Drug Discovery and Evaluation: Methods in Clinical Pharmacology [2 ed.] 9783319688657, 9783319688633, 9783319688640

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Franz J. Hock Michael R. Gralinski Editors

Drug Discovery and Evaluation: Methods in Clinical Pharmacology Second Edition

Drug Discovery and Evaluation: Methods in Clinical Pharmacology

Franz J. Hock • Michael R. Gralinski Editors

Drug Discovery and Evaluation: Methods in Clinical Pharmacology Second Edition

With 249 Figures and 161 Tables

Editors Franz J. Hock CorDynamics Dieburg, Germany

Michael R. Gralinski CorDynamics Chicago, IL, USA

ISBN 978-3-319-68863-3 ISBN 978-3-319-68864-0 (eBook) ISBN 978-3-319-68865-7 (print and electronic bundle) https://doi.org/10.1007/978-3-319-68864-0 1st edition: © Springer-Verlag Berlin Heidelberg 2011 2nd edition: © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

General Introduction

Drug discovery and evaluation is a multidisciplinary process. The discovery of a new drug starts with experiments in isolated organs or in biochemical assays, for example, in vitro receptor binding’s studies. New chemical compounds have to be compared with known drugs used in specific therapies. Positive results have to be confirmed in various animal tests. The therapeutic advances may be higher potency, fewer side effects, longer activity, or a new mode of action. Many methods are described in the literature and are reviewed in Drug Discovery and Evaluation: Pharmacological Assays, the first book of this series. The strategy of drug development has changed in recent years. Instead of sequential studies in toxicology and pharmacokinetics, the parallel involvement of various disciplines has been preferred. Exposure to the body is investigated by pharmacokinetic studies on absorption, distribution, and metabolism at an early stage of development and contributes to the selection of drugs. The term safety pharmacology, formerly general pharmacology, has been coined to describe a specific issue in addition to traditional toxicology tests. These studies are reviewed in the second book of the series as Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Clinical pharmacology and clinical pharmacokinetics belong together. There is no pharmacodynamics without pharmacokinetics and vice versa. We, therefore, combined both disciplines in the third book of this series as Drug Discovery and Evaluation: Methods in Clinical Pharmacology, with the aim of demonstrating the mutual dependency to the reader. An important objective of clinical pharmacology is the early and ongoing assessment of the safety and tolerability of a new drug. This is done by assessing the type, frequency, and severity of side effects; assessing in which patient population these side effects may occur at which dose or exposure; for what duration; and whether these side effects are reversible. The importance of an adequate selection of animal models, assessing the significance of the preclinical data obtained in the first-in-man study, has recently been shown quite dramatically. The first dose step in the first-in-man study with a humanized monoclonal antibody induced a cytokine release syndrome in all actively treated healthy volunteers, all of whom suffered life-threatening, acute shock and subsequent multiorgan failure. Obviously, these severe and serious adverse results were v

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General Introduction

not predicted by the animal studies conducted prior to human studies. Because of this incident, the regulators worldwide changed several processes so that this mishap further could be prevented. Pharmacogenomics has an interesting input to drug development. Genomic information should enable the pharmaceutical industry to target specific patient populations that are more likely to respond to the drug therapy or to avoid individuals who are likely to develop specific adverse events in clinical studies. In this volume, the possibilities of pharmacogenomics-guided drug development are discussed. Another new and promising development is personalized medicine. These new areas are discussed in this edition. This second edition of Drug Discovery and Evaluation: Methods in Clinical Pharmacology is completely new organized and extended. It contains besides the former chapters several new ones, such as Clinical Studies in Infants and Geriatric Population, Traditional Chinese Medicine, Space Pharmacology, Nanotechnology in Medicine, etc. Franz J. Hock Michael R. Gralinski

Acknowledgment

We would like to express our gratitude and our sincere thanks to all authors who contributed their knowledge to this book. Furthermore, we personally would like to thank the founder of this series H. Gerhard Vogel, who passed away in 2011. He was the Editor-in-Chief of the first edition and all of the Drug Discovery and Evaluation titles at Springer consisting of Pharmacological Assays, Safety and Pharmacokinetic Assays, and Methods in Clinical Pharmacology.

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Contents

Volume 1 Part I 1

2

3

Human Studies in Clinical Pharmacology

.............

1

Methodologies of Safety Assessment in Clinical Pharmacology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Werner Seiz

3

Pharmacodynamic Evaluation: Cardiovascular Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ivana I. Vranic

19

Characterization of Cardiac Electrophysiology Including ECG-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ivana I. Vranic

51

4

Pharmacodynamic Evaluation: CNS Methodologies . . . . . . Lynne Hughes, Marie Trad, Stacey Boyer, Deborah Lee, and Wei Yin

81

5

Pharmacodynamic Evaluation: Pain Methodologies . . . . . . . Pieter Siebenga, Pieter Okkerse, Guido van Amerongen, Robert Jan Doll, Alex Mentink, Justin Hay, and Geert Jan Groeneveld

95

6

Pharmacodynamic Evaluation: Drug Dependency and Addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 V. Tenev and M. Nikolova

7

Pharmacodynamic Evaluation: Ocular Pharmacology . . . . . 163 Najam A. Sharif

8

Clinical Pharmacology of Tinnitus: Design and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Agnieszka J. Szczepek

9

Clinical Aspects in Sleep Disorders and Apnea . . . . . . . . . . . 223 Thomas Penzel and Ingo Fietze

ix

x

Contents

. . . 243

10

Pharmacodynamic Evaluation: Diabetic Methodologies Juergen Sandow

11

Pharmacodynamic Evaluation: Gastroenterology . . . . . . . . . 263 Petar Nikolov, Georgi Banishki, and Milena NikolovaVlahova

12

Pharmacodynamic Evaluation: Endocrinology . . . . . . . . . . . 283 Michael A. B. Naafs

13

Pharmacodynamic Evaluation: Dermatology . . . . . . . . . . . . 299 Liora Bik and Hok Bing Thio

14

Pharmacodynamic Evaluation: Inflammation/ Immunology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Jörg Schüttrumpf and Matthias Germer

15

Pharmacodynamic Evaluation: Infectious Diseases . . . . . . . 325 Smita Bhuyan, Sebastian Felgner, Dino Kocijancic, and Vinay Pawar

16

Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer . . . . . . . . . . . . . . . . . 343 Fatih M. Uckun and Sanjive Qazi

17

Pharmacodynamic Evaluation: Gene Therapy . . . . . . . . . . . 361 Nicolas Grandchamp

18

Pharmacological Therapy in Inborn Errors of Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Anibh M. Das and Sabine Illsinger

19

Clinical Studies in Infants (Pediatric Pharmacology) . . . . . . 401 Karel Allegaert

20

Clinical Studies in Geriatric Population . . . . . . . . . . . . . . . . . 417 Petra A. Thürmann

21

Personalized Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 Federico Goodsaid, Felix Frueh, and Michael E. Burczynski

22

Noninvasive Methodology (NMR) Mitul A. Mehta

23

Traditional Chinese Medicine and Clinical Pharmacology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455 Anita Chen Marshall

24

Pharmacodynamic Evaluation: Herbal Medicine . . . . . . . . . 483 Gulam Mohammed Husain, Mohammad Ahmed Khan, Mohd Urooj, and Munawwar Husain Kazmi

25

Chronopharmacology in Drug Development . . . . . . . . . . . . . 499 Björn Lemmer

26

Space Pharmacology: How Space Affects Pharmacology . . . 519 Virginia Wotring

. . . . . . . . . . . . . . . . . . . . . 439

Contents

xi

27

Nanotechnology in Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . 533 M. Nikolova, R. Slavchov, and G. Nikolova

28

Gender Differences in Drug Therapy . . . . . . . . . . . . . . . . . . . 547 Anthony G. Fenech and Vanessa Petroni Magri

29

Methodologies of PD Assessment: Scales . . . . . . . . . . . . . . . . 571 Roman Görtelmeyer

30

Methods in Clinical Pharmacology . . . . . . . . . . . . . . . . . . . . . 593 Lorraine M. Rusch and Clayton Dehn

31

Pharmacodynamic Drug–Drug Interactions . . . . . . . . . . . . . 603 Ming Zheng

32

Medical Devices: Definition and Clinical Testing Lea Wettlaufer and Daniela Penn

. . . . . . . . . 613

33

Food Supplements: Definition and Classification Evelyn Breitweg-Lehmann, Birgit Liebscher, and Carolin Bendadani

. . . . . . . . . 625

34

Stem Cell Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637 Nina Graffmann, Lucas-Sebastian Spitzhorn, Soraia Martins, Md Shaifur Rahman, Lisa Nguyen, and James Adjaye

Volume 2 Part II

Clinical Pharmacokinetics . . . . . . . . . . . . . . . . . . . . . . . . . . .

669

35

Dose Finding in Single Dose Studies by Allometric Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 671 Zheng Lu, Rüdiger Kaspera, Yoichi Naritomi, and Tianli Wang

36

Pharmacokinetic Aspects of Multiple Dose Studies . . . . . . . . 683 Steven G. Woolfrey and James Gilmour Morrison

37

Dose Linearity and Proportionality . . . . . . . . . . . . . . . . . . . . 695 Tanja Eisenblaetter, Lenore Teichert, Ronald Burnette, and Paul Hutson

38

Effects of Food Intake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 715 Teodora Handjieva-Darlenska

39

Special Populations: Profiling the Effect of Obesity on Drug Disposition and Pharmacodynamics . . . . . . . . . . . . . . . 723 Kenneth T. Moore

40

Special Populations: Renal Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 Gerard Sanderink and Andreas Kovar

41

Special Populations: Influence of Hepatic Impairment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759 Gerard Sanderink and Andreas Kovar

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Contents

42

Special Populations: Protein Binding Aspects . . . . . . . . . . . . 765 Italo Poggesi

43

The Human ADME Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . 773 Andrew McEwen

44

Synthesis of Radiolabelled Compounds for Clinical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Jens Atzrodt, Volker Derdau, and Claudia Loewe

45

Drug–Drug Interaction Studies Peter Stopfer

46

In Vitro/In Vivo Correlation for Drug-Drug Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 847 Jan Wahlstrom and Larry Wienkers

47

Specific Studies for Formulation Development . . . . . . . . . . . 867 Roland Wesch

48

Absolute and Relative Bioavailability . . . . . . . . . . . . . . . . . . . 879 Khaled Abo-EL-Sooud

49

Bioequivalence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887 Henning H. Blume

50

Population Pharmacokinetics and PharmacokineticPharmacodynamics in Clinical Pharmacology . . . . . . . . . . . 903 Daniel F. B. Wright, Chihiro Hasegawa, and Hesham S. Al-Sallami

51

In Silico Drug Repositioning Using Omics Data: The Potential and Pitfalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929 Enya E. Scanlon and Jaine K. Blayney

52

Toxicokinetics and Safety Ratios Ferdi Sombogaard

53

In/In Vivo Correlation for Transporters Sandra Cvijic

54

Relevance of Transporters in Clinical Studies . . . . . . . . . . . . 989 Bruno Hagenbuch

55

Role of Clinical Pharmacokinetics Studies in Contemporary Oncology Drug Development Fatih M. Uckun and Sanjive Qazi

. . . . . . . . . . . . . . . . . . . . . . . 827

. . . . . . . . . . . . . . . . . . . . . . 949 . . . . . . . . . . . . . . . . 957

. . . . . . . . . . . . 1005

56

Pharmacogenomics in and Its Influence on Pharmacokinetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019 Guy Montay, Jochen Maas, and Roland Wesch

57

PK/PD Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047 Yichao Yu, Diether Rüppel, Willi Weber, and Hartmut Derendorf

Contents

xiii

58

General Principles of Pharmacovigilance in Clinical Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1071 Rainer Heissing and Anne-Ruth van Troostenburg

Part III Regulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1083 59

Regulatory Guidance: ICH, EMA, FDA . . . . . . . . . . . . . . . . 1085 Gerd Bode

60

Clinical Quality Management System Beat Widler

61

Future of Regulatory Safety Assessments Gerd Bode and Petra Starck-Lantova

. . . . . . . . . . . . . . . . . . 1139 . . . . . . . . . . . . . . . 1145

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169

About the Editors

Franz J. Hock Since retiring from Aventis in 2002, Dr. Hock has leveraged his experience as a freelance consultant specializing in safety pharmacology. Dr. Hock was a research scientist at HOECHST, Hoechst Marion-Roussel, and Aventis from 1976 to 2002. He initially worked on methods in general pharmacology and nephrology, before becoming Head of Laboratory and devoting to pharmacological methods for drugs influencing memory and learning. He was ultimately Head of Laboratory for General/Safety Pharmacology at the Frankfurt site of Aventis Pharma Deutschland GmbH. Dr. Hock received his M.Sc. in Neurobiology from the Technical University Darmstadt and his D.Sc. in Zoology from the University Kassel, Department of Biology, Institute of Neuroethology and Biocybernetics. He received the degree of Fachpharmakologe DGPT (“Certified Expert Pharmacology”) in 1981. In 1983, Dr. Hock spent a sabbatical year at the University of California, Irvine, at the Center for the Neurobiology of Learning and Memory (Director Prof. Dr. James L. McGaugh). He lectured for several years to student of Biology at the University of Kassel and the Technical University Darmstadt. He has published over 100 original papers on methods in pharmacology and on new compounds. Furthermore, he held 28 patent applications to protect or broaden the application of lead structures. He is currently a member of the Task Force General/Safety Pharmacology German/Swiss Pharmaceutical Companies and is also member of several national and international scientific societies. Dr. Hock is founding member of “Safety Pharmacology Society,” “Neurowissenschaftliche Gesellschaft e.V.,” and “European Behavioural Pharmacology Society.” For several years, he is serving as a member of the Program Committee of the Safety Pharmacology Society. Dr. Hock is member of several domestic and international scientific societies. Michael R. Gralinski Dr. Gralinski is an internationally recognized authority and advisor in cardiovascular pharmacology. He has founded multiple contract research and consulting companies and is currently the Chief Executive Officer of CorDynamics, a position he has held since 2002. He has more than 25 years of experience in pharmaceutical research and development with deep expertise in cardiovascular pharmacology and toxicology experimental design, data analysis, and interpretation. xv

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Prior to CorDynamics, Dr. Gralinski garnered extensive management experience working for several pharmaceutical organizations including WarnerLambert/Parke-Davis, G.D. Searle, Pharmacia, and Pfizer. His past responsibilities included directing product safety and toxicology operations, leading resolution efforts for cardiovascular issues involving discovery and development, and marketed compounds including Rezulin, Celebrex, and other COX-2 inhibitors as well as cultivating relationships with international colleagues throughout small and large biopharma. He was involved in the genesis of the Safety Pharmacology Society and was elected to multiple positions on its Board of Directors including Treasurer, Vice-President, and President. Dr. Gralinski earned a Bachelor of Science degree in Pharmacology and Toxicology from the University of Wisconsin-Madison and a Doctor of Philosophy in Cardiovascular Pharmacology from the University of Michigan.

About the Editors

Contributors

Khaled Abo-EL-Sooud Pharmacology Department, Faculty of Veterinary Medicine, Cairo University, Giza, Egypt James Adjaye Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Karel Allegaert Intensive Care and Department of Pediatric Surgery, Erasmus MC-Sophia Children’s Hospital, Rotterdam, The Netherlands Department of Development and Regeneration, KU Leuven, Leuven, Belgium Hesham S. Al-Sallami School of Pharmacy, University of Otago, Dunedin, New Zealand Jens Atzrodt Integrated Drug Discovery, Medicinal Chemistry, Isotope Chemistry and Metabolite Synthesis Departments (ICMS), Sanofi-Aventis Deutschland GmbH, Frankfurt a. M, Germany Georgi Banishki University of Aberdeen, Aberdeen, UK Carolin Bendadani Federal Office of Consumer Protection and Food Safety, Berlin, Germany Smita Bhuyan Department of Microbial drugs, Helmholtz Center for Infection Research, Braunschweig, Germany Liora Bik Dermatology, Erasmus University Medical Center, Rotterdam, The Netherlands Jaine K. Blayney Stratified Medicine Group, Centre for Cancer Research and Cell Biology, Queen’s University of Belfast, Belfast, UK Henning H. Blume Frankfurt am Main, Germany SocraTec C&S GmbH, Oberursel, Germany Gerd Bode Institute for Pharmacology and Toxicology, University Medical Center, University of Goettingen, Goettingen, Germany Stacey Boyer Clinical Research Operations, Cavion, Charlottesville, VA, USA Evelyn Breitweg-Lehmann Federal Office of Consumer Protection and Food Safety, Berlin, Germany xvii

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Michael E. Burczynski Department of Translational Medicine, Global R&D, Teva Pharmaceuticals, Frazer, PA, USA Ronald Burnette University of Wisconsin School of Pharmacy, Madison, WI, USA Sandra Cvijic Department of Pharmaceutical Technology and Cosmetology, University of Belgrade-Faculty of Pharmacy, Belgrade, Serbia Anibh M. Das Department of Paediatrics, Hannover Medical School, Hannover, Germany Clayton Dehn Highpoint Clinical Trials Center, High Point, NC, USA Volker Derdau Integrated Drug Discovery, Medicinal Chemistry, Isotope Chemistry and Metabolite Synthesis Departments (ICMS), Sanofi-Aventis Deutschland GmbH, Frankfurt a. M, Germany Hartmut Derendorf Department of Pharmaceutics College of Pharmacy, University of Florida, Gainesville, FL, USA Robert Jan Doll Centre for Human Drug Research, Leiden, The Netherlands Tanja Eisenblaetter München, Germany Sebastian Felgner Department of Molecular Immunology, Helmholtz Center for Infection Research, Braunschweig, Germany Anthony G. Fenech Department of Clinical Pharmacology and Therapeutics, University of Malta, Msida, Malta Ingo Fietze Interdisciplinary Sleep Medicine Center, Charitécentrum für Herz- Kresilauf- und Gefäßmedizin CCM11, Charité – Universitätsmedizin Berlin, Berlin, Germany Felix Frueh Opus Three LLC Life Sciences, Pescadero, CA, USA Matthias Germer Corporate R&D, Biotest AG, Dreieich, Germany Federico Goodsaid Opus Three LLC Life Sciences, Pescadero, CA, USA Roman Görtelmeyer Scientific Consulting, Frankfurt am Main, Germany Biological and Clinical Psychology, University of Mannheim, Mannheim, Germany Nina Graffmann Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Nicolas Grandchamp Biosource / GEG Tech, Paris, France Geert Jan Groeneveld Centre for Human Drug Research, Leiden, The Netherlands Bruno Hagenbuch Department of Pharmacology, Toxicology and Therapeutics, The University of Kansas Medical Center, Kansas City, KS, USA

Contributors

Contributors

xix

Teodora Handjieva-Darlenska Department of pharmacology and toxicology, Medical Faculty, Medical University of Sofia, Sofia, Bulgaria Chihiro Hasegawa School of Pharmacy, University of Otago, Dunedin, New Zealand Translational Medicine Center, Ono Pharmaceutical Co., Ltd., Osaka, Japan Justin Hay Centre for Human Drug Research, Leiden, The Netherlands Rainer Heissing Heissing Partenheim, Germany

Pharmacovigilance

Consulting

GmbH,

Lynne Hughes Neurology Center of Excellence, IQVIA, Reading, Berkshire, UK Gulam Mohammed Husain Pharmacology Research Laboratory, Central Research Institute of Unani Medicine (Under CCRUM), Hyderabad, India Paul Hutson University of Wisconsin School of Pharmacy, Madison, WI, USA Sabine Illsinger Department of Neuropaediatrics, Children’s Hospital Oldenburg, Oldenburg, Germany Rüdiger Kaspera Clinical Pharmacology and Exploratory Development, Astellas Pharma Europe B.V., Leiden, The Netherlands Munawwar Husain Kazmi Department of Ilmul Advia (Unani Pharmacology), Central Research Institute of Unani Medicine (Under CCRUM), Hyderabad, India Mohammad Ahmed Khan Pharmacology Research Laboratory, Central Research Institute of Unani Medicine (Under CCRUM), Hyderabad, India Dino Kocijancic Department of Molecular Immunology, Helmholtz Center for Infection Research, Braunschweig, Germany Andreas Kovar Translational Medicine and Early Development, Sanofi, Frankfurt, Germany Deborah Lee CNS-TAU, Takeda Pharmaceutical International Co., Cambridge, MA, USA Björn Lemmer Institute of Experimental and Clinical Pharmacology and Toxicology, Medical Faculty, Mannheim Ruprecht-Karls-University of Heidelberg, Mannheim, Germany Birgit Liebscher Federal Office of Consumer Protection and Food Safety, Berlin, Germany Claudia Loewe Integrated Drug Discovery, Medicinal Chemistry, Isotope Chemistry and Metabolite Synthesis Departments (ICMS), Sanofi-Aventis Deutschland GmbH, Frankfurt a. M, Germany Zheng Lu Clinical Pharmacology and Exploratory Development, Astellas Pharma, Northbrook, IL, USA

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Jochen Maas R&D Metabolism and PK Germany, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany Anita Chen Marshall Sequoia Healing Center for Acupuncture and Herbal Medicine, Alameda, CA, USA American College of Traditional Chinese Medicine (ACTCM), California Institute of Integral Studies (CIIS), San Francisco, CA, USA Bastyr University, Seattle, WA, USA John A. Burns School of Medicine, University of Hawaii, Honolulu, HI, US Soraia Martins Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Andrew McEwen Reverisco Solutions, Peterborough, UK Mitul A. Mehta Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK Alex Mentink Centre for Human Drug Research, Leiden, The Netherlands Guy Montay GMPK/PK Sanofi-Aventis, Vitry-Sur-Seine, France Kenneth T. Moore Cardiovascular and Metabolism Medical Affairs, Janssen Pharmaceuticals Inc., Titusville, NJ, USA James Gilmour Morrison GW Pharmaceuticals, Cambridge, UK Michael A. B. Naafs Naafs International Health Consultancy, Oldenzaal, Netherlands Yoichi Naritomi Analysis and Pharmacokinetics Research Labs., Astellas Pharma Inc., Ibaraki, Japan Lisa Nguyen Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Petar Nikolov Gastroenterology, Ardea Medical Center, Sofia, Bulgaria G. Nikolova University Hospital Acad I. Penchev, Medical University, Sofia, Bulgaria M. Nikolova University Hospital Alexandrovska, Clinic of Nephrology, Medical University, Sofia, Bulgaria Milena Nikolova-Vlahova Clinic of Nephrology, University Hospital Alexandrovska, Medical University, Sofia, Bulgaria Pieter Okkerse Centre for Human Drug Research, Leiden, The Netherlands Vinay Pawar Department of Molecular Immunology, Helmholtz Center for Infection Research, Braunschweig, Germany Institute of Immunology, Medical School Hannover, Hanover, Germany Daniela Penn Medisis, Neustadt, Germany

Contributors

Contributors

xxi

Thomas Penzel Interdisciplinary Sleep Medicine Center, Charitécentrum für Herz- Kresilauf- und Gefäßmedizin CCM11, Charité – Universitätsmedizin Berlin, Berlin, Germany Vanessa Petroni Magri Department of Clinical Pharmacology and Therapeutics, Faculty of Medicine and Surgery, University of Malta, Msida, MSD, Malta Italo Poggesi Global Clinical Pharmacology, Quantitative Sciences/ModelBased Drug Development, Cologno Monzese, Italy Sanjive Qazi AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA Bioinformatics Program, Gustavus Adolphus College, St. Peter, MN, USA Md Shaifur Rahman Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Diether Rüppel PKDM/TMED, Sanofi-Aventis Germany, Frankfurt, Germany Lorraine M. Rusch Highpoint Clinical Trials Center, High Point, NC, USA Gerard Sanderink Translational Medicine and Early Development, SanofiAventis R&D, Vitry-sur-Seine, France Juergen Sandow Centre of Pharmacology, Frankfurt-Main University, Glashuetten, Germany Enya E. Scanlon Stratified Medicine Group, Centre for Cancer Research and Cell Biology, Queen’s University of Belfast, Belfast, UK Jörg Schüttrumpf Corporate R&D, Biotest AG, Dreieich, Germany Werner Seiz R&D Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany Najam A. Sharif Global Alliances and External Research, Global Ophthalmology Research and Development, Santen Incorporated, Emeryville, CA, USA Pieter Siebenga Centre for Human Drug Research, Leiden, The Netherlands R. Slavchov Cambridge University, Cambridge, UK Ferdi Sombogaard Department of Clinical Pharmacology and Pharmacy, Amsterdam University Medical Centers – VUmc, Amsterdam, The Netherlands Lucas-Sebastian Spitzhorn Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University, Düsseldorf, Germany Petra Starck-Lantova The University of Bonn/German Society for Regulatory Affairs, Bonn, Germany

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Peter Stopfer Boehringer Ingelheim Pharma GmbH & Co. KG, Ingelheim, Germany Agnieszka J. Szczepek Department of ORL, Head and Neck Surgery, Charité University Hospital, Berlin, Germany Lenore Teichert Biostatistics, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany V. Tenev Department of Psychiatry, University Hospital, University of Iowa, Iowa city, IA, USA Petra A. Thürmann Philipp Klee-Institute of Clinical Pharmacology, HELIOS University Clinic Wuppertal, Wuppertal, Germany University Witten/Herdecke, Witten, Germany Hok Bing Thio Dermatology, Erasmus University Medical Center, Rotterdam, The Netherlands Marie Trad Neurology Center of Excellence, IQVIA, Paris, France Fatih M. Uckun AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA Mohd Urooj Pharmacology Research Laboratory, Central Research Institute of Unani Medicine (Under CCRUM), Hyderabad, India Guido van Amerongen Centre for Human Drug Research, Leiden, The Netherlands Anne-Ruth van Troostenburg Gilead Sciences International Ltd, Cambridge, UK Ivana I. Vranic Clinical Center of Serbia, Faculty of Medicine, University of Belgrade, Belgrade, Serbia Jan Wahlstrom Amgen, Inc, Pharmacokinetics and Drug Metabolism, Thousand Oaks, CA, USA Tianli Wang Clinical Pharmacology and Exploratory Development, Astellas Pharma, Northbrook, IL, USA Willi Weber Frankfurt, Germany Roland Wesch R&D Metabolism and PK Germany, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany Lea Wettlaufer Medisis, Neustadt, Germany Beat Widler Widler & Schiemann AG, Zug, Switzerland

Willi Weber has retired.

Contributors

Contributors

xxiii

Larry Wienkers Amgen, Inc, Pharmacokinetics and Drug Metabolism, South San Francisco, CA, USA Steven G. Woolfrey Independent Pharmaceutical Consultant – Clinical Pharmacology and Pharmacokinetics, Newcastle Upon Tyne, UK Virginia Wotring Center for Space Medicine and Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, TX, USA Daniel F. B. Wright School of Pharmacy, University of Otago, Dunedin, New Zealand Wei Yin Quantitative Clinical Pharmacology, Takeda Pharmaceuticals, Cambridge, MA, USA Yichao Yu Department of Pharmaceutics College of Pharmacy, University of Florida, Gainesville, FL, USA Ming Zheng Clinical Pharmacology and Pharmacometrics, Bristol-Myers Squibb Company, Princeton, NJ, USA

Part I Human Studies in Clinical Pharmacology

1

Methodologies of Safety Assessment in Clinical Pharmacology Werner Seiz

Contents Introduction/General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Definition of Adverse Events as the Parameter to Assess Safety . . . . . . . . . . . . . . . . . . . . . . . . . 3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Categorization of Adverse Events for Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Assessment of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modifications of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 4 5 5 5 6

Decision Making on Dose Increase and to Stop the Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 6 6 6

Clinical Adverse Events Monitoring (Report by Subjects) . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 6 6 6

Clinical Adverse Events Monitoring (Physical Examination by the Clinical Investigator) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Assessment of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 7 7 7 7

Timing of Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Vital Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Heart Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

W. Seiz (*) R&D Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany e-mail: werner.seiz@sanofi.com © Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_30

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W. Seiz Vital Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Blood Pressure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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ECG Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PR Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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ECG Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . QT Interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glucose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Potassium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Alanine Aminotransferase (ALT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Aspartate Aminotransferase (AST) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Phosphatase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Bilirubin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Creatinine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Albumin in Urine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Creatinphosphokinase (CPK) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Hemoglobin (Male Subjects) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Polymorphonuclear Leucocytes (PMN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Platelets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Coagulation Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Activated Partial Thromboplastin Time (aPTT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Laboratory Parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Kidney Injury Molecule-1 (KIM-1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Visual Analogue Scale for Semiquantitatively Assessing Pain and Other Subjective Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Abstract

An important objective of clinical pharmacology is the early and ongoing assessment of the safety and tolerability of a new drug. This is done by assessing the type, frequency, and severity of side effects, assessing in which patient population these side effects may

occur at which dose or exposure, for what duration and whether these side effects can be monitored and whether they are reversible. The terminology for the safety assessment of drugs has some specifics that need to be explained right at the beginning of this chapter.

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Methodologies of Safety Assessment in Clinical Pharmacology

Introduction/General Considerations An important objective of clinical pharmacology is the early and ongoing assessment of the safety and tolerability of a new drug. This is done by assessing the type, frequency, and severity of side effects, assessing in which patient population these side effects may occur at which dose or exposure, for what duration and whether these side effects can be monitored and whether they are reversible. The terminology for the safety assessment of drugs has some specifics that need to be explained right at the beginning of this chapter.

Definition of Adverse Events as the Parameter to Assess Safety The term “side effect” used for marketed drugs is replaced by the term “adverse event” (AE) in studies with investigational drugs. An adverse event is defined as any unfavorable and unintended sign, symptom, or disease temporally associated with the use of a drug, whether or not considered related to the drug. In this chapter the term “adverse event” is explicitly also used for any abnormal laboratory value, as the consequence of abnormal values will be evaluated in the same scheme as for clinical adverse events. The term “treatment related” is often added as a modifier in order to remove preexisting conditions from consideration. A further term “serious adverse event” is used to describe any untoward medical occurrence that, at any dose, results in death, is life-threatening, requires hospitalization or prolongation of an existing hospitalization, results in persistent or significant disability or incapacity, or is a congenital anomaly or a birth defect. Serious adverse events have to be reported to the health authorities in an expedited manner, typically. The severity of an adverse or serious adverse event is classified as either mild, moderate, or severe. Standardized definitions for adverse events and classification of severity have been published by the National Cancer Institute (NCI)

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of the National Institute of Health (NIH) and are also used for clinical trials in nononcology indications (NCI 2017). It is important to distinguish between the severity and the seriousness of an adverse event. A severe adverse event is not necessarily serious (e.g., severe abdominal cramps not causing hospitalization), and a serious adverse event is not necessarily of severe intensity (e.g., mild to moderate, prolonged dizziness of an outpatient causing hospitalization) (Herson 2000).

How to Manage the Safety Assessment of a Drug One of the most critical steps in the development of a new drug is the first administration of a drug to humans, the first dose escalation, the first multiple dosing, and the first switch from healthy patients to the targeted patient population. In order to acquire the safety data in a responsible way, it is necessary to consider all of the following areas for each clinical study and to plan these items in advance: • Expect, plan, and manage the occurrence of adverse events. This administrative part of the safety method includes the selection of the right preclinical animal models for the prediction of the target organ, the definition of the exposure to the drug at the no-observed-adverse-effect-level (NOAEL), the adequate calculation of the safe starting dose in humans, the decision about the dose escalation and when to stop it, the proper organization of the clinical trial, and the definition of the expected adverse event profile. • Plan and manage the acquisition of adverse events data. This includes – based on the expected adverse event profile – the selection of the clinical, technical, and laboratory observations, by which the expected adverse events are to be monitored. • Plan and manage the interpretation of the adverse events data and their impact on the subsequent development or study conduct. In order to avoid bias, the statistical analysis

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of the safety data obtained has to be predefined, using commonly accepted criteria. For each parameter assessed, it should be clear prior to the analysis, which deviation is considered relevant and therefore an adverse event. This is usually done in the statistical analysis plan, which has to be finalized prior to closing the database of a study and prior to breaking the randomization code. In clinical pharmacology studies, data monitoring committees (DMC) are typically not included; however, in studies with adaptive designs DMCs might be installed very early in clinical development.

Case Study The importance of an adequate selection of animal models, assessing the significance of the preclinical data obtained for humans and planning adequately the study conduct in the first-inhuman study has been shown quite dramatically a decade ago. The first dose step in the first-inman study with the biotherapeutic TGN1412, a humanized agonistic anti-CD28 IgG4 monoclonal antibody (present on regulatory T-cells), induced a cytokine release syndrome in all six active-treated healthy volunteers, all of whom suffered from life-threatening, acute shock and subsequent multi-organ failure. At least in one of the participants of the TGN1412 first-in-man study, several fingers and toes were to be amputated finally. Obviously this severe and serious adverse events were not predicted by the animal studies conducted prior to human studies, the dose administered was obviously above the minimum active biological effect level (MABEL) for humans, and all volunteers were already dosed before the first dosed person suffered from the symptoms of the upcoming cytokine release syndrome, that is, within less than 90 min. Although a complete explanation of the event was never unanimously accepted (http://www.circare.org/ foia5/clinicaltrialsuspension_interimreport.pdf), at least it appears that the drug was given too fast to each subject (3–6 min infusion time) and to too many subjects within too short a time (every 10 min the next subject was dosed) (Horvath and Milton 2009). As a consequence

of this event with TGN1412, the regulators worldwide have changed several processes so that this should not happen again. The European Medicines Agency (EMA) has revised their guidance on first-in-human clinical trials to identify and mitigate risks for trial participants (EMA 2017) after a case of death of a human volunteer in a first-in-human clinical trial in 2016. In that case, a 49-year-old healthy subject who experienced neurological symptoms after the 5th out of 10 planned doses in the first multiple ascending dose study with a nonselective fatty acid amid hydrolase inhibitor was submitted to hospital and died about 7 days later (Brentano and Menard 2016). A major new request by the EMA is the treatment of a sentinel at least 24 h before subsequent subjects are to be treated. It is self-evident that the evaluation and interpretation of the safety data obtained as a whole is of utmost importance to a drug development program; however, here in this chapter the topic will be the technical description of the most often used clinical, technical, and laboratory methods to acquire safety data and how this will influence decisions on dose escalation or termination of a study. No more thoughts are given to analyze the safety data as a whole and in the context of the already accumulated clinical safety data.

Categorization of Adverse Events for Decision Making Purpose and Rationale Adverse events should be categorized in the same way across studies so that the decisions based on these categories are consistent within a development program and across programs. The NCI-CTCAE v5.0 terminology is defining the following five grades of severity for each adverse event (AE): Grade 1

Definition Mild; asymptomatic or mild symptoms; clinical or diagnostic observations only; intervention not indicated (continued)

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Methodologies of Safety Assessment in Clinical Pharmacology

Grade 2

3

4 5

Definition Moderate; minimal, local, or noninvasive intervention indicated; limiting ageappropriate instrumental activities of daily living (ADL) Severe or medically significant but not immediately life-threatening; hospitalization or prolongation of hospitalization indicated; disabling; limiting self-care ADL Life-threatening consequences; urgent intervention indicated Death related to AE

Procedure Each adverse event or finding is classified into one of five categories, where grade 1 indicates a deviation from the norm without an obvious relevance for the subject, grade 2 indicates a mild interference with daily activities for the subject but without need for treatment except non-opioid analgesics, for example. Occurrences of events of grade 2 have to be seen as an alert on reaching doses, where tolerability to the test compound decreases. Grade 3 indicates that the event or finding requires medical or other treatment or prevents daily activities of the subject. Grade 4 is reserved for definitely inacceptable adverse events, which typically, if not occurring in the placebo group, leads to a termination of the study at least of the dose, when the grade 4 event has been observed (e.g., rhabdomyolysis, angioedema). Grade 5 of course is an inacceptable consequence of treatment with an investigational drug and may even terminate a drug development project. If there is a rapid change in a parameter, this also might lead to an increase in grading. For laboratory parameters (chemistry, ECG), the grading is done based on the likelihood for further consequences or risks according to the categories above. In order to categorize laboratory values as abnormal, they have to be different from the normal range, which is specific to each laboratory. The numbers given here are suggestions and are based on published normal ranges (Kratz et al. 2004).

