis or gallstones (Mackenzie et al., 2008). UGT8 (UGT8A1) is expressed in kidney and gastrointestinal tract (intestine, colon), participates in the biosynthesis of glycosphingolipids, cerebrosides, and sulfatides of nerve cells (Bosio et al., 1996; Meech et al., 2015) and also acts as a modulator of bile acid homeostasis and signaling (Meech et al., 2015). The UGT1 and UGT2 families comprise 19 members that have overlapping substrate profiles (Miners et al., 2010) even if UGT1 enzymes are active on phenolic and heterocyclic compounds while UGT2B enzymes have shown reduced activity (Tukey and Strassburg, 2000) for these substrates. The UGT1 gene locus is located on chromosome 2 and encodes nine functional enzymes (isoforms 1A1, 1A3-1A10) (Mackenzie et al., 2005), while the UGT2A and 2B genes form a cluster on chromosome 4. Among the UGT proteins (UGT1A1, UGT1A3, UGT1A4, UGT1A5, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT1A10, UGT2A1, UGT2A2, UGT2A3, UGT2B4, UGT2B7, UGT2B10, UGT2B11, UGT2B15, UGT2B17, UGT2B28, UGT3A1, UGT3A2, and UGT8A1) (Mackenzie et al., 2008; Sneitz et al., 2009) the UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A9, UGT2B7, and UGT2B15 are the main liver xenobiotic-conjugating enzymes, whereas UGT1A7, UGT1A8, and UGT1A10 are extrahepatic. Individual UGT enzymes tend to display somewhat unique substrate preferences. For example, UGT1A1 has bilirubin as its substrate (Wang, 2006) while UGT2B7 is involved in opioid metabolism (Coffman et al., 1998). UGT1A3, UGT1A4, UGT1A9, and UGT2B7 are involved in metabolism of several non-steroidal anti-inflammatory agents (Tukey and Strassburg, 2000), steroid hormones and dietary and tobacco carcinogens, while acetaminophen (paracetamol) is glucuronidated by UGT1A1, UGT1A6, and UGT1A9 (Court et al., 2001). Moreover, UGT1A8 and UGT1A10 decrease the bioavailability of several oral drugs including raloxifene, a selective estrogen receptor modulator used in the therapy of osteoporosis (Mizuma, 2009; Kemp et al., 2002). UGTs are important also in the inactivation of carcinogens from the diet or cigarette smoke (Dellinger et al., 2006), but can be inhibited by different compound such as analgesics, NSAIDs, anxiolytics, anticonvulsants, or antiviral agents (Dellinger et al., 2006). Several classes of drugs including analgesics, antivirals, or anticonvulsants are considered to act as human UGT inducers. As well, various xenobiotics or endogenous molecules (such as hormones) act as inducers via nuclear receptors and transcription factors. An example is the aryl hydrocarbon receptor, which plays a role in UGT1A1 induction; key players in UGT1A6 and UGT1A9 induction include PXR and CAR (Mackenzie et al., 2003b; Xie et al., 2003). UGT2B7 is involved in the glucuronidation of steroid hormones (androsterone, testosterone, dihydroprogesterone), fatty acids, estrogens and their metabolites (catechol estrogens) and exercise a role in the formation of proximal carcinogens, such as quinine estrogens (Mackenzie et al., 2003a; Starlard-Davenport et al., 2010). Moreover, Arbitrio et al. (2019) demonstrated the correlation of five SNPs in NR1I3 (rs11584174) and UGT2B7 (rs7438284, rs7662029, rs7439366, and rs7668258) with taxane related peripheral neurotoxicity of grades  2–3 in breast cancer patients (Arbitrio et al., 2019), characterized by neuroprotection and loss of drug efficacy related to the UM phenotype. UGT2B7 also mediates the conversion of morphine to its active metabolite, morphine-6-glucuronide (Madadi et al., 2013). The most important genetic polymorphism in UGTs involves the UGT1A1 gene, whose altered activity underlies the deficient bilirubin detoxification capacity seen in Crigler-Najjar syndrome type I and type II and Gilbert’s syndrome (Kadakol et al., 2000). Patients affected from the type I syndrome show lack of UGT1A1 enzymatic activity and toxic effects of bilirubin on the central nervous system. Other substrates of UGT1A1 include SN-38 (active and toxic metabolite of irinotecan, an anticancer drug), raltegravir (inhibitor of the HIV integrase enzyme), clozapine, bazedoxifene (a selective estrogen receptor modulator for prevention and treatment of postmenopausal osteoporosis), and eltrombopag (a thrombopoietin receptor agonist for thrombocytopenia). The genetic variant characterized by the presence of an additional TA repeat in the TATA sequence of the UGT1A1 promoter, ((TA)7TAA, instead of (TA)6TAA), with a reduction in transcriptional and enzymatic activity, is identified as UGT1A1*28 (Beutler et al., 1998). This variant, together with UGT1A1*6 (rs4148323, c.211G > A), whose activity is also reduced, have important impacts on irinotecan metabolism (Jinno et al., 2003) with impaired SN-38 glucuronidation seen especially in patients who are homozygous carriers (UGT1A1*28 TA7/TA7). The 80% of patients who suffered from life-threatening toxicities have variant sequences due to UGT1A1*6 (211G> A) and UGT1A1*27 (686C> A). The *28 variant is relatively common in Caucasians (29%–40%) and Africans (36%–43%) but less frequent in Asians (13%–16%), while the *6 variant is found only in Asians (16%–23%) (Ando et al., 1998). Severe diarrhea and neutropenia are correlated to the abnormally high SN-38 concentrations resulting from deficient glucuronidation (Wasserman et al., 1997). Prospective trials have confirmed this correlation (Iyer et al., 2002; Wasserman et al., 1997; Liu et al., 2017b) and the FDA approved a revision of the irinotecan label with recommendations for initial dose reduction for patients homozygous for the UGT1A1*28 variant, although genetic testing is not mandatory before starting irinotecan therapy (Table 2). Recently, genetic variants UGT1A1*28, UGT1A1*60, UGT1A1*93, and UGT1A1*6 were studied for their correlation to neonatal hyperbilirubinemia severity in an Indonesian population (Amandito et al., 2019). UGT1A1*60 (3279T > G) is frequent in Indonesians and associated with decreased transcriptional activity of the UGT1A1 promoter (Yusoff et al., 2010; Mazur-Kominek et al., 2017) while UGT1A1*93 (3156G > A) shows a high extent of LD with UGT1A1*28 (Yusoff et al., 2010). Besides, several case-control studies have assessed genetic polymorphisms of UGT genes and consequent

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reduction of their expression or enzymatic activity (primarily UGT1A and UGT2B genes) as genetic risk factors for a wide variety of cancers (Hu et al., 2016).

1.26.3.2.3

Thiopurine S-methyltransferase (TPMT)

Methylation is a minor pathway of xenobiotic and drug biotransformation and is involved in the biosynthesis of endogenous compounds (epinephrine and melatonin) and the metabolism of small endogenous compounds (neurotransmitters) and macromolecules (nucleic acids). Under this process, compounds can undergo N-, O-, S- and arsenic (As)-methylation during their metabolism (Feng et al., 2010). S-Adenosylmethionine (SAM) is the cofactor required to form methyl conjugates and is subsequently utilized in the transfer of the activated methyl group to the acceptor molecule (RXH). Among these processes, thiol methylation is implicated in metabolism of aromatic and heterocyclic sulfhydryl compounds. Two distinct enzymes are expressed in human tissues: thiol methyltransferase (TMT), a membrane-bound enzyme involved in catalysis of the S-methylation of compounds such as captopril and D-penicillamine, and thiopurine S-methyltransferase (TPMT), a cytoplasmic enzyme catalyzing the S-methylation of anticancer and immunosuppressive thiopurines such as 6-mercaptopurine, 6-thioguanine, and azathioprine. These thiopurines are important agents in the treatment of cancer, Crohn’s disease and rheumatoid arthritis and in the prevention of allograft transplant rejection. Several TPMT gene polymorphic variants result in reduced TPMT enzymatic activity. Among 44 variants identified at present (https://www.hmv.liu.se/tpmtalleles) and correlated to large interindividual variations in thiopurine drug efficacy/ toxicity, the most studied are TPMT*2 (rs1800462, G238> C, reduced activity), TPMT*3A (G460 > A and A719 > G, no activity), TPMT*3B (rs1800460, G460 > A, reduced activity) TPMT*3C (rs1142345, A719 > G, reduced activity), and TPMT*4 (rs1800584, G626 > A, very low activity). Patients with absent and low TPMT activity can be treated with 5 and 50% of the standard 6mercaptopurine regimen. The frequency of patients with TPMT variants is 8.1%–10.1% for Caucasians but only 2.3%–4.2% for Asian populations (Kim et al., 2015). Homozygous deficient individuals had a phenotype of IM or PM with a high risk of myelosuppression after azathioprine treatment. In this case, patients need a reduction of the dose of thiopurines (Zhou, 2006). Variants in the non-coding regions such as the TPMT-promoter and introns can also affect TPMT activity by influencing the gene transcription (Kotur et al., 2015). For example, there is evidence for a variable number of tandem repeats (VNTR) region with microsatellite instability in the TPMT gene promoter whose activity modulates TPMT transcription and enzyme activity. TMPT has wide tissue expression, including in erythrocytes, which facilitates the measurement of enzyme activity. At present, TPMT is the enzyme with widespread testing in clinical practice recommended by the FDA, with CPIC guidelines related to thiopurine usage (Relling et al., 2013).

1.26.3.2.4

Glutathione S-transferases (GSTs)

The cytosolic enzymes of the human GST family are organized into eight classes identified as alpha (a), kappa (k), mu (m), omega (u), pi (p), sigma (s), theta (q) and zeta (z) (Hayes et al., 2005). The most clinically relevant genes are GSTM1 of the m class, GSTT1 of the q class, GSTP1 of the p class, and GSTA1 of the a class. By catalyzing glutathione conjugation reactions, the enzymes of the m class play roles in the metabolism of several drugs (such as busulfan, cisplatin, ethacrynic acid, cyclophosphamide, thiotepa), detoxification of environmental carcinogens and reactive intermediates produced from chemicals, herbicides, and pesticides (acrolein, lindane, malathion, tridiphane) (Hayes et al., 2005). Moreover, GSTs can provide intracellular protection against oxidative stress and are also involved in synthesis and metabolism of derivatives of arachidonic acid and steroids (van Bladeren, 2000). Reactive intermediates resulting from lipid peroxidation, nucleotide peroxidation or catecholamine peroxidation are inactivated by GSTs (Dagnino-Subiabre et al., 2000). The epoxides and other reactive intermediates resulting from P450-mediated metabolism of several environmental procarcinogens (aflatoxin B1, polycyclic aromatic hydrocarbons, styrene, heterocyclic amines) are substrates of GSTs. The genes coding for this class of enzymes are organized in a gene cluster on chromosome 1 and are highly polymorphic. For m and q class GSTs, gene deletions have been identified resulting in null variants (GSTM1*0 and GSTT1*0) of GSTM1 and GSTT1 with loss of function and linked to cancer susceptibility. The literature reports two polymorphisms of the GSTP1 gene, rs947894 or rs1695 with a 1404A> G SNP and with substitution I105V at codon 105 (Ge et al., 2013) associated with breast cancer risk and better response to cyclophosphamide, and rs1799811 with 2294C > T SNP and substitution A114V at codon 114. Four different variants have been identified: GSTP1*A (105Ile-114Ala), GSTP1*B (105Val114Ala), GSTP1*C (105Val-114Val), and GSTP1*D (105ILe-114Val) (AliOsman et al., 1997). A point mutation in the promoter of the GSTA1 gene can result in lower promoter activity associated with the GSTA1*B allele; GSTP1 and GSTA1 polymorphisms can also give rise to low-activity enzyme variants. GSTP1*A is cytoprotective against chemotherapy toxicity whereas (Sweeney et al., 2003) GSTP1*B increases toxicity of anticancer drugs as a consequence of impaired enzyme activity. The two GST null alleles (GSTM1*0 and GSTT1*0) have a variable distribution among populations. The GSTM1*0 frequency is 42%–58% in Caucasians while in 27%–41% of Africans the GSTM1 gene is lacking. The null-allele of the GSTT1 gene has a frequency between 2%–42% for Caucasians, 50%–60% in Asians, 15%–20% in African Americans, and less than 10% in Hispanics (Davies et al., 2001; Navarro et al., 2009). For the GSTP1 and GSTA1 polymorphisms, the distribution is up to 40% of Caucasians and 41%–54% of Africans. Cancer chemotherapeutic agents such as oxaliplatin and chlorambucil are GST substrates (Goekkurt et al., 2009; Crettol et al., 2010) and it was demonstrated that in metastatic breast cancer patients with GSTM1*0 or GSTT1*0 genotypes there is poorer response and reduced overall survival (Zhang et al., 2017) and severe drug-related toxicity. Also in metastatic colorectal cancer patients who had received oxaliplatin-based chemotherapy a higher frequency of grade 4 neutropenia was seen in homozygous carriers of GSTM1*0 (McLeod et al., 2010). Evidence of the detoxification role of GST has been reported in pediatric patients with GSTM1 and CYP2C9 variants in which combination treatment with busulfan and cyclophosphamide causes higher risk of developing hemorrhagic cystitis (Uppugunduri et al., 2017). Individuals

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lacking functional GSTM1, GSTT1, and GSTP1 have been shown to have a higher incidence of bladder, breast, colorectal, head/neck, and lung cancer. On the other hand, several drug substrates of GSTs require a well-functioning enzyme and acute myeloid leukemia patients treated with doxorubicin had a better survival rate correlated to the active GSTM1 gene compared to patients with at least one null allele (Autrup et al., 2002).

1.26.3.2.5

Sulfotransferases (SULTs)

Another important gene superfamily of enzymes involved in metabolism of exogenous and endogenous compounds encodes the sulfotransferases (SULTs). Sulfoconjugation (or sulfonation) mediated by these enzymes involves the transfer of a sulfonate (SO3 ) from the sulfonate donor 30 -phosphoadenosine 50 -phosphosulfate (PAPS) to the appropriate hydroxyl or amino group of an acceptor molecule. SULT-mediated detoxication or metabolic activation utilize as substrates a wide range of xenobiotics, endogenous neurotransmitters, hormones, bile acids, fatty acids, carbohydrates, proteins (Gamage et al., 2006), retinol or vitamin D. SULT expression is regulated by members of the nuclear receptor (NR) superfamily that function as sensors of xenobiotics and endogenous molecules (Runge-Morris et al., 2013). These NRs include the peroxisome proliferator-activated receptors, PXR, CAR, VDR, liver X receptors, farnesoid X receptor, retinoid-related orphan receptors, and estrogen-related receptors. The localization of these enzymes is membrane-bound (Golgi apparatus), with no demonstrated xenobiotic-metabolizing activity (Habuchi, 2000), and cytosolic for the enzymes playing roles in detoxification and metabolic activation processes (Coughtrie, 2016). The superfamily of cytosolic sulfotransferases is classified into families (members have amino acid sequence identity of  45%) and subfamilies ( 60% identity). Four human SULT families, SULT1, SULT2, SULT4, and SULT6, have been identified, while less characterized are SULT3 and SULT5. The members of SULT1 family (1A1, 1A2, 1A3, 1A4, 1B1, 1C1, 1C2, 1C3 and 1E1) are found in liver, brain, breast, intestine, jejunum, lung, adrenal gland, endometrium, placenta, kidney and blood platelets. The SULT2 family includes SULT2A (SULT2A1) and SULT2B (SULT2B1a and SULT2B1b) while the SULT4A1 and SULT6B1 enzymes belong to the SULT4 and SULT6 families, respectively (Lindsay et al., 2008). SULT1A1 participates in the metabolism of xenobiotics in humans and in the sulfoconjugation of phenolic compounds (monocyclic phenols, naphthols, benzylic alcohols, aromatic amines or hydroxylamines) (Glatt et al., 2001), N-hydroxyderivatives of arylamines, allylic alcohols and heterocyclic amines with risk of mutagenic and carcinogenic activity (Glatt and Meinl, 2004). SULT1A2 plays a role in the toxification of several aromatic hydroxylamines (Meinl et al., 2002) and is located in the SULT1A cluster on chromosome 16. SULT1A3 metabolizes catecholamines (dopamine) while the SULT1B1 acts in thyroid hormone metabolism. SULT1E1 plays a key role in estrogen homeostasis and a down-regulation or loss of SULT1E1 could have a tumorigenic role for breast or endometrial cancer (Cole et al., 2010). Among the SULT2A subfamily, SULT2A1, also called dehydroepiandrosterone sulfotransferase (DHEA ST) conjugates various hydroxysteroids and participates in metabolism of quinolone drugs (Senggunprai et al., 2009). By acting on neurosteroids synthesized in the brain, SULT2A1 can modulate the activity of gamma-aminobutyric acid-A (GABA-A) receptors with an involvement of sulfonated neuroactive steroids in the etiopathogenesis of Parkinson’s disease and other CNS disorders linked to GABAergic neurotransmission alterations (Luchetti et al., 2010; Gartside et al., 2010). Evidence for the clinical importance of other cytosolic sulfotransferases is lacking. Genetic polymorphisms have been reported for SULT1A1, SULT1A3, SULT1C2, SULT2A1, SULT2A3 and SULT2B1 enzymes (Coughtrie, 2016). SULT1A1 polymorphism might play a role in the pathophysiology of several cancers (lung cancer, urothelial carcinoma, meningiomal brain tumors, breast cancer and colorectal cancer) (Arslan et al., 2009; Brockton et al., 2000; Bardakci et al., 2008; Huang et al., 2009).

1.26.4

PGx of drug transporters

Drug transporters, embedded at the membrane level of many endothelial and epithelial barriers (blood-brain barrier (BBB), intestinal epithelial cells, hepatocytes, and renal tubular cells), together with the phase I/II enzymes discussed above, may also influence the PK/PD of many clinically used drugs. As well, drug transporter genetic polymorphisms can influence interindividual variability in drug response. Drug transporters play physiological roles and influence drug disposition and response by facilitating the entrance of chemical substances into the target cells (uptake transporters) or by opposing such entry (efflux transporters) (Huang et al., 2009). In humans, drug transporters are broadly categorized in two groups: the efflux transporters belonging to the ATP-binding cassette (ABC) superfamily with its 49 members grouped into seven subfamilies, and the uptake transporters belonging to the solute carrier (SLC) superfamily with over 400 members grouped into 65 families. Among ABC transporters, the ABCB1 (P-glycoprotein [Pgp] or multidrug resistance 1 [MDR1]), ABCC1 (multidrug resistance-associated protein 1 [MRP1]), ABBC2 (multidrug resistanceassociated protein [MRP2]), and ABCG2 (breast cancer resistance protein [BCRP]) are the most studied. Among SLC transporters, the most studied are the organic anion-transporting polypeptides (OATP), organic cation transporters (OCT), and organic anion transporters (OAT). Genetic variants in these genes (http://www.pharmGKB.org) affect their expression, substrate specificity, and/or intrinsic transport activity as well as the disposition, efficacy, and safety/toxicity of their substrates. At present there is increasing evidence for important roles of transporters in both normal physiology and disease; in some cases transporters are identified as therapeutic targets to treat diseases such as diabetes, major depression, hypertension and constipation (Liang et al., 2015). Either ABC (for unidirectional efflux) or the ABC and SLC (for both uptake and efflux) transporters can mediate the vectorial transport characterized by unidirectional movement of compounds and the equilibrative distribution of compounds across the two sides of the plasma membrane. ABC transporters alone mediate the vectorial transport of lipophilic compounds while both types of transporters mediate the vectorial transport of hydrophilic organic compounds (Raj and Raveendran, 2019).

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1.26.4.1

Efflux transporters: ABC superfamily

The transmembrane ABC proteins are primary active transporters that utilize the free energy from ATP-binding for active transport of different substrates and are involved in the absorption and disposition of many clinically used drugs, including anticancer drugs. ABC transporters are located also in the blood-brain barrier (BBB), often serving to exclude lipophilic xenobiotics and drugs from the central nervous system (Hermann and Bassetti, 2007). In ischemic brain, ABC transporter expression changes in bibB endothelial cells can prevent the central access of active compounds leading to compromised efficacy of stroke therapies, while long-term impairment of ABC transporter function by selective inhibitors can contribute to neurodegeneration, with impaired neurological recovery (Manrique-Castano et al., 2019). Based on the sequence homology of ABC transporters, 49 ABC genes are categorized into seven subfamilies (ABCA to ABCG), with several transporters playing a pivotal role in multidrug resistance of cancer cells. Clinically important ABC transporters belong to the ABCB, ABCC and ABCG subfamilies. Some of the ABC transporters (e.g. ABCC1 or ABCC3) at the basolateral membrane of intestinal cells transport some vitamins into the blood.

1.26.4.1.1

ABCB1 or P-glycoprotein (P-gp)

ABCB1 belongs to the ABCB subfamily, which includes 11 transporters; this subfamily is also known as the MDR (multidrug resistance) or TAP (transporters of antigen presentation) groups. Together with ABCB2, ABCB3, ABCB4, and ABCB11, P-glycoprotein (Pgp, MDR1, ABCB1) is very relevant as it mediates drug movement from the intracellular space to extracellular space and shows propensity to cause multidrug resistance. P-gp substrates include multiple cancer chemotherapeutic agents and expression of this transporter at the bibB plays important roles in modulating the pharmacology of anticonvulsants and fexofenadine (a thirdgeneration antihistamine) by limiting drug entry into the central nervous system. Moreover, ABCB1 plays a role in the metabolism of drugs, drug bioavailability, clinically important drug-drug interactions and the transport of substrates into the bile and urine. ABCB1 is highly polymorphic and the most studied SNPs in strong LD are the c.C1236T (rs1128503) and the c.C3435T (rs1045642), silent polymorphisms in exon 12 and 26 respectively, and the c.G2677A/T (rs2032582) polymorphism in exon 21. Allelic distribution patterns show ethnic differences (Gradhand and Kim, 2008; Marzolini et al., 2004). In particular, the C3435T SNP causes lower expression and activity of ABCB1 such that TT homozygotes show increased digoxin bioavailability and plasma concentration after oral administration (Sakaeda et al., 2001); the CC genotype is associated with reduced atorvastatin efficacy and risk of myalgia after treatment (Hoenig et al., 2011) and related increase in serum creatine kinase (Hoenig et al., 2011) as a consequence of lower intracellular concentration and higher plasma concentration of the statin. The polymorphism also affects protease inhibitors (Fellay et al., 2002). Instead, the ABCB1 haplotype TTT (rs1128503, rs2032582, rs1045642) is linked to increased morphine exposure and sensitivity in a patient (Madadi et al., 2013); as well, carriers of the GG and GA genotypes for the rs9282564 SNP show increased risk of morphine induced respiratory depression (Sadhasivam et al., 2015). Nevertheless, conflicting results have been reported for other polymorphisms and alternative splicing and further studies are needed to clarify the functional and clinical significance of the polymorphisms in strong LD for different substrates including psychotropics, antiretroviral protease inhibitors, immunosuppressants, colchicine resistance and inflammatory bowel disease, and anticancer drugs (Kimchi-Sarfaty et al., 2007).

1.26.4.1.2

ABCC1, ABCC2 and ABCC5 or multidrug resistance-associated proteins MRP1, 2, and 5

ABCC1 and ABCC2 mediate the biliary excretion of conjugated forms of diverse substrates such as tamoxifen glucuronides and sulfates and SN-38 glucuronide (Sugiyama et al., 1998), organic anions, methotrexate and pravastatin, and exhibit overlapping substrate specificities for several drugs. Although ABCC1 gene variants are rare, polymorphisms of the ABCC2 gene are more common. Examples are the c.1249G > A SNP (rs2273697) in exon 10 with lower protein expression and the c.3972C> T SNP (rs3740066) in exon 28 (Itoda et al., 2002). Patients with the 1249G > A variant show higher risk of drug-induced renal proximal tubulopathy when treated with tenofovir. ABCC2 polymorphisms and haplotypes (ABCC2*2 haplotype) were associated with lower irinotecan clearance in a cohort of Caucasian patients with solid tumors (n ¼ 167) and a significant reduction of severe diarrhea was seen in patients with the ABCC2*2 haplotype but not carrying a UGT1A1*28 allele (de Jong et al., 2007); protection from irinotecan-related diarrhea may be a consequence of reduced hepatobiliary secretion of irinotecan (or SN-38) by ABCC2. Instead, another retrospective study in irinotecan treated metastatic colorectal cancer patients identified 3 SNPs mapping in ABCG1 (rs425215), ABCC5 (homozygous genotype C/C in the rs562) and OATP1B1/SLCO1B1 transporter gene (rs2306283) associated with irinotecan related GI toxicity (Di Martino et al., 2011). Also in Fabry disease, nine SNPs within four genes (NR1I3, ABCC5, ABCB11, SLCO1B1) were potentially associated with gastrointestinal symptoms (GIS), which represent the earliest presenting events of disease (Di Martino et al., 2016b).

1.26.4.1.3

ABCG2 or breast cancer resistance protein (BCRP)

ABCG2 gene encodes the BCRP, which is also known as mitoxantrone resistance protein (MXR), or placenta-specific ATP-binding cassette transporter (ABCP). It may be responsible for cancer multidrug resistance and can export both unmodified drugs and drug conjugates, including mitoxantrone, bisantrene, epipodophyllotoxins (e.g. etoposide), apixaban, dolutegravir, rosuvastatin (Ueshima et al., 2017; Tsuchiya et al., 2017; Zhang et al., 2006), camptothecins (topotecan and irinotecan) and flavopiridol (Sharom, 2008). ABCG2 (C421A) and the novel ABCG2 mutation (ABCG2-M71V) are also involved in uric acid elimination and its impaired function leading to higher serum uric acid levels causes gout (Nakayama et al., 2017). ABCG2 is highly expressed in the liver, the intestine, the bibB, and the placenta (Zambo et al., 2018). Moreover, C421A correlates with increased concentrations

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of gefitinib and topotecan and higher incidence of gefitinib-induced diarrhea in cancer patients (Li et al., 2007; Sparreboom et al., 2005). Elevated risk of diarrhea was reported as associated with ABCG2 genetic polymorphism in cancer patients treated with rituximab plus cyclophosphamide/doxorubicin/vincristine/prednisone (R-CHOP) therapy (Kim et al., 2008). ABCG2 showed a 7-fold lower affinity for the glucuronide conjugate of SN-38 compared to unconjugated SN-38 (Nakatomi et al., 2001). The C421A variant was also associated with altered statin efficacy (Chasman et al., 2012; Lee et al., 2013). The ABCG5 and ABCG8 proteins form heterodimers, while overexpression of the putative stem cell marker EpCAM with ABCG5 within the buds of colorectal tumors is frequently observed and associated with poor prognosis (Hostettler et al., 2010).

1.26.4.2

Uptake transporters: SLC superfamily

SLCs can be either facilitative or secondary active transporters of uptake and efflux. In the first case, the passive transport of their substrates is down the electrochemical gradient, while in the second case the transport is against the concentration gradient across the membrane by energy-coupling cofactors. These transporters are involved in the transport across biological membranes of several solutes (drugs, inorganic ions, amino acids, lipids, sugars and neurotransmitters) (Fredriksson et al., 2008). The human SLC superfamily includes over 400 members classified into 65 families with almost 20% sequence homology among them (http://slc. bioparadigms.org), but their dysfunction causes several different diseases. Limited studies are available on several SLC proteins. The exception is represented only by SLC51A and SLC51B, members of SLC51 family lacking sequence similarity with each other, encoding the subunits alpha and beta of the organic solute transporter. However, the SLCs are membrane-bound uniporters, antiporters or symporters with different substrate profiles and transport roles. The gene nomenclature system utilizes the root symbol SLC, followed by a numeral indicating the family and a letter indicating the subfamily, followed by a numeral designating the individual transporter gene (e.g., SLC1A2, solute carrier family 1, subfamily A, transporter gene 2). Although named an “organic anion transporting polypeptide” (OATP), substrates of OATP are not only organic anions but also cations, neutral and zwitterionic compounds. The most studied SLC transporters are OATPs, organic cation transporters (OCT), and organic anion transporters (OAT). SLC genetic variants (http://www.pharmGKB.org) may affect their expression, substrate specificity, intrinsic transport activity and disposition, efficacy, and safety of many drugs. SLC proteins are involved as proton or sodium cotransporters in intestinal absorption as well as transport of water soluble vitamins into cells of systemic tissues (Foraker et al., 2003; Hediger, 2013). Of the human OATPs, OATP1A2, OATP1B1, OATP1B3, and OATP2B1 are the best characterized and are involved in transport of thyroid hormones, neutral and cationic compounds, and organic anions as well as in drug disposition and hepatic uptake.

1.26.4.2.1

OATP1B1

OATP1B1, also known as OATP-C, is encoded by the human SLC21A6 gene (formerly SLCO1B1) and is located at the basolateral membrane of hepatocytes where it is expressed uniformly throughout the lobules. It transports endogenous substances (bile acids, bilirubin, glucuronide conjugates, and peptides) as well as exogenous substrates. The first SNP identified was 521T > C (Tamai et al., 2000) but many other SLCO1B1 SNPs have been reported, with 17 different SLCO1B1 alleles identified (Tirona et al., 2001). The rs4149056 (521T > C) SNP is associated with reduced transport activity and is more frequent in Caucasians (15%) and Asians (15%) than in Africans (2%). The frequency of the 388G allele (*1b) in Caucasians, Asians, and African-Americans is about 40%, 60%, and 75%, respectively (International Transporter Consortium et al., 2010). Conflicting evidence is reported on the rs2306283 (388A > G) SNP, in LD with the 521T> C SNP, regarding changes in transport activity. These two SNPs together form four functionally distinct haplotypes and the *5 and *15 haplotypes have been associated with reduced activity. OATP1B1 mediates hepatic uptake of pravastatin, rosuvastatin, as well as of simvastatin, all inhibitors of the 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase (Voora et al., 2009; Link et al., 2008; Schwabedissen et al., 2015). Another noncoding rs4363657 SNP in nearly complete LD with the rs4149056 SNP is considered a strong SNP marker correlated to simvastatin-induced myopathy (Link et al., 2008). The condition of simultaneous and complete deficiency of OATP1B1 and OATP1B3 gives rise to the Rotor syndrome (OMIM #237450) as a consequence of mild hyperbilirubinemia (Dhumeaux and Erlinger, 2013; van de Steeg et al., 2012). Moreover, SLCO1B1 genetic variation and DDIs related to OATP1B1 transport function can modulate statin transport and metabolism, increasing risk for statin-related ADRs (Tamraz et al., 2013). Cyclosporine A markedly increases the plasma concentrations of OATP1B1 substrates, while rifampicin and some HIV protease inhibitors are also OATP1B1 inhibitors (Shitara, 2011). Based on the importance of SNP label requirements, dose adjustments, and product withdrawals, the FDA and EMA recommend in vitro testing of OATP1B1 interactions for drug candidates that are eliminated in part via the liver and/or will be coadministered with OATP1B1 substrates. Nuclear hormone receptors FXR, HNF1a, HNF3b and HNF4a regulate OATP1B1, OATP1B3 and OATP2B1 expression (Jung and Kullak-Ublick, 2003). Clinical inhibitors of OATP1B1 include cyclosporine, gemfibrozil, some statins, antibiotics, and antiretroviral drugs. For example, reports indicate an elevated risk of myopathy and rhabdomyolysis as a consequence of OATP1B1 and OATP1B3 inhibition by gemfibrozil causing an 8-fold elevated AUC for drug substrates of CYP2C8 and CYP3A4 (e.g. repaglinide) and a 2–3-fold elevated AUC for other drugs that are not or partly metabolized by CYP2C8 (e.g. pravastatin, rosuvastatin, and simvastatin) (Niemi et al., 2003). When drug transporters and metabolic enzymes counteract each other in complex ways, there are important DDIs whose prediction, through in vitro tests, is difficult. An example is the interaction of OATP1B1/CYP2C9/CYP3A4/CYP2C8 in the disposition of fluvastatin (Niemi et al., 2005). It is important to consider that polymorphic variants of OATP1B1 (521C) give rise to changes in drug exposure with clinically relevant relapse and related myopathy in the case of statins like pravastatin (Link et al., 2008); FDA recommendations include identification of the

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SLCO1B1 521C variant allele prior to the initiation of statin therapy (https://cpicpgx.org/content/guideline/publication/ simvastatin/2012/22617227-supplement.pdf).

1.26.4.2.2

OATP2B1

OATP2B1, also known as OATP-B, is encoded by the SLC21A9 gene (formerly SLCO2B1) and has transport activity toward substrates similar to OATP1B1 (Tamai et al., 2001). OATP2B1 is expressed on the lumenal membrane of the small intestinal enterocytes (Kobayashi et al., 2003), as well as in liver, lung and ovary, and mediates drug absorption. Intestinal OATP2B1 expression seems to be higher in neonates than in adult (Brouwer et al., 2015). OATP2B1 is a primarily an intestinal lumen organic anion uptake transporter whose activity appears to be pH-dependent with increased activity at acidic pH (Nozawa et al., 2004), although information about its localization remains controversial (Keiser et al., 2017). According to in vivo evidence from DDI studies with inhibitors such as fruit juices, OATP2B1 is a mediator of oral drug absorption with inhibitors causing decreased systemic exposure to OATP2B1 substrates including fexofenadine, atorvastatin, and rosuvastatin (Johnston et al., 2014). Other drugs transported include glibenclamide, glyburide, estrone 3-sulfate (E3S), dehydroepiandrosterone 3-sulfate (DHEAS), prostaglandin E2, and taurocholate. However, proteomics data suggested overlapping expression profiles of OATP2B1 and OATP1B3 in the liver, supporting the hypothesis that OATP2B1 may act directly in liver uptake and indirectly in biliary excretion of xenobiotic substrates (Badee et al., 2015; Prasad et al., 2016). However, the actual contribution of OATP2B1 to liver uptake is still unclear. The accumulation of [11C]erlotinib in A431 cells overexpressing OATP2B1, but not OATP1B1/OATP1B3, was shown to be increased via PET imaging, while the uptake of erlotinib in the liver of human subjects pretreated with rifampin (OATP inhibitor) was decreased (Bauer et al., 2018). These results need additional confirmation due to the lack of specificity of rifampin as a substrate/inhibitor of OATP2B1; nevertheless, OATP2B1 is recognized as a transporter of emerging clinical importance by the International Transporter Consortium (ZamekGliszczynski et al., 2018). Several sequence variants of OATP2B1 have been described: the 1457C > T SNP (rs2306168, SLCO2B1*3), 601G > A SNP (rs35199625), 935G > A SNP (rs12422149), 43C > T SNP (rs56837383), and 26–28, p.QNT of OATP2B1 characterized by a nine-nucleotide and three amino acid (26–28) deletion (Nozawa et al., 2002). In vitro studies demonstrated decreased transport activity for most genomic variants but there are conflicting results among studies. High ethnic variation in allele frequencies was reported for these SNPs; for example, the 1457C > T SNP is distributed in 31% of Asians compared to only 3% of Caucasians. In Japanese subjects, a similar fexofenadine PK profile was observed among all genotypes of the 1457C > T SNP (Imanaga et al., 2011), which was also found to have no effect on absorption of the leukotriene receptor antagonist montelukast. Instead, patients who carry the 935A variant allele of the 935G > A SNP showed lower plasma concentrations of montelukast and lesser pharmacological response (Mougey et al., 2009), but also in this case another study reported the lack of association. It is likely that the effects of SLCO2B1 SNPs on drug absorption could be substrate dependent.

1.26.4.2.3

OATP1B3

OATP1B3, also known as OATP-8 or LST-2, is encoded by the SLC21A8 gene (formerly SLCO1B3). Like OATP1B1, OATP1B3 is considered to be mainly liver-specific in humans. OATP1B3 protein expression was variable and high expression of OATP1B3, OATP2B1, OATP1A2, but not OATP1B1, was found in isolated adult human pancreatic islets. Abundant OATP1B3 expression was revealed in b and a cells as well as in ductal cells of patients affected by pancreatitis or pancreatic adenocarcinoma (Hays et al., 2013) dependent on age and pancreatic disease, but further studies are needed to better clarify OATP1B3’s role and tissue expression. OATP1B3, like OATP1B1, mediates the uptake of bile acids and bilirubin into the liver, and these transporters are involved principally in enterohepatic circulation of bile acids and bilirubin metabolism (Hagenbuch and Stieger, 2013). Several polymorphic variants are present in the SLCO1B3 gene. In Caucasians, the most frequent variants are the 334T> G SNP (rs4149117) and the 699G> A SNP (rs7311358). OATP1B3 influences the hepatic uptake of several drugs, including taxanes (Smith et al., 2005), but conflicting information exists on the associations between paclitaxel clearance and the two OATP1B3 SNPs (Smith et al., 2007), while no associations were found for docetaxel (Chew et al., 2011; Baker et al., 2009). Although several studies have been performed, more information is needed to clarify the role of OATP1B3 polymorphisms in substrate disposition and response. Moreover, SLCO1B3 influences intracellular concentrations of docetaxel and cabazitaxel and thereby influences taxane efficacy, while the loss of SLCO1B3 may be responsible for taxane resistance in prostate cancer (de Morree et al., 2016). OATP1B3 was demonstrated to be expressed de novo in prostate tumors and influences the transport of androgen into these cells (McCrea et al., 2017), representing a mechanism of tumoral resistance to androgen deprivation therapy (ADT) with elevated androgen uptake. Polymorphic variations in the SLCO1B3 gene encoding OATP1B3 are related to clinical outcome in men with prostate cancer receiving ADT or those with castration-resistant prostate cancer. OATP1B3 is also involved in the hepatocellular uptake of several xenobiotic and endogenous metabolites, and a guideline on DDI studies for OATP1B1 and OATP1B3 substrates or inhibitors was released by the FDA.

1.26.4.2.4

Organic anion transporters (OATs)

This class of transporters belongs to the SLC22A gene subfamily (www.ucl.ac.uk/functional-gene-annotation), located on chromosome 6, and the different proteins (OAT1/SLC22A6, OAT2/SLC22A7, OAT3/SLC22A8, OAT4/SLC22A11, OAT5/SLC22A10, OAT6/ SLC22A20, OAT7/SLC22A9, OAT10/SLC22A13 and URAT1/SLC22A12) are localized in the proximal tubules of the kidney to regulate the transport of small hydrophilic organic anions. There is also expression in liver, brain, and placenta. OAT proteins have similar amino acid sequence compared to OCT proteins. OAT1, OAT2 and OAT3 are localized on the basolateral side of the proximal tubule and are encoded by SLC22A6, SLC22A7, and SLC22A8 genes, respectively. OAT1 and OAT3 are involved in the transport of a broad range of drugs including antibiotics, antivirals, diuretics, nonsteroidal anti-inflammatory drugs, toxic metals such as

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mercury, aristolochic acid, vitamins and flavonoids, whereas OAT2 is involved in the transport of zidovudine, cephalosporins, tetracycline, and salicylates. OAT1 and OAT2 play key roles in the transport of gut microbiome metabolites and uremic toxins of kidney disease. OAT3 is expressed in retinal vascular endothelial cells where it modulates the efflux (from vitreous humor to the blood) of organic anions and drugs (Hosoya et al., 2009). OAT4 is expressed at the apical side of the proximal tubule, and mediates the uptake and efflux of estrone sulfate and cilastatin (Khamdang et al., 2004). Moreover, OAT4 in placental syncytiotrophoblast cells (Uehara et al., 2014) mediates the clearance of sulfated steroids across the maternal-fetal barrier showing its important role in regulating the transport of hormones, drugs, and toxins from the fetal blood (Ugele et al., 2008). Human OAT5 is expressed in embryonic and adult liver but all the evidence about its function and substrates is derived from mouse studies (Nigam et al., 2015), which demonstrated that its expression is sex dependent, with higher levels in female probably due to testosterone-dependent downregulation in males (Breljak et al., 2010). OAT6, identified initially in mice, has a human homologous sequence whose activity needs to be clarified (Jacobsson et al., 2007). Its expression is strong in nasal epithelium and weaker in testis (Schnabolk et al., 2006), acting at barrier epithelia. Dexamethasone is an inducer of its expression (Thiebaud et al., 2010). OAT6 substrates include steroids, some drugs and interestingly, volatile odorants. OAT7 is located on the sinusoidal membrane of hepatocytes (Sun et al., 2001) but little information is available at present. Human OAT7 mediates the uptake of estrone sulfate and dehydroepiandrosterone sulfate, like other OATs, but it is not inhibited by probenecid (Shin et al., 2007). OAT8/SLC22A9 (rat) (Yokoyama et al., 2008) and OAT9/ SLC22A27 (mouse) (Wu et al., 2009) were described in animals but human homologs have not been reported to date. OAT10/ SLC22A13 is expressed in kidney, small intestine, and colon and acts at the apical membrane of proximal tubule cells. This transporter shows a sex difference with higher expression in female kidneys (Bahn et al., 2008). URAT1/SLC22A12 is related to OAT1, OAT3, and OAT6, and this gene is paired with OAT4 in the genome (Eraly et al., 2003). Polymorphic variants of URAT1 influence human urate metabolism and are correlated with hyperuricemia and hypouricemia (Enomoto et al., 2002). OAT expression is modulated by nuclear receptors and transcription factors such as HNF4a and HNF1a in conjunction with other phase I/II enzymes. OAT1 has an important role in DDIs and its activity is inhibited by probenecid coadministration, reducing clearance of cidofovir, furosemide, and acyclovir by 32%, 66%, and 32%, respectively (International Transporter et al., 2010). Other drugs are associated with inhibitory effects on OATs including pravastatin, cimetidine, cephalosporin antibiotics, thiazide and loop diuretics, acetazolamide, and some NSAIDs (Cundy, 1999). OATs function, together with ABC transporters, to maintain body fluid and cellular homeostasis through a communication between cells and tissues/organs (Nigam et al., 2015).

1.26.4.2.5

Organic cation transporters (OCTs)

OCT1, OCT2 and OCT3 have been identified in humans, belonging to the SLC22A subfamily and are encoded by SLC22A1, SLC22A2, and SLC22A3 genes, respectively. OCT1 is expressed in hepatocytes and mediates hepatic cellular uptake of drugs, OCT2 has been detected in the proximal tubules of the nephron, while OCT3 is more ubiquitously distributed. Their role is the transport of hydrophilic, low molecular weight organic cations (Takeda et al., 2002). Cimetidine is an inhibitor of OCT2 with consequent reduced clearance of several administered drugs in polytherapy including metformin, pindolol, and dofetilide (International Transporter et al., 2010). Cetirizine also inhibits OCTs, being responsible for a 41% reduction in pilsicainide clearance (Tsuruoka et al., 2006). SLC22A1 is located on chromosome 6, is highly polymorphic and reduced or lost function relates to four coding polymorphisms: rs122083571 (181C > T), rs34059508 (1393G > A), rs34130495 (1201G > A), and rs72552763 (OCT1 Met420 deletion of three bases ATG at codon 420 of exon 7) (Goswami et al., 2014). OCT1 has several drugs as substrates including ondansetron, metformin, morphine, thiamine (vitamin B1) and tramadol and its transport activity shows considerable interindividual variability (Tzvetkov et al., 2011, 2009; Hendrickx et al., 2013). Carriers of low-activity OCT1 alleles showed an increase of 20%–30% (Tzvetkov et al., 2009) in metformin renal clearance, with this effect being population-specific (Mato et al., 2018). The same polymorphism, correlated to low-activity OCT1 variant alleles, decreases hepatic uptake of metformin and consequently is associated with lower blood glucose response (Shu et al., 2007). Moreover, polymorphisms in OCT1 strongly affect the variability of patient response to morphine, which is a substrate of OCT1 and OCT2 and whose hepatocellular uptake might be altered by co-administration of an OCT1/2 inhibitor, such as irinotecan (Zhu et al., 2018). OCT1 affects ranitidine uptake and modulates the role of ranitidine in determining DDIs (Meyer et al., 2017). Five identified polymorphic alleles characterized by slow and deficient activity are OCT1*2 (deletion of Met420), *3 (Arg61Cys), *4 (Gly401Ser), *5 (Gly465Arg/Met420del), and *6 (Cys88Arg/Met420del (Kerb et al., 2002). These variants are loss-of-function alleles (identified as poor OCT1 transporters) and a study of 253 Europeans and White Americans (Tzvetkov et al., 2012) showed that 12% carried two deficient OCT1 alleles and 38% carried one deficient and one fully active OCT1 allele. Poor OCT1 transport activity is known to alter the PK and efficacy of substrates such as metformin, morphine, tropisetron, tramadol, bendamustine, sumatriptan, and fenoterol (Arimany-Nardi et al., 2015; Sundelin et al., 2017). The phenotype of poor OCT1 activity is rare in East Asians, but quite common (more than 80%) in certain populations in South America (Seitz et al., 2015). Sorafenib sensitivity is reduced in individuals carrying germline polymorphisms and tumor-specific somatic mutations (Herraez et al., 2013). Other OCT1 alleles such as OCT1*2, *7, *10, *11, and *13 have a substrate-specific loss of activity (Meyer et al., 2017). Infact, the OCT1*2 allele confers reduced uptake of metformin, morphine, and thiamine and complete loss of uptake of tropisetron and O-desmethyltramadol, while the OCT1*10 (Ser189Leu) allele showed a reduction in metformin, thiamine, and tropisetron uptake but an increase in morphine and debrisoquine uptake (Seitz et al., 2015). The SLC22A2 gene is less polymorphic but the most relevant genomic variant is the rs316019 (808G> T, Ser270Ala) SNP, which modifies the excretion of metformin and cisplatin as well as some endogenous compounds (creatinine and tryptophan) (Ciarimboli et al., 2012). Individuals homozygous for the variant allele (270S) have low-activity and lower renal clearance with higher metformin plasma concentrations in comparison to homozygous wild-type carriers (270A) (Wang et al., 2008b).

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1.26.5

PGx of other enzymes and antigens

1.26.5.1

Vitamin K epoxide reductase complex 1 (VKORC1)

The VKORC1 gene, located on chromosome 16, is an important PD gene involved in warfarin dosage requirement variations. This reductase complex is targeted directly by coumarins considered as vitamin K antagonists (Jackson and Suttie, 1977). Vitamin K is essential for coagulation but requires enzymatic activation for the conversion into its active form required for the carboxylation of glutamic acid residues in some blood-clotting proteins. VKORC1 protein plays a crucial role in Vitamin K cycle. When an individual has a deficiency in vitamin K-dependent clotting factors, the consequent bleeding could be fatal. In humans, VKORC1 mutations can be correlated with a deficit in vitamin K-dependent clotting factors, and with warfarin resistance in both humans and rats (warfarin is also used as a rodenticide). Two variants within the VKORC1 gene, the rs9923231 ( 1639G> A) and rs9934438 (1173C > T) SNPs, are associated with reduced gene expression (Wang et al., 2008a) and a warfarin dose adjustment that varied depending on ethnicity. The 1639A allele of rs9923231 is common in Asians ( 90%), with a lower frequency in Europeans ( 40%), and the lowest frequency in Africans (10%) (Limdi et al., 2010). As previously described, CYP2C8, CYP2C9 and CYP4F2 gene polymorphisms affect warfarin responses along with variations in VKORC1 enzyme expression or activity. For a particular CYP4F2 variant, the 1639AA, AG, and GG genotypes required warfarin-dose adjustment of 3, 5, and 6 mg/day, respectively. In addition, CYP2C9 genotype is important for a more accurate warfarin dose prediction and, together with the VKORC1 genotype, provides predictive biomarkers for reduction of the risk for ADRs during warfarin therapy (Epstein et al., 2010; Anderson et al., 2012).

1.26.5.2

Glucose-6-phosphate dehydrogenase (G6PD)

G6PD deficiency is an X-linked genetic disorder representing the most common enzymopathy worldwide, in which 187 allelic mutations have been identified (Minucci et al., 2012). G6PD is a key enzyme in the pentose phosphate pathway; patients with diminished activity have compromised production of protective intracellular thiols against oxidative stress. Although the deficiency shows ubiquitous cellular distribution, erythrocytes are highly vulnerable to oxidative stress in the G6PD-deficient state with outcomes including neonatal hyperbilirubinemia, acute hemolysis, and chronic nonspherocytic hemolytic anemia. According to the magnitude of the enzyme deficiency and hemolysis severity, G6PD deficiency was classified by the World Health Organization (WHO) into five classes. The most important triggers for hemolytic anemia in G6PD-deficient patients are infections, certain foods, and certain drugs. Historically, G6PD deficiency was known as “favism,” a term describing the hemolytic effect that fava beans showed in patients with G6PD deficiency (McMillan et al., 1993). G6PD deficiency causes diminished cellular capability to regenerate glutathione with higher vulnerability for red blood cells to oxidative damage and the risk of death. Hemolysis in G6PD-deficient patients may evolve under treatment with numerous medications. Several drugs should be avoided by all G6PD-deficient patients (Youngster et al., 2010) (i.e. methylene blue, niridazole (Ambilhar), nitrofurantoin (Furadantin), phenazopyridine (Pyridium), primaquine, rasburicase (Elitek), sulfacetamide, sulfanilamide, sulfapyridine, diaminodiphenyl sulfone (dapsone), while for other drugs there are recommendations for their use with caution and dose reduction in conditions of G6PD-deficiency but without nonspherocytic hemolytic anemia (Youngster et al., 2010) (i.e. acetaminophen (Tylenol), vitamin K, acetylsalicylic acid (aspirin), L-dopa, quinine, streptomycin, chloramphenicol, chlorguanidine (Proguanil, Paludrine), chloroquine, sulfadiazine, sulfaguanidine, etc.). These agents can be used with strict caution and with monitoring, evaluating case by case the potential benefits and risks of their use.

1.26.5.3

Human leukocyte antigen (HLA)

The human leukocyte antigen (HLA) or major histocompatibility complex (MHC) is a group of more than 200 genes on chromosome 6 categorized into three subgroups: class I, class II, and class III. Class I MHC is recognized by CD8þ T cells and consists of three main genes (HLA-A, HLA-B, and HLA-C); Class II MHC is recognized by CD4þ T cells and consists of 6 main genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, and HLA-DRB1); Class III MHC is poorly defined structurally and functionally. HLA class I molecules are expressed ubiquitously among cells and mediate the presentation of peptides to immune cells. HLA genes are highly polymorphic and play a role in determining the susceptibility to autoimmune disease and infections; they have a crucial role in the field of transplantation where the donors and the recipients must be HLA-compatible (Bray et al., 2008). Sporadically, some drugs can induce immune responses, activating the MHC molecules and, in some cases showing elevated risk of ADRs. During treatment, drugs and their metabolites can be recognized by T cell receptors (TCR) with consequent activation of an immune response, because they are considered as foreign antigens. Among drugs, two are of particular importance for ADRs and will be considered here: abacavir (HIV-1 nucleoside-analog reverse-transcriptase inhibitor) and carbamazepine. Abacavir is used to treat human immunodeficiency virus (HIV) infections. Hypersensitivity syndrome appears in 5%–8% of abacavir treated patients, which starts within 6 weeks of treatment and requires treatment discontinuation. Replease change the style of “Re”-exposure to abacavir is dangerous because symptomatology appears rapidly and with greater severity, including fever, rash, malaise/fatigue, nausea, vomiting, and diarrhea (Hetherington et al., 2001). Moreover, 30% of cases show dyspnea, cough, and pharyngitis. Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN) is a rare outcome, but with mortality rate ranges from 10% to more than 30% (Bossi et al., 2002). These reactions were associated with the presence of the HLA-B* 57:01 allele in a study including Australian and British cohorts (Mallal et al., 2002). Another study demonstrated hypersensitivity to abacavir in Caucasians and African-Americans in which the HLA-B* 57:01 allele frequency was 44% and 100%, respectively (Mallal et al., 2002). This allele was distributed in other populations with a frequency much higher in

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Caucasians than in other ethnicities, while in Taiwan it occurs in approximately 0.3% of HIV patients due to the low frequency of the HLA-B* 57:01 allele in the Taiwanese population (Sun et al., 2007). The mechanism underlying abacavir hypersensitivity is probably due to the reaction of short peptide fragments, derived from either the drug or its metabolites, with HLA-B* 57:01 to form a peptideHLA complex that activates CD8þ T cells and a cytokine cascade and the hypersensitivity response. Another important mechanism explaining hypersensitivity is that abacavir might occupy a space below the region of HLA that presents peptides with an alteration in the mechanism for presenting antigen and activation of an autoimmune reaction (Pirmohamed et al., 2015). Yerly et al. hypothesized a role in the hypersensitivity reaction linked to both types of T cells that recognize self-peptide/HLA-B* 57:01 complexes and crossreact with viral peptide/HLA-B* 57:01 complexes, which leads to altered activation of an immunological cascade (Yerly et al., 2017). Based on the above evidence, the US FDA recommends prospective screening for HLA-B* 57:01 for patients who intend to undergo abacavir treatment (Martin et al., 2012; Fan et al., 2017). Another important phenotype- and ethnicity-specific association was demonstrated between carbamazepine-induced hypersensitivity reactions and HLA genes (HLA-B*15:02 allele, HLA-A* 31:01 allele) (Chung et al., 2004). In particular, the correlation of HLA-B* 15:02 with carbamazepine-induced SJS/TEN could be ethnicity-specific according to different genetic backgrounds and the variable allele distributions among ethnic groups (Tangamornsuksan et al., 2013), whereas the HLA-B* 15:11 allele was associated with carbamazepine-induced SJS/TEN in Japanese and Korean populations (Kaniwa et al., 2010). Also for oxcarbazepine, an important drug used in the treatment of epilepsy, the risk of ADRs is linked to the HLA-B* 15:02 allele and genetic tests should be recommended for populations with higher frequency of this allele (Chen et al., 2017). A correlation with other HLA alleles was shown for ADRs in patients treated with allopurinol (HLA-B* 58:01), dapsone (HLA-B* 13:01), flucloxacillin (HLA-B* 57:01) or antithyroid drugs (HLA-B* 38:02-HLA-DRB1* 08:03 haplotype) (Fan et al., 2017).

1.26.6

Conclusion

All scientific evidence cited in this article and the many other articles not reported confirm the great efforts that have occurred since the HapMap-project, which has allowed the LD analysis by Tag-SNP genotyping, in understanding the biological differences that underlie the interindividual variability in drug response. PGx research has allowed the discovery of a multitude of gene-drug response associations that can genetically guide treatment and dosing decisions in attempts to avoid ADRs (Table 2) in patients affected by common and rare diseases. The recognition of specific genetic polymorphisms that significantly influence drug response and/or toxicity has led many companies to develop suitable in vitro diagnostic tests (IVD), an important step during the long process of biomarker validation (Arbitrio et al., 2020). To date, several PGx tests have been cleared or approved by the Center for Devices and Radiological Health for use in clinical laboratories upon prescription by the attending physician (https://www. fda.gov/medical-devices/vitro-diagnostics/nucleic-acid-based-tests). These tests analyze variations in the sequence of known ADME or target genes, such as CYP2C9, VKORC1, CYP2D6, CYP2C19 and UGT1A1 in order to determine genetic carrier status from human peripheral blood or saliva samples. Genotyping technology of these assays is based on the detection of Tag-SNPs by labeled probes on a microarray or real time system. These IVD tests are useful for clinicians to identify high-risk individuals and to improve patient risk stratification, according to personal genetic make-up, in determining therapeutic strategies. However, in order to enable clinical decisions, it is important to integrate personal PGx data with PGx guidelines, reference databases and electronic platforms. For example, information about the CYP2C9 and VKORC1 genotypes may be used as an aid in the identification of patients with greater risk for warfarin sensitivity. In this context, the eQ-PCRÔ LC Warfarin Genotyping kit (TrimGen Corporation), eSensorÒ Warfarin Sensitivity Test (Osmetech Molecular Diagnostics), Sensorg Warfarin Sensitivity Saliva Test (GenMark Diagnostics) and INFINITI 2C9 & VKORC1 Assay for Warfarin (AutoGenomics, Inc.) are specifically designed for the detection and genotyping of CYP2C9*2, CYP2C9*3 and VKORC1 1173C > T alleles. The Gentris Rapid Genotyping AssaydCYP2C9 and VKORC1 (ParagonDx, LLC) and Verigene Warfarin Metabolism Nucleic Acid Test and Verigene System (Nanosphere, Inc.) genotype the single-point polymorphism 1173C > T of the VKORC1 gene in addition to CYP2C9*2 and CYP2C9*3. Other examples of PGx tests are Spartan RX CYP2C19 Test System, Verigene CYP2C19 Nucleic Acid Test and INFINITI CYP2C19 Assay, three assays designed for determining the therapeutic strategy for drugs that are metabolized by the CYP2C19 gene product, and specifically the impact of CYP2C19* 2, *3, and *17 alleles. AmpliChip CYP450 Test, from Roche Molecular Systems, Inc., is a microarray test for CYP2C19*1, *2 and *3 genotyping. An example of a PGx test for CYP2D6 is xTAGÒ CYP2D6 Kit v3 (Luminex Molecular Diagnostics, Inc.), a device used to simultaneously detect and identify a panel of genetic variants in the highly polymorphic CYP2D6 gene including rearrangements associated with the deletion (*5) and duplication genotypes. The Invader UGT1A1 Molecular Assay (Third Wave Technologies Inc.) is an IVD test designed to identify known sequence polymorphisms using a series of probes that are specifically complementary to TA repeat sequences in the “TATA Box” of the UGT1A1 promoter region. In this context, the full translation of PGx knowledge in clinical practice has been slow until now and not applied to a broad variety of biomarkers and drugs. Although the implementation of PGx in clinical practice needs to overcome organizational, medical, technical, ethical and legal issues, all these data encourage progress in PGx research to achieve the vision of personalized medicine.