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Evaluation For each subject, the maximal adverse events’ grading can easily be assessed. For gradings 3 or 4, unblinding is recommended; for grade 5, unblinding and immediate termination of the clinical trial is a must. An individual should not be further dosed if on active drug and grading 3 occurs. If only placebo-treated subjects suffer from an adverse event and if this event is not study-procedure related, it has no impact on further study conduct. If placebo- and activetreated patients suffer from grading 3, but with less than 50% of active-treated subjects, doses should be adapted (=lowered), the number of subjects treated at a time need to be reduced, and the time interval between subjects should be increased, in order to minimize the risk for treated subjects. If the dose step is well tolerated, additional subjects could be treated at the dose with the grade 3 events. Finally, if more than 50% of active-treated patients suffer from grade 3 adverse events at a given dose, the dose below is qualified as the maximal tolerated dose. At all grades, clinical judgment is needed based on the nature, reversibility, and monitorability of adverse events.

Critical Assessment of the Method The categorization of information leads to a loss of information and therefore has to be used with care. Everyone using this method needs to be aware that the full picture and information needs to be taken into account and not just the results from the categorization. The grading system suggested and described here is modified from (Sibille et al. 2010) and not approved by any authority but should be seen as way to consistently aggregate and interpret information. Grading adverse events is in use in oncology and vaccine studies already (Cancer therapy evaluation program 2009; FDA Guidance 2007). These systems use four or five gradings, where grade 5 is always “death” and grade 4 is mostly of life-threatening adverse events, which is not in contradiction with the grading used here.

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Modifications of the Method

Evaluation

An alternative to formal categorization of adverse events for subsequent decision making on dose escalation or study progress is the repetitive assessment of the uncategorized clinical and laboratory data by the investigator, the sponsor, and additional experts to achieve a common understanding on how to proceed. The precategorization of events as described here in this chapter, however, does not prevent such an approach and has the advantage to provide a consistent assessment of the information across dose steps, studies, and compounds.

In case grade 3 or 4 adverse events do occur, treatment of ongoing subjects should be stopped immediately.

Decision Making on Dose Increase and to Stop the Study Purpose and Rationale The decision to stop dose escalation should be based on the observation of adverse events no longer tolerable (by frequency or severity) and by the observed exposure information.

Procedure The grading of the adverse events and their frequency need to be assessed. As long as no adverse events are observed and the exposure is not above the exposure in the most sensitive species, dose escalation may go on as planned in the protocol. If the exposure is above the NOAEL exposure, careful further dose escalation may be reasonable to define the maximal tolerable dose. If the severity of adverse events is below grade 3 and the exposure of the NOAEL is not reached, dose escalation can proceed. No further dose escalation should be considered, if more than 50% of activetreated subjects suffer from adverse events of grade 3.

Clinical Adverse Events Monitoring (Report by Subjects) Purpose and Rationale Most drug-related adverse events are based on the spontaneous reporting of clinical signs by the clinical trial participants. Subjects participating in a clinical trial can realize these adverse events at any time.

Procedure The subjects are asked to report any events, signs, or abnormal observations and feelings to the study personnel immediately. In addition, subjects should be asked direct questions from time to time, such as “Did you make any disagreeable or unexpected observations since you took the drug?” The information obtained need to be documented without interpretation at first. The (preliminary) diagnosis and decision about next steps (physical, and if indicated additional laboratory or technical examinations) will be based on the interpretation by the responsible MD on this report.

Evaluation Categorization of adverse events reported by the subjects is to be done by experienced medical personnel taking into account possible differential diagnosis and their time course. The reference http://www.fda.gov/ BiologicsBloodVaccines/GuidanceCompliance RegulatoryInformation/Guidances/Vaccines/ucm 074775.htm gives an example of recommendations by the FDA.

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Methodologies of Safety Assessment in Clinical Pharmacology

Clinical Adverse Events Monitoring (Physical Examination by the Clinical Investigator) Purpose and Rationale Physical examination based on spontaneous reporting of adverse events will be conducted as needed.

Procedure Physical examination can include auscultation, investigation of reflexes, or orientation, etc.

Evaluation The investigator needs to decide on the classification of physical findings based on changes to baseline and their relevance.

Critical Assessment of the Method Typically there will not be many clinical findings on physical examination. If there are some, this indicates already quite substantial effects (e.g., angioedema, rashes, ankle edema). Exceptions are findings in the vital signs of heart rate or blood pressure (see below), where effects are frequently seen.

Timing of Monitoring Purpose and Rationale Timing of clinical and laboratory assessments need to be in line with the timecourse of drug concentration over time.

Procedure Standard monitoring needs to be done at baseline and repetitively after drug administration. For

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orally administered drugs, this is typically at baseline before drug administration – 30 min, 1, 2, 4, 8, 12, and 24 h after dosing for once daily drugs. Timing has to be tailored to the specific pharmacokinetic and pharmacodynamic profile of a drug.

Vital Signs Heart Rate Purpose and Rationale The heart rate is influenced by the sympathic and parasympathic system, which can be affected by drugs directly or indirectly. Heart rate as a vital parameter has to be quite stable as heart rate effects in patients with ischemic heart disease could lead to angina pectoris. In phase I studies, heart rate typically is most affected by increased vagal tone and subsequent bradycardia and occasional fainting. Procedure Continuous ECG monitoring by telemetry or Holter ECG is the method of choice to observe effects on heart rate. Evaluation Normal range: 50–80/min in supine position. Grade 3 definition: 130/min. Critical Assessment of the Method Basic method for safety and tolerability assessment. Modifications of the Method Holter monitoring allows continuous 24-h assessment of heart rate including analysis of cardiac arrhythmias. Holter monitoring should be used whenever there is evidence of proarrhythmic potential of a drug. Central analysis of Holter ECGs is recommended to also be able to compare acquired data to larger groups of subjects.

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Vital Signs Blood Pressure Purpose and Rationale Blood pressure is dependent on stroke volume, heart rate (stroke volume  heart rate = cardiac output), and peripheral resistance. Decrease in cardiac output and/or resistance decreases blood pressure and vice versa. A decrease in blood pressure is most often a result of either vasodilatation or decrease in heart rate, both of which can occur during increased vagal stimulation. Procedure Blood pressure can be measured manually or by a machine, in supine, sitting, or standing positions. For functional assessments the Schellong test is an easy to conduct procedure to measure the effect of a physical challenge on heart rate and blood pressure. After 10 min in supine position, the subject is asked to take a standing position. Heart rate and blood pressure are measured 2 min after end of supine position. Timepoints of blood pressure measurements need to be adapted based on the observed effects. Evaluation Normal range: 100–140 mmHg systolic in supine position, 50–85 mmHg diastolic in supine position. Grade 1 definition: 140–159 mmHg systolic in supine position and 90–99 mmHg diastolic in supine position for pressure increase; a quantity of 80–100 mmHg systolic in supine position for pressure decrease. Decrease in systolic blood pressure after 2 min standing by more than 20 mmHg together with increase in heart rate. Grade 2 definition: 160–179 mmHg systolic in supine position and 100–110 mmHg diastolic in supine position for pressure increase; a quantity of 70–80 mmHg systolic in supine position for pressure decrease. Cannot stay standing after 10 min of supine position for pressure decrease. Grade 3 definition: >180 mmHg systolic in supine position and >110 mmHg diastolic in supine position for pressure increase; below 70 mmHg systolic in supine position for pressure decrease or syncope during Schellong test.

W. Seiz

Critical Assessment of the Method Basic method for safety and tolerability assessment. Modifications of the Method Twenty-four-hour ambulatory blood pressure monitoring (ABPM) is the method of choice for any compound with known or suspected effect of blood pressure as the effect over time can be best followed by continuous monitoring. Some drugs affect the nocturnal decrease in blood pressure (so-called dipping); whenever there is evidence that a drug has such an effect, ABPM should be used early in clinical development. Blood pressure (and heart rate) will be measured every 15–20 min during day time (defined as 6 a.m. to 10 p.m.). During night time, the measurement intervals are 30 min. Full 24 h should be measured, if ABPM is used. ABPM allows to calculate precisely peak and trough effects and the duration of effect on blood pressure.

ECG Parameter PR Interval Purpose and Rationale The PR interval in the ECG is the time during which the electrical excitation is conducted from the atria to the AV-node. Prolongation of the PR interval is a potential side effect of drugs affecting repolarization and bears the risk of AV blockade. Procedure Evaluators should be trained in ECG analysis. Automated analysis is frequent but needs to be validated in order to rely upon it. Evaluation Normal range: 120–200 ms. Grade 1 definition: 1.1-fold ULN. Grade 2 definition: >250 ms. Grade 3 definition: AV-block 2nd degree or syncope. Critical Assessment of the Method Basic method for safety and tolerability assessment.

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Methodologies of Safety Assessment in Clinical Pharmacology

ECG Parameter QT Interval Purpose and Rationale The QT interval in the ECG is the time during which the electrical excitation and repolarization of the ventricles takes place. Prolongation of the QT time and especially the QTc time (QT time corrected for effect of heart rate) is a risk factor for torsades des point, a ventricular arrhythmia associated with an increased incidence of drug-induced sudden cardiac death. QT prolongation of drugs is one of the most frequent reasons for termination of a drug development program. Procedure Evaluators should be trained in ECG analysis. Automated analysis is frequent but needs to be validated in order to rely upon it. Evaluation Normal range for QTc: 360–425 ms for men, 380–445 for women, increase below 40 ms. Grade 1 definition: Increase above 40 ms and QTc below 475 ms. Grade 2 definition: 476–499 ms and increase below 60 ms. Grade 3 definition: Above 500 ms or increase exceeding 60 ms. Critical Assessment of the Method Basic method for safety and tolerability assessment.

Laboratory Parameter

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Procedure Blood glucose should be measured from capillary or venous blood at predefined timepoints and in addition in cases of suspected hypoglycemia or impaired consciousness. Evaluation Normal range 3.8–6.4 mmol/L. Grade 1 definition for hypoglycemia: 3.5–3.8 mmol/L. Grade 2 definition: 2.2–3.4 mmol/L. Grade 3 definition: 1.7–2.1 mmol/L. Critical Assessment of the Method Basic method for safety and tolerability assessment.

Laboratory Parameter Potassium Purpose and Rationale Potassium concentration in cells is 25-fold higher than in blood. In all cases were potassium is released into the peripheral blood (e.g., during and after hypoxic events) or a decrease in renal excretion occurs, potassium increases will have the potential for cardiac bradyarrhythmias. Hypokalemia can lead to ventricular tachyarrhythmias. Therefore close monitoring of potassium concentration in serum is very important in early phases of development as long as the effect on its concentration in serum is not yet known. Procedure Potassium values are measured from serum taken from peripheral veins at predefined timepoints.

Glucose Purpose and Rationale A sufficient glucose concentration in blood (>2 mmol/L or >40 mg/dL at minimum) is essential for all life processes. Whenever there are signs of decreased consciousness, this vital parameter has to be assessed immediately.

Evaluation Normal range 3.5–5.0 mmol/L. Grade 1 definition: 3.1–3.4 for hypokalemia and 5.1–6.0 for hyperkalemia. Grade 2 definition: 2.5–3.0 mmol/ L for hypokalemia and 6.1–6.5 mmol/L for hyperkalemia. Grade 3 definition: 2.0–2.4 mmol/L for hypokalemia and 6.6–7.0 mmol/L for hyperkalemia.

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Critical Assessment of the Method Basic method for safety and tolerability assessment.

Laboratory Parameter Alanine Aminotransferase (ALT) Purpose and Rationale Hepatic damage is one of the most frequent drugrelated adverse events and needs to be monitored in every clinical pharmacology study. Transaminases (SGPT/ALT and SGOT/AST), alkaline phosphatase, and total and conjugated bilirubin are the serum assays to detect liver damage.

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Laboratory Parameter Aspartate Aminotransferase (AST) Evaluation Normal range is 0–40 IU/L, strongly dependent on lab. Grade 1 definition: Increase >1.2-fold ULN. Grade 2 definition: Increase 2.5- to 5-fold. Grade 3 definition: >5- to 10-fold. Critical Assessment of the Method Supporting parameter for ALT analysis.

Laboratory Parameter Phosphatase

Procedure ALT, AST alkaline phosphatase, and bilirubin are taken from serum of peripheral blood at predetermined timepoints and more frequently, if any increases are seen. If increase of ALT is above threefold upper limit of normal (ULN), ALT needs to be followed until normalization (below ULN) or until no further decrease of ALT after termination of treatment is observed. Evaluation Any transaminase elevation above the upper limit of normal should be considered as an indicator for hepatic damage. ALT increase is the enzyme specific for liver damage. Normal range is 0–60 IU/L, strongly dependent on lab. Grade 1 definition: Increase >1.2-fold ULN. Grade 2 definition: Increase 2.5- to 5-fold. Grade 3 definition: >5to 10-fold. Critical Assessment of the Method Basic method for safety and tolerability assessment. Increases of ALT are very specific to the liver. Alkaline phosphatase can be increased in other diseases as well, for example, bone disease. Depending on preclinical data of potential liver toxicity and upcoming clinical data early in development, the reporting thresholds for increases in liver enzymes should be adapted specifically.

Evaluation Normal range is 30–120 IU/L, strongly dependent on lab. Grade 1 definition: Increase >1.1-fold ULN. Grade 2 definition: Increase two- to threefold. Grade 3 definition: three- to tenfold. Critical Assessment of the Method Supporting parameter for ALT analysis.

Laboratory Parameter Bilirubin Purpose and Rationale Bilirubin assessment together with ALT measurement is used to identify potential risks of hepatic toxicity. Evaluation Normal range is 5–27 μmol/L. Grade 1 definition: Increase >1.3-fold ULN. Hy’s law (FDA 2009) is a prognostic indicator that a drug-induced liver injury leading to jaundice has a case fatality rate of 10–50%. Hy’s law cases have the three following components: • The drug causes hepatocellular injury, generally shown by more frequent threefold or

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Methodologies of Safety Assessment in Clinical Pharmacology

greater elevations above the ULN of ALT or AST. • Among subjects showing such ALT/AST elevations, often much greater than 3  ULN, some subjects also show elevation of serum bilirubin to >2  ULN, without initial findings of cholestasis (serum alkaline phosphatase [ALP] activity >2  ULN). • No other reason can be found to explain the combination of increased transaminases and bilirubin, such as hepatitis A, B, or C, preexisting or acute liver disease, or another drug capable of causing the observed injury.

Critical Assessment of the Method Together with ALT, a very powerful parameter to identify true drug-related hepatic damage.

Laboratory Parameter Creatinine Purpose and Rationale Creatinine is solely excreted by the kidney, primarily by glomerular filtration, and therefore is a good marker of renal perfusion and filtration. Drugs affecting renal perfusion or filtration lead to an increase in creatinine. Increases in creatinine only occur if there is already a significant decrease in renal glomerular filtration rate.

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Critical Assessment of the Method Serum creatinine levels are not very sensitive to large changes in GFR as long as the GFR is still above 60 ml/min/m2, but then a rapid increase will be observed. A more sensitive method for renal function is the GFR or the fractional excretion of electrolytes.

Laboratory Parameter Albumin in Urine Purpose and Rationale Presence of albumin in urine is an indicator of glomerular damage. Procedure Albumin is measured from morning urine. Evaluation Normally no albumin is excreted via urine. Any finding of albumin above 300 mg/24 h in urine is indicative of a renal issue that needs to be further evaluated (if prior to treatment with investigational drug the value was negative). Critical Assessment of the Method Albumin in urine is always a pathological sign, which needs further analysis.

Laboratory Parameter Creatinphosphokinase (CPK)

Procedure Creatinine concentration needs to be measured in plasma and urine. Together with the urine production per time (either within 24 h, or time overnight sampling; for example, 1,500 ml excreted between 8 pm and 7 am), the glomerular filtration rate can easily be calculated. Evaluation Normal range: 80–130 μmol/L. Grade 1 definition: >1.1-fold ULN. Grade 2 definition: >1.5fold ULN. Grade 3 definition: >1.9- to 3.4-fold ULN.

Purpose and Rationale CPK is released during damage of skeletal muscle, a frequent side effect of lipid lowering compounds like statins. Procedure CPK is taken from serum of peripheral blood at predetermined timepoints and more frequently, if any increases are seen. If increase of CPK is above threefold ULN, CPK needs to be followed until normalization (below ULN) or until no further decrease of CPK after termination of treatment is observed.

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Evaluation Normal range: 50–400 IU/L. Grade 1 definition: 480–1,000 IU/L. Grade 2 definition: 1,000–2,000 IU/L. Grade 3 definition: 2,000–5,000 IU/L.

Laboratory Parameter Hemoglobin (Male Subjects) Purpose and Rationale Hemoglobin can be affected by acute bleeding, by chronic suppression of erythrogenesis, or by dilution/concentration due to changes in the intravasal volume.

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Procedure PMN count is assessed from whole blood taken from peripheral veins at predetermined timepoints. Evaluation Normal range: 1.7–7.5 G/L. Grade 1 definition: 1.3-fold ULN. Grade 2 definition:1.0–1.3 G/L. Grade 3 definition: 1.5 g/dL. Grade 2 definition: 10.0–11.9 g/dL. Grade 3 definition: 1.5 g/dL. Grade 2 definition: 9.5–10.9 g/dL. Grade 3 definition: 0) (Arendt-Nielsen et al. 2000; Hay et al. 2016). Critical Assessment of the Method

As facilitated temporal summation is a feature in neuropathic pain patients, it has been hypothesized that induction of temporal summation using electrical stimulation can be used as a biomarker of drug effects on neuropathic pain (Arendt-Nielsen et al. 2007b). In a recent study, drug effects of analgesic compounds, including several used in the treatment of neuropathic pain, could not be established using this evoked pain paradigm, while other evoked pain paradigms manage to demonstrate pharmacological effects convincingly. This appeared to be related to a higher intra-subject variability that may necessitate larger subject groups (Okkerse et al. 2017). High-Frequency Electrical Stimulation Purpose and Rationale

High-frequency electrical stimulation (HFS) of the human skin induces increased pain sensitivity in the surrounding unconditioned skin (Van den Broeke et al. 2014). It has been shown that sustained nociceptive input can induce activitydependent changes in synaptic strength within nociceptive pathways, leading to an amplification of nociceptive signals (Ikeda et al. 2003). This is thought to play a key role in the development and maintenance of chronic pain and in particular some forms of hyperalgesia (Latremoliere and

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Woolf 2009; Sandkühler 2009). HFS-induced hyperalgesia within the surrounding unconditioned skin mimics the phenomenon of secondary hyperalgesia (Meyer and Treede 2004). As such, it constitutes a suitable model to study the mechanisms underlying central sensitization of nociceptive pathways (Klein et al. 2008). Procedure and Evaluation

HFS is delivered to the test site, e.g., the volar forearm, and consists of 5 trains of 100 Hz pulses lasting 1 s, (10 s interstimulus interval; 2 ms single pulse duration) at 10 times the detection threshold (Pfau et al. 2011). The electrical stimulation is generated by a constant-current electrical stimulator and delivered to the skin using a specifically designed electrode that has been demonstrated to activate peptidergic nociceptive afferents in the skin (Klein et al. 2004). The heterotopical effect of HFS is usually characterized using mechanical punctate stimuli. The test stimuli are applied to the skin surrounding the area onto which HFS is applied as well as to the same skin area on the contralateral arm, which serves as control to take into account a possible time-dependent habituation (van den Broeke et al. 2014). The intensity of perception elicited by the three types of test stimuli is assessed using a numerical rating scale (NRS). After approximately 1 h, the level of heterotopical hyperalgesia starts to diminish, however is still measurable and significant from baseline up to 8 h after HFS (Pfau et al. 2011).

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hyperalgesia is measurable can be carefully regulated and is relatively stable over the first hour after application of HFS (Pfau et al. 2011). The major disadvantage of this method is that is has not been used to demonstrate pharmacodynamic effects of analgesic drugs, although one recent study did show that the model can be combined with the assessment of drug effects (Vo et al. 2016). Modifications of the Method

Modifications of the method are primarily related to the type of sensory stimulus to determine the heterotopical hyperalgesic effect and to the quantification. Heterotopical hyperalgesia can be demonstrated for mechanical punctate stimuli, but also for thermonociceptive stimuli induced by heat probes or laser stimulation. Van den Broeke et al. used the model in conjunction to event related potentials to objectively demonstrate the hyperalgesic phenomena (Van den Broeke et al. 2014).

Electrical Muscle Stimulation

Critical Assessment of the Method

Purpose and Rationale Electrical stimulation of muscle tissue can be used to elicit both local and referred muscle pain. It possesses the ability to generate referred muscle pain in an “on and off” manner, and it is capable of maintaining referred pain for at least 10 min (Laursen et al. 1997). Intramuscular electrical stimulation appears to be used more often to study the nature of muscle pain than as a model to determine the pharmacodynamic effects of new analgesic compounds.

HFS offers an alternative to other models that lead to secondary hyperalgesia, such as the capsaicin model or the UVB model with some important advantages. The major advantage versus the UVB model is that the mechanism underlying the secondary hyperalgesia is thought to involve heterosynaptic facilitation and, hence, to constitute a suitable model of central sensitization of nociceptive pathways (Klein et al. 2008), while the secondary hyperalgesia in the UVB model is thought to be due to a more peripheral sensitization of nociceptors, induced by inflammation (Bishop et al. 2009). The interval during which the secondary

Procedure and Evaluation In the intramuscular electrical stimulation paradigm, two needle electrodes with uninsulated tips are inserted into a muscle (e.g., the musculus tibialis anterior). A computer-controlled constant current stimulator is used to induce referred pain in the ventral part of the ankle by stimulating the muscle (Laursen et al. 1997). Each stimulation consists of five constant current rectangular pulses (1 ms) delivered at 200 Hz. The referred pain threshold is defined as the lowest stimulus intensity required for the subject to notice a “just barely

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painful” sensation in the referred pain area. Referred pain thresholds are determined by a staircase regime consisting of five ascending and four descending series of stimuli (Gracely 1994; Laursen et al. 1997). Critical Assessment of the Method Electrical muscle and skin stimulation can use the same modalities which makes it possible to compare both models. A disadvantage of the model is that referred pain due to intramuscular electrical stimulation does not occur in all subjects; approximately, three quarters of patients experience it (Laursen et al. 1997). The referred pain typically arises approximately 40 s after the onset of electrical stimulation, which may mean that temporal summation is involved (Laursen et al. 1997). Modifications of the Method Modifications can be made with the stimulation settings. Pulse range of 100–200 Hz has been described, as well as a pulse width of 1–2 s (Laursen et al. 1997; van den Broeke et al. 2014).

Electrical Visceral Stimulation In the viscera, it is difficult to determine the pain threshold to a single stimulus, whereas the pain threshold is easily determined if a train of stimuli is used. Furthermore, the referred pain area gradually expands if stimulation is continued for 120 s (Arendt-Nielsen et al. 1997).

Chemical Stimulation Administration of algogenic substances to the skin, muscle, or viscera is believed to be a close resemblance of clinical inflammation. Various substances have been used to induce cutaneous hyperalgesia. The most commonly used are capsaicin, nerve growth factor (NGF), glutamate, mustard oil, and menthol, but other chemical stimulation models exist as well. Intramuscular injection of chemical substances is less common and harder to control in a clinical trial. The esophagus is the target organ when it comes to chemical viscera stimulation because of its easy access.

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Chemical Skin Stimulation Capsaicin Purpose and Rationale

Capsaicin is a highly selective agonist for TRPV1, notorious for its pungent property in red chili peppers. TRPV1 channels are major transducers of physically and chemically evoked sensations (Hauck et al. 2015). The vanilloid 1 subtype is activated by noxious heat (
43  C) (Frølund and Frølund 1986) and is expressed on C-fibers, and on a subset of Aδ-fibers (Le Bars et al. 1979). The direct effects of applying topical capsaicin are burning sensations, hyperalgesia, allodynia, and erythema. In addition, it triggers the release of proinflammatory agents at peripheral terminals, such as substance P and calcitonin gene-related peptide (CGRP) (Kakigi 1994; Yarnitsky et al. 2010). Procedure and Evaluation

Capsaicin can be administered topically and intradermal. Intradermal injection with capsaicin 0.1 mg can cause hyperalgesia, but a dose of 100 mg or higher is needed to produce hyperalgesia for an hour (Simone et al. 1987). A dose of 100 mg is most frequently used (Baron et al. 1999; Serra et al. 1998; Torebjörk et al. 1992). Topical administration of capsaicin in low concentrations (up to 3%) can cause temporary mechanical and heat hyperalgesia. Sensitization can be induced by preheating the skin to 45  C for 5 min with a thermode directly before capsaicin application. Sensitization can be rekindled throughout a study by reheating the skin up to 40  C for 5 min. Application of the capsaicin is most commonly done on the forearm or the back, but can be done on any area of the skin. Topical application of capsaicin can induce peripheral and central sensitization shown respectively by primary mechanical/thermal hyperalgesia and by secondary mechanical hyperalgesia/allodynia. This pain model can therefore be used to study novel analgesic compounds targeting these typical symptoms of neuropathic pain. Peripheral sensitization is caused by modulation of peripheral

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afferents and is therefore restricted to the site of injury, i.e., primary hyperalgesia. Central sensitization is caused by modulation of the nociceptive processing in the central nervous system. To quantify the effects of this pain model, laser stimulation (LS) can be used in combination with electro-encephalogram (EEG). Critical Assessment of the Method

Peripheral sensitization is closely linked to primary hyperalgesia, and central sensitization is partly explained by hyperalgesia in the surrounding area, i.e., secondary hyperalgesia. Moreover, nociceptive integration at spinal cord level may include non-nociceptive mechanoreceptors. Therefore, central sensitization may also cause Aβ-fiber mediated pain (allodynia). Higher concentrations (capsaicin 8%) initially causes increased sensitivity but is then followed by a decrease in sensitivity due to a reduced TRPV1 expression (Messeguer et al. 2006; van Amerongen et al. 2016). High concentration capsaicin is indicated in postherpetic neuralgia. Besides, capsaicin may also have a neurolytic property, where it (partly) eliminates epidermal nerve fibers (ENFs) in treated areas over time (Dworkin et al. 2010). Re-innervation occurs over time (Hüllemann et al. 2015).

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nociceptors so that they show an increased response to thermal and chemical stimuli (Bennett 2007). Administration of NGF to human skin evokes mechanical sensitization and profound hyperalgesia to thermal stimuli that develops within 3 h postinjection and peaks between day 1 and 7 (Dyck et al. 1997). Sensitization to heat and hyperalgesia to cold develops within days after injection and lasts up to 21 days, while hypersensitivity to mechanical impact stimuli develops over a longer period and persists for at least 49 days (Rukwied et al. 2010). Intradermal NGF administration provokes a pattern of sensitization that can be used as experimental model for neuropathic pain (Rukwied et al. 2010). Procedure and Evaluation

Nerve Growth Factor Injection

One microgram of human recombinant lyophilized NGF is dissolved in 50 μL saline and injected intradermally into the central volar forearm. The same volume of saline is administered into the contra-lateral site as vehicle control (Rukwied et al. 2010). Vasodilatation upon NGF- and saline-injection can be recorded by laser Doppler imaging. Nociceptor sensitization can be explored to mechanical (touch, pinprick, pressure), thermal (cold, heat), and electrical (current pulses) stimuli. Stimuli for investigating static and dynamic allodynia and pinprick hyperalgesia are administered 5–7 cm distal from the injection site and continued in steps of 1 cm until the subject reports a definite increase of pinprick pain or switch from touch to an aversive sensation (Rukwied et al. 2010). The point where this starts is marked on the skin and the distance to the injection site measured. Pain thresholds and subjective scores with NRS/VAS can be used to evaluate the mechanical, thermal, or electrical stimulation.

Purpose and Rationale

Critical Assessment of the Method

NGF is a member of the neurotrophin family, which also includes brain derived neurotrophic factor (BDNF), neurotrophin-3 (NT3) and neurotrophin-4/5 (NT4/5). NGF binds to both a high affinity tyrosine kinase receptor trkA and a low affinity receptor p75. NGF can sensitize

Increased levels of NGF have been reported in human painful disorders including arthritis (Kidd and Urban 2001). Injection of NGF therefore appears to mimic processes found in clinical disease (Olesen et al. 2012). Even though NGF may also be upregulated in the UVB burn (Bishop et al.

Modifications of the Method

There are several variations that need to be addressed when designing a study utilizing capsaicin, e.g., concentration of the capsaicin, dose administration (intradermal or topical), vehicle of the capsaicin (alcohol or cream), duration of the application, location of administration, and pre-/ rekindling.

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2007), anti-NGF has been shown to only partially reduce UVB induced hyperalgesia (Bishop et al. 2007). Apparently, the NGF induced mechanism of mechanical sensitization is different to UVB evoked primary hyperalgesia. NGF induces a particularly long lasting mechanical sensitization including static allodynia and cold hyperalgesia without any visible signs of inflammation and therefore adds to the spectrum of human evoked pain models (Rukwied et al. 2010). The longlasting local allodynia and hyperalgesia after subcutaneous or intradermal injection, up to 49 days after injection, form the most important disadvantage of the model. Even though considered a model for neuropathic pain, it is unlikely that central sensitization plays a role. Modifications of the Method

Systemic administration of NGF 1 μg/kg i.v. has been shown to lead to mild to moderate muscle pain mainly in the bulbar and truncal musculature that lasted 2–8 days (Petty et al. 1994). NGF has been injected into the musculus masseter to induce allodynia and hyperalgesia and as a model of myofascial temporomandibular disorder pain (Svensson et al. 2003). Other Chemical Mediated Models Mustard Oil

Mustard oil is a plant-derived irritant. The noxious effects of mustard oil are currently ascribed to specific activation of the cation channel transient receptor potential, subfamily A, member 1 (TRPA1) in nociceptive neurons (Olesen et al. 2012). Topical administration leads to a burning pain in the area exposed to mustard oil as well as secondary allodynia and hyperalgesia in the surrounding unaffected area, similar to the topical capsaicin model (Koltzenburg et al. 1992).

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expressing C-nociceptors and activates coldspecific Aδ-fibers (Binder et al. 2011). Topical application has been used as an evoked pain model of cold hyperalgesia, which is a clinical symptom that occurs frequently in patients with peripheral or central nervous system lesions (Hatem et al. 2006). In addition to cold hyperalgesia, the model elicits primary and secondary mechanical (pinprick) hyperalgesia combined with the sensation of burning (Binder et al. 2011). The menthol model has been shown to be sensitive to a range of analgesics (Altis et al. 2009).

Chemical Muscle Stimulation Nerve Growth Factor Intramuscular injection with NGF is most commonly done in the musculus tibialis anterior or musculus masseter (Andersen et al. 2008; Svensson et al. 2008). It induced a long-lasting hyperalgesia and lower pressure pain threshold can be observed, lasting up to 4 days in the musculus tibialis anterior and up to 14 days in the musculus masseter (Andersen et al. 2008; Svensson et al. 2008). An advantage of the intramuscular NGF paradigm is the long-lasting hyperalgesia which can simulate clinical pain more than most other paradigms, but this is also the disadvantage where ethical consideration may play a role. The paradigm is difficult to control where hyperalgesia is dependent on the dose and the size of the muscle (Andersen et al. 2008).

Chemical Visceral Stimulation Esophagal (Gut) Perfusion with Acid, Alcohol, Glycerol, Capsaicin, and Hypertonic Saline Purpose and Rationale

Menthol

Menthol acts as an agonist on the transient receptor potential cation channel subfamily M member 8 (TRPM8) receptor. The topical application of high concentration (40%) menthol is thought to activate and sensitize cold-sensitive TRPM8-

Chemical stimulation of the GI tract may be used to stimulate C-fibers selectively via TRPV1 receptors and modulate the visceral pain system due to their sensitization effects. Having a model of central sensitization of the viscera can be helpful in the development of new analgesics, as this is

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thought to be an important element of chronic visceral pain.

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with the mucosa, and the motility has minor impact on the results.

Procedure and Evaluation

Using acid to stimulate the esophagus is the most used method to sensitize the gut (Bernstein and Baker 1958; Demedts and Tack 1998; Drewes et al. 2005; Reddy et al. 2005). However, other chemicals such as alcohol, glycerol, capsaicin, and hypertonic saline are used to stimulate the gut as well (Louvel et al. 1996; Drewes et al. 2003a, b). The chemical compound is usually infused into a container/bag residing in the esophagus with a small perfusion hole to release the compound into the esophagus. Chemical stimulation is able to modulate the visceral pain system by selectively activating nonmyelinated C-fibers for a longer amount of time. This tonic activation may result in central sensitization effects, which can be quantified by subsequent thermal, electrical, or mechanical stimulation. Critical Assessment of the Method

A high variation in the outcome measures is seen with this model. The reproducibility is challenging because several factors are hard to control, like exposure time to the chemical stimulus, size of the treated area, and latency time to onset of effects. Furthermore, tissue injury results in the release of multiple molecules working together, and to mimic this situation it may be necessary to use a mixture of chemical substances (Reddy et al. 2005). Blinding this procedure is difficult, since subjects are able to taste the compound. Therefore, the experimental setup requires that both subject and assessor are ignorant of the possible influence of the compound on the pain threshold (Drewes et al. 2003b). Modifications of the Method

Each chemical substance will have an impact on the results. For example, the motility may interfere with the results when glycerol is used. Other stimuli, such as injection of hypertonic saline and application of capsaicin, the pain is elicited shortly after the chemical comes into contact

Discussion Healthy Subjects Versus Patients Despite many advances in the last decades in understanding pain, the development of new analgesic compounds lacked behind. In almost 60 years, only 59 compounds were registered for the treatment of pain, of which two thirds were specifically developed as analgesics (Kissin 2010). Historically, pain states have been classified and investigated on the basis of a disease state. Based on preclinical animal models, target patient populations were selected. In patient studies, efficacy is then reported as change in the patient’s response to pain (McQuay and Moore 2013). Unfortunately, several promising compounds have failed in this late-stage development where pharmacotherapy only provides meaningful pain relief in less than 50% of patients with neuropathic pain (Finnerup et al. 2010, 2015). But a negative outcome does not automatically mean inefficacy of the compound. Pathophysiological mechanisms of pain vary between individuals with the disease state. Selecting and clustering the patients in groups of pathophysiology rather than disease might be necessary to obtain meaningful results. The use of human evoked pain models can provide more information. Multimodal testing in healthy volunteers can provide information about the analgesic activity of the compound and possibly find the active dose level range. In a way, by using different pain modalities, the results will create a certain pain profile of the compounds (Okkerse et al. 2017). These results may reflect effects of analgesic drugs on mechanisms involved in clinical pain. Thus, multimodal pain testing may aid in determining the optimal target population for new analgesic compounds based on their profile of effects on a diversity of pain mechanisms and depending on the contribution of each of these mechanisms in clinical pain phenotypes. In several chronic pain populations, such as chronic whiplash,

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rheumatoid arthritis, vulvodynia, and fibromyalgia, changes in pain tolerance levels, pain modulation, and augmented brain responses and altered responses to analgesics have been found (Daenen et al. 2014; Hampson et al. 2013; van Laarhoven et al. 2013). Using evoked pain in these patients can provide insight into the analgesic mechanisms – or lack thereof – in these altered pain states (Olesen et al. 2012). In patients with chronic (neuropathic) pain, different sensory profiles exist. These profiles possibly match with different neurobiological mechanism of pain (Baron et al. 2017).

Predictive Value of Models for Drug Development Human evoked pain models in healthy volunteers can be conducted in standardized laboratories. Factors like stimulus intensity, frequency, duration, and location can be controlled, and when a model is stable and reproducible, it can be regarded as suitable for pharmacodynamic evaluation of new analgesic drugs. Using pain models in healthy volunteers has important advantages over assessing the effects of new drugs in patients with pain; the pain elicited in human pain models is predictable in its intensity while clinical pain will naturally fluctuate, and in pain models analgesic properties can be investigated without the influence of accompanying symptoms that are often seen in patients with pain. However, it should always be asked whether a pain model at all resembles naturally occurring pain. Clinical pain is a subjective perception, influenced by cognitive processes, by emotions, social context, and even cultural background, while pain models are solely based on the infliction of a noxious stimulus and its response. An important question is whether or not a positive result in a certain evoked model is also predictive of clinical efficacy. Two approaches have been used to investigate this. Moore and colleagues investigated which naturally occurring pain was physiologically most in agreement with evoking a pain response causing the same type of pain. For instance, they concluded that intramuscular electrical stimulation closely matched clinical acute musculoskeletal pain

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(Moore et al. 2013). Oertel and Lötsch evaluated the differences between human pain models and clinical efficacy. First they looked at which drugs were effective in different pain conditions (e.g., NSAIDs were effective for inflammatory arthritis), then they investigated which drugs were effective in which pain model (e.g., NSAIDs influence pain response in laser evoked pain). If a certain drug was effective both in the model and in the particular clinical setting, the model was concluded to possibly be predictive for the type of clinical pain. Some level of agreement could be observed for a large number of pain models with many different clinical forms of pain (Oertel and Lötsch 2013). In another review, the mutual agreement between pain models and clinical efficacy was statistically assessed. It was observed that a small set of pain models seemed predictive for efficacy in the clinic, for example, capsaicin induced hyperalgesia with mechanical stimulation is associated with trigeminal neuralgia and renal colic, and UVB induced hyperalgesia in combination with heat stimulation can be linked to burn injuries or postoperative pain (Lötsch et al. 2014). Several reviews investigated which evoked pain models were sensitive to the analgesic effects of different classes of analgesics in healthy subjects (Oertel and Lötsch 2013; Okkerse et al. 2017; Staahl et al. 2009a, b). With the aid of these studies, well-considered decisions can be made on which evoked pain models to include in studies investigating potentially analgesic compounds.

Multi-model Assessment of Pain Pain comes in various types and can originate in many different tissues. It is obvious that different analgesics will influence different types of pain according to their respective mechanism of action. If an analgesic drug with a novel mechanism of action is studied, it can occur that a single pain model, thought to relate to a specific clinical pain syndrome, demonstrates lack of efficacy of the new compound. In these cases, a combination of human evoked pain models can be used to screen for possible analgesic effects of these compounds. For instance, a combination of a mechanical,

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thermal, and electrical pain models: pressure stimulation assesses the nociception generated from within the muscle (Polianskis et al. 2001); cold pain induced by the cold pressor test mainly activates C-fibers in the skin (Olesen et al. 2012); heat stimulation initially activates Aδ-fibers in the skin, followed by C-fiber activation; induction of inflammation via sunburn or UVB induces the production of cytokines that lead to sensitization of cutaneous nociceptors (Bishop et al. 2009); and electrical stimulation directly stimulates sensory nerve endings of both Aδ and C-fibers in the skin (Handwerker and Kobal 1993). This multimodal testing with a battery of different pain models has been performed by multiple study groups (Enggaard et al. 2001; Okkerse et al. 2017; Olesen et al. 2014; Staahl et al. 2006). The batteries have in common that they induce pain via different modalities and in different tissues and mimics clinical pain better than a single pain model can. The multimodal batteries can be used to profile the analgesic effects of new drugs, to obtain the optimal dose of new analgesics, and to benchmark new drugs against profiles of well-known analgesics (Okkerse et al. 2017).

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124 and Fmri and their correlation with evoked pain. BMC Anesthesiol 8:8 Roberts K, Shenoy R, Anand P (2011) A novel human volunteer pain model using contact heat evoked potentials (CHEP) following topical skin application of transient receptor potential agonists capsaicin, menthol and cinnamaldehyde. J Clin Neurosci 18:926–932 Rosendal L, Larsson B, Kristiansen J, Peolsson M (2004) Increase in muscle nociceptive substances and anaerobic metabolism in patients with trapezius myalgia: microdialysis in rest and during exercise. Pain 112:324–334 Rukwied R, Mayer A, Kluschina O, Obreja O (2010) NGF induces non-inflammatory localized and lasting mechanical and thermal hypersensitivity in human skin. Pain 148:407–413 Ruscheweyh R, Weinges F, Schiffer M, Bäumler M (2015) Control over spinal nociception as quantified by the nociceptive flexor reflex (RIII reflex) can be achieved under feedback of the RIII reflex. Eur J Pain 19:480–489 Sandkühler J (2009) Models and mechanisms of hyperalgesia and allodynia. Physiol Rev 89:707–758 Sandrini G, Alfonsi E, Ruiz L, Livieri C et al (1989) Age-related changes in excitability of nociceptive flexion reflex. An electrophysiological study in school-age children and young adults. Funct Neurol 4:53–58 Sayre RM, Desrochers DL, Wilson CJ, Marlowe E (1981) Skin type, minimal erythema dose (MED), and sunlight acclimatization. J Am Acad Dermatol 5:439–443 Schaffler K, Nicolas LB, Borta A, Brand T et al (2017) Investigation of the predictive validity of laser-EPs in normal, UVB-inflamed and capsaicin-irritated skin with four analgesic compounds in healthy volunteers. Br J Clin Pharmacol 83:1424–1435 Schouenborg J, Weng HR, Kalliomäki J, Holmberg H (1995) A survey of spinal dorsal horn neurones encoding the spatial organization of withdrawal reflexes in the rat. Exp Brain Res 106:19–27 Schulte H, Segerdahl M, Graven-Nielsen T, Grass S (2006) Reduction of human experimental muscle pain by alfentanil and morphine. Eur J Pain 10:733–741 Sengupta J, Gebhart G (1994) Gastrointestinal afferent fibers and sensation. In: Johnson LR (ed.) Physiology of the Gastrointestinal Tract, 3rd edn. Raven, New York, pp 483–519 Serra J, Campero M, Ochoa J (1998) Flare and hyperalgesia after intradermal capsaicin injection in human skin. J Neurophysiol 80:2801–2810 Shukla S, Torossain A, Duann JR, Keung A (2011) The analgesic effect of electroacupunture on acute thermal pain perception – a central neural correlate study with fMRI. Mol Pain 7:45–56 Simone DA, Ngeow JYF, Putterman GJ, LaMotte RH (1987) Hyperalgesia to heat after intradermal injection of capsaicin. Brain Res 418:201–203 Skljarevski V, Ramadan NM (2002) The nociceptive flexion reflex in humans – review article. Pain 96:3–8

P. Siebenga et al. Smith GM, Egbert LD, Markowitz RA, Mosteller F (1966) A VAS is used to assess the subject’s pain intensity. J Pharmacol Exp Ther 154:324–332 Song J, Davey C, Poulsen C, Luu P et al (2015) EEG source localization: sensor density and head surface coverage. J Neurosci Methods 256:9–12 Staahl C, Drewes AM (2004) Experimental human pain models: a review of standardised methods for preclinical testing of analgesics. Basic Clin Pharmacol Toxico l95:97–111 Staahl C, Christrup LL, Andersen SD, Arendt-Nielsen L et al (2006) A comparative study of oxycodone and morphine in a multi-modal, tissue-differentiated experimental pain model. Pain 123:28–36 Staahl C, Olesen AE, Andresen T, Arendt-Nielsen L et al (2009a) Assessing analgesic actions of opioids by experimental pain models in healthy volunteers – an updated review. Br J Clin Pharmacol 68:149–168 Staahl C, Olesen AE, Andresen T, Arendt-Nielsen L et al (2009b) Assessing efficacy of non-opioid analgesics in experimental pain models in healthy volunteers: an updated review. Br J Clin Pharmacol 68:322–341 Svendsen O, Edwards CN, Lauritzen B, Rasmussen AD (2005) Intramuscular injection of hypertonic saline: in vitro and in vivo muscle tissue toxicity and spinal neurone c-fos expression. Basic Clin Pharmacol Toxicol 97:52–57 Svenson P, Arendt-Nielsen L (1995) Induction and assessment of experimental muscle pain. J Electromyogr Kinesiol 5:131–140 Svensson P, Cairns BE, Wang K, Arendt-Nielsen L (2003) Injection of nerve growth factor into human masseter muscle evokes long-lasting mechanical allodynia and hyperalgesia. Pain 104:241–247 Svensson P, Wang K, Arendt-Nielsen L, Cairns BE (2008) Effects of NGF-induced muscle sensitization on proprioception and nociception. Exp Brain Res 189:1–10 Thalhammer JG, LaMotte RH (1982) Spatial properties of nociceptor sensitization following heat injury of the skin. Brain Res 231:257–265 Torebjörk HE, Lundberg LE, LaMotte RH (1992) Central changes in processing of mechanoreceptive input in capsaicin-induced secondary hyperalgesia in humans. J Physiol 448:765–780 Tracey I, Mantyh PW (2007) The cerebral signature for pain perception and its modulation. Neuron 55:377–391 Treede RD, Lorenz J, Baumgärtner U (2003) Clinical usefulness of laser-evoked potentials. Neurophysiol Clin 33:303–314 Tuveson B, Leffler AS, Hansson P (2006) Time dependent differences in pain sensitivity during unilateral ischemic pain provocation in healthy volunteers. Eur J Pain 10:225–232 Vo L, Hood S, Drummond PD (2016) Involvement of opioid receptors and α2-adrenoceptors in inhibitory pain modulation processes: a double-blind placebo-controlled crossover study. J Pain 17:1164–1173 Wager TD, Atlas LY, Lindquist MA, Roy M et al (2013) An fMRI-based neurologic signature of physical pain. N Engl J Med 368:1388–1397

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Pharmacodynamic Evaluation: Drug Dependency and Addiction V. Tenev and M. Nikolova

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Opioids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Pharmacological Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Pharmacologic Interactions of Opioids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Stimulants: Cocaine and Amphetamine and Its Derivatives . . . . . . . . . . . . . . . . . . . . . . . 135 Cocaine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Amphetamine and Its Derivatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Marijuana and Synthetic Cannabinoids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 Benzodiazepines and Barbiturates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Benzodiazepines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 Barbiturates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Hallucinogens (LSD, Mescaline, Magic Mushrooms, Ayahuasca, Psilocybin, Dimethyltryptamine, DMT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Dissociative Anesthetics (Nitrous Oxide, Ketamine, Dextromethorphan, Phencyclidine, Salvia divinorum) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Alcohol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Drug Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 Nicotine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

V. Tenev (*) Department of Psychiatry, University Hospital, University of Iowa, Iowa city, IA, USA e-mail: [email protected] M. Nikolova University Hospital Alexandrovska, Clinic of Nephrology, Medical University, Sofia, Bulgaria e-mail: [email protected] © Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_49

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V. Tenev and M. Nikolova Bath Salts (Synthetic Cathinones) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Pharmacodynamic Interactions Between Addictive Substances . . . . . . . . . . . . . . . . . . . . . . . 157 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Abstract

The intake of psychoactive substances has accompanied mankind since the dawn of human race. Different psychoactive substances have been extracted and synthesized in human history, and their pharmacodynamic properties have been studied thoroughly. This chapter presents the current knowledge on the pharmacodynamic profile of the most common illicit and recreational drugs and their influence on drug dependency and addiction. The substances with abuse potential grow exponentially. Our clinical and practical knowledge has still a long way to go in catching up with these realities. The dynamic interactions between different drugs observed in vitro cannot be fully replicated in vivo. We rely on randomized clinical trials, case reports, and own clinical experience with patients. Different clinical scenarios could provide further evidence and hypotheses regarding the sought and adverse effects of substances, their interactions with legal drugs and medications, and their impact on different stages of metabolism. In this chapter we attempted to summarize the available reliable data and suggest some ideas for future observation and research.

Introduction Pharmacodynamics is a branch of pharmacology that studies the molecular, biochemical, and physiological effects and the mechanisms of action of a substance (i.e., a drug) on the human body (Campbell and Cohall 2017). All substances that affect the human body either influence normal biochemical or physiological processes or inhibit the vital processes of an “invader” to the body – microorganism or parasite. On molecular level, overall seven major mechanisms of drug action have been described in the human body:

– Stimulation/activation of receptor systems (agonism, e.g., beta-agonists in asthma) – Inhibition/depression of receptor systems (antagonism, e.g., calcium channel blockers in hypertension) – Blocking of receptors without further activation or inhibition (“silent” antagonism, e.g., naloxone in opioid intoxication) – Stabilization of receptors (e.g., buprenorphine in opioid dependency) – Exchange of substances (e.g., digitalis glycosides, anesthetics, etc.) – Initiation/activation of beneficial chemical reactions (acetyl cysteine as initiator of free radical scavenging) – Initiation/activation of harmful chemical reactions (e.g., cytotoxic treatment) Pharmacodynamics of a substance comprises of three major types of processes: (1) binding to structures (receptors) in the body and resulting in desired and undesirable (adverse) effects, (2) postreceptor effects, and (3) interactions with other substances within the body. The binding to certain structures within the body (receptors, membrane structures, proteins, enzymes, ion pimps, etc.) leads to further molecular, biochemical, and physiological effects. The difference between the doses leading to desired effects and the one that leads to adverse events is the therapeutic window of a drug. The duration of action of a drug is the length of time that the drug remains effective. From pharmacodynamic point of view, the receptor binding, the therapeutic and adverse effects, the therapeutic window, and the duration of action in drug dependency and addiction depend mainly on the receptor target, the properties of the drug itself and the dose taken, and the concentration of the drug at the receptor site and on certain physiological changes in the body (aging, intake of other substances/drugs, genetic polymorphisms

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Liberation (the release of a drug from its formulation) ↓ Absorption (the process of internalization of the drug to the circulation) ↓ Distribution (the dissemination of the drug within the body fluids and tissues/organs) ↓ Metabolism (the transformation of the drug to active and/or inactive substances) ↓ Excretion (the clearance of the substance and its metabolites from the body) Fig. 1 LADME scheme of a pharmacokinetic profile of a drug

and conditions, metabolic disturbances [thyrotoxicosis, malnutrition, renal or hepatic failure, dyselectrolytemia, etc.]). Moreover, the concomitant abuse of several substances, including alcohol and prescription medications, could potentiate the effects of illicit drugs due to pharmacodynamic (and pharmacokinetic) interactions. All these factors alter the pharmacodynamics of illicit and recreational drugs and modify the profile of dependency, addiction, and withdrawal. On the other hand, pharmacokinetics studies the effects of the body on the drug: drug absorption, distribution, metabolism, and excretion (Ruiz-Garcia et al. 2008). The pharmacokinetic profile of a drug can be presented schematically in the so-called LADME sequence (Ruiz-Garcia et al. 2008) (Fig. 1). Sometimes the terms metabolism and excretion are grouped together in the term “elimination.” In terms of drug abuse and dependence, pharmacodynamics and pharmacokinetics are often referred to as toxodynamics and toxokinetics because of the toxic effects of illicit drugs and the high rate of adverse and toxic reactions in this patients’ population. The pharmacodynamic and the pharmacokinetic profiles of illicit and recreational drugs define their effects, adverse reactions, addiction, and withdrawal symptoms. In general, the illicit drugs with more rapid absorption and entry into the circulation and the central nervous system, higher bioavailability, shorter half-life, high free drug levels, smaller volume of distribution, and higher clearance rate are more toxic and tend to cause higher rate of addiction and more severe withdrawal symptoms. Most of the drug users

tend to adapt the route of administration, the dose and the additives, and/or the coadministered illicit drugs with additive/synergistic effects to their individual cravings in order to produce maximum drug effect for maximum time. Before we start discussing the pharmacodynamics of addictive substances, we have to answer the following important questions: Which substances actually make us feel happy, and which parts of the brain give the signals of happiness? The substances that make us feel happy are physiological mediators in the brain that are secreted in response to a stimulus giving us the sensation of comfort or reward. These are the endorphins, serotonin, dopamine, and oxytocin. The physiological sites where these stimuli act are the limbic system (and particularly nucleus accumbens), the memory/experience part of the brain (hippocampus and amygdala), and the cortex (the frontal and prefrontal areas that supervise the first two parts stated) (Powledge 1999; Volkow and Morales 2015). This “reward pathway” in the brain is a very ancient dopaminergic pathway that was present long before humans in the brain of mammals. It plays crucial role for the motivation of behavior. It starts in the midbrain (in the ventral tegmental area) and extends to nucleus accumbens, hippocampus, amygdala, and the prefrontal and frontal cortical areas that are meant to inhibit all the structures before them in the pathway (Powledge 1999; Volkow and Morales 2015). After disinhibition of the subcortical structures, a vicious circle of constant “reward” stimulation is closed (“the reward cycle”).Virtually all illicit drugs follow this pathway of addiction, along with nicotine, caffeine,

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and alcohol (Powledge 1999). This neuromediator pathway is stimulated not only by natural stimuli (success, victory, self-content from the achieved, etc.) but by stimulant medications and addictive types of self-destructive behavior, including eating disorders and gambling (Powledge 1999; Volkow and Morales 2015). All addictive substances tend to bind specific receptors in the brain that lead to liberation of serotonin, dopamine/norepinephrine, and/or oxytocin and therefore to imitate the state of comfort and/or the reward ensured by other natural stimuli in our everyday life, but the artificially induced state of happiness and/or excitement is more intense. Still, the administration of recreational drugs leads to structural and functional changes in neurons, called neuroplasticity. These adaptive changes alter the drug effect and metabolism and generate the need for more frequent administration in higher doses, which is called tolerance with further dependence and addiction. The sudden cease in drug intake leads to withdrawal symptoms. Withdrawal symptoms are often mediated by dopaminergic pathways and/or by extrahypothalamic corticotropin-releasing factor (CRF) system – release of CRF outside the hypothalamus (e.g., from the amygdala). This could explain the common withdrawal symptoms (sweating, changes in blood pressure and heart rate, abnormal peristaltics, joint pains, headache, etc.) for different illicit drugs, including opiates, stimulants, alcohol, nicotine, cannabinoids, etc. In other words, the majority of psychoactive substances tend to bind specific receptor in the central nervous system (CNS) and to mimic the effects of endogenic substances with the effect being more potent and with longer duration: opioid, benzodiazepine/gamma-aminobutyric acid (GABA), serotonin, dopamine, and cannabinoid receptors (Quinn et al. 1997; Sharma et al. 2012). Moreover, these receptors are known to interact and to lead to a neurochemical correlation between substances abused within the brain (Quinn et al. 1997). Stimulant drugs (cocaine, amphetamines, and amphetamine derivatives) act by causing an increase in dopamine levels within the synaptic cleft – by facilitating dopamine release and inhibiting dopamine reuptake (Nestler

V. Tenev and M. Nikolova

2005; Calipari and Ferris 2013). Cocaine also blocks sodium channels on cell membranes and has anesthetic effect (Volkow and Morales 2015). Nicotine activates N-cholinergic receptors and is known to stimulate catecholamine (dopamine, norepinephrine), glutamate, serotonin, acetylcholine, endorphin, and GABA release (Quinn et al. 1997; Benowitz 2009). Moreover, its primary metabolite, cotinine, is known to increase serotonin levels in the brain (Quinn et al. 1997). Only alcohol has no specific receptors in the brain and acts by altering the physiological properties of lipid membranes, modifying their fluidity and changing the receptor sensitivity to natural stimulant, and inhibiting neurotransmitters (Quinn et al. 1997). As it was stated above, the changes in the human body in response to the intake of substances, especially in receptor systems and signaling pathways in the brain, are referred to as plasticity (respectively, neuroplasticity). These changes are responsible for the development of tolerance, i.e., the need for more frequent administration of the addictive substance and in higher doses. Once this need evolves to imperative urge, an addiction has developed. From pharmacodynamic point of view, the major mechanisms underlying neuroplasticity are as follows: changes in receptor structure, type, distribution, and functionality, changes in signaling pathways, development of tolerance and cross-tolerance, involvement of other receptor pathways due to the cross-reaction between receptor systems, and development of new pathways for signal channeling (Dumas and Pollack 2008). Moreover, all addictive substances used for recreational purposes are known to cause permanent structural alteration in cells due to epigenetic and genetic effects, including microtubular toxicity, chromothripsis, genotoxicity, oncogenesis and embryo-/fetotoxicity, inhibition of tumor-suppressor genes (e.g., p53 by marijuana smoke) and activation of proto-oncogenes, etc. (Reece and Hulse 2016). The major mechanism behind the inheritable genetic abnormalities in illicit substance abuse is thought to be the process of chromothripsis – extensive genomic rearrangements and an oscillating pattern of DNA copy number levels due to microtubular

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damage and changes in the mitotic spindle. These alterations are curiously restricted to one or a few chromosomes. All illicit drugs are known to cause changes in sister chromatid exchange levels, in the mitotic spindle, in DNA fragmentation, and in gene systems that regulate the cell processes (including growth and development) (Dumas and Pollack 2008; Reece and Hulse 2016; Li and Lin 1998; Reece 2009). Having in mind the wider prevalence of illicit drug intake worldwide, the possibilities for transgenerational genotoxicity (terato- and oncogenicity and toxicity) raise serious and increasing concern (Benowitz 2009). A very important aspect of both pharmacodynamics and pharmacokinetics of illicit drugs are the drug interactions that determine the potentiation or inhibition of effect and the possibilities to influence withdrawal and cessation of illicit drug abuse by the administration of their analogues or medications that block their action or ameliorate abstinence symptoms. Pharmacodynamic profiles of commonly abused drugs and their significance for the treatment of withdrawal and addiction.

Opioids The term “opiate” refers to a substance derived from opium, i.e., the alkaloids found in the plant Papaver somniferum (opium poppy). Three main psychoactive compounds are isolated from this plant – morphine, codeine, and thebaine – along with several alkaloids that lack psychoactive properties and have only spasmogesic effect (papaverine, noscapine, and about 24 more substances). Other morphine-like substances that have been isolated in small amounts from the opium poppy are dihydrocodeine, metopon, oxycodone, and oxymorphol. The term “opioids,” on the other hand, includes a large group of substances that interact with the opioid receptors in a morphine-like way, producing analgesic, anesthetic, and psychoactive effects. Opioids have been familiar to humans for thousands of years for their analgesic and psychoactive properties. These substances are widely used for recreational purposes, including

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their euphoric, hallucinogenic, and other psychoactive effects. In 2013 up to 0.8% of the population aged 15–65 years worldwide reported using opioids for recreational purposes (Status and Trend Analysis of Illicit Drug Markets 2015). Their rewarding effects, explained by activation of dopaminergic pathways in the “reward cycle” of the brain (including parts of the limbic system), are the main cause of opioid abuse, addiction, and dependence. Severe withdrawal symptoms develop in abrupt drug cessation; therefore proactive treatment is needed. According to their presence in nature, opioids are classified in several groups (Ghelardini et al. 2015; Koob and Le Moal 2006): – Natural – morphine, codeine, thebaine, and salvinorin A (kappa-agonist) – Morphine esters – morphine diacetate (heroin), morphine dinicotinate, morphine dipropionate, etc. – Semisynthetic (created from natural opiates or their esters) – hydromorphone, hydrocodone, oxymorphone, oxycodone, buprenorphine, ethylmorphine, etc. – Synthetic – fentanyl, methadone, tramadol, tapentadol, dextropropoxyphene, pethidine, levorphanol, etc. – Endogenic – endorphins, enkephalins, dynorphins, and endomorphins The adverse effects of opioid abuse include cognitive impairment, gastrointestinal symptoms (constipation, nausea, vomiting), hypotension, sexual dysfunction, and respiratory and cardiovascular center depression. Three major types of opioid receptors have been identified and cloned: mu, delta, and kappa. An additional opioid substance binding type of receptor is the ORL-1 (opioid receptor-like 1, or nociceptin receptor). Three additional types of opioid ligand binding receptors have been discovered – zeta and epsilon opioid receptors and sigma receptors. All types of opioid receptors have different distributions and physiological roles (Ghelardini et al. 2015; Koob and Le Moal 2006; Stein et al. 2003; Gosnell et al. 2013):

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– Mu: distributed in the brain (cortex, thalamus, striosomes, periaqueductal gray matter, rostral ventromedial medulla), in the spinal cord, in the peripheral sensory neurons, and in the gastrointestinal tract and other peripheral structures. Subtypes: mu1 (analgesia, dependence), mu2 (euphoria, miosis, respiratory and gastrointestinal motility depression, physical dependence), and mu3 (vasodilation?). – Kappa: distributed in the brain (hypothalamus, periaqueductal gray matter, claustrum), spinal cord, and peripheral sensory neurons. Subtypes: kappa1, kappa2, and kappa3, responsible for analgesic, depressive, hallucinogenic, miotic, diuretic, dysphoric, neurodepressive, sedative, and neuroprotective effects. – Delta: distributed in the brain (deep cortex, pontine nuclei, amygdala, olfactory bulb) and peripheral sensory neurons. Subtypes: delta 1 and delta 2, responsible for analgesic and antidepressant effects, convulsogenic properties, and dependence. – ORL-1 (nociceptin receptor): distributed in the brain (amygdala, hypothalamus, hippocampus, cortex, septal nuclei) and the spinal cord. Responsible for anxiety, depression, appetite changes, and dependence to mu-agonists and affects both pain and reward signaling within the brain. – Epsilon (binding beta-endorphin): distributed in the brain and peripheral sensory neurons, probably a splice variant or a heteromer of existing opioid receptors, antagonized by buprenorphine. Responsible for analgesic effect and for the release of met-enkephalin. – Sigma: referred to as antitussive receptors, binding 4-phenyl-1-(4-phenylbutyl) piperidine and other substances (including dextromethorphan, phencyclidine, cocaine and methamphetamine, morphine and diacetyl morphine, fluvoxamine, dimethyltryptamine, etc.). Known to interact with kappa-opioid and NMDA glutamate receptors. Known two subtypes – sigma1 and sigma2 (sigma1 having no structural similarity to the opioid receptors). Their activations mimic acute stress reactions

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– tachycardia, mydriasis, overall stimulation, antitussive effect, and euphoria/dysphoria. Sigma-receptors bind to several hormones – dehydroepiandrosterone and gestagens. – Zeta (opioid growth factor receptor): distributed in peripheral tissues (parenchymal organs – heart, liver, kidney, brain, pancreas, fat tissue, and skeletal muscles). Responsible for tissue growth, embryonic/fetal growth, wound healing, and development and cancer proliferation. The activation of these receptors decreases cell proliferation (i.e., acts as “negative” growth factor). Opioid receptors are abundant in all tissues and organs, including the brain, peripheral nerves, gastrointestinal and immune system, endocrine glands, and skin, where they have different analgesic and non-analgesic physiological effects, as described above. All opioid receptors represent G protein-coupled receptors acting via changes (decrease) in adenylate cyclase activity and cAMP levels, protein kinase activity, CREB protein, and calcium and potassium ion transport. Moreover, the activation of opioid receptors leads to changes in substance P and GABAergic, glutamatergic, and dopaminergic transmission, leading to decrease in pain sensation and psychoactive properties, including activation of the reward cycle and euphoria (Quinn et al. 1997; Ghelardini et al. 2015; Koob and Le Moal 2006; Stein et al. 2003; Gosnell et al. 2013; Pasternak and Pan 2013). Tramadol and tapentadol also affect monoamine uptake (Quinn et al. 1997; Ghelardini et al. 2015; Koob and Le Moal 2006; Stein et al. 2003; Gosnell et al. 2013; Pasternak and Pan 2013). Opioid agonists (mu, kappa, and delta) are known to interact with oxytocin, neuropeptide Y, and melanocyte-stimulating hormone signaling systems (Gosnell et al. 2013; Pasternak and Pan 2013), which could explain their effects on feeding and appetite. According to their effect on opioid receptors, the ligands can be classified as agonists, antagonists, partial agonists, and mixed agonists/ antagonists:

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Pharmacodynamic Evaluation: Drug Dependency and Addiction

– Agonists – bind strongly to opioid receptor and undergo strong conformational changes to exert effect: morphine, heroin, hydrocodone, hydromorphone, fentanyl, methadone, oxycodone, and oxymorphone. – Partial agonists – bind less strongly and cause less conformational changes with less receptor activation and at low doses cause similar analgesic effects like full agonists; increasing the dose does not increase analgesic activity: buprenorphine, tramadol, and butorphanol. – Mixed agonists/antagonists – agonists to some and antagonists to other opioid receptors and dose-dependent effect (i.e., agonists at some and antagonists on other doses): buprenorphine, butorphanol, nalbuphine, and pentazocine. For instance, buprenorphine is a partial mu-agonist and kappa-antagonist and weak delta-antagonist; butorphanol is a mu-antagonist and partial kappa-agonist, pentazocine is a partial mu-agonist and kappaagonist, and nalbuphine is a mu-antagonist and kappa-agonist. – Antagonists: naloxone and naltrexone. To make the long story short, most psychoactive opioids are mu-agonists with different actions on kappa-receptors. As described above, the activation of mu-opioid receptors leads to G protein-mediated decrease of adenylate cyclase activity and inhibition of cAMP production with subsequent inhibition of calcium influx and potassium efflux with membrane hyperpolarization and analgesic effect. Moreover, these substances change the levels of substance P and GABAergic, glutamatergic, and dopaminergic transmission with suppression of pain signaling and activation of the reward cycle. In addition, many synthetic opioids have supplementary effects (i.e., inhibition of norepinephrine uptake and NMDA receptor inhibition with increased glutamate and GABA signaling), so other signal systems in the brain are also used to mediate their psychoactive effects (Koob and Le Moal 2006; Gosnell et al. 2013; Pasternak and Pan 2013).

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Pharmacological Effects Mu-receptor opioid agonists have the following pharmacological effects (Ghelardini et al. 2015; Koob and Le Moal 2006): – Analgesic – mediated by mu-opioid receptors (at spinal and supraspinal levels). This effect is a result of complex ion- and mediator-induced changes in neuron interactions. At supraspinal level, it is a result of activation of mu-receptors on GABAergic neurons with subsequent activation of serotoninergic neurons. At spinal level, this effect is due to increase in the pain threshold and is mediated by inhibition of the release of mediators participating in the pain signaling – substance P and glutamate and nitric oxide from the nociceptive afferent neuron cells. Methadone also interacts with the mu-receptors on glutamatergic neurons and thus additionally decreases the transmission of the pain signal. Mesangial cells are also known to have opioid receptors which at least partially can explain the development of heroin-associated nephropathy. – Psychotropic effects – these effects are mediated by the opioid receptors on structures of the limbic system, including the cortical areas, hypothalamus, locus coeruleus, and amygdala. – Effects on respiratory functions – mediated by the opioid receptors in the brainstem, along with miosis. – Gastrointestinal effects (decreased mobility, suppressed nausea) – via the opioid receptors on peripheral neurons and on the gastrointestinal tract. – Respiratory effects – suppression of cough, in larger doses, and suppression of breathing. – Endocrine effects (via hypothalamic mu-receptors with subsequent suppression of pituitary functions) – inhibition of pituitary function with decreased levels of LH, FSH, and ACTH. – Paradoxical effects of morphine – at low doses morphine can increase the sensation of pain – hyperalgesia, probably due to activation of pronociceptive mediation via stimulation (not

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inhibition) of adenylate cyclase and increase in neuron excitability. This effect is dosedependent. The development of tolerance and addiction is explained by several phenomena, including decrease in receptor number and affinity and internalization of receptors and changes on post-receptor level that decrease the ligand effect and lead to the need of more frequent administration of higher doses. The withdrawal symptoms of opioid dependence are very unpleasant and further increase the craving. They are mediated via changes in adrenergic and cholinergic mediation and neuropeptide Y changes in CRF receptor system (Koob and Le Moal 2006). According to their severity, these symptoms can be classified into 5 grades (from 0 to 4) (Koob and Le Moal 2006): – Grade 0 – craving (for the drug) and anxiety – Grade 1 – grade 0 plus yawning, increased perspiration, runny nose, and lacrimation – Grade 2 – grade 0 and 1 with increased intensity plus sympathetic activation (mydriasis, gooseflesh with piloerection (“cold turkey detox”), marked tremor and twitches/spasms, hot and cold flushes); severe pain in the joints, bones, and muscles; and loss of appetite – Grade 3 – all of the above, with increased intensity, plus insomnia, signs of sympathetic activation (increased blood pressure, body temperature, heart and respiratory rate, restlessness, muscle twitches), and nausea – Grade 4 – all of the above, with increased intensity, plus vomiting, diarrhea, loss of appetite, weight loss, embryonic position, spontaneous ejaculation/orgasm, dehydration with hemoconcentration and eosinopenia, and high blood glucose These symptoms can be alleviated with the administration of beta-blockers, sedatives, and antipsychotics, supportive treatment (hydration, parenteral feeding, gastroprotective agents, etc.), and addition of morphine analogues (Quinn et al. 1997; Pasternak and Pan 2013).

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Natural Opioid-Like Substances Several endogenic substances mimic the effects of opioid and are classified as endogenous opioids (Ghelardini et al. 2015; Pasternak and Pan 2013): – Enkephalins (pentapeptides containing the sequence Tyr-Gly, linked to leucine or methionine and called, respectively, leu-enkephalin and met-enkephalin) – bind predominantly to kappa-receptors. – Dynorphins A and B – bind mainly to deltareceptors. – Endorphins – the beta-endorphins bind equally to mu- and delta-receptors. – Endomorphin-1 and endomorphin-2 – bind mainly to mu-receptors. – Endogenous morphine synthesis has been proven in humans and in animals, but the role of this “animal” morphine and its precursors and derivatives remains unclear. All these substances take part in the pain and reward signaling, both central and peripheral, but their exact role in human physiology remains unclear. Several peptides have been shown to modulate opioid action, including cholecystokinin and neuropeptide FF that reduce opioid effects (Mollereau et al. 2005) via changes in intracellular second messengers of nociception. The pronociceptive opioid analogues nociceptin and dynorphin (Mollereau et al. 2005) paradoxically are able not only to potentiate but also to attenuate the analgesic effects of opioids due to changes in pain circuit signaling.