See Also: 1.19: Drug Metabolism: Cytochrome P450; 1.20: Drug Metabolism: Other Phase I Enzymes; 1.21: Drug Metabolism: Phase II Enzymes; 1.22: Drug Transport-Uptake; 1.23: Drug Transporters: Efflux; 2.11: Translating Pharmacogenomic Research to Therapeutic Potentials (Bench to Bedside)

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1.27

Drug-Drug Interactions With a Pharmacokinetic Basis

Lisa Cheng, Thomas K.H. Chang, and Harvey Wong, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada © 2022 Elsevier Inc. All rights reserved.

1.27.1 1.27.2 1.27.2.1 1.27.2.2 1.27.2.2.1 1.27.2.3 1.27.2.4 1.27.2.4.1 1.27.2.4.2 1.27.3 1.27.3.1 1.27.3.1.1 1.27.3.1.2 1.27.3.1.3 1.27.3.2 1.27.3.3 1.27.3.4 1.27.4 1.27.4.1 1.27.5 1.27.6 1.27.7 References Relevant Websites

Introduction General pharmacokinetic principles Area under the curve and AUCR Clearance The well-stirred model Oral dosing pharmacokinetic concepts Influence of pharmacokinetic properties on magnitude of DDIs for victim compounds: Intravenous vs. oral administration Intravenous (IV) dosing of victim compound Oral (PO) dosing of victim compound Metabolism Reversible inhibition Influence of perpetrator/inhibitor concentrations Involvement of multiple drug-metabolizing enzymes Time-dependent inhibition Induction Putting it together: Evaluating the overall effect of reversible inhibition, TDI, and induction on AUCR Favorable pharmacokinetic properties to avoid being a good DDI victim or perpetrator Alterations to AUC and unbound AUC (AUCu) based on the well-stirred model DDIs based on alterations in plasma protein binding Absorption Transporters Conclusion

699 699 699 700 700 702 702 702 703 703 705 705 706 707 708 709 710 710 711 711 712 713 714 715

Glossary Adverse drug reaction Mild to severe side effects due to administration of one or more therapeutic drugs. Area under the curve (AUC) Area under the blood/plasma concentration vs. time curve and serves as a measurement of overall drug exposure. Bioavailability (F) The fraction of dose administered via an extravascular route (e.g., oral) that enters systemic circulation compared to reference (i.e., intravenous dosing). Clearance (Cl) The volume of blood that is cleared of drug per unit time (units are in volume/time). Drug-drug interaction (DDI) A change in victim drug concentrations occurring with co-administration of a perpetrator that results in toxicity or lack of efficacy. Extraction ratio (E) The fraction of drug that is irreversibly removed from the blood as it passes through an eliminating organ; the value ranges from 0 to 1. Intrinsic clearance (Clint) A measure of the organ’s ability to remove a drug, independent of any limiting or external factors (e.g., protein binding and hepatic blood flow). Pharmacokinetics The study of the body’s effect on the drug focusing on absorption, distribution, metabolism, and excretion. Polypharmacy The administration of two or more therapeutics prescribed for one or more health conditions in the same patient. Systemic drug concentration The amount of unbound or free drug per volume of blood/plasma (units are typically in mass per volume).

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Comprehensive Pharmacology, Volume 1

https://doi.org/10.1016/B978-0-12-820472-6.00179-1

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1.27.1

699

Introduction

The administration of two or more complementary therapeutics, or combinatory drug therapy, is a common approach in treating one or multiple health conditions. Although this method is often effective, the major clinical concern is adverse drug-drug interactions (DDIs). Clinically significant interactions can impact a drug’s pharmacokinetics and/or pharmacodynamics, resulting in treatment failure or adverse reactions relating to toxicity. These effects may be attributed to changes in absorption, distribution, metabolism, or excretion (ADME) and/or altered pharmacological effects (e.g., receptor binding inhibition or cooperativity). Investigations into DDIs are critical during drug discovery and development where clinical outcomes ultimately inform marketable indications and appropriate drug labels. However, cases of severe or fatal adverse drug reactions due to DDIs have led to market-approved drugs being recalled and withdrawn. Mibefradil (PosicorÒ) was discontinued (1998) (Backman, 1999) and for similar reasons, dofetilide (TikosynÒ), sorivudine (UsevirÒ, BrovavirÒ), mebanazine (ActomolÒ), and phenoxypropazine (DrazineÒ) have been withdrawn (Stephens, 2005). DDI-caused adverse drug reactions are often preventable with adequate and appropriate studies and in the ongoing search of new medicines, learned lessons from past mistakes promote the need to monitor and assess potential interactions. The risk of DDIs increases with the number of drugs used concurrently, which can typically be associated with an increase in agedgeriatrics are overall more likely to be prescribed three or more prescription drugs for the treatment of chronic health conditions (National Center for Health Statistics, 2021). Thus, the identification of compounds involved in DDIs and evaluating the consequences (risk vs. benefit analyses) are significant in reducing comorbidities and mortalities. Alternatively, there are favorable, although rare, DDIs that can be beneficial in therapeutic treatments. In these situations, combinatorial drug therapy will alter the active drug’s pharmacokinetics, thereby enabling increased drug exposure. Preliminary assessments of a drug’s profile provide clues regarding its potential as a perpetrator or victim of DDIs. Perpetrators alter the pharmacokinetics of a concurrently or sequentially administered compound, the victim. Among the current methods of drug delivery, the frequency of observed DDIs is greatest with oral administration of both the perpetrator and victim drugs. Following oral drug delivery, ingested formulations travel through a dynamic gastrointestinal environment to be absorbed into the portal vein and undergo hepatic first-pass metabolism prior to entering the systemic circulation. Acidity (or pH) within the gastrointestinal tract can be a determinant of the solubilized drug concentrations available for permeation across the enterocytes and for absorption-mediated DDIs, acid-reducing agents (ARAs) are the prevailing culprits. ARAs act to increase gastric pH, which may impact the solubility of the co-administered drug and in turn, the solubility of the victim decreases. Less of the victim drug freely diffuses across the enterocytes resulting in a diminished fraction absorbed. These events are not applicable to other routes of administration (e.g., intravenous, subcutaneous, rectal, and transdermal), and consequently compounds delivered via other routes of administration are less impacted by concurrent drug therapy. A drug’s elimination route provides crucial information when determining the likelihood of DDIs. Drugs metabolized hepatically have a greater probability of being a perpetrator or victim of DDIs compared to compounds cleared via other pathways. This is largely due to the greater number of therapeutic agents that are metabolized by the liver, as well as hepatic first-pass metabolism, after oral administration. Hepatic-based DDIs can be a result of enzyme inhibition resulting in drug accumulation and toxicity, and/ or induction which reduces systemic concentrations to sub-therapeutic levels. The clinical impact of these interactions is most pronounced with narrow therapeutic index drugs (e.g., anticancer drugs, antipsychotics, and immunosuppressants), which may necessitate therapeutic drug monitoring to optimize dose and dose regimen. The scope of this chapter is to examine DDIs from a pharmacokinetic perspective. Equations involved in DDI risk assessments will also be presented, but the primary focus will be on pharmacokinetic characteristics of perpetrator and victim drugs and their impact on DDIs. The liver is the most common route of elimination and as such, there is an added emphasis on drugs that are hepatically metabolized. Common mechanisms of observed clinical DDIs such as changes in hepatic drug metabolism and hepatic Clint (due to inhibition and induction) and alterations in drug absorption due to acid reducing agents will be a primary focus.

1.27.2

General pharmacokinetic principles

Pharmacokinetics refers to the study of how the body acts on the drug and can be described as the ADME of a drug. We review several important pharmacokinetic concepts that are important for the understanding of DDIs.

1.27.2.1

Area under the curve and AUCR

The area under the curve (AUC) can be estimated from plots of drug concentration in blood/plasma as a function of time, and it is a measure of overall drug exposure across a specified time and is impacted by all aspects of ADME processes. A clinically significant DDI is defined as the change in AUC by twofold or greater when a perpetrator and victim are co-administered, compared to administration of the victim drug alone (Fig. 1). Changes in AUC are expressed as the AUC ratio (AUCR) defined by Eq. (1). Influences on oral absorption by perpetrator drugs such as acid-reducing agents would also be considered clinically relevant when coadministration with these agents results in AUCR that exceeds the defined thresholds (Patel et al., 2020). AUCR ¼

AUCvictimþperpetrator AUCvictim

(1)

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Fig. 1 Plot of plasma concentration (mg/mL) vs. time (h) to visually describe clinically relevant differences in area under the curve (AUC) for an orally administered victim drug. The blue shaded area represents exposure when a victim drug is dosed alone. When the victim is administered in combination with a perpetrator that inhibits (green) or induces (gray) drug metabolizing enzymes, its overall exposure in the body shifts.

Anecdotally, an AUCR  2 (inhibition) or  0.5 (induction) defines a clinically significant DDI as changes less than twofold are unlikely to be detected due to pharmacokinetic variability. Regulatory agencies take a more cautious approach. In early stages of DDI assessment, there are stringent cutoffs to predicted changes in AUCR that trigger additional preclinical experiments and subsequent formal clinical DDI evaluations. For example, the United States Food and Drug Administration (FDA) recommends different thresholds of AUCR for risk assessment of metabolism-based DDIs. If the AUCR falls below or rises above the thresholds, respectively, further evaluations are required to characterize the DDI potential(s) of drug candidates as they move further along the drug development process. Oftentimes, these AUCR thresholds are much lower than the twofold change cited above. Overall, assessments of AUCR are integral to provide the necessary information to determine whether DDIs are a clinical concern. Parent drugs are naturally the focus of interaction studies, but the risk of metabolites being an inhibitor or instigator of DDIs is possible. This phenomenon is rarely observed since metabolites are not typically present at high enough concentrations to result in a clinically relevant DDI. Potential DDI studies should investigate if the AUC of the metabolite is greater than that of the parent drug (AUCmetabolite  AUCparent), or if the metabolite is less polar than the parent drug and has an AUC larger than 25% of the parent drug’s AUC (AUCmetabolite  25% of AUCparent) (U.S. Food and Drug Administration, 2020).

1.27.2.2

Clearance

Clearance (Cl) is defined as the volume of blood that is cleared of drug or compound per unit time. Total body clearance (Cltotal) is additive and incorporates hepatic clearance (ClH), renal clearance (ClR), and clearance by other organs (Clother) (Eq. 2). Hepatic clearance is by far the most common primary route of clearance of many prescription drugs and often Cltotal is approximately equal to ClH. Following hepatic clearance, renal clearance by the kidneys serves as the second most common primary route of drug clearance. Cltotal ¼ ClH þ ClR þ Clother

(2)

Impacts on clearance are reflected in changes in a drug’s half-life (t1/2) (Eq. 3). Changes in t1/2 are detected when a DDI affects Cltotal. t1=2 ¼

lnð2Þ  V d 0:693  V d ¼ Cl Cl

(3)

where Vd is the volume of distribution. The well-stirred model is a common model of hepatic clearance. It will be described in detail because hepatic clearance serves as the main route of elimination for many drugs and the model will enable a better mechanistic understanding of how drug pharmacokinetics impact the magnitude of DDIs.

1.27.2.2.1

The well-stirred model

The well-stirred model (Fig. 2) is a frequently utilized physiologically-based liver pharmacokinetic model that relates hepatic clearance to intrinsic clearance, hepatic blood flow, and the fraction unbound in blood. This model assumes that the drug in the liver compartment is homogeneously mixed or “well-stirred” and is in instantaneous equilibrium with venous blood (Pang and Rowland, 1977). Based on the well-stirred model, hepatic clearance (ClH) is calculated as hepatic blood flow (QH) multiplied by the extraction ratio (E). QH is reported as 20.7 mL/min/kg or for the average 70 kg adult human, 1450 mL/min or 1.45 L/min (Davies and Morris, 1993). E is defined as the fraction of the influent concentration that is eliminated with each pass through the liver (Eq. 4).

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Fig. 2 Schematic of a well-stirred model. The cylindrical tube represents a liver with instantaneous equilibration between drug in the liver and drug exiting the liver. The rate of input is equal to hepatic blood flow (QH)  influent concentration (Cin) and the rate at which the drug exits the liver is QH  effluent concentration (Cout). The difference between the rate of input and output is known as the elimination rate: QH  (Cin  Cout). The dashed line represents the concentration of drug in the liver relative to Cin and Cout. Adapted from Pang KS and Rowland M (1977) Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. Journal of Pharmacokinetics and Biopharmaceutics 5: 625–653. https://doi. org/10.1007/BF01059688.

ClH ¼ QH  E ¼ QH 

Cin  Cout Cin

(4)

where Cin is the influent (or incoming) concentration and Cout is the effluent (or outgoing) concentration from the liver. The E value range is between 0 and 1. Compounds whose extraction ratios are 0.7 or greater are classified as high extraction/ clearance compounds, extraction ratios of 0.3–0.7 correspond to moderate extraction/clearance compounds, and extraction ratios less than 0.3 describe low extraction/clearance compounds. The ratio is typically constant under conditions of linear pharmacokinetics but can vary depending on the dose administered, the patient’s pathophysiological state, and DDIs. Hepatic extraction or clearance is an important property that influences a candidate compound’s potential as a perpetrator or victim in a DDI. The E value can also be expressed in terms of QH, the fraction unbound in the blood (fu), and intrinsic clearance (Clint) (Eq. 5). Clint is a measure of the organ’s ability to clear a volume of blood of drug or compound independent of any limiting, external factors (e.g., protein binding and hepatic blood flow). Experimentally, the Clint value is typically determined in vitro, using liver microsomes or hepatocytes. Clint involves drug-metabolizing enzymes that are finite in capacity and therefore, it is a saturable mechanism (Eq. 6). Alterations in the Clint of DDI victim compounds by DDI perpetrators constitute a large majority of clinically observed DDIs. DDIs that result from alterations in Clint will be discussed in the next section (see Section 1.27.3). E¼ Clint ¼

f u  Clint QH þ f u  Clint

V max V max ðnon  linear conditionsÞ or Clint ¼ ðlinear conditionsÞ Km þ C Km

(5) (6)

where Vmax is the maximum velocity rate of the reaction, Km represents the compound concentration at which 50% of Vmax is achieved, and C denotes the concentration of the compound. Eq. (4) expressed in reference to Clint is show below (Eq. 7). An understanding of Eq. (7) describes ClH for high and low clearance compounds and aids in the understanding of how pharmacokinetic properties of compounds can impact the magnitude of DDIs. ClH ¼ QH 

f u  Clint QH þ f u  Clint

(7)

For low clearance drugs, Clint is much lower than QH such that Eq. (7) can be simplified to Eq. (8), demonstrating that ClH is predominantly a function of Clint and fu. ClH zf u  Clint

(8)

In contrast, Clint is much greater than QH for high clearance compounds such that Eq. (7) can be abbreviated to Eq. (9), which shows that for high extraction compounds, ClH is dependent mainly on QH (flow-limited) and is relatively insensitive to changes in Clint and fu. ClH zQH

(9)

The evaluation of ClH for moderate clearance drugs in DDIs requires the use of the full Eq. (7), signifying Clint or QH can be influential variables.

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1.27.2.3

Oral dosing pharmacokinetic concepts

Bioavailability (F) is the proportion of drug that enters the systemic circulation with respect to the total dose administered. IV administration yields 100% bioavailability since the solubilized drug is injected directly into the systemic circulation, whereas bioavailability of an orally administered compound (Fpo) is dependent on the fraction of dose absorbed (Fa), fraction of drug bypassing gut metabolism (Fg), and fraction of drug escaping elimination by the liver (Fh) (Eq. 10). Fpo ¼ Fa  Fg  Fh

(10)

In a simplified scenario where Fa and Fg are 1, bioavailability is only limited by the liver (Eq. 11) and any change in Clint due to a metabolic DDI can have an impact on oral bioavailability. Fpo ¼ Fh ¼ 1  E ¼ 1 

f u  Clint QH þ f u  Clint

(11)

The liver is uniquely positioned in relation to the gut and systemic circulation (Fig. 3). Orally administered compounds are absorbed in the gut and enter the portal vein where it encounters hepatic “first-pass” metabolismda phenomenon where the absorbed drug passes through the liver once prior to entering the systemic circulation. Here, the liver is situated in the prime position to be exposed to greater concentrations of the drug arriving via the portal vein (accounting for  70% of blood flow entering the liver). In contrast, following intravenous dosing, drug is diluted in the entire blood volume before reaching the liver. These vast differences have DDI implications as the higher concentrations of a perpetrator drug can act on the hepatic drug-metabolizing enzymes to alter Clint of victim drugs following oral dosing (PO) in comparison to intravenous administration. Impacts on Clint that alter ClH of a victim drug will result in subsequent changes in t1/2 (Eq. 3) with inhibitors increasing and inducers decreasing victim drug t1/2. Of note, DDIs due to alterations in Fa (as in the case of interactions with acid reducing agents) would not be anticipated to alter t1/2 in a similar manner to metabolism-based DDIs as ClH should not be affected. Based on concepts discussed above, the route of administration is a contributing factor when determining DDIs.

1.27.2.4

Influence of pharmacokinetic properties on magnitude of DDIs for victim compounds: Intravenous vs. oral administration

Prediction of changes in Clint (due to drug inhibition or induction), ClH, and subsequent AUC changes due to DDIs can be performed based on Eqs. (8) and (9) when a low or high extraction victim compound is co-administered with a perpetrator.

1.27.2.4.1

Intravenous (IV) dosing of victim compound

To illustrate differences in how low and high clearance drugs are impacted by DDIs following IV administration, simulations similar to those of Wilkinson and Shand (1975) were performed and are shown in Fig. 4. For the low extraction/clearance compound, ClH is directly affected by changes in Clint. The outcome of inhibition and induction by a perpetrator, for example, resulting in a two-fold decrease or increase of Clint, respectively, will correspondingly result in a change of the same magnitude for ClH and AUC (Fig. 4). In contrast, the clearance of high extraction/clearance IV drugs is largely insensitive to changes in Clint such that induction or inhibition of drug-metabolizing enzymes associated with an observed twofold change in Clint does not significantly alter ClH and AUC following IV administration. Finally, IV dosed victim compounds are not subject to DDIs based on perpetrators that cause DDIs due to alterations in Fa.

Fig. 3 Orally administered drugs are presented with several obstacles before entering systemic circulation. Compounds are shuttled from the gut via the portal vein and into the liver where they undergo hepatic “first-pass” metabolism. A fraction of hepatically metabolized drugs will be eliminated prior to their entry into the body. QH: hepatic blood flow.

Drug-Drug Interactions With a Pharmacokinetic Basis

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Fig. 4 Representative plasma concentration (mg/mL) vs. time (min) profiles for intravenous administration of (A) low extraction and (B) high extraction drugs. The blue line illustrates the profile of a victim drug when dosed alone. The gray and brown line is representative of the victim drug concentration vs. time profile when co-administered with an inducer and inhibitor, respectively. Intrinsic clearance (Clint) and hepatic clearance (ClH) are in units of L/min. The sample calculations of Clint and ClH are based on a dose of 10 mg. The area under the curve (AUC) is calculated using the equation AUC IV ¼ Dose Cl H and reported in units of mg  min/L. It is assumed that the unbound fraction (fu) remains at 1 and hepatic blood flow (QH) is 1.45 mL/min (Davies and Morris, 1993) and does not change in these simulations. The volume of distribution (Vd) is set a 2.0 L/kg or 140 L for the d average 70 kg adult and can be used to calculate t1/2 (minutes) using t1=2 ¼ 0:693V . Adapted from Wilkinson GR and Shand DG (1975) A Cl physiological approach to hepatic drug clearance. Clinical Pharmacology and Therapeutics 18: 377–390. https://doi.org/10.1002/cpt1975184377.

1.27.2.4.2

Oral (PO) dosing of victim compound

PO administration of victim compounds that are primarily cleared by the liver exhibit different characteristics than those dosed via the IV route. Simulations performed with low and high extraction/clearance compounds are shown in Fig. 5. Following PO administration, both high and low extraction/clearance drugs are sensitive to changes in Clint such that a twofold change in Clint caused by DDI perpetrators would result in an approximate proportional change in AUC (Fig. 5). The difference between the effect of a DDI perpetrator on high and low clearance victim compounds is the cause of change in AUC. For the low clearance drug, Clint and thereby, ClH (Eq. 8), is modified because of the perpetrator. Inhibitors decrease the Clint value whereas Clint increases with inducers. In contrast, for the high clearance drug, the change in AUC is a component of the change in Fh (and consequently F) and, therefore, would have little impact on t1/2 (Fig. 5). Finally, unlike IV administered compounds, orally dosed DDI victim compounds would be sensitive to perpetrators that cause DDIs due to alterations in Fa. In summary, the pharmacokinetic concepts based on the well-stirred model illustrate that route of administration and the pharmacokinetic properties of the victim compound can influence the magnitude of DDI that is observed when victim compounds are co-administered with DDI perpetrators. Fig. 6 maps the location of interactions that may be associated with changes in Fa, Fg, and Fh. We have discussed DDIs with respect to the liver in prior sections. However, DDIs with respect to the gut can be examined using similar pharmacokinetic principles.

1.27.3

Metabolism

The goal of enzyme-facilitated metabolism is detoxification via irreversible biotransformation that increases the molecule’s polarity to promote excretion. Metabolites from phase I metabolism may be further conjugated with endogenous substances (i.e., glucuronide and sulfate) to facilitate elimination. Inhibition or induction of the drug-metabolizing enzymes’ activities result in

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Drug-Drug Interactions With a Pharmacokinetic Basis

Fig. 5 Representative plasma concentration (mg/mL) vs. time (min) profiles for oral administration of (A) low extraction and (B) high extraction drugs. The blue line illustrates the profile of a victim drug when dosed alone. The gray and brown line is representative of the victim drug concentration vs. time profile when co-administered with an inducer and inhibitor, respectively. Intrinsic clearance (Clint) and hepatic clearance (ClH) are in units of L/min. The sample calculations of Clint and ClH are based on a dose of 10 mg. The area under the curve (AUC) is calculated using the equation AUC PO ¼ F Dose and reported in units of mg  min/L. It is assumed that the unbound fraction (fu) remains at 1 and hepatic blood flow (QH) Cl H is 1.45 mL/min and does not change in these simulations (Davies and Morris, 1993). The volume of distribution (Vd) is set a 2.0 L/kg or 140 L for d . Adapted from Wilkinson GR and Shand DG (1975) A the average 70 kg adult and can be used to calculate t1/2 (minutes) using t1=2 ¼ 0:693V Cl physiological approach to hepatic drug clearance. Clinical Pharmacology and Therapeutics 18: 377–390. https://doi.org/10.1002/cpt1975184377.

Fig. 6 Drug-drug interactions may lead to changes in fraction absorbed (Fa), fraction of drug bypassing gut metabolism (Fg), and fraction of drug escaping elimination by the liver (Fh). Perpetrators acting on the gastrointestinal tract (e.g., acid-reducing agents) will have an impact on Fa, whereas those altering the activity of drug-metabolizing enzymes or intrinsic clearance (Clint) can influence Fg and Fh. This figure maps the major changes associated with absorption- and metabolism-mediated interactions.

consequences such as the accumulation of the parent drug or metabolites that may trigger adverse drug reactions or the acceleration of clearance such that drug concentrations fall below the therapeutic window, respectively. Outcomes of preclinical studies can establish a preliminary understanding of the metabolic pathway and the enzymes that contribute to  25% of the candidate compound’s clearance. These clearance pathways are considered to be clinically relevant.

Drug-Drug Interactions With a Pharmacokinetic Basis

705

Laboratory experiments typically use in vitro systems such as microsomes, cytosolic fractions, hepatocytes, and purified enzymes to inform whether an investigational drug should be profiled as a substrate, inhibitor, or inducer. Numerous phase I and II drug-metabolizing enzymes are present throughout the body. They promote xenobiotic biotransformation, which commonly acts to increase solubility, promoting excretion via urine or bile. Cytochrome P450s (P450s, named with the root symbol CYP) are a superfamily of phase I enzymes that facilitate substrate bioactivation and detoxification by catalyzing oxidative reactions. They are major contributors to drug metabolism in the liver and intestine. P450s are ubiquitously associated with metabolic DDIs because they participate in the elimination of most prescription medicines. Certain P450s involved in the oxidation of a wide range of medications are examined for investigational drugs to identify potential DDIs. These include, but are not limited to CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4/5. Regulatory agencies provide a list of index inhibitors and inducers of each relevant P450. Alterations in metabolism via either enzyme inhibition or induction are responsible for the changes in Clint causing decreases and increases, respectively. Based on the well-stirred model, Eq. (12) shows the relation between AUCR and Clint following oral drug administration. AUCR ¼

AUC0po AUCpo

¼

Clint Cl0int

(12)

where AUCpo is the AUC after oral dosing in the absence of the inhibitor, AUC0 po is the AUC in the presence of the inhibitor, Clint is the intrinsic clearance in the absence of the inhibitor and Cl0 int is the intrinsic clearance in the presence of the inhibitor. This relationship shown in Eq. (12) also holds true for low clearance compounds that are dosed via IV administration. For high clearance compounds that are dosed intravenously, ClH is only sensitive to changes in QH negating the applicability of Eq. (12). Mechanistic static equations used in quantitative assessment of DDI magnitude described in the subsequent sections utilize forms of the above relationship to describe the effect of induction and inhibition on Clint and subsequently the overall impact on AUCR.

1.27.3.1

Reversible inhibition

Inhibitors act by blocking drug-metabolizing enzymatic activity reversibly or in a time-dependent manner, thereby decreasing metabolism, Clint, and ClH, and increasing AUC of victim compounds. As most cases of reversible inhibition involve P450s, we will discuss reversible inhibition in the context of these enzymes. However, these mathematical concepts are readily applicable to other drug-metabolizing enzymes such as uridine 50 -diphospho-glucuronosyltransferases (UGTs). Specific to reversible inhibition of one P450 isoform, Eq. (13) is an equation that is commonly used to guide estimations of DDI potential for perpetrators (U.S. Food and Drug Administration, 2020). As discussed, an AUCR  2 for the victim compound is defined as a clinically relevant DDI. AUCR ¼ 1 þ

½I Ki

(13)

where [I] represents the relevant concentration of the perpetrator or interacting drug at steady-state, and Ki is the inhibitor constant. Compounds with lower Ki values are more potent reversible inhibitors. The DDI potential of a compound is dependent on both [I] and Ki. Ki is typically determined from in vitro experiments using liver microsomes in the case of reversible inhibition of hepatic P450 enzymes. DDI potential can be evaluated with [I] being total or unbound systemic or portal vein (for oral compounds) concentrations. Likely, the concentration that is closest to liver concentrations available to interact with P450 enzymes is the most quantitatively relevant. In the current FDA regulatory guidance, DDI potential evaluation utilizes [I] defined as the maximal unbound plasma concentration of the interacting drug at steady-state (U.S. Food and Drug Administration, 2020). In addition to evaluation of inhibition of hepatic P450s, Eq. (13) applies to inhibition of intestinal metabolism. In the case of intestinal metabolism inhibition, [I] can be defined as the intestinal luminal concentration of the interacting drug calculated as the dose/250 mL (U.S. Food and Drug Administration, 2020).

1.27.3.1.1

Influence of perpetrator/inhibitor concentrations

The assessment of AUCR typically involves the use of inhibitor steady-state concentrations, [I]. However, in vivo, inhibitor concentrations oscillate with each dosing. The effects of perpetrator (i.e., inhibitor) concentrations on a victim drug’s intrinsic clearance can be described by Eq. (14). Clint ¼

V V max  ðlinear conditionsÞ
max 
ðnon  linear conditionsÞor Clint ¼ ½I Km  1 þ Ki þ C Km  1 þ K½Ii

(14)

Based on Eq. (14), with increasing inhibitor concentrations or more potent inhibitors (lower Ki value), the term (1 þ K½Ii ) serves to functionally increase Km, which leads to a decrease in Clint. Fig. 7 illustrates the impact of varying inhibitor concentrations on the pharmacokinetics of a victim compound. For this simulation, it is assumed that the victim compound is eliminated entirely by the liver (fm ¼ 1). ClH of the victim compound is determined by the well-stirred model, Clint of the victim compound is governed by Eq. (14), and both the victim and perpetrator are assumed to be administered intravenously. Further, in these simulations, [I] is

706

Drug-Drug Interactions With a Pharmacokinetic Basis

Fig. 7 Simulation of the effect of inhibitor on the pharmacokinetic profile of a victim compound following (A) an infusion of the inhibitor and (B) a bolus of the inhibitor. The simulation assumes that the liver is the primary organ of elimination, hepatic clearance (ClH) is described by the wellstirred model and change in Clint are governed by Eq. (14). Simulations were performed assuming a hepatic blood flow of 1.8 L/h/kg, a Vmax of 1800 nmol/h/kg, a compound Km of 1000 nM, and an inhibitor Ki of 100 nM.

assumed to be the total plasma concentration of the inhibitor and [I]/Ki is assumed to be equal to 1 at time 0. The inhibitor concentration in both Fig. 7A and B is 100 nM at time 0 and Ki is 100 nM indicating that both compounds are equally potent reversible inhibitors. In Fig. 7A, the inhibitor concentration is kept stable, whereas in Fig. 7B, the concentration is assumed to drop rapidly to < 1 nM at 12 h post-dose. Inhibitors with sustained systemic concentrations are likely to be better perpetrators of a metabolic DDI. This would imply that given the same Ki, compounds that are low clearance (with their associated higher systemic exposure) and/or are dosed more frequently would serve as better perpetrators of a metabolism-based DDI. In terms of the victim compound, one would anticipate both an increase in AUC coupled with a longer t1/2 where there is a substantive metabolism-based DDI as illustrated in Fig. 7A. In the case of orally dosed compounds, they will typically have a higher hepatic [I] value due to greater portal vein concentrations following oral absorption. These events result in a higher degree of interaction when perpetrator and victim compounds are coadministered via this route when compared to IV dosing. Additional characteristics following PO dosing have been previously described (see Section 1.27.2.4.2). As with IV dosing, if there is a substantive metabolic DDI following PO dosing, one would anticipate both a higher AUC as well as a longer t1/2 of the victim compound if the victim compound has a low clearance. For victim compounds that are dosed orally and have a high extraction ratio, one would anticipate a higher AUC but no change in t1/2.

1.27.3.1.2

Involvement of multiple drug-metabolizing enzymes

The influence of metabolism by multiple drug-metabolizing enzymes (i.e., P450s, UGTs and others) is an important concept to understand for evaluation of potential DDIs for victim compounds. The impact of reversible inhibition on AUCR for potential victim compounds is described by Eq. (15) (Williams et al., 2004). AUCR ¼


f m f m;enzyme 1þ½I=Ki



1

  þ 1  f m  f m;enzyme

(15)

where fm is the fraction of drug that is cleared by liver metabolism and fm,enzyme is the fraction of drug that is eliminated by the specific enzyme of interest.

Drug-Drug Interactions With a Pharmacokinetic Basis

707

Fig. 8 Effect of fraction metabolized by inhibited pathway (fm,enzyme) on changes in AUCR following administration of inhibitor. The dashed line signifies a significant change (two-fold change) in AUC of the victim compound (AUCR ¼ 2). The ratio of the perpetrator/inhibitor concentration to the Ki value ([I]/Ki) was assumed to be 10 for the simulation. The simulation was based on Eq. (15).

Based on Eq. (15) and Fig. 8, compounds that are primarily eliminated by metabolism and primarily by one enzymatic pathway (fm ¼ 1 and fm,enzyme ¼ 1), are more vulnerable to metabolic DDIs. The simulation demonstrates that a theoretical inhibitor, where its relevant inhibitory concentration [I] is 10 times higher than Ki, will generate an increase in AUC of the victim compound of 11fold when co-administered. When the percentage of the victim compound metabolized by the inhibited enzyme drops to  50% (fm,enzyme ¼ 0.5), the increase in AUC of the victim compound drops drastically to slightly less than a threefold increase. Midazolam is an example of a drug that is metabolized primarily by one enzyme, as it is eliminated primarily by CYP3A4. Often serving as an index substrate for in vitro and in vivo investigations of CYP3A4-based DDIs, midazolam is a gold standard probe to evaluate the changes in clearance and AUC when co-administered with a perpetrator acting on CYP3A4’s activity (inhibition or induction). Ketoconazole is a demonstrated CYP3A4 inhibitor where the co-administration of midazolam with ketoconazole increased the AUC by 7.7-fold (Lam et al., 2003). In contrast, rifampin is a known CYP3A inducer and decreases the AUC of midazolam by 96% (Backman et al., 1996). Consistent with the simulations in Fig. 8, drugs whose elimination is not dependent on one drug-metabolizing enzyme are less likely to be involved in metabolic DDIs. UGTs are typically not the primary clearance pathways, and most compounds are not metabolized by only one UGT isoform. DDIs for UGT substrates have been observed; however, an AUCR  2 is not achieved with these drugs in the presence of UGT inhibitors, as substrates are eliminated by multiple pathways (Williams et al., 2004). When multiple drug-metabolizing enzymes are involved, a change to one enzyme’s activity will not significantly affect the molecule’s elimination because the other enzymes can act as compensatory pathways. As such, victim compounds that are eliminated via more than one enzymatic option are more resistant to large changes in AUC in the presence of inhibitors of one of the clearance pathways. Of note, this concept of resistance to DDI by victim compounds applies for not just clearance via metabolism by P450s or UGTs as discussed, but rather all clearance pathways.

1.27.3.1.3

Time-dependent inhibition

Time-dependent inhibition (TDI) involves binding of the inhibitor to the enzyme (typically a P450) such that degradation of the complex and/or resynthesis of a new enzyme is required to regenerate catalytic activity. There are two types: irreversible (mechanism-based inactivation) and quasi-irreversible (Deodhar et al., 2020). Mechanism-based inactivation occurs when an inhibitor is metabolized forming a reactive intermediate that covalently binds to and inactivates the P450 enzyme. For P450 activity to return to baseline, protein synthesis must occur to replenish inactivated enzymes. The rate at which enzymes can be formed depends on intracellular processes and during this time, substrates will be metabolized to a much lower degree if they are exclusively cleared by the P450 isoform that has been inactivated (Silverman, 1995). Fig. 9 illustrates mechanism-based inactivation. A less common mechanism responsible for TDI is quasi-irreversible inhibition, which occurs when the metabolite’s dissociation kinetics from the heme iron within the P450 active site is so extremely slow that the enzyme is functionally unable to metabolize other substrates. TDI’s impact on the AUC of a co-administered substrate/DDI victim is evaluated using Eq. (16) (Grimm et al., 2009; Vieira et al., 2014; Yang et al., 2008).   kobs þ k deg ðk inact  ½IÞ (16) AUCR ¼ where k obs ¼ k deg ðKI þ ½IÞ

708

Fig. 9

Drug-Drug Interactions With a Pharmacokinetic Basis

Scheme of drug-metabolizing enzyme inactivation by a mechanism-based inactivator. E represents the drug-metabolizing enzyme and

exhibits reversible kinetics for the inhibitor (I) binding to form an enzyme-inhibitor (E • I) complex. The rate constants k1 and k 1 are governing the

formation and dissociation of the E • I complex. k2 characterizes the conversion of E • I to the inactivating species E • I*. There are three possible

outcomes for the E • I* complex: (i) I* is reactive and covalently binds (governed by k4) to E and generates the E-I** complex, (ii) I* is non-reactive and binds to E to form a tight binding complex (E • I*), and (iii) I* is metabolized by E to form metabolite P (governed by k3). The inactivation rate

2 k4 . KI is the inactivator concentration at which the inactivation rate is ½ of kinact and can be constant (kinact) is calculated using: kinact ¼ k2 kþk 3 þk4

 2 3 þk4 determined by KI ¼ k1kþk  k2 kþk . Adapted from Silverman RB (1995) [10] Mechanism-based enzyme inactivators. In: Methods in 1 3 þk4

Enzymology, pp. 240–283. Elsevier. https://doi.org/10.1016/0076-6879(95)49038-8.

where [I] is the relevant concentration of the time-dependent inhibitor at steady state; kobs represents the apparent first-order inactivation rate constant of the targeted enzyme; kdeg denotes the degradation rate constant of the enzyme; kinact is the maximum inactivation rate constant; and KI is the inactivator concentration at which the inactivation rate is ½ of kinact (Vieira et al., 2014). Evaluation of TDI relies on estimates of compound specific kinetic parameters (kinact and KI) shown in Eq. (16) using in vitro studies performed in microsomes or with individual recombinant enzymes. The parameter kdeg is a first-order rate constant used to define the natural degradation of drug-metabolizing enzymes. Estimates of kdeg can influence the quantitative assessment of a TDI. There is a degree of variability in the literature regarding the turnover of P450s and kdeg (Yang et al., 2008). Wang (2010) discusses the importance in the accuracy of the kdeg value in predicting AUC changes using static and physiologically-based pharmacokinetic models. They found that a CYP3A kdeg value of 0.03 h 1 better predicted AUC changes with a series of time-dependent inhibitors, when compared to 0.0077 h 1. Despite this, some simulations of TDI overpredicted observed interactions when kdeg ¼ 0.03 h 1 was used (Wang, 2010). There is currently no consensus established for the kdeg value and the inability to define an accurate kdeg is a hindrance to predicting TDI. Time-dependent inhibitors typically have a greater magnitude of impact on a victim’s AUC, when compared to that for reversible inhibitors. Unlike reversible inhibition, the maximal effect of TDI takes longer to reach, is not reliant on sustained concentrations of the DDI perpetrator, and generally has longer-lasting effects compared to reversible inhibition. Reversal of the effects of a timedependent inhibitor on the victim compound’s AUC will depend on the resynthesis of the inhibited or inactivated enzyme (typically a P450) to baseline concentrations. As with reversible inhibition, if a victim compound is cleared by multiple clearance pathways, there will be a weaker observed effect from TDI as other compensatory pathways are available. Changes in Clint with TDI involve adjustments to both enzyme capacity (Vmax or numerator of Eq. 17) and enzyme affinity (Km or denominator of Eq. 17 (see also Eq. 6). Time-dependent inhibitors have an impact on the total enzyme activity as they inactivate the enzyme upon complex formation and further alter the Km value. V max  kdeg V max  k deg   ðlinear conditionsÞ

ðnon  linear conditionsÞ or Clint ¼ (17) Clint ¼ ½IÞ ðk inact ½IÞ þ C Km  k deg þ ðkðKinact K  k þ m deg ðKI þ½IÞ I þ½IÞ Several time-dependent inhibitors have been withdrawn from the market (see Section 1.27.1), while others related to less severe consequences remain in use with careful indications and warnings (e.g., clopidogrel and gemfibrozil) (Backman et al., 2016). Clopidogrel, sold under brand name PlavixÒ, is administered as a pro-drug and is subsequently converted into the active metabolite by CYP2C19 and CYP3A4. However, glucuronidation of clopidogrel generates the time-dependent inhibitor of CYP2C8, clopidogrel acyl-b-D-glucuronide (Tornio et al., 2014). Similarly, gemfibrozil glucuronide, a phase II metabolite of gemfibrozil, is a timedependent inhibitor of CYP2C8 (Ogilvie et al., 2006).

1.27.3.2

Induction

Inducers promote P450 expression by activating transcriptional regulators, thus increasing their level and activity for victim compound clearance. Cytosolic and nuclear receptors play an important role as xenobiotic sensors that modulate P450 expression levels. Inducers either bind directly to or indirectly activate the aryl hydrocarbon receptor (AhR), pregnane X receptor (PXR), or constitutive androstane receptor (CAR), to trigger nuclear translocation and subsequently promote P450 gene transcription followed by mRNA translation. Most reported cases of clinically relevant DDIs due to induction are related to induction of CYP3A4 (Fahmi and Ripp, 2010; Kenny et al., 2018). Overall, less is known about P450 induction in comparison to P450 inhibition. Early evaluation of induction potential often involves using a simple fold-change method. This method involves measurement of increases in P450 mRNA in human hepatocytes (from a minimum of three different donors) in the presence of the investigational drug compared to a vehicle control. If the results show both (1) concentration dependence and (2) an increase over a threshold of twofold at therapeutically relevant concentrations,

Drug-Drug Interactions With a Pharmacokinetic Basis

709

the investigation drug is considered a positive signal for induction. Further, a response  20% of a positive control (e.g., rifampicin for CYP3A) is included as criteria of a positive signal for induction by the FDA (U.S. Food and Drug Administration, 2020). Calibration or correlation approaches improve on the fold-change method by attempting to assess the magnitude of an induction DDI. These approaches calibrate the induction performance of an investigational drug against a set of known inducers of varying degree of clinical induction in the same experimental system. The evaluations of the relative induction score (RIS) (Eq. 18) or [I]/EC50 value (Eq. 19) are examples of calibration approaches (Fahmi and Ripp, 2010; U.S. Food and Drug Administration, 2020). RIS ¼

Emax  ½I EC50 þ ½I

(18)

½I EC50

(19)

where Emax refers to the maximum induction effect observed (usually from in vitro sources), and EC50 is the concentration when the half-maximal effect is reached. In Eqs. (18) and (19), [I] represents the relevant inducer concentration (considered the maximal unbound plasma concentration of the inducer [perpetrator] at steady-state in the current FDA guidance). As mentioned, RIS and [I]/EC50 estimates for the investigational drug are translated to a level of induction by application of a calibration curve generated using a set of known inducers of the same enzyme being investigated. Calibration methods are simple but have no accommodations for examination of DDI potential for compounds that are both enzyme inducers and inhibitors. Mechanistic mathematical models can also be used to predict quantitative changes in AUC of victim compounds in the presence and absence of the investigation drug. A model quantifying AUC changes (Shou et al., 2008) based on alterations in Clint of the victim compound caused by an inducer/perpetrator is shown in Eq. (20). AUCR ¼

1 
Emax ½In f m  1 þ EC n þ½In þ ð1  f m Þ

(20)

50

where fm refers to the fraction of the compound metabolized by the P450 of interest, n is the Hill coefficient. In its original form, fm in Eq. (20) referred to the fraction of the dose metabolized by CYP3A4 as most reported cases of induction involve regulation of this enzyme. However, the equation is applicable to induction of any phase I or II drug-metabolizing enzyme. [I], Emax and EC50 are defined as shown in Eq. (18). Application of Eq. (20) has advantages over the calibration approaches that apply Eqs. (18) and (19) as the work required for generation of calibration curves is not necessary. Further, as this equation is mechanistic in nature and is based on changes in Clint of the victim compound, it can be incorporated into larger models that address both the effects of induction and inhibition. Eq. (21) shown below, illustrates specifically how induction parameters introduced in Eq. (20) impact the Clint of induction DDI victims.  

Emax ½In Emax ½In V max  1 þ EC V  1 þ n n n n max EC50 þ½I 50 þ½I ðnon  linear conditionsÞ or Clint ¼ Clint ¼ ðlinear conditionsÞ (21) Km þ C Km

1.27.3.3

Putting it together: Evaluating the overall effect of reversible inhibition, TDI, and induction on AUCR

As the changes in AUCR in mechanistic equations are often derived from concepts consistent with the well-stirred model, the net effect on AUCR by a perpetrator can be estimated using a global static mechanistic equation (Eq. 22) (Fahmi and Ripp, 2010; U.S. Food and Drug Administration, 2020). DDIs with respect to the gut can be examined using similar pharmacokinetic principles as for the liver and are incorporated into Eq. (22). !
 1 1   (22) AUCR ¼   ½A h  Bh  Ch   f m þ ð1  f m Þ A g  Bg  Cg  1  Fg þ Fg where A, B, and C denote the effects of reversible inhibition, TDI, and induction, respectively. The first component on the right side of Eq. (22) in parentheses reflects the changes in gut metabolism (the gut is represented by subscript “g”), whereas the second component on the right side in parentheses within the same equation represents the changes in hepatic metabolism (subscript “h” designated for the liver). k deg  , and C ¼ 1 þ dEmax ½I. A, B, and C are derived from AUCR models for reversible inhiIn Eq. (22), A ¼ 1 ½I , B ¼
1þK

i

ðkinact ½I KI þ½I

þkdeg

EC50 þ½I

bition (Eq. 13), TDI (Eq. 16), and induction (Eq. 20), respectively. Fg is the fraction available after gut metabolism, fm is the fraction of hepatic substrate clearance that is mediated by the affected P450 via inhibition or induction, and d is the scaling factor (assumed to be 1 unless prior experience exists where it is another value

710

Drug-Drug Interactions With a Pharmacokinetic Basis

with the specific in vitro system used). Induction decreases the AUC of the victim drug (resulting in AUCR values of < 1) and vice versa, both reversible inhibition and TDI increase the AUC of the victim drug (resulting in AUCR values of > 1). The relevant perpetrator concentrations [I] for Eq. (20) differ in the gut ([I]g) and the liver ([I]h) and can be estimated using Eqs. (23) and (24), respectively. ½Ig ¼

Fa  ka  Dose Qen

(23)

0

!1 Fa  Fg  k a  Dose A @ ½Ih ¼ f u;p  Cmax þ Q h

(24)

RB

where Fa is the fraction of dose absorbed after oral administration, ka is the absorption rate constant, Qen is the blood flow through enterocytes, fu,p is the fraction unbound in the plasma, Qh is the hepatic blood flow, and RB is the blood-to plasma concentration ratio. The use of Eq. (22) allows for the DDI evaluation of an investigational compound that is both an inducer and inhibitor. It is a static equation that combines the three main areas of metabolic DDIs within the gut and liver and describes the magnitude of AUCR. An example of a compound that acts as both a metabolic inhibitor and inducer is the HIV protease inhibitor, ritonavir (brand name NorvirÒ); at therapeutic concentrations, ritonavir potently inhibits CYP3A4 and CYP2B6 and induces CYP1A2, CYP2C9, and CYP2C19 (Foisy et al., 2008; Hsu et al., 1998).

1.27.3.4

Favorable pharmacokinetic properties to avoid being a good DDI victim or perpetrator

The preceding sections describing metabolism-based DDIs include discussion on various pharmacokinetic properties favorable for compounds to avoid being an effective DDI victim or perpetrator. These properties are summarized in Table 1 and are more specific to compounds that are cleared primarily by hepatic metabolism. Favorable pharmacokinetic properties to avoid being an effective victim of DDIs include clearance of the compound by more than one metabolic pathway. Compounds that are dosed using an intermittent dosing regimen are also favorable for avoidance of being a victim of DDIs since there is opportunity to administer the compound in the absence of the inhibitor. Pharmacokinetic properties that aid compounds to avoid being a metabolism-based DDI perpetrator are focused around achieving lower liver exposure to the perpetrator compound. This includes administering the compound via the IV route rather than PO in order to avoid high portal vein concentrations associated with oral dosing, selecting high clearance compounds which typically have lower systemic and liver exposures, and administering the compound by an intermittent dosing regimen, thereby providing an opportunity to avoid co-administration with potential victim compounds. It must be noted that selection of compounds with some of these properties may not be feasible as they may be incompatible with therapeutic efficacy requirements.

1.27.4

Alterations to AUC and unbound AUC (AUCu) based on the well-stirred model

DDIs are defined by changes in AUC (Eq. 12). Based on the presented concepts of the well-stirred model, the influence of alterations in unbound fraction and intrinsic clearance on both total and unbound AUC (AUCu) needs to be examined. In the simplified scenario where Fa and Fg are 1, dependence of AUC and AUCu on fu, Clint and QH following both IV and PO dosing are shown in Table 2. The relationships are derived from Cl ¼ Dose/AUC for IV administration, Cl ¼ Fpo  Dose/AUC for oral administration, and Eqs. (7) and (11) (Table 2). With the exception of a high clearance compound being delivered by the IV route, all other scenarios show dependence on fu and Clint for total AUC and Clint for AUCu. As such, we will examine the impact of alterations in fu and Clint and their impact on DDIs.

Table 1

Favorable pharmacokinetic properties to avoid being a good victim or perpetrator of drug-drug interactions (DDIs).

Favorable compound properties that reduce the potential to be a metabolism-based DDI victim

• •

Compound is cleared by more than one (>1) metabolic pathway Compound is administered using an intermittent dosing regimen

Favorable compound properties that reduce the potential to be a metabolism-based DDI perpetrator

• • • •

Lower perpetrator liver concentrations Compound is dosed IV rather than PO Compound is high Cl resulting in low systemic and liver exposures Compound is given by intermittent dosing regimen

Drug-Drug Interactions With a Pharmacokinetic Basis Table 2

Influence of unbound fraction (fu), intrinsic clearance (Clint), hepatic blood flow (QH) on AUC (area under the curve) and AUCu (unbound AUC) for high and low extraction/clearance compounds. Intravenous dosing

AUC AUCu

1.27.4.1

711

Oral dosing

High ClH

Low ClH

High ClH

Low ClH

Dose QH

Dose fu Clint Dose Clint

Dose fu Clint Dose Clint

Dose fu Clint Dose Clint

fu  Dose QH

DDIs based on alterations in plasma protein binding

Variation in plasma protein binding activity can influence changes in fu, which could potentially lead to an altered pharmacodynamic effect according to the free drug hypothesis or an adjustment to drug clearance. Only compounds that are > 90% protein bound are considered to show clinically significant plasma protein binding. The most common plasma proteins that bind to many drugs are albumin and alpha-1-acid glycoprotein, and their concentrations may fluctuate due to various disease states. Albumin features two pockets and acidic, neutral, and basic compounds exhibiting diverse structures are ligands at these binding sites. Albumin concentration at homeostasis is driven by the difference between synthesis and elimination (i.e., degradation by catabolism or excretion via gastrointestinal and renal routes) (Levitt and Levitt, 2016). Hypoalbuminemia is commonly detected in patients diagnosed with end-stage renal disease. The damaged kidney allows for an increased level of albumin to pass into the urine while protein synthesis in the liver remains constant. From a pharmacokinetics perspective, decreased albumin levels can result in an increase in fu. Alpha-1-acid glycoprotein, a highly acidic protein, exhibits greater affinity for basic compounds. In the healthy state, alpha-1acid glycoprotein represents a considerably smaller percentage ( 1–3%) of total plasma protein in comparison to albumin ( 60%) (Smith and Waters, 2019). However, alpha-1-acid glycoprotein levels significantly increase by up to sixfold in disease states such as infection (bacterial or viral) and inflammation, suggesting that it is a relevant plasma protein for investigations especially in patients with certain diseases (Smith and Waters, 2019). There are documented changes in drug exposure due to alterations in plasma protein binding, although these interactions rarely produce outcomes that are clinically relevant. This lack of clinical impact of plasma protein binding fluctuations has been previously reported (Benet and Hoener, 2002). The scarcity of clinically relevant DDIs is consistent with relationships established in Table 2, where increases in protein binding and the subsequent decrease in fu can cause an increase in total AUC in all situations with the exception of an IV dosed high clearance drug. Despite the change in fu, unbound or active concentration exposure (AUCu) does not change. Therefore, the level of unbound drug that is responsible for both efficacy and toxicity remains the same, which ultimately leads to a lack of clinically relevant outcomes. The only scenario where there is potential for a clinically significant DDI caused by a change in plasma protein binding is in the case of an IV dosed high clearance drug (Rolan, 1994).

1.27.5

Absorption

Interactions that may occur during the absorption of an orally administered compound are a crucial consideration to achieve the desired systemic concentration. The route of administration is a weighted factor when estimating the probability of an absorption-based DDI event. IV administration is traditionally not examined as the dose is injected directly into the systemic circulation, mitigating the absorption phase. Similarly, the delivery of active ingredients by the transdermal and rectal routes rarely exhibits interactions since their indications relieve a localized symptom or health condition. In dermatological practices, many skin conditions are treated with a mixture of compounds with varying pharmacological effects. A frequently prescribed combination of hydroquinone and tretinoin, a derivative of vitamin A, for melasma takes advantage of desquamation induced by the retinoic acid to facilitate the enhanced absorption of hydroquinone (Cheong et al., 2017). In contrast, oral administration is associated with the greatest number of potential interactions because the therapeutic formulation must travel through the dynamic gastrointestinal tract. A compound’s passage through the digestive system starts in the mouth, propels down the esophagus into the stomach where it undergoes mechanical and chemical-induced breakdown, transits through the small, then large intestine and finally, defecated. Its journey into the hepatic portal vein is entirely dependent on the dissolution and permeation processes which are the major events transpiring within the stomach and small intestine, two organs which offer a very different physiology in relation to absorption. Dissolution is the process of disintegrating and dissolving the ingested formulation and precedes permeation, the passive diffusion or active transport of free drug across the intestinal epithelium. The rate at which dissolution occurs is influenced by drug-specific physicochemical characteristics, mechanical stress by the stomach, and the gastrointestinal environment. Permeation is primarily driven by the concentration gradient (between drug dissolved in intestinal fluid and drug in the systemic circulation), and in more rare cases the activity of transporters. The rate-limiting step for oral absorption differs among compounds and can be manipulated by the addition of a second drug or multiple chronic medications that may alter the gastrointestinal microenvironments which can introduce a DDI leading to a fluctuation in dissolution and permeation.

712

Drug-Drug Interactions With a Pharmacokinetic Basis

The stomach houses an acidic environment (baseline z pH 2) and acid-reducing agents (e.g., proton-pump inhibitors (PPIs) and histamine H2 receptor antagonists (H2RAs)) are prescribed to inhibit gastric acid secretion or relieve gastroesophageal reflux disease (GERD) or gastritis symptoms. Geriatrics and cancer patients are most commonly prescribed these medications chronically, and these are the populations that are also likely taking multiple concurrent therapeutics. The caveat to elevating gastric pH and thereby improving the unpleasant feeling of stomach acid overproduction is the impact on the dissolution of ionizable compounds, which comprises a range of chronic medications and most drug candidate compounds in development. Weakly basic and acidic drugs demonstrate pH-dependent solubility, and they are at risk of therapeutic failure or toxicity-induced adverse drug reactions because their dissolution is correlated with fluctuations in gastric pH. With an elevated pH, weakly basic compounds experience a reduction in absorption. Azole antifungals are classified as a basic drug and their co-administration with a PPI is reported to result in a clinically significant decreased exposure (Budha et al., 2012; Krishna et al., 2009). Conversely, weakly acidic drugs would exhibit improved solubility with an increase in pH. Furthermore, contents within the gastric chyme are also a consideration as the introduction of metal ions from, for example, supplements or antacids can contribute to chelation concerns. Coadministration of chelators with certain statins and antibiotics can be problematic as this effectively decreases the concentration of freely soluble drug available for permeation. These concepts can be translated into mathematical equations to better understand how one variable, typically the solubilized concentration within the intestinal tract, can have an impact on absorption. Dissolution is often represented by the modified NoyesWhitney equation (Eq. 25) or Wang-Flanagan model (Dokoumetzidis and Macheras, 2006; Noyes and Whitney, 1897; Wang and Flanagan, 1999). The anatomy of the stomach and small intestine remain constant indicating that the diffusion coefficient (D), surface area (A), and thickness of the diffusion layer (h) are not adjusted. As drug solubility (Cs) can be altered with changes in stomach pH, the rate of dissolution (dxsol/dt) is subject to changes in the concentration gradient, O C or (Cs  Ct), which is observed with DDIs. The modified dissolution rate triggers a domino effect, thereby enabling or impairing the subsequent drug permeation. dx sol =dt ¼

DA ðCs  Ct Þ h

(25)

where Ct correspond to the concentration dissolved in intestinal fluid at time t. Membrane permeability is governed by Fick’s law of diffusion, which dictates that flux (J) is driven by a concentration gradient (Eq. 26). The gradient is formed because there is a difference in concentration across the enterocytes. Flux, or the rate at which the molecule permeates a membrane (dM/dt), is also dependent on the human effective permeability (Peff), the cross-sectional area across which diffusion occurs (A), and the thickness of the barrier (h). J ¼ dM=dt ¼

 A  Peff   Cint  Cpv h

(26)

where the concentration of dissolved compound in the small intestinal tract and portal vein are represented by Cint and Cpv, respectively. The upper limit of Cint is Cs because the concentration available for diffusion is capped at the drug solubility in intestinal fluid. The co-administration of acid-reducing agents can alter the victim drug’s solubility (Cs) and as a result the upper limit of Cint is also lowered. Therefore, acid-reducing agents can have a domino effect on absorption of the victim where an impact on dissolution subsequently affects the concentration available for permeation. Overall, these alterations will ultimately have an impact on Fa. Following permeation, the compound travels to the liver via the portal vein and undergoes hepatic first-pass metabolism (see Section 1.27.2.3). Absorption-based DDIs typically involve adjustments in the intestinal solubility of the compound being evaluated and can impact both dissolution and permeation processes. Oral co-administration of the compound of interest with acidreducing agents can increase pH resulting in either an increase or decrease in oral AUC of the DDI victim. Absorption-based DDIs act to influence fraction absorbed (Fa) of the victim compound. This would imply that an absorption-based DDI would alter F and AUC but have no effect on half-life as Clint would not be altered. In some cases, the outcomes of absorption-based DDIs are not clinically meaningful because the drug concentration in the systemic circulation remains within the therapeutic window despite changes in AUC. Additionally, some ionizable compounds with pKa(s) outside the range over which the gastric pH fluctuates will not be affected by PPIs or H2RAs. Overall, the physicochemical properties of a victim compound can provide insight into the potential consequences when it is co-administered with a perpetrator.