Pharmacologic Interactions of Opioids The epidemic of opioid prescription abuse makes it even more important to focus our clinical attention on their drug interactions. Methadone and buprenorphine, as with most of the psychoactive medications, are substrates of CYP450 3A4. The hepatic metabolism of opioids also goes through other isoenzymes from the CYP family, such as 2B6, CYP2C19, CYP2C9, and CYP2D67 for

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methadone and 2C8 for buprenorphine. A classical interaction would be a strong 3A4 inhibitor (e.g., ketoconazole) increasing the plasma levels of methadone. Same interaction could be observed with an antifungal and an antibiotic – ciprofloxacin. Per FDA criteria, strong inhibition leads to fivefold and higher increase of the plasma concentration of the inhibited substrate. On the other hand, inducing strongly CYP450 system would lead to lower plasma level of its substrates. However, the correlations are not always that linear. One of the reasons is the influence of medications on the glucuronidation. Methadone inhibits glucuronidation of zidovudine, thus decreasing its elimination and increasing the risk of toxicity (McCance-Katz et al. 1998). There are several important class interactions: (1) opioids with medications treating infectious diseases (HIV, tuberculosis, Hep C, etc.), (2) opioids with psychopharmacologic agents (antidepressants, antipsychotics, benzodiazepines), and (3) opioids with alcohol or illicit substances. Three types of consequences due to interactions: 1. Toxicity, higher rate, and more severe side effects – related to slowing the rate of metabolism/elimination, increasing the plasma levels of: (a) The concomitant drugs administered with opioids, e.g., zidovudine (lactic acidosis, transaminitis, myopathy, severe anemia or neutropenia, etc.) (b) Opioids, e.g., methadone (cognitive dysfunction, respiratory depression, QTc prolongation, arrhythmias) with cotreatment with azoles and ciprofloxacin or discontinuation of CYP450 inducers (such as carbamazepine, phenytoin, phenobarbital) (c) Synergistic and pharmacodynamic effects: Increased sedation, delirium, and respiratory drive (opioids with alcohol, benzodiazepines, antihistamine medications (diphenhydramine), dextromethorphan) 2. Poor therapeutic response to concomitant drugs – Related to increased rate of metabolism/elimination of antiretrovirals and poor

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efficacy. Complications: viral mutations, antiretroviral resistance, and increased risk for viral transmission (lower concentrations of didanosine and stavudine) 3. Opioid withdrawal (a) Related to increased rate of metabolism/ elimination of opioids (methadone, buprenorphine), e.g., HIV medications (efavirenz, nelfinavir, lopinavir/ritonavir, etc.), tuberculosis medications (rifampin), anticonvulsants (carbamazepine, phenytoin, phenobarbital), and stimulants (cocaine (CYP3A4, P), glycoprotein inducer) (b) Discontinuation or lowering the dose of CYP450 inhibitors, medications which increase the plasma concentrations of opioids (fluvoxamine, fluoxetine) and antibiotics (including azoles) (c) Pharmacodynamic interactions – Cocaine during sublingual use of buprenorphine (vasoconstriction)

Drug Interactions for Specific Opioids See Table 1 Interactions with Clinical Importance Morphine delays the absorption of clopidogrel, prasugrel, and ticagrelor and enhances gabapentin pain tolerance in healthy volunteers. Quinidine can enhance the activity of opioids – morphine, fentanyl, oxycodone, codeine, dihydrocodeine, and methadone. Antimycotic medications increase the plasma concentrations of opioids – buprenorphine, fentanyl, morphine, oxycodone, methadone, tilidine, and tramadol. Protease inhibitors induce metabolism of opioids – oxycodone and fentanyl. Paroxetine inhibits the metabolism of hydrocodone, oxycodone, and tramadol. Escitalopram inhibits the metabolism of tramadol (Feng et al. 2017).

Stimulants: Cocaine and Amphetamine and Its Derivatives All psychostimulants act by increasing monoamine (norepinephrine, dopamine, and serotonin) release in the synaptic space and by inhibiting

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Table 1 Pharmacokinetic and pharmacodynamic interactions of opioids with other medications Opioid

Absorption Metabolism Elimination

Absorption

Metabolism

Metabolism

Morphine " own concentration – toxicity Metoclopramide Quinidine Itraconazole Other azoles Amantadine Methadone Quinidine Voriconazole Ketoconazole Grapefruit juice Delavirdine Amitriptyline Dextromethorphan Quetiapine Ciprofloxacin

" the other agent’s concentration toxicity P2Y12 inhibitors Gabapentin

# the other agent’s concentration

# own concentration – withdrawal Rifampin Rifampin St. John’s wort

AZT (zidovudine) Desipramine

Buprenorphine Antimycotics

Rifampin

Didanosine, stavudine

Darunavir Efavirenz Nelfinavir Nevirapine Lopinavir/ritonavir Carbamazepine Phenytoin Phenobarbital Carbamazepine Phenytoin Phenobarbital

Oxycodone Antimycotics Macrolides Ketolides Protease inhibitors Voriconazole Ketoconazole Grapefruit juice Paroxetine Quinidine

their reuptake leading to increased neurotransmitter levels for a longer time in the synaptic cleft (Quinn et al. 1997).

Cocaine Cocaine is the second most frequently used recreational drug worldwide after cannabis. It is a natural alkaloid extracted from the leaves of the coca plant (Erythroxylum coca var. coca, var. ipadu, var. novogranatense, and var. truxillense), growing in South America. Cocaine can be extracted from coca leaves or synthesized and used as a

recreational substance, or further processed to crack cocaine – a freebase form of cocaine that can be smoked. Between 14 and 21 million people are estimated to have used this drug every year (Pomara et al. 2012). Cocaine has been used for more than 1000 years by the indigenous South American people as a stimulant and for religious and recreational purposes in the form of Erythroxylum coca leaves that can be chewed or processed to extract the alkaloid. There are proofs that cocaine has been used as anesthetic in ancient times (Gay et al. 1975). In the seventeenth century when the Spanish arrived to the New World, they described

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the stimulant, hunger-suppressing, anesthetic, and recreational effects of coca leaves. The alkaloid was first isolated by Friedrich Gaedcke in 1855 and was initially named erythroxyline. Approximately 40 years later, in 1989, the first synthetic cocaine appeared. The drug was initially used as a painkiller; subsequently its local anesthetic properties were used. Cocaine was found to be a unique anesthetic because unlike all other anesthetics it decreased bleeding due to its local vasoconstriction effect. In 1879 cocaine was introduced for the treatment of morphine addiction, and in the next few year, its use as a psychostimulant and appetite-suppressing drug started. In the beginning of the twentieth century, it was marketed as stimulant and was subsequently used in world wars as stimulant and anesthetic. Gradually, cocaine has become the second most abused illicit drug worldwide. It is used by all socioeconomic strata, age, and demographic, economic, social, political, and religious groups all over the world. Cocaine can be insufflated (snorted), taken orally (gingival administration and chewing coca leaves), smoked, administered rectally, and injected intravenously or intramuscularly. It can be taken alone or in combination with heroin (speedball). In modern medicine its use is limited as local/topical anesthetic, mainly in ophthalmology. Cocaine has sympathomimetic effects, influencing serotonin receptor and membrane ion transport. Cocaine is also known to have longterm endocrine and genetic effects. The pharmacodynamic effects of cocaine are determined by its three major actions (Quinn et al. 1997; Pomara et al. 2012; Gay et al. 1975): – Increased release of catecholamines in the synaptic cleft due to stabilization of the dopamine transporter – Decreased mediator reuptake via blockage of the presynaptic dopamine transporter – Blockage of neuronal membrane sodium channels with local anesthetic effect Additionally, cocaine interacts with serotonin 5-HT3 and 5-HT2 receptors, and these

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interactions explain its effect on appetite and locomotion. The effects on locomotion could also be explained by its interaction with dopamine levels in the substantia nigra. Cocaine also interacts with kappa-opioid, sigma, D1, and NMDA receptors. Unlike amphetamine, cocaine does not inhibit monoamine oxidase (MAO) (Quinn et al. 1997). The net effect of these ligand-receptor interactions is sympathomimetic effect with buildup of dopamine in the limbic system structures (especially in the nucleus accumbens) and stimulation of pleasure and reward feeling that explains the addiction and dependence in long-term abuse (Nestler 2005). The increase of dopamine levels in the nucleus accumbens is a normal physiological process, part of the fight-or-flight response to stress, giving the body and the mind the assurance that the stress-inducing stimulus has been eliminated and generating the sensation of comfort and pleasure – i.e., when a thirsty person drinks water, or when a reward for achievement has been given (Nestler 2005). Thus, the external stimulation and the buildup of dopamine levels in the nucleus accumbens by cocaine are far more potent than the physiological effect and give the sensation of euphoria and stimulation. This is the underlying mechanism of addiction and dependence. Cocaine also exerts its dopamine buildup effects in other regions of the brain, associated with the limbic system, including memory centers (hippocampus and amygdala) and the frontal cortex. It is believed that the repeated exposure to cocaine with increase in dopamine availability in the hippocampus and amygdala leads to functional and organic changes that every memory of cocaine intake urges an almost compulsory craving for repeated intake (Volkow and Morales 2015). The repeated increase in dopamine levels in the frontal cortex by cocaine abuse is associated with changes in this region and decrease of its inhibitory effect over the urges generated in the nucleus accumbens, hippocampus, and amygdala and subsequent addictive pattern. The interactions with serotonin receptors may explain the mood and appetite-suppressing effects of cocaine.

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A more serious molecular effect that can explain the addiction in cocaine intake is the genetic impact of this alkaloid. Cocaine is known to change the amount of dopamine transporters and dopamine receptors on nerve cells via alteration of gene expression (genetic effects of cocaine). ΔFosB is a natural protein substance present in small amount in nerve cells, especially in the nucleus accumbens. It plays a role in the genetic mechanisms of the basic cell functions – the integrity and the interaction with other cells. In chronic cocaine intake, this protein accumulates in large quantities in the nucleus accumbens and is thought to be the part of the mechanisms explaining the addiction to cocaine. Changes in ΔFosB levels in the nucleus accumbens have been demonstrated in long-term cocaine intake in mouse models. It is known that one of the genes stimulated by ΔFosB, the enzyme cyclindependent kinase 5 (CDK5), promotes nerve cell growth. This factor also affects nuclear factorkappa B and MEF2 (myocyte enhancer factor-2) expression. These effects are not well understood. It has been speculated that probably these transcriptional and epigenetic changes could be the genetic mechanism of the very long-term effects of cocaine. In a very long term, intake of cocaine increased dendrite growth and increases the number of the neurons in the nucleus accumbens that has been observed, i.e., increased cell contacts with other parts of the nervous system with altered information pathways and increased amount of signals coming to and originating from these cells with stable behavioral changes (Volkow and Morales 2015; Robison and Nestler 2011). These very long-term effects, based on genetic and epigenetic changes in the brain, probably make cocaine addiction very difficult to counteract. Another long-term effect of cocaine is dopamine depletion that is probably responsible for withdrawal symptoms (Quinn et al. 1997). The main medical strategies to treat cocaine addiction and withdrawal are (Quinn et al. 1997) the following: the use of antidepressants (in order to inhibit neurotransmitter reuptake, particularly desipramine), dopamine agonists (to counteract dopamine depletion in the central nervous system), dopamine antagonists, anticonvulsants, and

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opioids (buprenorphine – probably through affecting the linkage between opioid and dopaminergic pathways). The treatment of acute intoxication with cocaine and amphetamines is generally supportive: regulation of hydration and electrolyte disturbances; treatment of hypertension, rhythm, and conduction disturbances; use of vasodilators; gastroprotection; etc. Similar to heroin, cocaine and amphetamines are known to cause severe endothelial dysfunction and hemolytic-uremic syndrome (Kavannagh et al. 2006). Therefore, antithrombotic prophylaxis should be administered. Cocaine has several metabolites: benzoylecgonine, ecgonine methyl ester, and norcocaine. Benzoylecgonine is a potent vasoconstrictor in vitro, but does not cross the blood-brain barrier in vivo. Ecgonine methyl ester (EME) is actually a vasodilator. It is produced by metabolization of cocaine by plasma cholinesterase (also known as pseudocholinesterase, or butyrylcholinesterase). “Pseudocholinesterase deficiency” due to BCHE gene mutations, is a specific condition that could render patients more vulnerable to severe intoxication with cocaine, to prolonged paralysis with succinylcholine and mivacurium. Cocaine drug interactions could be examined in the light of three situations: cocaine intoxication, withdrawal, and long-term treatment and craving prevention. It seems there is scarce evidence of interaction between cocaine and CYP3A4 inhibitors, ketoconazole, erythromycin, and clarithromycin. There are other factors whose importance has to be established in the future, such as glutathione peroxidase-1 deficiency and microRNAs (Gallelli et al. 2017). Cocaine intoxication leads to tachycardia, hypertension, and vasospasm. Treatment of these sometime fatal symptoms is done through the use of benzodiazepines, calcium channel blockers, and nitric oxide-mediated vasodilators. Nitroglycerine could induce reflex tachycardia through severe hypotension, so it should be used with extreme caution. Alpha-1 blockers had been tried with limited evidence. Alpha-2-adrenoceptor agonist trials had better results, especially with the

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use of dexmedetomidine. There had been a widespread belief that beta-blockers could dangerously worsen hypertension during cocaine intoxication (Lange et al. 1990). However, this concept had been challenged recently. There were several Level I/II, Level III, and Level IV/V studies of β-blockers, with 1744 subjects, 7 adverse drug events, and 3 treatment failures. There were no adverse events reported for labetalol and carvedilol, mitigating hypertension and tachycardia (Richards et al. 2016). Antipsychotics have been used and studied for the treatment of hypertension and tachycardia, improving agitation and psychosis (paranoia), but there are significant risks with QTc prolongation and extrapyramidal adverse effects. Since second-generation antipsychotics have serotonergic effects, clinicians need to be aware of the potential risk of serotonin syndrome, by potentiating serotonergic effects of cocaine. Other medications include lidocaine, sodium bicarbonate, amiodarone, procainamide, propofol, intravenous lipid emulsion, and ketamine. Cocaine withdrawal and cravings is a challenging condition due to several phenomena, including behavioral sensitization. Antipsychotics have been tried with mixed results. The biological mechanism of counteracting the effects of cocaine is thought to be due to presynaptic action on dopaminergic and serotonergic, while cocaine affects directly and indirectly the postsynaptic cascades. Data analysis shows that actually antipsychotics do not have advantages over placebo in regard to cocaine use and cocaine abstinence or craving. They could even cause more discomfort, even depression related to discontinuation (Kishi et al. 2013). Cochrane review did not support the notion of using antidepressants in the treatment of cocaine withdrawal (Pani et al. 2011). There had been some serious adverse reactions reported regarding the use of citalopram and cocaine – potentiation of serotoninergic vasoconstriction (Medicines and Healthcare products Regulatory Agency 2016). The more successful medication interaction is the one with GABAergic medications, topiramate, although there is still not enough conclusive unequivocal evidence for its efficiency.

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Drug Interactions of Cocaine and Other Substances Antipsychotics – increased risk of antipsychotic induced acute dystonias, both in intoxication and chronic treatment. Clozapine could lead to increased cocaine plasma concentrations and reduced psychotic and pressor effects. Mood stabilizers (carbamazepine) – plasma concentrations of norcocaine increase – higher risk of hepatotoxic and cardiotoxic effects (Tenev 2008). Benzodiazepines – oversedation and increased risk of benzodiazepine abuse. Disulfiram – threefold increase of plasma levels of cocaine and increased risk of cardiotoxic complications. β-blockers – very high risk of myocardial ischemia. Nicotine – has a synergistic effect on dopamine release in the reward areas of the brain; lowers the oxygen supply, arterial pressure, and cardiac contractility; and increases the incidence of cardiac complications arising from cocaine use. Alcohol – ethanol-induced metabolite, cocaethylene, of cocaine is more reinforcing than cocaine and is potentially more toxic.

Amphetamine and Its Derivatives Amphetamine and its derivatives are not present in nature and represent purely synthetic substances. Amphetamine was first synthesized in 1887 and was initially used for the treatment of nasal congestion and subsequently as stimulant, athletic performance and cognitive enhancer, aphrodisiac, and euphoria inducer. Amphetamine and its derivatives (methamphetamine and methoxy-substituted amphetamines; 3,4-methylenedioxyamphetamine (MDA); 3,4-methylenedioxy-methamphetamine (MDMA) or ecstasy; N-ethyl-3,4-methylenedioxyamphetamine (MDEA); 2,5-dimethoxy-4methylamphetamine (DOM); p-hydroxydimethoxy-4-methylamphetamine (PMA)) are purely synthetic stimulants that act by increasing the monoamine levels in the synaptic

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cleft (Quinn et al. 1997; Volkow and Morales 2015). The half-life of MDMA in humans is 8–10 h. – Inhibition of monoamine uptake (competitive inhibition of dopamine uptake) – Increase in neurotransmitter release (facilitation of dopamine release from the vesicles and increase in dopamine transporter-mediated reverse transport of the mediator into the synaptic cleft, independently from the action potential-induced vesicular release) – Inhibition of monoamine oxidase (MAO)

The first and the second mechanisms are mediated by binding to trace amine-associated receptor 1 (TAAR1). Ecstasy is also known to increase serotonin liberation (Rudnik and Wall 1992) and the release of oxytocin. The molecular and physiological effects of amphetamines are similar to those of cocaine, but they are known to inhibit MAO and to have virtually no local anesthetic effect. Methamphetamine has two enantiomers with the S-(+) being five times more active. The physiological, psychological, and toxic effects of amphetamines are similar to those of cocaine and are mediated by their sympathomimetic and serotonin-mediated effects. The underlying mechanisms of addiction, dependence, and withdrawal in amphetamine intake are associated with changes in gene expression (transcriptional and epigenetic changes) in the mesocorticolimbic projection. The major transcription factors responsible for these alterations are ΔFosB, CREB (cAMP response elementbinding protein), and nuclear factor-kappa B (Rudnik and Wall 1992). The crucial role of ΔFosB overexpression in the development of drug addiction to many substances (including alcohol, cannabinoids, cocaine and amphetamines, nicotine, opioids, dissociative anesthetics, and hallucinogens) is demonstrated by the profound effect of ΔJunD in such cases. ΔJunD is an enzyme that blocks ΔFosB overexpression, and when

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brought to the nucleus accumbens by a viral vector, it could reverse the behavioral changes in chronic drug abuse and addiction. The overexpression of ΔFosB in amphetamine abuse (like in cocaine abuse) leads to marked and long-standing functional effects and changes in dopaminergic neurons, especially in the nucleus accumbens, hippocampus, amygdala, and frontal cortex with the development of addiction. This addiction pattern is due to deep receptor, mediator, and structural changes in the neurons, and currently there are no known medications to counteract addiction in such patients. Medical strategies have been developed to treat acute intoxication – i.e., for the treatment of cardiac (tachycardia, rhythm and conduction disturbances, hypertension) and vascular (vasoconstriction, endothelial dysfunction) symptoms, hyperthermia, dehydration, dyselectrolytemia, inadequate antidiuretic hormone secretion, intracranial complications (ischemic stroke and hemorrhage), respiratory failure and acute respiratory distress syndrome, and hepatic and liver failure. The hepatic failure is known to develop due to the oxidation of mitochondrial proteins and acute microsomal toxicity, combined with ischemia (vasoconstriction plus thrombosis), and renal failure is usually due to dehydration in combination with rhabdomyolysis and/or development of hemolytic-uremic syndrome (Kavannagh et al. 2006; Moon et al. 2008). There are acute and long-term toxicity phenomena. There are several sources of data: in vitro experiments, animal models, and in vivo observations. There are still a lot of studies to be done to unequivocally prove the specific interactions and their clinical significance. Acute toxicity Euphoria, well-being, happiness, stimulation, increased energy, extroversion, feeling close to others, increased empathy, increased sociability, enhanced mood, and mild perceptual disturbances. In addition, cardiovascular-

Chronic toxicity Neurotoxicity Impairment in serotonin function Neurodegeneration Phenocopying phenomenon – compromising the extensive metabolizer capability; developing (continued)

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Acute toxicity

Chronic toxicity

related somatic symptoms, autonomic effects (dry mouth, sweating, tremor, mydriasis tremor, jaw clenching, and restlessness), and moderate derealization have been observed (de la Torre et al. 2004) Hyponatremia – uncommon, associated with inappropriate antidiuretic hormone (SIADH) secretion and excessive water intake (also in polymorphic reduced COMT activity) Fulminant hepatitis and hepatic necrosis have been described too

low tolerance to methamphetamine after a short period of experiencing less toxicity of the substances (EM to PM status change)

The toxic effects are related to the metabolism of MDMA and methamphetamine and their metabolites. MDMA is a substrate to CYP2D6, but also a potent inhibitor through the so-called mechanism-based inhibition, by the phenocopying phenomenon. The effective enzyme amount decreases, so even genotypically active metabolizers become similar to poor metabolizers. Regardless of the genotype/phenotype, it could take up to 10 days to resynthesize CYP2D6 and restore it back to its baseline level of activity after even a single recreational dose. It was thought that there were sex differences, with 67% of males and 100% of females having such phenotyping effect, exposing them to the adverse effects of the drugs. Female subjects in the study setting would display more intense physiological (heart rate and oral temperature) and negative effects (dizziness, sedation, depression, and psychotic symptoms). Currently it had been proven that the wide genotype allelic variations of CYP2D6 actually do not play the role they had assigned before. That could be due to the alternate pathways during the first phase of methamphetamine and MDMA metabolism: CYP1A2, CYP2B6, CYP2C19, and CYP3A4. They also have multiple genotype/ phenotype variations and could undergo the same phenocopying phenomenon, thus making the occurrence of acute and chronic adverse effects dose-independent and unpredictable.

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The second phase of metabolism of MDMA is through COMT. It converts the catechol metabolites HHMA and HHA into HMMA and HMA. The same enzyme inactivates dopamine (DA) and noradrenaline (NE). It exists in two forms. MB-COMT is in the brain and S-COMT in the liver/kidneys. There are two basic functional polymorphisms – valine (val) to methionine (met) substitution at codon 108 in S-COMT and at codon 158 in MB-COMT. The latter variant, the Met allele, is associated with low enzymatic activity, while the former, val allele, has higher activity. Roughly one fourth of the population has low activity, and one fourth has high activity. The lower the activity, the higher the toxicity through the accumulation of the immediate active MDMA metabolites – HHMA and HHA. This could subsequently increase the risk of clinical symptoms including hyperthermia, hypertension, tachycardia, seizures, serotonin syndrome, and rhabdomyolysis. HMMA plasma concentrations play significant role, regardless if these are linked to CYP2D6 genotype (higher with two functional alleles). Genotypes of COMT val158met or 5-HTTLPR with high functionality (val/val or l/*) determine greater cardiovascular effects and with low functionality (met/* or s/s) negative subjective effects, such as dizziness, anxiety, and sedation. An important role is attributed to glutathione S-transferase (GST) in the detoxification of HMMA. There had been some data in vitro showing differences in toxicity related to GST polymorphism, which actually had not been observed in vivo. The conjugation during elimination process in phase II of metabolism goes through SULT system, leaving sulfate conjugated MDMA urinary metabolites and UGT system – glucuronide conjugate urinary metabolites. There are also some genetic variants, especially in UGT system, which could lead to decreased enzymatic activity, hence longer elimination and increased toxicity of MDMA (UGT2B15). MDMA and amphetamine toxicity is dynamically related to individual differences in DAT expression both at pre- and postsynaptic levels. The dopamine transporter gene could modify

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indirectly the receptor signaling done by the drugs. Reduced SERT potentiates selfadministration of MDMA and cocaine (Brox and Ellenbroek 2018). Types of consequences due to interactions or enzymatic polymorphisms 1) MDMA-induced toxicity Enzymatic polymorphism Phase I – CYP2D6, CYP1A2, CYP2B6, CYP2C19, CYP3A4 Phase II (COMT, GST, SULT, UGT) – Less active isoenzyme genotype, or depletion of normal activity genotype (phenocopying) NT reuptake transporters: SERT, NET, DAT NT synthesis, breakdown: TH, TPH, COMT, MAO NT receptors

Interactions CYP2D6 inhibitors SERT inhibitors – Fluoxetine, paroxetine, citalopram NET inhibitors – Duloxetine, reboxetine DAT inhibitors – Bupropion, duloxetine 5-HT2 antagonists – Ketanserin, mirtazapine α-β-adrenergic antagonists – Carvedilol Antipsychotics (Rietjens et al. 2012)

The most dangerous toxic phenomena related to MDMA and methamphetamine are as follows: Serotonin syndrome: (1) Mental status changes, (2) autonomic hyperactivity, and (3) neuromuscular abnormalities, all with varying signs from tremor and diarrhea to delirium, neuromuscular rigidity, and life-threatening hyperthermia. Death could occur due to increased serotonin levels via MDMA-induced 5-HT release and inhibition of 5-HT degradation via MAO inhibitors (Rietjens et al. 2012). The highest risk for this syndrome is in combination with antidepressants. Hyperthermia: MDMA-induced cutaneous vasoconstriction and metabolic heat production. Several dangerous reactions related to CYP2D6 inhibition had been described. Ritonavir and antiretroviral drugs have had lifethreatening effects described. The unpredictability of these reactions is derived from the genetic polymorphism of CYP450 and alternate pathways for Phase I of MDMA metabolism. Pharmacodynamic interactions that could lead to MDMA tolerance and increase the

recreational dose, due to lack of the desired effect (no “high,” less intense sensation of euphoria) This effect could be protective against neurotoxicity, exerted directly by MDMA or its toxic metabolites. Two mechanisms for that had been suggested. The first is the reduced 5-HT release and SSRI exerted prevention of MDMA to interact with SERT, blocking the efflux of serotonin through SERT. The second seems to be direct inhibition of CYP2D6 by such antidepressants like paroxetine and fluoxetine. Thus, MDMA metabolism is blocked. Concentration of toxic metabolites HHMA and HMA and their reactive quinones remains low. The risk of this type of interaction is that consumer could increase the dose.

Drug Interactions It is important to clarify the timeline for assessing and predicting the drug interactions, since there is a differentiated response to MDMA and methamphetamine after infrequent one dose or seldom binges vs chronic daily use. It is possible that one and the same medication has different effect on the metabolism of these drugs after sporadic or chronic stimulation of the enzymatic activity and the genotype predisposition of CYP450, UGT, GST, COMT, and SULT. The level of affected NAT, DAT, and SERT transporters is also worth mentioning. In this context some varieties could be anticipated with regard to reaction and side effects of MDMA and methamphetamine with patients taking antidepressants, antipsychotics, or antiepileptic medications. Another very important issue is if there had been pretreatment, i.e., the person had been receiving a medication before using MDMA (or amphetamines). It could potentially change the reaction during acute intoxication, withdrawal, or maintenance of sobriety treatment. Further research needs to be conducted to elucidate individual differences (Table 2). Of note: severe MDMA intoxication is addressed by cooling measures and use of benzodiazepines. Antidepressants – Bupropion (CYP2D6 inhibitor) (could lower the pharmacological effects (both cardiovascular and euphoric) of

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Table 2 Pharmacokinetic and pharmacodynamic interactions of MDMA, methamphetamine and other medications at different time points in treatment timeline Timeline of medication intervention related to meth/ MDMA use Pretreatment Antidepressants Citalopram Prevention of depletion of 5-HT (Schmidt and Taylor 1987) (in rat models) Paroxetine Inhibition of Fluoxetine CYP2D6, low level of (FLX) active, toxic metabolites

Duloxetine

Mirtazapine

Bupropion

Imipramine Sertraline

Antipsychotics Haloperidol

Clozapine

" MDMA levels, through inhibiting SERT and NET, # tachycardia, # hypertension, weak DAT inhibitor Could # consumption, # erratic sexual behaviors " MDMA levels, through CYP2D6 block, # adverse effects

During intoxication Could " locomotor activity, through " D2 receptor expression (rat models) Limited data FLX reduces 5HT depletion, does not affect hyperthermia SSRIs could " risk of serotonin syndrome Not suitable to start treatment, since FLX needs 6–8 h to reach therapeutic plasma concentration, same time for elimination of MDMA Could be used, not enough data

Not enough data

During withdrawal

During maintenance of abstinence

Could exacerbate physiologic effects Reverse reward deficits during amphetamine withdrawal (Harrison et al. 2001)

Do not improve significantly depressive symptoms Nonconclusive reports regarding prolongation of abstinence or treating depressive symptoms

Not enough data

No adverse effects noted

Further studies need to be done

Not enough data

Could prolong abstinence

" risk of cardiovascular, GI, " abstinence time anticholinergic effects Should not be administered to patients with methamphetamine-related disorders, due to adverse effects on abstinence, AWMF (Arbeitsgemeinschaft Wissenschaftlicher Medizinischer Fachgesellschaften www.awmf.org) Reduces hyperthermia # depletion of 5-HT

Reversal of MDMAinduced cutaneous vasoconstriction (Blessing et al. 2003) and inhibition of MDMA-induced Increases in metabolic heat production

Change subjective MDMA effects from a pleasurable state of wellbeing and euphoria to a more dysphoric state with slightly increased anxiety, i.e., akathisia (Rietjens et al. 2012)

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methamphetamines by blocking its toxic metabolites). It had been tried in treating moderate and non-daily users. It could be administered for prolonging the abstinence period, although data is still not conclusive (HärtelPetri et al. 2017b). From clinical perspective, one could make the case that increasing dopaminergic transmission could facilitate resolution of temporary depressive symptoms after “methamphetamine crash.” Bupropion could also give false-positive urine drug screen for amphetamines, in 41% of cases (Casey et al. 2011). Trazodone could yield false-positive urine drug screen for amphetamines, especially after pretreatment with phenothiazines. Mood stabilizers – Lithium (dehydration more pronounced). Antipsychotics – Quetiapine and risperidone for the treatment of depressive and psychotic symptoms in the chronic methamphetamine use syndrome. In acute phase antipsychotics and methamphetamine could reduce the efficacy of each other. Antiretroviral drugs – Ritonavir (severe CYP2D6 inhibitor, increased level of MDMA). α-β-adrenergic receptor antagonists – Carvedilol (could potentially decrease hyperthermia). Alcohol – Slows down the effects of MDMA and increases nephrotoxicity, leading to high risk of lethal dehydration. Urinary alkalinizers’ (OTC medications) use leads to increased tubular reabsorption, via the increased amounts of non-ionized amphetamine. Thus, methamphetamine could have its half-life increased two- to threefold, while MDMA’s half-life could increase by twofold. Antihypertensive medications – MDMA and methamphetamine counteract their effects and could render the hypertension control more difficult to maintain in the long term. Tobacco/nicotine – Smoking methamphetamine in combination with tobacco creates the pyrolysis product cyanomethylmethamphetamine. This metabolite has some stimulant properties (Dean 2006).

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Methamphetamine-related, post-acute persistent or comorbid syndromes such as methamphetamine-associated psychosis (MAP), depressive syndromes, anxiety, and sleep disorders are usually treated in a symptom-oriented manner. The interactions could be unpredictable, could happen on many different levels, and could change dynamically. This makes it very important to use medications with the most available data for efficacy possible. Further research is warranted (Härtel-Petri et al. 2017a). Methamphetamine and MDMA could lower the seizure threshold.

Marijuana and Synthetic Cannabinoids Cannabis is the most widely used illicit substance all over the world (Sharma et al. 2012). It has been used for centuries for recreational purposes. It contains more than 400 active substances, 61 of which are cannabinoids and have certain psychoactive properties. The main psychoactive substance is delta-9-tetrahydrocannabinol (THC). In the human body, THC binds to specific psychoactive and functional effects outside the CNS. It is used for recreational purposes, but because of its wide spectrum of effects and tendency to cause dependency and profound behavioral changes, THC is illegal in the most part of the world. THC is derived from the leaves, stems, and seeds of the Indian hemp (Cannabis sativa). The parts of the plant can be smoked or taken orally, even mixed with food and cooked. When smoked, Cannabis sativa leaves, stems, and seeds liberate more than 2000 substances, most of which are produced via pyrolysis (Sharma et al. 2012), but the major psychoactive substance is THC. In the human body, cannabinoids bind to specific cannabinoid receptors (CB1 and CB2) that have physiological ligands (anandamides) that belong to the arachidonate derivatives. The latter act via affecting cAMP intracellular levels and ion transport (calcium and potassium) in different organs. The multiple physiological effects of cannabinoids are mediated by two types of receptors – CB1 and CB2. CB1 are expressed mainly in the brain areas responsible for the cognitive, memory, pain, reward and anxiety, and endocrine and

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motor functions, while CB2 are expressed by the peripheral tissues and organs. The exact mechanisms of action of cannabinoid in the body are not well understood, but it is assumed that the binding to cannabinoid receptors leads to the activation of several signal pathways, including dopamine, serotonin, and norepinephrinergic, GABAergic, opioid, cholinergic, glucocorticosteroid, and prostaglandin systems (Sharma et al. 2012). It is also known that cannabinoids can directly interact with opioid and benzodiazepine receptors and can affect protein, nucleic acid, and prostaglandin synthesis (Sharma et al. 2012), hormone secretion, and DNA repair and replication (Sharma et al. 2012; Reece and Hulse 2016; Li and Lin 1998; Reece 2009). CB2 receptors are expressed in multiple tissues and organs, including the gastrointestinal tract, endocrine glands, and immune systems, and this can at least partially explain the effects of cannabinoids on these structures. Moreover, there is evidence that cannabinoids interact with vanilloid and vanilloid-like receptors (Pertwee 2005) on glutamatergic and alphaadrenergic receptors and on multiple peripheral tissues. Cannabinoids, both natural and synthetic, have certain adverse effects on the mental status (including triggering overt psychoses), respiratory tract (including the development of obstructive lung disease and lung cancer), cardiovascular system (changes in blood pressure, ischemic organ damage, inflammatory angiitis, arrhythmias, worsening of the metabolic profile, etc.), bone loss, fetal retardation, etc. (Reece 2009). Of special interest is the mutagenic, teratogenic, and genotoxic effect of cannabinoids that has become even more visible due to the widespread abuse of cannabis. Of crucial importance are the permanent genetic changes arising during in utero exposure to cannabinoids, leading to the formation of inheritable malignancies, such as childhood neuroblastoma, leukemia, and rhabdomyosarcoma (Reece 2009). These effects are mediated by at least three major mechanisms (Reece 2009): – Oxidation of DNA plus inhibition of DNA repair (via induction of the formation of

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nitrogen-centered species and by uncoupling of mitochondrial oxidative phosphorylation), with deoxidation of guanosine to oxo-guanosine being a normal part of the endocannabinoid signaling – Changes in enzyme activity: stimulation of MAP kinase pathway (an important factor for the induction of non-lymphoblastic leukemia), inhibition of topoisomerase II pathway, and RAD-1 inhibition and damage – Changes in telomeres due to the inhibition of telomerase (this enzyme is present in stem cells, gonadal/germ cells, and cancer cells but not in the normal somatic cells)

In cell cultures, marijuana smoke condensates have been shown to increase the formation of reactive oxygen species (ROS) and to inhibit the synthesis of the transcription factor p53 that acts as a tumor-suppressor protein (Kim et al. 2012). The synthetic cannabinoids (fake weed, spice, K2, etc.) are synthetic cannabinoid derivatives with stronger affinity toward the cannabinoid receptors with more pronounced psychomodulating and adverse effects and unknown safety. Their peripheral, long-term, and genetic effects are unknown and hard to predict. The multiple receptor targets of cannabinoids, their epigenetic and genetic effects, and the unclear mechanism of signaling changes, in combination with their widespread abuse, make the treatment of cannabinoid addiction extremely difficult. THC and CBD are metabolized mainly in the liver by cytochrome P450 isoenzymes (mainly CYP2Cs and CYP3A4). In vitro studies indicate that THC and CBD both inhibit CYP1A1, CYP1A2, and CYP 1B1 enzymes, and recent studies have indicated that CBD is also a potent inhibitor of CYP2C19 and CYP3A4. Both cannabinoids may interact with other medications metabolized by the same pathway or by inducers/inhibitors of the isoenzymes. It is important to distinguish different pathways of metabolism, related to different ways of administering both substances. Preparations which have Δ9-THC inhibit CYP2C9 and CYP3A4. CBD inhibits mostly CYP2C19 and

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CYP3A4. Marijuana inhalation (pyrolysis) inducts CYP1A1 and CYP1A2. Patients with lower activity of CYP2C9 and/or CYP3A4 (phenotypical or by genotype) could have increased plasma concentrations of other substances they take together with Δ9-THC, while CBD exposure in patients with diminished CYP2C19 and/or CYP3A4 function could lead to unpredictable risk of adverse effects of medication substrates of these enzymes. There are very few documented interactions that are proven in vitro and in vivo, such as TCAs or anticholinergic drugs that can produce significant tachycardia. This may be due to beta-adrenergic effects of cannabis coupled with the anticholinergic effect of tricyclic antidepressants. Most of the other interactions are actually hypothesized, and there is still lack of sufficient controlled trials to state evidencebased approach. Nevertheless, it could be very well expected to have increased plasma concentrations with these substrates, due to the occurrence of possible drug interactions.