1.27.6

Transporters

In addition to passive diffusion described by the mathematical equations, molecules can traverse the epithelium by facilitated passive diffusion, pinocytosis, and active transport. Facilitated passive diffusion and pinocytosis do not contribute significantly to DDI-mediated variability, whereas transporters mediating active uptake and efflux should be assessed. Nuclear receptors modulate not only P450 enzymes levels, but also the expression of P-glycoprotein and other transporters. Molecules interacting with these ligand-activated transcription factors are capable of inducing multiple enzymes and transporters in concert. Although the

Drug-Drug Interactions With a Pharmacokinetic Basis

713

magnitude of fluctuations in systemic drug exposure elicited by modulation of P450 enzymes typically exceeds that of transporters, they are also a concern for DDIs. Organic cation transporters (OCTs), organic cation/carnitine transporters (OCTNs), and plasma membrane monoamine transporters (PMATs) are important contributors to drug translocation in the intestinal epithelium, among other organs in the body. The events following the drug molecule’s permeation through enterocytes can be summarized into three pathways: (1) shuttled back into the gut lumen by efflux pumps; (2) metabolized by intestinal drug-metabolizing enzymes (see Section 1.27.3); and (3) transported to the liver via the portal vein (see Sections 1.27.2.2.1 and 1.27.2.3) (Fig. 6). Efflux transporters such as the multidrug resistance protein (MDR1, P-glycoprotein, ABCB1) and breast cancer resistance protein (BCRP, ABCG2) are present on the apical membrane. They are responsible for active drug efflux in the small intestine, kidney, liver, and at blood-tissue barriers and their expression throughout the body indicates that inhibition and induction of a transporter can have a multifaceted effect. Transporters have a broad range of substrate specificity and for this reason, their activities can be altered by a variety of ligands. These proteins’ activities are often investigated during preclinical studies, but predictions of transporter-mediated DDIs are difficult because their expression is heterogenous and only more recently has research emerged in this area. Concomitant use of metformin, a widely prescribed antidiabetic medication and OCT substrate, with known inhibitors of OCT results in transporter-mediated interactions that reduces metformin absorption (Liang and Giacomini, 2017). However, these interactions may not be detrimental to metformin absorption for two reasons: (1) metformin has a wide therapeutic window and (2) OCT is also expressed in kidneys and its inhibition will prevent the drug from being secreted into the nephron tubules, thus increasing the plasma concentration and half-life of metformin. Higher systemic drug concentrations can likewise be observed with decreased efflux transporter activity. Increased intracellular drug concentrations are available for intestinal metabolism and entry into the portal vein. All orally administered investigational drugs are tested as potential P-glycoprotein and BRCP substrates, and physicochemical properties of these molecules dictate that they are not likely to be highly permeable and highly soluble drugs. Digoxin is a low permeability compound and a P-glycoprotein probe substrate. It is well documented that concomitant therapy with some P-glycoprotein inhibitors may induce toxicological effects (Elmeliegy et al., 2020; Nader and Foster, 2014). Interestingly, this particular example showcases a transporter-mediated DDI that is not defined by AUCR  2. These adverse signs and symptoms are a result of increased quantities of digoxin traveling through the blood-brain barrier and not being expelled by P-glycoprotein. A potential patient-specific approach to compensate for this phenomenon, if there are no other external factors, is adjusting the dosage such that the proportion of drug available for pharmacological effects does not differ significantly. Despite the potential to be involved in DDIs, there are few drugs that are impacted by transporter-mediated DDIs and variability in pharmacokinetics is more serious in metabolism-based interactions. Generally, there may not be significant influences on AUC with altered transporter activity, yet adverse drug reactions are occasionally observed because impaired transporter activities can result in alterations in drug distribution that can occasionally result in toxicities. Despite the existence of quantitative equations/ models to describe transporter inhibition, the performance of these models requires additional refinement. The deficiency of information on transporter expression in tissues, the lack of specific substrates and inhibitors (compared to P450s) and the scarcity of clinically observed transporter-based DDIs that show changes in AUC are among the reasons that quantitative prediction of transporter DDIs is far less developed compared to metabolism-based DDIs. Additional details regarding the involvement of drug uptake and efflux transporters in DDIs can be found elsewhere in this reference work.

1.27.7

Conclusion

Potential DDIs are an important consideration from the perspective of the design and assessment of new drugs. The most common interactions include reversible inhibition, TDI, and induction of drug-metabolizing enzymes, as well as modifications in oral absorption due to co-administration with acid-reducing agents. Metabolism- and absorption-based DDIs can be differentiated by characterizing the pharmacokinetic behavior of victim compounds. The overall knowledge of the well-stirred model and physiologically-based pharmacokinetic (PBPK) models have increased and as a result, the understanding of metabolism-based DDIs has shown large improvements. Adaptation of mechanistic models for drug inhibition and induction presented in this chapter within the dynamic framework of PBPK models has emerged in recent years and advanced the science of quantitative prediction of metabolism-based DDIs. Despite the advancements in quantitative prediction, potential DDIs require confirmatory assessment in clinical trials when a risk is identified. With the prevalence of polypharmacy ever rising, DDI assessment will remain a crucial part of the characterization of new drug candidates.

See Also: 1.17: Oral Drug Delivery, Absorption and Bioavailability; 1.19: Drug Metabolism: Cytochrome P450; 1.20: Drug Metabolism: Other Phase I Enzymes; 1.21: Drug Metabolism: Phase II Enzymes; 1.22: Drug Transport—Uptake; 1.23: Drug Transporters: Efflux; 1.25: Mathematical Aspects of Clinical Pharmacokinetics

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Journal of the American Chemical Society 19, 930–934. https://doi.org/10.1021/ ja02086a003. Ogilvie, B.W., Zhang, D., Li, W., Rodrigues, A.D., Gipson, A.E., Holsapple, J., Toren, P., Parkinson, A., 2006. Glucuronidation converts gemfibrozil to a potent, metabolismdependent inhibitor of CYP2C8: Implications for drug-drug interactions. Drug Metabolism and Disposition 34, 191–197. https://doi.org/10.1124/dmd.105.007633. Pang, K.S., Rowland, M., 1977. Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel tube” model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. Journal of Pharmacokinetics and Biopharmaceutics 5, 625–653. https:// doi.org/10.1007/BF01059688. Patel, D., Bertz, R., Ren, S., Boulton, D.W., Någård, M., 2020. A systematic review of gastric acid-reducing agent-mediated drug–drug interactions with orally administered medications. 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Appendix I: Drug products withdrawn from the market for safety reasons. In: Talbot, J., Waller, P. (Eds.), Stephens’ Detection of New Adverse Drug Reactions. John Wiley & Sons, Ltd, Chichester, UK, pp. 667–702. https://doi.org/10.1002/0470014199.app1. Tornio, A., Filppula, A.M., Kailari, O., Neuvonen, M., Nyrönen, T.H., Tapaninen, T., Neuvonen, P.J., Niemi, M., Backman, J.T., 2014. Glucuronidation converts clopidogrel to a strong time-dependent inhibitor of CYP2C8: A phase II metabolite as a perpetrator of drug-drug interactions. Clinical Pharmacology and Therapeutics 96, 498–507. https://doi.org/ 10.1038/clpt.2014.141. U.S. Food and Drug Administration, 2020. In Vitro Drug Interaction StudiesdCytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions Guidance for Industry. U.S. Food and Drug Administration, Silver Spring, MD, USA.

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Vieira, M.L.T., Kirby, B., Ragueneau-Majlessi, I., Galetin, A., Chien, J.Y.L., Einolf, H.J., Fahmi, O.A., Fischer, V., Fretland, A., Grime, K., Hall, S.D., Higgs, R., Plowchalk, D., Riley, R., Seibert, E., Skordos, K., Snoeys, J., Venkatakrishnan, K., Waterhouse, T., Obach, R.S., Berglund, E.G., Zhang, L., Zhao, P., Reynolds, K.S., Huang, S.M., 2014. Evaluation of various static in vitro-in vivo extrapolation models for risk assessment of the CYP3A inhibition potential of an investigational drug. Clinical Pharmacology and Therapeutics 95, 189–198. https://doi.org/10.1038/clpt.2013.187. Wang, Y.H., 2010. Confidence assessment of the Simcyp time-based approach and a static mathematical model in predicting clinical drug-drug interactions for mechanism-based CYP3A inhibitors. Drug Metabolism and Disposition 38, 1094–1104. https://doi.org/10.1124/dmd.110.032177. Wang, J., Flanagan, D.R., 1999. General solution for diffusion-controlled dissolution of spherical particles. 1. Theory. Journal of Pharmaceutical Sciences 88, 731–738. https:// doi.org/10.1021/js980236p. Wilkinson, G.R., Shand, D.G., 1975. A physiological approach to hepatic drug clearance. Clinical Pharmacology and Therapeutics 18, 377–390. https://doi.org/10.1002/ cpt1975184377. Williams, J.A., Hyland, R., Jones, B.C., Smith, D.A., Hurst, S., Goosen, T.C., Peterkin, V., Koup, J.R., Ball, S.E., 2004. Drug-drug interactions for UDP-glucuronosyltransferase substrates: A pharmacokinetic explanation for typically observed low exposure (AUCi/AUC) ratios. Drug Metabolism and Disposition 32, 1201–1208. https://doi.org/ 10.1124/dmd.104.000794. Yang, J., Liao, M., Shou, M., Jamei, M., Yeo, K., Tucker, G., Rostami-Hodjegan, A., 2008. Cytochrome P450 turnover: Regulation of synthesis and degradation, methods for determining rates, and implications for the prediction of drug interactions. Current Drug Metabolism 9, 384–393. https://doi.org/10.2174/138920008784746382.

Relevant Websites https://www.fda.gov/regulatory-information/search-fda-guidance-documents/vitro-drug-interaction-studies-cytochrome-p450-enzyme-and-transporter-mediated-drug-interactions. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-drug-interaction-studies-cytochrome-p450-enzyme-and-transporter-mediated-druginteractions.

1.28

ADME of Biologicals and New Therapeutic Modalities

Robert S. Foti, Preclinical Development (ADME & Discovery Toxicology), Merck & Co., Inc, Boston, MA, United States © 2022 Elsevier Inc. All rights reserved.

1.28.1 1.28.1.1 1.28.1.2 1.28.1.3 1.28.1.4 1.28.1.5 1.28.1.6 1.28.1.7 1.28.2 1.28.2.1 1.28.2.2 1.28.2.3 1.28.2.4 1.28.2.5 1.28.2.6 1.28.2.7 1.28.2.8 1.28.3 1.28.3.1 1.28.3.2 1.28.4 References

Introduction and basic overview to ADME properties of biologics Receptor-mediated endocytosis Target-mediated clearance FcRn-mediated clearance Formation of anti-drug antibodies Post-dose modification of biological therapeutics Excretion of biological therapeutics Current strategies to alter the clearance of biological therapeutics Novel therapeutic modalities Peptide therapeutics Hybrid/fusion proteins Antibody drug conjugates Bispecific antibodies Antisense oligonucleotide and siRNA therapeutics Adoptive cellular therapy Oncolytic viruses Proteolysis targeting chimeras Approaches to characterizing the ADME properties of novel modalities Bioanalytical techniques In vitro assays Conclusion

716 717 717 718 718 718 719 719 719 719 722 723 724 726 727 728 730 731 731 732 732 733

Glossary ADME Absorption, distribution, metabolism, elimination. Adoptive cellular therapy Immunotherapy utilizing administration of T-cells (often engineered) to patients to illicit an immune response against a given disease state. Bispecific antibody Antibody specifically engineered to bind multiple antigens or epitopes. Fusion protein Engineered protein produced by fusing two genes together. Oncolytic virus A virus specifically engineered to selectively target and lyse cancer cells. PROTAC Proteolysis targeting chimera.

1.28.1

Introduction and basic overview to ADME properties of biologics

The identification and optimization of new drug interventions has seen a shift in recent decades from a primarily small molecule oriented field to a target biology-driven approach that requires alternative drug modalities to illicit complex pharmacological responses (Fig. 1) (Morrison, 2019; Mullard, 2018; Reichert, 2003). In addition to traditional small molecules and monoclonal antibodies, the pharmaceutical field is increasingly turning to fusion proteins, engineered endogenous proteins, bispecific antibodies, oligonucleotides and gene-based therapies, many of which have seen their initial regulatory approvals in recent years (Fig. 2). The increase in the complexity of drug modalities has driven a simultaneous increase in the development of new experimental approaches to characterize the molecular attributes of drug candidates, including their pharmacokinetic and ADME (absorption, distribution, metabolism and elimination) attributes. It is generally considered that the understanding of the role of drug metabolism in the clearance of therapeutic biologics is still evolving. The following section will serve to give a brief overview of the various mechanisms of protein therapeutic clearance as well as the various approaches currently used to optimize the pharmacokinetic and drug metabolism properties of therapeutic biologics. There are a number of mechanisms by which therapeutic biologics are cleared from the body. While neonatal Fc receptor (FcRn)and target-mediated clearance have received a significant amount of attention in recent years, pre-systemic clearance, receptormediated endocytosis, Fcg-mediated clearance, immune responses or general proteolytic degradation can also play a role in

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https://doi.org/10.1016/B978-0-12-820472-6.00141-9

ADME of Biologicals and New Therapeutic Modalities

717

Percentage of FDA Approved Drugs

100 80

Small Molecule

60 40 20

Biologic

0 1980

1990

Fig. 1

Emergence of novel modalities in recent years (moving average).

Fig. 2

Initial approval dates for novel therapeutic modalities.

2000

2010

2020

eliminating therapeutic biologics from circulation (Lin, 2009; Meibohm, 2006; Meibohm and Braeckman, 2007). Pre-systemic clearance of therapeutic biologics occurs in the interstitial space following subcutaneous or intra-muscular administration and is mediated by soluble peptidases that result in the degradation of the therapeutic. The additional clearance mechanisms are covered in greater detail below.

1.28.1.1

Receptor-mediated endocytosis

Cellular endocytosis can generally be separated into two distinct mechanisms, namely phagocytosis and pinocytosis. While the former is primarily limited to macrophages, monocytes and neutrophils, pinocytosis is a process common to all cells and plays an important role in the transport and clearance of biological therapeutics (Conner and Schmid, 2003; Kruth et al., 2005). The process can be governed either by simple fluid phase dynamics where no binding event occurs or through electrostatic and/or receptor-mediated binding mechanisms. Receptor-mediated endocytosis is the only mechanism noted that is generally considered to be selective and saturable (Lin, 2009). The process is a kinetically efficient mechanism that is initiated by binding of the therapeutic to a cell surface receptor and results in the internalization and endosomal degradation of the biological therapeutic followed by recycling of the receptor back to the cell surface (Goldstein et al., 1985).

1.28.1.2

Target-mediated clearance

Target-mediated clearance, a process functionally similar to receptor-mediated endocytosis, is observed when the pharmacokinetics of a biological therapeutic are altered by a large percentage of the therapeutic being tightly bound to its pharmacological target (Levy, 1994; Mager, 2006). The classic hallmark of target-mediated clearance is the inability to superimpose dose-normalized concentration-time plots, often due to the effects of an extended terminal elimination phase and alterations in the apparent volume of distribution at steady-state drug concentrations (Mager, 2006). This may be primarily due to a rapid equilibrium between target and therapeutic under initial low dose conditions or may be due in part to receptor-mediated endocytosis of the therapeutic that is facilitated by target binding and uptake into cells (Mahmood and Green, 2005). The result in the latter case is often saturable clearance properties of a biological therapeutic that decrease as dose increases and that are also dependent upon physiological factors which may result in an alteration in the expression levels of the target of interest.

718

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1.28.1.3

FcRn-mediated clearance

Extensive research efforts have recently focused on understanding the interactions between IgG and the neonatal Fc receptor (FcRn) and their implications for the clearance of therapeutic biologics (Roopenian and Akilesh, 2007). FcRn binding of monoclonal antibodies and fusion proteins is highly dependent upon the pH of the local environment and as such is able to protect the therapeutics from intracellular lysosomal degradation (Fig. 3). Cellular uptake of IgG molecules is followed by binding to the FcRn receptor under the acidic conditions of the endosome (pH
6.0). Unlike free IgG, FcRn-bound IgG molecules do not undergo lysosomal degradation and instead are trafficked back to the cell surface where the neutral pH results in the release of the antibody or fusion protein from the FcRn and the subsequent return to circulation (Datta-Mannan et al., 2007; Ober et al., 2004a,b; Ward et al., 2003). To this end, a number of approaches to increasing the affinity of IgG therapeutics for the FcRn receptor at pH 6.0 have been used to improve the pharmacokinetics of therapeutics, a topic which will be covered in greater detail later in this section.

1.28.1.4

Formation of anti-drug antibodies

A phenomenon often encountered when evaluating human biological therapeutics in preclinical species but one which may also affect the clearance of therapeutics in the clinic is the formation of anti-drug antibodies (Harding et al., 2010; Lin, 2009; Stas and Lasters, 2009a,b). The presence of anti-drug antibodies is a result of an immunogenic response to the biological therapeutic and can be affected by the species the protein originated from (i.e., human or pre-clinical species), the dosing regimen of the therapeutic and patient-specific properties such as genetic influences and disease state (Lin, 2009). In addition to the potential toxicological consequences of anti-drug antibody formation, there is also the potential for the process to act as a clearance mechanism if the formation of neutralizing antibodies results in an alteration to the clearance parameters of the biological therapeutic (Meibohm, 2006; Milella et al., 1993; Wadhwa et al., 1996). It is important to note that the binding of anti-drug antibodies to biological therapeutics does not always result in an increase in the clearance of the therapeutic but may actually function as a “protective sink” for the antibody, thus decreasing its susceptibility to degradation and in turn increasing the terminal half-life of the therapeutic (Lin, 2009).

1.28.1.5

Post-dose modification of biological therapeutics

Understanding the “metabolism” of biological therapeutics can often be crucial to evaluating the overall pharmacokinetic and pharmacodynamic properties of a biological therapeutic. The basic metabolism or degradation of most proteins is achieved through either lysosomal degradation or through the ubiquitin-proteasome/aggresome pathways (Hideshima et al., 2005; Kisselev et al., 1999; Pshezhetsky and Ashmarina, 2001). Peptide therapeutics, conversely, may undergo degradation either by endo- or exopeptidases that serve to either break peptide bonds within the therapeutic or to remove multiple amino acids from either terminus of the protein (Lobo et al., 2004; Werle and Bernkop-Schnürch, 2006). Furthermore, glycoprotein therapeutics such as lenercept, a fusion protein containing a human IgG-1 Fc domain coupled to two tumor necrosis factor receptor p55 extracellular domains, may be cleared by receptors such as the asialoglycoprotein receptor or the mannose receptor, each of which recognizes specific structural motifs such as complex-type or high mannose N-linked glycans or terminal N-acetylglucosamine residues (Ashwell and Harford, 1982; Jones et al., 2007; Rice and Lee, 1993; Stockert, 1995).

Fig. 3

FcRn-mediated recycling of IgG-type protein therapeutics.

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Recently, more complex post-dose modifications of biological therapeutics are beginning to become clearer. For example, the disulfide bonds of IgG type molecules can be altered through a b-elimination mechanism resulting in the formation of dehydroalanine and persulfide or through multiple reaction intermediates resulting in the formation of a trisulfide bond (Florence, 1980; Galande et al., 2003; Liu and May, 2012; Nashef et al., 1977; Pristatsky et al., 2009). Monoclonal antibodies such as herceptin (anti-HER2) have also been shown to undergo disulfide scrambling, a process that can occur at basic pH or through interactions with free thiols, resulting in the shuffling of disulfide bonds and the formation of a heterogeneous mix of protein isomers (Chang and Li, 2002; Wang et al., 2011).

1.28.1.6

Excretion of biological therapeutics

One final elimination pathway of biological therapeutics that warrants discussion is elimination via excretion. In general, renal excretion via glomerular filtration of biological therapeutics is relatively facile for those therapeutics with a molecular weight of less than 10,000 Da. Between 10,000 and 60,000 Da, the excretion properties of the therapeutic are highly dependent upon physicochemical characteristics such as protein structure and charge, and renal excretion is not often observed for molecules over 60,000 Da (Deen et al., 2001; Haraldsson and Sorensson, 2004). In general, because of the short half-lives often observed for peptide therapeutics and the relatively slow rate of glomerular filtration, metabolic clearance is considered to play a greater role in the clearance of these drugs as compared to renal excretion (Lin, 2009). However for biological therapeutics such as filgrastim that display longer half-lives and still meet the physical requirements for renal excretion, this pathway has been shown to account for a significant portion of the drug’s clearance (Kuwabara et al., 1995; Yang et al., 2004, 2008).

1.28.1.7

Current strategies to alter the clearance of biological therapeutics

In order to extend or improve the pharmacodynamic effects of biological therapeutics or to decrease the frequency of dosing, efforts are often undertaken to increase the half-life of the therapeutic (Fig. 4). Current strategies to extend the half-lives of peptides and proteins often involve increasing the FcRn affinity of the therapeutic or increasing the hydrodynamic radius of the therapeutic (Kontermann, 2009). Examples of approaches to increasing the hydrodynamic volume of biological therapeutics include insertion of polyethylene glycol or polyethylene glycol mimetics into the therapeutic, coupling techniques such as polysialylation or HESylation and mutagenesis approaches to introduce additional N-glycosylation sites into the protein or peptide (Chapman, 2002; Cohen et al., 2004; Fishburn, 2008; Fresenius, 2009; Gregoriadis et al., 1993, 2005; Hamidi et al., 2006; Kontermann, 2009; Newkirk et al., 1996). Parallel approaches to capitalize on the protective effects of FcRn binding have included genetically engineered Fc regions as well as fusion of the therapeutic to Fc regions, albumin, albumin-binding domains or additional proteins, the latter of which will also serve to increase the hydrodynamic volume of the therapeutic as well as its affinity for FcRn (Chaudhury et al., 2003; Chen et al., 2011; Dall’acqua et al., 2006; Dennis et al., 2002; Hinton et al., 2004, 2006; Holt et al., 2008; Jazayeri and Carroll, 2008; Kontermann, 2009; Muller et al., 2007; Petkova et al., 2006; Smith et al., 2001; Tijink et al., 2008; Yazaki et al., 2008). As the majority of the aforementioned mechanisms are applicable to traditional monoclonal antibody therapeutics, more specific examples focused on individual classes of novel and emerging modalities will be discussed in greater detail below.

1.28.2

Novel therapeutic modalities

1.28.2.1

Peptide therapeutics

Peptide therapeutics encompass a class of molecules comprised of a sequence of amino acids with molecular weights generally ranging from 1 kDa to 10 kDa. As a result of their molecular properties which fall somewhere in between traditional small molecule therapeutics and those of larger biologics, peptides have been viewed as an attractive option to elicit pharmacology generally restricted to biologics (i.e., blocking protein-protein interactions) in privileged spaces that are generally more amenable to the biodistribution properties of small molecules (Erak et al., 2018; Fosgerau and Hoffmann, 2015; Kaspar and Reichert, 2013; McGonigle, 2012; Rafferty et al., 2016; Tsomaia, 2015). Indeed, the past two decades have seen the approval of over 30 peptide drugs, with global sales expected to reach nearly $50 billion by 2025 (Ghosh, 2016). Examples of peptide therapeutics with annual sales of over $1 billion include copaxone (glatiramer acetate, exact mechanism unknown for the treatment of multiple sclerosis), lupron (leuprorelin or leuprolide; gonadotropin-releasing hormone analog for the treatment of hormone-dependent cancers or estrogen-dependent diseases), sandostatin (octreotide; somatostatin mimetic for multiple indications), velcade (bortezomib; 26S proteasome inhibitor for the treatment of multiple myeloma and mantle cell lymphoma) and zoladex (goserelin acetate; gonadotropin releasing hormone receptor agonist/antineoplastic agent for the treatment of multiple cancers) (Adams and Kauffman, 2004; Dhib-Jalbut, 2003; Dlugi et al., 1990; Duda et al., 2000; Field-Smith et al., 2006; Fløgstad et al., 1997; Ge et al., 2000; Johnson et al., 1998; Jonat et al., 2002; Kane et al., 2003; Lancranjan et al., 1995; Rinke et al., 2009; Thompson et al., 1991; Williams et al., 1986). While potentially attractive from a pharmacological aspect, the ADME properties of peptide therapeutics provide a number of significant challenges. Peptides often suffer from poor membrane permeability (affecting oral absorption and cellular uptake), short half-lives, limited biodistribution, low oral permeability and need to be administered via parenteral routes of administration

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ADME of Biologicals and New Therapeutic Modalities

Fig. 4 Strategies to extend the half-life of protein therapeutics. Reproduced with permission from Kontermann RE (2011) Strategies for extended serum half-life of protein therapeutics. Current Opinion in Biotechnology 22: 868–876.

(intravenous, subcutaneous, intramuscular or intradermal) (Di, 2015). As such, a number of well-established molecular design approaches, combined with emerging technologies, are in place to attempt to improve the ADME characteristics of peptide therapeutics. Absorption of peptides across cellular membranes and intestinal barriers represents one of the major hurdles to developing a successful peptide therapeutic. Transcellular permeability of all orally administered drugs occurs by a combination of passive diffusion and carrier- or vesicle-driven transport, all of which are governed by the physicochemical properties of the drugs and their binding interactions with transporters or cell-surface receptors. To this end, the ability to form numerous hydrogen bonds coupled with the polar nature of most peptides tends to limit their ability to cross cellular membranes (Rezai et al., 2006). Paracellular absorption is regulated by the architecture of the tight junctions between intestinal epithelial cells, with paracellular pore sizes (fenestrae) estimated to be between 3 Å and 10 Å (Nellans, 1991). Again, the physicochemical properties of most peptides, primarily molecular weight and hydrodynamic radius in this case, limit the ability of peptides to be absorbed through paracellular absorption in the intestine. As with small molecules, peptides can also be substrates for transporter-mediated uptake and efflux mechanisms, such as PepT1 and PepT2 (uptake) or P-glycoprotein (efflux) (Brodin et al., 2002; Inui et al., 2000; Sharom et al., 1996; Sugawara et al., 2000; Wacher et al., 1998). Peptide absorption following intramuscular or subcutaneous administration involves a combination of blood and lymphatic uptake, depending on the molecular weight of the peptide (Di, 2015; Diao and Meibohm, 2013; Lin, 2009). With regard to peptides designed to interact with intracellular targets, nonspecific binding to membrane components, endosomal trapping and lack of stability in the intracellular milieu must all be accounted for as well (Torchilin, 2008). As such, oral delivery of peptide therapeutics and interactions with intracellular targets remain two of the primary challenges with developing peptide drugs. The tissue distribution properties of peptides, like all drugs, are driven by their physicochemical properties, including molecular weight, charge, lipophilicity, extent of binding to endogenous proteins and susceptibility to transport by active processes (Diao and Meibohm, 2013). As the aforementioned properties of peptide therapeutics fall somewhere in between those of small molecule drugs and larger protein biologics, the distribution of peptides out of the central vasculature combines both passive diffusion

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and convective transport into tissues (Lin, 2009). In general, peptides exhibit a low volume of distribution, often 0.2 L/kg or less, similar to extracellular fluid volume (Lin, 2009; Meibohm and Derendorf, 2006; Tang and Meibohm, 2006). Metabolic or chemical instability in circulation or in the gastrointestinal tract further affects the plasma half-lives and oral bioavailability of peptides, with most peptides exhibiting short half-lives and an oral bioavailability of less than 1% (Zhou and Po, 1991). Coupled with the limited absorption characteristics of peptides, the majority of marketed peptide therapeutics are subject to frequent dosing regimens and incorporate intramuscular or subcutaneous routes of administration. The metabolic instability of peptide therapeutics is a direct result of the labile nature of the peptidic structure. Similar to the well-recognized instability of amide bonds in small molecules therapeutics, the multiple amide bonds that are intrinsic to the peptide structure make peptides prime substrates for proteases such as trypsin, chymotrypsin, elastase, pepsin, aminopeptidases, carboxypeptidases and others expressed in blood, in the gastrointestinal tract and in other tissues throughout the body, including the liver and kidneys (Table 1) (Yao et al., 2018). More hydrophilic peptides, such as bradykinin and glucagon-like peptide-1 (GLP-1) are metabolized by soluble enzymes in circulation, including angiotensin converting enzyme and dipeptidyl peptidase IV (DPP-IV), respectively (Dorer et al., 1974; Kieffer et al., 1995; Mentlein et al., 1993). Examples of hepatic peptide metabolism include principal metabolites of GLP-1 in human and mouse hepatocytes as well as human parathyroid hormone peptide in rat liver homogenate (Liao et al., 2010; Sharma et al., 2013). In the kidney, peptides can be eliminated through glomerular filtration, and subsequently subject to endocytosis, lysosomal trapping and ultimately hydrolyzed through proteolysis at the brush border of renal proximal tubule cells (Carone and Peterson, 1980; Ganapathy and Leibach, 1982). As such, in vivo clearance of peptides can often exceed cardiac output, with proteolytic by-products subsequently eliminated through urinary or biliary excretion. Finally, the elimination of peptides and their proteolytic byproducts is thought to occur primarily through renal excretion, though recent reports implicate transporter-mediated biliary excretion as another elimination pathway for peptides (Yamada et al., 1996). The overall extent of renal excretion is highly dependent on the molecular weight of the peptide and as mentioned above, peptides are often conjugated to macromolecules to decrease their rate of renal excretion. As noted, a number of approaches have been utilized to enhance the cellular and oral permeability of peptides. Metabolic structure-activity relationships have been well-described for peptides, with the N-terminal amino acid residue, the number of aromatic amino acids or specific sequence motifs all shown to correlate to peptide stability (Adessi and Soto, 2002; Erak et al., 2018; Gentilucci et al., 2010). Perhaps owing to the many advances in the field of mass spectrometry-based proteomics in recent years, the characterization of suspected cleavage sites by specific proteases is generally well understood. From a structural standpoint, the introduction of intramolecular hydrogen bonds, cyclization or cross-linking (stapled peptides) to decrease the flexible nature of linear peptide sequences and the capping of amide nitrogen atoms with methyl groups are all commonly used by medicinal chemists to increase permeability (Di, 2015; Dougherty et al., 2019; Vorherr, 2015; Yang and Hinner, 2015). However, even with structural modifications to increase permeability, a number of additional challenges remain. For peptides intended to be dosed via oral administration, pH- and protease-mediated instability represent additional challenges to overcome (Damgé et al., 1990; Hamman et al., 2005; Pauletti et al., 1997; Renukuntla et al., 2013; Shaji and Patole, 2008). An emerging area of research with regard to enhancing the oral bioavailability of peptide therapeutics involves the use of permeation enhancers or encapsulating formulations (Maher and Brayden, 2012; Maher et al., 2016; Sadeghi et al., 2008; Wearley, 1991). In a similar fashion, the flexible nature of linear peptides also makes them highly susceptible to proteolytic degradation, and as such, designing cyclic peptide structures can serve to improve both the permeability and stability of peptides, leading to an increased focus on cyclic peptides in recent years (Choi and Joo, 2020; Joo, 2012; Ong et al., 2017; Yuki et al., 2020). Beyond cyclization, capping terminal amino acids

Table 1

Common in vivo proteases and specific sites of peptide cleavage (MEROPS Peptidase Database accessed 6/18/2020).

Enzyme

Site(s) of Expression

Enzyme Class

Specific Cleavage Site(s)

Carboxypeptidase A Carboxypeptidase B Chymotrypsin Elastase Pepsin Trypsin Aminopeptidase A Aminopeptidase N Aminopeptidase P Aminopeptidase W Carboxypeptidase M Carboxypeptidase P Dipeptidyl Peptidase IV Endopeptidase 24.11 Endopeptidase 24.18 Endopeptidase 3 Microsomal dipeptidase

Pancreatic peptidase

Exoprotease Exoprotease Endoprotease Endoprotease Endoprotease Endoprotease Exoprotease Exoprotease Exoprotease Exoprotease Exoprotease Exoprotease Exoprotease Endoprotease Endoprotease Endoprotease Exoprotease

Gly, Leu, Phe, Trp Arg, Lys Leu, Phe, Trp, Tyr Ala, Gly, Ser Leu, Phe, Trp, Tyr Arg, Lys Asp, Glu Many Pro Phe, Trp, Tyr Arg, Lys Ala, Gly, Pro Ala, Pro Ile, Leu, Phe, Tyr Many Arg Many

Intestinal Brush Border

722 Table 2

ADME of Biologicals and New Therapeutic Modalities Structural modifications to improve ADME properties of peptides.

Peptide Modification

Desired Outcome

Linear Peptide Cyclization Stapled Peptide N-methylation Ester substitution D-amino acid substitution Addition of Intramolecular H-bonds N-acetylation/C-amidation Addition of unnatural amino acids Lipidation Conjugation to macromolecules

– Increase rigidity, stability, permeability Increase “helicity,” stability, potency, permeability Reduce hydrogen bonding potential, increase active uptake Increase permeability without altering conformation Stability against proteases Reduce intermolecular H-bonds and add rigidity Protect termini from exopeptidases Introduce steric hinderance/protease recognition Binding to lipoproteins/albumin, increase T1/2, GI stability and oral permeability Reduce renal clearance, engage protein recycling mechanisms

via N-acetylation or C-amidation, the introduction of non-natural amino acids and replacing L-amino acids with D-amino acids are all methods used to increase the stability of peptides. The conjugation of peptides to albumin, polyethylene glycol, FcRn-binding motifs or full monoclonal antibodies can also be used to take advantage of cellular recycling pathways while decreasing the rate of renal elimination and will be described elsewhere in this article. Examples of structural modifications to enhance peptide permeability and stability are shown in Table 2.

1.28.2.2

Hybrid/fusion proteins

Fusion (also referred to as hybrid or chimeric) proteins comprise a genetically engineered class of molecules designed to improve the pharmacokinetic or pharmacodynamic properties of protein therapeutics by taking advantage of additional protein interactions (Beck and Reichert, 2011). The engineering of fusion proteins involves linking multiple genes or gene fragments that encode for different protein sequences and expressing the linked genes through recombinant DNA approaches. Fusion approaches have been applied to enhance the properties of cytokines, blood coagulation factors, growth factors, cell surface receptors, endogenous enzymes, as well as other therapeutic molecules (Czajkowsky et al., 2012; Huang, 2009; Schmidt, 2009; Wu and Sun, 2014; Yang et al., 2018). The more common protein modifications include fusion to the Fc-binding region on an antibody (Fc-fusion), to recombinant serum albumin or to XTEN (a recombinant, biodegradable, hydrophilic polypeptide designed to increase half-life) (Podust et al., 2016). Other fusions include protein domains designed to interact with the transferrin receptor (further discussed in below in Section 1.28.2.4), carboxy-terminal peptides, or elastin-like peptides. The initial proof of concept for the therapeutic potential for fusion proteins came in 1989, through engineering of CD4 to a Fc domain for administration to HIV-1 patients (Traunecker et al., 1989). Etanercept (EnbrelÒ) was the first U.S. Food and Drug Administration (FDA)-approved Fc-fusion protein, approved in 1998 for the treatment of autoimmune diseases through the inhibition of tumor necrosis factor a (TNFa) (Amgen and Pharmaceuticals W, 2008). From a pharmacokinetic standpoint, fusion proteins represent a strategy to increase the half-life of many of the aforementioned molecules which may suffer from rapid in vivo elimination (Beck and Reichert, 2011). Efforts to increase the half-life of GLP-1, which has a half-life of less than 5 min in vivo (due to rapid renal elimination and catabolic cleavage of the GLP-1 amino terminus by DPP-IV), demonstrate that conjugation to albumin can increase the circulating half-life of GLP-1 to approximately 11 h while conjugation to an immunoglobulin Fc domain increases the half-life of GLP-1 to over 30 h (Baggio et al., 2004; Kim et al., 2003; Picha et al., 2008). A major advantage of Fc-fusion proteins is their ability to avoid cellular degradation through interaction with the FcRn receptor (Czajkowsky et al., 2012; Kontermann, 2011; Levin et al., 2015; Sockolosky and Szoka, 2015). However, even with the addition of an FcRn binding region, many Fc-fusion proteins display inferior pharmacokinetic profiles relative to unmodified IgG proteins (Hopp et al., 2010; Levin et al., 2015; Wu and Senter, 2005). Potential reasons include altered binding to FcRn, the effects of target-mediated drug disposition and the potential for catabolic degradation of engineered proteins (Souders et al., 2015; Unverdorben et al., 2016). Altered glycosylation patterns of Fc-fusion proteins relative to full antibodies have also been shown to result in the more rapid elimination of Fc-fusions (Liu, 2015). Species differences in FcRn binding affinities must also be taken into account when translating PK/PD profiles from preclinical species into human outcomes. For example, while interactions with human FcRn appear to be fairly specific (interacting only with Fc domains from human, rabbit or guinea pig), mouse FcRn will bind Fc domains from multiple species, including human (Ober et al., 2001). Beyond half-life extension, fusion to an engineered Fc may also enhance the effector function of the resulting fusion protein, allowing it to effectively exhibit mechanisms of antibody-dependent or complement-dependent cell-mediated cytotoxicity through interactions with the Fc receptor.

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A number of potential challenges to fusion proteins must also be considered. First, the engineered structure of fusion proteins lends itself to potential unfolding and aggregation not only in vivo, but also under conditions commonly used in protein expression, purification and storage (Yang et al., 2018). Next, catabolic instability either in vitro (i.e., production cell lines) or in vivo (i.e., stability in blood, plasma or serum) can hamper the development of Fc-fusion proteins into successful drug candidates (Chakrabarti et al., 2016; VÉniant et al., 2012). Fusion of endogenous molecules to Fc domains can also alter primary sites of catabolic instability. For example, fibroblast growth factor 21 (FGF21) is thought to be primarily catabolized through N-terminal clipping by DPP-IV and by fibroblast activation protein-mediated clipping between proline-171 and serine-172 (Zhen et al., 2016). Fusion of an Fc domain to the N-terminus of FGF21 resulted in attenuation of the N-terminal catabolism, however catabolism at proline171 and subsequently alanine-180 were identified in the Fc-fusion (Hecht et al., 2012). As the C-terminus of FGF21 is critical to maintaining pharmacological activity, additional protein engineering efforts identified that introduction of a glutamic acid residue in place of the alanine at position 180 resulted in decreased catabolism and prolonged pharmacological activity (Stanislaus et al., 2017).

1.28.2.3

Antibody drug conjugates

Antibody drug conjugates (ADC) represent a class of targeted chemotherapeutics that utilizes a monoclonal antibody linked to a cytotoxic warhead to direct the warhead to cancer cells overexpressing a target of interest (Devita and Chu, 2008). ADCs achieve their pharmacological effect by binding to an antigen located on the surface of target cells, followed by internalization of the ADCantigen complex (Beck and Reichert, 2014; Jain et al., 2015; Joubert et al., 2020). The internalized complex is subsequently degraded within the lysosomal compartment of the cells, releasing the cytotoxic warhead which is then free to bind to its intended intracellular target (Jain et al., 2015). Examples of approved ADCs include Kadcyla (trastuzumab-DM1), Adcetris (brentuximab-MMAE), Besponsa (inotuzumab-ozogamicin) and Mylotarg (gemtuzumab-ozogamicin). The majority of ADCs approved or in development are generally comprised of either an auristatin, maytansine, calicheamicin, or duocarmycin derivative conjugated to an IgG1-type antibody (Adair et al., 2012; Deslandes, 2014). The chemical warhead is covalently linked to lysine or cysteine residues (engineered cysteines or reduced disulfide chains) via various linker chemistries. Linkers can either be cleavable or non-cleavable, with the total number of warhead-linkers that are conjugated to an antibody being defined as the drug-to-antibody ratio (DAR) (Ford et al., 1983; Trail et al., 1993). The pharmacological efficacy of an ADC is a function of the potency of the warhead, the affinity of the antibody for a target receptor, the expression level of the receptor on the cell surface and the internalization rate of the receptor. ADCs are subject to many of the ADME concepts previously covered in Section 1.28.1. Optimizing the ADME and pharmacokinetic properties of ADCs involves assessing and enhancing the properties of the intact ADC as well as each of the warhead, linker and antibody individually, as ADCs retain many of the properties of both small molecules (cytochrome P450 metabolism, protein binding, drug interactions) and protein therapeutics (target binding, FcRn recycling, effector function). The clearance of ADCs is driven by a combination of cleavage of the warhead from the antibody (i.e., linker stability) and clearance of the intact ADC via many of the aforementioned mechanisms. Target mediated drug disposition, FcRn recycling, and proteolysis all play roles in the clearance of ADCs, with clearance generally being linear with respect to dose at clinically relevant doses of ADCs (Lin et al., 2013). Linker chemistry, warhead, and DAR all contribute to the overall pharmacokinetic profile of an ADC (Hamblett et al., 2004; Lyon et al., 2015). In general, ADCs with higher DAR ratios exhibit a propensity for aggregation and higher clearance, leading to an ensemble of pharmacokinetic properties for an ADC as payloads are released (Hamblett et al., 2004). ADC aggregation has been hypothesized as potentially leading to hepatotoxicity and immunogenicity (Joubert et al., 2012). Relative to unconjugated antibodies, ADCs often tend to show higher clearance and shorter half-lives (Strohl, 2018). In the oncology therapeutic area, a primary goal of an ADC approach is to increase efficacy while reducing off-target effects by limiting tissue distribution primarily to tumor tissue (Dan et al., 2018; Peters and Brown, 2015; Strebhardt and Ullrich, 2008; Su et al., 2018). Similar to unconjugated antibodies, the volume of distribution of ADCs tends to be low (
0.15–0.2 L/kg at steady state), suggesting that the majority of the dose remains in the vasculature or central compartment (Boswell et al., 2011; Erickson and Lambert, 2012; Mould and Green, 2010; Tabrizi et al., 2006). However, nonspecific distribution into healthy tissue remains an issue, especially for ADCs targeting highly expressed antigens (Coats et al., 2019; Ponte et al., 2021). Choice of linker chemistry and site(s) of conjugation are two major determinants of the ADME properties of ADCs. Linkers are classified into cleavable or non-cleavable, with the former utilizing pH, protease activity or reduction by glutathione to release their warheads (Jain et al., 2015). Non-cleavable linkers are released during lysosomal degradation of the antibody. While initial efforts often utilized non-specific lysine conjugation via 3-amine side chains, more recent advancements have allowed for site-specific conjugation resulting in more uniform populations of ADCs (Junutula et al., 2008a,b; Panowski et al., 2014). Linkers are often optimized to reduce the potential for chemical decoupling or enzymatic cleavage while in circulation (Hengel et al., 2014; Kaur et al., 2013; Kellogg et al., 2011; Pillow et al., 2017; Shen et al., 2012; Su et al., 2018; Tumey et al., 2015; Zhang et al., 2016). Ester hydrolysis, acetate cleavage and amide hydrolysis have all been shown to be key mechanisms of linker cleavage and subsequent warhead “deactivation” (Su et al., 2018). The site of conjugation of a linker to an antibody can also affect the overall ADME properties of the ADC (Biswas et al., 2017; Murray et al., 2019; Shen et al., 2012).

724 1.28.2.4

ADME of Biologicals and New Therapeutic Modalities Bispecific antibodies

Bispecific antibodies are antibodies with the ability to interact with more than one antigen or epitope (Kontermann and Brinkmann, 2015; Sedykh et al., 2018). Numerous bispecific antibody formats have been engineered, often grouped into IgG-like and non-IgGlike proteins (Fan et al., 2015; Kontermann, 2011, 2016; Kontermann and Brinkmann, 2015). IgG-like bispecific antibodies retain the Fc region found in traditional monoclonal antibodies and as such, can bind to Fc receptors to activate the immune system (effector function) while also utilizing FcRn receptor binding as a cellular recycling pathway, resulting in the typically long halflife observed for monoclonal antibodies (Fig. 5). Non-IgG-like bispecific antibodies, on the other hand, lack the Fc region and are comprised of Fab fragments, multivalent single-chain variable fragments or fusion proteins that incorporate the variable chains of two monoclonal antibodies. As a result of the decreased molecular weight of non-IgG-like bispecific antibodies, this class often displays increased tissue distribution relative to IgG-like bispecific antibodies, however suffers from shorter half-lives due to the lack of the FcRn recycling pathway (Kontermann and Brinkmann, 2015). The pharmacological advantages of bispecific antibodies lie in their aforementioned ability to facilitate tumor growth inhibition through enhanced immune function, to simultaneously agonize or antagonize multiple biochemical pathways which may result in additive or synergistic pharmacology and to simultaneously bind multiple cell-surface proteins, resulting in improved binding specificity for a given cell type (Fan et al., 2015). Bispecific T-cell engaging antibodies are a subset of bispecific antibodies specifically designed to bind to receptors on the surfaces of both cytotoxic T lymphocytes and tumor cells, thus engaging the T cells to directly kill tumor cells (Perez et al., 1985; Staerz et al., 1985). Bispecific antibodies combining anti-CD3 binding on T cells with binding against over 40 antigens expressed on the surface of various tumor cells are currently reported to be in various stages of preclinical development (Wu and Cheung, 2018). Similar to other antibody formats, the in vivo half-lives of bispecific T-cell engaging antibodies are determined by a combination of targetmediated drug disposition, FcRn recycling and renal elimination (Liu et al., 2017; Zhu et al., 2016). Another subset of bispecific antibodies worth noting in any ADME-focused discussion are bispecific antibodies designed to bind to the transferrin receptor as well as a target antigen of interest within the brain or central nervous system. Crossing the blood-brain barrier (BBB) presents a formidable biodistribution challenge for protein therapeutics owing to their size and physicochemical properties. To overcome this, bispecific antibodies have been designed with one complementarity determining region (CDR) targeting the transferrin receptor, a membrane protein which is expressed on brain endothelial cells as well as in other tissues. Antibodies which bind to the transferrin receptor then undergo receptor-mediated transcytosis and are released on the abluminal side of the blood-brain barrier, thus enabling interaction with targets in the CNS (Fig. 6) (Pardridge, 2015, 2017). Recent work by Kariolis et al. and Ullman et al. describe the development of a bispecific anti-BACE1 (beta-secretase 1) transferrin-binding antibody, dubbed

Fig. 5 Examples of bispecific antibody formats. Reproduced with permission from Kontermann RE and Brinkmann U (2015) Bispecific antibodies. Drug Discovery Today 20: 838–847.

ADME of Biologicals and New Therapeutic Modalities

Fig. 6

725

Bispecific antibody targeting the transferrin receptor facilities transport across the blood brain barrier (BBB).

a BBB transport vehicle, which resulted in a nearly 40-fold increase in the uptake of the bispecific antibody relative to the antibody targeting only the CNS target BACE1 (Kariolis et al., 2020; Ullman et al., 2020). Similar approaches have been evaluated using bispecific antibodies that target the insulin receptor or the low density lipoprotein receptor related protein (Xiao and Gan, 2013). The relative affinities of the individual CDRs for their specific targets can also affect the biodistribution properties of bispecific antibodies. Seminal work characterizing an anti-HER2/CD3 bispecific T-cell engaging antibody demonstrated that plasma AUC and distribution into the spleen and lymph nodes was dependent on the CD3 affinity of the antibody (Mandikian et al., 2018). Tumor distribution was more dependent on the HER2-targeting CDR affinity, with high CD3 affinity limiting the distribution of the bispecific antibody into tumors, suggesting the need to optimize the relative binding affinities of each CDR. Further, higher CD3 affinity resulted in increased biodistribution to secondary lymphatic tissues and subsequent increased catabolism (Mandikian et al., 2018). Similar to many of the ADME challenges faced with peptides or antibody fragments, the short in vivo half-lives of non-IgG-like bispecific antibodies has resulted in numerous half-life extension (HLE) strategies aimed at reducing the frequency of dosing for these therapeutics. Conjugation to polyethylene glycol or fusion to albumin, albumin-binding motifs or Fc-fragments represent common approaches to decrease the clearance and increase the half-life of bispecific antibodies, though it should be noted that such approaches can also negatively affect the biodistribution properties of the molecule and as such, a balance must be achieved between clinically acceptable clearance parameters and the ability of the antibody to distribute to its pharmacological site of action (Mandikian et al., 2018). Further, as a result of their relatively small molecular weight, non-IgG-like bispecific antibodies may be subject to rapid renal filtration, another parameter which must be optimized in the development of bispecific antibodies (Fan et al., 2015; Wang et al., 2019). A number of clinical dosing paradigms have been utilized to overcome the ADME and pharmacokinetic challenges of bispecific antibodies. Blinatumomab, the first FDA approved bispecific T-cell engaging antibody (BiTeÒ) that targets CD3 on T-cells and CD19 on tumor cells employs an intravenous infusion in the clinic to overcome its short systemic half-life of approximately 2 h (Zhu et al., 2016). Catumaxomab, the first bispecific antibody approved by the European Medicines Agency (EMA) against T-cell CD3 and tumor cell EpCAM (epithelial cell adhesion molecule) is dosed by intraperitoneal administration and intrapleural dosing regimens have been explored in order to maximize efficacy-driving local exposure near tumors while limiting systemic exposure, a primary determinant of the safety profile for catumaxomab (Sebastian et al., 2009). Systemic bioavailability of catumaxomab following intraperitoneal administration was shown to be less than 1% (Ruf et al., 2010). The subcutaneous or intramuscular bioavailability

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of antibodies ranges from 50% to 100%, with these dosing routes having been explored for bispecific antibodies such as blinatumomab (subcutaneous administration; https://clinicaltrials.gov/ct2/show/NCT02961881). As more bispecific antibodies reach clinical testing, it is likely that subcutaneous and intramuscular dosing routes will be further examined.

1.28.2.5

Antisense oligonucleotide and siRNA therapeutics

Antisense oligonucleotide (ASO) and small interfering RNA (siRNA or RNAi) are molecules capable of silencing gene expression and thus eliciting a therapeutic outcome. Both ASO and siRNA-based strategies utilize a complementary oligonucleotide that binds to its target through Watson-Crick base pairing (Watts and Corey, 2012). However, the mechanism leading up to the inhibitory base pairing differs between ASOs and siRNA (Fig. 7). Gene silencing using an ASO is a relatively simple concept, whereby a singlestranded oligonucleotide directly binds to its target RNA after being delivered into cells or dosed in vivo. Double-stranded siRNA, on the other hand, forms a complex with argonaute (AGO) within RISC (RNA-induced silencing complex), which then functions to remove the passenger strand and direct the guide to its RNA target (Fire et al., 1998). Similar to proteolysis targeting chimeras (described in Section 1.28.2.8), ASOs and siRNA function through catalytic, event-driven pharmacology (Lai and Crews, 2017). Examples of approved ASO therapeutics include nusinersen (spinal muscular atrophy), eteplirsen (Duchene muscular dystrophy) and inotersen (familial amyloid polyneuropathy), while more recent examples of siRNA therapies include patisiran (hereditary transthyretin amyloidosis), givosiran (acute hepatic porphyria) and lumisiran (primary hyperoxaluria type 1) (Humphreys et al., 2020; Scoles et al., 2019).

A ASO

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Mechanisms of gene silencing by (A) antisense oligonucleotides (ASOs) and (B) siRNA.

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While using similar molecular principles to affect disease outcome, the physicochemical and subsequent ADME properties of ASOs and siRNA are relatively different. For example, the relative instability of single-stranded oligonucleotides has been identified as a key concern for ASOs, while double-stranded siRNA tends to be much more stable. Chemical modifications of ASOs, such as introducing phosphorothioate linkages can be used to decrease catabolism by endogenous nucleases while increasing plasma protein binding, effectively serving to extend the half-life of ASOs (Levin, 1999). Similarly, introducing 2’-O-methoxyethyl (2’MOE), 2’-O-methyl (2’-O-Me) or 20 -fluoro (2’-F) modifications can serve to increase the affinity of ASOs for the target RNA and allowed for the development of shorter ASOs with more favorable ADME properties (Straarup et al., 2010). While chemical modifications are generally unnecessary in order to convey intrinsic stability to siRNA, they have been introduced to decrease in vivo clearance, prolong pharmacologically efficacious levels and block sites prone to cleavage by endogenous nucleases (Allerson et al., 2005; Braasch et al., 2004; Morrissey et al., 2005a,b; Watts et al., 2008). Covalent conjugation of N-acetylgalactosamine (Gal-NAc) to siRNA is a well-studied approach to facilitate uptake of siRNA into hepatocytes via the asialoglycoprotein receptor (Nair et al., 2014; Sehgal et al., 2015; Springer and Dowdy, 2018). The physicochemical properties of ASOs and siRNA can often present challenges to achieving intracellular exposure and administration in vivo. Endosomal uptake and subsequent lysosomal degradation result in only a small percentage of the therapeutic being released intact into the cytosol of target cells (Gilleron et al., 2013; Johannes and Lucchino, 2018; Wittrup et al., 2015). In vitro, transfection of ASOs and siRNA into cells is generally achieved through the use of cationic lipids. In vivo, ASOs rely on many of the aforementioned chemical modifications, while the siRNA field has relied on the use of lipid- or polymer-based formulations. Often, the approaches utilized to facilitate in vivo dosing also convey additional ADME advantages. For example, it has already been noted that utilizing a phosphorothioate instead of a phosphodiester backbone increases plasma protein binding, thus reducing renal filtration of ASOs (Levin, 1999). Similarly, utilizing a pegylation approach to formulate siRNA for in vivo administration also has the benefit of extending the half-life of the siRNA (Papahadjopoulos et al., 1991). ASOs and siRNA generally utilize intravenous or subcutaneous routes of administration in vivo. Maximum systemic exposure is generally achieved within 4 h following subcutaneous administration, with bioavailabilities as high as 100% (Geary et al., 2015). As noted above, in vivo elimination is generally through catabolism by endogenous nucleases, renal filtration or, in the case of Gal-NAc conjugated ASOs, biliary excretion. Nuclease catabolism of ASOs and siRNA has been described in vitro and in vivo in both preclinical species and humans (Humphreys et al., 2020). In vivo studies of the catabolism of volanesorsen, an ASO with a 2-MOE modification, suggest endonuclease-catalyzed hydrolysis is the primary catabolic step across species, followed by exonuclease catabolism of the newly formed catabolites (Post et al., 2019). In vitro studies designed to assess the drug interaction potential of ASOs and siRNA against cytochrome P450 enzymes and common drug transporters have generally indicated no evidence of inhibition of any enzymes or transporters tested at clinically relevant concentrations of the oligonucleotides (Ramsden et al., 2019; Shemesh et al., 2017).

1.28.2.6

Adoptive cellular therapy

Utilizing a patient’s own living cells to mount an immune response towards a tumor or pathogen is one of the emerging pillars of personalized medicine. The use of chimeric antigen receptor T-cells (CAR-T therapy), tumor infiltrating lymphocytes (TIL therapy), natural killer cells (NK therapy) or T-cell receptor T-cells (TCR-T therapy) are the primary approaches to personalized cellular immunotherapy and are collectively referred to as adoptive cellular therapy (Feldman et al., 2015; Paucek et al., 2019). Indeed, a simple PubMed search of literature reports relating to “CAR-T therapy,” “tumor-infiltrating lymphocyte therapy,” “T cell receptor therapy,” or “natural killer cell therapy” shows the significant increase in research in these areas in recent years. One of the earliest forms of adoptive cellular therapy involves isolation and re-infusion of tumor infiltrating lymphocytes (TIL therapy), T-cells which have already migrated into a patient’s tumor. TIL therapy involves obtaining a biopsy of tumor tissue and sequencing the oncolytic DNA for specific mutations (Rosenberg et al., 1988). The mutated DNA is transfected into dendritic cells which are then co-cultured with TILs originally isolated from the tumor. TILs which respond to the new antigen-presenting dendritic cells are subsequently isolated, expanded and re-infused back into the patient (Dudley et al., 2003; Figlin et al., 1997; Goff et al., 2016). A limitation to the use of TIL therapy is the requirement for post-dose infusions of relatively high doses of interleukin-2, a therapeutic regimen with a well-documented narrow therapeutic window (Nguyen et al., 2019). Current standards-of-care often involve myeloablative lymphodepletion using chemotherapy prior to the re-infusion of the autologous tumor infiltrating lymphocytes (Besser et al., 2013; Dudley et al., 2005, 2008; Gattinoni et al., 2005; Goff et al., 2016; Klebanoff et al., 2005; Nguyen et al., 2019; Pilon-Thomas et al., 2012; Radvanyi et al., 2012; Rosenberg et al., 2011). The optimization of pre-infusion chemotherapy and post-infusion IL-2 dosing regimens remains a key area of TIL research. CAR-T therapy involves the engineering of a scFv (single-chain variable fragment) onto the surface of CD8 þ T-cells that is designed to bind to a cell-surface protein on a target cell such as a tumor cell in the case of oncolytic immunotherapy (June et al., 2018). By doing so, CAR-T therapy couples immune system effector function with the high degree of selectivity afforded by the binding of the engineered scFv domains (Kawalekar et al., 2016). CAR-T cells may also express signaling (CD3z) or costimulatory (OX40, CD137, CD28 or ICOS) domains that allow the CAR-T cells to further induce immune system activation (Dotti et al., 2014; Kawalekar et al., 2016; Sadelain et al., 2013). While CAR-T therapy is not limited by the class of major histocompatibility complex (MHC) formation (as will be discussed for TCR-T cells below), CAR-T cells are limited to recognizing cell-surface antigens only (Zhao and Cao, 2019; Zhao et al., 2015). The pharmacokinetic-pharmacodynamic relationship of CAR-T cells is

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dependent upon the duration that the CAR-T cells remain in patients and the corresponding cytokine release and extent of tumor regression. The extent to which engineered CAR-T cells remain in a patient, analogous to a half-life measurement for more traditional therapeutics, has been shown to be highly dependent on the choice of co-stimulatory domain (Quintarelli et al., 2018; Zhao et al., 2015). For example, CAR-T cells expressing a CD28 co-stimulatory domain remain in patients for approximately 1 month, while CAR-T cells expressing the same antigen receptor but utilizing the CD137 (4-1BB) co-stimulatory domain are expressed and continue to elicit effector function for up to 4 years post infusion, with some studies suggesting the presence of engineered cells for over 10 years (Brentjens et al., 2011; Lee et al., 2015; Porter et al., 2015; Scholler et al., 2012). The differences in cellular survival have been attributed to the distinct signaling pathways of CD28 versus CD137, with the former resulting in increases in aerobic glycolysis through activation of the PI3k-Akt pathway, while the latter causes TRAF1-dependent alterations to the BcL-xL and ERK-dependent Bim pathways, resulting in a more favorable environment for activation (Frauwirth et al., 2002; Kawalekar et al., 2016; Sabbagh et al., 2008). Dose selection for CAR-T therapy can be complicated, with a balance needing to be achieved between tumor-killing potential and severe adverse effects, such as cytokine storms (O’rourke et al., 2017). Doses are calculated in number of cells per kilogram body weight, with reported doses ranging from 1  105 engineered cells/kilogram to greater than 1  1010 engineered T-cells/kg (Zhao and Cao, 2019). While oncolytic CAR-T cells are designed to recognize primary cell-surface antigens on tumor cells, engineered TCR-T cells are capable of targeting intracellular proteins that have been degraded and trafficked to the cell surface in conjunction with MHC proteins (Xu et al., 2018). Rather than an engineered scFv domain, TCR-T cells expressed a modified T-cell receptor capable of binding to its intended target with a much higher affinity relative to normal T-cell receptors (Barrett et al., 2015). The need for antigen presentation by MHC proteins has often been considered a mechanistic disadvantage for TCR-T cells as compared to CAR-T therapy, though the number of targets able to be recognized by TCR-T cells relative to CAR-T cells is significantly increased (Paucek et al., 2019). Similar to the signaling and co-stimulatory domains expressed on CAR-T cells, TCR-T cells maintain the full capacity of the TCR signaling pathway and as such, are able to be fully activated to produce tumor growth inhibition or tumor killing (Garber, 2018; Kershaw et al., 2013). It has been suggested that TCR-T cells may also exhibit a larger therapeutic safety window relative to CAR-T cells owing to a decreased rate of cytokine release, though examples are limited (Xu et al., 2018). One drawback to TCR-T cell therapy is the limited biodistribution of activated T-cells to solid tumors, often requiring the need for intra-tumoral administration as opposed to systemic routes of dosing (Kavunja et al., 2017). In general, doses for TCR-T cells tend to be higher than those for CAR-T cells, perhaps due to the liabilities associated with MHC presentation and the lack of an antibody-like (scFv) interaction with their intended targets. Natural killer (NK) cell therapy has received increased attention in recent years because of its potential to overcome a number of the challenges associated with the previously discussed types of adoptive cellular therapy (Farag and Caligiuri, 2006; Locatelli et al., 2014; Miller et al., 2005; Moretta et al., 2011; Rubnitz et al., 2010; Yoon et al., 2010). NK cells were first discovered by Herberman and colleagues as a T-cell independent mechanism of tumor cell killing (Herberman et al., 1975a,b). NK cells have the ability to degrade neoplastic tumor cells through efficient stimulation of the innate immune system and without the need for tumor specific antigen interactions, though the latter is being evaluated as “CAR-NK” cellular therapies begin to enter preclinical testing (Chu et al., 2014; Han et al., 2015). NK cells also do not carry the same graft-versus-host-disease liabilities that come with other forms of adoptive cellular therapy (Asai et al., 1998; Glienke et al., 2015). The initial lack of an HLA (human leukocyte antigen) specific mechanism for NK cells has made them an area of interest for an allogenic, “off-the-shelf” approach to cellular immunotherapy (Rezvani et al., 2017). From a “half-life” standpoint, infused NK cells generally tend to have a limited duration of persistence in patients relative to engineered T cells, which, when coupled with the inability for clonal expansion and lack of HLA-mediated immune responses against the NK cells, suggest NK cell therapy may also avoid the cytokine release liabilities associated with CAR-T or TCR-T cell therapies (Hu et al., 2019; Klingemann, 2014; Martinet and Smyth, 2015). Conversely, the limited duration of modified NK-cells in patients may provide challenges with regard to the overall efficacy of NK cell therapies. While the traditional concepts of ADME and pharmacokinetics may not directly apply to adoptive cellular therapies, a number of connections can be made. Following administration of cellular immunotherapy, the expansion and persistence of the engineered cells can be monitored by quantitative polymerase chain reaction or flow cytometry and used to calculate the cellular kinetics of the intervention (Kakkanaiah et al., 2018). Similar to other therapeutic modalities, kinetic parameters such as Cmax and Tmax (indicating the maximum amount of cellular expansion and the time at which the maximum amount of engineered cells are present, respectively), AUC (area under the curve), and half-life can be determined. Expression levels of engineered cells can be quantified in either the blood compartment or in various tissues. An example of the cellular kinetic profile of CTL019 (tisagenlecleucel), a CART therapy being investigated for use in chronic lymphocytic leukemia and relapsed/refractory B-cell acute lymphoblastic leukemia is shown in Fig. 8. Of note in the CTL019 cellular kinetic profile in whole blood is a rapid “distribution” phase, followed by an equally rapid expansion of CTL019 cells in the central compartment (Mueller et al., 2017). The initial distribution phase was hypothesized to be a result of cells translocating from the central compartment into peripheral blood, bone marrow as well as other tissue compartments (Ho et al., 1993; Maude et al., 2014; Read et al., 1990; Walker et al., 2000). Time to maximum expansion of CTL019 (Tmax) was approximately 2 weeks, with the engineered T-cells still able to be detected more than 2 years post infusion.

1.28.2.7

Oncolytic viruses

Oncolytic viruses represent one of the more recent modalities to emerge in the immuno-oncologist’s arsenal. The therapeutic approach utilizes attenuated viruses, often injected directly into tumors, to infect tumors and subsequently initiate or enhance

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Fig. 8 Cellular kinetics of CAR-T CTL019. Reproduced with permission from Mueller KT, Maude SL, Porter DL, Frey N, Wood P, Han X, Waldron E, Chakraborty A, Awasthi R, Levine BL, Melenhorst JJ, Grupp SA, June CH and Lacey SF (2017) Cellular kinetics of CTL019 in relapsed/refractory Bcell acute lymphoblastic leukemia and chronic lymphocytic leukemia. Blood 130: 2317–2325.

immune responses against the tumor (Bell et al., 2003). Viral replication can either directly result in tumor killing through inhibition of protein synthesis within the tumor cells or indirectly through the activation of tumor-infiltrating lymphocytes and subsequent local release of anti-tumor cytokines (Bai et al., 2019; Gujar et al., 2019; Kaufman et al., 2015; Li et al., 2020). Such viruses are able to proliferate specifically in tumor cells, sparing host cells in non-oncogenic tissues. The first FDA approved oncolytic virus therapy was talimogene laherparepvec (Imlygic), which is approved for use in metastatic melanoma patients (Dharmadhikari et al., 2015; Dolgin, 2015; Killock, 2015; Pol et al., 2016). The virus is comprised of oncolytic herpes simplex virus type 1 (HSV-1), coupled with granulocyte-macrophage colony-stimulating factor. The HSV-1 virus is engineered with two specific gene deletions, g34.5 and a47, with the former conferring tumor cell selective replication and the latter attenuating downregulation of MHC class I expression, subsequently increasing the severity of the immune response against the tumor cells (Fukuhara et al., 2016; Goins et al., 2014; Liu et al., 2003). The need for intratumoral delivery is in part predicated on rapid clearance by the reticuloendothelial system in the spleen and liver, resulting in inadequate tumor exposure after other routes of administration (Barton et al., 2008; De Silva et al., 2010; Raja et al., 2018; Russell et al., 2012; Xu et al., 2008). The rapid clearance, similar to first-pass hepatic clearance of small molecules, coupled with limited distribution to extravascular tumor sites results in an overall poor pharmacokinetic-pharmacodynamic relationship for many oncolytic viruses (Fisher, 2006). Of note is the need for the viruses to be stable and active in whole blood, where

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they may bind to cell surface receptors on circulating erythrocytes and ultimately be targeted for degradation (Alemany et al., 2000; Carlisle et al., 2009). Also of interest is the potential for species differences to exist in the binding of oncolytic viruses to host erythrocytes, thus complicating the translation of preclinical PK/PD relationships into clinical outcomes (Carlisle et al., 2009). With that said, efforts have demonstrated the utility of intravenous dosing of oncolytic viruses. Preclinical studies have shown intravenously injected oncolytic viruses to be effective against mouse squamous cell lung carcinoma, muscle-invasive bladder cancer and prostatic carcinoma in mice (Atherton et al., 2018; Hu et al., 2018; Saito et al., 2011). Clinically, intravenous administration of a human oncolytic reovirus to patients with high-grade gliomas resulted in detectable amounts of viral DNA in tumor biopsies, suggesting the ability of oncolytic viruses to cross the blood brain barrier (Samson et al., 2018). Many strategies are being evaluated to improve the clearance, distribution, and subsequent PK/PD properties of oncolytic viruses after systemic or intratumoral administration. To facilitate the use of intravenous routes of administration that are in theory, more capable of reaching primary and distal metastatic tumors, most research has focused on so-called delivery vehicles to transport the virus to the tumoral site of action intact. Encapsulation of the virus in various nano- or polymeric particles, polyethylene glycol or liposome-based carriers have all been explored with the goal of reducing viral clearance by the host immune system (Choi et al., 2015; Doronin et al., 2009; Green et al., 2004). Similar to other non-small molecule modalities, oncolytic viruses are also hampered by their ability to fully distribute throughout solid tumors (Mok et al., 2007; Nemunaitis et al., 2001). Factors such as the size and type of viral envelope, as well as the surrounding tumor microenvironment all affect the distribution properties of the virus within the tumor (Guedan and Alemany, 2018). In particular, the stromal barrier functions encountered within the extracellular matrix of solid tumors, as well as the immunosuppressive nature and subsequent antiviral immunity of the tumor microenvironment all present significant challenges to the distribution of the virus within the tumor and subsequent pharmacological efficacy (Jain and Stylianopoulos, 2010; Reale et al., 2019). The viral kinetics or “ADME” properties of oncolytic viruses can be measured in various tissues by quantitative polymerase chain reaction using primers designed specifically for the viral DNA (Titze et al., 2017). For example, following intratumoral doses of talimogene laherparepvec, viral DNA has been detected in both blood and urine (Amgen, 2015; Burke and Zager, 2018; Andtbacka et al., 2019). Commonly calculated kinetic parameters include viral replication, clearance, distribution, and shedding. Traditional pharmacokinetic parameters are more difficult to estimate as the viral kinetics of oncolytic viruses will be highly dependent on immune responses, which will serve to directly reduce viral titers while indirectly reducing viral replication through removal of tumor cells (Ikeda et al., 1999; Todo et al., 1999). An example of the dynamic interplay between viral replication, clearance and subsequent tumor killing is shown in Fig. 9.

1.28.2.8

Proteolysis targeting chimeras

Proteolysis targeting chimeras (PROTACs) are small molecule-like compounds that generally exhibit many properties in the “beyond rule of 5 (BRo5)” space and as such, are often considered to be a separate modality from traditional small molecules. PROTAC molecules are designed with three primary structural features, namely a portion of the molecule designed to bind to the target protein of interest (POI), a second moiety designed to bind ubiquitin E3 ligase and a chemical linker to tether the two together (Fig. 10) (Burslem and Crews, 2017; Huang and Dixit, 2016; Paiva and Crews, 2019; Schapira et al., 2019; Sun et al., 2019). By bringing the target POI into close proximity with ubiquitin E3 ligases, the POI is polyubiquitinated and ultimately degraded by

Fig. 9 Oncolytic virus PK/PD. Reproduced with permission from Titze MI, Frank J, Ehrhardt M, Smola S, Graf N and Lehr T (2017) A generic viral dynamic model to systematically characterize the interaction between oncolytic virus kinetics and tumor growth. European Journal of Pharmaceutical Sciences 97: 38–46.

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Fig. 10

731

Representative structure of a proteolysis targeting chimera (PROTAC).

the ubiquitin proteasome system (Edmondson et al., 2019). The mechanism is catalytic or “event-driven” as opposed to occupancy driven, allowing individual PROTAC molecules to catalyze multiple cycles of POI degradation (Bondeson et al., 2015). The catalytic mechanism can result in the ability to achieve pharmacology with very low drug exposure, and often requires more complex modeling approaches to predict efficacious dose levels. The efficacy of PROTACs is only beginning to be tested in clinical trials, with ARV-110 (metastatic castration resistant prostate cancer) and ARV-471 (metastatic breast cancer) representing the first two PROTAC molecules to advance into the clinic (clinical trials.gov, accessed 18 July, 2021). The need for PROTAC molecules to essentially engage two targets at once results in a number of physicochemical properties which violate Lipinski’s “rule of 5”, including molecular weight, cLogP, hydrogen bond donors and acceptors, total polar surface area and the total number of rotatable bonds, often to the point of eliminating the possibility for oral administration (Lipinski et al., 1997; Maple et al., 2019; Veber et al., 2002). The molecular properties of PROTAC molecules are often highly dependent on the portion of the molecule targeting the E3 ligase, with ligands against the mouse double minute-2 (MDM2) E3 ligase, the inhibitor of apoptosis (IAP) E3 ligase, the Von Hippel-Lindau (VHL) E3 ligase and the cereblon (CRBN) E3 ligase being the most common. As CRBN ligands tend to be smaller and less lipophilic than ligands targeting other E3 ligases, it has been suggested that these PROTACs may be more amenable to oral administration, with several CRBN E3 ligase targeting PROTAC molecules achieving an oral bioavailability of greater than 30% (Edmondson et al., 2019; Pike et al., 2020). The oral absorption properties of a PROTAC molecule can also be affected by choice of linker, which can affect the overall three-dimensional structure and subsequent rigidity of the PROTAC (Edmondson et al., 2019; Krämer et al., 2016). Commonly used linker chemistries include polyethylene glycol, peptidic, alkyl or alkynyl, and heteroaromatic or heterocyclic chains (Troup et al., 2020). It has been suggested that PROTAC molecules utilizing polyethylene glycol or piperidine-based linkers have superior solubility properties as compared to other linker chemistries (Wang et al., 2020; Wurz et al., 2018). In addition to low oral absorption, the structural characteristics of PROTAC molecules also present challenges with regard to tissue distribution, metabolic stability and subsequent elimination. Similar to many peptides, the high lipophilicity of PROTACs often results in very low unbound fractions in plasma (fu,plasma) and in vitro incubations (fu,inc), creating difficulty in accurately determining these parameters for subsequent use in predicting efficacious dose levels in patients. Subsequently, unbound intrinsic clearance (Clintrinsic/fu) values are often high for PROTAC molecules, and many of the metabolites formed are also able to bind to the POI. As with oral absorption, linker chemistry also plays a role in PROTAC stability and the resulting metabolic profile (Troup et al., 2020). Metabolic profiles are generally a function of the entire PROTAC and cannot be readily inferred from their individual moieties, with linkers and points of linker attachment often being the least metabolically stable portion of a PROTAC molecule (Goracci et al., 2020). While linker length can often be modified to improve metabolic stability, the ideal choice of linker length and chemistry must ultimately be balanced against the proper conformation needed to bring the POI and ligase into the correct proximity to form the necessary ternary complex. Biliary and passive renal clearance are generally considered to be low for PROTAC molecules (Pike et al., 2020).

1.28.3

Approaches to characterizing the ADME properties of novel modalities

1.28.3.1

Bioanalytical techniques

With the emergence of biological therapeutics in recent years, a number of new analytical techniques have been developed to assess the metabolism of new drug candidates. For example, both intact and protein digest methods are readily available for the analysis of proteins and peptides in biological matrices (Anderson et al., 2009; Bandow, 2010; Chen, 2008; Lu et al., 2009; Pernemalm et al., 2008; Wang and Hanash, 2005, 2009; Wu et al., 2009). Intact approaches generally utilize high-resolution mass spectrometry platforms and use direct gas phase fragmentation methods to analyze the protein structure or degradants. Protein digest techniques,

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often referred to as “bottom-up” approaches, use various cleavage reagents to form peptides from complex protein mixtures, which are then subject to analysis by liquid chromatography-mass spectrometry analysis. The data that are obtained from the mass spectral analyses can be compared to numerous proteomic databases in order to identify the proteins and peptides of interest (Xu and Vugmeyster, 2012). Approaches that couple traditional sample preparation techniques such as enzyme-linked immunosorbent assays (ELISA) with more advanced proteomic mass spectrometry methods are also being developed. For example, the use of a capture antibody specific for the human Fc region has been successfully used to prepare human antibody samples from preclinical species prior to analysis by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (Hall et al., 2010). Such methods can allow for the identification of labile sites within a biological therapeutic as well as estimate relative levels of protein fragments or metabolites that result from degradation of the therapeutic. One additional analytical technique often used to identify protein structures is nuclear magnetic resonance, though this method is generally limited to proteins and peptides with molecular weights of less than 25,000 Da (Powers, 2009). As the pharmacokinetic and ADME properties of many protein therapeutics are directly related to physicochemical properties such as charge, isoelectric point and overall hydrophobicity, analytical techniques which measure these properties have also been used to study or predict the in vivo ADME properties of these molecules. One example of such a technique is hydrophobic-interaction chromatography (HIC), where test articles are eluted through a hydrophobic stationary phase, with the subsequent hydrophobic interactions between the two phases determining the retention time of each molecule (Ochoa, 1978; Queiroz et al., 2001). The simplicity and limited material requirements of the HIC technique allow it to be readily used in triaging molecules for in vivo outcomes. Recent examples using HIC to study a set of anti-CD70 antibody-drug conjugates and Nav1.7 peptide-drug conjugates identified direct correlations between longer HIC retention times (increased hydrophobic nature) and higher in vivo clearance values (Foti et al., 2019; Lyon et al., 2015).

1.28.3.2

In vitro assays

In addition to emerging bioanalytical techniques, the in vitro ADME field has also continued to develop new assays to support the growing set of therapeutic modalities. Though the in vitro ADME field has vastly expanded their capabilities over the years with regard to characterizing small molecule therapeutics, study of the ADME and pharmacokinetic properties of many of the aforementioned novel modalities remains highly dependent on in vivo studies in preclinical species (Avery et al., 2018; Jaramillo et al., 2017). It is also now known that the more a protein is engineered away from a traditional antibody format, the more likely it is to have different factors governing its ADME properties (Boehm et al., 1999; Ramsland et al., 2015; Ueda, 2014; Vugmeyster et al., 2012). As such, many of the initial in vitro screens designed to test monoclonal antibodies may not be relevant when studying more highly modified modalities. In order to bridge the gap, multiple in vitro and in silico approaches are being evaluated to more efficiently characterize novel modalities, including many assays or in silico approaches which bear resemblance to their small-molecule focused counterparts. From a computational standpoint, sequence-based and/or three dimensional structural modeling techniques are finding a greater foothold in identifying drug candidates (Dostalek et al., 2017). Plasma protein binding and nonspecific binding assays (utilizing CHO and HEK293 cells) have been developed for both antisense oligonucleotides as well as modified antibodies, with the hydrodynamic radius of ASOs being key to selecting the proper size exclusion limits for the assay (Datta-Mannan et al., 2012, 2015; Humphreys et al., 2019; Xu et al., 2013). Rapid in vivo clearance for protein therapeutics has also been studied using hepatocytes and liver sinusoidal endothelial cells, with higher in vivo clearance correlating with higher degrees of nonspecific cellular binding (Datta-Mannan et al., 2016; Foti et al., 2019). Perhaps owing to the overall molecular complexity of many of the newer modalities, it has been recently proposed that multiparametric approaches (such as combining affinity for FcRn with thermal stability data) may be more appropriate for predicting in vivo outcomes (Goulet et al., 2018). As mentioned previously in this article, many of the newer drug modalities are subject to proteolytic catabolism resulting in rapid clearance and/or altered PK/PD relationships. Similar to approaches used for small molecule metabolite identification experiments, in vitro assays coupled with corresponding bioanalytical techniques have also been developed to identify the primary sites of catabolism for newer modalities, thus allowing modifications and substitutions aimed at increasing the resistance of the molecules to proteolysis. A comprehensive catabolite identification/excretion study with volanesorsen, a triglyceride-reducing antisense oligonucleotide was key in identifying the primary mechanisms of elimination for the ASO (Post et al., 2019). Similarly, in vitro catabolism studies with TN-ApoA1 identified the DPP-IV catalyzed formation of pharmacologically active catabolites, consistent with results obtained in vivo (Schadt et al., 2019; Zell et al., 2016).

1.28.4

Conclusion

Rapid advancements in target biology and the continual hunt to identify and understand more complex pharmacological pathways has led to the development of many novel therapeutic modalities in recent years. As the properties which underwrite the pharmacokinetic and ADME properties of these newer modalities may differ significantly from small molecules and more traditional

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monoclonal antibodies, a significant amount of effort has been placed on understanding these properties and optimizing them prior to conducting in vivo studies, either pre-clinically or in humans. It is without doubt that the field will continue to rapidly evolve well beyond what has been covered in this article as newer and more highly engineered therapeutics are identified. Ultimately, understanding the ADME characteristics of such therapeutics will be vital to developing new drugs which are both safe and efficacious.

See Also: 1.17: Oral Drug Delivery, Absorption and Bioavailability; 1.18: PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations; 1.24: Drug Excretion

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1.29 Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development Samuel L.M. Arnolda,b and Nina Isoherranena, a Department of Pharmaceutics, School of Pharmacy, University of Washington, Seattle, WA, United States; and b Division of Allergy and Infectious Diseases, School of Medicine, University of Washington, Seattle, WA, United States © 2022 Elsevier Inc. All rights reserved.

1.29.1 1.29.2 1.29.2.1 1.29.2.2 1.29.2.3 1.29.2.4 1.29.2.5 1.29.2.6 1.29.2.7 1.29.2.8 1.29.2.9 1.29.2.10 1.29.3 1.29.4 1.29.5 1.29.5.1 1.29.5.2 1.29.6 References

Introduction Absorption Permeability, solubility, and dissolution rate limited absorption Solubility, dissolution, and precipitation Dissolution models Impacts of formulation factors on drug dissolution Drug permeation Carrier mediated transport Intestinal drug metabolism PBPK models of oral absorption Application of physiologically based absorption models Model verification and validation Distribution Metabolism Excretion Renal excretion Biliary Excretion Conclusions

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Glossary Absorption The process by which drugs reach systemic circulation from the site of drug administration. Clearance The process and pharmacokinetic parameter that describes the efficiency of drug removal from the body. Distribution A process that describes at what rate and to what extent the drug reaches various sites in the body. Excretion The elimination of the drug from the body in its chemically unchanged form. Metabolism Enzymatic process responsible for chemical modifications to the administered drug. Physiologically based pharmacokinetic modeling A modeling technique that combines knowledge of physiological values and known variables with known characteristics of a drug to describe the plasma concentrations versus time curves of the drug.

1.29.1

Introduction

The decisions of how much, how often and how long a drug should be given in a specific patient population or for healthy volunteers rely on a fundamental understanding of the dose-exposure and concentration-response relationships of a given drug. While pharmacological response can be graded or quantal and involve complex signaling pathways that ultimately result in an effect, an understanding of the concentrations of the drug required in circulation and at the target site is needed to safely and effectively administer drugs to patients. Drug specific pharmacokinetic characteristics for Absorption, Distribution, Metabolism and Excretion (ADME) ultimately define the time course of drug exposure and the extent of the exposure in patients. The ADME characteristics of a drug are typically assessed throughout the drug discovery and development process using in vitro methods, animal models, clinical studies and modeling and simulation approaches. Throughout the drug development process, knowledge is gained and incorporated from all of these sources to design and optimize drug formulation, define optimal dosage strengths and dosing intervals in different patient populations and guide the ultimate dosing regimen design in clinical practice. An integral portion of drug development is predictive simulation and modeling. While compartmental models were used to characterize and simulate pharmacokinetics for many years, the role of physiologically based pharmacokinetic (PBPK) modeling has grown substantially and now supports multiple stages of preclinical and clinical drug development. This methodology uses a model structure in which tissues and organs are included with compartments parameterized to reflect known physiological values

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Fig. 1

Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

Physiologically based pharmacokinetic (PBPK) model structure. The solid arrows indicate blood flows (Q) to each tissue.

(e.g., blood flow and tissue volume) in the target species (humans or preclinical models) (Fig. 1). With this whole-body approach, the drug specific parameters (ADME characteristics) and physiological values (i.e., model structure) are integrated to simulate the time course of drug concentrations in plasma and tissues. Since drug and system components are handled separately, the potential impact of physiological changes (e.g., reduced hepatic or renal function in specific populations) or drug physicochemical variations (e.g., lead optimization in preclinical development) on drug exposure can be investigated independently. While the PBPK approach was originally devised in the 1930s, it has taken many years for PBPK modeling to firmly insert itself within the drug development process (Teorell, 1937). With increasing adoption in both the preclinical and clinical phases of drug development, there has been substantial effort to improve harmonization and confidence in modeling approaches. As a result, both the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) have produced guidance documents for PBPK modeling (FDA, n.d.b; EMA, n.d.). The expanded regulatory acceptance of PBPK modeling is reflected by the increasing number of drug labels impacted by PBPK modeling (Shebley et al., 2018). While PBPK model application has largely focused on drug-drug interaction (DDI) predictions and pediatric modeling, PBPK modeling is gradually replacing empirical approaches to predict first in human (FIH) dosing as well (Miller et al., 2019). Regardless of its application, PBPK modeling capitalizes on the collective ADME knowledge gained during the drug development process and, together with other experimental, prediction and data analysis methodologies, forms an integral part of decision making on dosage regimen design. The individual ADME concepts and their integration to PBPK modeling are described in this chapter.

1.29.2

Absorption

When discussing drug absorption, the conversation tends to be heavily weighted towards oral drug administration, but the principles of absorption pertain to many routes of drug administration. Regardless of the route of administration, the site of action is usually not confined to the administration site, and drug molecules will require movement through at least one barrier prior to reaching their biological target. For example, with transdermal drug administration, drug molecules must transit across multiple layers of the epidermis prior to entering dermis capillary blood (i.e., absorption). Another example is pulmonary drug delivery for treatment of systemic diseases which requires drug to cross over the alveolar membrane before reaching systemic circulation. Thus, while the vast majority of this section focuses on oral drug absorption, many of the concepts are directly applicable to absorption models for other routes of administration as well. The convenience of oral drug administration is the main reason most marketed drugs are delivered orally. Therefore, drug development in disease areas that require chronic self-administration will usually require the final drug product as an oral formulation. Limits imposed by parenteral administration can create a barrier that reduces the potential impact of the therapeutic. These hurdles are well illustrated by the current COVID-19 setting where treatment with the small molecule therapeutic remdesivir or monoclonal antibodies casirivimab and imdevimab appears to improve outcomes for specific COVID-19 patient populations. However, both therapies require intravenous (iv) dosing, which is resource intensive and restricts therapeutic administration and availability of treatment. Moving forward, any therapeutic developed as a treatment for COVID-19 (or any other pervasive condition) will benefit from an oral dosage form to facilitate widespread implementation.

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The importance of an oral dosage form underscores the value of identifying and mitigating oral absorption issues that may prevent adequate systemic exposure in the target patient population. For drug delivery, bioavailability (F) provides a measure of success for drug reaching systemic circulation from the dosage form. After oral drug administration, a drug’s bioavailability is a composite of three separate terms that reflect the sequential processes that control the amount of drug reaching systemic circulation: absorption into the gastrointestinal (GI) epithelium (e.g., enterocytes), GI metabolism, and hepatic elimination. F ¼ Fa ∙Fg ∙Fh For orally administered drugs, Fa is the fraction of drug absorbed into the gut wall, Fg is the fraction of drug escaping GI metabolism, and Fh is the fraction escaping hepatic elimination. Each of these parameters will have a value between 0 and 1, and the oral bioavailability, F, cannot be larger than any individual term. In addition to the extent of drug absorption (i.e., Fa), the rate of drug absorption from the GI lumen is a critical parameter for modeling drug absorption. We can think of the body as single compartment with drug entering the body from an absorption site (e.g., GI lumen) and leaving the body via excretion and/or metabolism. In this conventional compartment model, the rate of drug absorption from the GI lumen can be described by a first order absorption rate constant (ka). Immediately following oral drug administration, the rate of drug absorption will be much greater than the rate of elimination from the body resulting in gradually increasing plasma concentrations. Although elimination processes also impact drug plasma concentrations during these early points, mathematical techniques can be used to determine a value for ka using the plasma concentration versus time data at these early time points together with knowledge of the elimination kinetics of a drug. However, it is not uncommon to observe plasma concentration-time profiles that cannot be described with simple first order absorption kinetics, and many current PBPK absorption models incorporate time dependent absorption rate coefficients to model drug absorption (discussed in detail later) (Higaki et al., 2001). While ka is a useful term, it simplifies a complex process with multiple potential pathways for the active pharmaceutical ingredient (API) to reach the blood (Fig. 2). The role of each pathway can be influenced by both drug and system parameters, and it can be difficult to predict how alterations in each parameter will impact the absorption rate (Table 1). By incorporating these drug and system components, PBPK absorption models have proved to be powerful tools to simulate drug absorption. Furthermore, incorporation of inter-individual variability within each system component can provide important information on how drug absorption is expected to vary across populations.

Fig. 2

Representation of drug absorption from oral dosage form.

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1.29.2.1

Drug and system parameters impacting oral drug absorption.

Drug/Formulation

System

-

- Lumen environment (pH, bile salts, fluid volume, fluid dynamics) - Intestinal motility - Intestinal blood flow - Drug metabolizing enzyme and transporter abundance - Gastric emptying - Food effects

Solubility (pKa, Solubility Factor) Particle size distribution Disintegration De-aggregation Precipitation Permeability Intestinal metabolism Intestinal transport Intra-luminal degradation

Permeability, solubility, and dissolution rate limited absorption

The lumen of the GI tract is essentially a busy one-way street, and drug molecules have a finite residence time in the lumen during which absorption can occur prior to excretion. In the absence of active transport and/or GI drug metabolism, the absorption of the API will be driven by the drug’s physicochemical properties which have a large role in both drug dissolution into the GI fluid and permeation across the gut wall into the blood. For a given API, the rate limiting process in drug absorption may be permeability, solubility, or dissolution. The overall oral absorption will be controlled by this rate limiting step and it should be identified as early as possible in drug development. In addition, while not discussed in depth in this section, drug stability in the fluids of the GI tract is critical as both metabolic and chemical degradation can reduce drug levels prior to absorption. Dissolution, solubility and permeability rate-limited absorption can all lead to reduced drug absorption after oral administration. Permeability rate limited drugs will have sufficient aqueous solubility and a relatively fast dissolution rate, but poor permeation into the GI epithelium. This is a common characteristic of drugs with high aqueous solubility but poor lipid solubility. Transcellular drug permeation in the GI tract requires passage through intestinal epithelial cell membranes composed of phospholipids and cholesterol. For this passage to occur, the drug must be sufficiently lipophilic to partition into the lipid bilayer but not too lipophilic so that drug molecules are sequestered within the cell membrane. A common measure of lipophilicity is the partition coefficient of a molecule between lipophilic and aqueous solvents (logP), usually octanol and water. ! ½soluteoct logPoct=wat ¼ log ½solutewat;neutral In general, logP values between  0.5 and 6 are suitable for oral drug administration, but current drug development efforts tend to produce candidates that are more lipophilic in nature. While logP is an informative measure of lipophilicity, most drugs contain at least one ionizable group, and logP does not consider the extent of ionization at physiologically relevant pH values. For weak acids and bases, dissociated species (i.e., ionized) are much less permeable than undissociated species (i.e., neutral), and the proportion of drug that is ionized will depend on the pKa of the ionizable group and local pH. The distribution coefficient, logD, is a pH dependent value (e.g., logD6.8) and is usually considered a superior descriptor of molecule lipophilicity. ! ½soluteoct logDpH ¼ log ½solutepH;ionized þ ½solutepH;neutral With the wide range of pH values across the GI tract ( 1–7), knowledge of relevant logD values for an ionizable drug can be critical for estimating drug absorption. However, along with logD, there are additional parameters crucial for estimating permeability (e.g., molecular weight) and these will be discussed later. Returning to our example of permeability rate limited absorption, a drug molecule with high solubility and a logD6.8 < 0 may have a permeation rate that limits the overall rate of drug absorption. In contrast, solubility rate-limited drugs tend to have sufficient lipid solubility (i.e., logD values > 1) but poor aqueous solubility. As discussed later in the chapter, the concentration gradient established between a solid particle’s surface and the bulk solution influences the drug dissolution rate. Poor aqueous solubility will lead to a relatively small concentration gradient and slow dissolution rate. In addition to aqueous solubility, other factors (e.g., particle size) can impact drug dissolution. For cases of dissolution-rate limited absorption, the relationship between the dose and the amount of drug absorbed into the body will be linear, and a reduction in particle size (i.e., increased surface area) will increase the observed rate of drug absorption. While disintegration can also impact the rate of drug absorption, this is usually not a limitation in well formulated dosage forms. Overall, when a drug product has solubility or dissolution rate limited absorption, both the rate and extent of drug absorption will depend on the drug formulation. By carefully selecting excipients and particle shapes/sizes, the final drug form can be optimized to provide sufficient disintegration, disaggregation, and dissolution. Parameter sensitivity analysis (PSA) provides a useful tool to predict how changes in drug (e.g., solubility, permeability) or system parameters will influence drug pharmacokinetics (e.g., Fa). For example, let us say we are interested in synthesizing a series of molecules based on “Drug X” which has 100% oral absorption (i.e., Fa ¼ 1), and we want to know how changes in solubility, permeability, or particle size may impact the absorption of our Drug X derivatives. Drug X has aqueous

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Fig. 3 Impact of drug solubility, permeability, and particle size on drug fraction absorbed (Fa). A parameter sensitivity analysis (PSA) was used to determine how changes in the aqueous solubility (A), effective permeability (B), and mean particle radius (C) of Drug X are expected to impact the Fa after oral administration.

solubility of 0.005 mg/mL, effective permeability (Peff) of 0.00035 cm/s, and a mean particle radius of 5.0 mm. When we perform a PSA for these parameters and Fa, we see that reduced aqueous solubility and increased particle size are expected to dramatically reduce Fa, but a reduction in drug permeability is not expected to adversely impact drug absorption (Fig. 3). Based on the premise that drug absorption is governed by solubility, dissolution, and permeability, the biopharmaceutics classification system (BCS) was developed to predict in vivo drug absorption from in vitro solubility and permeability measurements (Amidon et al., 1995). The BCS framework classifies drugs according to their solubility and intestinal permeability: Class Class Class Class

1: 2: 3: 4:

High solubility, High permeability. Low solubility, High permeability. High solubility, Low permeability. Low solubility, Low permeability.

The solubility classification is based on the highest strength of an immediate release (IR) drug product, and the substance is considered “highly soluble” if it is soluble in 250 mL aqueous media within a pH range 1–6.8 at 37  C. In addition, a drug is considered to be “highly permeable” when the extent of absorption (i.e., Fa) in humans is at least 85%. However, in lieu of human data (e.g., mass balance and/or bioequivalence studies), preclinical animal studies and in vitro models can be used to predict the extent of drug absorption in humans. However, it should be noted that there are serious concerns associated with human bioavailability predictions based on preclinical data (Musther et al., 2014). Drug dissolution is not explicitly addressed with the BCS, but the FDA considers a drug product as rapidly dissolving when at least 85% of the drug substance dissolves within 30 min under established conditions. Not only does the BCS provide a standardized framework to consider how critical factors may influence in vivo absorption, it also provides a mechanism to establish bioequivalence for certain BCS compounds. With the BCS approach, in vitro data can be used to justify a waiver for in vivo bioavailability and/or bioequivalence studies (i.e., biowaiver) for BCS class 1 and 3 drug products. This is a useful tool for investigational new drug applications (INDs), and applicants who submit new drug applications (NDAs) and abbreviated new drug applications (ANDAs). However, there are still well-established limitations to the BCS approach, and the BCS does not account for the potential impact of drug transport and metabolism. In an effort to partially address these limitations, the Biopharmaceutics Drug Disposition Classification System (BDDCS) utilizes drug metabolism instead of intestinal permeability (Wu and Benet, 2005).

1.29.2.2

Solubility, dissolution, and precipitation

For drug absorption, solubility provides a measure for how much drug (solute) can dissolve in a given volume of solvent, and this value is provided either in descriptive or precise terms. Dissolution is a kinetic process that describes the rate at which a solute goes into the solution. A solute may have very poor solubility in a solvent, but the dissolution may be rapid. Drug products with very poor solubility (< 10 mM) tend to exhibit highly variable, low oral bioavailability and non-linear dose responses. For this reason, drug solubility should be characterized early in the development process. While drug aqueous solubility and pKa values can be predicted with quantitative structure–property relationships (QSPR), drug aqueous solubility across a range of pH values should be characterized in vitro to confirm these parameters. In addition, predicted solubility factors, which are the ratio of solubilities for ionized and unionized forms, should be confirmed when possible. There are various experimental methods to obtain an equilibrium solubility, and generally these values are determined by 1) suspending solid drug in liquid until equilibrium is reached, 2) removing residual solid material by filtration and 3) quantifying the amount of drug remaining in the filtrate. The upper small intestine is the primary site of drug absorption, and this section of the GI tract will have the highest bile salt concentrations. Bile salt micelles can increase the apparent solubility of drugs, and solubility should be characterized in biorelevant media with well-defined pH and bile salt concentrations. Options for biorelevant media include fasted-state simulated gastric and

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intestinal fluid (FaSSGF, FaSSIF) and fed-state simulated intestinal fluid (FeSSIF). When experimental data are not available to support a bile salt solubilization ratio, this value can be estimated using the API’s logP value. The surface of a bile micelle is negatively charged by sulfonate and carbonate functional groups, and the charge state of the drug will impact bile micelle partitioning. Bile salt micelles can increase the apparent solubility of the drug, but there may also be a corresponding reduction in drug diffusion which can impact dissolution, precipitation, and permeation. In humans, most oral drugs are not administered as solutions, and hence dissolution rate is an additional critical parameter to consider for drug absorption. Dissolution experiments are usually initiated at the early stages of drug development to assist in formulation optimization for preclinical studies (efficacy and safety) and the final drug product development. When appropriate experimental approaches are selected, the resulting in vitro data can assist in in vitro-to-in vivo correlation (IVIVC) studies. For solid oral dosage forms, the industry standard dissolution testing methodologies are the United States Pharmacopoeia (USP) Apparatus 1 (basket) and USP Apparatus 2 (paddle). Most approved drug products are immediate, modified, and extended release that are usually tested with the USP 2 apparatus. While USP 1 and 2 apparatuses are the most common, other techniques are available for dissolution testing. For the many drugs that are weak bases, the API may go into solution quickly in the stomach (pH  1–2) but will eventually precipitate out of solution in the more alkaline environment of the small intestine (pH  5–6). In addition, many BCS class 2 drug products include supersaturating drug delivery systems (e.g., salts, cocrystals, nanosuspensions, etc.) to improve solubility, and precipitation will be an issue for these drugs as they traverse through the GI tract. A common in vitro method to characterize drug precipitation is the transfer assay. Data collected with the transfer assay can be used to guide decision making prior to in vivo testing and to inform PBPK absorption models that incorporate drug precipitation in the GI tract.

1.29.2.3

Dissolution models

Many years before drug developers recognized the importance of drug dissolution in oral drug absorption, the physical chemistry research community directed substantial effort to studying the dissolution process. Their progress provided a strong foundation that still supports many of the dissolution models commonly used in PBPK absorption models. These dissolution models are based on experiments conducted by Noyes and Whitney in the late 1890s that investigated the dissolution of benzoic acid and lead chloride (Noyes and Whitney, 1897). Noyes and Whitney discovered that dissolution rates were proportional to a concentration gradient that existed between the compound in the bulk solution and the compound’s saturation solubility. The authors postulated that the dissolution process involved diffusion of molecules through a diffusion layer that formed near the surface of each particle, and their published model was inspired by Fick’s first law of diffusion. The Noyes-Whitney model instigated a series of dissolution layer models (DLMs) that all assume equilibrium is achieved rapidly at the solid surface and that the rate limiting step for drug dissolution is diffusion across the hydrodynamic boundary layer surrounding each particle. While these initial studies were conducted in a manner that kept the material surface area constant over time, follow-up studies quickly determined that the dissolution rate was proportional to the solid surface area. By 1904, a two part-publication from Walther Nernst and Erich Brunner considered the impact of solute diffusion across a boundary of unstirred solvent (unstirred boundary layer) surrounding each solid particle with effective thickness (heff) and the surface area of the particle (Brunner, 1904; Nernst, 1904). With this model, compound diffusion across the unstirred boundary layer is inversely proportional to the length of this unstirred boundary layer. Furthermore, a diffusion coefficient is included in the equation to describe the movement

Fig. 4

Parameters for drug dissolution rate.

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of a solute across the boundary layer of thickness heff in a given solvent (Fig. 4). A modified version of the Nernst-Brunner model is described by: dMd D∙At ¼ ðCs  Cb Þ dt heff in which Md is the total dissolved mass in the time interval dt, D is the diffusion coefficient for the dissolved drug in a given solvent, At is the surface area of the drug particle at time (t), Cs is the solubility of solute in the solvent, and Cb is the concentration of the dissolved species in the given solvent volume. At initial times during drug dissolution, Cb < < Cs and there are negligible changes in the surface area. Under these conditions, the modified Nernst-Brunner model simplifies to: dMd D∙A∙Cs ¼ dt heff Using the Nernst-Brunner equation as a foundation, the dissolution models implemented within PBPK absorption models include additional factors to improve drug dissolution simulations. For example, the Johnson model modifies the NernstBruner equation by accounting for the dissolution of both spherical and cylindrical particles: dMd D ð1 þ 2sÞ ðCs  Cl ÞMu;t ¼ p∙heff ∙rt s dt in which p is the particle density, rt is the spherical radius at time (t), s is the shape factor that accounts for non-spherical particle shapes, Cl is the drug concentration in the lumen, and Mu,t is the undissolved drug at time (t). The shape factor, s, is very important for particles that do not conform to a spherical shape. While particles may have the same volume, they will have significantly different surface areas. In addition, the dissolution model can use a constant heff or time varying heff that is dependent on particle size. Experimental evidence suggests that drug density, p, does not exhibit a large amount of variation, and a value of 1.2 g/cm3 can be used as a placeholder in dissolution models. The Johnson model is the default dissolution model incorporated into Gastroplus, but other options are available for selection. For Simcyp’s ADAM model, the default dissolution model is a modified WangFlanagan model:   dMd D ¼N 4p∙rt rt þ heff ðCs  Cl Þ heff dt in which N is the number of particles in a given particle size bin (discussed later). The Z-factor model incorporates the variable “z” which is obtained by fitting the following equation to in vitro dissolution data:   dMd Mu;t 2=3 ¼ z∙Mu;0 ðCs  Cl Þ dt Mu;0 where Mu,0 is the initial amount of undissolved drug. A pH dependent Z-factor can be incorporated into the final dissolution model when dissolution is characterized under multiple pH conditions. It is important to note that particle size may not be identical across all species, and particles can be binned according to their size (i.e., radius for spherical particles), and dissolution rates will be calculated separately for each particle size bin. The distribution of particle sizes can be defined within the absorption model as lognormal, normal, or user defined. While additional dissolution models have been described in the literature, the dissolution models described here are sufficient to simulate drug dissolution from suspension and solid dosage forms in many cases. Drug dissolution in the GI tract will depend on drug and system parameters, and PBPK models provide a powerful tool to incorporate both sets of parameters to simulate the entire process of drug dissolution. For example, there is considerable variation in pH and bile salts within the GI tract, and this can generate region specific API solubility. For all dissolution models discussed here, the local solubility, Cs, in a particular section of the GI tract can be incorporated into the final dissolution model to adjust for the impact of both pH and bile salts. In addition, the Johnson and Wang-Flanagan models can be adjusted for bile acid impacts on drug diffusion (D) in different GI sections. The effective diffusion coefficient, Deff, is a drug and medium-dependent parameter that is a composite of neutral and ionized drug diffusion coefficients which account for both monomeric drug and bile saltphospholipid drug aggregates. These aggregates tend to form with lipophilic drugs and these interactions can result in a relatively small Deff value compared to hydrophilic drugs that exist predominantly as monomeric species. Deff is rarely an empirical value and there are numerous options for parameter prediction. Since the Z-factor model does not include a diffusion coefficient, bile salt impacts on API diffusion are not considered with this model. Finally, the effective diffusion layer thickness (heff) and lumen drug concentration (Cl) will vary over time, and time dependent changes in both variables can be incorporated into the dissolution models. While the number of particles in each bin will decrease as solid drug goes into solution, precipitation out of solution can result in particle size growth over time. This is especially a concern for weakly basic drugs that display higher solubility at low pH environments found in the stomach (i.e., pH ¼ 1–2) compared to higher pH conditions in the small intestine (pH ¼ 5–6). Undissolved free base in the small intestine can provide a nucleus for drug precipitation which may result in particle growth. How drug precipitation is handled by PBPK absorption models varies. Robust in vitro testing (e.g., transfer assays) for poorly soluble drugs can be used to identify the potential for supersaturation and to characterize precipitation rates. If sufficient data are available to suggest

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Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

in vivo precipitation may occur within the GI tract, options to simulate precipitation include first order models and mechanistic models based on classic nucleation theory. While the first order precipitation model has proven very useful for many drugs, a mechanistic precipitation model that incorporates solution thermodynamics can provide an important tool to investigate processes impacting drug precipitation in vivo. In addition to absorption, precipitation and excretion, drug dissolved in the GI fluid can be removed from the lumen by enzymatic degradation, chemical instability, and microbiome metabolism. If sufficient data are available, these individual processes can be incorporated into the PBPK absorption model.

1.29.2.4

Impacts of formulation factors on drug dissolution

Dissolution rates for weakly acidic and basic drugs can be improved by administering a salt form with greater solubility compared to the non-salt form. The salt form will generate an alkalinized saturating film surrounding drug particles that will act as a buffer (improved Cs). While there are a few examples of organic salts having lower aqueous solubility compared to that of a free acid, sodium and potassium salts of weak acids tend to dissolve much more rapidly than free acids. This enhanced solubility is also observed for hydrochloride salts of weak bases compared to free bases. For some therapeutics, physical or chemical instability may preclude the use of salts in drug dosage forms. For example, sodium and potassium salts of weak acids can pose storage problems due to deliquescence. However, this issue can be bypassed by administering a buffering agent with the weak acid which will increase dissolution rate by increasing the pH and solubility in the dissolution layer. Solid drug can exist in multiple crystal forms that are referred to as polymorphs. While these crystal polymorphs will have the same chemical structure, their differing crystal structures can result in distinct physical properties (e.g., density, solubility, and melting point). Often, there is one preferred polymorph at a given temperature and pressure, but multiple metastable polymorphs can coexist. Furthermore, when these metastable polymorphs have improved aqueous solubility, they can be formulated into a final dosage form if the metastable crystal form has sufficient stability. Pseudo-polymorphism occurs when solvates within the crystal lattices alter the crystal structure in a manner that results in solvate forms of the drug. Hydrates are the most common form resulting from a water adduct, and the impact on drug dissolution should be considered for each individual case. Furthermore, co-crystal formation with crystalline solid excipients has been explored as a pathway to improve drug solubility and stability.

1.29.2.5

Drug permeation

Once a drug molecule dissolves within GI fluid, the drug is available for intestinal membrane permeation. The rate of drug absorption will depend on the surface area available for absorption, drug permeability, and drug concentration. dMabs ¼ Surface area∙Permeability∙Concentration dt When considering drug absorption from the GI fluid, the absorption rate can be described by: dMabs ¼ SAGI ∙Peff ∙Cl dt where SAGI is the surface area of the GI tract available for absorption and Peff is the effective permeability of the drug. If we multiply the above equation by VGI/VGI (i.e., 1) where VGI is the intestinal fluid volume, the product is: dMabs SAGI ¼ ∙P ∙C ∙VGI dt VGI eff l At this stage, we can assume the GI tract is a cylindrical tube and thus SAGI/VGI simplifies to 2/RGI where RGI is the radius of the GI tract. In addition, the product of the GI lumen drug concentration (Cl) and volume (VGI) is the amount of dissolved drug in the GI lumen (Ml). dMabs 2 ¼ ∙P ∙Ml RGI eff dt A common form of this equation replaces 2/RGI and Peff with a first-order rate coefficient for drug absorption (ka): ka ¼

2∙Peff RGI

dMabs ¼ ka ∙Ml dt In this equation, ka assumes a perfectly smooth surface and constant radius throughout the GI tract, a physiologically unreasonable assumption for the GI tract. Within the jejunum, the presence of intestinal folds, villi, and microvilli drastically increases the surface area available for drug absorption. Therefore, scaling factors are usually integrated into absorption models to account for the increased surface area. Since PBPK models separate the GI tract into separate compartments, absorption models can assign each

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section of the GI tract individual scaling factors that reflect the variations in intestinal physiology (i.e., ka value will vary along the GI tract). For oral drug absorption, Peff can be calculated in vivo by measuring drug disappearance from a single pass perfused segment of the intestine and fitting the data with an appropriate model (e,g, well-stirred Dahlgren et al., 2015; Lennernas, 1997; Amidon et al., 1980, parallel tube Lennernas, 2014a,b). Although perfusion studies to determine drug Peff values in humans have been conducted for a small set of drugs, this value is not routinely measured and most approaches include in vitro, ex vivo, and/or animal studies to estimate Peff in humans. Common in vitro permeability models include the cell free Parallel Artificial Membrane Permeability Assay (PAMPA) and mammalian cell Caco-2 cell transwell model. Both models include a donor and receiver compartment that represent the apical and basolateral sides of a physiological barrier (e.g., gut wall). After addition of drug to the donor compartment, the appearance of drug in the receiver compartment is measured over time and used to calculate an in vitro apparent permeability (Papp). There are many factors that can influence the observed Papp (e.g., pH, protein concentration, shaking, solvents) and this complicates drug comparison and Peff predictions. Options to predict both Papp and Peff have improved significantly, and there are many QSPR models available to predict how individual parameters may impact both Papp and Peff. While we have treated intestinal permeability, Peff, as a singular term, the parameter captures multiple routes of transit for intestinal drug absorption by diffusion. In each section of the GI tract, dissolved drug in the lumen can diffuse to the blood by two different routes (Fig. 2). The predominant mechanism, transcellular diffusion, will include drug absorption into the cells of the gut epithelium (e.g., enterocytes) prior to entry into blood. For the second mechanism, paracellular transit, drug molecules travel to the blood through gaps separating the intestinal epithelial cells, and this rate is influenced by drug size, shape, and ionization state. Since paracellular transit bypasses intestinal epithelial cells, paracellular absorption circumvents exposure to intestinal drug metabolizing enzymes and transporters that may impact drug disposition. Usually, drug and physiological parameters are used to estimate paracellular permeability (Ppara). The transcellular permeability, Ptrans, is calculated by subtracting the paracellular permeability from the total permeability (observed or predicted). Based on Fick’s first law of diffusion, the concentration gradient driving drug diffusion will include the unbound intestinal epithelial cell concentration or unbound blood concentration for transcellular and paracellular transit, respectively. As a result, PBPK absorption models can consider the contribution of each to the observed drug absorption rate:     dMabs ¼ SF trans ∙Ptrans ∙VGI ∙ Cl  Cu;enterocyte þ SF para ∙Ppara ∙VGI ∙ Cl  Cu;blood dt where SF is the scaling factor for each route of absorption (in the unit length1), P is the permeability for each route, Cu,enterocyte is the unbound enterocyte concentration, and Cu,blood is the unbound blood concentration. In addition to changes in surface area, scaling factors in PBPK absorption models can be included to account for additional regional differences that are not reflected in surface area changes (e.g., variation in tight junction gaps).

1.29.2.6

Carrier mediated transport

For drug molecules absorbed via the transcellular route, carrier-mediated transport may have an important role in drug absorption. Within the intestinal epithelia, there are both efflux (e.g., P-gp, BCRP, MRP3) and uptake (e.g., OATPs, OCT1, PepT1) transporters localized to the apical and basolateral membranes. Substrates of drug transporters will have absorption rates that are regulated by both passive diffusion and carrier mediated transport rates: dMent ¼Apical diffusion rate þ Apical carrier mediated transport rate  Basolateral diffusion rate dt þ Basolateral carrier mediated transport rate While the integration of carrier mediated transport in PBPK absorption models varies, a common approach considers the overall rate of carrier mediated drug transport at the apical and basolateral membranes as the difference between the influx and efflux rates (i.e., influx-efflux). If sufficient data are available, individual carrier mediated transport kinetics (e.g., Km, Vmax) for each influx and efflux transporter can be included in the absorption model: ! ! Vmax;influx ∙Ci Vmax;efflux ∙Cu;enterocyte Carrier mediated transport rate ¼ DF influx  DF efflux Km;influx þ Ci Km;eflux þ Cu;enterocyte in which DF is a distribution factor accounting for the difference in transporter expression/activity in the GI tract compared to the in vitro model used to determine Vmax, Vmax is the maximum rate of drug transport, Km,influx is the Michaelis-Menten constant for the uptake transporter and substrate, Km,efflux is the Michaelis-Menten constant for the efflux transporter and substrate, Ci is the drug concentration in the GI lumen (apical) or blood (basolateral), and Cu,enterocyte is the unbound enterocyte concentration. By incorporating MichaelisMenten kinetic parameters, the final absorption model can account for non-linear kinetics due to saturable transport. Since many PBPK absorption models separate the intestine into multiple compartments, the drug concentrations used to calculate carrier mediated transport rates will depend on the location in the GI tract. For this reason, at a given time, carrier mediated transport can be saturated in the proximal small intestine (duodenum) but not saturated in the distal intestine (ileum). Along with drug concentration, the transporter expression/activity also controls the rate of carrier mediated transport. Transporter

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Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

expression is not homogenous throughout the GI tract, and there is increasing data supporting how transporter expression varies across the small and large intestines. Even within the small intestine, there will be large differences in expression between proximal jejunum and distal ileum. By including distribution factors, transporter expression profiles can be incorporated into the final absorption model to account for differences in transport rate due to transporter expression.

1.29.2.7

Intestinal drug metabolism

Similar to carrier mediated transport, drug metabolism can significantly impact the rate and extent of drug absorption. If we consider the possibility of both carrier mediated transport and drug metabolism at the gut wall, the rate of change for drug in enterocytes can be described by: dMent ¼ Apical diffusion rate þ Apical carrier mediated transport rate  Basolateral diffusion rate dt þ Basolateral carrier mediated transport rate  Gut metabolism rate While luminal, gut wall, and bacterial enzymes are known to metabolize orally administered drugs, most significant impacts on drug absorption are attributed to the Phase I and Phase II drug metabolizing enzymes expressed in the enterocytes. Of the Phase I enzymes expressed in the intestine, the Cytochrome P-450 superfamily (CYP) has received the overwhelming majority of research attention given its dominant role in hepatic drug metabolism. Compared to the liver, only a subset of hepatically expressed CYPs have been observed in the intestine (e.g., CYP3A4, CYP2C9, CYP2C19, and CYP2D6), and their expression varies across intestinal regions. The rate of drug metabolism within each intestinal region can be calculated as follows:   Vmax ∙Cu;enterocyte Gut metabolism rate ¼ GEDF Km þ Cu;enterocyte in which GEDF is a distribution factor accounting for the metabolic enzyme amount in enterocytes in each GI section relative to the whole liver, Vmax is the maximum rate of drug metabolism for the enzyme in the whole liver, Km is the Michaelis-Menten constant for the enzyme and drug, and Cu,enterocyte is the unbound enterocyte concentration. Given the difficulty in experimentally determining Cu,enterocyte (i.e., fraction unbound (fu) $ Centerocyte), PBPK approaches will often compare prediction success with fu set to 1 or fu values in the enterocytes set equal to fu values in plasma or blood.

1.29.2.8

PBPK models of oral absorption

While current PBPK models including oral administration consist of many heterogenous compartments, these models are based on earlier, simpler models that treated the GI tract as a single well-stirred compartment. Early model examples include a mixing tank model “Single-Tank” which assumes instantaneous mixing throughout the GI tract and incorporates a single intestinal transit time for the entire GI tract (Fig. 5) (Dressman and Fleisher, 1986). Importantly, this mixing tank model considers drug dissolution from solid particles which allowed the model to play an important role in early studies investigating cases of dissolution and permeability rate limited absorption. While the mixing tank model was a critical step forward in PBPK modeling of oral dosing, there are many obvious limitations imposed by a single GI compartment. Therefore, to bypass the constraints of a single compartment, more recent modeling efforts incorporated multiple compartments representing various sections of the GI tract. An early, pivotal result of these efforts was the compartmental absorption and transit (CAT) model that separated the small intestine into seven compartments (Yu et al., 1996; Yu and Amidon, 1998). This dynamic model describes the transit of a drug through the individual compartments of the intestine with a set of ordinary differential equations and does not assume instantaneous mixing throughout the GI. The rate of drug movement is described by the following equation: dMðiÞ ¼ kt ∙Mði1Þ  kt ∙MðiÞ  ka ∙MðiÞ dt in which M(i) is the drug amount in compartment i, kt is the transit rate constant, and ka is the absorption rate constant. Both the kt and ka rate constants are shared across each of the seven compartments. However, as shown in the above equation, the model does

Fig. 5

Schematic of mixing tank model that represents the GI tract as a single compartment.