Δ9-THC preparation

CBD preparations

CYP3A4 substrates Antidepressants: amitriptyline, citalopram, clomipramine, fluoxetine, imipramine, mirtazapine, paroxetine, sertraline, trazodone, venlafaxine Antipsychotics: pimozide, quetiapine, risperidone, ziprasidone, aripiprazole, chlorpromazine, clozapine, haloperidol, perphenazine Benzodiazepines: clonazepam, diazepam, nitrazepam, alprazolam, midazolam Sedatives: zaleplon, zolpidem

CYP2C9 substrates Antidepressants: Fluoxetine, sertraline, amitriptyline Selective AT1 angiotensin II receptor antagonists: losartan, valsartan Oral hypoglycemic: sulfonylureas, glimepiride, glipizide, glyburide NSAIDs Others: phenytoin, S-warfarin, zolpidem CYP2C19 substrates Antidepressants: amitriptyline, citalopram, clomipramine, fluoxetine, imipramine, (continued)

CYP2C9 CYP3A4 substrates substrates Analgesics: buprenorphine, codeine, fentanyl, hydrocodone, tramadol, lidocaine. Antiarrhythmics: amiodarone Ca channel blockers: amlodipine, diltiazem, nimodipine, verapamil Beta-blockers: metoprolol, carvedilol Protease inhibitors: ritonavir, lopinavir, nelfinavir, indinavir NNRTIs: efavirenz Antiepileptics: carbamazepine, ethosuximide, valproic acid, zonisamide Statins: atorvastatin, simvastatin Antibiotics: azithromycin, clarithromycin, erythromycin Antifungals: ketoconazole, fluconazole, miconazol Others: dextromethorphan, sildenafil, tamoxifen, ondansetron, PPIs Marijuana 1A1 substrates smoking Compounds of lowers the tobacco smoke: concentration heterocyclic of CYP1A1, amines and CYP 1A2 polycyclic aromatic substrates, hydrocarbons potentially CYP1A1 is a carcinogenmetabolizing enzyme. Its activation or inhibition could

sertraline, venlafaxine Barbiturates: hexobarbital, mephobarbital PPI: lansoprazole, omeprazole, pantoprazole, esomeprazole Benzodiazepines: alprazolam, diazepam, flunitrazepam Others: moclobemide, propranolol, nelfinavir

1A2 substrates Antidepressants: amitriptyline, clomipramine, fluvoxamine, mirtazapine Antipsychotics: chlorpromazine, clozapine, fluphenazine, haloperidol, olanzapine, perphenazine, (continued)

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CYP2C9 CYP3A4 substrates substrates modify the cancer risk factors

thiothixene, trifluoperazine, ziprasidone Others: propranolol, caffeine, acetaminophen, riluzole, ropinirole, melatonin, R-warfarin, naproxen, ondansetron

Cannabis produces sedation, impairs psychomotor performance, and increases blood pressure and heart rate. Pharmacodynamic interactions with other sedatives can potentiate the central effects but can be decreased by psychostimulants. This review focuses on the interactions between cannabinoids and alcohol, other drugs of abuse, and prescription medicines. It is important to note that the ratio between CBD and THC had changed over the years, with Δ9-THC having higher concentration now than in the past. Cannabidiol (CBD) had been used more and more in the treatment of epilepsy, including very recent promising results in the possible improvement of schizophrenia (McGuire et al. 2018). Case reports suggest that concurrent use of cannabis with other illicit substances could lead to toxic interactions (Lindsey et al. 2012).

Benzodiazepines and Barbiturates Benzodiazepines (BZDs) are among the most widely prescribed medications all over the world for a broad spectrum of indications, including insomnia, epilepsy, muscle spasms and contractures, alcohol withdrawal, and anxiety (Griffin et al. 2013). They are used in anesthesiology for premedication before general surgery because of their marked anxiolytic effect and the ability to cause anterograde amnesia. The use of some benzodiazepines (i.e., flunitrazepam) has been severely restricted due to their ability to induce

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abulia in combination with retrograde amnesia and the potential to be used for sexual assault and for “zombification.” Other point of concern is their ability to induce tolerance and dependence/addiction, the changes in their pharmacological profile with age, and the interactions with multiple medications. The pharmacological effects of benzodiazepines (sedative, anxiolytic, muscle relaxation, antiepileptic) are mediated via modulation of GABAA receptor activity – i.e., in the central nervous system BZD represent GABAA agonists. The gamma-aminobutyric acid (GABA) is a universal inhibitory mediator in the central nervous system that decreases neuronal activity and excitability. The GABA receptors have three major types – A, B, and C – that represent chlorine channels, and BZDs bind selectively to GABAA. The latter is composed of five subunits – two alpha, two beta, and one gamma subunit. BZDs bind to the pocket created by α and γ subunits and change the space structure of the receptor that leads to increased binding of GABA and stimulation of GABA mediation; increased chlorine channel permeability, along with changes in sodium, potassium, and calcium membrane permeability; inhibition of calcium-dependent neurotransmitter release; and inhibition of adenosine neuronal uptake with unknown clinical significance (Quinn et al. 1997; DeVane 2016). BZDs also have peripheral benzodiazepine receptors (DeVane 2016) that are unrelated to GABAA – in the peripheral nervous system, glia, immune system structures, etc. BZDs also act as mild adenosine reuptake inhibitors thought to explain, at least partially, their anticonvulsant and anxiolytic effects. Barbiturates bind to a different part of the same receptor as BDZ bind. Barbiturates cause similar effects to the ones that BDZ have (DeVane 2016). While BZDs increase the frequency of the chlorine channel opening, barbiturates increase the duration of the opened state. This leads to increased risk of toxicity of barbiturates. Moreover, barbiturates are known to bind and affect other CNS and peripheral receptors, including inhibition of ionotropic glutamate receptors (kainate and AMPA receptors) and inhibition of

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P/Q type of voltage-dependent calcium channels (leading eventually to inhibition of glutamate release). Barbiturates also bind to ligand-gated ionic channels (cationic – nAChR, 5-HT3 receptor, and glycine receptor ionic channels), causing depression of the CNS. The stated extra-GABAA receptor and ionic channel effects of barbiturates leading to more pronounced CNS depression compared to BZDs plus the lack of specific antagonist have led to significant restriction of barbiturate use in the clinical practice (DeVane 2016).

Benzodiazepines BZDs were first introduced to the clinical practice in the 1960s as tranquilizers and sedatives and subsequently were administered as anticonvulsants and hypnotics. Depending on the duration of action, BZDs are classified as having short, intermediate, and long duration of action. Short and intermediate acting are used mainly for the treatment of insomnia and long acting for anxiety. There are two main mechanisms of tolerance and addiction in BZDs (Quinn et al. 1997): downregulation of GABAA receptors in the limbic system and increased sensitivity of the benzodiazepine-GABAA receptor complex to inverse agonists. Moreover, similar to alcohol addiction and withdrawal, mechanisms have been described, including, respectively, changes in the expression of corticotropin-releasing hormone (CRF) and CRF receptor sensitivity and neuropeptide Y and increased NMDA and AMPA receptor sensitivity (affecting glutamate neurotransmission). BZD withdrawal syndrome is characterized by sleep disturbance, irritability, anxiety to panic attacks, confusion, nausea, weight loss, changes in blood pressure and heart rate, muscle stiffness, irritability, headache, perceptual changes, psychotic reactions including hallucinations and delusions, and suicidal thoughts and attempts. In the elderly, BZDs tend to have more unfavorable profile of side effects and are therefore included in the Beers List of inappropriate medications in the elderly.

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Specific adverse effects of BZDs on the central nervous system that are even more pronounced in elderly are (Griffin et al. 2013; DeVane 2016): – Cognitive impairment and other toxic effects on the CNS, including sedation, drowsiness, inattentiveness, motor impairment, anterograde amnesia, and ataxia. – High risk for development of tolerance and addiction with severe withdrawal symptoms in abrupt cessation. – Anterograde amnesia, especially concerning the long-term memory and impaired implicit and explicit memory; these effects are particularly dangerous because of the possibility for drug-facilitated sexual abuse, especially in flunitrazepam intake. – Accumulation and subsequent disinhibition with impaired perception of inherent risk of inappropriate behavior (reckless driving, sexual behavior, etc.). – BZD-induced delirium states, especially in the elderly and/or hypoxic patients with parenchymal organ failure. BZDs have significant drug interactions with many prescription, nonprescription, and illicit drugs, including benzodiazepines, opioids, alcohol, and over-the-counter sleep medications. Benzodiazepines are metabolized through the liver, mainly through CYP450 to CYP3A4 isoenzyme. On the other hand, BZDs have antagonist, flumazenil, used for the acute treatment of overdose, along with the supportive treatment (infusions, antibradycardic, antihypotensive medications, diuretics, etc.). The treatment of BZD addiction is difficult, as dependence (both psychological and physical) develops relatively quickly and includes flumazenil and cognitive behavioral therapy.

Drug Interactions Benzodiazepines could have their plasma levels increased through the inhibition of CYP3A4. Potent inhibitors, such as fluoxetine, imipramine, erythromycin, clarithromycin, etc., increased the plasma levels of benzodiazepines through the

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inhibition of CYP3A4 by the antidepressants. They could have their plasma concentration lowered (or half-life shortened) when interacting with enzymatic stimulants such as carbamazepine. Benzodiazepines could have synergistic increased depressing effect on CNS and respiratory suppression with mirtazapine, alcohol, barbiturates, and antihistaminic medications.

Barbiturates Barbiturates were first discovered in 1864, but their widespread use in the clinical practice as hypnotics began in the beginning of the twentieth century. Because of their marked side effects and the risk of dependence and addiction, currently barbiturates are used mainly as hypnotics, anticonvulsants, sedatives, and general anesthetics (sodium thiopental) and for the treatment of severe withdrawal symptoms of alcohol and illicit drug abuse (as sedatives, like BZDs) (DeVane 2016). Their profile of addiction and withdrawal is similar to benzodiazepines, but they have no known receptor antagonist. Therefore, the intoxication and dependence are more difficult to manage compared to BZDs, having in mind the broader spectrum of receptor and mediator systems affected by barbiturates.

Hallucinogens (LSD, Mescaline, Magic Mushrooms, Ayahuasca, Psilocybin, Dimethyltryptamine, DMT) The classical hallucinogens are natural substances or their derivatives that cause perceptual alterations of real stimuli, described by the patients as hallucinations, via stimulation of serotonin mediation. These “hallucinations” actually represent distortion of the reality due to changes in perception. Classical hallucinogens have been used for religious and recreational purposes for hundreds of years. Classical hallucinogens are taken orally in the form of “magic drinks or potions,” tablets, etc., and are rarely smoked. All

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act via activation of 5-HT2A receptor pathways. Depending on their chemical structure, classical hallucinogens can be divided into three major groups (Baumeister et al. 2014): – Tryptamines: psilocin (the psychoactive compound of psilocybin, found in the magic mushrooms) and DMT (N,N-dimethyltryptamine, the psychoactive compound of ayahuasca) and its derivatives – Lysergamides: LSD (lysergic acid diethylamide) and LSP (lysergic acid 3-pentylamide) and its derivatives – Phenylamines: mescaline (the psychoactive compound of the peyote cactus) and DOI (2,5-dimethoxy-4-iodoamphetamine) and their derivatives Psilocybin, dimethyltryptamine, and mescaline occur in nature, and the rest are synthetic. Ketamine, MDMA, and salvinorin A are able to induce similar changes in the state of consciousness but are not classified as classical hallucinogens because their effects are mediated via other receptor pathways. All three types of classical hallucinogens act via the stimulation of 5-HT2A receptors (Baumeister et al. 2014) and cause distortion of environmental stimuli (i.e., temporary distortion of reality), perceived as hallucinations, traveling to or contact with other worlds, etc. They also bind to metabotropic serotonin receptors and affect large number of intracellular signaling pathways, the significance of which is not clear (Baumeister et al. 2014). These substances also tend to have antidepressant effects. The pharmacodynamic studies on these substances have shown that they act via the stimulation of 5-HT2A receptors and can therefore cause cross-tolerance (i.e., between psilocybin and LSD). 5-HT2A antagonists (ketanserin and risperidone) can block their action. Classical hallucinogens cause rapid downregulation of the stated receptors and the subsequent development of tolerance. The activation of serotonin receptors initiates several signal transduction pathways: G q/11

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signaling route activating phospholipase C, decreasing the activity of protein kinase C and increasing the release of calcium ion from the cells. Classical hallucinogens are also known to stimulate phospholipase A2 (with formation of arachidonate) independently of the described pathways. These substances are known to change gene expression in the brain: induction of c-fos (LSD and derivatives), egr-1 and egr-2 (LSD), etc. Hallucinogens affect the Gi/o proteins with subsequent activation of Src. They also affect metabotropic receptors (e.g., glutamate receptor mGluR2) and lead to metabolic and behavioral effect changes. It has been suggested that their hallucinogenic effects are mediated by the co-activation of 5-HT2A and mGluR2, as well as Gi/o proteins and their cascades. 5-HT2A receptors are located in several brain areas: pyramidal neurons of layer V projecting into layer I of the cortex and the thalamus (reticular nucleus, regulating the signal processing from the thalamus to the cortex). The reticular nucleus sends inhibitory GABAergic projections to the thalamus and allows the transfer of more sensory stimuli to the cortex. Psilocybin is known to decrease the metabolic activity in the thalamus, and this could be the mechanism of sensory alterations in its abuse. Probably the changes in information transfer via the stimulation of 5-HT2A receptor pathways represent the mechanism for the development of sensory hallucinations (literally opening doors of perception, described by Huxley in 1954). Classical hallucinogens have different receptor affinities and different half-lives, the longest having LSD up to 12 h). The physiology of addiction to these substances is associated with persistent activation of 5-HT2A receptors (Baumeister et al. 2014) with epigenetic modifications leading to changes in information transfer and channeling and the need for repetitive stimuli to maintain the same level of information transfer. Medical strategies that counteract these changes are 5-HT2A receptor antagonists, such as ketanserin and risperidone. Still, some of the effects of hallucinogens, including the changes in information channeling, tend to

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persist in time and cause flashbacks months later, even years after the cessation of drug abuse.

Dissociative Anesthetics (Nitrous Oxide, Ketamine, Dextromethorphan, Phencyclidine, Salvia divinorum) Dissociative drugs are hallucinogenic substances that alter the perceptions and the connection with the environment. They generate the feeling of detachment from the reality and from self. The classic dissociative drugs have also anesthetic properties, and some of them are currently used in anesthesiology (both in humans and in animals). The following substances are classified as dissociative anesthetics: nitrous oxide, phencyclidine (PCP), ketamine, dextromethorphan (DXM), and Salvia divinorum. Three main mechanisms of action of dissociative anesthetics have been described (JevtovicTodorovic et al. 1998; Sleigh et al. 2014; Anis et al. 1983; Capasso et al. 2006): – Disruption of glutamate-mediated neurotransmission of signals via antagonizing N-methylD-aspartate (NMDA) receptors – nitrous oxide, phencyclidine, ketamine, dextromethorphan. – Activation of kappa-opioid receptors: Salvia divinorum. – Interaction with other receptors – e.g., phencyclidine inhibits nicotine acetylcholine (nACh) receptors and directly interacts with endorphin and enkephalin receptors and sigma2 receptors; phencyclidine and ketamine are partial dopamine D2-receptor agonists; and nitrous oxide blocks beta-2-subunit containing nACh channels; inhibits kainite, GABAc, AMPA, and 5HT-3 receptors; potentiates GABAa and glycine receptors; and activates two-poredomain potassium channels. The inhibition of NMDA receptors is known to have three effects: dissociative, neuroprotective (via inhibition of glutamatergic stimulation of neurons, which is beneficial in ischemic brain injury), and neurotoxic (inhibition of GABA and

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cholinergic stimulation that could cause neuronal damage) (Jevtovic-Todorovic et al. 1998). These effects are age-dependent (Jevtovic-Todorovic et al. 1998). The first dissociative anesthetic synthetized was the nitrous oxide – discovered in 1772 by Joseph Priestley and later used as “laughing gas.” Subsequently, its anesthetic purposes were discovered, and it became clear that besides its anesthetic properties, it can be administered as recreational and neuroprotective agent with risk of neurotoxicity (Jevtovic-Todorovic et al. 1998). Subsequently, ketamine and phencyclidine were discovered and were introduced as general anesthetics. Dextromethorphan was synthetized as an opioid analogue cough-suppressing agent and is currently part of many over-the-counter syrups against cough, in combination with antihistamines, paracetamol, and decongestants. Salvia divinorum is a plant abundant in Mexico and South America, traditionally used for religious purposes (for divination) and gastrointestinal motility problems [Capasso]. Its major psychoactive substance, a structurally unique transneoclerodane diterpenoids, is known as salvinorin A. It represents a potent kappa-opioid receptor agonist. Moreover, it inhibits enteral cholinergic transmission (explaining its anti-diarrheic effect) and has some mu-opioid receptor agonist action (Capasso et al. 2006). Dissociative anesthetics can be administered via inhalation (nitrous oxide) and ingestion, intravenously (ketamine, phencyclidine, dextromethorphan), or chewing of the leaves (Salvia divinorum). A major problem of ketamine is that the dry substance has no taste, odor, nor color and if added to a drink may cause dissociative state that can be used for sexual assault and kidnapping. As it was mentioned above, the classic dissociative anesthetics (PCP, ketamine, and DXM) act mainly via antagonizing NMDA receptors, i.e., inhibition of glutamate-mediated neurotransmission. This mediator transfers the excitatory signal to the adjacent cells, and as it is one of the major players in cognition and nociception, the inhibition of its signal pathways leads to changes in cognitive functions and inhibition of pain

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sensation. Moreover, the inhibition of glutamate transmission may lead to disruption of vital functions, changes in the mood, sensation of detachment from the environment and from self, depersonalization, derealization, etc. One should not forget that PCP and ketamine affect dopaminergic transmission, i.e., the “reward pathway.” As a parallel with classical hallucinogens, the exact mechanisms of receptor and postreceptor action of dissociative drugs are not well understood, but currently it is assumed that these substances act via temporary blocking of the communications between neurotransmitter systems in the brain and in the spinal cord and causing disorganization of the information channeling that regulates perception (including nociception), vital functions (regulation of sleep, hunger, body temperature, muscle control, sexual behavior), mood, and cognition. In time, due to the phenomenon of neuroplasticity, permanent changes in these information channeling systems may develop, resulting in permanent changes in mood, sleep, hunger, motion, etc. Moreover, the combined intake of dissociative anesthetics with other addictive and psychoactive substances can be extremely dangerous, due to their strong effects on vital and mental functions. The concomitant intake with antidepressant drugs can cause serotonin syndrome with lethal consequences. In combination with stimulants, dissociative anesthetics can increase the heart rate and the blood pressure to life-threatening levels. In combination with sedatives and alcohol, they can suppress breathing. In anesthesiology the medication with nitrous oxide and ketamine is accompanied by oxygen supplementation because without oxygen these substances can decrease oxygen saturation, especially the inhalation of pure nitric oxide. The latter also leads to depletion of vitamin B12 stored and subsequent development of megaloblastic anemia and peripheral neuron damage. DMX is taken largely in the form of cough syrup where it is combined with other substances, such as antihistamines, paracetamol, and decongestants. Extremely large quantities of cough syrup are needed to achieve hallucinations with

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DMX, and these contain high quantities of antihistamines, paracetamol, and decongestants that could be toxic for both the body and the brain, causing inhibition of vital functions, hepatotoxicity, rhythm and conduction disturbances of the heart, etc. Approximately 5–10% of the Caucasian population have genetic polymorphism affecting DMX metabolism that leads to increased risk of overdose. The treatment of addiction and withdrawal to dissociative anesthetics is very difficult, because multiple receptor systems are engaged and frequently changes in vital functions, cognition, and behavior have already developed. Because their mechanism of action involves loss of GABAergic inhibition of the cholinergic excitatory mediation, GABA agonists and cholinolytic medications have beneficial effect in such cases.

Alcohol The ethylic alcohol (spiritus vini) is the most frequently abused mood-changing substance in the world. It affects the life of millions of people worldwide causing alcohol-related diseases. Every year approximately two million people die of alcohol-related conditions, including cirrhosis, cancer, alcohol dependence syndrome, and traumatism (Quinn et al. 1997). Alcohol has been known to mankind since the dawn of human history. Some cultures and beliefs even have their gods of happiness in alcohol-containing beverages. From pharmacodynamic point of view, the effects of alcohol are not mediated by any specific receptor systems but rather are triggered by changes in membrane fluidity, disruption of ion channels, and changes in phospholipase and protein kinase C activity (similar to that in LSD use, but much less severe). The alcohol is known to stimulate glutamate-mediated transmission in the central nervous system via N-methyl-D-aspartate (NMDA) receptor activation. This effect is thought to be responsible for its sedative and amnesic effects, and the overstimulation of this receptor can cause neuronal death. The latter

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mechanism is probably the underlying process of development of organic brain syndrome in chronic alcohol abuse. Alcohol is known to stimulate GABAA receptors. Moreover, chronic alcohol consumption leads to alterations in GABAA benzodiazepine receptor, and probably the withdrawal symptoms of alcohol (especially anxiety and seizures) are related to these changes. Alcohol consumption changes norepinephrine levels due to persistent inhibition of alpha2-adrenergic receptors in chronic abuse and leads to dopamine release and subsequent depletion of the nucleus accumbens. Therefore, withdrawal symptoms may be, at least partially, related to norepinephrine over-reactivity and dopamine release and depletion. In favor of this hypothesis is the beneficial effect of alpha2-adrenergic blockers, dopamine and serotonin antagonists, in alcohol withdrawal syndrome (Quinn et al. 1997). Alcohol is also known to increase the release of 5-HT from central and from peripheral nerve endings and to interact with the opioid receptors in the prefrontal cortex and cause euphoria (Quinn et al. 1997). The administration of opioid receptor antagonists, such as naltrexone, is known to suppress alcohol dependence. The risk of alcoholism is determined by certain environmental factors, by its interaction with multiple receptor systems (including adrenergic, dopamine, serotonin, NMDA, glutamate, and opioid receptor systems), and by genetic polymorphisms of alcohol and aldehyde dehydrogenase (Quinn et al. 1997; Higuchi et al. 1995). The alcohol withdrawal syndrome is managed with sedatives, including benzodiazepines, betaadrenergic blockers, dopamine and 5-HT antagonists, and naltrexone, used to counteract the described receptor and signaling pathways. Alcohol dependence is very difficult to manage because this substance is widely available and easy to access. Several strategies have been applied, including sedative and disulfiram intake and behavioral therapy (Quinn et al. 1997). To exert its desired effect, a drug generally must travel through the bloodstream to its site of action, where it produces some change in an organ or tissue. The drug’s effects then diminish as it is processed (metabolized) by enzymes and

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eliminated from the body. Alcohol behaves similarly, traveling through the bloodstream, acting upon the brain to cause intoxication, and finally being metabolized and eliminated, principally by the liver. The extent to which an administered dose of a drug reaches its site of action may be termed its availability. Alcohol can influence the effectiveness of a drug by altering its availability. Alcohol has three main pathways of metabolism: 1. Alcohol dehydrogenase (ADH) metabolizes alcohol to acetaldehyde. It is a toxic compound. It is also a proven carcinogen, with its high activity. 2. Aldehyde dehydrogenase (ALDH) turns acetaldehyde into acetate, which is less active and not a carcinogen. Acetate ends up being turned into water and carbon dioxide. Genotype of ADH, such as ADH1B*2, is more active. It increases the levels of alcoholderived acetaldehyde quickly and with high intensity. It is common in people of Chinese, Japanese, and Korean descent but rare in people of European and African descent. This could be a protective factor, considering that intoxication leads to unpleasant experience and possibly would lower the risk for repetitive use (and developing of addiction). ALDH1A1*2 and ALDH1A1*3 on the other hand are the most frequently formed enzymes in patients with alcohol use disorder from AfricanAmerican descent. There are many environmental factors (food, stress levels, other genetic influences) that shape the alcohol metabolism in different populations. This perhaps could explain the equal distribution of alcoholism and alcohol use disorders among Caucasians, Asians, Native Americans, and African-Americans per recent observations. Acetaldehyde alters glial cells’ function. It has psychiatric and behavioral repercussions. Normal amounts could have euphoriainducing, anxiety-reducing, hypnotic, and memory-inhibiting effects. With higher plasma levels, aggression could occur. Acetaldehyde could also lower the preference of alcohol, i.e., aversion to voluntary ethanol consumption.

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This is the key concept in the use of disulfiram. The disulfiram reaction occurs when alcohol is consumed in the presence of disulfiram, which blocks irreversibly aldehyde dehydrogenase, thus increasing steadily the levels of acetaldehyde. The latter’s side effects are flushing, headaches, tachycardia, arrhythmia, nausea, vomiting, and hypotension. Disulfiram also inhibits CYP2E1 too, thus increasing the plasma levels of warfarin, phenytoin, and theophylline and also decreasing the clearance of benzodiazepines such as diazepam, oxazepam, and chlordiazepoxide and also caffeine and tricyclic antidepressants such as desipramine and imipramine. Disulfiram has two toxic metabolites: diethyldithiocarbamate (DDC) and its metabolite carbon disulfide (CS2). DDC blocks the activity of dopamine betahydroxylase, through copper chelation. With higher levels of DDD, dopamine can no longer turn into norepinephrine. Presynaptic norepinephrine gets depleted. Dopamine accumulation leads to secondary cardiac abnormalities. 3. Alcohol could be also metabolized through P450 2E1 (CYP2E1) and catalase. These enzymes break down alcohol to acetaldehyde. CYP2E1 “switches on” after large amounts of alcohol, that is, after the ADH and ALDH capacities are overwhelmed. Catalase also contributes to alcohol metabolism only at a very small extent (Edenberg 2007). CYP2E1 accounts for roughly 7% of all CYP450 isoenzymes. It is located on chromosome 10q26.3. There are three key polymorphisms in CYP2E1 gene studied as of recently. CYP2E*5 on 50 -regulatory region has two variants, G1293C (PstI) and C1053T (RsaI). CYP2E*6 is the third variant, which is detected by Dra I, one of the restriction enzymes used to digest complete genomes and pulsed field gel electrophoresis. This variant has lower activity and could lead to potentially toxic levels of acetaminophen, ethanol, and styrene (Haufroid V et al. 2002; McGraw 2014). There is different data regarding its ethnic distribution. Per Mittal et al. (2015) it is distributed as follows: Caucasians 9%, African-Americans 9%, and

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Japanese 35%; while CYP2E1*6 seems to have been found in 19.6% of Asians, 10.1% of Africans, and 7.7% of Caucasians (Gurusamy and Shewade 2014). 4. Fatty acid interactions, forming fatty acid ethyl esters (FAEEs), which damage the liver and pancreas (Vonlaufen et al. 2007).

Drug Interactions The alcohol-related types of drug interactions have three main dimensions: temporal, enzyme specific, and pharmacodynamic. They could be very complex and difficult to predict at times: 1. Temporal – Acute intoxication, especially after prolonged period of sobriety and low concentration and grade for enzyme synthesis – potential for inhibition of drugs’ metabolism, through competing interaction with the same enzymes. Exposure to acute alcohol intoxication while on any of the antibiotics such as furazolidone, griseofulvin, and metronidazole can lead to disulfiram-like reaction – headaches, nausea, vomiting, and possibly convulsions. Some tricyclic antidepressants could become toxic after acute ingestion of alcohol. Warfarin could increase its plasma concentration leading to problematic bleeding. Gastrointestinal bleeding could happen with combination of non-opioid pain medications, aspirin, and alcohol, since aspirin increases the availability of alcohol. – Chronic use leads to a decrease in the drugs’ availability, diminishing their effects, even in the absence of alcohol, for weeks after cessation of drinking. The clinical importance of this is marked as the need for increasing the dose of certain medications, which patient with chronic alcohol abuse had been taking before entering early remission from alcohol. The doses of these medications required by nondrinkers might be way lower. Chronic alcohol use could decrease the availability of rifampin. To

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the same token, the dose of propofol required to induce anesthesia might be increased in patients with chronic alcohol abuse. There is also increased risk of liver damage by the anesthetic gases enflurane and halothane. Chronic use of alcohol lowers the dose of antiepileptic medications, due to the stimulation of the same enzymes responsible for their metabolism, thus increasing the risk of seizures, even during sobriety periods. Propranolol could have its plasma concentration reduced, thus increasing the risk of hypertensive crisis. Acetaminophen could be transformed into toxic metabolites. 2. Enzyme-specific reactions Chronic alcohol use could influence carcinogenesis by several mechanisms. Acetaldehyde is a carcinogen, binding to DNA. It could form active substances, such as malondialdehyde adduct, which mediate lipid peroxidation and nucleic acid oxidation. Inducing CYP2E1 pathway also contributes to forming acetaldehyde and radicals and enhances degradation of retinoic acid affecting signaling pathways, such as estrogen signaling, favoring proliferation and malignant transformation of precancerous cells. Chronic ethanol intake is also associated with the failure of immune surveillance of tumor cells (Ratina and Mandrekar 2017). CYP2E1 catalyzes the metabolism of procarcinogens such as N-nitrosamines, aniline, vinyl chloride, benzene, styrene, and urethane. There is consistency in different sets of epidemiological data showing a dose-response correlation between chronic alcohol consumption and increase in the risk for breast cancer (Baan et al. 2007; Schwab 2011). The full impact of chronic alcohol use on cancer is yet to be elucidated fully. CYP2E1 is involved in the metabolism of drugs such as acetaminophen, isoniazid, chlorzoxazone and fluorinated anesthetics, hormones, and xenobiotic toxins (Schmidt and Taylor 1987). 3. Pharmacodynamic interactions Alcohol can potentiate the sedative effect of opioids (morphine, codeine, meperidine),

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tricyclic antidepressants, antihistamine medications, antipsychotics, benzodiazepines, and hypnotics. Specific alcohol drinks such as beer and wine could lead to hypertensive crises even in moderate amounts, if combined with monoamine oxidase inhibitors, especially if taken also with foods containing tyramine (cheese, some processed meat) or specific alcohol drinks such as beer and wine with monoamine oxidase inhibitors that could lead to hypertensive crises, even in moderate amounts. Dizziness and risk of falls could be exacerbated during acute intoxication with alcohol for someone taking antihypertensive medications such as nitroglycerin, hydrazaline, or with medications for Parkinson’s disease, such as methyldopa.

Nicotine Tobacco has toxic effects on virtually all organs in the human body. These effects are generally caused by substances other than nicotine, but still, this is the major addictive substance in tobacco smoke. Nicotine is a tertiary amine, found in the tobacco plant. Both (S)- and (R)nicotine bind stereoselectively to nicotinic cholinergic receptors (nAChRs) with the (S)-type being a more potent aAChR agonist. When nicotine enters the body (with the cigarette smoke, when dried tobacco is sniffed or when tobacco leaves are chewed), it quickly enters the bloodstream and reaches two major sites where it exerts its physiological effects – the brain and the adrenal gland. In the brain, the stimulation of CNS nAChRs leads to activation of dopaminergic transmission (also within the “reward circle” – midbrain – nucleus accumbens and further activation of parts of the limbic system, including cortical areas). The activation of nAChRs leads to activation of other receptor pathways, including acetylcholine, norepinephrine, serotonin, GABA, glutamate, and endorphins (Benowitz 2009). In tobacco smoking, dopamine release in the brain is facilitated by nicotine-mediated augmentation

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of glutamate release and by inhibition of GABA release (Benowitz 2009). Moreover, in chronic tobacco smoking, inhibition of monoamine oxidase (MAO) A and B is observed, which is associated with further increase of dopamine and norepinephrine in the synaptic cleft. Therefore, the two major pathways of nicotine addiction are (1) the increase in dopamine and norepinephrine in the CNS (the nAChR- mediated stimulation of the brain reward function) and (2) activation of the limbic system. The second binding site of nicotine are the ganglion-type nAChRs in the chromaffin cells within the adrenal medulla with further epinephrine release leading to increased pulse rate, blood pressure, and contrainsular effects (Benowitz 2009). The pharmacological interventions in nicotine addiction are directed against stopping the tobacco smoking, generally because of the undesired effects of other smoke ingredients. It consists generally of nicotine replacement – via transdermal patches, etc., and nicotine-blocking treatment (Quinn et al. 1997).