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Fig. 6 Representative compartmental absorption and transit models. Blue arrows represent drug release/dissolution and movement between compartments. Red arrows represent drug elimination (e.g., chemical degradation, metabolism, excretion).

not differentiate between dissolved and undissolved drug, and the CAT model was later modified with seven additional compartments to account for the two different drug forms transiting through the GI tract (Fig. 6) (Yu, 1999). Most current PBPK models used to simulate GI absorption from oral dosage forms are modified versions of the CAT model that incorporate various levels of additional physiological factors that will impact drug absorption. An example of a modified CAT model is the advanced compartmental absorption and transit (ACAT) model which serves as a foundation for the model used by Gastroplus (Fig. 6) (Agoram et al., 2001). The ACAT model expands the CAT model structure to account for additional sites of drug absorption, drug release from controlled release dosage forms, and the intestinal epithelial cells lining the gut wall. Furthermore, kt is no longer considered constant across compartments and is based on the mean transit time for each individual compartment. Undissolved and dissolved drug transiting through the GI tract are subject to multiple different processes at each step. Dissolved drug can precipitate into solid form, permeate into enterocytes (transcellular) or blood (paracellular), or break down due to metabolism or degradation in the lumen. Each of these processes will be influenced by the conditions assigned to the GI location (e.g., pH, bile salt concentration) and these conditions can be treated as dynamic variables at each site. For example, conditions induced by fed conditions (e.g., increased pH, increased bile salts, reduced gastric emptying rate) will change over time and this behavior can be included in the PBPK model. While the original ACAT model assumed a constant volume for each GI compartment, updated versions include the option for dynamic fluid volumes. These updated models provide the option to simulate changes in intestinal volumes based on fluid intake, absorption, and secretion. Changes in luminal volume can have a large impact on drug dissolution and permeation because both are influenced by a concentration gradient that includes the lumen drug concentration. An additional critical aspect of the ACAT model is the intestinal epithelial cell compartment assigned to each section of GI tract. While early definitions of absorption were based on the fraction of drug entering the blood, the modern definition is based on the amount of drug absorbed into gut epithelia. The enterocytes in the model are considered well-mixed compartments from where drug can either transit into the gut lumen or blood (through active or passive processes) or undergo gut metabolism. Similar to variations in luminal pH and bile salt concentrations across the GI tract, differences in drug metabolizing enzyme and transporter expression can be incorporated in the model structure. Furthermore, interindividual variability can be considered for all of the variables incorporated into the model. While not shown in Fig. 6, the ACAT model can also include enterohepatic circulation (EHC) by incorporating drug transit to the duodenum after biliary secretion. The secreted drug will be available for reabsorption into enterocytes or will be excreted in the feces. Furthermore, many PBPK models now include the option to simulate hydrolysis of glucuronidated metabolites to the parent drug in the GI following biliary secretion into the duodenum by EHC. In addition to the ACAT model, the advanced dissolution, absorption, and metabolism (ADAM) model is another popular absorption model that is implemented within Simcyp (Jamei et al., 2009). The ADAM model is also based on the CAT model, and drug absorption from each GI section will be controlled by drug dissolution, precipitation, permeability, metabolism, transport, luminal degradation, and transit. While it has not been clearly described, a difference between the Gastroplus and Simcyp absorption models appears to be how drug basal transfer in the intestine is handled by each model. One of the physiological

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components impacting basal drug transfer is the blood perfusion at each GI section. Traditionally, physiologically based absorption models set blood flow to the GI absorptive region (small intestinal enterocyte region) as 100% of the superior mesenteric artery flow. However, in humans, only a fraction of blood supplied to the small intestine by the mesenteric artery supplies the epithelial cells of the villi which is the location of many drug metabolizing enzymes and transporters in the intestinal epithelium. Not only will this impact first pass drug metabolism, but it can also have a significant effect on the predicted metabolism and transport of systemically circulating drug and the rate of drug absorption.

1.29.2.9

Application of physiologically based absorption models

At present, PBPK modeling provides a well-established approach to support DDI predictions involving drug absorption and gut metabolism in NDAs and ANDAs. While these DDI assessments usually focus on drug metabolizing enzymes and/or transporters, the application of PBPK models that incorporate advanced absorption models to DDI predictions has expanded recently. For example, an alternative mechanism for drug interactions involves gastric pH-dependent DDIs with acid-reducing agents (ARAs). As discussed in a previous section, gastric pH can have a major impact on drug solubility and dissolution, and ARAs may alkalinize the gastric lumen in a manner that alters the bioavailability of weak bases (reduced) or weak acids (increased). PBPK absorption models offer a useful approach to assess the potential for pH-dependent DDIs and can inform clinical study design. For example, coadministration of ranitidine with ketoconazole, a poorly soluble weak base (BCS 2 compound), was shown to reduce ketoconazole exposure by 95% (Piscitelli et al., 1991). To investigate whether a ranitidine induced increase in stomach pH could partially explain the observed reduction in ketoconazole exposure, PSA can be used to explore the impact of gastric pH on drug absorption (Fig. 7). A PSA demonstrates that ketoconazole Fa is sensitive to perturbations in gastric pH that increase the pH to a value > 3.0, and the reduction in Fa will depend on the administered ketoconazole dose. Given the significant impact ARAs may have on drug disposition, the FDA published a draft guidance for evaluation of gastric pH-dependent drug interactions in November 2020 (FDA, n.d.a). There has been a push to expand PBPK modeling applications to FIH dosing recommendations for applications submitted to regulatory agencies. When comparing PBPK methodology to traditional, empirical methods for FIH dosing accuracy, many industry groups have confirmed that PBPK modeling provides an improved approach to predict FIH pharmacokinetics. In 2018, the EMA acknowledged PBPK modeling as a useful approach to select appropriate starting doses in healthy volunteers. Furthermore, for NDAs, recent applications to the FDA have included simulations to support predicted drug exposure (lumen and enterocyte) in the GI tract, and the inclusion of these data illustrates the increasing confidence in PBPK absorption model application. While the PBPK models are constantly improving, there are still well documented limitations in this approach (Poulin et al., 2011). In 2011, the Pharmaceutical Research and Manufacturers of America reported that their evaluation of PBPK modeling for FIH studies identified significant accuracy issues and this was especially true for oral administration of poorly soluble compounds (Poulin et al., 2011). However, the lack of preclinical verification and diverse sources of in vitro data for the study may have contributed to the observed issues. PBPK models provide an important tool to predict food effects on drug absorption. Food ingestion can impact drug absorption through a variety of different mechanisms that include reduced gastric emptying rates, increased bile salt secretion, and altered blood flow to the gut. Since PBPK absorption models include these system parameters in the model structure, physiological changes anticipated with food consumption can be investigated to determine how individual mechanisms impact drug absorption. For example, while a food induced reduction in gastric emptying may delay and/or limit absorption in some cases, the concomitant increase in bile salts may enhance the absorption of other drugs by improving drug solubility. Food effect investigations with PBPK modeling have been described in detail for multiple therapeutics including zolpidem (Andreas et al., 2017) and alectinib (Parrott et al., 2016; Tistaert et al., 2019).

Fig. 7 Coadministration of an acid reducing agent (ARA) is predicted to reduce the ketoconazole fraction absorbed (Fa) in a dose dependent manner.

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1.29.2.10 Model verification and validation Model verification is key to improving confidence in modeling drug absorption via PBPK methods, and this process includes substantial preclinical verification of absorption predictions. In a broader effort to boost confidence in PBPK approaches, there have been considerable efforts to improve harmonization across PBPK modeling groups. While these efforts are applicable to all stages of PBPK model verification and validation, approaches for modeling drug absorption have received substantial attention since inaccuracies and errors in modeling absorption are responsible for the inaccuracies in predicting human pharmacokinetics in many cases. In general, prior to developing an oral absorption model, iv drug pharmacokinetic data are critical to establishing drug disposition parameters in the absence of oral absorption. The PBPK absorption model can be treated separately from the systemic model, so iv data can be used with a calibrated systemic PBPK model or a compartmental model can be fitted to the pharmacokinetic data. While all drug specific parameters that govern drug absorption can be predicted with both free and commercially available QSPR models, these values should be confirmed with in vitro data when possible. In an effort to verify formulation specific models, preclinical oral pharmacokinetic data should include human-relevant doses and formulations. Model complexity will increase from iv to oral dosing with compounding error at each step (Margolskee et al., 2017a,b; Darwich et al., 2017; Ahmad et al., 2020).

1.29.3

Distribution

Drug distribution into various organs in the body is complex and is a major driver of the shape of the plasma-concentration versus time curve. Distribution is governed by a combination of physiological parameters and processes such as tissue composition and blood perfusion to different organs, and drug specific characteristics such as lipophilicity, ionization, pKa and molecular size. The protein binding of the drug in plasma and tissues is also a key component in defining drug distribution characteristics. The distribution of drugs is usually characterized by the pharmacokinetic parameter of Volume of distribution (V) which, depending on the type of pharmacokinetic analysis used, may be reported as volume of the central compartment (Vc), volume of the b-phase (Vb, also sometimes called Varea or Vz), or volume of distribution at steady state (Vss). Of these parameters, only Vss should be considered a true measure of the extent of distribution with Vc and Vb used predominantly for compartmental analysis of plasma concentration-time data. The conceptual mathematical definition of volume of distribution is that it is the proportionality constant between the amount of drug in the body (A) and plasma concentration (C) defined by: V¼

A C

This concept makes measurement of volume of distribution as a pharmacokinetic parameter challenging in research projects and in drug development. The amount of drug in the body is rarely known and can practically only be well defined at the time of instantaneous iv bolus. As such the volume to which the drug distributes instantaneously after iv bolus dosing can be measured as Vc from: Vc ¼

D C0

in which C0 is the plasma concentration at time 0. The overall exposure to a drug (measured by the area under the plasma concentration-time curve, AUC) depends only on the bioavailable dose (F∙ D) of the drug and the clearance (CL) of the drug as show by: AUC ¼

F∙D CL

However, the shape of the plasma concentration-time curve is greatly affected by distribution kinetics and the kinetic processes driving specific distribution to tissues. It is important to emphasize that CL and V are independent parameters and CL can always be calculated from D/AUC regardless of distribution kinetics and the shape of the plasma concentration-time curve. The critical role of distribution kinetics in defining the shape of the plasma concentration-time curve is perhaps best described by the relationship between drug half-life (t1/2), clearance (CL) and volume of distribution (V): t1=2 ¼

V∙ln2 CL

As shown in Fig. 8, the safety and efficacy of drugs and the considerations of dosing regimen design are closely related to distribution kinetics, and therefore predicting distribution kinetics prior to human dosing is important. All the drugs shown in Fig. 8 after iv and oral dosing have the same clearance and bioavailability as well as absorption rate constant (ka). Yet, due to the different distribution kinetics, the half-lives are very different and the maximum concentration (Cmax) values for these drugs are different. Importantly, depending on the concentration-response characteristics of the drugs of interest, the different distribution characteristics may define whether a once a day or three times a day dosing would be required. As such, determination of volume parameters in clinical studies is important for adequate understanding of the sojourn of the drug in the body, and distribution kinetics plays a key role in considerations of how long and how often plasma samples should be collected in clinical studies. For example, the decision on

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Fig. 8 Illustration of the impact of distribution kinetics on the shape of the plasma concentration versus time curves. The plasma concentrations for five drugs that all have the same clearance, are completely bioavailable and have identical absorption kinetics are simulated. The shape of the plasma concentration curve is strongly impacted by distribution kinetics including the rate and extent of distribution both after iv (left panel) and oral (right panel) dosing. Following iv dosing (left panel) the impact of distribution kinetics is clearly seen in the terminal slope of the plasma concentration versus time curve and the rate at which the plasma concentrations decline early on after drug dosing. After oral dosing, the same differences in terminal half-life can be observed but the maximum drug concentration (Cmax) is also significantly impacted by the distribution kinetics due to the interplay of the individual rate constants defining the concentration curve.

when to collect the first plasma sample following an iv bolus dose should consider the initial distribution kinetics as it can impact the determination of AUC in case the initial distribution phase is not captured and the y-intercept after an iv bolus dose is incorrectly extrapolated. Furthermore, due to the effect of distribution kinetics on half-life of the drug, the value of Vb plays an important role in determining how long plasma samples should be collected to adequately characterize the AUC. If sample collection does not capture the terminal half-life, CL of the drug will be overestimated as the AUC is calculated (and extrapolated to infinity) based on a shorter distribution half-life. Distribution kinetics has traditionally been described mathematically either using compartmental models or in the context of extent of distribution by noncompartmental analysis (NCA). The most commonly used compartmental model is the 2compartment model that is schematically shown in Fig. 9 with an illustration of the processes that occur during the process of distribution. The 2-compartment model is a purely mathematical way of describing plasma concentration-time curves and does not have a mechanistic basis. The model compartments do not reflect any specific physiological space but rather mathematically describe compartments to which the drug distributes at different rates. As such, one must emphasize the apparent nature of the distribution terms in the 2-compartment model (Vc, Vb). Importantly, compartmental models cannot be used to predict drug disposition without real-life pharmacokinetic data. However, compartmental analysis of plasma concentration-time curves in preclinical species can be extrapolated via allometric scaling to humans. Yet, this is often a low confidence approach. Therefore, compartmental analysis of distribution kinetics is generally of use during drug development for population-pharmacokinetic (pop-PK) analyzes and pharmacokinetic-pharmacodynamic (PK-PD) modeling. In addition, when a terminal half-life is determined, compartmental analysis is useful in deciding on dosing strategies. In reality, Vb is not a good measure of the distribution characteristics of a drug and tends to overestimate the true distribution volume of the drug as described in the following text. This is because of the complex interplay of extent and rate of distribution and the characteristics of the pseudo-equilibrium that occur after an iv bolus or oral administration of a drug. Instead, Vss is a reflection of the true distribution volume of a drug and a reasonable measure of distribution if determined correctly. Conceptually, Vss is the distribution volume when equilibrium is reached. However, distribution equilibrium might never be reached after an iv bolus dosing causing additional challenges in determining distribution kinetics. Still, conceptual derivation of what determines the magnitude of

Fig. 9 Schematic of the two-compartment model with A1 depicting the amount of drug in the central compartment in which the drug distributes instantaneously and A2 depicting the amount of drug in the peripheral compartment. After an iv-bolus dose the entire dose distributes instantaneously into the central compartment and the amount in this compartment drives the rate of distribution into the peripheral compartment as shown in the differential equations. The accumulating amount of drug in the peripheral compartment then provides the driving force for rate of return of the drug to the central compartment resulting ultimately in a distribution equilibrium when k1,2$A1 ¼ k2,1$A2. Subsequently, due to elimination (k1,0), the rate k1,2$A1 will be lower than k2,1$A2.

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volume of distribution is useful and this is best accomplished by considering general distribution under distribution equilibrium (steady state) and considering physiological determinants in the body. The total amount of drug in the body is determined by A ¼ Ap þ At, where A is the amount of drug in the body and subscripts p and t refer to plasma and tissues, and the volume of the body can be considered in the context of plasma and tissue volumes and written as Vtotal body ¼ Vp þ Vt where Vp and Vt are physiological volumes of plasma and tissues (3 L and 67 L respectively for a 70 kg human). In this case, the tissue volume includes the volume of extracellular fluid as well as the cellular compartments. Knowing that the amount of drug in each respective physiological space or tissue is related to the concentration of drug in the tissue by volume, via A ¼ V$C, the amount in the body can be written as: A ¼ Cp ∙Vss ¼ Cp ∙Vp þ Ct ∙Vt : For each tissue, a partition constant Kp can be determined which is the tissue-to-plasma equilibrium concentration ratio (Ct/Cp). By dividing the equation above by Cp and substituting the ratio Ct/Cp with Kp the overall description of Vss is obtained: Vss ¼ Vp þ Vt ∙Kp It is clear from the above equation that Kp is critical for determining Vss. However, it is well known that the Kp is usually dependent on the tissue composition and therefore there is no single Kp-value for the entire body. The overall volume of distribution of a drug is the sum of distribution into specific organs: Vss ¼ Vp þ VT1 ∙Kp1 þ VT2 ∙Kp2 þ VT3 ∙Kp3 þ .:VTn þ 1∙Kpn þ 1 From this follows that Kp to certain large organs can have a major impact on Vss while smaller organs could have very high Kp values without impacting overall distribution. This concept is illustrated in Table 2 using fentanyl distribution parameters as an example. In Table 2, the Kp values for individual organs are reported based on measurements of fentanyl distribution in rats (Rodgers et al., 2005) and the human tissue volumes are used to predict human volume of distribution. It is important to keep in mind that for this approach it is assumed that the Kp values (concentration ratios) measured are obtained under distribution equilibrium. As shown in Table 2, fentanyl Vss is largely dependent on the Kp value for adipose and muscle as these two tissues constitute about 94% of the total distribution of fentanyl. The kidney and pancreas, despite the relatively high Kp values, each contribute < 1% of fentanyl distribution due to the relatively small size of these tissues. During drug development, distribution to individual tissues is often not experimentally determined although total body radiography or other imaging techniques could be used to estimate tissue distribution as an alternative to classic bioanalytical measurements of concentration ratios. It is also noteworthy that experimental measurement of the Kp values for large organs, such as muscle, adipose and bone together with the highly perfused organs of liver and skin would likely be sufficient to predict human distribution kinetics with relatively high confidence. This can be mathematically shown via PSA of the impact of individual tissue Kp values on the overall predicted distribution kinetics. In the case of fentanyl, the Vss value is highly sensitive to adipose tissue Kp. If that value is halved to 13 from 27, the Vss will decrease to 343 L. The value of differentiating the specific organs that contribute to the distribution volume comes from the impact of different blood flows to these organs and consequently different rates of distribution. Unlike compartmental models, this approach allows mechanistic prediction of both rate and extent of distribution to specific organs and the shape of the plasma concentration time curve, offering a unique strength of physiologically based modeling. In many cases, experimental determination of Kp values is not possible and in silico methods are used for predicting Kp values. A number of methods have been proposed for the prediction of Kp values and the reader is referred to the specific publications for the

Table 2

Predicted human tissue distribution characteristics of fentanyl based on tissue-to-plasma concentration ratios (Kp) measured in rat and known tissue volumes in a representative 70 kg human.

Parameter

Fentanyl Kp values (rat data)

Volume of the tissue (L)

VT$Kp (L)

% of distribution volume

Adipose Bone Brain Gastrointestinal tract Heart Kidney Liver Lung Muscle Pancreas Skin Spleen Blood Total

26.7 1.0 3.5 8.4 4.5 12.1 3.8 13.5 3.1 21.3 2.1 27.6 1

15 10 1.4 1.2 0.33 0.31 1.8 0.53 28 0.098 2.6 0.18 5

400.5 10 4.9 10 1.5 3.8 6.8 7.2 87 2.1 5.5 5.0 5 549

73 1.8 0.9 1.9 0.3 0.7 1.3 1.3 16 0.4 1.0 0.9 0.9

From Huang W and Isoherranen N (2020a) Sampling site has a critical impact on physiologically based pharmacokinetic modeling. The Journal of Pharmacology and Experimental Therapeutics 372: 30–45.

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detailed mathematical derivation of the methods that are used for predicting Kp values (Poulin and Theil, 2000, 2009; Poulin et al., 2001; Berezhkovskiy, 2004; Rodgers et al., 2005; Rodgers and Rowland, 2006, 2007; Jones et al., 2011; Chan et al., 2018; Lukacova et al., 2008). At present, none of the methods has been well verified in humans, and all of these methods lack some critical parameters that relate to human drug distribution. For example, the phospholipid content of specific tissues is often extrapolated from rats or mice, and the partitioning to lipids is predicted from sometimes poorly characterized lipophilicity of the compound of interest. Similarly, the measured or predicted blood-to-plasma ratio can have a disproportionate effect on the predicted values depending on the calculation method. The in silico methods do not account for specific binding to unique tissue proteins such as fatty acid binding proteins nor active transport and hence can fail to predict true extent of distribution. Conceptually, the Kp prediction methods have their foundations in the assumption that the free (unbound) drug concentration is the same within the cell and in plasma (i.e., distribution equilibrium applies). Drug molecules in the body are either bound to plasma proteins (mainly albumin and a-acid glycoprotein), tissue proteins and lipids, or are free (unbound) in plasma or cytosol and extracellular fluid. Since proteins in tissues and plasma are different, the drugs bind differently in tissues and plasma. Similarly, since lipids and proteins in different tissues and cell types vary, drug binding in different tissues is expected to vary despite free concentrations being the same across all organs at steady state and in the absence of active transport. The unbound fraction (fu) of drugs in plasma and tissue is defined as the ratio of the free concentration and total concentration (i.e., fu,plasma ¼ Cu,plasma/Ctotal,plasma and fu,tissue ¼ Cu,tissue/Ctotal,tissue). At steady state, the free drug concentration is the same on both sides of any biomembrane regardless of the extent of binding. This allows rationalization of Kp values for specific tissues as the ratio of unbound fractions: Kp ¼

Ct ¼ C

Cu;t fu;t Cu fu

¼

fu fu;t

and as such the magnitude of Vss is governed by the ratio of plasma protein binding and tissue binding. The above description of Vss and Kp values predominantly relates to the extent of distribution and to what apparent volume the drug ultimately distributes to if infused intravenously to steady state. However, another important consideration for distribution kinetics is the rate of distribution. Mechanistically, drug distribution is easiest to understand by considering the physiological basis of distribution, and PBPK models are emerging as critical tools to predict and understand the rate and extent of drug distribution to various organs in the body (Fig. 1). Drugs get delivered to each tissue by blood flow (Q) but delivery also depends on the concentration of the drug in arterial blood (CA): Rate of presentation ¼ Q$CA CA is the same for each tissue while Q is tissue dependent resulting in differences in rate of delivery of the drug to different tissues. However, the time it takes to reach distribution equilibrium in a given tissue also depends on the Kp for a given tissue (i.e., how big a sink a given tissue is for the drug binding). As such the rate of distribution to a given tissue is governed by the distribution rate constant kT which is a first order rate constant and described by: Q

kT ¼

Q∙CV V ¼ T VT KP CV KP

Understanding the nature of distribution rate constant kt and the corresponding tissue-specific distribution half-life allows the realization of the power of PBPK modeling in predicting the shape of the plasma concentration-time curve. As can be seen, the kT that determines how rapidly a drug reaches distribution equilibrium within a given organ depends only on physiological values of Q and Vt and of the drug specific Kp. Again, it needs to be emphasized that the Kp value refers to distribution equilibrium. If the tissue to plasma concentration ratio is measured at non-steady state concentrations, the values obtained can differ considerably from the true steady state values. Table 3 illustrates this for ketamine showing apparent Kp values in the rat measured after iv bolus

Table 3

Distribution parameters for Ketamine measured in the rat following an iv infusion to steady state and following an iv bolus. The tissue to plasma concentration ratios (Kp) are shown for each individual tissue.

Tissue

Ketamine Kp values (iv infusion) Ketamine Kp values (iv bolus)

Adipose Brain Gastrointestinal tract Heart Kidney Liver Lung Muscle

2.4 5.1 4.6 6.4 10.0 4.4 6.9 1.8

18.9 3.1 2.0 8.3 60.9 3.5 25.3 –

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Fig. 10 Simulated arterial, venous and select tissue concentrations of fentanyl following an iv bolus dose of fentanyl. The simulations were done using the values described in Table 2 for fentanyl tissue partitioning and the fentanyl PBPK model described in Huang and Isoherranen (2020a,b). The right panel shows the data for the early time points zoomed in from the left panel to depict different distribution rates to the individual organs. The lines depict arterial (red), venous (blue), skin (gray), liver (yellow) and adipose (purple) concentrations.

and iv infusion (Edwards and Mather, 2001). While some Kp values are relatively close to each other between iv infusion and iv bolus dosing, and likely within experimental error and variability, in other tissues such as adipose, kidney and lung the Kp values after iv bolus are considerably higher than those measured after iv infusion. This discrepancy can be rationalized by considering the differences in plasma concentrations across an organ and the arteriovenous concentration difference that arises from distribution kinetics into the specific organ. Fig. 10 shows simulated data of fentanyl concentrations in the artery, vein, adipose, skin and the liver following an iv bolus dose of fentanyl. Fentanyl distributes to the adipose, skin and liver based on the data in Table 2. For example, the distribution half-lives are 17 h for adipose, and 0.22 h for skin. After iv bolus dosing, fentanyl starts to distribute to each of these organs based on the blood flow, tissue volume and Kp value as defined by kT. As shown in Fig. 10 during the first hours after iv bolus dosing, the concentrations of fentanyl in the skin and adipose increase while the plasma concentrations decrease leading to time-dependent changes in the apparent Kp value before distribution equilibrium is reached. Fig. 10 also illustrates how the knowledge of individual tissue Kp values combined with PBPK modeling allows mechanistic prediction of observed compartmental kinetics of fentanyl as the rate of distribution can be simulated using the known physiology and Kp values to the key organs. However, a striking observation from Fig. 10 is that the concentration ratios between tissues and plasma for fentanyl are significantly different from the Kp values used in the PBPK model and reported in Table 2. This is due to the elimination of fentanyl from the body which causes arterial concentrations perfusing each organ to be lower than the venous concentration as shown in Fig. 10 as the tissue becomes a reservoir of the drug during elimination phase. As such, when Kp values are experimentally measured, determining them during the elimination phase of the drug may cause significant error in the Kp value that is measured and intended for use in PBPK modeling. In the case of fentanyl, the error in the Kp value for the adipose is about twofold as shown in Fig. 11. When fentanyl is infused to steady state, the true Kp values can be obtained while after iv bolus dosing the Kp values are artificially elevated. The data in Fig. 11 also illustrate why Vb typically overestimates the true steady state distribution volume of a drug and Vb is always greater than Vss. Taken together these examples emphasize the important applications of PBPK modeling in predicting plasma-concentration time curves and the unique strength of PBPK modeling in the ability to simulate and predict drug concentrations in individual tissues including target sites for drug action. This application provides a unique strength for potentially predicting the time course of pharmacological activity in addition to predicting the potential manifestations of multicompartment kinetics in humans.

1.29.4

Metabolism

Drug metabolism and metabolic elimination typically occur in the liver and to some extent in the intestines. As clearance is perhaps the most important pharmacokinetic parameter to characterize accurately, it is not surprising that metabolic clearance has received considerable attention over the years. For the current section, metabolism is considered in the context of hepatic clearance and the importance of understanding hepatic clearance phenomena in predicting drug exposures (AUC), CL and F. For detailed reference on clearance concepts and hepatic clearance models the reader should refer to previous discussions on these topics (Wilkinson, 1987; Pang et al., 2019).

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Fig. 11 Simulated venous, arterial and adipose tissue concentrations of fentanyl following an iv bolus (top panels) and iv infusion (middle panel) of fentanyl with the adipose to venous plasma concentration ratio depicted in the bottom panel. The simulations were done using the values described in Table 2 for fentanyl tissue partitioning and the fentanyl PBPK model described in Huang and Isoherranen (2020a,b). In top panel fentanyl was dosed as an iv bolus and the arterial (red line), venous (blue line) and adipose (purple line) concentrations were simulated up to 170 h after bolus dose. In middle panel fentanyl was dosed as continuous iv infusion for 170 h and the arterial (red line), venous (blue line) and adipose (purple line) concentrations were simulated. Note that adipose tissue concentrations are depicted on a separate scale than plasma concentrations. The bottom panel shows the adipose tissue to venous plasma concentration ratios after the iv bolus and iv infusion dosing illustrating the impact of nonequilibrium on apparent tissue-to-plasma ratios. The black line shows the simulated adipose tissue to venous plasma concentration ratio following an iv infusion and the red line shows the corresponding ratio after iv bolus dosing. Note that based on the Kp value and the perfusion rate of adipose tissue the distribution half-life of adipose tissue is 17 h and the simulations capture 10 distribution half-lives and at 170 h during iv infusion the adipose to plasma concentration ratio equals the Kp value used in the simulations. The simulations are shown to illustrate the role of the interplay between clearance and distribution in defining dynamic concentration ratios under non-steady state circumstances.

They key model of hepatic clearance for drug development purposes is the well-stirred (venous equilibrium) model of the liver: CLh ¼

Q∙fu;b CLint Q þ fu;b CLint

which continues to be used as the main method to predict hepatic elimination and hepatic clearance of drugs. In the well-stirred model, Q refers to hepatic blood flow, fu,b is the fraction unbound in blood and CLint is the intrinsic clearance by the liver. An aspect that is often ignored is the fact that the well-stirred model does explicitly use blood flow and blood binding rather than plasma flow and plasma protein binding. Drugs typically distribute to blood cells by passive diffusion (i.e., unbound drug in plasma is available to diffuse into erythrocytes). At distribution equilibrium, the unbound concentrations in erythrocytes and in plasma are the same and the blood to plasma concentration ratio is defined similarly to other distribution characteristics based on the binding/

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partitioning of the drug to blood cell components and to plasma proteins. Hence, drug concentration in whole blood is not necessarily the same as the concentration in plasma. The blood to plasma ratio can be mathematically described as: B CBC ¼ ð1  Hcr Þ þ Hcr ∙ P CP where Hcr is the hematocrit, the fraction of whole blood that is blood cells. Since B/P can be significantly different from 1, hepatic blood clearance can also be quite different from plasma clearance, and parameters such as extraction ratio should be calculated from blood clearance and blood flow unless it is known that B/P ¼ 1. The plasma clearance can be converted to blood clearance via: CLblood ¼

CLplasma B P

The well-stirred model can be derived considering a system of an extracting organ such as the liver connected by a blood flow to a reservoir (Rowland et al., 1973). For this derivation one must assume venous equilibrium in which liver concentrations are in equilibrium with the concentrations emerging from the liver and that the concentrations entering the liver are the same as the concentration in the reservoir. As the blood emerging from the liver is in equilibrium with the liver, the concentration ratio between the liver concentration and venous blood concentration is described by the liver partition ratio Kp. The elimination of the drug within the liver is assumed to be first order and described by the first order rate constant kelimination. These concepts yield the equation for the well-stirred model: CLh ¼

Q∙kelimination ∙Kp ∙Vliver Q þ kelimination ∙Kp ∙Vliver

in which liver is associated with a volume (Vliver) and elimination rate constant k (kelimination) and venous equilibrium (Cliver/ Cplasma ¼ Kp,liver) is assumed. Q refers to liver blood flow, approximately 1.5 L/min. First order kinetics of dA/dt ¼  kA and the basic definition of CL (CL ¼

dA dt

Cblood

CLint;liver ¼

) can be used to also derive the intrinsic clearing capacity for the liver as dAliver dt

Cblood

¼

kelimination ∙Aliver kelimination ∙Vliver ∙Cliver ¼ ¼ kelimination ∙Vliver ∙Kp;liver Cblood Cblood

showing that the product kelimination ∙ Kp ∙ Vliver in the well stirred model is equal to the intrinsic clearing capacity of the liver. In essence, the amount of drug driving the elimination in the liver can be expressed as a function of the partitioning of the drug to the tissue. Based on fundamental kinetics, k ∙ V ¼ CL and as such kelimination ∙ Vliver ¼ CLint. Based on distribution concepts, under steady state conditions the unbound concentrations in the liver and plasma are the same. Therefore, the Kp,liver value can be written as the ! ratio between unbound fractions in blood and liver

f

KP;liver ¼ fu;B . This yields: u;L CLint;liver ¼

fu;B ∙CLint fu;L

The CLint in this equation refers to the maximum enzymatic ability of the liver enzymes to clear the substrate based on MichaelisMenten kinetics: v¼

C∙Vmax C þ Km

in which C is the substrate concentrations in the liver, Km is the Michaelis-Menten constant and Vmax is the maximum velocity of product formation defined as kcat ∙ [E]. A well characterized relationship arising from Michaelis-Menten kinetics and from the dA definition of CL as CL ¼ dtC yields CL ¼

v Vmax ¼ C C þ Km

And under circumstances when C  Km this simplifies to CLint ¼

Vmax Km

Substituting this to the equation above for CLint,liver yields: CLint;liver ¼ fu;b ∙

Vmax Vmax ¼ fu;b ∙ Km fu;L Km;u:

It is important to note that it is believed that the unbound concentration is what interacts with metabolic enzymes in vitro and in the liver and hence in vivo Km$fu,L is considered equal to the unbound Km,u values from in vitro experiments. This illustrates why it is critical to determine the unbound Km values in vitro rather than the total Km values in a given experiment and why the common

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form of well-stirred model used to predict hepatic clearance requires accurate measurement of unbound intrinsic clearance as well as unbound fraction in blood. The CLint,liver can be substituted to the well-stirred model yielding its common form of CLh ¼

Q∙fu;b CLint Q þ fu;b CLint

Numerous publications and book chapters have been dedicated to the accurate determination of intrinsic clearance in in vitro systems and for the prediction of the intrinsic clearance from in vitro systems, and the reader is referred to these documents for in depth analysis of intrinsic clearance predictions (Obach et al., 1997; Obach, 1996, 1999; Brown et al., 2007; Wan et al., 2010). The well-stirred model assumes that the liver is a single well-stirred compartment in which sinusoidal, arterial and venous blood mix instantaneously, and as shown above the well-stirred model makes the assumption of a venous equilibrium. However, hepatic clearance is generally defined in relation to arterial (reservoir) concentration (Rowland et al., 1973). For high clearance and high extraction ratio drugs, the arterial and venous concentrations for the liver can be very different leading to a need to address the decrease in drug concentrations across the liver. More complex liver models have been developed to address this issue for high clearance drugs and the declining plasma concentrations across the liver. Of the more complex liver models, the dispersion model represents a unifying (well-stirred and sinusoidal) model that is consistent with hepatic physiology and the non-ideal movement of solute through a packed bed of hepatocytes. It assumes a pulse input, varying paths and mixing, sinusoidal elimination and probability output peak. The model defines hepatic clearance based on two parameters RN and DN. RN is an efficiency number for drug elimination and is equivalent to fu,b ∙ CLint,u/Q under first order conditions. DN is an axial dispersion number and is a measure of the dispersion or spread in residence time of drug molecules moving through the liver. The higher the value of DN, the greater the dispersion. Typical dispersion numbers reported are 0.2–0.4. As elegantly described by Pang et al. (2019) the well-stirred model and parallel tube model of the liver are limiting cases of the dispersion model representing situations where DN ¼ 0 or infinity. Much academic attention has been dedicated to the assumptions of the various liver models because from a physiological context the assumptions of instantaneous mixing and equilibrium employed in the well-stirred model are obvious oversimplifications. However, in drug development, the well-stirred model is still the predominant liver model used and typically performs sufficiently well for low-to-intermediate clearance compounds. Therefore, the remainder of this section will deal with the well-stirred model and its use in drug development. The well-stirred model also allows for incorporation of uptake and efflux transporters and permeability limitations, mathematical factors that add to the power of the model in drug development. The universal expression of the hepatic clearance can be written as: CLh ¼ Q

fu;b CLinflux CLint;H   QCLefflux þ CLint;H Q þ fu;b CLinflux

It is important to note that for drugs that are mainly cleared by metabolism, the hepatic clearance defines the systemic clearance and hence the half-life of the drug (together with volume of distribution). However, the exposure to the drug (i.e., AUC) after oral administration is defined by the oral clearance of the drug (CL/F). In other words, the relationship between orally administered dose (D) and exposure to the drug (AUC) is defined by oral clearance of a drug after oral administration: D CL ¼ AUC F This is significant as oftentimes a drug with high hepatic extraction may still have a relatively long half-life after iv administration due to a high volume of distribution, but the exposure after oral administration is limited by extensive first pass metabolism. For compounds with high in vitro intrinsic clearances, the systemic clearance may be relatively easy to predict within a twofold accuracy, a common standard for clearance predictions, as the hepatic clearance becomes blood flow limited. At the same time, overall F for such compounds can be very sensitive to prediction errors as only a small fraction of the dose absorbed from the GI-tract escapes hepatic metabolism and a twofold error in Fh might be within normal variability (i.e., 2% vs. 5%). The well-stirred model can be useful to fully appreciate the differences in oral and systemic clearance for drugs that are mainly cleared by metabolism and to clarify the common problems in clearance predictions. When a drug is predominantly cleared by the liver and Fa and Fg are equal to 1 (i.e., the drug is completely absorbed and has no gut metabolism), the bioavailability of the drug can be described by F ¼ Fh ¼ 1  ER. ER in this equation refers to the hepatic extraction ratio which is the fraction of the drug entering the liver that is eliminated by the liver upon a single pass. The relationship between ER and CLh can also be described as CLh ¼ ER ∙ Q. If the clearance of the compound is entirely by the liver under these circumstances the oral clearance can be written as: AUC ¼

D fu CLint

and this relationship between AUC, D and unbound intrinsic clearance applies to both high and low clearance compounds. Therefore, if unbound fraction or intrinsic clearance are altered, the AUC of the compound will always be altered proportionately regardless of the extraction ratio. However, the impact on the shape of the plasma concentration time curve will be different for high and low clearance drugs due to the different effects of altered unbound intrinsic clearance on half-life and systemic clearance of high and low ER drugs. For high clearance compounds, hepatic clearance tends to become limited by blood flow (i.e., CLh  Q). In the

Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

763

case of the systemic clearance for such compounds, changes in unbound intrinsic clearance have minimal effect on overall half-life and systemic clearance. However, for low clearance compounds for which CLh  fuCLint, changes in unbound intrinsic clearance will change the half-life and systemic clearance of the drug. A question may be asked on how AUC can be altered for a high clearance drug when systemic clearance and half-life are unchanged. Conceptually, this can be rationalized by understanding the altered hepatic bioavailability, but for the plasma concentration-time curve this will be manifested largely as an increased or decreased Cmax with the plasma concentrations declining with the same slope. When designing a drug dosing regimen or adjusting dosing regimen to address a drug interaction, this is relevant as due to the unchanged half-life the best approach to addressing such changes is to adjust the dose rather than dosing interval. In contrast, for low extraction ratio drugs, both the Cmax and half-life are impacted and one may consider adjusting dosing interval rather than the dose given within a dosing interval to address potential DDIs. It is noteworthy that the common predictions of drug interactions are based on predicting altered intrinsic clearance in the liver or the gut and rely on the above assumptions of the well-stirred model. In addition, the Fg ratio is often predicted based on CLint changes in the gut to fully predict the overall magnitude of drug interactions. An aspect that is often discussed is how changes in plasma protein binding may impact the disposition of drugs. For drugs that are mainly cleared by the liver the equations above show that, regardless of the extraction ratio of the drug (i.e., whether hepatic clearance is limited by blood flow or unbound intrinsic clearance) the AUC of the drug will decrease if fu increases. However, based on the free drug hypothesis, only the unbound drug causes pharmacological effects. In this context, unbound AUC (AUCu ¼ fu ∙ AUC) should be considered. The AUCu after oral administration can be written as: AUCu ¼ fu ∙AUC ¼ fu

D D ¼ fu CLint CLint

and as such it can be shown that changes in unbound fraction will not alter the unbound AUC for orally administered drugs and it is important to not adjust dosing strategies based on total AUC under such circumstances. In recent years PBPK modeling of drug disposition has become increasingly popular for predicting hepatic clearance and how changes in metabolic clearance such as DDIs may affect drug exposure. PBPK models provide an exciting opportunity to consider dynamic changes in drug concentrations as function of time and account for distribution kinetics of an inhibitor or inducer and the drug of interest in simulating plasma concentration-time curves. However, it should be noted that PBPK models typically assume venous equilibrium and incorporate the well-stirred model as the chosen liver model. This may not harness the full power of PBPK models and does not account for zonal distribution of liver enzymes nor for the arteriovenous differences in the concentrations of the inhibitor and the drug of interest across the liver. For scientists who employ PBPK models for drug interaction predictions, it is critical to familiarize oneself with the structure and assumptions of the liver model incorporated in the PBPK model and the inherent areas in which the model is likely to not predict drug disposition well or fully capture the true concentration profile within the organ.

1.29.5

Excretion

1.29.5.1

Renal excretion

One of the key elimination processes for drugs is excretion that includes both renal and biliary excretion. Renal elimination has been recognized as a major drug elimination pathway for decades and is believed to contribute to the elimination of majority of drugs on the market. A unique aspect of renal clearance is that it can be measured after any route of administration directly as: CLr ¼

dAe dt

C

in which the dAe/dt is the amount of drug excreted into urine in unit time and C is the concentration in plasma at midpoint of the collection interval. This aspect is often capitalized in drug development via characterization of drug excretion into urine and by quantifying the fraction of the dose recovered in urine. If the bioavailability of the drug is known or the drug is given iv, the knowledge of the fraction of dose excreted unchanged is important as it can be used to derive the fraction of the overall total body clearance that is via the kidneys. The excretion of drugs by the kidney is closely tied to the physiological processes in the kidney that include glomerular filtration, tubular secretion and passive and active reabsorption (Fig. 12). By anatomy, glomerular filtration occurs first followed by tubular secretion and reabsorption, and hence renal clearance involves both parallel and sequential (active and passive) processes that can be mathematically more complex to capture than metabolic clearance. The functional subunit of the kidney is the nephron, and each kidney contains about 1 million nephrons. Of importance in drug development is the fact that the kidney cannot regenerate nephrons, and hence individuals experience a gradual decrease in the number of nephrons with age (after age 40 about 10% every 10 years). Therefore, the pharmacokinetic changes that occur with declining renal function need to be established during drug development. Glomerular filtration in the kidney is a passive process that involves filtration of plasma water. In healthy young humans, the glomerular filtration clearance is 120 mL/min meaning that 120 mL/min of plasma water is filtered from blood to the kidney tubule. When this filtration is compared to the renal blood flow (1.2 L/min), it is immediately apparent that renal filtration clearance is a low efficiency process. In addition, it is important to note that only drug that is unbound to plasma proteins is believed to

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Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

Fig. 12 Illustration of the basic components of renal clearance. Unbound drugs are filtered via glomerular filtration followed by passive reabsorption in the tubule due to reabsorption of water and possible simultaneous active secretion from blood to renal tubule via active transport and secretion clearance. Portions of Fig. 12 were created with BioRender.com.

be filtered, as common proteins that bind to drugs such as albumin are assumed to be too big for filtration. The filtration size cutoff is commonly considered to depend on the size and shape of the protein (or nanoparticle) with a size of 6–8 nm preventing filtration or molecular weight around 45,000 g/mol. As such the rate of filtration of drugs (dAfiltered/dt) is equal to the flow of filtrate (GFR) times unbound concentration of drugs: dAfiltered ¼ fu ∙C∙GFR dt From the basic definition of clearance, the filtration clearance is: CLfiltration ¼

dAfiltered dt

C

¼ fu ∙GFR

Hence, filtration clearance of a given drug only depends on plasma unbound fraction and kidney function (GFR). However, due to passive reabsorption, CLr can be much lower than filtration clearance (fu $GFR). An excellent example of the impact of passive tubular reabsorption to renal clearance can be found in Scotcher et al. (2016). The passive reabsorption of drugs in the kidney tubule is a challenging parameter to predict and PBPK models (Huang and Isoherranen, 2018, 2020b) as well as static models (Scotcher et al., 2016) of predicting fraction reabsorbed in the kidney tubule have been proposed. Overall, passive reabsorption depends on the permeability of the drug and is driven by the concentration gradient of the drug between kidney tubule and blood. The tubular flow decreases from 120 to 1 mL/min from the glomerulus to the bladder as water is reabsorbed. This water reabsorption causes drug concentrations to increase along the tubule (depending on permeability of the compound). Initially, the unbound concentration in the filtrate is equal to the unbound concentration in plasma, and the reabsorption of water generates a concentration gradient that serves as the driving force for tubular

Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development Table 4

765

Summary of plasma protein binding, filtration clearance, permeability and renal clearance values for a subset of drugs cleared via the kidneys.

Fluconazole Dexamethasone Ribavirin Metronidazole

fu

fuGFR (mL/min)

Peff 10 6 cm/s

CLr (mL/min)

0.86 0.32 1 0.98

103 38 120 118

12.5 30 1.6 65

15.7 6.3 110 9.6

reabsorption. Whether a drug is effectively reabsorbed depends on drug permeability. The passive permeability clearance in the kidney is defined as: CLPD ¼ renal tubular surface area$permeability across cell and the rate of drug reabsorption as: dAreabsorption ¼ CLPD ∙DC dt In essence, drugs that are unionized in the tubule lumen and have high permeability tend to be almost entirely reabsorbed via passive reabsorption while drugs with low permeability or high ionization status in the tubule lumen, such as metformin or tenofovir, will not get reabsorbed and end up extensively excreted into the urine. In the case of compounds like iothalamate, that are polar enough to not have any passive reabsorption and at the same time do not have active secretion transport, the renal clearance ultimately is equal to fu$GFR. For other compounds, active reabsorption may decrease the renal clearance. For example, gentamicin is reabsorbed by megalin in the proximal tubule resulting in a CLr < fu$GFR despite the low permeability of gentamicin and lack of passive reabsorption. The impact of passive permeability and plasma protein binding on renal clearance is illustrated in Table 4 showing drugs that are not subject to active secretion but with different filtration clearances and permeabilities. As illustrated, fluconazole, ribavirin and metronidazole have very similar filtration clearances but over 10-fold different renal clearances largely driven by the differences in passive permeability and consequently different fractions reabsorbed. Due to its low permeability, ribavirin is not reabsorbed at all while fluconazole and metronidazole are extensively reabsorbed due to their good permeability. Notably, for drugs that are acids and bases, apparent permeability depends on the pH of the urine (or the pH in the tubule lumen). Classic examples of such drugs include methamphetamine, amphetamine and salicylic acid for which changes in urine pH are sufficient to significantly alter the renal clearance and subsequently plasma exposure. Another important aspect of renal clearance is the active secretion of drugs by renal transporters. The key transporters present in the kidney include uptake transporters OCT2, OAT1, OAT3, and efflux transporters MATE1, MATE2-K, MDR1 and MRP2. Together these transporters work in concert to excrete drugs from plasma into tubule lumen. Some drugs that have extensive tubular secretion include metformin, tenofovir, methotrexate, penicillins and cephalosporins which have all been identified to be substrates for renal transporters. As filtration is a low efficiency process, drugs that have efficient renal clearance that approaches renal blood flow must be excellent substrates of kidney transporters and have low permeability. For example, para-amino hippuric acid (PAH) which does not bind to plasma proteins (fu ¼ 1, filtration clearance in 120 mL/min) and has low permeability (0.72 $ 10 6 cm/s) has a total renal clearance of 550 mL/ min. Similarly, cimetidine with fu ¼ 0.8 and low permeability (1.37 $ 10 6 cm/s) has renal clearance of 543 mL/min. While both of these drugs have a renal extraction ratio of  0.5 (the renal clearance is still not blood flow limited), it is clear that both drugs must be actively secreted by kidney transporters and secretion by kidney transporters can be a significant clearance process for drugs. Overall renal clearance processes can be described by the static equation: CLr ¼ ð1  FR Þðfu ∙GFR þ CLsecretion Þ in which one needs to define the fraction reabsorbed (FR) to be able to define the magnitude of secretion clearance. It is critical to realize that defining the fraction reabsorbed requires quantitative knowledge of active secretion clearance. Similarly, quantification of the contribution of active secretion requires quantification of reabsorption clearance hence making renal clearance predictions challenging. However, PBPK models provide an opportunity to predict and simulate the fraction reabsorbed and as such provide added confidence in assessing the significance of secretion clearance in drug excretion.

1.29.5.2

Biliary Excretion

For many drugs, biliary drug clearance is a significant route of elimination. Biliary clearance in humans is very difficult to measure in vivo, and in vitro studies with human sandwich-cultured hepatocytes (SCH) are often used to predict in vivo biliary clearance. The standard SCH protocol includes characterization of drug uptake and efflux in the model without and with disruption of canalicular tight junctions. These studies are based on the premise that removal of Ca2þ from SCH culture media leads to the disruption of canalicular tight junctions. Comparison of drug accumulation between SCH experiments  Ca2þ can be used to estimate biliary drug clearance. With PBPK modeling, biliary clearance can be included as a pathway for drug elimination in the liver. The simplest

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Role of Pharmacokinetics and Pharmacokinetic Modeling in Drug Development

method to set up biliary clearance in a PBPK model is to set biliary clearance as a fraction of liver clearance. In this case, the rate of drug cleared by biliary clearance is: dMb ¼ MliverCl $FbiliCl dt in which Mb is the mass of drug clearance by biliary excretion, MliverCl is the mass of drug cleared by the liver, and FbiliCl is the biliary clearance fraction. If sufficient data are available, biliary clearance can be specified as a combination of active efflux and passive diffusion at the apical membrane. In this case, the rate of biliary excretion is described by:   dMb SFA$Vmax $Cu ¼ þ ðPStcAp$Cu Þ dt Cu þ Km in which SFA is the relative activity/expression of the transporter at the liver apical membrane, Km and Vmax are drug specific Michaelis-Menten parameters for the efflux transporter, PStcAp is the permeability-surface area product for the apical membrane, and Cu is unbound drug concentration in the liver (perfusion-limited tissue) or intracellular space (permeability-limited liver tissue). After drug secretion into the bile, the drug transits to the gall bladder where it is stored (fasted state) or directly transits to the duodenum. PBPK models can account for drug released into duodenum and this released drug will be available for absorption. Furthermore, the PBPK model can be designed to release drug from the gall bladder under fed conditions, and this drug release will be controlled by the specified gallbladder emptying time.

1.29.6

Conclusions

Mechanistic understanding of drug absorption, distribution, metabolism and excretion processes is critically important during the drug development process, and models with increasing complexity can integrate each of these processes to predict drug disposition in patients. When coupled with solid understanding of fundamental pharmacokinetic principles and rigorously determined preclinical data, PBPK models allow translation and in silico prediction of plasma concentration time curves prior to FIH studies. A unique strength of PBPK models is that with well characterized absorption mechanisms, understanding of major distribution organs, measurement of protein binding and kinetic characterization of metabolism and active transport of the compound of interest, each process can be independently probed to determine how alterations may impact drug concentrations within specific tissues. Thus, PBPK models provide a powerful tool to simulate how drug disposition may change when system or drug parameters are altered as is observed in organ impairment scenarios, in specific populations and in drug-drug interactions. Confidence in PBPK modeling continues to increase with growing computational power, method harmonization, and development of novel PBPK modeling tools. As a result, this in silico approach is quickly becoming a key component of many drug development programs. In addition, with improved understanding of disease processes and targeted drug delivery, PBPK models and pharmacokinetic understanding are likely to become more useful in predicting the time course of drug action and the magnitude of effect. In particular, PBPK modeling offers the unique strength of combining mechanistic knowledge of physiology and drug disposition with the mechanistic understanding of pharmacology that will likely result in improved safety and efficacy in the clinic with support of dosing regimen designs.

See Also: 1.17: Oral Drug Delivery, Absorption and Bioavailability; 1.18: PK Interpretation of Drug Distribution: General Concepts and Application to Special Populations; 1.19: Drug Metabolism: Cytochrome P450; 1.22: Drug Transport—Uptake; 1.23: Drug Transporters: Efflux; 1.24: Drug Excretion

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1.30 Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools Jonathan D Tyzack, EMBL-EBI, Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom © 2022 Elsevier Inc. All rights reserved.

1.30.1 1.30.1.1 1.30.1.2 1.30.2 1.30.2.1 1.30.2.2 1.30.2.3 1.30.2.4 1.30.2.5 1.30.2.6 1.30.2.7 1.30.2.8 1.30.2.9 1.30.2.10 1.30.2.11 1.30.2.12 1.30.2.13 1.30.3 1.30.3.1 1.30.3.1.1 1.30.3.1.2 1.30.3.1.3 1.30.3.1.4 1.30.3.2 1.30.3.2.1 1.30.3.2.2 1.30.3.3 1.30.3.4 1.30.3.5 1.30.3.6 1.30.3.6.1 1.30.3.6.2 1.30.3.6.3 1.30.3.7 1.30.3.8 1.30.4 References

Introduction Cytochrome P450s Introduction to in silico predictions Ligand-based methods in CYP450 metabolism prediction SMARTCyp StarDrop FAME fast metabolizer RS-predictor Xenosite SOMP BioTransformer Other expert knowledge predictors Data-mining methods: PROXIMAL Use of reactivity descriptors Other methods using semi-empirical reactivity CYP specificity prediction Inhibitor prediction Structure-based methods in CYP450 metabolism prediction CYP450 structure Key structural elements Access channels Pocket size and shape Site flexibility and multiple occupancy Selected recent insights from structures for drug metabolizing CYP450s CYP2D6 structural insights CYP3A4 structural insights Structure-based SoM prediction 3D alignment MetaSite Docking Docking coupled with reactivity descriptors SoM prediction tools based on docking Docking with multiple or ensemble of protein structures Molecular dynamics Modelling reactivity QM/MM Summary and outlook

770 770 771 772 773 774 775 775 776 776 776 777 777 777 777 778 779 779 780 780 781 781 782 782 782 782 783 783 783 783 784 784 784 784 785 786 787

Abbreviations CpdI Compound I (reactive form of CYP450) CYP450 Cytochrome P450 QM/MM Quantum mechanical/molecular mechanical QSAR Quantitative structure activity relationship SoM Site of metabolism SVM Support vector machine

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1.30.1

Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools

Introduction

The design and development of a drug is a complex multi-parameter optimization balancing many diverse factors (Segall, 2014; Yusof et al., 2014). The pharmacodynamics of the drug, i.e. how the drug affects the organism, is of course important, and it is essential to have activity at the identified site with sufficient specificity to avoid side-effects. However, it is also essential to consider the pharmacokinetics of the drug, i.e. how the organism affects the drug, often summarized with the well-known ADME acronym: Absorption, Distribution, Metabolism and Excretion (Ferreira and Andricopulo, 2019). Absorption and distribution are important to ensure that the drug is taken up by the body and sufficient concentrations are transported to the site of action to deliver the therapeutic outcome. These properties are largely determined by the physicochemical properties of drugs (Faller et al., 2011), such as solubility, molecular weight, logP, etc., but the effect of transporters can also come into play to move molecules across cell boundaries (DeGorter et al., 2012; Kell and Oliver, 2014). These factors will influence the dosing regimen such as administration route, i.e. oral vs intravenous, and dosage frequency and concentration. As well as encountering physical barriers such as crossing membranes the drug molecules will also encounter the organism’s natural defence mechanisms against foreign molecules, or xenobiotics. These consist of metabolic enzymes that have evolved to provide protection against potentially hazardous molecules such as toxins and poisons present in the environment (Testa et al., 2012). They act to transform the xenobiotic into more readily excretable metabolites and can be classified into two broad categories: Phase I metabolism involves making the molecule more polar and hydrophilic; and Phase II involves conjugation with endogenous hydrophilic compounds such as glucuronide, sulfate or glutathione. In this way, metabolism will contribute to the excretion of the drug, facilitating its removal from the body to avoid the accumulation of xenobiotic molecules to harmful concentrations. The net result is that metabolism is responsible for the clearance of about 75% of all drugs (Rendic and Guengerich, 2015) producing metabolites with different physicochemical, physiological, pharmacological, and toxicological properties (Di, 2014). Drug metabolism prediction is therefore an important step in the early lead optimization phase and preclinical studies. It is important to understand which enzymes are likely to interact with a drug candidate and identify the metabolically labile softspots in the molecule (sites of metabolism; SoM). A molecule that is readily transformed into excretable metabolites before it has had a chance to accumulate at the site of action and exert its therapeutic outcome is unlikely to make a suitable drug candidate. Successful drug metabolism prediction will help to avoid late stage (Phase I–III) or post-marketing withdrawals which usually result in a significant financial loss to a pharmaceutical company. Experimental screening of drug candidates against known metabolic enzymes is time consuming and resource intensive and extrapolation from animal models can be problematic (Burkina et al., 2017; Gottardi et al., 2018), so there is much interest in the development of fast and accurate computational methods to predict drug metabolism. Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy (Kirchmair et al., 2013c). The most effective computational approaches allow the profiling of large datasets and enable the interactive optimization of lead compounds but at vastly lower expense compared to experimental methods. These predictions can then be used as part of the multiparameter optimization drug discovery process, helping to satisfy metabolic stability constraints, increase in vivo half-life and avoid toxic metabolites. Developments to experimental approaches have enabled the elucidation of ADME properties at an unprecedented level of detail (Wilson, 2014). For example, immortalized hepatocyte cell lines, liver microsomal fractions and recombinant enzyme systems are commercially available and can be used to investigate the metabolism of a xenobiotic. Mouse, rat, dog and primate animal models are used at various stages to assess the differences between in vitro and in vivo data and to extrapolate to human metabolic profiles. Samples collected from these experiments are subjected to chromatographic separation, detection and identification using mass spectrometry and sometimes NMR to confirm the structure of a complex or unexpected metabolite. High-resolution mass spectrometry (HRMS) and LC–MS/MS techniques in combination with novel data acquisition and mining tools have greatly simplified the metabolite identification and profiling. However, these experimental methods remain costly and time-consuming, so it is desirable to have efficient and reliable in silico methods in place (Kirchmair et al., 2015). The focus of this article is to review important developments in the field of computational metabolism prediction. The most important group of enzymes involved in Phase I metabolism are the cytochrome P450s (CYP450s), a superfamily of monooxygenase enzymes. Therefore, there will be significant focus on methods to predict the action of these enzyme on xenobiotics. Other non-CYP450 enzymes like flavin monooxygenases (Phillips and Shephard, 2017) (FMOs), monoamine oxidases (Benedetti, 2001) (MAOs) and aldehyde (Marchitti et al., 2008) and alcohol (Cederbaum, 2012) dehydrogenases (ALDH and AD) are also known to make smaller contributions to Phase I drug metabolism. Further discussion on these enzyme systems is beyond the scope of this article since there is not a significant body of work to predict their metabolism of xenobiotics computationally, but the understanding of non-CYP450 xenobiotic metabolism continues to grow (Cerny, 2016; Foti and Dalvie, 2016; Nishiya et al., 2020).

1.30.1.1

Cytochrome P450s

A detailed review of the cytochrome P450 superfamily of enzymes (CYP450) can be found in Chapter 4 of this Volume. A concise summary is included here to discuss the key aspects that are particularly relevant to the in silico tools described in this article. There are 57 documented CYP450 isoforms in humans which can be classified into two major classes: those involved in the biosynthesis of endogenous compounds such as sterols, fatty acids, eicosanoids, and vitamins; and those involved in xenobiotic

Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools

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detoxification found mainly in the liver (such as the CYP450 2 and CYP450 3 families) (Guengerich, 2017). The latter class have remarkable ligand promiscuity driven by varying pocket sizes, shapes, binding surfaces, and flexibility, directing metabolism toward different parts of small molecules. CYP450 enzymes are classified based on amino acid sequence similarity. Their nomenclature has the CYP450 abbreviation followed by a number for the gene family, then a letter for the subfamily, and another number for the individual gene. Thus, CYP3A4 is an enzyme from the third family belonging to subfamily A and 4 is the individual enzyme number. The 57 CYP450 genes in humans code for 18 families and 44 subfamilies of the protein. Members from different families share
40% sequence identity while members within subfamilies have  55% sequence similarity. CYP450s are involved in the metabolism of  90% of all chemicals, including general chemicals, natural compounds and drugs, with  75% of human drug metabolism being performed by 5 P450s: 1A2, 2C9, 2C19, 2D6 and 3A4 (Rendic and Guengerich, 2015). Methods to make in silico xenobiotic metabolism predictions for these and other isoforms will be described in this article. The possibility of interactions with multiple isoforms and oxidation at multiple sites in a given molecule creates a complex picture for the computational models to rationalize. This range and diversity of CYP450 enzyme function has evolved to provide protection against the wide variety of chemical structures that an organism is exposed to over the course of its life, coming from a variety of environmental sources including animal, plant and microbial. At the core of the protein is a heme group that acquires an oxygen atom to form a highly reactive moiety known as CpdI. This has been described as ‘Nature’s blowtorch’ due to its oxidative power and ability to catalyze a wide variety of different reactions. The mechanism of action of CYP450 enzymes is to catalyze molecular dioxygen reduction by splitting the dioxygen molecule in two: one oxygen atom is inserted in the substrate to yield the oxidized product, and the second is reduced to water, a typical monooxygenase activity (EC:1.14.14.1). The net result of CYP450 metabolism is typically the insertion of an oxygen atom at a SoM to form the product, where possible transformations include the most common CeH oxidation (aliphatic and aromatic), N/O-dealkylation, N/S-oxidation, double bond epoxidation and some unique cyclizations and reductions. The reaction proceeds either via initial hydrogen abstraction or direct oxygen transfer. These mechanisms and the catalytic cycle have been studied both experimentally and computationally and have been discussed in some excellent reviews (Guengerich, 2018; Dubey and Shaik, 2019). Most of the human CYP450s are membrane bound and require dioxygen, NADPH and CYP450 reductase to complete the catalytic cycle. There are many factors that make understanding the action of CYP450 enzymes in vivo challenging, including expression patterns, inhibition levels, and genetic polymorphisms (Zanger and Schwab, 2013). Expression patterns vary significantly across organs, the highest human concentrations being found in the liver and small intestine, but expression is also influenced by gender, age, disease, stress, lifestyle, diet, and medication. These factors will all influence in vivo CYP450 expression and the rate of drug clearance. Furthermore, CYP450 inhibition and induction can be hugely influential, such as the flavonoid CYP450 inhibitors found in grapefruit juice (He et al., 1998) that can result in higher drug concentrations than anticipated in the dosing regimen. Conversely, CYP450 induction can cause drug concentrations to fall below therapeutic levels, such as the dietary supplement St John’s Wort, a potent inducer of CYP3A4 (Roby, 2000). CYP450 genetic polymorphisms manifesting as loss or gain of function variants also need to be considered in the drug development process so that undue reliance is not placed on CYP450 isoforms that have known deficiencies in certain ethnic populations. These factors all contribute to an overall complex picture and make prediction of drug metabolism highly challenging but essential for drug discovery and development.

1.30.1.2

Introduction to in silico predictions

The metabolic outcome is a result of a substrate migrating to the CYP450 active site, orienting in the pocket in a favorable pose, and interacting with the reactive CpdI moiety at the core of the enzyme. The high reactivity of CpdI makes it capable of catalyzing multiple reactions and the diversity in binding pockets of the different isoforms presents a highly complex picture for in silico prediction tools. The accessibility of different parts of the substrate to the reactive center, and the inherent reactivity of different molecular fragments to the reactive center are key determining features. These important concepts should be kept in mind throughout. A number of computational approaches have been devised to model the reactivity and accessibility of ligands towards CYP450 enzymes and bring order to this complexity (Kirchmair et al., 2015; Bezhentsev et al., 2016; Dixit and Deshpande, 2016; Kar and Leszczynski, 2018; Tyzack and Kirchmair, 2019). In silico methods are commonly categorized as ligand-based or structure-based (Sliwoski et al., 2014). As the names suggest, ligand-based methods rely on modelling data on small molecules only, whereas structure-based methods incorporate data on the protein structure. Many in silico metabolism prediction tools can be broadly categorized on this basis, but there are also numerous hybrid methods that draw on aspects of each. Ligand-based methods make use of the large amount of data that has been collected and published on the metabolism of xenobiotics in the presence of CYP450 enzymes and tend to have the advantage of being quick to run, enabling them to be applied routinely to large data sets of molecules. The popularity of machine learning methods has stimulated a number of in silico tools to learn trends and patterns in the published data. However, methods built on data are inherently limited by that data and are only relevant within the domain of applicability of the model. Structure-based methods tend to have greater computational complexity as they explicitly model the enzyme-substrate binding event. As a consequence they tend to require more expert knowledge to set up and be more computationally demanding which limits the number of candidate molecules that can be studied within a reasonable time frame. However, they often allow a more in-depth study of a particular enzyme-substrate interaction and can provide predicted poses as an output. They can also

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Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools Xenobiotic metabolism prediction software tools discussed in this article.

Table 1 Name

Description

Availability

SMARTCyp

Reactivity: library of activation energies from DFT Accessibility: SPAN descriptor Machine learning using Random forests with MetaQSAR data Machine learning using Support Vector Machines with the Zaretski dataset Machine learning using Neural Networks with the Zaretski dataset Bayesian probabilistic classifier using data from Metabolite database Expert system based on curated reaction rules

Public webserver https://smartcyp.sund.ku.dk/mol_to_som Public webserver https://nerdd.zbh.uni-hamburg.de/fame3/ Public webserver http://reccr.chem.rpi.edu/Software/RS-WebPredictor/ Public webserver https://swami.wustl.edu/xenosite Public webserver http://way2drug.com/somp/ Public webserver http://biotransformer.ca Commercial

FAME RS-Predictor Xenosite SOMP Biotransformer StarDrop Meteor Nexus SyGMa MetabolExpert Metasite

Reactivity: semi-empirical QM calculations Accessibility: steric and orientational descriptors fitted to observed SoM Expert system based on curated reaction rules Expert system based on curated reaction rules Expert system based on curated reaction rules Reactivity: library of activation energies from QM Accessibility: Pseudo docking using Molecular Interaction Fields for steric and chemical properties

Commercial Commercial Commercial Commercial

be used to predict novel metabolic reactions on molecules where published data are sparse and would lie outside the domain of applicability of ligand-based models. Where the structure of the enzyme is known then state of the art simulations can be applied using Quantum-Mechanical/ Molecular-Mechanics (QM/MM) methods to model the interaction of the substrate with the enzyme. These involve representing the drug and the enzyme active site quantum mechanically and the remainder of the protein molecular mechanically (using classical Newtonian mechanics). However, these methods are highly computationally expensive rendering then unsuitable for routinely screening drug candidates in a drug development program. The challenge for computational methods is to achieve high accuracy without having to resort to the complexity of full simulations, making simplifications but minimizing the penalty on accuracy. This article will review the development of computational methods to predict drug metabolism and discuss their associated strengths and weaknesses. The initial focus will be on primarily ligand-based methods as these form the basis of most available in silico prediction tools. The article will then move onto structural methods and present the commonly applied computational technologies in this field. Rather than try to produce an exhaustive, chronological list of all the developments in the field of computational metabolism prediction, the approach taken here will be to take a journey through the most important contributions to metabolism prediction, explaining the simplifications that have been made in each case. A high-level summary of the software tools covered in this article that are available either as a freely available public webserver or as a commercial offering is provided in Table 1 below.

1.30.2

Ligand-based methods in CYP450 metabolism prediction

In contrast to structure-based methods, ligand-based methods avoid modelling the binding event entirely and are either datadriven, identifying patterns in the data from published metabolic studies in the literature, or make simplifications towards estimating accessibility and reactivity. These push the boundaries of simplifying the modelling of enzyme-substrate interactions to achieve a compromise between accuracy and speed. Typically, these methods work with respectable accuracy giving 80–90% correct SoM and isoform specificity prediction rates. Despite this remarkable success the remaining 10–20% of cases that are harder to predict can often be due to reactive metabolite formation, resulting in adverse drug reactions, toxicity and failures in Phase III clinical trials, or post-marketing withdrawals of drug candidates. A well-known example is the metabolism of Troglitazone, where most software tools can correctly identify SoM relating to aliphatic C hydroxylation, S-oxidation and OH glucuronidation. However, they fail to identify the reactive metabolites that led to the liver toxicity, namely quinone, quinone methide and isocyanate resulting from the oxidation at these sites (Kassahun et al., 2001). The high-performance rates of ligand-based methods are useful to perform high-throughput virtual screening but the elucidation of more complex pathways such as toxic metabolite formation can require more in-depth simulations and experimental validation. The more complex in silico methods such as QM/MM simulations that enable more detailed analysis will be discussed later in the article. Some of the important ligand-based methods are presented next.

Prediction of Drug Metabolism: Use of Structural Biology and In Silico Tools 1.30.2.1

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SMARTCyp

SMARTCyp (Olsen et al., 2019) has the advantage of being extremely quick to run, enabling its deployment as a freely available public webserver, and producing results of high accuracy. A screenshot of the output from the SMARTCyp webserver when submitting Diclazepam is shown in Fig. 1(a). In many ways SMARTCyp acts as the benchmark for studies into metabolism prediction as it demonstrates that strong performance can be obtained in real time. In fact, many other studies have used part or all of the outputs from SMARTCyp to produce their enhanced predictions. To avoid the prohibitive computational expense of running live QM simulations, the reactivity component of SMARTCyp is based on precomputed QM calculations on a predefined library of molecular fragments. Density Functional Theory (DFT) was used to measure the CeH bond dissociation energies and activation energies of common molecular fragments using a methoxy

Fig. 1 Example output when submitting Diclazepam to (A) SMARTCyp, (B) FAME and (C) SOMP. All programs identify the major sites of metabolism, namely hydroxylation at the carbon site vicinal to the ketone and imine in the 7-membered ring and N-demethylation (Moosmann et al., 2014), in the top-3 predictions.

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radical as a simplification of the Cpd I active center. In this way the reactivity has been simplified to a ligand-based view and neglects any electronic contribution from the enzyme binding pocket. A query molecule is searched against this library of molecular fragments and activation energies are assigned. Of course, this approach is only as good as the coverage of the library fragment and assumes all matching fragments have the same reactivity regardless of neighboring moieties, but this is a trade-off worth making for the benefit of exceptional speed from a simple matching and look-up. However, it doesn’t make correct predictions when faced with groups for which accurate information on reactivity is not available in the database. The latest version also has a metric based on Morgan fingerprints to report the similarity of the fragment to the database to give an idea of whether the predictions are within the domain of applicability. The approach to modelling accessibility is again ligand-based and is probably about as extreme an approximation as you could possibly get. An accessibility score called the SPAN descriptor (Sheridan et al., 2007) is calculated for each atom from the 2D molecular graph which measures the relative distance to the “edge” of a molecule by finding the ratio of the shortest bond path to the edge of the molecule over the longest such bond path in the molecule. It might seem remarkable, but this simple descriptor of accessibility combined with the lookup of activation energies provides accurate predictions of SoM. This sets the bar high for more complex methods as it is challenging to justify the extra computational expense for sometimes marginal performance gains. One limitation of the simple SPAN descriptor is that a site in the molecule with SPAN value near 0.5 need not necessarily be hidden from the heme group due to either conformational freedom or geometry at the site. One example is Pazopanib, a tyrosine kinase inhibitor used in the treatment of renal carcinoma, which undergoes N-demethylation of the pyrimidine-2,4-diamine. This is almost in the middle of the 2D molecular structure, but SMARTCyp fails to identify this site in the Top-2 metric for CYP450 3A4, 2C9 or 2D6 predictions, over-penalizing this site with the SPAN descriptor. One further limitation of the SPAN descriptor is that it fails to differentiate between different CYP450 isoforms and ignores the differences in the binding pockets. It is known that the size, shape and polar nature of the binding pockets vary significantly between isoforms. However, key binding residues are known for some isoforms that are crucial to orient the ligand via charged interactions. For example, isoform 2D6 has two negatively charged residues in the upper part of the binding cavity, Glu216 and Asp301, which facilitate the binding of positively charged groups such as protonated amines. The favorable electrostatic binding between these charged moieties favors metabolism at sites 5–10 Å, most commonly 7 Å, from the positively charged atom in the ligand (de Groot et al., 1999). The simple SPAN descriptor can be adjusted to improve predictions for this isoform (Rydberg and Olsen, 2012a). A similar approach was taken with the 2C9 isoform where Arg108 has been documented to perform an important orientation role by interacting with negatively charged carboxylic acid groups in a substrate (Rydberg and Olsen, 2012b). The favorable distance between the charged carboxylic acid group in the substrate and the SoM was consistent with the 2D6 isoform due to similarities between the active sites of these two isoforms where the distances between the charged amino acids and the heme iron are 12.6 Å and 12.8 Å in published 2D6 (Rowland et al., 2006) and 2C9 (Wester et al., 2004) structures respectively. The inclusion of these adjustments to the SPAN descriptor improved predictive performance for 2D6 and 2C9 and enabled these isoforms to be included alongside 3A4 predictions in the SMARTCyp webserver. However, these fixes highlight the limitation of the SPAN descriptor and the manual adjustments described above require expert knowledge of the differences between binding sites. The same reactivity scores are used for all isoforms since the reactive CpdI is the same in all isoforms and any electronic effects from the protein environment are ignored. The adjustment of the accessibility score based on expert knowledge of CYP450 isoforms means that separate predictions can be made for the three main CYP450 isoforms that contribute to Phase 1 metabolism, although as we will see other methods provide predictions for a more comprehensive range of isoforms. A further key contribution of the work on SMARTCyp was the release of a dataset of 394 3A4 substrates for benchmarking (Rydberg et al., 2010) the performance of CYP450 SoM predictors, often referred to in the literature as the Zaretzki dataset. Much data are held in-house within big pharmaceutical companies and there is a dearth of high-quality SoM data in the public domain. This dataset (Zaretzki et al., 2013b) remains an important resource, and was updated in 2017 (de Bruyn Kops et al., 2017). An example of a software package that incorporates SMARTCyp is the open-source Toxtree (Patlewicz et al., 2008). This contains a metabolism prediction module that predicts SoMs using SMARTCyp and then applies a small set of reaction rules to the predicted SoMs in order to generate metabolite structures.

1.30.2.2

StarDrop

The CYP450 metabolism prediction module (Tyzack et al., 2016) in the StarDrop cheminformatic platform is one of the market leading commercial offerings. Metabolism predictions are possible for 7 of the CYP450 isoforms involved in drug metabolism (3A4, 2D6, 2C8, 2C9, 2C19, 2E1, and 1A2) giving a comprehensive view of CYP450 metabolism. The key output is metabolic lability which gives an absolute indication in the range 0–1 of the likelihood of metabolism at each site in the molecule. Another key feature is the generation of metabolite molecular structures enabling the products from metabolism to be visualized and used in the other StarDrop modules such as to calculate important physicochemical properties. StarDrop typically takes a few minutes to generate predictions for a typical drug-like molecule. Whereas SMARTCyp had a precalculated library of molecular fragments and activation energies, StarDop directly models the reaction pathway on-the-fly using parameterized semi-empirical AM1 QM methods from 3D structures generated with Corina. These semi-empirical methods run much faster than DFT calculations but there are well-known limitations in modelling open shell systems and transition states. However, they do capture relative differences in bond dissociation energies across a series of related fragments and parameterization is used to make the energies comparable across different series of fragments. The direct modelling

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of the activation energy comes with extra computational expense, although a methoxy radical is used as a proxy to the reactive CpdI moiety at the heart of the CYP450 to streamline the calculations and the protein environment is completely ignored compared to full QM/MM methods. The payback for this increased effort is the fact that activation energies are calculated for each molecular fragment in the context of the molecular environment in which it resides, rather than treating each fragment as identical regardless of its neighboring atomic moieties. An accessibility adjustment to the activation energy is calculated from regression models trained on isoform specific data sets of substrates and SoMs. The goal is similar to that achieved in SMARTCyp for isoforms 2D6 and 2C9 by adjusting the SPAN descriptor for known important orientational interactions. However, the approach behind StarDrop is more rigorous by using a number of steric and orientational descriptors to represent SoM and fitting to a training set with regression techniques rather than mere inspection. This enables predictive models to be created for many more isoforms where sufficient published data are available, instead of just 3A4, 2D6 and 2C9 in SMARTCyp. Significant efforts were made at Optibrium to collate enlarged, high-quality datasets from the literature, extracting major/minor sites of metabolism and major/minor metabolizing isoforms. Many publicly available datasets only record the major sites and metabolites and often significant secondary or tertiary data points are overlooked, leading to pessimistic false positive rates being reported when a non-recorded SoM is predicted.

1.30.2.3

FAME fast metabolizer

As suggested by the name, the emphasis of the freely available FAME (Kirchmair et al., 2013b) software is to generate highly accurate metabolism predictions efficiently and quickly. A screenshot of the output from the FAME webserver when submitting Diclazepam is shown in Fig. 1(B). Whereas SMARTCyp and StarDrop both estimated reactivity and accessibility separately, FAME is our first example of a completely data-oriented approach applying machine learning methods to a dataset of known metabolic reactions. FAME is available as a web server and a self-contained Java software package, free for academic and non-commercial research, enabling molecules to be run locally without the security concerns of submitting over the internet. FAME offers prediction of Phase 1 and Phase 2 metabolism and metabolite structures are obtained from GLORYx (de Bruyn Kops et al., 2020) to enable the metabolic endpoints to be easily visualized. The latest release of FAME is FAME3 ( Sícho et al., 2019) and is an evolution of the previous versions (Kirchmair et al., 2013a; Sícho et al., 2017). To allow machine learning methods to be applied, it is necessary to have a dataset representing the problem at hand, in this case xenobiotics with annotated sites of metabolism. FAME3 uses the MetaQSAR (Pedretti et al., 2018) database, which contains metabolic data for over 2100 substrates and 6300 experimentally confirmed SoMs. These data must be represented in a computer readable format but capturing the important features in the data that are driving the observed outcomes. A key aspect of machine-learning models is creating feature vectors that provide high quality inputs to drive strong prediction performance. Machine learning methods are powerful as they can identify non-linear relationships in complex datasets, identifying those features that contain the richest information. This means that many descriptors can be calculated and passed to the machine learning algorithm. However, this can be inefficient and leads to outputs that are difficult to interpret, the so-called ‘black-box’ criticism of machine learning methods. FAME took the approach of trying to retain interpretability by using a minimal set of descriptors, applying Occam’s Razor to strip away complexity where it is not justified in terms of extra performance. The initial version of FAME (Kirchmair et al., 2013b) contained only seven descriptors, selected from a larger pool using information gain analysis, to represent the known SoM, consisting of electronic descriptors such as partial charge, electronegativity and polarizability coupled with atom type and accessibility descriptors. The descriptors were supplemented in FAME2 ( Sícho et al., 2017) with additional reactivity descriptors calculated by MOPAC and topological descriptors based on circular fingerprints describing atom type counts at different bond depths from the site in question. This approach is supported by another study that showed that 2D topological descriptors based on MOLPRINT2D fingerprints coupled with machine learning methods were sufficient to produce strong classification performance (Tyzack et al., 2014). Once the data set has been represented with informative feature vectors, in this case capturing the key drivers of metabolism, it is split into a training set and a test set. There are a variety of different algorithms to train machine learning models, where popular choices include neural networks, support vector machines and random forests. The performance of the model is then tested on the independent test set, taking care to avoid contamination between training and test sets. The FAME software employs random forest methods, where multiple decision trees are built to produce a classifier.

1.30.2.4

RS-predictor

Given the speed of modern computers and sophistication of machine learning algorithms it is feasible to generate a plethora of descriptors to represent the metabolic data. This can be passed to the machine learning algorithm to identify those descriptors that capture the most information to discriminate SoM from non SoM, and those that are redundant and carry very little information. However, this contributes to one of the commonly documented drawbacks of machine learning methods, namely that they are black boxes offering very little interpretability. This approach, in contrast to the minimal set of descriptors in FAME, was taken by RS-Predictor (Zaretzki et al., 2011) which reports stronger performance than SMARTCyp and StarDrop from classifiers built from 148 topological descriptors and 392 quantum chemical descriptors, although the relative performance of methods can vary significantly across different test sets. The

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topological descriptors are quickly derived from the 2D structure while the quantum chemical descriptors are calculated by MOPAC using AM1. Generating the semi-empirical wave function is the rate limiting step but results can be generated for a drug-like molecule in under a minute even for a large drug molecule. These descriptors were applied to a subset of the Zaretzki dataset to generate a training set, with a support vector machine based machine learning method called MIRank used to generate the SoM predictors. The inclusion of SMARTCyp reactivity descriptors into the RS-Predictor process was investigated (Zaretzki et al., 2012) and was found to give improved performance. An important further benefit of using SMARTCyp reactivities is the high speed of the look ups to pre-calculated activation energies. It was found that combining the RS-Predictor topological descriptors with SMARTCyp reactivity measures gave equivalent performance but at a fraction of the computational cost. This version of RS-Predictor formed the basis of the publicly available web-server (Zaretzki et al., 2013a) which returns prediction results for drug like molecules in a few seconds of compute time.

1.30.2.5

Xenosite

Another example of a machine learning based SoM predictor is Xenosite (Zaretzki et al., 2013b), also available as a web-server (Matlock et al., 2015). The approach is similar to that taken in RS-Predictor using the same sets of topological, quantum chemical and SMARTCyp descriptors but there are a few notable exceptions. Firstly, Xenosite incorporates 15 molecular descriptors thought to strongly impact drug metabolism, such as molecular size, solubility, flexibility and polar surface areas, calculated using MOE (Chemical Computing Group, 2020). This makes biophysical sense since a small molecule will be able to rotate more freely in the binding pocket, with all potential SoMs having more equal access to the reactive CpdI. This contrasts with larger molecules which may be more constrained in the binding pocket, where potential SoMs will not have equal access to the CpdI moiety and an atom’s reactivity may not be as important as its position in the molecule. Molecule-level descriptors enable the isoform specific CYP450 metabolism prediction models to take molecular size into account when balancing the relative importance of reactivity and accessibility/position to the metabolism of each specific substrate. Also, Xenosite uses neural networks rather than support vector machines to build the predictors which gives a performance advantage in terms of speed when training and at run-time. One big improvement is the fact that SoM scores generated by Xenosite can be treated as probabilities rather than the rank orderings seen in RS-Predictor.

1.30.2.6

SOMP

Site of Metabolism Predictor (SOMP) (Rudik et al., 2014) is freely available as a webserver and a screenshot of the output when submitting Diclazepam is shown in Fig. 1(C). It uses descriptors solely based on 2D atomic neighborhoods as the basis for its predictions for the 5 major CYP xenobiotic metabolizing isoforms (1A2, 2C9, 2C19, 2D6 and 3A4) and also UGT (glucuronidation). The method is based on the PASS (Lagunin et al., 2010) (Prediction of Activity Spectra for Substances) and uses the same LMNA (Labelled Multilevel Neighborhoods of Atom) descriptors. Training data were obtained from the Biovia (Accelrys) Metabolite database, which is unfortunately no longer available, and from extracting data from source literature. The positive and negative examples of SoM and non SoM feed into a Bayesian probabilistic classifier to produce the classifiers, which produce results in under a second for a typical drug like molecule. It is important to exclude training examples form the test set, and the results for SOMP on an independent test set were below that of SMARTCyp but ahead of RS-Predictor. Care should be taken with reported performance figures as they can vary greatly from one test set to another and the field of in silico metabolism prediction would benefit from standardized test sets to allow comparability. SOMP has been made available as a web-server (Rudik et al., 2015) and also embedded in the MetaTox (Rudik et al., 2017) platform which also generates metabolite structures from SOMP and provides graphical representations of the molecules.

1.30.2.7

BioTransformer

BioTransformer is our first example of a so-called expert system that encodes an expert knowledge base in the computer with manually curated reaction rules. This has the advantage of being highly interpretable, avoiding the black-box criticism associated with machine learning methods. The significant disadvantage is of course the time taken to assemble the reaction rules from large datasets. This process is performed automatically by machine learning algorithms allowing the computer to identify complex patterns in training data without expert guidance. Generating predictive software based on expert knowledge involves creating a biotransformation dictionary of structural fragments with reported metabolic instability. A query molecule can then be searched for fragments matching the expert rules, identifying potential metabolic hot-spots. However, a common criticism of expert systems is the lack of prioritization of the reported hits and a combinatorial explosion of predictions can occur, particularly when applying the reaction rules in successive generations. BioTransformer (Djoumbou-Feunang et al., 2019) is a recent example of the expert system approach, where reaction rules are derived from the metabolic reactions in MetXBioDB (Djoumbou-Feunang et al., 2019), the same database used for Fame3. The release of this data set is an important contribution since the size and quality of available data and the features used to describe them are of paramount importance in the development of computational prediction tools. BioTransformer predicts metabolite structures for human Phase 1 CYP450 and Phase 2 metabolism as well as gut microbial, environmental microbial, and other human metabolic enzymes. BioTransformer also offers an option for identifying metabolites

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based on masses from mass spectrometry data. One significant drawback to BioTransformer is the lack of ranking in the predictions, so the user is not able to prioritize the output.

1.30.2.8

Other expert knowledge predictors

Meteor Nexus (Valerio and Long, 2010) is a knowledge based SoM prediction tool developed by Lhasa Limited based on expert curation of reaction rules from detailed literature review. When applying the rules to a query molecule, each transformation is classified in likelihood categories. However, ambiguities in where to set the cut-off between categories can cause a combinatorial explosion when applying the rules iteratively. However, the 2017 addition of a k-nearest neighbor approach (Marchant et al., 2017) helps to prioritize transformations towards those most commonly observed. Meteor also includes models for SoM prediction via a reimplementation of SMARTCyp and tools for linking mass spectrometry data with predicted metabolites of phase I and phase II biotransformations. MetabolExpert (Darvas, 1987) is an expert system from Compudrug with an open architecture, allowing users to understand, expand, modify or optimize the data on which the metabolism predictions are generated. Similar to other commercial offerings there is a user-friendly sophisticated graphical user interface representing predicted metabolic structures as tree graphs. The software can predict metabolic pathways in a number of different animals, plants or through photodegradation. There also exists an upgrade that extends predictions to new metabolic reactions including ring opening and closing reactions and covering the production of toxic metabolites collected from the scientific literature. SyGMa (Ridder and Wagener, 2008) (Systematic Generation of potential Metabolites) is another expert knowledge based method to predict and rank the likely products of Phase I and Phase II metabolism. Metabolites are scored based on an empirical probability assigned to each rule. It was one of the earliest freely available offerings to generate metabolite structures but remains a highly effective and useable tool. More recently, in addition to being made available as a software package, SyGMa has been released as a KNIME (Berthold et al., 2008) node as part of the 3D-e-Chem project (Kooistra et al., 2018). SyGMa is also available as a python library.

1.30.2.9

Data-mining methods: PROXIMAL

Data-mining methods can be effective where there is sufficient data to generate statistics about a particular molecular fragment based on the number of times it is observed as a SoM against the total number of occurrences in the dataset. It is necessary to have a description of the molecular environment in which each molecular fragment resides in order to make these statistical analyses. These descriptors need to be specific enough to allow differentiation of sites, but not too specific so as to prevent reliable statistics being generated. Some studies have explored the use of randomized sub-sets of descriptors to allow fuzzy matching (Tyzack et al., 2014) which is useful particularly where data-sets are small, a common situation in metabolism prediction. The MetaPrint2D (Adams, 2010) method adopted a data mining approach based on the Metabolite database but unfortunately support for both the web-server and the underlying dataset has been withdrawn. PROXIMAL (Yousofshahi et al., 2015) (Prediction of Xenobiotic Metabolism) picked up the baton where MetaPrint2D left off and derives likelihood of metabolic reactions for atoms in defined atomic environments obtained from the KEGG and DrugBank databases. Matches are scored by activity and abundance. The source code is available from the authors on request.

1.30.2.10 Use of reactivity descriptors The approaches presented so far to consider reactivity involve using quantum mechanics to precalculate activation energies of common fragments (SMARTCyp), run semi-empirical QM calculations on the fly (StarDrop) or use reactivity descriptors as part of the input to machine learning (FAME, RS-Predictor, Xenosite). There are studies that have investigated particular reactivity descriptors to provide greater insights into their importance than is often obtained from machine-learning approaches. These descriptors can be used as components to approximate the hydrogen and electron abstraction processes fundamental to the CYP450 catalytic cycle. The calculation of reactivity descriptors often requires QM minimization to be carried out on a query molecule which is less complex and requires less computational expense than modelling a reaction pathway. For example, some studies have identified reactivity descriptors based on the energies of molecular orbitals (Tyzack et al., 2013) and hardness (Pragyan et al., 2014) as important metrics to help determine SoMs. One such method is QMBO (Afzelius et al., 2007), which uses bond orders determined at the B3LYP/3-21G level of theory to compute CeH bond strengths, coupled with the solvent accessible surface area of each hydrogen to calculate accessibility. In this way, the weakest and most accessible CeH bonds are predicted as SoM. Despite the main disadvantages such as the small basis set used to represent atomic orbitals and only modelling H abstraction pathways competitive performance was reported.

1.30.2.11 Other methods using semi-empirical reactivity The pioneering work in the early 1990s by Korzekwa et al. paved the way for semi-empirical methods to predict CYP450 mediated metabolic reactions (Korzekwa et al., 1985, 1990). The models used AM1 methods to estimate the energy of hydrogen abstraction and ionization potential of the resulting radicals to estimate the relative ease of oxidation of CeH bonds for aromatic and aliphatic

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compounds. Newer semi-empirical methods such as PM3 and SAM1 have been shown to give slightly better correlations than AM1 but all are inferior to DFT calculations. These semi-empirical calculations use small molecules such as a methoxy radical or triplet oxene (oxygen) to mimic the CpdI fragment. The simplifications enable the calculations to be used in SoM predictors to estimate the activation energy and explain relative reactivities within a molecular series. However, further parameterization must be carried out to enable comparisons to be made across different molecular series. Although the reaction barriers and relative product stabilities can be predicted fairly accurately, results from the semi-empirical method with proxies to CpdI should be interpreted with caution. The protein environment is known to play a crucial (sometimes decisive) role in modulating the actual metabolic reaction pathway. However, such subtlety is neglected in these approaches and is only considered in the more computationally expensive QM/MM methods to be discussed later. A SoM predictor developed at Bayer Schering Pharma called CYPScore (Hennemann et al., 2009) uses atomic reactivity descriptors obtained with AM1 for seven CYP450 catalyzed reactions, including bond orders, atomic valence and Coulsen charge. These are coupled with non QM descriptors such as accessible surface area to produce a model for each type of metabolic reaction but with results produced on the same scale to allow comparability. CYPScore can be obtained for a free trial period but full ownership requires a license. These ideas continue to be developed and MetScore (Finkelmann et al., 2018) builds on the foundations laid by CYPScore. It is a predictor of Phase 1 and Phase 2 metabolism using molecular representations built on quantum chemical partial charges to build machine learning classifiers based on Random Forests. However, it is only available as an in-house tool for Bayer’s research platform Plx. EaMEAD (Kim et al., 2009) (Activation energy of Metabolism reactions with Effective Atomic Descriptors) also uses AM1 methodology to predict the activation energies Ea of four CYP-catalyzed reactions: aliphatic hydroxylation, aromatic hydroxylation, N-dealkylation and O-dealkylation. Various atomic reactivity descriptors, including effective atomic charge, effective atomic polarizability, and bond dipole moments, are calculated using AM1 to predict the activation energy using empirical models. The commercial software ADMET Predictor (Simulations Plus) uses reactivity descriptors derived from semi-empirical molecular orbital calculations such as partial charges. ADMET Predictor applies artificial neural network ensembles to feature vectors based on these descriptors to predict SoM and generates propensity scores to give an estimate of the likelihood of metabolism at that site. The software also allows prediction of enzyme kinetics for the 5 major CYP450 isoforms 1A2, 2C9, 2C19, 2D6 and 3A4.

1.30.2.12 CYP specificity prediction The software described so far has predicted SoM for specific CYP450 isoforms. However, it is important to predict which CYP450 isoforms are likely to show activity with a given xenobiotic so the correct SoM predictors can be applied. It is also beneficial to know the relevant CYP450 isoforms to identify cases where metabolism and clearance of a drug is reliant on only one or a few CYP450 isoforms. In these cases, there is much greater risk from genetic polymorphism in the active CYP450 isoform which can compromise the only clearance route. A number of computational tools have been developed to predict which CYP450 isoforms will metabolize a given xenobiotic. The task of predicting CYP450 specificity differs from predicting SoM as the feature vectors are required to describe properties of the molecule rather than being specific to particular atomic sites. There are many molecular fingerprint methods that have been developed to describe molecules (Cereto-Massagué et al., 2015) as this type of molecule level approach is useful in other forms of virtual screening such as target prediction for activity of a drug candidate at a particular receptor (Koutsoukas et al., 2011). In the CYP450 specificity paradigm it is of course prediction of activity with a particular CYP450 isoform that is required. Once data sets have been prepared containing molecules with known activity with each particular CYP450 isoform, the task is to apply the most suitable and effective fingerprinting method to classify the data. If activity data are known, such as rate of metabolism, then the models can go beyond classifiers into the QSAR (Quantitative Structure-Activity Relationship) paradigm where activity predictions can be made. The development of fingerprints to capture relevant features of molecules has been an active area of research and can be broadly split into the categories substructure key-based fingerprints, pharmacophore fingerprints, topological or path-based fingerprints, circular fingerprints, and hybrid methods containing a mixture of the above (Willett, 2006; Cereto-Massagué et al., 2015). Substructure key-based and pharmacophore methods set the bits of a feature vector depending on the presence or absence of certain important chemical moieties with examples including MACCS keys (Durant et al., 2002) and Pubchem (Bolton et al., 2008) fingerprints. Topological or path-based methods work by analyzing all the fragments of a molecule following a usually linear path up to a specified number of bonds and hashing into a feature vector, with the Daylight fingerprint (Daylight Chemical Information Systems, Inc., n.d.) being the most prominent. Circular fingerprints are also hashed topological fingerprints but atom environments up to a specified radius are encoded rather than linear paths. The most common example is the Extended Connectivity Fingerprint (ECFP) based on the Morgan algorithm (Morgan, 1965) where there are a number of commonly used variants at describing circular environments of different diameter. The methods described in this section apply these fingerprints to the task of CYP specificity prediction. Machine learning methods such as neural networks (Xiong et al., 2018), support vector machines (SVMs) (Daina et al., 2017) and random forests (Hunt et al., 2018) allow the modelling of the complex nonlinear relationships observed in large collections of enzyme–substrate interaction data and have become established as the mainstay for predicting enzyme specificity. The published

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approaches show variation in the data on which they are trained, the descriptors used to represent the data, the scope of predictions, and the type of machine learning methodology employed. Machine learning models once trained produce accurate results quickly and so are suitable for usage in web applications where results can be returned in real time. However, the quantity and quality of the available data is often a limiting factor and determines the coverage and performance of these models. WhichCyp (Rostkowski et al., 2013) is a web-based isoform specificity prediction tool to predict the CYP450 isoform (1A2, 2C9, 2C19, 2D6 and 3A4 isoforms) involved in the metabolism of a xenobiotic. Training data were obtained from the PubChem BioAssay database (Wang et al., 2012b) consisting of molecules with known CYP450 specificity. The molecules were represented with molecular signatures built from atomic signatures (Faulon et al., 2003), a form of circular fingerprint, with support vector machine methodology applied to produce specificity classification models. The support vector machine models were optimized to achieve a high Matthews correlation coefficient (MCC) (Matthews, 1975) but with the constraint that specificity and sensitivity should be similar. When working with potentially imbalanced datasets it is important to have a good balance between specificity and sensitivity to avoid biasing the model to over-predicting false positives or false negatives (Banerjee et al., 2018). A plot of sensitivity (true positive rate) against ‘1-specificity’ (false positive rate) is called a receiver operating characteristic (ROC) curve, where the area under the curve (AUC) is often used to assess classification performance. Like the MCC, the AUC is a balanced metric where a score of 1.0 indicates perfect classification whereas a score of 0.5 indicates that the classifier is not able to distinguish between positive and negative class points and performance is no better than random. There are also a number of commercial offerings for the prediction of enzyme inhibition and specificity. WhichP450 (Hunt et al., 2018) from the StarDrop platform identifies enzyme–substrate interactions for seven major xenobiotic metabolizing isoforms. It uses random forest models based on datasets manually extracted from the primary literature, where care was taken to annotate the training data with major and minor metabolizing isoforms for completeness. The random forest models enable the confidence of predictions to be calculated, enabling the isoform predictions to be rank ordered. The descriptors used to train the models included whole molecule descriptors such as molecular weight, logP, proportion of rotatable bonds and counts of 290 structural descriptors, a type of substructure key-based fingerprint. ADMET Predictor (Simulations Plus Inc., n.d.) has functionality to both predict inhibitors for five major drug metabolizing CYP450s and substrates for nine CYP450 isoforms using data acquired from the BIOVIA Metabolite database, the Drugbank database (Wishart et al., 2018), and other public resources. CypReact (Tian et al., 2018), used in the BioTransformer platform, employs a machine learning method called LBM (learning base model) to predict substrates for nine major xenobiotic metabolizing isoforms. It calculates over 2279 features for each molecule in its training set of 1632 molecules compiled from various sources including the Zaretski data set. These include many physicochemical properties such as molecular weight and logP but also many structural features including MACCS keys, Pubchem fingerprints and other structural descriptors from ClassyFire (Djoumbou Feunang et al., 2016). However, in order to avoid overfitting to the training set and to improve the speed of training a feature selection protocol was followed to remove redundancy in the training data.

1.30.2.13 Inhibitor prediction It is known that some molecules are inhibitors of CYP450 enzymes, which can lead to accumulation of xenobiotics to toxic levels. It is important to ascertain whether a drug candidate shows similarity to known inhibitors, or indeed activators, of important metabolic enzymes to allow drug dosing to be managed safely. The types of approaches described in the preceding section to predict isoform specificity can also be applied to the task of inhibition prediction when trained on a suitable training set of known CYP450 inhibitors. SwissADME (Daina et al., 2017) is a web service that offers, among many other tools, SVM models for the prediction of inhibitors for the five major CYP450 isoforms (i.e. CYP450s 1A2, 2C19, 2C9, 2D6, and 3A4). The classifiers were trained on data from the PubChem Bioassay 1851 dataset using 50 molecular and physicochemical descriptors. CypRules (Shao et al., 2015) is another web service that predicts inhibitors and non-inhibitors of the same major CYP450s based on the same data. It utilizes decision trees in combination with the concept of information entropy. Where activation/inhibition data for compounds are available then QSAR models can be produced to predict levels of activation/inhibition with regression models based on feature vectors describing the molecules. A QSAR method known as PASS (Rudik et al., 2014) (Prediction of Activity Spectra for Substances) makes use of substructure based descriptors to predict probabilities of activation/inhibition of a variety of enzymes including CYP450 and non-CYP450 enzymes. More details on the numerous QSAR models for predicting CYP450 inhibition can be found in this review (Kato, 2020) which covers the CYP450 isoforms 1A2, 2C9, 2D6 and 3A4.

1.30.3

Structure-based methods in CYP450 metabolism prediction

The number of structures of CYP450 enzymes deposited in the PDB (Gutmanas et al., 2014) continues to grow, providing an invaluable resource for the modelling and simulation of this important family of enzymes. Structure-based methods allow interrogation of the substrate-enzyme binding event and can reveal important aspects such as binding site flexibility to explain the remarkable promiscuity of some CYP450 enzymes. Before discussing some of the key structure-based computational technologies to model

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metabolism in silico, such as docking, molecular dynamics (MD) and quantum mechanics (QM), important structural features of CYP450 enzymes will be discussed.

1.30.3.1

CYP450 structure

The CpdI moiety at the center of CYP450s, consisting of an activated heme group, performs the oxidation reaction and reactivity towards different molecular fragments will influence the SoM. This reactive moiety is conserved across all CYP450 isoforms, so substrate specificity is largely determined by the size, shape and physicochemical properties of the enzyme active site. These bind different substrates in different orientations to give the observed variation in SoM across isoforms. However, there are consistent features across CYP450 isoforms and the numerous crystal structures available show a similar fold with a shared common triangular prism-like domain rich in a-helices (Guengerich et al., 2016). The wide range of substrate specificities is surprising given the high degree of similarity across a diverse range CYP450 sequences and is due to variation in the size and arrangement of secondary structure elements which will be discussed in more detail next.

1.30.3.1.1

Key structural elements

The CYP450s share a similar architecture and differences are not immediately apparent from visual inspection. Membrane P450s contain at least 12 alpha helices designated with the letters A-L and an N terminus beta sheet domain. Fig. 2 shows these secondary structure elements for the human CYP450 3A4 isoform color coded with rainbow coloring from blue (N-terminus) to red (Cterminus). The protein core consists of helices C, D, I, K and L and the beta sheets that contribute to the heme binding site and the adjacent region where partner proteins bind. The I and L helices contact the heme, and there is a b-bulge segment of the cysteine ligand, just prior to the L-helix. There is also a basic ‘patch’ on the CYP450 surface that comprises arginines located adjacent to the heme and on the same side as the catalytic cysteine. These basic residues are generally considered to be involved in binding the redox partner cytochrome b5, which can play an accessory role in some (but not all) eukaryotic CYP450 reactions as a redox partner or as some type of allosteric modulator. The same basic region is also postulated to bind the eukaryotic accessory flavoprotein, NADPHP450 reductase (Guengerich et al., 2016). Substrates bind in the pocket above the heme with several channels opening to the surface via the F and G helices. The F and G helices form a structural unit that links to the distal surface of CYP450s and are important in ligand access, binding and egress. The F and G helices define the most common access channels, and the F helix can be broken in crystal structures of some CYP450s, such as

Fig. 2 CYP450 secondary structure elements. The figure was created from PDB 1TQN Crystal Structure of Human Microsomal P450 y (Yano et al., 2004) with PyMOL (Schrödinger, 2016) using rainbow coloring from the C terminus (red) to the N terminus (blue).

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CYP1A enzymes. The catalytic cysteine sits on the other side of the heme in the axial position opposite the bound oxygen. Helices B, C, F and G, in addition to the N- and C-terminal ends, are located more on the periphery of the protein and exhibit greater structural variations and dynamic behavior (Guengerich et al., 2016).

1.30.3.1.2

Access channels

The first X-ray crystallography work on CYP450s described the structure of camphor-bound CYP450 101A1 and raised the question of how the substrate gains access to the active site, which was described by the authors as “inaccessible to the outside world” (Poulos, 2003). However, some crystal structures of bacterial P450s show an active site that is deep in the core protein, but with an access channel that is clearly visible. The buried pocket in human isoforms implied the formation of access channels to allow substrate entry and exit as part of the dynamic behavior of the enzyme. This hypothesis can be tested via the use of computational methods such as molecular dynamics to explore the conformational space of the enzyme over time in the presence of substrates (Urban et al., 2018). Some of the earliest work to uncover the channels by which substrates enter and leave the active site also focused on CYP450 101A1 (Lüdemann et al., 2000a,b) using a type of molecular dynamics called RAMD (random acceleration molecular dynamics) (Markwick and McCammon, 2011). Molecular dynamics simulations are described in a later section. In these simulations, camphor is bound in the pocket and a randomly orientated force is applied to the center of mass of the ligand where the direction of the force is changed according to certain criteria, such as comparing ligand displacement to a defined threshold. The key advantage of RAMD simulations is that it allows for many unbiased ligand egress events to be observed in a short simulation time. A similar variant called steered molecular dynamics (SMD) (Isralewitz et al., 2001) proposes a potential access channel a priori and a force is inserted to attempt to pull the bound ligand through the putative access channel. These computational methods have identified several access/egress channels in the crystal structure of CYP450cam and demonstrated the insights that can be gained by applying these methods to CYP450s. Since these important papers, various algorithms have been developed to discover access channels in proteins (Kingsley and Lill, 2015) to help to understand the influences of channels to both substrate specificity and catalysis in enzymes. Two of the most commonly used methods are CAVER (Chovancova et al., 2012) and MOLE (Sehnal et al., 2013) that employ methods reminiscent of RAMD but use a sphere of specified diameter rather than a specific ligand. The sphere is inserted into the known binding pocket where it is rolled out of the active site in different directions to identify all the direct paths connecting the buried active site to the protein surface. There are also a number of freely available web-tools such as MOLEonline (Pravda et al., 2018b) which calculates access channels and their physicochemical properties in a user submitted protein and ChannelsDB (Pravda et al., 2018a) which provides a database of annotated access channels in enzyme structures including CYP450s. The database includes information about channel locations, geometry and physicochemical properties via a user-friendly interactive interface. The studies enabled by these tools have revealed a number of access channels between the CYP450 active site and the protein surface (Urban et al., 2018) and reference numbers have been assigned (channels 1, 2, 3, 4, W and S).(Cojocaru et al., 2007) These channels are lined by different secondary structure elements in topologically different parts of the structure, with most channels forming in the F/G loop region to connect the reactive center CpdI to the distal surface of the CYP450 protein (Urban et al., 2018). This region is impacted most by ligand binding and the secondary structure arrangement in the F/G regions is influenced by the ligand that is bound. A group of aromatic residues at the entrance to channel regions on the protein surface play an important role in ligand recognition and controlling the opening and closing of access channels in response to the presence of a ligand. Channels may be opened by an induced rearrangement of an aromatic side-chain on ligand binding as observed in molecular dynamics simulations in CYP450s 3A4, 2C9 and 2A6 (Skopalík et al., 2008). This recognition event coupled with the physicochemical properties of the channels means that different ligands may access the catalytic core by different routes. The possibility of making mutations presents an opportunity to explore the impact of these access channels on substrate specificity but comes with the associated difficulties of destabilizing the enzyme.

1.30.3.1.3

Pocket size and shape

The size of CYP450 active sites varies considerably across isoforms (Guengerich et al., 2016), from  190 Å3 in human CYP450 2E1 (PDB 3E6I) (Porubsky et al., 2008) to  1400 Å3 in human CYP450 3A4 (PDB 1TQN) (Yano et al., 2004). The sizes of CYP450 active sites are broadly in agreement with sizes of known substrates, where CYP450s with large pockets such as CYP3A4 show the broadest substrate range. The plasticity of the CYP450s is highlighted by the change in pocket volume upon ligand binding. For example, the active site of unliganded CYP3A4 was estimated to be 950 Å3 (Yano et al., 2004) but increased to 1650 Å3 and 2000 Å3 (Ekroos and Sjögren, 2006) when bound to erythromycin and ketoconazole respectively. Similarly, the binding pocket of CYP2E1 more than doubles in size in the presence of fatty acid ligands (Porubsky et al., 2010). Molecular dynamics simulations on three main drug metabolizing CYP450 enzymes (2C9, 2D6 and 3A4) demonstrated this marked flexibility with more than 50% variation in binding pocket volumes over a 100ns simulation (Hendrychova et al., 2012). This variation is consistent with the remarkable ability of CYP450s to metabolize a myriad of substrates of different shapes and sizes. The shape of the active site is also important to determine substrate specificity, with the more open cavity of CYP3A4 (PDB 1TQN) (Yano et al., 2004) explaining the broader substrate specificity as compared to the kinked “L-shaped” cavity of CYP450 2C8 (PDB 2NNI) (Schoch et al., 2008). In another example the active site of CYP450 1A2 (PDB 2HI4) (Sansen et al., 2007)

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was shown to favor the binding of flat planar molecules such as polycyclic aromatic hydrocarbons, similar to other members of Family 1 CYP450s such as 1A1 and 1B1.

1.30.3.1.4

Site flexibility and multiple occupancy

The CYP450s are malleable and the size and shape of the binding pocket can adjust on ligand binding (Guengerich et al., 2016; Nair et al., 2016). Therefore, a CYP450 structure in the absence of a ligand may not be representative of the CYP450-substrate complex. The CYP450 3A4 isoform has a large open binding active site above the heme moiety but has also been demonstrated to show internal rearrangement of secondary structure elements to accommodate the substrates erythromycin and ketoconazole (Ekroos and Sjogren, 2006). At the other end of the scale, isoform CYP450 2E1 has a small active site but through significant internal rearrangement has been demonstrated to accommodate much larger substrates such as fatty acids (Porubsky et al., 2010). However, care must be taken even when there is a bound ligand, as the crystal structure may not be representative of a catalytic pose. This is particularly apparent with crystal structures where the known SoM is located away from the reactive CpdI center in a pose that would not facilitate metabolism. For example, in the published structures of CYP450 1A1 (Walsh et al., 2013) and 1A2 (Sansen et al., 2007) complexed with a-naphthoflavone, the SoM for 5,6-epoxidation is actually located furthest from the heme iron at a distance of  12 Å. The same situation occurs in the previously referenced erythromycin bound CYP450 3A4 structure (Ekroos and Sjogren, 2006) where the crystal structure pose does not reflect the experimentally observed SoM. This demonstrates the importance of using computer simulation to give insights where experiment can sometimes only provide unrepresentative snapshots. The methods discussed later such as molecular dynamics enable protein-substrate conformational space to be explored and interrogated with a wide variety of substrates to understand the structural rearrangements that are occurring. Structural data were also important to uncover examples of multiple ligand occupancy in the binding sites of CYP450s. This was essential in explaining some of the cooperative behavior of CYP450 3A4, both homotropic and heterotropic (Shou et al., 1994; Ueng et al., 1997), which could otherwise be attributed to other binding modes such as allosteric binding sites. Evidence for ‘dual occupancy’ was discovered in 2006 when two molecules of the inhibitor ketoconazole were identified in the active site of CYP450 3A4 (Ekroos and Sjogren, 2006). Since then, two ligands have now been found in crystal structures of other CYP450 isoforms such as 2C8 (Schoch et al., 2008) and 2D6 (Wang et al., 2015) and understanding homotropic and heterotropic cooperativity continues to be an active area for research.

1.30.3.2

Selected recent insights from structures for drug metabolizing CYP450s

As mentioned before, structures are now available for all of the major human CYP450s involved in drug metabolism. Some recent publications for CYP450 isoforms 2D6 and 3A4 have been presented below highlighting some of the key insights that can be achieved with the support of structural analysis, such as important binding interactions, inhibitory interactions, and cooperative and allosteric binding (Guengerich et al., 2016).

1.30.3.2.1

CYP2D6 structural insights

CYP450 2D6 is involved in the metabolism of 20–25% of marketed drugs (Don and Smiesko, 2018) and was the first CYP450 to show monogenic distribution and genetic polymorphism (Mahgoub et al., 1977). The first crystal structure involved several amino acid substitutions and was in the absence of ligand (PDB 4F9Q) (Rowland et al., 2006) but subsequent work reported a structure containing a bound ligand prinomastat (PDB 3QM4) (Wang et al., 2012a). Two residues, Asp-301 and Glu-216, are known to be very important in the CYP450 2D6 binding pocket which favor the binding of ligands with a basic, positively charged nitrogen atom. This feature was exploited in SMARTCyp to direct prediction towards sites approximately 7Å from the positively charged nitrogen. Many structures have confirmed the importance of charge stabilized hydrogen bonds between these resides and basic nitrogens in ligands such as thioridazine, quinine, and ajmalicine (Wang et al., 2015). The elasticity of the binding pocket and conformational shifts on substrate binding are evident in the open and closed complexes observed with the substrate thioridazine. Multiple ligand occupancy is also evident in the open conformation where a second molecule of thioridazine was observed bound in an expanded substrate access channel stabilized by a hydrogen bond to a glutamate residue. One of the reasons for studying metabolic CYP450 enzymes is of course to design drugs with the desired metabolic properties. A further example demonstrates the extra insights that can be gained from structural studies to achieve this goal. In this example a b-secretase inhibitor drug candidate was known to be almost exclusively metabolized by CYP450 2D6, exposing individuals with genetic polymorphisms in this isoform to lower drug clearance. Using contact sites established by crystallizing the drug, an N-methylpyrazole, and its N-demethylated product with CYP450 2D6, an alternate isoxazole drug candidate was designed that had similar IC50 values to the initial lead compound but with multiple metabolism routes with different isoforms (Brodney et al., 2015).

1.30.3.2.2

CYP3A4 structural insights

CYP3A4 is the major isoform in the human liver and intestine and is therefore of crucial importance to understanding drug metabolism. Sometimes it is necessary to administer CYP450 inhibitors with drugs to act as pharmacoenhancers to reduce the rate of metabolism and elimination from the body before the therapeutic impact can take effect. One such example is the known inhibitor ritonavir and its derivative cobicistat that are co-administered with HIV drugs to enhance the therapeutic outcome (Von Hentig, 2008). Crystal structures with bound inhibitor revealed that the most effective heterocycle in terms of close binding to the heme

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was a pyridine and removal of the backbone hydroxyl improved interactions. Inspection of the holo structures also revealed two binding sites, labelled Phe-1 and Phe-2 pockets, that were important for the binding of ritonavir analogues. These finding were used to develop a new inhibitor with a new pharmacophore (Sevrioukova and Poulos, 2014). Structural studies can also reveal more complex behavior such as allosteric binding and multiple binding. In one study (Sevrioukova and Poulos, 2015), steroid molecules including progesterone, testosterone, androstenedione and cholesterol were observed in the same peripheral region as earlier studies (Williams, 2004) in a position that makes it unlikely to be acting as a substrate or effector. Citrate from the buffer was also observed in the crystal structures where it could be contributing to the efficiency of the catalytic cycle. This hypothesis has since been supported by biochemical assays that have shown that citrate and other anions such as phospholipids and glutathione could intervene at various points in the mechanistic cycle although further evidence is required (Imaoka et al., 1992; Gillam et al., 1993).

1.30.3.3

Structure-based SoM prediction

Structure-based methods explicitly model the accessibility criteria by placing the molecule into the CYP450 binding pocket. The output from these methods is easy to interrogate as they generate binding poses that can be inspected by the user and are rooted in the physical reality of explicitly modeling the binding event. Today, crystal structures are available for almost all CYP450 isoforms relevant to xenobiotic metabolism. Malleability of these enzymes, their complex interplay with water, and the hydrophobic character of their variably sized binding sites pose significant challenges to the application of structure-based approaches, in particular docking. For this reason, structure-based methods are primarily applied for the detailed investigation of the interaction of CYP450s with individual compounds rather than for the profiling of small-molecule libraries. In this setup, structure-based methods can be particularly valuable for rationalizing distinct biological properties of enantiomers. Some of the key structural-based methods are discussed next, including a discussion on docking, molecular dynamics and QM/ MM methods.

1.30.3.4

3D alignment

This type of approach involves comparing the molecule of interest against a database of reference molecules for which the 3D alignment in the pocket and the SoMs are known. It was used to study the CYP450 2C9 metabolism of the antibiotic Flurbiprofen and related similar molecules. The authors overlaid the test molecules against a small data set of CYP450 2C9 substrates with known SoM with the alignment program ROCS (OpenEye Scientific, n.d.) to predict the SoMs of Flurbiprofen and related molecules (Sykes et al., 2008). The success of 3D alignment methods is of course dependent on the availability of high-quality structures of the substrate in the binding pocket in catalytically relevant poses. The 3D alignment method was enhanced by combining the accessibility measure with a reactivity-based prediction to account for intrinsic reactivity in compounds (de Bruyn Kops et al., 2017). This hybrid approach improved predictive accuracy, demonstrating the importance of accessibility and reactivity considerations in SoM prediction.

1.30.3.5

MetaSite

MetaSite (Cruciani et al., 2005) was one of the pioneers of structure-based methods for SoM prediction and has been developed into a commercial software package. It uses a pseudo-docking approach where steric and chemical properties of CYP450s such as hydrogen bonding or hydrophobic regions are described by Molecular Interaction Fields (MIFs), calculated by placing various chemical probes in a grid system embedded over the CYP450 structure. Potential SoMs can be identified in a target molecule by aligning to the MIFs, coupled with matching molecular fragments to pre-computed reactivity scores from QM approaches.