Bath Salts (Synthetic Cathinones) Synthetic cathinones are phenylalkylamine derivatives chemically similar to the natural monoamine alkaloid cathinone (benzylethanamine, β-keto amphetamine) derived from the plant khat (Catha edulis). These substances were first synthesized approximately a century ago but became popular as recreational drugs in the first decade of the twenty-first century. The commonly abused synthetic cathinones (called “bk-amphetamines” for their beta-ketone moiety) resemble amphetamine in their chemical structure and mode of action (including binding to the monoamine transporters and monoamine release, reuptake and signaling within the brain, modulation of serotonin action). The natural alkaloid cathinone is (S)-2-amino1-phenyl-1-propanone – a beta-ketone amphetamine analogue. It is found in the fresh leaves of the khat plant. Khat leaves are popular for recreational purposes in the Middle East, particularly in

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Yemen. The intake of cathinone has sympathomimetic effect, close to that of amphetamine – euphoria, alertness, and increase in pulse rate and blood pressure. Well-known synthetic cathinones are mephedrone, methedrone, methylenedioxypyrovalerone, methylone, butylone, dimethylcathinone, ethcathinone, ethylone, fluoromethcathinone, and pyrovalerone. The first cathinone derivate – methcathinone – was synthesized in 1928, and 1 year later mephedrone was synthesized. The only synthetic cathinone currently approved for medical purposes is bupropion. Methcathinone was used for the treatment of depression in the 1930s–1940s and was administered for recreational purposes until the late 1990s. The administration of pyrovalerone for chronic fatigue and obesity has been investigated, but due to abuse and dependency, the drug was withdrawn. In the beginning of the twenty-first century, there was a renaissance of synthetic cathinones as recreational drugs, initially in the UK and subsequently in the USA. These substances have been often referred to as “designer drugs” and have been sold under the name of “bath salts.” The commonly sold bath salts in Europe usually contain mephedrone, and in the USA methylenedioxypyrovalerone, along with the different derivatives of pipradrol and pyrovalerone. The main routes of administration of synthetic cathinones are nasal insufflations (snorting) or oral ingestion, but rectal, gingival, and inhalation delivery and intramuscular and intravenous injection have also been described. Moreover, synthetic cathinones are often administered in combination with other recreational substances, with or without alcohol. Their psychoactive effect appears 10–15 min after the intake and is expected to last for ½–4 h, depending on the route of administration. As it was mentioned above, all synthetic cathinones are phenylalkylamine derivatives with bk-moiety that resemble amphetamines, so they can modulate the levels and action of biogenic amines (stimulant, sympathomimetic effects) and serotonin (effects on the mood and appetite, psychoactive effects) in the brain. Cathinones have higher polarity compared to amphetamines, and therefore they have lower

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penetration through the blood-brain barrier. Their pharmacodynamic and pharmacokinetic properties in humans are not well understood, and the majority of pharmacological data are derived from animal models and in vitro studies. Their effect is known to be due to increased synaptic concentration of dopamine, norepinephrine, and serotonin in the synaptic space via two major mechanisms: – Inhibition of the monoamine uptake transporters with subsequent inhibition of the synaptic clearance of monoamines – Release of the neurotransmitters from intracellular depots through the alteration of vesicular pH and concomitant inhibition of the vesicular monoamine transport VMAT2 receptor, responsible for the monoamine reuptake in the vesicles From a neurobiological point of view, the main factor for the self-administration behavior, abuse, and addiction is the mesolimbic dopamine transmission (Baumann et al. 2014). The mechanisms of action of the following synthetic cathinones have been elucidated, at least in animal models (Baumann et al. 2014; Prosser and Nelson 2012): – Methylone: inhibition of norepinephrine and dopamine via the suppression of monoamine uptake transporters (equally potent to that of methamphetamine and MDMA), inhibition of VMAT2 receptor (less potent than methamphetamine and MDMA), competitive for norepinephrine uptake and non-competitive for serotonin and dopamine), and reverse transport of neurotransmitters from the nerve terminal to the synapse (analogous to that in methamphetamine intake) – Mephedrone: the same mechanisms of action, but less potent in increasing serotonin brain levels and faster returning of mediator levels to the baseline compared to MDMA and amphetamine – Pyrovalerone: inhibition of norepinephrine and dopamine and little effect on serotonin reuptake with the S-enantiomer of pyrovalerone possessing higher biological activity

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Based on their similarity to amphetamines, the effects of synthetic cathinones resemble much to the effects of amphetamine derivatives. The most important adverse effects of their intake are related to their sympathomimetic and serotoninergic effects: palpitations; increased blood pressure due to vasospasm; epistaxis; abdominal pain; severe rhabdomyolysis due to vasoconstriction with dehydration due to decreased sensation of thirst and increased physical activity (i.e., dancing); mydriasis with vision abnormalities; increased activity of the central nervous system with agitation, aggression, paranoia, and delusions; tremor; seizures; tachypnea; dyspnea; diaphoresis; and fever. Cases of hyponatremia in bath salt intake have been reported that are thought to be related to overhydration (as in amphetamine/ MDMA users who voluntarily increase the intake of fluids because of the risk of dehydration) plus changes in antidiuretic hormone secretion [Prosser]. Cases of acute renal failure have been described that are thought to be due to rhabdomyolysis, dehydration, and severe vasoconstriction. Liver failure in cathinone users is thought to be associated with vasoconstriction, thrombosis, and concomitant use of other hepatotoxic substances. It is unknown whether cathinones have direct hepatotoxic effect, like MDMA, mediated by direct mitochondrial toxicity with oxidative modification of mitochondrial proteins (Moon et al. 2008). Synthetic cathinones are often consumed with alcohol. Studies show that the concomitant intake of mephedrone and alcohol in rodents leads to enhancement of the psychostimulant effect via additional increase in synaptic dopamine levels (A.Cuidad-Roberts et al., 2015). This alcohol-induced potentiation of the effect of cathione can be blocked by haloperidol, but not by ketanserin. Concerning the addiction and withdrawal, there has been no systematic research on these processes in cathinone-abusing humans. Observational studies have shown that synthetic cathinones are addictive (Prosser and Nelson 2012), with addiction/dependence symptoms and social dysfunction. Abusers report craving to repeat or increase the dose of mephedrone. Although no physical effect has been reported so

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far. Severe psychological dependence may be present, including depression, anxiety, and craving for continuous use, even without any reported physical effects (Prosser, Nelson JS, Nelson LS 2012). As cathinones are analogues of amphetamines, one could expect the development of marked and long-lasting changes in brain sympathies and serotoninergic receptor systems. The treatment of acute cathinone intoxication is generally supportive – hydration and correction of electrolyte disturbances; stimulation of diuresis; increase of gastrointestinal clearance; oral administration of absorptive agents, adrenergic antagonists (beta-blockers), sedatives (benzodiazepines as in amphetamine and cocaine intoxication to counteract monoamine release and reuptake inhibition), and anticonvulsants; treatment of hyperpyrexia; treatment of rhabdomyolysis (saline infusions, intravenous loop diuretics, urine alkalization, mannitol infusions, corticosteroids, dialysis); gastroprotection; antithrombotic prophylaxis; etc.

Pharmacodynamic Interactions Between Addictive Substances As all addictive drugs follow the same neurotransmitter pathways and the neuromediator systems tend to interact closely with each other and with endocrine signaling, the addictive drugs tend to show significant pharmacodynamic interactions that can have detrimental consequences for the body and the brain. These interactions are of importance for the treatment of drug addiction and withdrawal because of the need to decrease anxiety and CNS overexcitement via decrease in receptor sensitivity and/or inhibition of other receptor systems of the same neurons, affected by the addictive substance. The pharmacodynamic interactions between different psychoactive medications can be explained by four major phenomena: effect on the same receptor systems, presence of both types of receptors on the same neuron, presence of the two types of specific receptors on contacting/adjacent neurons that interact, and, last but not least, interaction on subcellular and intracellular levels (i.e., action on the same second

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messenger or enzyme systems within the cell) (Quinn et al. 1997). Shown below are some examples of pharmacodynamic interactions between psychoactive substances. Opioids are known to increase the sedative effects of benzodiazepines, and vice versa. Moreover, naloxone decreases this effect. This could be explained by at least one of the following three phenomena (Quinn et al. 1997): – Presence of both GABAA (for benzodiazepines) and opioid receptors on the same neurons – Presence of GABAA and opioid receptors on adjacent but related neurons – Pharmacodynamic synergism due to changes in intracellular mediators and second messengers – in this case changes in cAMP levels and/or GABAA receptor phosphorylation via cAMP-dependent process The sedative effects of BZDs have been shown to be diminished by naloxone, probably by downstreaming of GABAA receptors (Quinn et al. 1997). The sedative effects of GABAA pathwaymediated substances (opioids and benzodiazepines) are also increased by alcohol. Acute alcohol ingestion also potentiates GABAA. Antidepressants, antihistamines, and anticonvulsants that interact with GABAA mediation also show additive synergism with opioids, benzodiazepines, and alcohol. Cocaine and opioids, especially heroin, also tend to interact mainly in a pharmacodynamic way (Quinn et al. 1997) – interactions between opioid and dopaminergic pathways and changes in cAMP intracellular levels. Amphetamine and its derivatives possess MAO inhibitory properties and therefore tend to interact with MAO inhibitors in a potentially fatal way. The co-administration of both types of drugs leads to marked adrenergic activation with extreme elevation of blood pressure and pulse rate. This could result in confusion, coma, and death (Quinn et al. 1997).

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The combined intake of dissociative anesthetics with other addictive and psychoactive substances can also be extremely dangerous. The concomitant intake with antidepressants can cause serotonin syndrome, and in combination with stimulants, they can increase heart rate and the blood pressure. In combination with sedatives and alcohol, they can suppress breathing. Synthetic cathinones are often consumed with alcohol, and it tends to increase their psychostimulant effect (Ciudad-Roberts et al. 2015), probably via additional increase in synaptic dopamine levels. Their effects are probably stimulated by all illicit substances that increase dopaminergic mediation.

Conclusion The intake of psychoactive substances has followed mankind since the dawn of human history. These substances have been taken for religious and recreational purposes, as sedative or stimulant medications, and for the treatment of somatic conditions (i.e., gastrointestinal mobility disorders, for pain, etc.) and central and/or peripheral nervous system diseases and conditions. Their pharmacodynamic profiles are of crucial importance for the understanding of their pharmacological and toxic effects, addiction, possible drug interaction, and treatment of withdrawal. Pharmacodynamic drug interactions are of particular importance because of the high prevalence of multidrug abuse. The newer “designer drugs” are an emerging and potentially serious problem, because of the affection of many receptor and signaling pathways and the potential to interact with virtually all substances that affect dopaminergic mediation. Therefore, the good understanding of the pharmacodynamic characteristics of both older and newer addictive substances will aid the diagnostic and therapeutic process in the everyday clinical practice.

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References and Further Reading Anis NA, Berry NSC, Burton NR, Lodge D (1983) The dissociative anaesthetics, ketamine and phencyclidine, selectively reduce excitation of central mammalian neurons by N-methyl-aspartate. Br J Pharmacol 79:565–575 Baan R, Straif K, Grosse Y, Secretan B, El Ghissassi F, Bouvard V, Altieri A, Cogliano V, WHO International Agency for Research on Cancer Monograph Working Group (2007) Carcinogenicity of alcoholic beverages. Lancet Oncol 8:292–293 Baumann MH, Soris E, Watterson LR et al (2014) Bath salts, spice, and related designer drugs: the science behind the headlines. J Neurosci 34(46):15150–15158 Baumeister D, Barnes G, Giaroli G, Tracy D (2014) Classical hallucinogens as antidepressants? A review of pharmacodynamics and putative clinical roles. Ther Adv Psychopharmacol 4(4):156–169 Benowitz NL (2009) Pharmacology of nicotine: addiction, smoking-induced disease and therapeutics. Annu Rev Pharmacol Toxicol 49:57–71 Blessing WW, Seaman B, Pedersen NP, Ootsuka Y (2003) Clozapine reverses hyperthermia and sympathetically mediated cutaneous vasoconstriction induced by 3,4 methylenedioxymethamphetamine (ecstasy) in rabbits and rats. J Neurosci 23(15):6385–91 Brox B, Ellenbroek B (2018) A genetic reduction in the serotonin transporter differentially influences MDMA and heroin induced behaviours. Psychopharmacology (Berl) 235(7):1907–1914 Calipari ES, Ferris MJ (2013) Amphetamine mechanisms and actions at the dopamine terminal revisited. J Neurosci 33(21):8923–8925 Campbell JE, Cohall D (2017) Pharmacodynamics – a pharmacognosy perspective, Chapter 26. In: Pharmacognosy. Fundamentals, applications, strategies. Academic, Boston, pp 513–525 Capasso R, Borrelli F, Capasso F et al (2006) The hallucinogenic herb Salvia divinorum and its active ingredient salvinorin A inhibit enteric cholinergic transmission in the Guinea-pig ileum. Neurogastroenterol Motil 18(1):69–75 Casey ER, Scott MG, Tang S, Mullins ME (2011) Frequency of false positive amphetamine screens due to bupropion using the Syva EMIT II immunoassay. J Med Toxicol 7(2):105–108 Ciudad-Roberts A, Camarasa J, Ciudad CJ et al (2015) Alcohol enhances the psychostimulant and conditioning effects of mephedrone in adolescent mice; postulation of unique roles of D receptors and BDNF in place preference acquisition. Br J Pharmacol 172:4970–4984 de la Torre R, Farré M, Roset PN, Pizarro N, Abanades S, Segura M, Segura J, Camí J (2004) Human pharmacology of MDMA: pharmacokinetics, metabolism, and disposition. Ther Drug Monit 26(2):137–144 Dean A (2006) Illicit drugs and drug interactions. Pharmacist 25(9):684–689. www.erowid.org/psychoactives

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160 guidelines for the pharmacologic management of methamphetamine dependence, relapse prevention, chronic methamphetamine-related, and comorbid psychiatric disorders in post-acute settings. Pharmacopsychiatry 50:96–104 Higuchi S, Matsushita S, Murayama M et al (1995) Alcohol and aldehyde dehydrogenase polymorphisms and the risk of alcoholism. Am J Psychiatry 152:1219–1221 Haufroid V et al (2002) Interest of genotyping and phenotyping of drug-metabolizing enzymes for the interpretation of biological monitoring of exposure to styrene. Pharmacogenetics 12(9):691–702 Jevtovic-Todorovic V, Todorovic SM, Mennerick S et al (1998) Nitrous oxide (laughing gas) is a NMDA antagonist, neuroprotector and neurotoxin. Nat Med 4(4):460–463 Kavannagh D, Goodship THJ, Richards A (2006) Atypical haemolytic uraemic syndrome. Br Med Bull 77–78:5–22 Kim HR, Son BH, Lee SY et al (2012) The role of p53 in marijuana smoke condensates-induced genotoxicity and apoptosis. Environ Health Toxicol 27:c2012017 Kishi T, Matsuda Y, Iwata N, Correll CU (2013) Antipsychotics for cocaine or psychostimulant dependence: systematic review and meta-analysis of randomized, placebo-controlled trials. J Clin Psychiatry 74(12): e1169–e1180 Koob GF, Le Moal M (2006) Neurobiology of Addiction. Academic Press, Imprint of Elsevier 92:101–4495 Lange RA, Cigarroa RG, Flores ED, McBride W, Kim AS, Wells PJ, Bedotto JB, Danziger RS, Hillis LD (1990) Potentiation of cocaine-induced coronary vasoconstriction by beta-adrenergic blockade. Ann Intern Med 112(12):897–903 Li JH, Lin LF (1998) Genetic toxicology of abused drugs: a brief review. Mutagenesis 13(6):557–565 Lindsey WT, Stewart D, Childress D (2012) Drug interactions between common illicit drugs and prescription therapies. Am J Drug Alcohol Abuse 38(4):334–343 McGraw J (2014) Chapter 16: CYP450 and Ethnicity, Handbook of Pharmacogenomics and Stratified Medicine, Elsevier McCance-Katz EF, Jatlow P, Rainey P, Friedland G (1998) Methadone effects on zidovudine (AZT) disposition (ACTG 262). J Acquir Immune Defic Syn Hum Retrovirol 18:435–443 McGuire P et al (2018) Cannabidiol (CBD) as an adjunctive therapy in schizophrenia: a multicenter randomized controlled trial. Am J Psychiatry 175(3):225–231 Medicines and Healthcare products Regulatory Agency (2016) Citalopram: suspected drug interaction with cocaine. Drug Saf Update 9(12):2 Mittal B, et al, Advances in Clinical Chemistry; Chapter Four: Cytochrome P450 in Cancer Susceptibility and Treatment. Volume 71 Ed by Makowski GS, (2015), Elsevier.

V. Tenev and M. Nikolova Mollereau C, Roumy M, Zajac JM (2005) Opioidmodulating peptides: mechanisms of action. Curr Top Chem 5(3):341–355 Moon KH, Upreti VV, Yu LR et al (2008) Mechanism of 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy)-mediated mitochondrial dysfunction in rat liver. Proteomics 8(18):3906–3918 Nestler EJ (2005) The neurobiology of cocaine addiction. Sci Pract Perspect 3(1):4–10 Pani PP, Trogu E, Vecchi S, Amato L (2011) Antidepressants for cocaine dependence and problematic cocaine use. Cochrane Database Syst Rev (12):CD002950 Pasternak GW, Pan YX (2013) Mu opioids and their receptors: evolution of a concept. Pharmacol Rev 65(4):1257–1317 Pertwee RG (2005) Pharmacological actions of cannabinoids. Handb Exp Pharmacol 168:1–51 Pomara C, Cassano T, D’Errico S et al (2012) Data available on the extent of cocaine use and dependence: biochemistry, pharmacologic effects and global burden of disease of cocaine abusers. Cur Med Chem 19(33):5647–5657 Powledge TM (1999) Addiction and the brain. Bioscience 49(7):513–519 Prosser JM, Nelson LS (2012) The toxicology of bath salts: a review of synthetic cathinones. J Med Toxicol 8(1):33–42 Quinn DI, Wodak A, Day RO (1997) Pharmacokinetic and pharmacodynamic principles of illicit drug use and treatment of illicit drug users. Clin Pharmacokinet 33(5):344–400 Ratina A, Mandrekar P (2017) Alcohol and cancer: mechanisms and therapies. Biomolecules 7:(3) Reece AS (2009) Chronic toxicology of cannabis. Clin Toxicol 47:517–524 Reece AS, Hulse GK (2016) Chromothripsis and epigenomics complete causality criteria for cannabis and addiction-connected carcinogenicity, congenital toxicity and heritable genotoxicity. Mutat Res 789:1–11 Richards JR, Garber D, Laurin EG, Albertson TE, Derlet RW, Amsterdam EA, Olson KR, Ramoska EA, Lange RA (2016) Treatment of cocaine cardiovascular toxicity: a systematic review. Clin Toxicol (Phila) 54(5):345–364 Rietjens SJ, Hondebrink L, Westerink RHS, Meulenbelt J (2012) Pharmacokinetics and pharmacodynamics of 3,4-methylenedioxymethamphetamine (MDMA): interindividual differences due to polymorphisms and drug–drug interactions. Crit Rev Toxicol 42 (10): 854–76 Robison AJ, Nestler EJ (2011) Transcriptional and epigenetic mechanisms of addiction. Nat Rev Neurosci 12(11):623–637 Rudnik G, Wall SC (1992) The molecular mechanism of “ecstasy” [3,4-methylenedioxymethamphetamine (MDMA)]: serotonin transporters are targets for MDMA-induced serotonin release. Proc Natl Acad Sci 89:1817–1821

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Ruiz-Garcia A, Bermejo M, Moss A, Casabo VG (2008) Pharmacokinetics in drug discovery. J Pharm Sci 97(2):654–690 Schmidt CJ, Taylor VL (1987) Depression of rat brain tryptophan hydroxylase activity following the acute administration of methylenedioxymethamphetamine. Biochem Pharmacol 36:4095–4102 Schwab M (2011) Encyclopedia of cancer, 3rd edn. Springer, Berlin Sharma P, Murthy P, Bharath S (2012) Chemistry, metabolism, and toxicology of cannabis: clinical implications. Iran J Psychiatry 7(4):149–156 Sleigh J, Harley M, Voss L, Denny B (2014) Ketamine – more mechanisms of action than just NMDA blockade. Trends Anest Crit Care 4(2–3):76–81 Status and Trend Analysis of Illicit Drug Markets (2015) World drug report. http://www.unodc.org/documents/ wdr2015/WDR15_Drug_use_health_consequences.pdf Stein C, Schäfer M, Machelska H (2003) Attacking pain at its source: new perspectives on opioids. Nat Med

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9(8):1003–1008, Publisher Iztok-Zapad, 5 Stara Planina Str, 2nd Floor, Sofia, 1000, Bulgaria, EU; ISBN 978-954-9854-19-0, 248 pp Tenev V (2008) Reference Book on Drug Interactions in Psychiatry and General medical practice. Bulgarian Psychiatric Association, Publisher Iztok-Zapad, 5 Stara Planina Str, 2nd Floor, Sofia, 1000, Bulgaria, EU; ISBN 978-954-9854-19-0, 248 pages Volkow ND, Morales M (2015) The brain on drugs: from reward to addiction. Cell 162:712–725 Vonlaufen A, Wilson JS, Pirola RC, Apte MV (2007) Role of alcohol metabolism in chronic pancreatitis. Alcohol Res Health 30(1):48–54 Yubero-Lahoz S, Pardo R, Farré M, O’Mahony B, Torrens M, Mustata C et al (2011) Sex differences in 3, 4-methylenedioxymethamphetamine(MDMA; ecstasy)induced MDMA, methamphetamine, and CYP2D6 cytochrome P450 2D6 inhibition in humans. Clin Pharmacokinet 50:319–329

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Pharmacodynamic Evaluation: Ocular Pharmacology Najam A. Sharif

Contents General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Introduction to Eye Anatomy and Basic Physiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Receptors, Ion-Channels Transporters, and Pharmacodynamics . . . . . . . . . . . . . . . . . 168 Application of Pharmacodynamic Principles in Ocular Drug Discovery and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Eye Diseases and Their Pharmacological Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 Primary Open-Angle Glaucoma (POAG) and Ocular Hypertension (OHT) . . . . . Receptor Binding and Functional Assays to Discover New IOP-Lowering Agents . . . Testing of Compounds for AQH/Fluid Extrusion in Ex-Vivo Systems . . . . . . . . . . . . . . . . Animal Models Used to Discover Novel Ocular Hypotensive Drugs . . . . . . . . . . . . . . . . . .

182 183 187 187

Neuroprotective Therapeutics for Treating Glaucomatous Optic Neuropathy . . . 188 Cell-Based Assays and Animal Models for Discovering Neuroprotective Drugs . . . . . 190 Age-Related Macular Degeneration (AMD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assay Systems Deployed for Anti-dAMD/Anti-GA Drug Discovery . . . . . . . . . . . . . . . . . Animal Models to Find Anti-dAMD/Anti-GA Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cell-Based Assays for Finding New Anti-wAMD/Anti-CNV Drugs . . . . . . . . . . . . . . . . . . Animal Models to Find Anti-wAMD/Anti-CNV Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

192 195 197 197 199

Diabetic Macular Edema and Diabetic Retinopathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Assay Systems and Animal Models for Discovering New Treatments for DR and DME . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

N. A. Sharif (*) Global Alliances and External Research, Global Ophthalmology Research and Development, Santen Incorporated, Emeryville, CA, USA e-mail: [email protected] © Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_54

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N. A. Sharif Ocular Surface Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Allergic Conjunctivitis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dry Eye Disease (DED) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bacterial Infection/Ocular Inflammation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Refractive Disorders/Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

200 200 201 202 204

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205

Abstract

The eye is a specialized organ that provides a relatively easy access for direct visualization of the different anatomical structures and assessment of the diseases associated with them. Despite this, however, the diagnosis and treatment of eye diseases has proven difficult over the years. Ocular surface diseases include allergic conjunctivitis, infection (viral, bacterial, and fungal), inflammation, and dry eye. Major anterior chamber and lens-associated disorders include cataracts, presbyopia, iritis/ ureitis, elevated intraocular pressureassociated glaucoma and pseudo-exfoliation glaucoma. Diseases that primarily effect the retina in the posterior segment of the eye include wet and dry age-related macular degeneration, diabetic macular edema and retinopathy, and glaucomatous optic neuropathy that involves the retinal sensory neurons (retinal ganglion cells) and their axons that form the optic nerve that connects the retina to the brain. Many decades of basic and applied research have resulted in the discovery and development of different types of pharmacological agents (small molecules), peptides, and antibodies that help clinically manage the various ocular disorders mentioned above. Recent advances in gene- and cellular-therapeutics, and production of suitable miniature devices, have also revolutionized ocular disease management. The pharmacotherapeutic and pharmacodynamic aspects of these modalities will be discussed here. This will include target protein localization, assessment of drug engagement with the target, and mechanism of action of the drug entities in cellular and whole-eye efficacy systems using normal and disease-

based assays and animal models. Such in vitro screening and in vivo evaluation and the types of results obtained from such studies will be also described and discussed.

General Introduction Due to the fact that this chapter aims to cover numerous types of eye diseases that have a diverse set of etiologies and disease pathways, it is the author’s opinion that a standardized format is unsuitable. As such, the chapter will cover the key elements of the requisite headings and subheadings but without strict adherence. It is hoped that the format followed is acceptable. The World Health Organization (WHO 2018), National Eye Institute (NEI 2014), and American Academy of Ophthalmology have estimated that ~250 million individuals, including 36 million blind people, have some form of eyesight impairment. Furthermore, it is believed that the number of blind people will increase to 38.5 million by 2020, and to 115 million by 2050. In the USA alone, the total economic burden related to vision loss is expected to reach ~USD 715 billion by 2050. In fact, chronic eye diseases are one of the main causes of vision loss globally, and an ~90% cases of visual impairment are due to such conditions, and indeed a large portion of these chronic ophthalmological disorders affect back of the eye (WHO 2018; NEI 2014; American Academy of Ophthalmology). Eyesight is an extremely vital sense in animals and humans and is critical for survival in most species that sense and interact with the environment through ocular cues. In fact, most humans place the highest value, among the five senses, on eyesight. While not life-threatening, visual

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impairment due to eye disorders has serious implications for quality of life for the patient, caregivers, and the society at large. As some of the eye diseases are age-related in terms of onset and severity, the incidence of ocular diseases continues to increase as the aging population on our planet increases. For example, cataracts were reported to impact >20 million people in 2010, and glaucoma afflicts >65 million people worldwide (Tham et al. 2014; Weinreb et al. 2014; Jonas et al. 2017) with similar numbers for age-related macular degeneration and related visual acuity disorders. Similarly, dry eye (15 million patients in the USA alone), diabetic retinopathy (>5 million patients), refractive errors (>4.1 million patients), and ocular allergies (>3 million patients/year in the USA) also have a high prevalence. These eye diseases, vascular eye disorders, and myopia, continue to cause a sustained undue suffering, medical burden and expense to the patients and the society (WHO 2018; NEI 2014;

American Academy of Ophthalmology). Accordingly, discovery and development of therapeutic agents and devices to treat ocular disorders has gained prominence and importance, eliciting an appropriate heightened sense of urgency to find better treatment modalities and ultimately cures for these maladies.

Introduction to Eye Anatomy and Basic Physiology Being a uniquely specialized sensory organ, it is important to briefly outline the key anatomical elements of the eye and how the eye encodes the light it receives into well-defined color-coded images for us to see (Fig. 1). Most of the eyeball is encased in a white relatively thick, tough, yet flexible fibrous tissue called the sclera, which provides protection from sharp objects and noxious chemicals and shapes the eyeball. The sclera at the

Eye lid Lacrimal caruncle Tear duct

Lateral rectus muscle

Sclera Choroid

Sclera

Retina

Iris Pupil Lens

Cornea Anterior chamber (filled with aqueous humor)

Vitreous body (filled with vitreous humor)

Macula lutea Fovea centralis (central depression)

Optic disc (blind spot)

Posterior chamber Suspensory ligaments

Optic nerve and retinal blood vessels

Ciliary body and muscle Medial rectus muscle

Fig. 1 Depiction of the anatomy and key structures of the human eye pertinent for the discussion of various eye diseases

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N. A. Sharif Light

Conventional Pathway Trabecular meshwork

Cornea

Canal of Schlemm Sclera

Anterior chamber Dilator AQH

(UVSC Outflow)

Unconventional Pathway

AQH Sphincter

Iris Posterior Chamber

Lens

Ciliary process Ciliary epithelium

Zonules Ciliary Body

Ciliary muscle

Fig. 2 Key elements of the anterior segment of the human eye are shown. Formation of the AQH by the ciliary processes, its flow from the latter in front of the lens,

followed by its drainage from the anterior chamber via TM/SC and via the UVSC pathways is also depicted

front of the eyeball is further specialized and forms the transparent cornea that permits light to enter the eye and that also provides a barrier to airborne chemicals, noxious agents, and pathogens. The eye is divided into anterior and posterior segments that are separated by the lens. The anterior chamber (ANC) contains a clear fluid (aqueous humor [AQH]), that contains various nutrients and oxygen, is generated by the ciliary epithelium within the ciliary body. As the AQH flows through the ANC, it nourishes the cells of the lens epithelium, corneal endothelium, and trabecular meshwork (TM) (Fig. 2). AQH also removes metabolites, dead cells, and other toxic waste as it drains through the TM/Schlemm’s canal (SC) and into the venous circulation. The posterior segment is filled with vitreous humor (VH), a gelatinous material, that does not turnover as much as the AQH. The AQH and VH, coupled with the sclera, provide the eyeball its unique shape and overall rigidity. In direct contact with the VH is a thin inner limiting membrane that

isolates the retina from the VH but one that is fairly permeable to chemicals and gases. The retina is a highly specialized tissue being composed of multiple layers of cells that have unique functions in light perception and neuronal communication to the brain (Fig. 3). For the sake of brevity and focus, the most important cell types related to pathological aspects and treatment of retinal diseases include the retinal ganglion cells (RGCs) and their axons (connected with various forms of glaucoma; glaucomatous optic neuropathy (GON)), retinal pigment epithelial cells (RPE cells; connected with wet age-related macular degeneration (AMD) and diabetic retinopathy (DR), and photoreceptor cells (connected with retinitis pigmentosa (RP) and dry AMD) (Fig. 3). However, the interplay between many of these and other retinal cell types, for example, Muller glia and retinal interneurons, is also very important for visual perception. Thus, in humans a million or so RGC axons are bundled together to form the optic nerve that projects to the brain centers that are involved in visual perception.

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Light

Inner limiting membrane NERVE FIBRE LAYER

Vitreous Humor

Cell bodies of ganglion cells

GANGLION CELL LAYER

INNER PLEXIFORM LAYER

Cell bodies of amacrine cells, bipolar cells, Müller cells,

INTERNAL NUCLEAR LAYER EXTERNAL PLEXIFORM LAYER

horizontal cells Cell bodies of rod & cone photoreceptors

EXTERNAL NUCLEAR LAYER

PHOTORECEPTOR LAYER

Rod & cone photoreceptors RPE

CHOROID

Fig. 3 Various layers of human retinal cells are shown indicating their relative positions and interconnections between them

Wrapped within the optic nerve are the central retinal artery and the central retinal vein that provide and remove, respectively, some of the blood to the retina. Another major artery supplying oxygenated blood to the retina is the posterior ciliary artery. In terms of visual perception, light from the environment is first focused by the cornea and then passes through the pupil and is focused further by the lens onto the retina. Here, photoreceptors (rods and cones) then convert the light photons into membrane potential changes by closing of Na+-channels and with resultant cellular hyperpolarization. This electrical change within the rods and cones decreases the release of the excitatory neurotransmitter glutamate (GLU) from their synaptic terminals onto bipolar cells. This changes the level of tonic electrical activity of bipolar cells that then release less GLU onto RGCs. In turn, since GLU activates many types of

ionotropic and metabotropic receptor subtypes that modulate various Ca2+-channels and a whole host of transient receptor potential channels, the electrical activity of the RGCs is well regulated. Also, since neighboring horizontal and amacrine cells modulate the activity of bipolar cells through release of inhibitory neurotransmitters such as gamma-butyric acid and glycine, the bipolar cells’ ability to encode and transmit signals to the RGCs is extremely well coordinated and finely controlled. Finally, the RGCs integrate all the information received from bipolar cells and relay this as electrical impulses down their axons, within the optic nerve, to the lateral geniculate nucleus/superior colliculus within the thalamus of the mid-brain. From there, visual information is relayed to the visual cerebral cortex for final processing and generation of the exquisite images that we see. In this manner, the eyes represent windows for the brain.