1.30.3.6

Docking

Docking is a method for inserting a molecule into a protein binding pocket to obtain a predicted pose. In this context, SoM can be identified as those atoms binding in close proximity to the reactive CpdI moiety at the heart of the CYP450 in the most favorable docking poses. To identify favorable poses it is necessary to perform a conformational search of the ligand in the binding pocket and have a scoring function to identify favorable poses. Scoring functions differ from program to program but generally consist of sums of energy terms, such as the energies of hydrogen bonds or van der Waals interactions between the ligand and protein. These are heuristic by nature and do not give reliable binding free energies. Docking programs allow the user to set the rigor of the search, where options such as allowing flexible, rotatable side chains and including crystallographic waters, may give improved results but at greater computational expense. Docking is more computationally expensive than many of the ligand-based methods that have been presented, but with increases in computational power and access to parallel processing, large datasets of thousands to millions of molecules can be docked in a single day. As a consequence, molecular docking has been used to predict SoM based on the criterion of distance between the heme iron and ligand non-hydrogen atom of
6 Å. The presence of a heme group and large contribution from hydrophobic interactions within the active site requires specific parameterization in the scoring functions, mostly derived from quantum chemical calculations and electron density maps of known

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crystal structures. The reactive form of CYP450 enzymes feature an oxidized heme complex while the crystal structures are often in the resting state (coordinated to a water molecule). Ideally, scoring would therefore require incorporating interactions with this oxidized heme but these are often not parameterized in docking programs. Modelling water interactions using the positions of crystallographic water is of course only beneficial if their location is relevant to stabilizing ligand binding. It is far from guaranteed to improve accuracy and is only really useful for ligands with high structural similarity to those present in the co-crystal structure.

1.30.3.6.1

Docking coupled with reactivity descriptors

AS has been discussed throughout this article, SoMs are determined by accessibility and reactivity considerations. There are many published studies that apply docking to consider accessibility coupled with some form of reactivity descriptor. One such study used AutoDock Vina (Trott and Olson, 2009) to insert substrates into an ensemble of CYP450 2C9 structures (Kingsley et al., 2015) and gave overall predictions by coupling with reactivities from SMARTCyp. The authors highlighted the importance of considering flexibility and sampling conformational space by the improvement in going from docking into a single structure to an ensemble of structures. Constrained docking using the covalent docking functionality in GOLD (Jones et al., 1997) has also been explored (Tyzack et al., 2013), reducing the size of the search space by attaching each ligand atom in turn to the oxygen in CpdI and explicitly obtaining a score for each potential SoM. However, it is far more common for unconstrained docking to be applied, predicting SoMs as those that are in close proximity to the heme in the best poses. The unconstrained approach places reliance on the docking algorithm to fully explore conformational space rather than forcing it to consider each potential SoM as in the constrained workflow. In this study, the electrostatic contribution of charged residues important for ligand orientation in CYP450 isoforms 2C9 and 2D6 was modeled with the spherical constraint feature within GOLD docking program. The docking scores for each site were combined with an implementation of the average local ionization energy using molecular orbitals obtained from running DFT minimization of the unbound ligand. Another publication used the top three poses from in-house docking software combined with reactivity descriptors based on molecular orbitals to build a SoM predictor (Mukherjee et al., 2015). The docking method carries considerable computational expense since its estimation of binding free energies requires the calculation of partial charges from QM software and energy minimization with MD software, but a good correlation to experimental binding free energies was observed. The docking software GLIDE has also been combined with reactivity descriptors based on hardness to identify metabolically labile sites (Pragyan et al., 2014).

1.30.3.6.2

SoM prediction tools based on docking

IDSite (Li et al., 2011) samples the conformational space of CYP450s as part of a flexible docking procedure with Glide. It generates protein conformations using PLOP (Protein LocalOptimization Program) and reactivity is estimated using DFT calculations using CpdI models from methoxy radical calculations. DR Predictor (Docking-Reactivity Predictor) (Zaretzki et al., 2012) combines docking with AutoDock Vina with SMARTCyp activation energies and an additional 31 reactivity descriptors. Methodology used in RS-predictor (Zaretzki et al., 2011) was extended to make consensus SoM predictions on CYP1A2 and 2A6 substrates. The docking protocol involved selection of flexible residues based on crystallographic B-factors and modeling heme-ligand interactions with special terms. A further study (Kingsley et al., 2015) followed the same approach by combined docking with AutoDock Vina with SMARTCyp activation energies but focused on the CYP450 2C9 isoform. Another hybrid method combining docking with reactivity descriptors has been named IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States) (Campagna-Slater et al., 2012). It uses the FITTED (Corbeil et al., 2007) docking program to obtain favorable docking poses combined with pre-computed activation energies obtained at the B3LYP/6-31G* level of theory for a series of relevant fragments that are then assigned to the molecule of interest, similar to the approach taken for SMARTCyp. IMPACTS is part of the drug-discovery platform FORECASTER, which is freely available for academia and provides metabolic predictions for 4 CYP450 isoforms: CYP1A2, CYP2C9, CYP2D6, and CYP3A4.

1.30.3.6.3

Docking with multiple or ensemble of protein structures

Any one crystal structure will not be representative of the conformational changes and flexibility of a protein, so it is common to perform ensemble docking into multiple instances of a protein. It is common to use molecular dynamics simulations to obtain a set of poses and it is preferable to obtain structures with bound ligands similar to the query series. Some studies have adopted an approach to run a series of short molecular dynamics trajectories starting with the smallest ligand and successively adding the next ligand in order of size. This allows the binding pocket to gradually expand to accommodate larger ligands. One such study generated many thousands of protein structures of CYP2D6 using 65 substrates (Hritz et al., 2008), using a decision tree to determine which structure should be used to dock a new query ligand based on similarity criteria.

1.30.3.7

Molecular dynamics

Molecular dynamics (Karplus and McCammon, 2002) is a computational methodology based on Newton’s equations of motion to generate configurations of a molecular system in successive small time steps. The dynamics of a protein can be modelled in silico by relaxing the structure, solvating with water for explicit solvent simulations and increasing to a specific temperature. From the starting

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configuration of positions and velocities of all the particles in the system, the force acting on each particle can be calculated to generate new sets of coordinates and propagate the system forward in time. Repeating this process over many time steps generates a trajectory corresponding to the evolution of the system in time. It is common to run molecular dynamics simulations to generate an ensemble of structures to more fully sample conformational space when performing docking. There are many molecular dynamics software packages available that use a classical Newtonian forcefield to model bonded (bond stretching, bending and dihedral angles) and non-bonded (van der Waals and Coulombic) interactions to simulate the motion of the protein in silico. These forcefields have been parametrized to fit quantum-mechanical and experimental spectroscopic data to accurately model bond stretching, bending and torsions. It is relatively straightforward to set up and run simulations using proteins containing standard amino acid residues as all the necessary parameters have been calculated in predefined forcefields. Software packages such as OpenMM (Eastman and Pande, 2010) provide a user-friendly python interface to allow molecular dynamics simulations to set up as scripts. However, running molecular dynamics on an apo structure may not be representative of the conformational changes that occur on substrate binding. This problem can be alleviated by running dynamics on a holo structure with the substrate bound, although ligand parameterization and constraining to the desired position adds to the complexity of setting up the simulation. Various methods have been developed to enhance conformational sampling, such as adding artificial forces to the ligand to hold it in a specific position or pull it through a channel (steered MD simulation) or in a random direction (random accelerated MD simulation). These methods were particularly well suited to identifying access channels in the CYP450 binding cavity as described previously. Ideally, long trajectories would be run with the molecules of interest, identifying the sites susceptible to metabolism in a molecule that orients close to the reactive CpdI. An advantage of using molecular dynamics is that it uses an energy function that enables binding energies to be calculated using free-energy perturbation, thermodynamic integration and linear interaction energy methods. These methods are computationally expensive so it is more common to use the trajectory to select poses for docking simulations where the scoring is heuristic but faster. The more complete modelling of protein dynamics of course comes with increased computational expense and the simulations can take a long time to run. Many software packages have been developed and optimized to run in parallel, where the vectorized nature of the calculations lends itself to running on Graphics Processing Units (GPU). It should also be remembered that CYP450s are bound to the membrane and also occur with partner proteins as part of the electron transfer chain. MD simulations with just the CYP450 are a simplification and may not be representative of the full picture in vivo. On obtaining a trajectory it is common to apply clustering algorithms to identify the structural clusters and use representative structures from each to contribute to the docking ensemble. A typical approach to sample molecular dynamics of the protein and select representative structures is given in this work (Tyzack et al., 2013). Molecular dynamics simulations were performed on the apo CYP450 3A4, 2D6 and 2C9, followed by tethered docking against the representative structures. The docking results were coupled with a novel implementation of average local ionization energy to model reactivity to give competitive SoM prediction results, albeit at significant computational cost. This study highlighted one of the issues with running apo simulations and cautions the user to be aware of artefacts that can emerge from the MD simulations. Arg212 in CYP3A4 was seen to come within 5 Å of the heme group during the first approximately 40 ns of the MD simulation which was considered unsuitable for docking and so representative structures from these clusters were avoided. MD simulations have revealed conformational changes that are induced by the binding of small molecules to various CYP450 isoforms and established a relationship between substrate specificity and enzyme malleability (Mustafa et al., 2014). They have been used to study the solvation of the active sites of various CYP450 isoforms, and the active site access and egress pathways, which might have a key role in substrate selectivity and specificity. They have also provided evidence of the flexibility of the F/G region of CYP450 enzymes to provide further support for the access channels that develop to allow entry and egress.

1.30.3.8

Modelling reactivity QM/MM

The molecular dynamics methods can model the trajectory of the protein governed by a classical forcefield. However, classical forcefields are not sufficient to model reactions and to model the exchange of electrons it is necessary to enter the quantum world. We have already covered ligand-based prediction tools that use pre-computed activation energies from QM modelling, such as DFT used in SMARTCyp or the semi-empirical methods used in StarDrop. However, in more complex cases that sit outside the domain of applicability of these models such as the formation of reactive metabolites it is necessary and justified to go to the extra computational expense of QM/MM modelling (Senn and Thiel, 2009). In these cases, modelling of the protein dynamics using molecular mechanics (MM) must be coupled with modelling the ligand and immediate protein environment, including the reactive CpdI, with quantum mechanical (QM) methods. The boundary between the QM and the MM region can be modeled using a variety of methods, e.g. the link atoms based methodology is routinely employed, or the ONION method (Chung et al., 2015). There are many excellent reviews and articles describing the set up and running of QM/MM simulations including some with a specific focus on CYP450s and containing many relevant examples (Shaik et al., 2010). QM/MM modelling requires high levels of expertise but the outputs can be informative regarding the mechanisms taking place. Sason Shaik and his co-authors pioneered the QM and QM/MM methodologies for studies involving CYP450 enzymes, such as understanding key aspects of the catalytic cycle like the influence of the axial ligand on reactivity (Shaik et al., 2010). These more detailed studies also allow the mechanisms underlying the formation of reactive metabolites to be understood, a feature associated with many withdrawn and black-boxed drugs. The potential to form reactive metabolites is an inherent property of the ligand

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but can be catalyzed by the electron transfer processes facilitated by CYP450s. One such example is S-oxidation followed by rearrangement to isocyanate metabolites in thiazolidinediones and oxidation of haloalkanes followed by loss of hydrogen halides. These more complex questions involving the interplay of ligand-CYP450 interactions and the redox potential range at which the CYP450s operate fall into the scope of QM/MM studies. One example is the study of N-hydroxylation of Mexiletine using docking, molecular dynamics and QM/MM simulations (Lonsdale et al., 2016). This study investigated two proposed mechanisms: (i) hydrogen abstraction followed by rebound of a nitrogen centered radical and (ii) direct oxygen transfer to nitrogen, followed by displacement of the oxidation product by water, and finally proton transfer to give the hydroxylamine metabolite. The mechanism where oxygen is inserted into the NeH bond was discarded due to high energy barriers reported in earlier QM studies. Other studies have applied QM/MM methodology to study CYP450 2C9 catalyzed metabolism of diclofenac, S-ibuprofen, and S-warfarin (Lonsdale et al., 2013). The lowest energy pathway from analyzing the potential energy surface was consistent with the experimentally observed metabolites for S-ibuprofen and S-warfarin. However, the lowest energy path was not found to correspond to the observed metabolite for diclofenac. Further experimental studies showed that diclofenac is instead metabolized by a sequential oxidation by CYP450 3A4 (aromatic hydroxylation) and CYP450 2C9 (aromatic hydroxylation and dehydrogenation) to form the toxic 2,5-quinone imine. QM/MM modelling is also important to understand mechanism-based inhibitors of CYP450s, to elucidate the pathways by which reactive intermediates are formed that bind tightly to the enzyme causing irreversible inhibition. For obvious reasons it is very important to identify these reactive fragments to avoid the adverse effects from CYP450 inhibition. Computational modelling has elucidated the pathways involved, corroborating the experimentally observed metabolites, and has led to the identification of many functional groups such as thiophenes, furans, alkylamines and terminal amines.

1.30.4

Summary and outlook

This article has summarized the different computational approaches taken to predict the metabolism of xenobiotics, including identifying metabolizing enzymes and sites of metabolism (SoM) within the substrate. There are a high number and diversity of different approaches to predict xenobiotic metabolism highlighting the fundamental importance of this topic to drug discovery. The article was broadly split along two lines: ligand-based approaches, many of which are available as web-servers or distributable software packages; and structure-based approaches, providing more detailed analysis but with extra demands on expertise and computation resources. The success rate of the commonly used methods achieve comparable accuracies in the range of 80–90% and are useful tools to identify SoMs early in the drug development process. The high accuracy reflects the ability to identify SoM amongst chemical moieties that are commonly seen in drug candidates and these tools form an essential resource to channel drug development down pathways to achieve drugs with good metabolic stability. This means that drug candidates that are unlikely to be metabolically stable can be filtered out, or design decisions can be made early to modify the metabolically labile fragment. It is anticipated that the focus on the important available in silico prediction tools, from both academic and commercial sources, will guide users towards relevant and available software. The ligand-based methods themselves fall into two strands: data-oriented methods that apply data-mining or machine learning to identify or learn patterns in the data; and methods that simplify the accessibility and reactivity considerations of how the ligand interacts with the enzyme. Methods implementing the former strategy are more relevant when applied within their domain of applicability, on molecules containing fragments that are represented in the training set. Some of the failures will be due to new molecules containing fragments that are not covered in the training set. It is important for these methods to express their domain of applicability or estimate the confidence of their predictions. This functionality is sometimes observed in commercial offerings such as the metabolic lability labels in StarDrop and the SoM propensity scores in ADMET Predictor but is often neglected in academic tools. However, FAME3 is a notable exception and now includes FAMESCORE to indicate confidence of its predictions. Methods that simplify accessibility and reactivity are limited by the extent of the approximations made and may not be able to correctly model more complex or unusual mechanistic details, but the methods can run on timescales to enable web servers and high throughput analysis. Many of the ligand-based methods have been made available as freely available web-servers. However, the users of metabolism prediction tools for drug development are primarily industry-based where proprietary drug candidates cannot be submitted via web services across the internet due to confidentiality reasons. For this reason, some have been made available as distributable packages, but many still do not take this route. To be more useful to industry these in silico tools should be made available for local installation securely behind firewalls on a client site. In cases where a potential substrate sits significantly outside the domain of applicability of ligand-based models or more complex rearrangements occur to form a reactive intermediate, more in depth structure-based modelling is required. However, to be truly reflective of the conformational space of the metabolic enzyme these methods rapidly gain in complexity, from docking into a single structure, through docking into an ensemble, all the way to running full MD dynamics and QM/MM to model the reaction. The increasing number of CYP450 crystal structures deposited in the PDB reflects the increased focus of structural methods to understand ligand-CYP450 interactions and dynamics. More recently MD, QM and QM/MM methods have also started to address

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more complex questions such as the formation of reactive metabolites. Due to computational resource, expertise and time constraints, these methods are normally reserved for complex cases involving unexpected drug metabolism and toxicity. The dream of simulating ligand binding, reaction and egress pathways routinely for all drug candidates is moving closer to reality with advances in computer technology, particularly GPU acceleration. This will enable insights into the entry and egress channels, structural changes as the catalytic cycle progresses and heterotropic cooperativity. The performance of the available tools is comparable but test set dependent and is probably approaching the limit based on the quality of the data available. It would be beneficial for the field of SoM prediction to define known standard training and test sets to enable direct comparisons to be made between software packages, with regular updates as new metabolic data become available. However, annotation (SoM assignment in particular) is time-consuming and requires expert knowledge hindering the availability of data in the public domain. Despite this, the amount of data is growing slowly with the Zaretzski dataset and the recent MetaX database notable contributions to freely available metabolic data. However, much data remain held in-house with pharmaceutical companies and the scientific community would benefit greatly from increased data sharing. The different metrics used to calculate the performance of predictors of isoform specificity and SoM also make comparisons difficult and it is common for studies to include a mixture of metrics such as top-2, top-3, Lift metric, AUC or other statistical methods. There is a need to develop universally agreeable guidelines for such model development as has been done for general QSAR studies. When presenting performance, it would also be beneficial to routinely make comparisons to pure chance to fairly reflect the predictions being made. The increases in computing power will continue to benefit structural methods. One issue is the fact that most CYP450 structural work has been done on single proteins ignoring the redox partners, such as NADPH-P450 reductase and cytochrome b5, but it is not fully understood how the structure and dynamics of the CYP450 will change on redox partner binding. It would be better to include redox partners to more completely model the proteins but current computational limitations make this type of simulation prohibitive. There is also limited structural information about single nucleotide variants. These can be identified routinely with modern genetic approaches, but it is the change in structure of the binding pocket that will influence xenobiotic binding and metabolism. It would be better to have structural information on common genetic polymorphisms in CYP450 that are known to affect drug metabolism. The importance of predicting drug metabolism efficiently and accurately will continue to drive momentum and improvements in metabolism prediction software for the foreseeable future but the rate of progress will inevitably depend on the amount and quality of available data. Currently, the focus has been on CYP450 metabolism due to its relevance to Phase I drug metabolism but more attention will turn to enzymes involved in other clearance pathways such as glutathione transferases and sulfotransferases. Ongoing technological advances in the field of scientific computation will also enable complex simulations to be applied more routinely to study drug-enzyme interactions. The reader is advised that the rate of development is likely to continue apace.

See Also: 1.19: Drug Metabolism: Cytochrome P450

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COMPREHENSIVE PHARMACOLOGY

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COMPREHENSIVE PHARMACOLOGY EDITOR IN CHIEF

Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States ASSOCIATE EDITOR IN CHIEF

Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany

VOLUME 2

Pharmacogenomics EDITED BY

Andrew Tobin University of Glasgow, Glasgow, United Kingdom AND

Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany

Drug Discovery EDITED BY

Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States

Elsevier Radarweg 29, PO Box 211, 1000 AE Amsterdam, Netherlands The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge MA 02139, United States Copyright Ó 2022 Elsevier Inc. All rights reserved No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers may always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-820472-6 For information on all publications visit our website at http://store.elsevier.com

Publisher: Oliver Walter Acquisition Editors: Blerina Osmanaj and Kelsey Connors Content Project Manager: Michael Nicholls Associate Content Project Manager: Ramalakshmi Boobalan Designer: Vicky Pearson-Esser

EDITOR IN CHIEF

Terry Kenakin received his BS in Chemistry and PhD in Pharmacology from the University of Alberta, Canada. After a postdoctoral fellowship at University College London, UK, he moved to the United States to take a position as a Research Scientist at Burroughs Wellcome in Research Triangle Park, NC. After 7 years, he moved to Glaxo (now GlaxoSmithKline) where he worked for 25 years in drug discovery. His research is on drug receptors, allosteric protein function, and the application of pharmacology to drug discovery. He is the Editor-in-Chief of the Journal of Receptors and Signal Transduction and is on numerous editorial boards. He is a Fellow of the British Pharmacological Society and has received a number of distinctions including the Goodman and Gilman award for receptor pharmacology from ASPET, the Gaddum Memorial award from the British Pharmacological Society, and awards from the Dutch and Norwegian pharmacology societies. He currently is a Professor of Pharmacology in the University of North Carolina School of Medicine in Chapel Hill, NC.

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ASSOCIATE EDITOR IN CHIEF

Martin C. Michel is a physician trained in experimental and clinical pharmacology in Essen (Germany) and San Diego (California). He headed the Nephrology and Hypertension Research Laboratory at the University of Essen (Germany; 1993–2002), the Department of Pharmacology & Pharmacotherapy at the University of Amsterdam (The Netherlands; 2003–2011) and was Global Head of Product and Pipeline Scientific Support at Boehringer Ingelheim (Germany; 2011–2016). His current affiliations include being a Professor of Pharmacology at the Johannes Gutenberg University in Mainz (Germany; since 2012) and being a Senior Partner at the Partnership for the Assessment and Accreditation of Science (PAASP, Heidelberg, Germany; since 2016). His research focuses on urogenital and cardiovascular pharmacology, where he has published more than 500 peer-reviewed articles cited >30,000 times. He is editor or serves on the board of many pharmacological journals including Mol Pharmacol and Pharmacol Rev.

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EDITORIAL BOARD Editor In Chief Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States

Associate Editor In Chief Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany

Section Editors Hamid Akbarali Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, United States Abhijit Bal Department of Microbiology, Queen Elizabeth University Hospital, Glasgow, & Honorary Clinical Associate Professor, University of Glasgow, United Kingdom Kelly A. Berg University of Texas Health San Antonio, Department of Pharmacology, San Antonio, TX, United States Gavin Bewick Kings College London, Diabetes Research Group, 2.21N Hodgkin Building, Guys Campus, London, United Kingdom William P. Clarke University of Texas Health San Antonio, Department of Pharmacology, San Antonio, TX, United States Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States Martin C. Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany Karnam S. Murthy Department of Physiology and Biophysics, Virginia Commonwealth University, Richmond, VA, United States David S. Riddick Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada Katerina Tiligada Department of Pharmacology, Medical School, National & Kapodistrian, University of Athens, Athens, Greece

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Editorial Board

Andrew Tobin University of Glasgow, Glasgow, United Kingdom Elizabeth Yeh Department of Pharmacology and Toxicology, Indiana University School of Medicine, Indianapolis, IN, United States

CONTRIBUTORS TO VOLUME 2 Davide Abbondandolo Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain David Aranda-Garcia Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain

Andrew Baxter Medicinal Chemistry, GSK Medicines Research Centre, Stevenage, United Kingdom Nick D Bergkamp Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Mariamena Arbitrio Institute of Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Catanzaro, Italy

Bijan J Borah Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States; and Mayo Clinic College of Medicine and Science, Rochester, MN, United States

Krishnan Balasubramanian School of Molecular Sciences, Arizona State University, Tempe, AZ, United States

Kyla Bourque Department of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada

Salete J Baptista Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal; and Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal

B Bueschbell Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, Coimbra, Portugal; and Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal

Carlos AV Barreto Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, Coimbra, Portugal; and Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal

Ingo Burtscher Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany; and School of Medicine, Department of Human Genetics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany Pedro J Caraballo Division of General Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, United States

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Contributors to Volume 2

Daniel F Carr Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, United Kingdom Yu-Shan Cheng National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States Miguel Dieguez-Eceolaza Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain Maria Teresa Di Martino Department of Clinical and Experimental Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy Christoph E Dumelin Novartis Institutes for Biomedical Research, Basel, Switzerland Kevin Dzobo International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), University of Cape Town Medical Campus, Cape Town, South Africa; and Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa Gianluca Etienne Novartis Institutes for Biomedical Research, Cambridge, MA, United States AT Gaspar Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal María Salud García Gutiérrez Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain David Hall Novel Human Genetics Research Unit, GlaxoSmithKline, Stevenage, United Kingdom Fatemeh Hashemi-Shahraki Department of Biology, Faculty of Science, Shahrekord University, Shahrekord, Iran

Alexander Sebastian Hauser Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark Terence E Hébert Department of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada Wenndy Hernandez Section of Cardiology, Department of Medicine, University of Chicago, Chicago, IL, United States Marie Saghaeian Jazi Stem Cell Research Center, Golestan University of Medical Sciences, Gorgan, Iran Anil G Jegga Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, United States; and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States Jace Jones-Tabah Department of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada Lyn H Jones Dana-Farber Cancer Institute, Boston, MA, United States Terry Kenakin Department of Pharmacology, University of North Carolina School of Medicine, Chapel Hill, NC, United States György Miklós Keser} u Medicinal Chemistry Research Group, RCNS, Budapest, Hungary Dóra Judit Kiss Medicinal Chemistry Research Group, RCNS, Budapest, Hungary Peter Konings Data Sciences and Quantitative Biology, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden Maria Kontoyianni Department of Pharmaceutical Sciences, Southern Illinois University Edwardsville, Edwardsville, IL, United States Maria Koromina Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece; and The Golden Helix Foundation, London, United Kingdom

Contributors to Volume 2

YW Francis Lam Department of Pharmacology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States Volker M Lauschke Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden Hanne Leysen Receptor Biology Lab, University of Antwerp, Antwerp, Belgium; and Department of Biomedical Science, University of Antwerp, Antwerp, Belgium Meina Li ARC Centre for Personalised Therapeutics Technologies, Department of Pharmacology & Therapeutics, School of Biomedical Science, University of Melbourne, Parkville, Victoria, Australia Julio Licinio College of Medicine, SUNY Upstate Medical University, Syracuse, NY, United States Heiko Lickert Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany; and School of Medicine, Department of Human Genetics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany Marta Lopez-Balastegui Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain Goran Malojcic Novartis Institutes for Biomedical Research, Basel, Switzerland Jorge Manzanares Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain C Marques-Pereira Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, Coimbra, Portugal; and Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal

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Bronwen Martin Faculty of Pharmaceutical, Biomedical and Veterinary Science, University of Antwerp, Antwerp, Belgium Kimberly Martins-Cannavino Department of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada Eric T Matey Ambulatory Care Pharmacist-Pharmacogenomics, Department of Pharmacy, Mayo Clinic, Rochester, MN, United States Stuart Maudsley Receptor Biology Lab, University of Antwerp, Antwerp, Belgium; and Department of Biomedical Science, University of Antwerp, Antwerp, Belgium Brian Medel-Lacruz Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain R Melo Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, Bobadela, Portugal Martin C Michel Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany Nourhen Mnasri Department of Pharmacology and Therapeutics, McGill University, Montréal, QC, Canada Adrian Morales-Pastor Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain IS Moreira Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; and Department of Life Sciences, Center for Neuroscience and Cell Biology, Coimbra University, Coimbra, Portugal J Mourão Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; and Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal

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Contributors to Volume 2

Ann M Moyer Department of Laboratory Medicine and Pathology, Personalized Genomics Laboratory, Mayo Clinic, Rochester, MN, United States

Minoli A Perera Feinberg School of Medicine, Department of Pharmacology Northwestern University, Chicago, IL, United States

Zahra Nazari Department of Biology, School of Basic Sciences, Golestan University, Gorgan, Iran

Seth W Perry College of Medicine, SUNY Upstate Medical University, Syracuse, NY, United States

Steven Novick Data Sciences and Quantitative Biology, Discovery Sciences, Biopharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States

I Pinheiro Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal

Shin Numao Novartis Institutes for Biomedical Research, Basel, Switzerland Gáspár Pándy-Szekeres Medicinal Chemistry Research Group, RCNS, Budapest, Hungary; and Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark George P Patrinos Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece; Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates; and Zayed Center of Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates Licia Pensabene Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy Alejandro Peralta-García Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain N Pereira Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal Naveen L Pereira Mayo Clinic College of Medicine and Science, Rochester, MN, United States; and Division of Circulatory Failure, Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States

M Pires Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal Munir Pirmohamed Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, United Kingdom AJ Preto Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; PhD Programme in Experimental Biology and Biomedicine, Institute for Interdisciplinary Research (IIIUC), University of Coimbra, Casa Costa Alemão, Coimbra, Portugal; and Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal D Ramalhão Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal N Rosário-Ferreira Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal; and Department of Chemistry, Coimbra Chemistry Center, University of Coimbra, Coimbra, Portugal Francisco Navarrete Rueda Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain

Contributors to Volume 2

Francisco Sala Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Av. Ramón y Cajal s/n, San Juan de Alicante, Alicante, Spain Enrico Schmidt Novartis Institutes for Biomedical Research, Basel, Switzerland Francesca Scionti Institute for Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Messina, Italy Jana Selent Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain Alireza Shahryari Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany; School of Medicine, Department of Human Genetics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany; and Stem Cell Research Center, Golestan University of Medical Sciences, Gorgan, Iran Zeenat A Shyr National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States Marco Siderius Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands D Silvério Center for Neuroscience and Cell Biology (CNC), University of Coimbra, Coimbra, Portugal; and Center for Innovative Biomedicine and Biotechnology (CIBB), University of Coimbra, Coimbra, Portugal

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Tomasz Maciej Stępniewski Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain Alastair G Stewart ARC Centre for Personalised Therapeutics Technologies, Department of Pharmacology & Therapeutics, School of Biomedical Science, University of Melbourne, Parkville, Victoria, Australia Pierosandro Tagliaferri Department of Clinical and Experimental Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy Paul Y Takahashi Division of Community and Internal Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States; and Mayo Clinic College of Medicine and Science, Rochester, MN, United States Alan Talevi Laboratory of Bioactive Research and Development (LIDeB), Faculty of Exact Sciences, University of La Plata (UNLP), Buenos Aires, Argentina Andrew B Tobin The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom Mariona Torrens-Fontanals Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain Richard M Turner Department of Pharmacology and Therapeutics, Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool, United Kingdom

Martine J Smit Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecular and Life Sciences (AIMMS), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

Jaana van Gastel Receptor Biology Lab, University of Antwerp, Antwerp, Belgium; Department of Biomedical Science, University of Antwerp, Antwerp, Belgium; and SGS Belgium, Mechelen, Belgium

David Sotillo-Núñez Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Pompeu Fabra University (UPF), Barcelona, Spain

Katharina Wißmiller Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany; and School of Medicine, Department of Human Genetics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany

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Contributors to Volume 2

Ma-Li Wong College of Medicine, SUNY Upstate Medical University, Syracuse, NY, United States Weiwei Xu Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany Jaswanth K Yella Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States; and Department of Computer Science, University of Cincinnati College of Engineering, Cincinnati, OH, United States

Wei Zheng National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States Yitian Zhou Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden Ye Zhu Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States

FOREWORD Unlike “pure” sciences such as chemistry, biochemistry, and genetics, contemporary pharmacology draws on all of these and many other disciplines and applies them in service of understanding and controlling physiologic processes with drugs. This amalgamation of different approaches generates a self-reinforcing cycle in which pharmacological knowledge leads to better understanding of physiology, which in turn enables the discovery of new drug targets and medicines to prevent, diagnose, and treat illnesses. The development of new technologies continuously accelerates these cycles. Thus, pharmacology is tightly linked to the technological advances applied to the study of physiology leading to an ever-changing scientific environment. One consequence of the heterogeneity and diversity of scientific disciplines leveraged by modern pharmacologists is that it has become almost impossible to find authoritative comprehensive collections of information on this variegated science. It is to meet this need that Co-Editors-in-Chief Terry Kenakin and Martin Michel and a superlative cast of Section Editors and authors have labored to create this remarkable all-encompassing compendium, Comprehensive Pharmacology. This work covers the myriad of drugs and techniques applied to the treatment of disease. It ranges from detailed discussions of pharmacological mechanisms of drugs at the molecular and cellular levels to the clinical application of those drugs. Encompassing 219 articles, it is arranged in volumes by various pharmacologic disciplines (Pharmacodynamics, Pharmacokinetics, Pharmacogenomics, Drug Discovery) as well as therapeutic areas (cardiovascular, central nervous system, cancer, gastrointestinal, immunology, endocrinology, anti-infectives) written by leading experts in each field. The encyclopedic and detailed coverage will make this work the first stop for anyone seeking up-to-date definitive information about essentially any topic in basic or clinical pharmacology. Robert J. Lefkowitz, MD, Nobel laureate Duke University School of Medicine James B. Duke Distinguished Professor of Medicine

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PREFACE Pharmacology draws from many other disciplines including chemistry, biochemistry, anatomy, and physiology. In contrast to those, pharmacology directly intends to better human life by improving the prevention, diagnosis, and treatment of human disease, and sometimes even providing cure, for instance, in infectious diseases. The past two decades have seen major achievements in science such as the sequencing of the human genome in general and its variations between populations and individuals. Crystal structures have been resolved at high resolution for many proteins that serve as drug targets, often even in multiple conformations. Concomitantly, we have seen the evolvement of key novel technologies including those for analyzing and manipulating DNA and creating novel drug candidates by combinatorial chemistry. The speed of progress has become revolutionary. Accordingly, a major reference book in pharmacology today must be considerably different from those written 15 years ago. Defining pharmacology as the chemical control of physiology, it is a science that touches a wide realm of other disciplines from medicine, therapeutics, physiology and just about any research endeavor that concerns living tissue. This being the case, this present book has relevance to practitioners of medicine, bench scientists and students at all levels from graduate to undergraduate. The generation of useful new drugs requires not only an interaction between pharmacologists and other scientists but also among various subspecialties within pharmacology. This includes vertical interactions, e.g., between medicinal chemists and clinical pharmacologists, but also horizontal interactions, e.g., between those specializing in the pharmacology of the central nervous and the respiratory system. To enable such interactions, scientists need sources of information that provide authoritative reviews of various techniques and fields that allow to quickly grasp the essence of fields in which one is not an expert, but needs a critical understanding for good inter-disciplinary work. Comprehensive Pharmacology is an attempt to meet this need with a compendium devoted to the study and application of drug therapy. This work reflects the myriad of drugs and techniques applied to the treatment of disease. It ranges from detailed discussions of pharmacological mechanisms of drugs to their clinical application. It is arranged in volumes on various pharmacologic disciplines (Pharmacodynamics, Pharmacokinetics, Pharmacogenomics, Drug Discovery) and therapeutic areas (cardiovascular, central nervous system, cancer, gastrointestinal, immunology, endocrinology, anti-infectives). These topics are presented by experts in each field, and this gives the volumes much more specific discussion of the topics than any single author work. Thus, this work provides a concise and detailed treatment of a diverse discipline. Terry Kenakin, Editor in Chief Martin C. Michel, Associate Editor in Chief

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CONTENTS OF VOLUME 2 Editor in Chief

v

Associate Editor in Chief

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Editorial Board

ix

Contributors to Volume 2

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Foreword

xvii

Preface

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PHARMACOGENOMICS 2.01

Pharmacogenomics: Overview Andrew B Tobin and Martin C Michel

1

2.02

Ethical Perspectives on Pharmacogenomic Profiling Francesca Scionti, Licia Pensabene, Maria Teresa Di Martino, Mariamena Arbitrio, and Pierosandro Tagliaferri

3

2.03

Pharmacogenomics in the Era of “Big Data” and Advanced Computational Approaches Maria Koromina and George P Patrinos

2.04

GPCR Patient Drug InteractiondPharmacogenetics: Genome-Wide Association Studies (GWAS) Minoli A Perera and Wenndy Hernandez

2.05

Computational Methods and Approaches in Pharmacogenomic Research Yitian Zhou and Volker M Lauschke

53

2.06

Computational Medicinal Chemistry to Target GPCRs Dóra Judit Kiss, Gáspár Pándy-Szekeres, and György Miklós Keser} u

84

2.07

Simulating Time-Resolved Dynamics of Biomolecular Systems David Aranda-Garcia, Mariona Torrens-Fontanals, Brian Medel-Lacruz, Marta Lopez-Balastegui, Alejandro Peralta-García, Miguel Dieguez-Eceolaza, Adrian Morales-Pastor, David Sotillo-Núñez, Davide Abbondandolo, Tomasz Maciej Ste˛ pniewski, and Jana Selent

115

2.08

Targeting GPCRs Via Multi-Platform Arrays and AI AJ Preto, C Marques-Pereira, Salete J Baptista, B Bueschbell, Carlos AV Barreto, AT Gaspar, I Pinheiro, N Pereira, M Pires, D Ramalhão, D Silvério, N Rosário-Ferreira, R Melo, J Mourão, and IS Moreira

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21

27

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Contents of Volume 2

2.09

Pharmacology of Viral GPCRs: All-Round Chemokine Receptor Homologs Nick D Bergkamp, Marco Siderius, and Martine J Smit

163

2.10

Personalized Medicine Through GPCR Pharmacogenomics Alexander Sebastian Hauser

191

2.11

Translating Pharmacogenomic Research to Therapeutic Potentials (Bench to Bedside) Ann M Moyer and Pedro J Caraballo

220

2.12

Applying Pharmacogenomics in Drug Therapy of Cardiovascular Disease Ye Zhu, Paul Y Takahashi, Naveen L Pereira, Eric T Matey, and Bijan J Borah

247

2.13

Applying Pharmacogenomics in Drug Therapy of Neurologic and Psychiatric Disorders YW Francis Lam

277

2.14

Personalized Pharmacotherapy: A Historical Perspective on the Pharmacogenomics of Depression Seth W Perry, Julio Licinio, and Ma-Li Wong

2.15

Pharmacogenomics of Anti-Cancer Drugs Daniel F Carr, Richard M Turner, and Munir Pirmohamed

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2.16

Gene Therapy Alireza Shahryari, Zahra Nazari, Marie Saghaeian Jazi, Fatemeh Hashemi-Shahraki, Katharina Wißmiller, Weiwei Xu, Ingo Burtscher, and Heiko Lickert

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302

DRUG DISCOVERY 2.17

Drug Discovery: Overview Terry Kenakin

369

2.18

Drug Discovery in Induced Pluripotent Stem Cell Models Kyla Bourque, Nourhen Mnasri, Jace Jones-Tabah, Kimberly Martins-Cannavino, and Terence E Hébert

372

2.19

Pharmacological Target Engagement and Validation Terry Kenakin

389

2.20

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery Kevin Dzobo

2.21

Drug Lead Optimization Terry Kenakin

423

2.22

Compound Screening Shin Numao, Gianluca Etienne, Goran Malojcic, Enrico Schmidt, and Christoph E Dumelin

442

2.23

Target ValidationdProsecuting the Target Lyn H Jones

476

2.24

Models for Lead Optimization David Hall

498

2.25

Structure-Based Virtual Screening: Theory, Challenges and Guidelines Maria Kontoyianni

539

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Contents of Volume 2

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2.26

Computational and Artificial Intelligence Techniques for Drug Discovery and Administration Krishnan Balasubramanian

2.27

Replicability and Reproducibility in Drug Discovery Steven Novick and Peter Konings

617

2.28

Holistic Assessment of Compound PropertiesdIn Vitro to In Vivo Pharmacology Andrew Baxter

627

2.29

Translational Pharmacology and Clinical Trials Meina Li and Alastair G Stewart

677

2.30

Biomarkers Jorge Manzanares, Francisco Sala, María Salud García Gutiérrez, and Francisco Navarrete Rueda

693

2.31

Systems Pharmacology: Enabling Multidimensional Therapeutics Stuart Maudsley, Hanne Leysen, Jaana van Gastel, and Bronwen Martin

725

2.32

Magic bullets: Drug repositioning and drug combinations Jaswanth K Yella and Anil G Jegga

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2.33

Drug Combinations Zeenat A Shyr, Yu-Shan Cheng, and Wei Zheng

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2.34

Drug Repurposing Alan Talevi

813

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2.01

Pharmacogenomics: Overview

Andrew B. Tobina and Martin C. Michelb, a The Centre for Translational Pharmacology, Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom; and b Department of Pharmacology, Johannes Gutenberg University, Mainz, Germany © 2022 Elsevier Inc. All rights reserved.

The wealth of data, on a population scale, of genetic variation arising from numerous Genome Wide Association Studies (GWAS) and initiatives such as the 1000 genomes project has recently allowed for an appreciation of the genetic basis of drug action that has been captured in the term pharmacogenomics. This emerging field raises the prospect that by understanding how genetic variation impacts on the biology of drug targets and the response of these targets to pharmacological agents as well as how this impacts on pharmacokinetic parameters, will assist in personalizing treatments to optimized efficacy and tolerability. This field is however very much in its infancy with significant barriers to be resolved if pharmacogenomics is to contribute to personalizing drug treatments and dosing regimes. Discussed in this Chapter are many of these challenges as well as the considerable opportunities presented by this nascent field. One hurdle in developing and implementing meaningful pharmacogenomic data have been ethical considerations. Francesca Scionti et al. discuss in “Ethical Perspectives in Pharmacogenomic Profiling” the need to design pharmacogenomic research with respect for the bioethical principles of beneficence, non-maleficence, autonomy, and justice and how this respect can lead to appropriate design of corresponding studies. Concomitant with recent research in pharmacogenomics, important new research approaches no longer focus on individual measurements within a laboratory or a clinical study but rather use various types of electronic approaches. Maria Koromina and George Patrinos discuss how insight can be generated from “big data” approaches including genome-wide association studies, next generation sequencing and whole genome sequencing. Genome-wide associated studies offer a hypothesis-free approach to systematically test hundreds of thousands of gene polymorphisms. Minoli Perrera and Wenndy Hernandez highlight methodological requirements in conducting such studies in their chapter “GPCR Patient Drug InteractiondPharmacogenetics: Genome-Wide Association Studies (GWAS)”, particularly those related to the analysis of such data. This includes considerations on how to avoid false positives when so many loci are tested concomitantly. The field of pharmacogenomics started by observations on inherited differences in drug metabolism. Yitian Zhou and Volker Lauschke discuss “Computational methods and approaches in pharmacogenomic research” with a focus on enzymes involved in drug metabolism. They also consider computational approaches to gene variants other than single nucleotide polymorphisms including missense, truncated or splicing variants. It was clear from the first description of the atomic structure a non-visual GPCR that structural biology would make a huge impact on our understanding of the mechanism of drug action and in drug development. The pace of the advances made have however been breath taking as evidenced by more than 500 receptor structures currently deposited in the Protein Data Base (PDB). The Chapter authored by Dóra Judit Kiss et al. entitled “Computational medicinal chemistry to target GPCRs” makes it clear however that resolving the structure is just of the start of the impact made in our atomic appreciation of receptor ligand interaction. In this chapter there is detailed description of the computational approaches that can be built from the current experimentally determined structures. Not least how the various molecular dynamic approaches methods can be used not only to understand ligand receptor interactions but also how this can be extrapolated to exploring ligand bias and mechanisms underpinning allosteric modulation. Importantly this contribution goes on to describe the computational tools available and how these might be applied to in silico screening and docking studies that can inform medicinal chemistry and drug design. The contribution by Kiss et al. is complemented by the Chapter authored by Aranda-Garcia et al. entitled “Simulating time-resolved dynamics of biomolecular systems” which provides the reader with both fundamental and applied aspects of molecular dynamic (MD) simulation theory. The authors start by describing the basics of MD simulation theory and the fundamental equations and atomic interactions that are taken into account in making MD measurements. The contribution goes on to provide excellent examples of how MD has been used to resolve issues of receptor structural diversity, allosterism and even signaling bias. Preto et al. expand this theme in their chapter “Targeting GPCRs via multi-platform arrays and AI”. This covers various forms of artificial intelligence such as machine learning and computer-aided design. Specifically, they discuss how these can be applied to GPCR research including receptor modelling or study of GPCR dimerization. The Chapter entitled “Pharmacology of viral GPCRs: all-round chemokine receptor homologs” authored by Bergkamp et al. focuses on the appreciation that viruses have hijacked GPCRs from host cells and that these receptors are used as components of essential aspects of viral biology. Described in this chapter is how these virally encoded receptors, that are homologs of human chemokine receptors, have a role in pathologies such as herpesvirus-associated diseases. The authors have particular expertise in the trafficking and signaling of these viral receptors in particular in their constitutive activity and how negative allosteric modulators whether small molecules or biologicals can be used as therapeutic strategies in virally-induced disease. While genetic differences in enzymes involved in drug metabolism have been the historic foundation of pharmacogenomics, increasing attention is focused on the pharmacogenomic differences in pharmacodynamic targets. Alexander Hauser discusses “Personalized Medicine Through GPCR Pharmacogenomics” for G protein-coupled receptors as a large group of molecular drug targets. He highlights how polymorphisms in genes encoding such receptors may affect drug action at the level of ligand binding and receptor signaling.

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Pharmacogenomics: Overview

Novel insight from pharmacogenetic research would have limited impact unless translated into actionable advice for clinical decision making. In this regard, Ann Moyer and Pedro Caraballo discuss “Translating Pharmacogenomic Research to Therapeutic Potentials (Bench to Bedside)”. A key part of this will be an understanding of appropriate diagnostic approaches by practicing physicians to enable pharmacogenomic-based decision making. Several subsequent chapters discuss aspects of pharmacogenomics with a focus on progress and/or methodological challenges in specific therapeutic areas. The chapter “Applying pharmagenomics in drug therapy of cardiovascular disease” by Ye “Julia” Zhu et al. explains that much work in this area was devoted to pharmacokinetic differences. In contrast, only limited robust data exist for pharmacodynamic differences. They present the case study of pharmacogenomics of warfarin based on a combination of genetic variations in drug metabolism by CYP 2C9 and drug target VKORC 1 that led to FDA-approved genetic testing for anticoagulant treatment. Neurological and psychiatric diseases remain one of the main areas of unmet medical need, and the treatment of many such conditions remains challenging. Francis Lam discusses “Applying Pharmacogenomics in Drug Therapy of Neurologic and Psychiatric Disorders”. The author points out that the promise of translating gene-based drug targets into treatment-based genetic stratification for optimal therapy has remained elusive until now. It is proposed that the usefulness of pharmacogenomics in this therapeutic area presently is limited to predicting adverse drug reactions. Whereas many clinicians and researchers approach the subject of pharmacogenomics from the perspective of the drug target less emphasis is placed on understanding how genomic variation might impact on pharmacokinetics. In the contribution made by Seth Perry et al. entitled “Personalized Pharmacotherapy: a historical perspective on the pharmacogenomics of depression” the genetic variation underlying drug metabolism associated with neuropsychiatric therapies is considered. The author presents a comprehensive review of how genetic variation impacts on the efficacy of monoamine uptake inhibitors and on the prospect that CYP450 variants underlie some of the personalized responses to anti-depressants. As mentioned above, oncology has become the prime area for the implementation of pharmacogenomics. Daniel Carr et al. discuss the “Pharmacogenomics of Anti-Cancer Drugs”. The key difference compared to other areas of pharmacogenomics is that that oncology primarily looks at genetic variation within the tumor cells, not within the healthy cells of the body. Of note, this can be a moving target as tumor cells keep mutating. However, genetic variability in healthy cells of a tumor patient may contribute to his or her susceptibility to the adverse events of anti-tumor treatments and, thereby, affect the overall benefit/risk ratio of a given treatment in an individual patient. Whereas most contributions in this volume focus on small molecule drug development the Chapter authored by Alireza Shahryari et al. entitled “Gene Therapy” provides a detailed analysis of the viral, non-viral and cell based gene therapies that are being used in clinic. By providing a description of each gene therapy product with its target and associated biology, the companies involved and the approval processes, the dosing and evidence of clinical efficacy as well as adverse responses, this chapter not only presents a description of the current clinically approved gene therapies but also provides an historic perspective that gives a sense of the journey taken by this constantly evolving field of medicine. These chapters in combination provide an in-depth picture of both modern technology in studying pharmacogenomics and on challenges and successes in their use. They confirm that the dream of personalized, more efficacious and/or better tolerated treatments based on pharmacogenomics remains alive and promising.

2.02

Ethical Perspectives on Pharmacogenomic Profiling

Francesca Sciontia, Licia Pensabeneb, Maria Teresa Di Martinoc, Mariamena Arbitriod, *, and Pierosandro Tagliaferric, *, a Institute for Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Messina, Italy; b Department of Medical and Surgical Science, University of Magna Graecia, Catanzaro, Italy; c Department of Clinical and Experimental Medicine, Magna Graecia University of Catanzaro, Catanzaro, Italy; and d Institute of Research and Biomedical Innovation (IRIB), Italian National Council (CNR), Catanzaro, Italy © 2022 Elsevier Inc. All rights reserved.

2.02.1 2.02.2 2.02.3 2.02.3.1 2.02.3.2 2.02.4 2.02.4.1 2.02.4.2 2.02.4.3 2.02.5 2.02.6 2.02.6.1 2.02.6.2 2.02.7 2.02.7.1 2.02.7.2 2.02.7.3 2.02.8 2.02.9 2.02.10 2.02.11 Conflict of Interest References

Pharmacogenomics in precision medicine Ethical concerns of genomic data management Ethical principles: Beneficence, non-maleficence, autonomy, and justice Association between genomic data and drug response Ethnic differences Informed consent process in pharmacogenomics research Data sharing and storage Confidentiality Incidental findings and familial implication Considerations for population studies Studies involving vulnerable groups Children participants Subjects who are unable to give consent Clinical trial design for pharmacogenomic research Pharmacogenomics biomarker validation studies Pharmacogenomics and drugs development studies Pharmacogenomic biomarkers test for clinical use Barriers for pharmacogenomics implementation Guidelines in pharmacogenomics Legal and social issues Conclusion

4 5 5 6 7 8 9 9 10 11 11 11 12 12 13 14 14 15 16 16 17 18 18

Glossary Bioethics The study of ethical, social, or legal issues arising in biomedicine a biomedical research. Biomarker A measurable characteristic that is an indicator of normal biologic processes, pathogenic processes, and/or response to therapeutic or other interventions. Governmental Regulations Means all directives, laws, orders, ordinances, regulations and statutes of any federal, state or local agency, court or office. Informed Consent Is an ethical and legal requirement for human participants enrolled in a clinical research study involving a process through which a participant is informed about all aspects of the trial with the aim to take a free and understanding decision to its voluntary participation. Pharmacogenomics The study of variations I DNA and RNA characteristics as related to drug response. Pharmacogenomic Pre-emptive test Pharmaco-genotyping test performed before it is known that a particular drug may be needed. Precision Medicine Tailored approach of medical treatment based to the individual characteristics of each patient. Research Participant A person who is the subject of a clinical research study and whose personal information is used in that research.

*

These authors contributed equally.

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2.02.1

Pharmacogenomics in precision medicine

The US National Institutes of Health (NHI, https://ghr.nlm.nih.gov/) has defined precision medicine as an “emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” Precision medicine aims to identify the disease-causing targets and the tailored therapeutic strategies by shifting the one-size-fits-all approach to a new approach that builds on the biological differences between individuals (Jameson and Longo, 2015). The development of this approach requires the collection and integration of a large amount of data (big data) and biological samples to stratify patients into molecular subgroups based on their predisposition to a particular form of the disease and/or their response to a particular treatment (Beckmann and Lew, 2016). In recent years, the implementation of precision medicine-based methods to diseases in different areas, such as oncology, psychiatry and cardiology has started to become visible (Le Tourneau et al., 2019; Quinlan et al., 2020; Tada et al., 2021). The impact of this shift has been especially observed in oncology, where several predictive biomarkers have already been established (Sousa et al., 2020; Constantinidou et al., 2019). For example, clinically available tests to stratify breast cancer patients according to their specific gene expression patterns, provide prognostic and/or predictive information that can be used by clinicians to drive the selection of adjuvant chemotherapy or endocrine therapy (Kwa et al., 2017). Therefore, we are gradually observing precision medicine entering into clinical settings. Pharmacogenomics (PGx) is an expanding area of precision medicine (Primorac et al., 2020). PGx focuses on the study of the relationships between a person’s response to drugs and variations in nucleic acids (DNA, RNA) to achieve a comprehensive characterization of biological pathways and underlying molecular processes (Arbitrio et al., 2021). The identification of patient’s molecular profiles together with toxicity risk factors (i.e., sex, age, comorbidity, environmental risk, polytherapy) has highlighted the need to broadly study and monitor patient’s for interindividual response to drugs in terms of safety and efficacy and to identify genomic markers related to inherited genetic diseases. The technological advances of high-throughput platforms, such as microarray to next-generation sequencing (NGS), have enabled the identification of germline and somatic variants providing great support for the implementation of tailored prescriptions with a major impact on patient’s health care costs. Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) play an important role in both pharmacokinetics (PK) and pharmacodynamics (PD) and influence individual phenotype with consequences in drug efficacy or toxicity, greatly impacting on patient’s outcome (Arbitrio et al., 2016b). Similarly, germline variants in drug targets can modulate a patient’s sensitivity or resistance to a drug. A variety of new drugs have been developed to target specific druggable somatic mutations in driver genes such as EGFR, RAS, BRAF, and ALK (Wheeler et al., 2013). Drug labels containing PGx information are approved by regulatory agencies worldwide, including the US FDA (US Food and Drug Administration), the EMA (European Medicines Agency), and the PMDA (Pharmaceuticals and Medical Devices Agency of Japan). Approximately 400 gene-drug interactions that can alter the therapeutic outcomes are known, and PGx recommendations are included in each drug’s labeling (https://www.fda.gov/drugs/science-and-research-drugs/table-pharmacogenomicbiomarkers -drug-labeling). This information has influenced several therapeutic areas of drugs from oncology/hematology, cardiology, infectious disease, psychiatry, and neurology. In cancer therapy, the profound impact of PGx recommendations for drug dosing, efficacy/toxicity, hypersensitivity/resistance, and outcome has improved patient management and healthcare costs. Single Nucleotide Polymorphisms (SNPs), along with insertions/deletions (INDEL), polymorphic short tandem repeats (STR), and copy number variations (CNVs) are germline variations identified as PGx biomarkers. They have a more direct role in diseases and differences in drug response affecting the function when located in a coding or regulatory region of a gene as well as they show an involvement in ethnic variability and clinical impact. In addition to common variants, other low-frequency (0.1%  Minor Allele Frequency (MAF) < 5%) and rare (MAF < 0.1%) variants could contribute to high functional gene and drug-specific functional alterations (Ingelman-Sundberg et al., 2018). SNPs (variations of a single nucleotide with a population frequency > 1%) are co-inherited in the same haplotype block and when in linkage disequilibrium, can act as “tag SNP” of a specific haplotype (Arbitrio et al., 2018; Hinds et al., 2006). These polymorphic variants also play a role in determining ethnic heterogeneity in drug response (Hovelson et al., 2017). Classic examples of genetic variants recognized to induce drug toxicity by decreased enzyme activity are CYP2C19*17, related to bleeding during clopidogrel therapy (Shuldiner et al., 2009); UGT1A*28, relatively to irinotecan toxicity (Iyer et al., 2002) or DPYD variants, associated to 5-fluorouracil or capecitabine toxicity (Henricks et al., 2018); CYP2D6 associated to tamoxifen toxicity; TPMT SNPs, related to thiopurine toxicity or SLCO1B1*5 drug transporter variant associated with simvastatin toxicity risk (Wilke et al., 2012) or the VKORC1 polymorphic variants related to warfarin resistance (Wadelius et al., 2005) and the HLA-B*5701 variant associated with abacavir hypersensitivity (Illing et al., 2017). However, although several PGx studies, from candidate gene to genome-wide association studies (GWAS), contributed to identify potential biomarkers related to drug efficacy or toxicity in many fields (Agapito et al., 2020; Di Martino et al., 2011a, b, 2016; Arbitrio et al., 2016a, 2019; Scionti et al., 2017). The process of validating a biomarker for translation into common clinical practice is very complex and also requires validation and qualification steps for the development and analytical confirmation of the biomarker assay (Dobbin et al., 2016). In this process, another key point is the clinical utility of the biomarker that must be tested and validated in clinical trials where the study design, with its endpoints, analysis, and reproducibility of results and their interpretation could be a source of bias. On the other hand, knowledge of individual genetic make-up could accelerate drug development or drug repurposing by clinical trials designed on the specific characteristics of enrolled participants (Pushpakom et al., 2019). Individuals, whose genetic profile is suitable for the drug under investigation, could be enrolled in specific phase III trials in which a safer, faster, and more suitable study will allow for better outcomes and benefits in a smaller, select population. In this context, when there is an association between drugs and genotype/phenotype information related to PK, PD, efficacy, and toxicity, or when a biomarker could help in the prevention of genetic diseases, several issues could be barriers to the implementation of PGx in clinical practice of the right and

Ethical Perspectives on Pharmacogenomic Profiling

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responsibility of all stakeholders, as well as the uncertainty derived from the use and availability of technologies. Regulatory, ethical, economic, educational, legal, and social issues represent important barriers to overcome for the full translation of medical innovation with PGx profile into clinical research and practice. In this chapter, we review key ethical challenges that could realistically or potentially cause problems, with PGx research that must be overcome in clinical settings.