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Receptors, Ion-Channels Transporters, and Pharmacodynamics In short, pharmacology is the study of drug action at a tissue and cellular level, while pharmacodynamics pertains to the study of mechanism of action of drugs at molecular, cellular, and tissue levels that reflects drug affinity and efficacy. In principle, ocular pharmacology is no different to pharmacology of other organs such as the heart, lungs, and brain. However, since the repertoire of receptors/ion-channels/transporters (target proteins; Alexander et al. 2017a, b, c, d) do not necessarily exist or function in exactly the same manner in each tissue/cell of these organs, it is worthwhile considering some of the basic aspects of pharmacology and their application to the eye. This necessitates the understanding of how neurotransmitters, hormones, cytokines, and other mediators and drugs exert their actions by interacting with receptors, enzymes, transporters, ion-channels, and nucleic acids that the cells possess (Alexander et al. 2017a, b, c, d). An important characteristic of most biologically and pharmacologically relevant molecules (whether they be small molecules, peptides, hormones, cytokines, or antibodies) is their affinity (“relative attraction”) for the target protein. This affinity parameter is the dissociation constant (Kd or Ki) as determined by the ratio of (rate of dissociation/ rate of association) of the ligand from the target protein. The Kd or Ki values are inversely proportional to the affinity of the ligand, thus a low Kd or Ki reflects a high affinity, usually represented as a concentration needed to occupy [or dissociate from half of the ligand binding sites of the target protein. Implied within the Kd and Ki values is a certain amount of specificity of the ligand for the receptor binding site such that mere adsorption onto the target protein can be discounted. Some examples of these pharmacological/pharmacodynamic parameters for ligands binding to various prostaglandin receptor subtypes can be seen in Tables 1 and 2. Of the overall many druggable target proteins expressed by cells, receptors (39–41% of total), transporters (4–5%), and ion-channels (8–11%) (Alexander et al. 2017a, b, c, d) are primarily

N. A. Sharif

embedded in cellular membranes, while most kinase enzymes (22–24%) and proteases (8–11%) are located within the cytoplasm, and of course nucleic acids and nuclear receptors (2%) are located within the cell nucleus. The interplay among these targets and their signal transduction mechanisms, including positive or negative cooperativity, feed-forward or feedback pathways, allows the cellular machinery to dampen, amplify, or subtly modulate the response mechanisms of the cells and tissues to the pharmacological agent, thereby providing extra fine control and specificity. Added to this complexity is the ability of endogenous ligands and exogenous agents to behave either as full-agonists (producing a maximal biological response), or partialagonists (only producing a submaximal response; e.g., Fig. 4, left panel), or inverse-agonists (inducing a response opposite to an agonist), or antagonists (blocking the actions of an agonist) (e.g., Fig. 4, right panel). Furthermore, there are instances when partial agonists at certain concentrations and under certain conditions can behave as antagonists (Griffin et al. 1999; Sharif and Klimko 2019), and this property can be exploited in the realm of disease management when truly bona fide high potency and high affinity antagonists are not available or are unsuitable as therapeutic drugs. In general terms, receptors are functional transmembrane proteins that are coupled to signal transduction components such as G-proteins (or kinase enzymes) coupled to certain catabolizing enzymes on the inner leaflet of the cell membrane. Activation of the receptor by an agonist ligand (such as norepinephrine or prostaglandin F2α) changes the conformation of the receptor to an active state that triggers specific G-protein(s) and the associated enzyme(s) to generate intracellular second messengers such as cAMP (by activation of Gs-linked to adenylyl cyclase [AC] to increase cAMP and via Gi to inhibit AC and Ca2+
channels, and open K+-channels), cGMP, inositol phosphates, and diacylglycerol (by activation of Gq-linked to phospholipase C) (Alexander et al. 2017a, b, c, d) (Fig. 5). These second messengers amplify the signal transduction by activating protein kinases, opening/closing

PG binding inhibition constants (Ki, nM) and receptor selectivity (x) DP EP1 EP2 EP3 81 6 5 >19,000 2,973  100 1,115  118 (x 234) (x 14) 26 6 10 4.9 6 0.5 3 6 0.2 >10,000 (x 667 vs EP1) 18,000  6,460 594  12 964  64 24  8 (x 0.2) (x 138) (x 5) 3,537 (x 3) >15,000 Nd 5,375  1,394 (x 11) (x 4) 8,074  254 (x 6)

433  25 (x 3)

EP4 2,139  180 (x 26) 0.9 6 0.03

1,398 6 724

53,708  2,136 (x 3,581 vs EP1)  50,000 (x 385)

3,400  710 (x 227 vs EP1) 130 6 6 >86,000 (x 62)

IP >140,000 (x 1,728)

FP 2,500  760 (x 31)

>10,000 (x 667 vs EP1)  190,000 (x 1,462) >65,000 (x 46)

TP >35,000 (x 432)

Data are means  SEM from 3–141 experiments. The bolded values represent the drug inhibition constant (Ki) of the PG for its primary preferred receptor. In the case of PGE2 it is clear that it lacks selectivity between the subtypes of the EP receptors. The Ki value is inversely related to affinity of the PG for the receptor, i.e., the smaller the Ki value the greater the affinity. The values in parentheses denote the relative selectivity of the PG for its preferred (cognate) receptor compared to its affinity for other PG receptors Nd not determined (Sharif et al. 2003)

PGI2

PGF2α

PGE2

PGD2

Natural PG

Table 1 Receptor binding inhibition constants and relative receptor selectivities for some key natural PGs

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11,700  2,710 (x 2) Nd 13,500  1,670 Nd (x
2)

>90,000 (x 14)

>43,000 (x 7) 90,000 (x 4)

Bimatoprost (Amide)

Unoprostone (UF-021; Acid) S-1033 (Na+-salt)

15,200  3,500 (x 3) 6,650  610 (x
3)

EP4 Receptors 41,000  2,590 (x 1,171) 14,400  1,550 (x 147) 25,700  2,060 (x 310) 75,000  2,830 (x 765) >100,000 (x 16)

IP Receptors TP Receptors  90,000  121,000 (x 3,457) (x 2,571) >60,500 (x 617) 121,063  20,714 (x 1,235) >100,000 >77,000 (x 928) (x 1,205)  90,000 (x 918)  60,000 (x 612)

>100,000 (x 16) >100,000 (x 16) 5,900 6 710 >30,000 (x 5) >30,000 (x 5) 22,000  2,600 >30,000 (x 1) >30,000 (x 1)

6,310 6 1,650

98 6 11

83 6 2

98 6 9

FP Receptors 35 6 5

Data are means  SEM from 3–8 experiments. The bolded values represent the drug inhibition constant (Ki) of the PG for its primary preferred receptor, except for S-1033 which is left unbolded since it appears to possess a higher affinity for the EP4 receptor than the FP receptor. The Ki value is inversely related to affinity of the PG for the receptor, i.e., the smaller the Ki value, the greater the affinity. The values in parentheses denotes the relative FP-receptor-selectivity of the PG analog compared to its affinity for the other PG receptors Nd not determined (Sharif et al. 2003)

 22,000 (x 4)  77,000 (x 4)

39,667  5,589# 7,519  879 (x 77) 19,100  1,450 (x 3) Nd >100,000 (x 16)

2,060  688 (x 21)

 20,000 (x 204)

PG receptor binding inhibition constants (Ki, nM) and FP receptor selectivity (x) EP2 Receptors EP3 Receptors DP Receptors EP1 Receptors 52,000  7,200 9,540  1,240 Nd 3,501  461 (x 1,486) (x 273) (x 100) >50,000 (x 510) 12,300  1,240 >100,000# 4,533  597 (x 126) (x 46) >90,000 (x 1,084) 95  27 (x 1) Nd 387  126 (x 5)

Bimatoprost acid (17-phenylPGF2α) Latanoprost acid (PHXA85)

Travoprost acid ((+)Fluprostenol) ()-Fluprostenol

PG Analog

Table 2 PG receptor inhibition constants and relative selectivities of synthetic PG receptor analogs of the FP-class

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Fig. 4 Left-side panel of this figure shows the increase in intracellular Ca2+-induced by AL-34662 and AL-34707, two enantiomers of a 5HT2-receptor agonist. Note that while AL-34662 is a full-agonist, AL-34707 is a much weaker agonist and is a partial-agonist based on its relative

potency and intrinsic efficacy compared with AL-34662.The right-side panel depicts the ability of various 5HT2 receptor subtype-selective antagonists concentration dependently blocking the actions of AL-34662. (Modified from Sharif et al. (2007))

ion-channels or releasing intracellular Ca2+, which ultimately results in a biological response such as hormone/cytokine release, muscle contraction, induction of inflammation and/or pain. On the other hand, kinase-linked receptors represent another large family of proteins that respond to certain growth factors and cytokines, and they trigger the phosphorylation of intracellular proteins that are involved in other types of signal transduction linked to cell growth, differentiation, and gene activation or inhibition. Furthermore, certain lipophilic ligands such as steroids enter the cell and have to move to the nucleus to activate gene transcription and thus influence protein synthesis (Fig. 5). Thus, rapid transmission of information (milliseconds to minutes) to modulate cellular activity and achieve communication among cells is undertaken by ion-channels/second messengers and intermediary mediators, while slow transfer of information (occurring over hours to days) is

accomplished via gene expression changes (Alexander et al. 2017a, b, c, d). When the actions of the endogenous ligand or exogenously delivered drug are completed, these molecules are either degraded by specific enzymes or are taken up by cells via transporter systems and then metabolized and ultimately recycled. Transporters, as the name implies, shuttle chemicals, drugs, metabolites, and/or nutrients from one side of the cell membrane to the cytoplasm or vice versa, and this process occurs over a seconds-minutes time-scale. Thus, by effective and timely use of different receptors, ion-channels, transporters, and diverse signaling mechanisms, the body conserves energy, enhances efficiency and tries to maintain homeostasis and thus good health. The degree to which the agonist ligand fits the receptor active-site and converts the inactive conformation of the receptor protein to a fully active or partially active state governs the relative

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Receptors as Enzymes: binding

nicotinic acetylcholine R glutamate R GABAA R glycine R 5HT3 serotonin R

G Protein-Coupled Receptor Systems

G proteincoupled receptors

α βγ

Cell Surface Multisubunit Ligand-gated lon channels

GTP

Effector

GDP

catalysis

Catalytic Activities: Cytoplasm

Tyrosine kinases growth factor receptors neurotrophic factor receptors Tyrosine phosphatases Serine/threonine kinases TGFβ-receptor Guanylyl cyclase ANF receptor guanylin receptor

G Proteins:

Effectors

Defined by α Subunit composition

Regulated by α Subunits:

αs αi αo αq α13 α Cytosolic αt olf

Receptor

Nucleus Regulation of transcription steroids retinoids thyroid hormone

adenylyl cyclase,

Ca2+ currents

adenylyl cyclase,

K+ currents

Ca2+ currents phospholipase Cβ Na+/H+ exchange αGMP-phosphodiesterase (vision) adenylyl cyclase (olfaction) regulated by βγ subunits: +

receptor-operated K currents adenylyl cyclase phospholipase Cβ

Fig. 5 The signal transduction systems associated with different kinds of receptor proteins present in most mammalian cells are shown

efficacy of the agonist (full- or partial agonist) to elicit the final cell or tissue response. The relative attraction of the ligand for the receptor to begin with is determined by the relative binding affinity of the ligand to fit the receptor-protein-pocket (“key and lock engagement”) and the ability of the ligand-receptor complex to be formed and thus to cause a biological effect (“opening the lock phenomenon”) (Fig. 5). For an agent to be classified as a pharmacologically relevant entity, its action at the receptor/ion-channel/transporter must follow concentration-response or doseresponse relationship characteristics, and other compounds in the same class must demonstrate various degrees of affinity/potency/efficacy (intrinsic activity) with parallel concentrationresponse curves. This means that an agonist must elicit an increasingly larger response as its

concentration or dose is increased until all the receptors/ion-channels/transporter ligand-binding sites are fully occupied and the induced response plateaus. In most cases, overstimulation of the receptor leads to dissociation of the receptor-Gprotein complex, and a diminished response is observed. This desensitization phenomenon is well known and is responsible for the development of tolerance to a drug. The potency of the agonist ligand is defined as the relative concentration needed to induce a given response via receptor activation or by engagement with its recipient protein. The relative potencies of agonist compounds (the concentration needed to induce 50% of the maximal response; EC50 or ED50) is used to rank order such compounds (e.g., Tables 3 and 4.). Likewise, the relative potencies of antagonist or inhibitor

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Table 3 Functional agonist potencies of selected PGs at various PG receptors Test PG Compound

PGD2 PGI2 PGE2 PGF2α Bimatoprost acid Travoprost acid Latanoprost acid (PHXA85) Cloprostenol S-1033 Unoprostone (UF-021)

Agonist potency (EC50; nM) at various prostaglandin receptors EP1-receptor EP2-receptor EP3-receptor DP(PI turnover; (" cAMP; or (various EP4receptor receptor or other other functional (" cAMP) (" cAMP) response) response) responses) 74 3,190 58,000 Nd >10,000 >10,000 319 >10,000 3,019 >10,000 >1,000 2.9 67 19.9; 45; 4.5 40 >10,000 29 >10,000 691; >10,000; >10,000 2,000 >10,000 2.6 >10,000 Nd >10,000

IP-receptor (" cAMP or other response) >10,000 7 3,310 3,000

TP-receptor (PI turnover; or other response) >10,000 >10,000 >10,000 >10,000

>10,000

>10,000

>10,000

Nd

>10,000

>10,000

>10,000

>10,000

>10,000

>10,000

119

20,000

12,000

>10,000

>10,000

>10,000

>10,000 >10,000 >10,000

93 >30,000 >30,000

>10,000 >10,000 >10,000

228 >10,000 >10,000

>10,000 >10,000 >10,000

>10,000 >10,000 >10,000

>10,000 >10,000 >10,000

Data are average values from up to three experiments. Nd not determined (Sharif et al. 2003)

compounds (the concentration needed to block/ inhibit 50% of the maximal response or event; IC50 or Ki) can be used to rank order such compounds in order to choose which compound(s) to pursue in animal studies, for instance. Furthermore, antagonists can be classified as competitive or noncompetitive. Competitive antagonists shift the concentration response of an agonist to the right (dextral shift) without diminishing the maximal effect of the agonist thereby reducing the agonist affinity for the receptor (Griffin et al. 1999). Noncompetitive antagonists invariably produce rightward shifts of the agonist concentration-response curves but prevent the agonist compound achieving its maximum effect (Sharif and Klimko 2019). Just as agonists cause receptor desensitization when the latter are exposed to excessively high a concentration (and/or exposed too often) of the agonist ligand, cells/tissue/animals challenged with very high levels of competitive antagonists (and/or on a high frequency) actually induce generation of

more receptors (receptor upregulation) as a compensatory mechanism. This is often associated with a so-called “rebound effect” when the agonist actually produces a greater response than it induced before exposure to the high levels of the antagonist. Ligand-gated ion-channels are important targets for neurotransmitter and drug interaction and thus for drug discovery/development. There are several different types of ion-channels that are present primarily on cell membranes of neurons and excitable cells where fast communication is needed. The most well-known ion-channels, that are made up of 3–5 protein subunits, are those permeable to Na+, K+, Cl
, and Ca2+ ions and are responsible for depolarization or hyperpolarization of cells. Binding of specific ligands (e.g., serotonin, glutamate, zinc, acetylcholine, ATP) to certain subtypes of receptors activates these types of ion-channels (Alexander et al. 2017a, b, c, d). Certain cation-channels of the transient receptor potential (TRP) protein superfamily

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Table 4 Functional agonist potencies of various FP-class PG analogs at native or cloned FP receptors in five different cell types of different species Compound

Travoprost acid ((+)fluprostenol ()fluprostenol Bimatoprost acid (17-phenylPGF2α) Latanoprost acid (PHXA85) Travoprost (Isopropyl ester) Latanoprost (Isopropyl ester) Bimatoprost (amide) Unoprostone (UF-021) Unoprostone isopropyl ester S-1033 PGF2α

Agonist potency for stimulating inositol phosphate production in different cell types (EC50; nM) Human Human cells (HEK-293) Rat A7r5 Human ciliary trabecular expressing cloned vascular muscle cells meshwork cells human ocular FP Mouse Swiss smooth (h-CM cells) (h-TM cells) receptor 3T3 fibroblasts muscle cells 1.4  0.2 3.6  1.3 2.4  0.3 2.6  0.2 2.6  0.5 4.3  1.3

11  2

4.6  0.4

3.7  0.4

4.4  0.2

3.8  0.9

28  18

3.3  0.7

2.8  0.2

2.8  0.6

124  47

35  2

45.7  8.4

32  4

35  8

123  65

103  27

40.2  8.3

81  18

46  6

313  90

564  168

173  58

142  24

110  19

9,600  1,100

3,245  980

681  165

12,100  1,200

6,850  1,590

3,503  1,107

3,306  1,700

3,220  358

617  99

878  473

8,420  912

2,310  1,240

9,100  2,870

560  200

458  85

4,701  2,031 104  19

7,000  2,600 62  16

2,610  463 29  2

670  320 26  3

767  93 31  3

Data are mean  SEM from 3–23 experiments. nd not determined. PGF2α (Ki = 122  40 nM) and latanoprost acid (Ki = 149  9 nM) exhibited relatively high affinity for the prostaglandin transporter transfected in host cells. The functional PI turnover activities of various PGs were blocked by the FP-receptor selective antagonist, AL-8810. The pooled antagonist potencies of AL-8810 were: cloned human FP receptor Ki = 1.9  0.3 μM; h-TM cell Ki = 2.6  0.5 μM; h-CM cell Ki = 5.7 μM; rat A7r5 cell Ki = 0.4  0.1 μM; and mouse 3 T3 cell Ki = 0.2  0.06 μM using a variety of FP agonists including fluprostenol, travoprost acid, unoprostone, bimatoprost, and bimatoprost acid (Sharif et al. 2003)

exist on nonexcitable and excitable cells and act as sensors of heat/cold, changes in osmolarity, odorants, and mechanical stimuli. The transient receptor potential vanilloid-1 (TRPV1) channel, for example, responds to capsaicin and detects “hot taste” associated with chili-peppers but is also activated by noxious heat (>43  C), low pH, voltage, and various lipids. All the pharmacodynamic aspects of drugreceptor (or drug-ion-channel) interactions mentioned above apply to in vitro and in vivo

situations. Obviously, all drugs have side effects, some toxicological in nature, and thus the riskbenefit ratio must be determined for each in order to ensure that the therapeutic index is high and that the side effects are minimized for the subject. The drug safety elements also have to account for the dose of the drug administered, routes of administration, the speed with which the active drug reaches its intended site of action, the duration of action of the drug, and its rate of and safe elimination from the body. Such data are obtained

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from pharmacokinetic (PK) and absorption/distribution/metabolism/elimination (ADME) studies conducted in suitable normal healthy animals or human subjects during the drug development processes. Direct efficacy of the drug can then be assessed at appropriate dosage(s) in animal models of disease and of course in humans in clinical trials in due course. Due to the diversity of cell types involved in the anatomy and physiology of the eye and the mediation of pathogenesis of various ocular diseases, the eye offers a great opportunity to apply the principles of pharmacology. There is thus a rich history of delineation of physiological and pharmacological actions of endogenous ligands and exogenous experimental chemical agents. A few examples include the role of endogenous epinephrine and norepinephrine (NE) in promoting the synthesis of AQH by the nonpigmented ciliary epithelial cells of the ciliary body in the anterior chamber (ANC) of the eye, and the role of vascular endothelial growth factor (VEGF) to stimulate the growth of new blood vessels into the vitreous humor in wet AMD and diabetic retinopathy. The NE-induced AQH production is mediated through action at alpha- and betaadrenoceptors and by raising the intracellular levels of cAMP, while the VEGF-induced neovascularization of the choroidal blood vessels is mediated through receptor-tyrosine kinase (RTK)-coupling. Hence, β-blockers and antiVEGF antibodies reduce AQH production and neovascularization, respectively. Similarly, allergen-induced conjunctival mast cell degranulation results in the release of histamine and other mediators into the tear film with subsequent activation of histamine receptor-1 in conjunctival and corneal epithelial cells (Sharif et al. 1994, 1996). This raises intracellular Ca2+ and then results in secretion of various proinflammatory cytokines, that together with histamine, cause increased vascular permeability of both tissues causing ocular itching and redness. And thus, treatment of the ocular surface with H1-receptor antagonists such as emedastine and olopatadine blocks the effects of histamine and curtails the itching and redness (Sharif et al. 1994, 1996; Yanni et al. 1999). Other

examples of ocularly relevant receptors/ion-channels/transporters, and drugs that interact with them, will be discussed below in more detail.

Application of Pharmacodynamic Principles in Ocular Drug Discovery and Development The first step in any drug discovery program is to identify the target protein whose activity needs to be modulated to achieve the therapeutic benefit. Next, it is important to verify the localization and relative distribution of the target protein in the specific tissue/cells connected with the disease process and in other areas where potential side effects may occur. Such target localization and visualization can be accomplished using techniques such as autoradiography (Fig. 6, left panel), immunohistochemistry (Fig. 6, right panel), and in situ hybridization. From an ocular perspective, the relative density and distribution and cellular localization of the target protein in animal and human eye sections is paramount. Examples of use of such techniques and results obtained are shown in Fig. 6 and Table 5. Testing funnel schemes are drawn up next that place the target protein into ligand binding assays (and/or directly into functional assays) using cell/ tissue homogenates enriched in the target protein (Fig. 7). If the target receptor/transporter/ionchannel can be found at high levels in animal/ human cells/tissues, then the naturally occurring protein can be used for screening purposes, and this is actually preferred (Sharif 2018a). The alternative strategy is to avail recombinant molecular biological techniques to express the desired protein at high levels in host cells to make the assay sensitive and reproducible. Using known high-affinity (usually nanomolar Kd) tritiated or iodinated radiolabeled ligands to tag the target protein, the relative affinity of unlabeled test compounds to compete for, and thus displace, the radioligand from its binding sites is determined using rapid vacuum filtration techniques to separate the free from bound radioligand followed by liquid

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Fig. 6 Two different techniques of localizing and visualizing membranebound receptors in human and monkey eye sections are illustrated. The top panel shows the autoradiographic distribution of DP PG receptor binding sites in the anterior segment of the human eye using radiolabeled DP-receptor antagonist, [3H]BWA868C. Top-left panel shows the total binding, while the top-right panel shows the nonspecific binding of the radioligand. (Modified from Sharif et al. 2000). The bottom panel depicts localization of bradykinin B2-receptors in human and monkey eye sections using the IHC technique– specific binding of the B2-receptor anti-body is shown in the left-side panels (russet color), while the control (nonspecific binding) is shown in the right-side panels for each species.(Modified from Sharif et al. (2014).

scintillation spectrometry (Fig. 8, left panel; Sharif 2018a). Alternatively, homogeneous proximityscintillation ligand binding assays can be utilized. The ligand binding affinity data obtained from such experiments are used to rank-order compounds and only those meeting specified pass criteria (e.g., Ki  50 nM) are then tested in functional assays. Here, isolated primary cells from target tissue are

used to determine if the compounds are agonists or antagonists (Sharif et al. 2007) (e.g., Fig. 8, right panel) or activators or inhibitors of enzymes (Chen et al. 2014) or channels (Patil et al. 2016). Compounds can then be ranked according to their relative functional potencies for instance for their ability to stimulate the production of intracellular cAMP (Crider and Sharif 2001) or cGMP (Katoli

14,459  3,683 (47%) 20,026  11,276 (20%) 4,151  2,762 (20%) 16,304  3,792 (40%)

14,232  7,937 (48%)

174,943  20,092 (74%) 2,225  582 (14%) 33,238  5,950 (60%) 78,140 (62%) 1,507 (18%) 19,244 (72%)

27,543 (83%)

β-Adrenoceptors [3H]-levobetaxolol binding (β-receptors)a Specific binding; DLU/mm2 and (% specific binding) 51,459 (76%)

55,500 (54%) 23,000 (5%) 39,700 (54%)

37,900 (79%)

DP PG Receptors [3H]-BWA868C binding (DP prostaglandin)b Specific binding; DLU/mm2 and (% specific binding) 67,000 (82%)

DLU digital light units. These units represent an index of the relative density of the receptor population found in the tissues studied

Ciliary epithelium (process) Longitudinal ciliary muscle Iris Lens Choroid

Tissue

5-HT2 Receptors [3H]-ketanserin binding (5-HT2 receptors) Specific binding; DLU/mm2 and (% specific binding) 13,683  5,870 (40%)

All 5-HT Receptors [3H]-5-HT binding (5-HT receptors) Specific binding; DLU/mm2 and (% specific binding) 71,780  2,725 (70%)

Table 5 Quantitative autoradiographic distribution of various drug receptors in postmortem human eye sections

3,776 (40%) 1,886 (12%) 1,671 (28%)

12,741 (68%)

FP PG Receptors [3H]-PGF2α binding (FP prostaglandin)c Specific binding; DLU/mm2 and (% specific binding) 2,554 (35%)

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Fig. 7 A schematic illustration of testing funnels for profiling and selecting compounds as “Hits” and fully characterized compounds is shown. Note that the target can be a

receptor, enzyme, transporter, or ion-channel expressed in isolated normal primary cells or genetically engineered cells or in isolated animal/human tissue samples

et al. 2010; Cavet and DeCory 2018; Fig. 9, left panel) or to mobilize intracellular Ca2+ (Kelly et al. 2003; Sharif et al. 2007; Fig. 8, right panel) or to induce cell/tissue contraction (Ohia et al. 2018). Sometimes the radioligand binding assays are omitted from the testing funnels and compounds are directly screened in functional activity assay systems using multiple compound concentrations to construct full concentration-response curves (e.g., Fig. 4, left panel). Once again relative potency data are used to triage and select most potent and efficacious agonists (e.g., EC50  10 nM), or most potent antagonists (Ki  10 nM), to advance into in vivo testing in animal models of ocular safety (e.g., guinea pigs or rabbits for ocular irritation, redness [hyperemia], or inflammation [mucus-containing discharge]), followed by testing in animal models of eye disease (e.g., ability of compounds to lower intraocular pressure (IOP) in rodent, rabbit, and monkey eyes, either normotensive or ocular hypertensive) (e.g., Fig. 9, right panel, Cavet and DeCory 2018; Fig. 10, Sharif et al. 2014).

In other cases where compound supply is limited, only one or two concentrations are tested to generate receptor binding and/or functionalresponse data to help make decisions. If none of the tested compounds meet the prespecified criteria of affinity/potency/in vitro efficacy, or in order to improve the overall profile of the compounds, the medicinal chemists would need to modify the chemical structure of the compounds and the biologists would need to retest the new compounds. This iterative process can be short or long depending on the complexity of the synthetic process for the compounds/peptides/antibodies and the degree of difficulty and complexity of the assay systems. Based on the physiochemical properties and structure-activity relationship (SAR) profile of the compounds, a suitable lead compound and possible backup compounds are selected for advancement to full development that requires gathering much more detailed information on the ocular/systemic safety, PK, ADME, and toxicology (genotoxicity; central and systemic toxicology in multiple species after repeated

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Fig. 8 Two different types of assays are shown. The leftside panel depicts a typical receptor-binding experiment where various compounds of interest are screened for their ability to displace a receptor-specific radioligand, in case from human retinal homogenates. Note how a compound can be shown to interact with the bind site in a high- and a low-affinity state(ifenprodil displacing [3H]-ifenprodil), or to only bind to a low-affinity state of the receptor. The

relative affinities of the different compounds are easily observed and determined in this manner. (Modified from Sharif and Xu, 1999). The right-side panel shows a typical profile of intracellular Ca2+ increase induced by different concentrations of the 5HT2-receptor agonist, AL-34662. The peak induction of responses from such traces are then used to construct concentration–response curves depicted previously in Fig. 4. (Modified from Sharif et al. (2007))

dosing at multiple doses) information in suitable animal species and test systems. Additional work requiring building a data package covering optimized formulation, route(s) of administration, duration, and mechanism of action of the lead compound (and a backup compound) would be undertaken next. These are lengthy, laborious, expensive, but necessary experiments to discover suitable drugs. Ultimately, the lead compound data package would have met all the necessary requirements to be considered for clinical trials in humans. At this stage, a formal request to conduct Phase-1 (primarily for ocular safety at multiple doses) clinical trials is made to the regulatory agency of the country whence the complete data package on the Investigational New Drug is submitted for approval. If the lead compound is pronounced safe in a limited number of healthy humans (e.g., 20–30) at multiple doses administered once daily

or multiple times/day via the best route of administration (e.g., topical ocular; intracameral; intravitreal; Hartman and Kompella 2018), Phase-II studies can be conducted. About 70% of drugs that enter Phase-I will be successful enough to proceed to Phase-II. Here, clinical studies are conducted in a small number of age-matched control patients lacking the ocular disease, and in those patients having the ocular disease. A control vehicle (placebo), multiple doses of the test drug candidate and a single dose of a suitable comparator drug (previously approved by the regulatory authority) are tested in patients who have the ocular disease (e.g., 30–50 per each treatment arm). Such Phase-II studies are conducted over several months and allow the selection of the most optimum dose of the lead compound. In Phase-III studies, the optimum dose of the lead compound is compared against specified marketed comparator drug (in

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Fig. 9 The left-side panel illustrates the production of a second messenger, cGMP, in h-TM cells by various ligands, while the right-side panel shows how the different

ligands lower IOP in rabbits, dogs, and monkeys. The cellbased and animal model-based data correlate well with each other. (Modified from Cavet and DeCory (2018).

the same drug class) in a much larger human population (e.g., 100–800 patients/treatment arm) of patients with the ocular disease and the trials conducted for several months. It is important to “statistically power” the clinical trials (with sufficient number of patients per treatment arm) in order to show statistically and clinically relevant efficacy and benefit to the patient suffering from the ocular disease. Having established the necessary safety and efficacy of the drug candidate molecule, the regulatory agency can be approached for approval to market the drug by submitting a New Drug Application. This dossier contains all the necessary guidance on drug manufacturing procedures, stability and formulation data, PK, ADME, and other necessary data, and of course all the preclinical and clinical safety and efficacy data, as required by the specific health authority. After meeting all the regulatory authority’s criteria and requirements, the agency may grant an approval to market the drug and thus make it available for use by clinicians to treat the patients with the ocular

disease with appropriate guidance on dose/frequency of dosing/route of administration and of course side effects, etc. Based on the above, it is not surprising that drug discovery, drug development and approval by a health authority is a very long (taking 10–12 years from discovery to marketing and before being introduced into clinical medicine) and very expensive process (typically costing $50–100 million depending on the type of drug/its cost of goods/frequency of dosing, etc.). Nevertheless, such due diligence, time, and cost is worthwhile in order to reduce and/or prevent visual impairment and preserve eyesight for a global population of humans afflicted with the eye disorders for which the treatment was sought.

Eye Diseases and Their Pharmacological Treatments As the earlier discourse above illustrates, the eye is a very complex organ being composed of a heterogeneous population of specialized tissues/

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Fig. 10 IOP reduction in conscious cynomolgus monkeys. This figure shows how the nonpeptide B2-bradykinin receptor agonist FR-190997 lowered IOP of ocular hypertensive eyes of the monkeys when delivered topical ocularly (t.o.). The effect of FR-190997 was reproduced in two different colonies of cynomolgus monkeys. Additionally, it was important to demonstrate that predosing t.o. with a B2-receptor antagonist FR-165649 could block the

IOP-lowering effects of the agonist. Likewise, in order to show that endogenous PGs were mediating some (or all) effects of activating the B2-receptor, a PG synthase inhibitor partially reduced the agonistic effects of FR-190997. Such studies illustrate the role of different pharmacological treatments to better understand the mechanism of action of drugs in vivo. (Modified from Sharif et al. (2014))

cells which have diverse functions ranging from providing structural support, limiting pathogenic infiltration, keeping corneal transparency, focusing light, transducing light into electrical impulses, absorbing excess light, phagocytosing toxins and cellular debris, production and drainage of AQH, etc. It is therefore not surprising that dysfunction of these cell types results in various ocular disorders such as cataracts (51% of total eye diseases), glaucoma (8%), AMD (5%), corneal opacity/scarring (4%), pediatric eye diseases (4%), trachoma (3%), uncorrected refractive errors (e.g., myopia; presbyopia; 3%), diabetic retinopathy (1%), and infections, ocular allergies, inflammation, retinoblastoma, etc. (21%) (WHO 2018; NEI 2014).

The other aspects worthy of note include the fact that while somewhat isolated from the rest of the body, medications administered to the eye topically still drain into the naso-pharynx area and eventually into the stomach and then into the blood stream. As the blood is supplied to each organ, the latter are then exposed to the drug that was administered to the eye, albeit at lower concentrations. Depending on the concentration/ affinity/potency of the drug or its metabolite(s), off-target side effects are always possible and thus the physician and the patient need to be aware of such issues. Likewise, drugs that are delivered to the anterior or posterior chambers of the eye via different routes of administration also are routed via the venous circulation and eventually

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metabolized by the liver and then eliminated via the kidneys and/or the alimentary canal. Due to these reasons, local ocular and systemic and central side effects of drug treatments to the eye have serious consequences and need to be considered in the treatment regimens. In order to emphasize the pharmacodynamic and translational aspects of ocular pathologies and their treatments, eye disorders that are more prevalent and which are debilitating from a visual impairment perspective will be covered first followed by others that are lesser common and where pharmacological elements are less well characterized from the structure activity and pharmacological viewpoint. Due to space limitations, only certain eye diseases will be discussed in detail where the pharmacology is quite well defined and robust. It is hoped that readers will follow-up on the cited references, especially review articles, to help advance their understanding of the eye disease pathogenesis and the treatment modalities available.

Primary Open-Angle Glaucoma (POAG) and Ocular Hypertension (OHT) Glaucoma is a heterogeneous group of ocular diseases that have their origin due to pathogenic events occurring in at least three distinct parts of the eye, namely, ANC, retina, and optic nerve. Over >65 million people around the world suffer from the most common form of glaucoma (primary open-angle glaucoma (POAG)). It is a slowly developing ocular disease that the patient only notices once some of the peripheral vision has already been lost and when only 60% of the original RGCs remain functional and capable of transmitting retinal signals to the brain. Eyesight is lost due to POAG and other types of glaucoma when the RGCs die, their axons atrophy and the RGCs are disconnected from the brain where neurons also die (Yucel et al. 2000). Other characteristic features of POAG are thinning of the retinal nerve fiber layer (RNFL) due to RGC axon atrophy, excavation of the optic nerve head (OHN), and subsequent cupping of the optic nerve disc (Tham et al. 2014; Weinreb et al. 2014; Jonas et al. 2017).

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Behind cataracts, POAG is the next most prevalent preventable blinding eye disease. Despite linkage of numerous risk factors to POAG, abnormally high IOP is the most high IOP accepted modifiable element in the disease process. This ocular hypertension (OHT) is caused by an imbalance between the amount of AQH being produced and the amount draining from the ANC. The blockage of the TM and SC with aberrant or age-related accumulation of extracellular matrix (ECM) and cell debris is primarily the culprit responsible for OHT (Xu et al. 2014). The OHT has been shown to be directly responsible for demise of RGCs and their axons in animal models of POAG and in humans such that it is calculated that every 1 mmHg reduction of IOP results in up to 13% lowering of the progression of POAG (Weinreb et al. 2014; Jonas et al. 2017). Therefore, reducing IOP has been an effective way to treat OHT/POAG for many decades. This has resulted from the fundamental understanding that either the production of AQH can be reduced and/or the efflux of AQH can be accelerated in order to lower IOP. Even though inhibiting AQH generation by blocking the Na+-K+-ATPase in the CEP cells of the ciliary processes is not usually recommended, since the AQH provides nutrients to the ANC cells and removes their waste products, early treatment options were quite limited and clinicians had no choice but to inhibit the inflow process. The relatively recent discovery and development of drugs, surgical procedures and devices that can effectively reduce IOP by stimulating the drainage of AQH via the trabecular meshwork and/or uveoscleral pathways and newly created pathways have revolutionized clinical management of OHT/POAG. Several decades ago, pharmacological management of IOP was achieved using pilocarpine, a plant-derived alkaloid muscarinic receptor agonist. However, while it reduced IOP it also caused miosis and brow ache by contracting the iris sphincter and ciliary muscle (CM). Additionally, its IOP-lowering effect was rather short-lived (4–6 h depending on the concentration of the drug and dosing frequency). Since that time, an enormous amount of progress has been made in discovering, developing, and marketing a variety of pharmacological agents that have

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diverse mechanisms of action to lower IOP. Today, prostaglandin FP-receptor agonist analogs (PGAs; e.g., latanoprost; travoprost; tafluprost; bimatoprost) (Hellberg et al. 2002) represent the drugs of choice for treating ocular hypertension and POAG since they exhibit excellent efficacy, long duration of action (at least 24 h) with relatively low incidence and severity of side effects after a single topical ocularly administered drop of the drug. These PGAs promote egress of AQH from the ANC of the eye via the uveoscleral pathway and to some extent via the conventional TM-SC outflow pathway by releasing matrix metalloproteinases that digest ECM and other cellular debris and thus enlarging existing spaces between CM bundles and the sclera and/or creating new drainage channels in the latter tissues. The notable side effects of PGAs, however, are hyperemia (eye redness), darkening of the iris and orbital skin, lengthening and thickening of eyelashes, deepening of the orbital sulcus, and to a lesser extent, cystoid macular edema. While alpha-2 adrenergic agonists like brimonidine and apraclonidine lower IOP by inhibiting inflow (generating cAMP that suppress Na+-K+-ATPase in ciliary epithelium) and by stimulating some outflow, their ocular (e.g., ocular allergy) and systemic and central (lowering CNS activity and causing lethargy) side effects limit their utility. Other agents that inhibit production of AQH, inflow inhibitors, also include carbonic anhydrase inhibitors (e.g., dorzolamide and brinzolamide), and beta-blockers (e.g., timolol and betaxolol) that do lower IOP but exhibit a number of ocular side effects (burning, stinging, foreign-body sensation) and systemic side effects (drop in blood pressure, bradycardia, palpitations, arrythmias, and bronchospasms). The latter inflow and outflow drugs have been combined in suitable formulations to generate so-called “combination products” with certain degree of enhanced efficacy (Holló et al. 2014), that have expanded the treatment options for treating elevated IOP and glaucoma. The exceptional value of the inflow suppressors and outflow stimulators is highlighted by the latter combination products but also their utility in glaucoma and OHT patients who become refractory to or are nonresponders to PGAs, especially to latanoprost.

In order to overcome some of the deficiencies and side effects of the aforementioned drugs for the treatment of OHT and glallcoma, two recent FDA-approved medications have been marketed: netarsudil (Rhopressa; Lin et al. 2018) and latanoprostene bunod (Vyzulta; Cavet and DeCory 2018). While netarsudil is a rho-kinase inhibitor that lowers IOP by relaxing the ciliary muscle and TM (Lin et al. 2018) thereby stimulating AQH to flow out of the ANC, latanoprostene bunod is a conjugate drug made up of latanoprost and a nitric oxide donating agent (Cavet and DeCory 2018), that reduces IOP by relaxing CM and TM tissues (engaging the outflow pathway) and by activating the UVS pathway. How these new drugs will fare after being introduced into clinical management of OHT/glaucoma remains to be seen, but in the meantime, the search for even better pharmacological agents with unique characteristics and better side effect profiles continues. It is encouraging that continued research in this area is poised to deliver additional drugs as judged by the multitude of reports published in recent years. The most succinct way to show this progress is via a tabular listing of such pharmacological agents (Table 6). Pharmacodynamic aspects, including mechanism of action, of each class of these agents is shown in this table and also described in detail in the relevant citations. Another exciting recent development is the ability of implanted microdevices, after Minimally Invasive Glaucoma Surgeries (MIGS), to literally drain the excess AQH from the ANC of OHT/POAG patients (e.g., Batlle et al. 2016; Fig. 11) without causing collapse of the ANC. The MIGS-related devices have revolutionized AQH drainage from the ANC and have added another means to lower IOP, which was previously dominated by tubes and trabeculectomies (Batlle et al. 2016).