2.02.2

Ethical concerns of genomic data management

The map of the entire human genome completed in 2003 by the international, collaborative research program Human Genome Project (HGP) marked the beginning of the genomic era (Collins et al., 2003). Research has then moved beyond the study of the structure of the human genome (structural genomics), going to understand the function of specific nucleotide sequences (functional genomics), the gene expression profile of various physiological and pathological conditions (transcriptomic), and the regulation of gene expression induced by genome modifications (epigenomics). In parallel, the advances of “omics” technologies, that refer to large-scale analysis, have impacted other different research areas, providing the opportunity to analyze samples in a highthroughput manner. Terms such as proteomics, metabolomics, nutrigenomics, microbiome, to name a few, become familiar. In this scenario, the methodological approach in PGx studies has changed from the single gene/pathway hypothesis-driven approach to a genome-wide hypothesis-generating approach (Ho and Giannoulatou, 2019). However, the vast volume of data produced by big data research studies has created new questions about how these data are generated, as well as collected, processed, and stored, thus raising ethical, legal, and social issues concerning privacy and discrimination (Tanjo et al., 2021; Carter, 2019). These challenging issues led to the founding of the ELSI (Ethical, Legal, and Social Implications) program within the HGP in 1990 (https://www.genome.gov/Funded-Programs-Projects/ELSI-Research-Program-ethical-legal-social-implications). The mission statement of the ELSI program was to both identify and address questions that were raised by genomic research which in turn can influence individuals, as well as families and society. If private health information is improperly disseminated by disclosure to third parties, it can lead to discrimination and stigma against the individual. This can also affect the individual’s trusting relationship with health care providers, which can lead to compromised behaviors such as withholding crucial information that impacts treatment. Thus, defined protocols for transferring data between research and clinical settings are needed in addition to complete and accurate information to research participants. Another ethical issue related to big data concerns the generation of additional information outside of the research object and their clinical management. Generally, they are not directly correlated to the objective of research study and thus to participant’s phenotype but may be related to the risk to develop other diseases or affecting certain drug outcomes. Examples of “secondary” data generated by NGS platforms are heterozygous recessive disease carrier status, mutations in genes known to be risk factors for cancer development, early and late-onset diseases such as Alzheimer’s disease (Martinez-Martin and Magnus, 2019). Patients should be aware about the risk related to generation of secondary information and the range of treatment choices available for their clinical management. However, differences between individuals, generated by omics platforms, are at risk of causing “genetic discrimination” in employment, insurance, or other issues as they can easily link differences with economic losses (Sariyar and Schlunder, 2019). For example, genomic data can provide information if an individual has the predisposition to develop a given disease and if the same belongs to a genetic group that will not be successfully treated with standard medications, thus representing increased morbidity, mortality risk, and higher healthcare costs. In addition, the genome has essential characteristics that make it possible to identify and potentially expose family relatives, which will affect not only the individual but other family members. Currently, there are a wide range of genetic tests, known as direct-to-consumer (DTC) tests, directly available for sale to customers in the web-based marketplace, including both health-related tests (i.e., monogenic diseases, susceptibility testing for common complex disorders, pharmacogenomics) and non-health-related tests (i.e., ancestry testing). Accessibility to DTCs lacks regulations regarding ethical issues such as consumer privacy, genetic counseling, and informed consent. In fact, DTCs increase the likelihood that genome data will be made available on the website and to for-profit companies, both of which can be considered less regulated environments (Parsons and Baker, 2020). Genome sequencing technology has become a routinely used practice with deep insight into medicine and fast use of genomic information by health systems. Preserving data confidentiality and personal privacy is expected to be a central and primary concern. A present challenge, therefore, is to address both the security and the confidentiality of all genomic data at once without compromising their use in research and health care. This will involve the need of explicit policies and laws to safeguard individuals’ data and the concomitant interdisciplinary communication among all stakeholders, for any efforts in developing efficient and secure bioinformatics tools for managing and storage of large genomic data.

2.02.3

Ethical principles: Beneficence, non-maleficence, autonomy, and justice

As discussed above, all research on DNA samples generates potentially sensitive genomic data which must be carefully protected and analyzed. The condition that related health data might be considered as valuable resources for disease diagnosis, prescription of therapies, information for public-funded research, target for the development of new drugs for-profit entities or biomarker assays, generate important ethical consideration. In PGx research, as well as in other biological fields, is important to proceed in all evaluation and approaches following the well-established principles based on “principlism” and conduct studies in accordance with basic ethical principles, which have their origin in the Declaration of Helsinki. Research involving humans should receive independent ethics committee/institutional review board (IEC/IRB) approval/favorable opinion prior to initiation. Failure to submit

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a protocol to the committee should be considered a clear and serious violation of ethical standards. Bioethical principlism is referred to four basic principles: beneficence, non-maleficence, autonomy, and justice (Beecher, 1959, 1966; Beauchamp, 2003). In particular, the term “beneficence” refers to an action whose purpose should achieve some good aim rather than being carried out for its own sake. In the research on human subjects, all studies should be carried out to increase scientific knowledge bringing a positive potential benefit either to those taking part or to other individuals which will be in a similar condition in the future. This consideration is valid also for a drug prescription and treatment which shouldn’t be ineffective or futile. For this principle, the discussion in the scientific community is open to debate for example as to what is really a benefit, when a benefit is proportional to the risks, how the future benefits can be weighed against present hazards. The term of “non-maleficence” refers to a condition in which if beneficence could not be demonstrated, it should be argued that the intervention at least should do no harm. In scientific research perhaps, it was often difficult to be confident about the benefits of a treatment or the outcomes of a trial, but uncertainty is part of the moral justification for research and experiment. The discussion in the bioethicist community is about the definition of harm (physical harms or social harms). The term “autonomy”, also referred to as respect for persons, is the full possession of his faculties when an individual makes his own choice which should determine his own fate. For this principle, other people should not take decisions for another or restrict his decision-making. The discussion in the scientific community is on the opportunity to criticize medical paternalism when a doctor gives only information and offers the therapeutic choice to patients in the belief that he knows better than the patients the effect of the drug relative to disease and patient conditions and if in his best interest. This principle is the basis of the notion of informed consent. The discussion is open when the therapeutic choice involves individuals whose “full possession of faculties” might be compromised (for disease or prisoner conditions) or in the case of a child. The term “justice” refers to people who are equal in relevant issues and should receive equal treatment. The difficulty is relative to the definition of “relevant respects” and “equal treatment.” It is important to respect the principle that the option should be non-discriminative on the basis of medically relevant criteria. The discussion is open on the possibility of discrimination on social criteria (ethnic, economic condition, severity of pathologic condition), which comes to the same discriminative conditions. These principles are highly relevant in PGx profiling because the identification of a polymorphic variant correlated to drug response might overlap with disease susceptibility and the knowledge of this kind of information could alarm an individual and his family. Moreover, a PGx test is included sensitive information on an individual’s genetic make-up, whose collection and archiving could raise questions of privacy, security, and ethical concerns relative to disease prognosis and treatment choices in the context of personalized medicine. The insufficient evidence for the advantage or the need to have preventive information derived from PGx test, with the exception of specific cases, are paralleled also by limitations due to relevant cost of time and money in the execution for PGx test and the lack of standardized guidelines for result interpretation and choice of therapeutic option. In this scenario, the relevant issues are as follows.

2.02.3.1

Association between genomic data and drug response

All the PGx evidence and associations to drug response need full confirmation for their translation in clinical practice and this is the first ethical challenge towards precision medicine (Tan-Koi et al., 2018). In fact, for a potential PGx biomarker it is important the validation of clinical and medical utility in terms of providing actionable information correlated to condition or disease and consequently to treatment decision for improving patient clinical outcome. The importance of the genetic association is a relevant claim for the genetics of drug response because undergoing a PGx test could bring to identify a class of patients as “at risk” of adverse drug reactions (ADRs) or considered as a “non-responder” and inappropriately excluded from treatment with the consequence of the lack of therapeutic options or to a wrong genetic association. The ethical issue related to the exclusion from treatment is a problematic condition especially in the case of a not very strong genetic association in a context without a therapeutic alternative. In clinical practice, to deny therapy for a patient, after the execution of a PGx test, should be considered a matter of professional judgment after the consideration of the balance of several factors whose objective is the improvement of patient’s treatment decision making according to the current standard of care. The same considerations must be made in the case of correlations between biomarkers and disease, because errors in the publication of the risk of developing a disease could have important psychological and ethical consequences if the role of the biomarker, used for risk prevention, is not safe and validated. Moreover, only when there is sufficient association, the PGx marker can be introduced in the clinical practice and accepted by all stakeholders without a doubt. Up to date, more than 400 drugs have Food and Drug Administration (FDA) PGx recommendations in their drug labeling (https://www.fda.gov/ drugs/science-and-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling) or European labels posted on the PharmGKB website (www.pharmgkb.org), including for some, but not all products, specific actions to be taken based on the PGx information. The clinical prescription for these drugs, the indication to perform a specific PGx test is inserted into treatment guidelines demonstrating an impact on treatment outcomes. Other considerations are needed in the case of polymorphic variants influencing the hepatic activity of CYP450, i.e., CYP2C19 or CYP2D6, and variability in the PK differences, but also age, race, comorbidities, and possible drug-drug interactions very important for this class of enzymes and involved in the interindividual variability. In fact, several genomic variants could be pleiotropic and associated with more than one disease or variability in drug effects. The predictive and protective effects of biomarker evaluation could be important for the introduction of specific PGx tests in the clinical practice only when strong and direct associations between gene and drug response are available with the specific aim to avoid potentially severe ADRs for patient’s health care. Also, the problems in the reproducibility of important genetic association studies represent an obstacle in the sprout of the PGx in drug response and the lack of a clear guide for translation in clinical practice. In this context, it will be helping to demonstrate, in well-designed clinical studies, that the risk-benefit ratio of a drug is improved by

Ethical Perspectives on Pharmacogenomic Profiling

7

PGx testing, also in terms of health costs and probably for treating selected conditions with standardized clinical approaches. However, patients must be informed about all risks correlated to disease condition and treatment, together with the potential risk associated to a specific genomic variant which might lead to important ADRs. On the other hand, clinicians will know if a patient, identified as non-responders or difficult to treat, cannot have potential chances of response when giving a drug. In this last case, patients might carry a “stigma” with them that could lead to the lack of opportunity to have adequate health care access. The risk could be personal economic crises or psychological troubles for individuals who consider themselves untreatable or a problem for the health care system. For these conditions become an urgent need to define rules for the class of orphan drugs or identify other measures to better classify patients defined as non-responders in order to minimize this category with respect to their level of health care.

2.02.3.2

Ethnic differences

Ethnicity, ancestry, genetics, and diseases can be strictly correlated in a dynamic way influencing people’s socioeconomic position at different levels and leading to inequalities in the access to resources and opportunities as health care with disproportionately high morbidity and mortality. The interindividual variability in the PK and PD profiles of several drugs is influenced by intra- and interethnic genetic ancestry differences becoming an important issue in PGx research not only for drug choice and the changes in riskbenefit but also for progress in drugs development. Genetic and non-genetic factors can influence individual genotype and phenotype relatively to the consequences in the regulation of activity of the polymorphic gene, or to ethnicity, disease, sex, age, lifestyle, and polytherapy. Environmental, style of life, and habitual factors influence the ethnicity, and these effects are important for drugs considered to be ‘ethnically’ sensitive as a consequence of metabolic polymorphic enzymes whose allele frequencies are variable across racial groups (http://www.gpo.gov/fdsys/pkg/FR-1998-06-10/pdf/98-15408.pdf). Several anticancer agents are prescribed at similar dosages to different ethnic populations without consideration of differences in pharmaco-ethnicity with risk of diversity in drug response or toxicity in terms of the recommended safe or effective dose of a drug or avoidance of ineffective therapy (O’Donnell and Dolan, 2009). This is an important matter for the progress of cancer PGx research because it should allow the identification of specific populations or subsets genetically predisposed to a given susceptibility or resistance or to the discovery of polymorphisms whose frequencies might be particularly important or relatively unimportant in certain populations. Although in literature there are examples of cancer PGx studies drowned in consideration of ethnic/genetic ancestry informative markers to determine the role of chemotherapies, this approach remains infrequent (Kishi et al., 2007; Schneider et al., 2017). When we refer to genetic ancestry, we consider all the race and genetic origin of one’s population which are indirectly correlated to genetic variants and involved in disease and health outcomes. Moreover, as exist genetic admixture among ethnic groups, also environmental and regional differences within a population should be considered on the basis of their genetic ancestry or genetic admixture (Cooper et al., 2008). Recent shreds of evidence reported that in PGx loci, located in both protein-coding and non-protein-coding regions, there is an enrichment of ancestry-informative markers and trans-ancestry differentiation should be considered in this kind of studies (Yang et al., 2021). The barrier to be overcome for population PGx studies is the need for whole-genome analysis in large, different populations to highlight the genetic or biological mechanism underline these differences. Among anticancer drugs, 5fluorouracil (5-FU) is the best studied agent with differences in pharmacy ethnicity probably amenable to decreased 5-FU tumor responsiveness or decreased tolerance of therapy among African Americans. Mattison et al. correlated the hematologic differences in toxicity to lower peripheral blood mononuclear cell levels of dihydropyrimidine dehydrogenase (DPD) activity influencing 5-FU catabolism in healthy African Americans compared with Caucasians (Polite et al., 2006; Mccollum et al., 2002; Mattison et al., 2006). Also, vincristine shows ethnic differences for CYP3A5 expression, responsible with CYP3A enzyme of its metabolism, between African Americans (70% prevalence) and Caucasians (20%), as well as for the ethnic differences demonstrated in the metabolism of other drugs like doxorubicin, cyclophosphamide, and EGFR inhibitors (Lal et al., 2007; Chang et al., 1993; Calvo and Baselga, 2006). Indications on personalized prescriptions based on genetic ancestry/ethnic recommendations are reported in the guidelines of FDA in medical device clinical studies (https://www.fda.gov/regulatory-information/search-fda-guidancedocuments/evaluation-and-reporting-age-race-and-ethnicity-specific-data-medical-device-clinical-studies). Several studies demonstrated variability in exposure/response due to genetic ancestry/ethnic differences and about one-fifth of new drugs approved are prescribed population-specific recommendations while, in the case of lack of specific data from diverse populations, should be mandatory additional post-marketing studies. This approach is important to avoid or discourage the inequality of race with genotype with the aim of accuracy and in the aim to minimize the risk deriving from genetic ancestry/ethnic stereotypes or racial injustice during drug development and marketing. In fact, even when different populations can have comparable income, the gravity of conditions, insurance status or age, genetic ancestry/ethnic minorities, as in the case of the USA black community, might access to a lower quality of health care than Caucasians (Noonan et al., 2016; Nsiah-Jefferson, 2003). Another risk is the participation of a small number of individuals of minorities in clinical drug trials with an understandable under-testing of drugs on women and minorities versus males. The genetic background of a population with ethnic differences is characterized by variability in genotype or gene frequency influenced also by differences in geographical environments with contrasts in culture, food, and habit which influence drug metabolism and effect (Ramamoorthy et al., 2015). Examples in genes of the CYP450 family demonstrate the influence of variability in genetic polymorphisms between ethnic groups. For example, genetic ancestry differences in CYP2B6 genotype and phenotype were observed in women associated with alteration in the metabolism of bupropion or efavirenz (Ilic et al., 2013; Naidoo et al., 2014). CYP3A4 activity and inducibility are also influenced by inter-genetic ancestry variability in midazolam clearance as observed in healthy age-matched males of South Asia compared to the Caucasian population (van Dyk et al., 2018). Also,

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Ethical Perspectives on Pharmacogenomic Profiling

CYP2C8, CYP2C9, CYP2C19, or VKORC1 variants are influenced by genetic ancestry/ethnic differences in the metabolism of several drugs as in the case of tamoxifen and warfarin (Shah and Gaedigk, 2018; Niinuma et al., 2014) or for Human Leukocyte Antigen (HLA) genes and carbamazepine-induced hypersensitivity reactions (Chung et al., 2004; Tangamornsuksan et al., 2013). Another example is ethanol metabolism in Chinese than that in Caucasians correlated to the higher acetaldehyde level linked to ALDH2 enzyme activity which is higher in Caucasians than that in Chinese or the case of the cardiovascular response of propranolol in Chinese than that in Caucasians or in the black population (Cai, 2020). Also, the treatment with primaquine in Africans compared to Caucasians is responsible for more frequent hemolysis (Shah and Gaedigk, 2018). The mortality rate of 20–50% in the gefitinibinduced interstitial lung disease Japanese population is more frequent than that of non-Japanese populations (Shah, 2016; Gemma et al., 2014).

2.02.4

Informed consent process in pharmacogenomics research

Informed consent (IC) is a process in which an individual gives voluntary agreement, based on an understanding of the relevant information, to participate in research or a clinical trial, or to undergo a particular medical intervention. The ability to express valid, free and IC is a fundamental prerequisite for the ethics of any clinical research. Therefore, the protection of the individual’s right to self-determination must be guaranteed in all areas in which an individual’s personality takes place. The IC process is characterized by five components: (1) competence, (2) disclosure, (3) understanding, (4) voluntariness, and (5) consent. The participant’s decision-making capacity depends on the individual’s ability to understand relevant information thus the IC process should take into consideration, among other things, the literacy level, language, and cultural expectations. A complete description of the research and/or intervention needs to be included in the information provided to research participants. In Fig. 1 are summarized some of the major challenges that must be attended by researcher to provide a complete and adequate IC. Important elements that should be provided during the IC process including but are not limited to the aims, methods, possible conflict of interest, potential benefits, and risks of the study. Specifically, in PGx research terminology and concepts may be intrinsically difficult to understand and the IC process should include also background information about the biological function of genes, how variations in genes could affect response to drug treatment, and how the PGx study may contribute to elucidate or take benefit from these mechanisms (Anderson et al., 2002). In order to ensure that information regarding the research is understood, additional materials, such as

Fig. 1

Challenges informed consent process.

Ethical Perspectives on Pharmacogenomic Profiling

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brochures and videos, could be provided to assist the subject in making decisions about participation. The IC should be an ongoing process throughout a study, and researchers should ensure that participants are made aware of any new information which might influence their decision about whether to take part or not. If the PGx study is a part of a clinical trial, it is necessary to clearly explain to the participant that he/she may or may not give his/her consent to the PGx, and can withdraw it at any time independently of his/ her inclusion in other parts of the clinical trial and without any negative consequences to his/her ongoing care or treatment (Howard et al., 2011).

2.02.4.1

Data sharing and storage

Each research participant has the right to request and obtain access to all data related to his/her own genetic information collected during a research study, including in several legislations genomic raw data generated from NGS platforms (Schickhardt et al., 2020). The results should be made available in a format that can understand. However, it should be made clear that this is only applicable when at least the functional significance of the identified genetic variant is known. PGx results that are preliminary in nature cannot be used to guide clinical management and must be confirmed by another study before they are of any clinical relevance and shared by sponsors or researchers. Although the clinical significance of some outcomes may be unknown, it should be made clear in the IC process that the absence of an outcome does not equate with absent or reduced disease risk and future research may change the clinical signs attributed to these results at the time of the study. At the end of the study, the research participant retains the right to request to destroy his/her biological samples as well as genomic and health-associated data or to allow their inclusion in a biobank (samples) or database (data). The privacy and storage of DNA and related genetic information are two crucial challenges involving both clinical trials and databanks. Specifically, the EMA has outlined five categories of coded samples and data with straight implications for the processing approach and level of confidentiality protection: (1) identified, (2) single coded, (3) double coded, (4) anonymized, and (5) anonymous. It is also important to note that any biological material that may be produced from specimens stored in a biobank is under the same regulation as the primary source (Peloquin et al., 2020). A clear example of the relevance of this issue comes from the controversy that has emerged after the publication and repository of the HeLa genome, a cell line that was isolated and immortalized by Henrietta Lacks, a 31-year-old African American woman with an aggressive form of cervical cancer 70 years ago. The availability of sequence information from HeLa could pose an identification risk to Henrietta Lacks’ family members. To avoid this, NIH policy in 2013 requires researchers to request access to full genome sequence data from HeLa cells (Wolinetz and Collins, 2020). The research participant must be informed about any sharing of biological samples and/or data, or summary results to investigators outside of the study team. If the participant decides not to keep the information relating to their genetic profile anonymous, then they will have to give new consent for future projects. The IC process should include a discussion of any potential re-contact of the participant for collection of additional information or requests to participate in other studies. In addition, subjects should have the option of limiting the future use of their biological samples to specific genetic research. The participant’s right to these different options can be realized through the inclusion in the consent of multiple levels of study acceptance with distinct signatures that allow the subject choice for example, a participant may choose to be enrolled in the proposed study, but disagree that, at the end of this, his/her biological samples will be used for future studies and therefore signing only IC sections that refer to the study proposed.

2.02.4.2

Confidentiality

A significant challenge of storing human biological samples is confidentiality ensured to the participant of a research study, who sign in the IC to donate their samples to a biobank. The data derived from these types of samples are, if disclosed to third parties (i.e., employees, agents, contractors), a potential source of stigma and discrimination. In this context, the need to guarantee confidentiality makes the coding of samples mandatory (as reported in the previous section) as well as the restriction of third-party access. Generally, samples stored in biobank were transferred to researchers for later studies in anonymized or coded form, leaving the possibility of decoding exclusively to the biobank managers. However, in parallel with the advancement of genomics, an explosion of bioinformatics analysis of omics data has been observed. Today it is possible to freely download large datasets of omics data and to integrate it using appropriate algorithms. Research participants should be made aware that absolute confidentiality cannot always be ensured during a research study for several reasons. First, even with good governance structures, there can be a risk, albeit minimal, that unauthorized third parties may come into possession and disclose sensitive information. Secondly, although research protocols provide for the use of anonymous or coded samples, it is possible that researchers or third parties can trace the identity of a participant, by crossing experimental data with clinical data and this due to the advancement of bioinformatics tools. This specific risk can increase in the case of small institutions or in the case where research concerns very rare diseases. Although it is possible in some research contexts to perform similar data pooling, the empirical risk of identifying a single individual would remain. It should also be considered that the risk of loss of confidentiality is intrinsic to the use of technological platforms, especially the whole exome sequencing (WES) and the whole genome sequencing (WGS) as the consequence of more available “variable” to correlate and the uniqueness of the genomic profile of an individual. Third, the disclosure of confidential data may be specifically requested by law. For instance, some legal jurisdictions now require that certain communicable diseases or proof of child abuse or neglect be reported to the appropriate agencies (https://www.childwelfare.gov/topics/systemwide/laws-policies/statutes/crossreporting/). Such and similar limitations on the ability to maintain confidentiality must be anticipated and conveyed to potential participants. Due to these limitations, research participants fear that their own information will be used by employers and health insurers to

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Ethical Perspectives on Pharmacogenomic Profiling

discriminate against them. In 2008 US congress passed the Genetic Information Nondiscrimination Act (GINA), a law that protects individuals against genetic discrimination in health insurance (but not long-term care insurance) and employment (Joly et al., 2020). Similarly, data protection in the EU was guaranteed by the General Data Protection Regulation entered into force in 2018 which aims to increase the harmonization of personal data protection law in healthcare and medical research across the EU and EEA (Cornock, 2018).

2.02.4.3

Incidental findings and familial implication

Changes in biomedical research technology have highlighted ethical questions about the management, treatment, and regulation of so-called “incidental findings” (IFs), defined as “a finding concerning an individual research participant that has potential health or reproductive importance and is discovered in the course of conducting the research, but is outside the objectives of the study.” The possibility that, during any research study, a scientific investigation could inadvertently lead to the acquisition of information that is unrelated to the objectives of the study, or that inference could be made by the researcher about this information due to the nature of the collected personal data, should be made explicit to participants as a possible risk. These disclosures may reveal aspects of privacy that are particularly sensitive or to which subjects are vulnerable due to condition, age, or personal history. Numerous legislative and policy initiatives have attempted to clarify the responsibilities of researchers in the return of IFs. However, divergent regulatory frames create additional complications for international research partnerships, prompting requests for harmonization (Thorogood et al., 2019). A very challenging condition is when information about participant’ personal life reveals that he/she has been subjected to (or committed) crimes, abuse, or other forms of violation of a fundamental human right. The researcher must communicate to participants, prior to the start of the study, the limits of the ability to ensure confidentiality in the cases of violence or abuse that require intervention by all the competent authorities. The benefits of receiving IFs, consisting of the ability to take therapeutic or preventive action, as well as the ability to make relevant existential choices, far outweigh the potential harms of receiving them, such as possible psychological repercussions or the increased risk of becoming the subject of discrimination if IFs were disclosed. Therefore, disclosure of IFs is an ethical duty for the researcher based on both the principle of beneficence towards research participants and the duty of reciprocity, arising from the professional relationship between the researcher and the research participant and the privileged access granted by the latter to information from the most intimate part of their sphere. In particular, to report IFs the three condition of analytical validity, clinical relevance and clinical actionability must coexist (Table 1). On the other hand, there are equally important arguments in support of non-disclosure of IFs when they do not present a net benefit to participants. For example, the finding of genetic variants of uncertain significance or variants associated with non-serious clinical conditions. IF related to genetic predispositions shared with family members that can potentially lead to the development of diseases for which there is the possibility of taking therapeutic or preventive action or with respect to which important reproductive or existential choices can be made. In this situation, the principle of beneficence towards family members should be able to prevail over the exercise of the research participant’s right not to know. There is a need to adequately design IC processes in order to ensure that the research participant can exercise his/her right to be informed of any IF or, conversely, his/her right not to know. In clinical setting, defined list of genes known to be associated to specific conditions are available to manage IFs arising during clinical diagnostic tests, although the boundaries between clinical and research settings are becoming more blurred. For example, in 2021, the American College of Medical Genetics and Genomics (ACMG) released a list of 73 genes for which secondary findings are reported linked with a variety of inherited disorders related to cancer, metabolic, and cardiovascular disease (Miller et al., 2021). The 73 genes were chosen because of defined clinical features, the opportunity for early diagnosis, feasible clinical genetic testing, and effective intervention or treatment. The variants reported are known to cause disease, while variants of unknown significance, whose influence in disease is currently unclear, are not reported. The ryanodine receptor gene (RYR1) is the only pharmacogene included in the ACMG list. People with RYR1 mutations may experience malignant hyperthermia if they receive particular anesthetic drugs (Sambuughin et al., 2001), but particular mutations of this gene have also been associated with a group of myopathy diseases (BharuchaGoebel et al., 2013). The impact of PGx IFs was assessed in a study by Lee et al., where authors are analyzed SNPs array data from 1101 individuals derived from 308 families and research exome sequence data from 645 individuals derived from 158 families. For the 868 annotated SNPs listed in the PharmGKB with pharmacological implications, 949 independent PGx findings using the SNP chip and exome sequence data were identified and found that each individual had at least one. Nine participants showed incidental PGx variants associated with altered drug efficacy (Lee et al., 2016).

Table 1

Conditions required for reporting incidental findings.

Analytical validity Clinical relevance Clinical actionability

A finding is analytically valid if it accurately and reliably identifies, within the standards normally applied in research, a genomic sequence of nucleotides. A finding has clinical relevance if it reveals a known risk factor for a disease and/or if it has significant implications for reproductive or lifestyle decisions. A finding is actionable if it is possible to intervene preventively or therapeutically, or through any other action that may change the clinical course of the disease or medical reproductive condition.

Ethical Perspectives on Pharmacogenomic Profiling

2.02.5

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Considerations for population studies

A particular area of genomic research is that aimed at population genetic studies to discover the genetic differences between different geographically well-defined groups or populations. It is well known that geographically isolated groups have a particular genetic architecture that, thanks to phenomena such as gene drift and natural selection, shows enrichment of variants that are rare in other populations. For this reason, they represent ideal models for the study of multifactorial diseases as well as autosomal recessive diseases. Research on genetic differences between groups or populations, however, has raised many ethical and legal issues. For example, emphasizing biological differences between ethnic groups has led in the past to assumptions of superiority and inferiority between groups, and in practice has led to stigmatization and discrimination (McGregor, 2007). In this context, it is therefore important that research protocols, primarily based on the protection of the individual, contemplate the specific benefits and risks of the group, as well as an appropriate process of IC. To make that, it is essential to first define what constitutes a group or population within any research project. Another important aspect concerns the risk that an individual has of being identified as belonging to a specific group if their genomic profile data is disseminated to third parties. Researchers enrolling members of identifiable groups should avoid any potential group risks and provide information about these challenges during the IC process, especially protecting individuals who are vulnerable to such risks within a group.

2.02.6

Studies involving vulnerable groups

In Barcelona Declaration, “vulnerability” is considered as a fundamental principle of bioethics together with autonomy, integrity, and dignity. Vulnerable is every person “whose autonomy or dignity or integrity are capable of being threatened” (https://hrcak.srce. hr/file/58331). From this emerges how vulnerability is a very broad concept that is not only related to an individual’s ability to provide informed consent, but also involves physical, psychological, and social aspects. In some cases, being vulnerable implies not being able to know how to protect one’s interests in part or completely. This may arise when persons have relative or absolute impairments in decision-making ability, education, resources, strength, or other attributes necessary to protect their interests. However, in other instances, vulnerability can be attributed to external factors that make the individual less vigilant or responsive to their interests. This can occur when people are marginalized, stigmatized, or excluded from society or are victims of prejudice that increases the likelihood that others will put their interests at risk, either intentionally or unintentionally. According to the Declaration of Helsinki, “medical research with a vulnerable group is only justified if the research is responsive to the health needs or priorities of this group and the research cannot be carried out in a non-vulnerable group. In addition, this group should stand to benefit from the knowledge, practices or interventions that result from the research” (https://www.wma.net/policies-post/ wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/). While ethics committees apply for special safeguards for prospective participants which will be collectively enrolled in a particular project, researchers must also consider the vulnerability of each participant and thus take appropriate measures to mitigate underlying factors. Indeed, a conventional approach in evaluating vulnerability in the context of research has been to define entire classes of individuals as vulnerable. However, several characteristics may also coexist making some individuals to be more vulnerable than others. This is highly contextdependent. For example, a person who is illiterate, is marginalized because of their social status or behavior, or who lives in an environment of authority can have multiple factors that makes them vulnerable. Individuals for whom the decision to volunteer in a clinical trial may be unduly influenced by the expectation, whether justified or not, of benefits to participation, or by possible retaliatory actions by hierarchically superior individuals if they refuse to participate.

2.02.6.1

Children participants

In research, when participants are children, the IC process becomes complex because pediatric participants are considered vulnerable subjects who are not of legal age for independent, autonomous IC to the treatments or procedures involved in the study. In this specific context, IC is more properly understood as a combination of informed parental permission and (when appropriate) the child’s assent. Before requesting IC for the participation of children in a research study, the following important conditions must be met

• • • • • • •

the research study is directly associated with a clinical condition from which the children suffer; there are scientific reasons to believe that participation in the research study will directly benefit the child more than the associated risks; the research study is designed to minimize pain, discomfort, fear, and any other risks, related to the child’s illness and stage of development; parental responsibility and rights must be considered for both parents children who are capable of understanding and making an informed decision must be both informed and involved in the decision-making process in a manner appropriate to their age and abilities, including considerations for each child’s environmental context, experiences and evolving capacities; methods of information must be differentiated according to age; IC consent must be obtained from both parents or guardian;

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• •

Ethical Perspectives on Pharmacogenomic Profiling the clear desire of a child who is competent to form his/her own opinion and evaluate the provided material to decline participation in or withdraw from the research at any time must be asserted; if the child reaches the age of majority during a research study, the specific IC is mandatory for the subject to continue to participate.

This principle is widely recognized, although it is sometimes difficult to determine whether absolute respect for the child’s opinion is appropriate and what weight to give to any differing views of the child compared with the parents. If appropriate, it is also possible to seek the help of a specialist to assess the child’s level of competence. In this context, the information process must take into account the fact that different actors must be involved, from the beginnings of the research study, including both parents/guardians and the child, in order to allow everyone to be informed and provide an opinion. On the other hand, in the case of a child who is not able to give his or her opinion, it is recommended to only deliver information to the child that allows him or her to understand what will happen after the parents or guardian have given their consent to participate.

2.02.6.2

Subjects who are unable to give consent

The definitions of “ability” and “competence,” as well as the modalities for their quantification, have been discussed extensively in the literature. In general, the ability to give IC is based on skills demonstrated in four main domains:

• • • •

the ability to communicate a choice: it can be considered an essential prerequisite; if it is missing, there is no need to consider other capabilities; understanding of the information concerning the nature of the patient’s disease, the type of the proposed treatment, potential risks and benefits related to the research or alternative approaches, including the possibility of no treatment; the ability to give due weight to the situation and its possible consequences: it differs from understanding, for example, a subject could understand the characteristics and consequences of the disease, but not adequately assess the fact that he himself is sick and that information is relevant to status; the ability to rationally use the available information to make a decision. The subject should be able to develop a logical process comprising the following steps: (1) define the problem; (2) identify alternative options; (3) imagine the consequences; (4) estimate the probability that the consequences will occur; (5) evaluate the desirability of the consequences based on one’s own scale of values; (6) make a final decision. These abilities refer to the way the choice is made, not to its nature: the fact that the subject makes a decision is not a sufficient requirement to consider the subject as competent. The abilities defined above may be impaired due to medical conditions or diseases, including chronic diseases, drugs, or impaired cognitive development.

The inability can be temporary or permanent, in relation to the specific condition. The ability to give IC is a continuum: it cannot be circumscribed only to the two conditions of presence or absence. A subject may indeed have an alteration of cognitive status, but this may not directly imply that the same person does not have sufficient skills to decide whether or not to participate in research. It is therefore advisable to use suitable tools to try to assess the ability of potential participants to communicate their IC to the study. When a subject is unable to express valid consent, the person who must be involved in the information process and from whom IC must be obtained is the legal representative who should be authorized in accordance with applicable law and in line with the recommendations of the World Health Organization and the Declaration of Helsinki. When benefits and risks of research involving people who cannot give their free IC were evaluated, it is necessary to balance two different needs: to protect the rights and well-being of individuals who, due to their disease, have lost or had never had the ability to make an informed decision, and at the same time guarantee the possibility of carrying out research activities able to identify strategies to prevent and treat the disease from which these people suffer. Withdrawing these patients from clinical trials would reduce the possibility of benefits of new therapies for future patients.

2.02.7

Clinical trial design for pharmacogenomic research

The appropriateness of a clinical trial design or conduction is mandatory in respect of an ethical issue for which all participants enrolled in a study have had to ameliorate perspective of response to the endpoint and expectations of the study, for the rights and interests of the individuals and society in general. These concerns are correlated to the impact of incentive strategies on consent and participation in research and to potential privacy concerns that may arise for technologically developed tools. Particular attention should be applied to the implications of data donation and receiving financial incentives in the case of genetic information shared with other members of their own family since genetic data carry family connections that should be taken into consideration. If the involvement of research participants is accompanied by no reasonable prospect of the novel contributes to scientific enrichment and knowledge to drug exposure, no justification can be considered. In particular, the design of a clinical trial should provide the data required to facilitate the choice of biomarker tested as an opportunity to stratify patients for a specific drug in a specific clinical context. The design and conduction of a clinical trial is a lengthy and costly process in which subject enrollment and retention are very important for cost and length reduction (Derenzo and Moss, 2006). Good quality research, Good Clinical Practice (GCP) guidelines, and International Conference on Harmonization (ICH) recommendations are the bases to consider during the initial steps of a well-designed clinical trial, for determining the clinical and cost-effectiveness of health care systems, for

Ethical Perspectives on Pharmacogenomic Profiling

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recruitment of sufficient participants to test hypotheses with confidence and lower bias and risks. Another important point to be considered during the design is the different sources of biological samples including cells, biopsy specimens, tissues removed during surgery or at death, and a sample of nucleic acid. Especially DNA or RNA sample and data storage are becoming a growing area of clinical research in which continuous advancements in the technological field could raise important ethical and legal issues related to the harvest and storage samples, and the future research aim to apply new research methodologies. All information must be explained and inserted in the specific informed consent in respect of rules established by the National Bioethics the National Bioethics Advisory Commission which highlights these ethical concerns in clinical research. All the efforts engendered by the completion of the Human Genome Project characterize several clinical research today in the inclusion of genetic components as endpoints because it recognized the value of relating human physiology and disease to inborn genetic determinants. This heightened attention during the preliminary phase of study genetic design is needed to determine most appropriately and effectively address in protocols and consent documents according to evolving recommendation in this particular field of research (Clayton et al., 2018; Shendure et al., 2019). In biomedical research, there is a distinction between the validation of a biomarker used for a medical product development tool and of a biomarker as a clinical test based on it for clinical purposes and decisions. In the FDA-NIH Biomarkers, Endpoints, and other Tools (BEST) glossary, available at https://www.ncbi.nlm.nih.gov/books/ NBK326791/, for example, medical product development tools are defined as “methods, materials, or measurements used to assess the effectiveness, safety, or performance of a medical product.” In particular, biomarkers used for better eligibility for a clinical trial, biomarkers used for early detection of drug-related adverse events, or biomarkers used as surrogate endpoints are considered biomarker-based tools used in clinical trials. Specifically, the surrogate biomarkers are a “laboratory measurement or a physical sign used as a substitute for a direct measure of how a patient feels, functions, or survives” and “changes induced by a therapy on a surrogate endpoint are expected to reflect changes in a clinically meaningful endpoint.” A table of surrogate endpoints considered important for drug approval or licensure is reported by FDA-NIH BEST, according to a case-by-case basis. In the case of medical product development, the FDA identifies the process of “qualification” to describe “a conclusion, based on a formal regulatory process within the stated context of use which can be relied upon to have a specific interpretation, application, and regulatory review. Qualification and validation are similar concepts but the first has specific regulatory requirements and implications. However, the validation of biomarker-based tests for clinical use and qualification of a biomarker as a medical product development tool accepted by regulatory authorities have several aspects in common and different.

2.02.7.1

Pharmacogenomics biomarker validation studies

The biomarker validation study represents a key step for the translation of the bench findings into clinical practice for tailored prescription or to accelerate the development of new drugs and other medical products. It includes both retrospective and prospective study design evaluation with large sample size and analysis of results to test its clinical utility in the relevant population and the impact on the disease burden within the stratified population. The agnostic discovery of a potential biomarker through highthroughput approaches (genome-wide or targeted genotyping) in a selected population enrolled in specific clinical conditions (learning set) starts in a clinical research context. An example of biomarkers discovery is case/control studies design in which is possible to compare patients experiencing toxicity vs. no-toxicity treated matched controls (Di Martino et al., 2011a, b, 2016; Arbitrio et al., 2016a, 2019). The internal validation (by cross-validation-based methods), the confirmation of technical methodology through orthogonal approaches, and the external validation in an independent patient series (validation set) are mandatory steps for moving towards the biomarker validation process. In these phases of discovery and validation very important are the study design with its primary endpoints, data analysis, outcome ascertainment, and assaying process and biomarker reproducibility. Each step of biomarker discovery could be the key source of bias from an error in study design, to the calculation of sample size or the choice of invalid surrogate endpoints from retrospective studies. Common study designs for PGx biomarker validation are represented: – by subject randomization for exploratory purposes (i.e., for the identification of a safety marker), independently of the results of PGx biomarker screening, as in the case of sample collection during a clinical trial for future evaluations. In this study design, any imbalance in enrollment of subjects between arms of treatment may be responsible for bias in biomarker data evaluation due to a small number of collected samples due to a limited number of signed consent for the analysis for future investigations which will be conducted after a later data from completion of the study with retrospective correlations (http://www.ema.europa.eu/ docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003864.pdf). Regulatory authorities (EMA, FDA or PMDA) have encouraged this kind of sample collection during clinical trials with a retrospective correlation of biomarkers and a revision of the same drug labeling was obtained as in the case of KRAS, EGFR (Maliepaard et al., 2013; Becquemont et al., 2011). This study design could be indicated to examine the correlation of genetic polymorphisms and drug PK with exploratory perspectives especially when high interindividual variability due to the polymorphic variants in ADME genes are expected. – by case-control study, with agnostic perspectives in the early discovery phase, in which it allowed the comparison of the prevalence of biomarkers between two matched groups distinguished by the presence of the outcome of interest individuals versus its absence. The risk of “convenient” samples collection or bias in individual selection operated according to factors not specifically correlated to pathologic conditions such as age, gender, and other biological parameters linked to the investigated biomarkers (http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2012/07/WC500129606.pdf), or as in the case of multi-center studies a wrong interpretation of enrollment criteria or discrepancy in population and sampling

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handling procedures. Moreover, the statistical bias due to sample size and the need for multiple comparison tests for avoiding false discovery or the inappropriate selection of control subjects is also a major cause of systemic bias (Zheng, 2018). – by clinical trials designed according to PGx-biomarkers stratification for patient selection, drug activity or toxicity, disease condition, or prognosis to evaluate the clinical utility of a PGx-biomarker in drug prescription and subsequent randomization in study drug/control arms according to the research of presence or absence of biomarker. This design although seems rigorous in stratifying the fit population for a drug and allows sample collection for future use, shows limits in the application in studies with a large number of treatments. – by enriched design trials in which randomization is done after the results of PGx-biomarker screening including only patients with the targeting PGx-biomarker as in the case of anti-cancer drugs also within phase I multi-institutional platforms as a proofof-concept purpose (Ishiguro et al., 2013). In this study design is important to establish the cut-off value of the biomarker for patient’s enrollment and selection of subjects who are likely to have drug efficacy with the exclusion of those who have a high risk of toxicity. Also, this study design is limited to assess the drug efficacy in PGx-biomarker positive subjects without considering efficacy/safety in PGx-biomarker negative subjects (Otsubo et al., 2013). Even here, there is the possibility to collect and store the biological samples for future investigations after obtaining informed consent.

2.02.7.2

Pharmacogenomics and drugs development studies

To allow a better and safer drug development, the impact of PGx approaches implementation during preclinical phases and early clinical trials might be important in order to exclude or to include particular genomically defined groups on the basis of pre-emptive test of PGx-biomarker. On these bases, high relevance has also the design and analysis of clinical trials beyond the avoidance of drug ADRs in selected genomic groups in late-stage trials or the opportunity to evaluate drug development targeting on the basis of the better response to treatment. Clinical trials designed and conducted according to genomic criteria might have technical and ethical issues about their reliability and validity. In fact, many problems could be due to a small subgroup of patient samples, a major risk to lack detection of rare ADRs, sample bias, minor consistency in controlling for PGx-biomarker in multicenter trials, and problems in replicating genetic association studies (Issa, 2002; Martin et al., 2006). Cautions are tacking by regulatory authorities in the design of clinical trials also in the case of early, preclinical studies or prospectively in Phase I trials with the aim to increase the chances of a drug being safe because this strategy could be accompanied to the risk of missing serious ADRs increasing the possibility of unsafe drugs reaching the market. Probably, companies will conduct PGx evaluation studies to ensure that selection of sample populations may be representative of the general population for the activity of specific alleles associated with drug metabolism (e.g., CYP2D6). This approach could reduce the risk of trial bias or avoid the rescue of a drug at a later stage of development with the improvement of its safety profile. However, if a trial is conducted on selection based on genotype this element could be of help for tight clinical governance and improved post-marketing surveillance because a drug developed in this way would only be licensed for use in specific subpopulations and in specialist secondary and tertiary settings. Alternatively, during a trial is possible to verify whether genotype-guided treatment is beneficial over standard care. Several limitations derived from the assessment of rare ADRs are represented by the large sample size required and its related high costs. In these cases, a valid alternative trial design, more efficient in terms of time, money, and/or sample size, is the adaptive trial design, which allows a planning modification prospectively during its progress without undermining the validity and integrity of the trial. This kind of study combines the biomarker identification and classifier development to the selection of candidate patients with the opportunity to reduce the number of patients exposed to an inferior/harmful treatment and allows to test in a statistically valid manner drug efficacy on a small subgroup for phase III clinical trials (Van Der Baan et al., 2012). Another opportunity is the introduction of PGx-biomarker evaluation in later stages of phase II and III trials to improve efficacy. In fact, in prospective studies, it is possible to test new drugs in a selected population believed to be “good responders” in order to increase the chance of a drug reaching the market. Moreover, the PGx approach might have a retrospectively use in the identification of a genomic subgroup who are particularly “good responders” to a drug whose overall benefit across the whole population is shown to be marginal. In these cases, the selected drug will be licensed purely for use in a specific genomic group. An example of this kind of approach is the herceptin (trastuzumab) for breast cancer therapy in patients whose tumors over express the HER-2 gene product (Martin et al., 2006).

2.02.7.3

Pharmacogenomic biomarkers test for clinical use

After the early stage of biomarker agnostic discovery and subsequent validation in clinical trials, is important and necessary to move towards the development of specific assays as companion diagnostics (CDx) parallelly with drug development. The long and complex process of implementation of a molecular genetic test for diagnostic use involves several steps of assessment and validation according to the ACCE framework (https://www.cdc.gov/genomics/gtesting/acce/index.htm). The other important issues to define after the decision to set up a diagnostic test are the choice of technology to be used and the suitable laboratory in which provides the process of test execution in terms of accuracy and adherence to required diagnostic standards. The key components are analytical validation, clinical validation, clinical utility, medical utility, commercial adoption, and consideration of the ethical, legal, and social implications of the test. Analytical Validation: is the phase during which is important to establish the performance characteristics of the performed test and its ability to measure the PGx-biomarker genotypes in an accurate and reliable way. It is important in the case of a specific drug

Ethical Perspectives on Pharmacogenomic Profiling

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to allow the identification of the variants to interrogate taking into account eventually the ethnic groups tested to maximize PGxbiomarker clinical validity. Clinical Validation: is the phase during which to test the ability of the assay to detect or predict the clinical disorder or the association genotype/phenotype related to the clinical condition for the drug’s intended use. The test has to demonstrate that the result is correlated to a clinical outcome or measure of its usefulness in the clinic in respect to sensitivity, specificity, precision, and reproducibility. Clinical Utility: is the phase during which to demonstrate that the application of the test, according to the intended use, provides improvements in treatment decision making with an important relapse in terms of benefit-to-risk balance for the patient compared to the current standard of care. Medical Utility: is the phase during which the PGx-biomarker related information tested by the assay typically enters clinical practice and whose routine application helps clinicians in their decision making. Commercial Adoption: is the phase during which PGx-biomarker test and treatment are associated and integrated into the clinical routine with the opportunity to have demonstrated health/cost-benefit from the routine use of this approach. In this case, it will be possible that standardized guidelines will be reported in the label of the specific treatment to allow better practicing clinicians. The conclusion of the formal regulatory process for a medical product development tool is defined with the term “qualification” by the FDA describing “a conclusion, based on a formal regulatory process, that within the stated context of use, a medical product development tool can be relied upon to have a specific interpretation and application in medical product development and regulatory review” (FDA-NIH BEST). Although, until now, regulatory oversight of many biomarker tests is minimal, exist specific requirements for the laboratory and the technical methodology for the test performance, when the assay is introduced in clinical practice. For example, Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory can develop PGx tests, according to FDA recommendation for their approval without clinical claims, providing full evidence of clinical validity and utility before the use in clinical laboratories (https:// www.fda.gov/regulatory-information/search-fda-guidance-documents/recommendations-clinical-laboratory-improvementamendments-1988-clia-waiver-applications). According to CLIA regulations the laboratory offering the test must be certified to meet Good Laboratory Practice (GLP) standards and appropriate quality assurance procedures with adequately trained personnel.

2.02.8

Barriers for pharmacogenomics implementation

The PGx test could be requested reactively (‘as needed’ on the time of specific drug prescription or after the occurrence of adverse drug reaction, ADRs) or pre-emptively (the test is requested independently for future prescriptions). The reactive approach is more frequently used in clinical practice and is accomplished through a single gene test when the decision on specific drug prescription is mandatory or due to ADR. Instead, the preemptive execution of a broad PGx screening, available in patient’s medical record, should be more user-friendly and practical for use in any drug prescribing decision. The amount of information returned by a single genetic test is manageable by physician and reimbursements cover the entire process. However, a single drug can be influenced by more than one biomarker. For example, prasugrel, a platelet inhibitor used to prevent thrombotic cardiovascular events in patients with acute coronary syndrome, is metabolized to its active metabolite (R-138727) by different hepatic cytochrome P450, primarily CYP3A5 and CYP2B6, less CYP2C9 and CYP2C19. At the same time, it has been reported that about 95% of patients has at least one “actionable” genotype (Van Driest et al., 2014). The execution of multiple tests using single assays require several days and its application in clinical practice risks delays of medical treatment. In the last years, the use of pre-emptive PGx screening, using multiple gene panels, have emerged to overcome the limitation of the single test. The main goal of these screenings is to collect in short time genetic data to generate a complete PGx portrait of the patient for inclusion in the personal electronic health record available to the clinician as needed. Despite these efforts, the application of preemptive PGx test in a routine patient care is limited by several barriers. Firstly, for acceptance, pre-emptive PGx strategy needs straight evidence demonstrating clear clinical benefit in order to afford corporate development. Presently, while information about the cost-effectiveness of PGx for single gene-one drug interactions is established, limited basic knowledge and FDA-approved technology are currently available for multigene/drug interaction based on preemptive strategies (Krebs and Milani, 2019). In addition, the use of preemtive PGx testing generates a considerable amount of data that must then be reviewed by the clinician and integrated with other clinical parameters in order to guide the choice of the most appropriate therapy for the patient. However, it must be considered that not all clinicians are familiar with the genetic data, so the use of bioinformatics tools, such as clinical decision support software, and educational programs are necessary. Clinical programs including multiplexed genetic testing to implement the preemptive PGx approach in the clinical practice, are ongoing in the United States (US), Europe, and Asia as the PGRN (Pharmacogenomics Research Network) translation PGx program (Shuldiner et al., 2013), PREDICT (The Pharmacogenomics Resource for Enhanced Decisions in Care and Treatment) (Pulley et al., 2012), eMERGE (Gottesman et al., 2013), SEAPharm (South East Asian Pharmacogenomics Research Network) (https://www.ims.riken. jp/english/projects/pj09.php.), IGNITE (Implementing GeNomics In practice) (Weitzel et al., 2016) and U-PGx (Ubiquitous Pharmacogenomics) (Cecchin et al., 2017). Moreover, several randomized clinical trials, including multiplexed genetic testing, are ongoing. In these studies, benefit of preemptive screening is evaluated comparing PGx-driven treatment vs standard of care (without PGx information).

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2.02.9

Ethical Perspectives on Pharmacogenomic Profiling

Guidelines in pharmacogenomics

The availability of evidence-based PGx guidelines is very important for the translation into daily clinical practice of PGx knowledge to allow personalized pharmacotherapy. Today, the lack of standardized guidelines for the association of specific drugs and genotypes or predicted phenotypes with PGx recommendation represent an important obstacle for PGx implementation together with ethical, social, and legal barriers. The role of the regulatory agency is to evaluate the relationships between the PGx-biomarker profile and the efficacy and/or safety of a drug in order to establish shreds of evidence during the phase of drug development and for standardized recommendation in clinical practice. In particular, International regulatory agencies (EMA, FDA, and PMDA) have issued guidelines harmonized in part according to the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) as in the case of ICH E15 (https://www.ema.europa.eu/en/ich-e15-definitionsgenomic-biomarkers-pharmacogenomics-pharmacogenetics-genomic-data-sample-coding) and ICH E16, (https://www.ema. europa.eu/en/ich-e16-genomic-biomarkers-related-drug-response-context-structure-format-qualification-submissions) which provide international definitions of PGx terminologies to avoid wrong interpretations between each agency. Other consortium experts in both PGx and clinical context created detailed drug-dosing guidelines for physician interpretation of genetic results derived from execution of pre-emptive genetic tests and implementation of PGx for all stakeholders. This is the case of Clinical Pharmacogenetics Implementation Consortium (CPIC), Royal Dutch Association for the Advancement of Pharmacogenetics Working Group (DPWG), The Canadian Pharmacogenomics Network for Drug Safety (CPNDS) and other professional societies. All guidelines, written in a standardized format giving background information on the gene, variant, drug correlations, association genotypephenotype, and specific therapeutic recommendations, derived from an extensive literature review. In this context, PharmGKB staff and other CPIC members collaborate for critical revision on all sources of PGx data according to quality and quantity of evidence as well as on drugs’ benefit or undesirable effects. Different levels of gene-drug pairs are reported for most dosing recommendations. On the other hand, other consortiums such as NIH Pharmacogenomics Global Research Network (PGRN, https://www.pgrn.org) are focused on promotion for research in PGx and disclose and communication of new genomic variants influencing drug’s efficacytoxicity for translational perspectives in precision medicine.

2.02.10 Legal and social issues The introduction in research and then in clinical practice of genetic testing has elevated several and general ethical but also social and legal problems. Near the ethical concerns on the necessity to make sure consent for the utilization and storage of DNA and genetic information as above discussed, legal and social problems with confidentiality and privacy, the storage and use of private genetic information by third parties, and therefore the potential for discrimination and stigmatization are important aspects extensively discussed by many authors and mentioned but not covered intimately here (Rothstein and Epps, 2001; Robertson, 2001). Variety of social and ethical issues specific to PGx and genetic testing are of great public alarm and focus of policy interest in the last years, as reported in the document of NIH Task Force on Genetic Testing, the Human Genetics Commission (UK), and the new EU Regulation on in vitro diagnostic (IVD) medical devices (IVDR), published on 5 April 2017 and that will apply from 26 May 2022 (32017R0746 - EN - EUR-Lex - EUR-Lex https://eur-lex.europa.eu/eli/reg/2017), and subsequent amending (EU Regulation 2020/561) regarding the production of PGx tests, the classification of PGx tests, and companion diagnostics which impact the manufacturers and the different economic operators involved. These reports introduce severe and stringent rules regarding different procedures on e.g. the conformity assessment procedures, traceability and transparency rules, safety and performance, and surveillance and vigilance systems. Moreover, all the social and ethical questions concern the IC for processing a genetic test, the disclosure and reporting of unexpected findings or the protection of genetic data remain opened and discussed aspects in the literature. These issues need an update of the legislation in all countries to address the concerns related to the implementation of PGx tests in the clinic. Moreover, another important issue for PGx with large-scale tests is the need for the obligation to inform about the use of algorithmic processing of big data and on the modalities by which the health worker gives the results of such tests. Also, the exclusion of specific groups from clinical trials, such as women, children, elderly people, and ethnic minorities on the basis of genotype raises important concerns about the loss of benefits and unfair representation from trial enrollment with important impact on the study (Moldrup, 2001; Mastroianni et al., 1994) as well as technical issues related the reliability and validity of the design of the trial are done in according to genomic criteria. Another important social issue is represented by the stratification of the population according to the sub classification of diseases into different diagnostic categories according to responses to selected drugs and their classification as “good responders” or “non-responders” or “difficult to treat” with the consequent implications for patients. In this context, another aspect that should be attentive and regulated is the potential risk of inappropriate off-label or non-label prescription and more stringent post-marketing surveillance. This is the case of PGx drugs with a restricted market as a consequence of the risk of serious ADRs in a specific population for which their successful use requires careful control so in that people with the wrong genotype cannot be administered while prescription of inappropriate off-label or non-label use of drugs might be a major concern. Moreover, when there are subjects to whom no effective therapy is available or existing therapy is inadequate, new products should not be developed for patients with the “wrong genotype” or considered for a prescription in a small population only for industrial speculation.

Ethical Perspectives on Pharmacogenomic Profiling

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2.02.11 Conclusion In this chapter, we have analyzed the most important concerns about ethical aspects related to PGx implementation towards clinical practice together with consequent legal and social issues, strictly correlated (Fig. 2). Some of the problems are in common with all clinical experimentations regarding the introduction in the study design of genetic tests and biological sample management, while others are specific for PGx advantages about the choice of fit drug and dosage prescription. All the efforts fielded so far should achieve an assessment of the policies, regulation, protection, standardized interpretation, and other measures that are required by genomic sample management with the aim to ensure the greatest social benefit from the technology while minimizing the risks to public health, civil liberties and the right for everybody to have equal opportunities in health care. In particular, it will be important to demonstrate the clinical advantages of the application and prescription of PGx tests for the most widely used drugs, especially in cancer disease in which many compounds have a narrow therapeutic index. According to the principle of justice is important that public policymakers and health care providers have to investigate how to fund clinical research on the application of the PGx test with the objective to promote cost-effective use in clinical practice. Another aspect to avoid is the case of not very strong genetic association in disease where there is no therapeutic alternative, a condition which creates ethical objection related to exclusion from treatment with the risk to be a matter of professional judgment. It should be fundamental to carefully evaluate and establish a shred of strong evidence, analysis approaches that appropriately evaluate the risks and benefits for the use of that PGxbiomarker in a specific context of action, availability of sufficient data, and standardized guidelines to allow a unique strategy of treatment. In this context, the quality and exhaustiveness of informed consent might protect against inevitable conflicts in clinical practice regarding PGx testing as discussed. From all these concerns it appears clear that several measures are needed to address different aspects treated in this chapter. They might include the tight regulation of drugs PGx-based clinical development; regulatory guidelines utilized for a better encouragement data sharing between all stakeholders; strict oversight to obtain rigorous validation of new tests; clear and detailed drug labeling and clinical governance measures to prevent off-label measure; improvement of postmarketing surveillance and the PGx-training for doctors for a better comprehension of the potentiality of PGx test for better precision medicine. The opportunity to have PGx information in the licensing of new drugs might represent a chance for the approval of another compound of the same class having activity against the same target of the drug with ADR data. Obviously, all PGx based information will contribute to the efficacy of well-established prescription drugs more rational and evidence-based with a consequence in potential cost savings. In particular, it allows lower genetically based ADRs and the use of ineffective drugs in selected patients although the pharmaceutical industry, differently by all the other stakeholders, might be several limits in offering genotyping that might limit their interest in marketing for a particular drug. However, a robust regulatory framework for optimal control of the general use of genetic testing will represent an important step toward the widespread use of PGx technology in daily clinical practice avoiding also the social and ethical issues linked to access to care and discrimination. This implementation will improve the potentially high cost of PGx-based treatment, also in developing countries, or the discrimination due to the incorrect use of ethnic and genetic ancestry categories. In order to achieve a high level of protection for patients, their families, and the disadvantaged social groups, political measures will promote equitable access to the technology, prevent the misuse of personal genetic data, and condemn the discrimination of specific groups of the population. However, all these aspects should be considered by

Fig. 2

Ethical, Legal and Social implications in PGx research.

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Ethical Perspectives on Pharmacogenomic Profiling

researchers, the industry, and healthcare professionals in the optic that the improvements derived by technology during drug discovery, development, and use could be realized while maintaining public confidence in genetic technologies. Yet in spite of these barriers, all stakeholders should not lose the great importance of the overall goal: to collect all information to improve the understanding of the link between the human genome and human disease with the aim to develop safer and more effective strategies of care. The situation is in great movement and the ethical issue discussed in this chapter should be overcome for the implementation of the opportunity derived by the introduction of PGx in clinical routine as a major step forward the advancements in the progress of medical science.

Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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