Receptor Binding and Functional Assays to Discover New IOP-Lowering Agents Testing of potential ocular hypotensive agents is simplified since in most cases the target protein and its signal transduction mechanism is known.

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Table 6 Pharmacological agents that lower IOP in various mammals and the mode of action of the compounds Compound classes Pharmacological agent Conventional outflow (TM) stimulators Pilocarpine; Acecledine; Carbachol Muscarinic receptor agonists (mostly M1 receptor agonists) Inhibitors of chloride Ethacrynic acid; Ticrynafen; Indacrinone transport Kinase inhibitors

Marine macrolids

Guanylate cyclase activators NO donors

Soluble guanylate cyclase activators κ-opioid receptor agonists

Rho kinase (ROCK) inhibitors: AR-12286 (Netarsudil); Ripasudil (K115); Y-27632; Y-39983; AMA-0076; H-7; ML-9; Chelerythrine; Staurosporin LIM-K inhibitor Myosin-II ATPase inhibitor: Blebbistatin Src kinase inhibitor Latrunculins A and B; Bumetanide; Swinholide Natriuretic peptides: ANP; CNP; SHP-639 Sodium nitroprusside; Hydralazine; 3-morpholinosyndnonimine; (S)-nitrosoacetyl-penicillamine; NCX-125 YC-1; BAY-58-2667; IWP-953 Bremazocine; dynorphin

Reported or potential mode(s) of action Contract ciliary muscle/TM to promote outflow of AQH via the TM-SC pathway Inhibition of Na+-K+-Cl
-transporter activity in the TM changes cell shape and volume and thus AQH efflux is increased Modification of actomyosin contractility that leads to changes in actin cytoskeleton of TM (relaxation) and this leads to AQH efflux

Promote sequestration of actin monomers and dimers in TM; cause cell TM shape change and thus AH efflux Type-A and type-B receptor activation leads to cGMP production, TM relaxation and AQH efflux via TM

Release natriuretic peptides and thus raise cGMP in TM leading to its relaxation and thus AQH efflux Cannabinoid receptor WIN55212-2; CP55940; SR141716A Receptor stimulation opens BKC-channels agonists and relaxes TM which then causes AQH efflux via TM and SC FP-class PG-receptor Latanoprost; Travoprost; Tafluprost; Some clinical evidence of promoting agonists Bimatoprost; Unoprostone isopropyl ester conventional outflow in addition to UVS outflow Serotonin-2 receptor BVT-28949; ketanserin and its analogs Unknown and unverifiable mechanism(s) of antagonists action (may block beta-adrenergic receptors indirectly?) Releasers of MMP FP-class PGs (see above); and Local production of MMPs; ECM and AP-1 t-butylhydroquinone (t-BHQ); degradation; stimulation of AQH efflux via β-naphthoflavone; TM Uveoslceral (UVSC) outflow stimulators (via gaps in CM fiber bundles and scleral tissue) FP-class PG-receptor Latanoprost; Travoprost; Tafluprost; FP receptor activation in CM causes release agonists Bimatoprost; Unoprostone isopropyl ester of MMPs that breakdown ECM (“clog”) around CM bundles and within sclera thus causing UVS outflow of AQH Omidenepag Isopropyl (DE-117); Butaprost; Receptor activation increases cAMP that EP2- and EP4PG-receptor agonists AL-6598; ONO-AE1-259-01; relaxes CM and TM; EP2 agonists also cause release of MMPs that breakdown ECM PF-04217329; PF-04475270 (“clog”) around CM bundles and within sclera thus causing UVSC outflow of AQH (R)-DOI; α-methyl-5HT; AL-34662 Contraction/relaxation of CM and TM by Serotonin-2 (5HT2) receptor agonists activation of 5HT2 receptors. May also release MMPs and/or PGs or other local (continued)

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Table 6 (continued) Compound classes

Pharmacological agent

Bradykinin B2-receptor agonists

Bradykinin; FR-190997; BKA278

Dual activity PGs, and conjugated compounds

FP/EP3 receptor agonist (ONO-954) AL-6598 (DP/EP2 receptor agonist)

Latanoprostene bunod (latanoprost-NO donor conjugate) Inflow inhibitors (reduce AQH production) β-adrenergic Timolol; Betaxolol; Levobetaxolol; antagonists Levobunolol; Metipranolol β-adrenergic receptor silencer α2-adrenergic agonists

SYL-040012; siRNA (Bamosiran)

Carbonic anhydrase inhibitors (CAIs)

Dorzolamide; Brinzolamide

Chloride channels inhibitors Na+-K+-ATPase inhibitors Dopamine receptor agonists

5-nitro-2-(3-phenylpropylamino)-benzoate (NPPB) Ouabain; Digoxin analogs

Na+-K+-ATPase inhibitors Aquaporin inhibitors

Brimonidine; Apraclonidine; Clonidine

PD-128907; CHF-1035; CHF-1024; SDZ GLC-756; (S)-(-)-3-hydroxyphenyl)-N-npropylpiperidine (3-PPP) Ouabain; Digoxin analogs

Various aromatic sulfonamides and dihydrobenzofurans Additional IOP-lowering agents Mas receptor DIZE via ACE-2 activation stimulator Angiotensin-II CS-088 receptor antagonists Ca2+-channel Lomerazine; Nivaldipine; Nifedipine; inhibitors Nimodipine; Verapamil; Brovincamine; Iganidipine Alpha-adrenergic Oxymetazoline; 5-methylurapidil; receptor antagonists Ketanserin Other prostaglandin AL-6598 (DP/EP2 receptor agonist); receptor agonists AGN192093 (TP receptor agonist); BW245C (DP receptor agonist); Sulprostone (EP3 receptor agonist)

Reported or potential mode(s) of action mediators that promote CM remodeling and thus promote UVS outflow B2-receptor activation causes PI hydrolysis production of IPs and DAG; cause PG release and release of MMPs that digest ECM and this promote UVS outflow in cynomolgus monkey; conventional outflow also stimulated in isolated bovine/porcine anterior eye segments Promotes UVSC outflow Inhibits inflow and stimulates outflow (TM and UVSC) Promotes UVSC and TM outflow

Block β-adrenergic receptors in the ciliary process, decrease cAMP generation and thus decrease AQH formation Downregulates endogenous β-adrenergic receptors and their signaling Intracellular cAMP reduced in CP that decreases AQH generation; may also prevent NE release Brimonidine also promotes TM outflow Inhibit ciliary process CA-II and CA-IV and thus reduce bicarbonate production that in turn reduces AQH generation Ion flux of CP NPE cells causes reduction of AQH formation Ciliary process Na+-K+-ATPase inhibited leading to inhibition of AQH production Inhibit release of NE and prevent AQH production; may also release natriuretic peptides Ciliary process Na+-K+-ATPase inhibited leading to inhibition of AQH production Inhibit release of NE and prevent AQH production Prevent ECM (including TGFβ) accumulation (outflow stimulation?) Various mechanisms of action; not robust IOP-lowering Enhance retinal blood flow; some may lower IOP; work well in normal tension glaucoma patients Work mostly via outflow mechanism but this needs to be defined These work through multiple mechanisms of action involving cAMP production, Ca2+ mobilization leading to relaxation/ contraction of ciliary muscles/TM (continued)

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Table 6 (continued) Compound classes PG-conjugates Combination products

Pharmacological agent Latanoprostene Bunod (NO donor coupled to latanoprost) Brinzolamide-brimonidine; Brinzolamidebrimonidine; Acetozolamide-TimololBrimonidine; Travoprost-brimonidine; Bimatoprost-brimonidine; TafluprostTimolol

Fig. 11 Top panel shows the dimensions of and the placement of the InnFocus Microshunt in the anterior chamber of the eye to drain the AQH in order to lower IOP. The efficacy of this MIGS device is clearly demonstrated by the

Reported or potential mode(s) of action Combination of NO-cGMP production and FP-receptor activation Complementary mechanisms of action encompassing inflow-outflow inhibition, and inflow-uveoscleral outflow inhibition

data in the lower panel in longitudinal studies performed in human OHT/POAG patients. (Modified from Batlle et al. (2016))

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Thus, for instance, if a new small molecule needs to be discovered for an EP2 or FP prostaglandin receptor, then ligand binding assays for all the known PG receptors and their subtypes, preferably human cloned or using fresh target tissues/ cells, and using selective radiolabeled ligand, are established and validated. Then test compounds are used to compete for the receptor-radioligand complex in cell/tissue homogenates suspended in suitable buffers at equilibrium using rapid filtration techniques and thus evaluated for their relative affinities, relative selectivities, and then ranked ordered accordingly. Examples of such receptor-ligand binding assays and the data obtained are shown in Tables 1 and 2 (Sharif et al. 2003; Kirihara et al. 2018). Since agonist compounds are being sought as novel ocular hypotensives, the compounds selected from the above assays are then tested for their ability to stimulate the desired receptor in freshly isolated target cells (preferably human CM or TM cells known to be involved in AQH dynamics and IOP regulation) (e.g., 5HT2-receptor-mediated intracellular Ca2+-mobilization, Fig. 4, left panel), or in host cells transfected with the human cloned receptors of interest, and appropriate second messengers detected and quantitated. The data are graphed to determine the relative potencies of the test compounds relative to a positive control drug. Thus, for instance, cAMP (Crider and Sharif 2001; Kirihara et al. 2018) or cGMP (Katoli et al. 2010) or inositol phosphates (Sharif et al. 1994, 1996) or intracellular Ca2+ (Sharif et al. 2007) are used as receptorinduced signal readouts (Fig. 4, left panel; Fig. 8, right panel) and the receptor-specific blockade of these responses (e.g., Fig. 4, right panel). Other assays to examine functional activity in vitro of potential IOP-reducing agents involve use of cell(Ramachandran et al. 2011) and/or tissue-based relaxation/contraction assays (Ohia et al. 2018) using cells/tissues known to be mediators of AQH dynamics and thus ocular hypotensive activity in vivo (e.g., CM and TM [Sharif et al. 2007, 2014] or SC [Dismuke et al. 2010] cells or CM/TM tissue strips [Wiederholt et al. 2000; Ohia et al. 2018]), coupled with cellular impedance changes that reflect cell relaxation/contraction (Wang et al. 2013) or cell-volume changes

(Dismuke et al. 2009, 2010). Examples of some of these data for different classes of ocular hypotensive drugs are shown in Figs. 4, 8, and 9; Tables 3 and 4.

Testing of Compounds for AQH/Fluid Extrusion in Ex-Vivo Systems The results from cell/tissue-based experiments are useful to define the agonist/antagonist nature of test compounds but they only provide a glimpse into the pharmacological activity of the latter. Therefore, researchers have utilized partially intact ANC segments of the eye from a number of species (human, bovine, and porcine) (e.g., Sharif et al. 2014) in culture to assess the ability of drugs to stimulate outflow of the fluid from such models. The perfusion buffer can also contain suitable inhibitors of receptors or enzymes in order to dissect potential molecular mechanisms of action of the perfused test agents.

Animal Models Used to Discover Novel Ocular Hypotensive Drugs Testing of potential IOP-lowering agents is obviously best performed in animal models where the initial baseline IOP is naturally high so that the change in IOP induced by the test compound is more easily identified. To this end, there are only a few animal species that express a naturally high IOP and that are readily accessible. These include DBA/2J strain of mice, Dutch-Belt rabbits, and Beagle dogs (see McNally and O’Brien 2014; Sharif 2018a, b, for reviews). In the absence of the latter, researchers have had to artificially elevate IOPs in rodents and monkeys by a number of different techniques. For rodents, the TM can be partially or completely destroyed using injection of hypertonic saline via the episcleral vein, by injecting latex or magnetic microbeads into the ANC of the eye, or by lasering the TM or episcleral veins (reviewed in McNally and O’Brien 2014; Sharif 2018a, b). In the monkeys, the preferred reproducible method of raising IOP is by lasering the TM where the IOP can be elevated and maintained around 30–40 mmHg

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for many months and in some cases for years. To obtain optimal results from testing new compounds for ocular hypotensive activity, it has been reported that the animals need to be unrestrained, well trained, comfortable, and conscious to have their IOPs measured on a frequent basis accompanied by suitable rewards. Some investigators anesthetize animals before they measure IOPs, but the data from such methods are somewhat suspect since unconscious animals have a different set of baseline IOPs and also respond differentially from fully awake animals. The age of the test animals also needs careful attention and requires standardization to older animals to ensure that OHT/POAG condition in humans is being well represented. Ocular normotensive and ocular hypertensive (naturally or induced) animals are used to directly assess IOP-lowering potential of new test compounds. Due to cost and genetic considerations, rodent models of OHT have been preferred for primary screening using t.o. dosing of known and new compounds dissolved or suspended in suitable ocularly compatible and previously approved vehicle solvents/buffers. First, test compounds are freshly prepared in the vehicle of choice and evaluated using either single installation (20–30 μl drop) or after multiple administrations for ocular safety using rabbits and/or guinea pigs. Albino New Zealand White rabbits are preferred since guinea pigs are overtly too sensitive to most t.o. drugs. When rodents are used for IOP-lowering efficacy studies, they can also be used for ocular safety assessments as well. Compounds are considered safe when they do not induce excessive irritation/redness, excessive blinking, and discharge, and when no vocalization is recorded. For the actual ocular hypotensive efficacy experiments, animals are grouped according to the treatment they will receive. The eye is first mildly numbed with proparacaine and IOP measured 3–4 times in a quiet and dimly lit environment using various pneumotonometers (e.g., Tono-Pen, Tono-Lab, Goldman appellation pneumotonometer) in order to establish the baseline IOP. Future IOPs are determined at the same

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time every day in the same room. Usually, a 5–10 μl drop of the vehicle or positive control drug (for rodents; or 20–30 μl drop for larger species) or the new test agent is instilled in one eye of the mice/rats and the IOPs recorded over time in each eye of each group of animals. A number of IOP readings are taken at each time point to ensure accuracy and reproducibility of the data. Those compounds that meet acceptable criteria of ocular safety (see above) and sufficient IOP reduction (e.g., 20% from baseline) are then tested in secondary models (e.g., ocular normotensive New Zealand White rabbits or naturally ocular hypertensive Dutch-Belted rabbits), and then ultimately in tertiary screening models (Beagle dogs; nonhuman primates such as Cynomolgus or Rhesus monkeys, either ocular normotensive and/or ocular hypertensive) (Fig. 12; Fig. 13, top panel) (Sharif et al. 2014; Kirihara et al. 2018; Fuwa et al. 2018). Typically, compounds that reduce IOP by >20% from baseline are considered worthy of further pursuit. Now, more detailed dose-response and mechanistic studies, involving testing of selective enzyme inhibitors and/or receptor antagonists can be undertaken (e.g., Sharif et al. 2014; Fig. 10). Additionally, AQH dynamic studies (Sharif et al. 2014) can be conducted in order to define whether the test compound(s) promote outflow of AQH from TM and/or UVSC pathways or inhibit the production of AQH (e.g., Fig. 13, bottom panel; Fuwa et al. 2018). In order for these results to be biologically and perhaps clinically relevant, the latter AQH modulation studies are usually performed in anesthetized ocular hypertensive nonhuman primates before being tested in human subjects in clinical trials.

Neuroprotective Therapeutics for Treating Glaucomatous Optic Neuropathy It is unfortunate that despite use of many different classes of ocular hypotensive agents for multiple decades to lower and control IOP, patients with OHT/POAG (and even those with normal IOPs),

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Fig. 12 A typical testing funnel for screening compounds directed at ocular hypertension and POAG is shown. Once a target protein is identified and incorporated into the in vitro screening paradigm, radioligand binding assays and/ or functional cell-based assays are conducted in order to rank-order the test compounds in terms of their affinity and functional potency/intrinsic activity (agonist or antagonist). This information is used to select compounds to be

tested for ocular/systemic toxicity/irritability and efficacy in rodents and/or rabbits. Those compounds meeting selection criteria are then tested in ocular normotensive and OHT monkey eyes for safety and efficacy. All this information is ultimately used to improve the structure–activity relationship of compounds by medicinal chemists. This iterative process is expected to yield clinical candidate drug molecules

continue to lose vision, and some may ultimately succumb to blindness. Thus, it is now accepted that direct protection of the retinal neurons (especially RGCs), their axons and terminals within the brain need to be strongly considered in addition to IOP reduction. Due to the multiplicity of damaging factors and insults impacting the RGC-brain axis that results in glaucomatous optic neuropathy (GON) (Fig. 14), including constriction of optic nerve axons at ONH (Hollander et al. 1995), complement activation (Tezel et al. 2010), locally released inflammatory/toxic substances (excess neurotoxic amino acids, cytokines, endothelins, NO, etc.) and tissue/cell remodeling enzymes (MMPs; calpains; caspases), blockage of neurotrophin (Quigley et al. 2000) and mitochondrial transport up and down the RGC axons (hence loss of energy [Thomas et al.

2000], etc., a multipronged neuroprotective strategy is necessary to preserve RGCs and their axons. As far as optic neuropathy (ON; encompassing Leber’s hereditary ON, nonarteritic ischemic ON) is concerned, there has already been a number of a drug launched/approved such as the powerful antioxidant and Ca2+-channel blocker idebenone, with others in late-stage clinical trials (e.g., lenadogene/ nolparvovec (mitochondrially encoded NADH dehydrogenease-4 expression enhancer), QPI-1007 (caspase-2 expression inhibitor), and elamipretide (apoptosis inhibitor). Other novel strategies for protecting RGCs and optic nerve components are discussed in more detail in recent publications (Smedowski et al. 2016; Hines-Beard et al. 2016; Venugopalan et al. 2016; Williams et al. 2017; Mead et al. 2018; Sharif 2018b).

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Fig. 13 A new-generation non-PG EP2-receptor agonist omidenepag isopropyl (OMDI) and its IOP-lowering effects in monkeys (top panel), and its stimulation of both

conventional outflow and uveoscleral outflow in OHT eyes of monkeys (lower panel) is shown.(Modified from Fuwa et al. (2018))

Cell-Based Assays and Animal Models for Discovering Neuroprotective Drugs

biological, mechanical, and local environmental factors simultaneously or in a close time frame at the level of the RGCs, their axons and terminals (Fig. 14), the majority of the reported test systems investigating neuroprotection in vitro have only introduced a single insult to the retinal/axonal cells/tissues. The types of challenges used have included hypoxia (to mimic retinal ischemia), elevated hydrostatic pressure (to mimic high IOP), glutamate- or NMDA- or TNFα- or IL-1β-induced cell death (to recapitulate neuronal toxicity), glucose or neurotrophic factor withdrawal (to mimic hypoglycemia as a result of ischemia

A number of cell/tissue-based assay systems have been developed to assess the potential protective activity of selected compounds of interest. These have included isolated primary rat RGCs, co-cultures of RGCs and other retinal cells, ONH or retinal astrocytes, surrogate CNS neurons, retinal explants, whole retinas, and whole retina-optic nerve explants (see He et al. 2018; Sharif 2018b for recent reviews). Despite the fact that GON is caused by a plethora of chemical,

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Fig. 14 Illustrationof the numerous sites within the eyebrain axis where various pathological events (e.g., mechanical, chemical, and environmental) occur to damage the anterior chamber cells (e.g., TM cells), retinal cells (e.g., RGCs), RGC-axons, and the optic nerve connections

to the brain. The net result of such damage is progressive visual impairment that can ultimately lead to blindness as in various forms of glaucoma. (Modified from Sharif (2018b))

(Osborne et al. 2014), and axonal/optic nerve constriction/blockage, respectively). Techniques and types of signal readouts that have provided means to gauge neuroprotective activity of test compounds in these in vitro systems included the following: cell viability (cellular toxicity) assays using measurement of extracellular lactate dehydrogenase and/or cytochrome c oxidase (released when cell membranes become compromised due to cell ill health), quantifying membrane potential using JC-1 dye, detecting mitochondrial destruction by fission, quantitating mitochondrial viability by cyan fluorescent protein labeling, monitoring cell apoptosis using the TdT-mediated dUTP nick-end labeling (TUNEL

assay), Brn3A-staining, propidium iodide labeling to assess cell death, measuring caspase-1/3 activity, multiple electrode array recording of cellular activity, measuring cellular levels of ATP using nuclear magnetic resonance, calcein AM staining, gelatin zymography for MMPs and other proteases, inhibition of [3H]-D-aspartate release from retinas as an index of neurotoxicity prevention, neurite elongation using high content screening, etc. (see references in: He et al. 2018; Sharif 2018b). If compounds show cytoprotective activity in a number of these assay systems, then chances are high that they will exhibit some level of neuroprotective efficacy in vivo.

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Animal models of GON are somewhat limited and generally tend to be labor-intensive, yielding data of variable value. The major reason for this is again the singularity approach of chemical, mechanical, and other insults/challenges used to evaluate the in vivo neuroprotective efficacy of compounds. This necessitates the utility of multiple animal models to robustly assess the neurotherapeutic activity of any compound that may ultimately be used in a clinical setting. Despite the above issues, much progress has been made in finding novel compounds and new pathways that represent useful intervention points with therapeutic end points for testing in animal models of retinal/optic nerve damage as encountered in GON/POAG. Consequently, numerous classes of compounds have been qualified as neuroprotective based on their ability to reduce the loss of RGCs and/or their axons. The following represent some of the most commonly used animal models to study GON and to screen compounds for their neuroprotective efficacy: rodent models of acute/chronically elevated IOP that results in retinal ischemia; rodent models of partial transection or crush of the optic nerve at the level of the ONH; ivt injections of neurotoxins such as NMDA, endothelin, amyloid-beta peptides, or staurosporin, or phorbol ester to capitulate endogenous inflammatory reactions common in GON; uveitic glaucoma model and inflammatory demyelination models (see references in Sharif 2018b). In the majority of the cases, the number of RGCs and their axons are quantified postmortem to assess the degree of damage/protection in control vs. the treated animals. This is achieved by retrograde-labeling of RGCs using fluorogold- or Brn3A-labeling after injection of the latter markers into the superior colliculus of the animals, and by axonal counts in transverse sections of the optic nerve, respectively. In some cases, intravenously injected fluorescently labeled annexin-5 has been used to monitor and quantify retinal cells undergoing apoptosis in living animals subjected to various experimental challenges pertinent to GON as described above. Interestingly, this technique has now been used in patients with POAG/OHT and certain neurodegenerative diseases and useful baseline data

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gathered (Cordeiro et al. 2017). Perhaps such diagnostic/prognostic biomarkers can be utilized to evaluate neuroprotective drugs in nonhuman primates and human subjects in the near future. Likewise, the recent use of flavoprotein fluorescence to monitor mitochondrial health of retinal cells in vivo in POAG and control patients appears a promising tool for assessing cytoprotective actions of compounds believed to possess neuroprotective efficacy (see references in Sharif 2018b). Furthermore, the use of novel technologies such as high-resolution adaptive optics and visible-light-OCT coupled with standard functional readouts like visual-evoked potential measurements will enhance our understanding of the GON and lead to better therapies in the future.

Age-Related Macular Degeneration (AMD) Another ocular disease that has a relatively rich history of drug discovery and pharmacodynamics associated with it is age-related macular degeneration (AMD) (Lambert et al. 2016). Unlike POAG where peripheral vision is lost, in AMD the loss of macular photoreceptors impacts central vision. AMD is believed to be responsible for nearly half of all severe vision loss in the US adults over the age of 40. AMD has been divided into two forms: nonexudative or “dry” AMD (dAMD; 90% of total) and exudative or “wet” AMD (wAMD; 10% of the total). While dAMD is characterized by the loss of photoreceptor cells in the macula following the death of supporting RPE cells, wAMD’s hallmarks are retinal edema and rampant neovascularization of choroidal capillaries (choroidal neovascularization or CNV) (Al-Zamil and Yassin 2017; Hernandez-Zimbron et al. 2018). Such aberrant angiogenesis causes retinal fibrosis, scar formation (and perhaps retinal detachment), culminating in loss of central visual acuity. While dAMD develops over months and years, wAMD is highly progressive and rapidly develops to rob vision of the patient unless treatment is sought. Advancing age is the strongest demographic risk factor associated with AMD

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although Caucasian heritage predisposes patients to this ocular disease. However, chronic excessive oxidative stress from cigarette smoke and other sources, autoimmune disease involving complement activation, and chronic local inflammation are also causative factors in the development of dAMD (Rickman et al. 2013). An increased blood plasma concentration of the proinflammatory proteins C-reactive protein, IL-6, cholesterol/triglycerides, and a family history of AMD also been positively correlated to some degree with AMD development and progression. Recent angiography-coupled-OCT evidence suggests that poor choriocapillary blood flow leads to poor clearance of cellular and other debris and may be the source of deposited drusen and related materials that ultimately lead to dAMD/GA (Qin et al. 2018). RPE cell dysfunction likely begins with intralysosomal accumulation of a fluorescent material called lipofuscin, a complex mixture rich in polyunsaturated lipids and probably is derived from phagocytosed photoreceptor outer

segments that cannot be broken down. Over time, this material renders lysosomal enzymes inactive and raises the pH level causing lysosomal membrane dysfunction and RPE cell death. A likely functionally important component of lipofuscin is the amphiphilic pyridinium ion N-retinyldene-N-retinylethanolamine (A2E) which is generated from the condensation of phosphatidylethanolamine with 11-trans retinaldehyde, followed by phospholipase D-catalyzed dephosphorylation. It has been postulated that A2E might be the major toxic chemical in lipofuscin producing reactive oxygen species and oxiranes in the presence of light and O2 (Fig. 15). Additionally, A2E likely acts as a detergent causing leakage of toxic reagents into the RPE cell, thereby killing the latter cells. Once RPE cells are injured, their ability to phagocytose photoreceptor cell outer segments is hindered and this causes the incomplete recycling of the components of the latter. This is responsible for accumulation of cellular debris, and the production and release of additional inflammatory

Oxidative stress

Aging

Homocysteine level

Smoking / Light

RPE dysfunction 2. Lipid oxidation CEP

1. mt.DNA deletion and rearrangement 3. Glycation (AGES) CML Pentosidine

4. Metabolic dysfunction (phagocytotic processing of outer segment)

Inflammation Complement system dysregulation

CRP level IL6

Activation of alternative pathway (CFH, CFB, C2, C3)

RPE damage

HDL,

LDL

Total cholesterol

AMD

IP - 10 Fibrinogen

Alu RNA

Geographic atrophy

Blindness

Choroidal neovascularisation

Fig. 15 Pathogenesis of dAMD/GA. (Modified from Ambati et al. (2003)).

(Angiogenesis) VEGF level + SNPs HTRA1 ARMS2 CML IL-8 CEP

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agents from the dying RPEs such as oxidized lipids, proinflammatory cytokines (e.g., IL-1β and IL-17A [Zhang et al. 2016]), and acute-phase inflammatory proteins, that accumulate between the RPE cell layer and the Bruch’s membrane that separates the RPEs from the choroidal capillaries. All this deposited toxic material (drusen) then kills more RPE cells and the homeostasis is disrupted further (Rickman et al. 2013) (Fig. 15). The drusen and other cellular debris also act as a barrier and less O2 and nutrients are made available to the rest of the retina, and this results in the development of a hypoxic environment at the back of the eye. In order to overcome this situation, hypoxia-inducible transcription factors like HIF-1α are upregulated and vascular endothelial growth actor (VEGF) and other angiogenic factors are locally released that cause the generation of new blood vessels from the existing choroidal capillaries. This aberrant neovascularization temporarily helps remove some of the metabolic and cellular waste and provides nutrients and O2 to the remaining RPE and photoreceptor cells. However, the new blood vessels breach the Bruch’s membrane and start to grow into the retina and eventually into the vitreous and start to interfere with light transmission to the retina and the communication among the inner retinal cells. Additionally, since the new blood vessels are leaky, blood starts to accumulate in the vitreous and local hemorrhages develop at the rear of the globe that begins to detach the retina. The patient loses more and more visual acuity and requires urgent care to curb the loss of all vision in that eye. This is how dAMD can cause wAMD. However, there are patients who do not develop wAMD, and their dAMD keeps progressing till their retina has widespread drusen deposited that is characterized as geographic atrophy (GA) (advanced dAMD) that robs the patient’s central and eventually peripheral vision and thus causes irreversible blindness (Fig. 16). As to the possible treatment options for combating dAMD/GA, there is currently no accepted pharmacological treatment in use in the clinical setting. While dietary supplementation with a combination of high-dose vitamins E and C, beta-carotene, and zinc afforded a 25% risk reduction for progression of high risk dAMD patients to wAMD form, the

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interpretation of the results of this study has been controversial. It is thought, however, that based on the involvement of oxidative stress and inflammation in the etiology of dAMD that drugs that can reduce the severity of the latter conditions may have beneficial effects in dAMD patients. Therefore, additional research and clinical trials are in progress using a number of agents and treatment options for dAMD/GA which are at various stages of development (Ishikawa et al. 2015; Waugh et al. 2018) (e.g., Table 7). Several wAMD treatments centered around reducing levels of VEGF have been successfully developed and introduced into the clinical management of the disease including use of bevacizumab and ranibizumab (both truncated antibodies bind VEGF and remove it), and aflibercept (VEGFreceptor as a VEGF trap) (Ishikawa et al. 2015; Al-Zamil and Yassin 2017; Hernandez-Zimbron et al. 2018). Despite the undeniable success of the anti-VEGF treatment modalities, there is increasing concern about the reported development of tolerance/resistance to these medications after the 2nd-year of treatment (Maguire et al. 2016; Yang et al. 2016). Thus, it is imperative that additional therapeutic agents be discovered and developed to mitigate such issues. Consequently, several therapeutic strategies have been proposed to reduce and/or prevent the development and progression of wAMD that appears to be driven by the angiogenic factors such as HIF-1α and VEGF (Table 8), along with other emerging growth factor culprits such as angiopoietins that trigger angiogenesis via the Tie-1/2 receptors. It may be necessary to also begin combinatorial therapy for wAMD (e.g., endogolin + anti-VEGF combination [Shen et al. 2018]), as was the case for OHT/POAG when it was realized that conventional single-agent therapies were unsuitable for recalcitrant patients and also those who were not maximally controlled by monotherapeutic agents (see above). Likewise, the novel cell-replacement therapies involving growth of polarized monolayer of human embryonic stem cell-derived RPE (hESC-RPE) cells on an ultrathin parlene substrate and placing them into human subjects’ retinas with severe vision loss due to dAMD/GA as a cell-patch (Kashani et al. 2018), or placing similarly engineered hESC-RPE patches

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Fig. 16 Pictorial depiction, via fundus photos, of human dAMD progressing to widespread GA due to increasing drusen deposition in the retina.(Modified from Ambati et al. (2003))

into subretinal space of patients with wAMD (da Cruz et al. 2018) to improve visual acuity holds tremendous promise.

Assay Systems Deployed for AntidAMD/Anti-GA Drug Discovery As with OHT/POAG and GON, cell-based assays utilize anatomically relevant retinal cells known to be involved in the etiology of or as comprised cell type(s) in the disease process of dAMD/GA for screening purposes. Since the cascade of events linked to the disease involve inflammation, deposition of ECM in Bruch’s membrane, and drusen accumulation in the RPE cells, the latter cells have been used as target cells to study ways to prevent the

former and latter phenomena. Anderson et al. (2013) used ARP-19 cells and tested various components of drusen (e.g., carboxyethypyrrole (CEP)-modified proteins, amyloid-β (1-42), Nε-(carboxymethyl) lysine (CML)-modified proteins, and aggregated vitronectin) and the key component of lipofucin (A2E) and monitored production of major inflammatory cytokines, chemokines, and VEGF-A as biomarkers. They concluded that A2E was the most active proinflammatory substance studied, and that it promoted the release the aforementioned biomarkers from RPE-19 cells by activating the inflammasome (NLRP3/caspase-1) pathway activation. Such a screening tools can thus be used to find suitable blockers of the NLRP3/caspase-1 system, and these may prove useful in ameliorating the dAMD/GA.

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Table 7 Therapeutic agents, devices and other treatments under development for dAMD/GA Therapeutic agent/ Mode of action product Small molecules Visual cycle Emixustat HCl modulator Modulating choroidal MC-1101 blood flow Tetracycline Oracea antibiotic 40 mg doxycycline Antioxidant; slows Metformin DNA damage, reduces ROS levels Aptamers/peptides/antibodies mAb fragment for Lampalizumab complement factor D

Sponsoring institution

Disease indication

Development phase

Route of delivery

Acucela

Phase-2b/3

Macuclear

GA/dry AMD Dry AMD

University of Virginia

GA/dry AMD

Phase-2/3

Oral daily tablet Topical, twice daily Daily oral capsules

University of California San Francisco

Nondiabetic GA/dry AMD

Phase-2

Daily oral tablets

Genentech

Phase-3

Intravitreal injection

Inhibition of complement C3 Antibody for C5 complement Aptamer; inhibits complement Factor C5 Hu-mAb for Aβ peptide

APL-2

Apellis Pharma

GA/ advanced AMD GA/AMD

Phase-2

LFG 316

Novartis

GA/AMD

Phase-2

Zimura

Ophthotech

GA/dry AMD

Phase-2/3

GSK933776

GlaxoSmithKline

Retinal amyloidosis/ GA/dry AMD

Phase-2a

Intravitreal injection Intravitreal injection Monthly intravitreal injection Intravenous infusion

Hu-CNS SC

StemCells Inc.

Phase-1/2

Subretinal transplantation

hESC-derived RPE cells

MA09-hRPE

Phase-1/2

Subretinal transplantation

Human umbilical tissue-derived cells

CNTO 2476

GA/AMD

Phase-1/2a

Subretinal administration

hESC-derived RPE cells seeded on polymeric substrate

CPCB-RPE1

Advanced dry AMD

Phase-1/2

Subretinal implantation

hESC-derived RPE cells Patient-derived iPSC transplantation

OpRegen

Advanced AMD Wet and dry AMD

Phase-1/2

Subretinal transplantation Transplantation

Autologous bone marrow–derived stem cells

BMSC- SCOTS study

Ocata Therapeutics – Astellas Pharma Janssen Research and Development Regenerative Patch Technologies (RPT) Cell Cure Neurosciences Moorefields Eye Hospital NHS Foundation Trust Retina Associates of South Florida and MD Stem Cells

GA/ Advanced dry AMD Advanced dry AMD

AMD

Early stage interventional study

Cellular therapeutics Stem cell transplantation

iPSC-derived RPE cells

Phase-2/3

Phase-1/2

Sub-Tenon injection

(continued)

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Table 7 (continued) Therapeutic agent/ Mode of action product Devices and other treatments Photobiomodula-tion LumiThera LT or low light level 300 light delivery therapy system Electrical stimulation Argus-II System

Transpalpebral microcurrent electrical stimulation

Nova Oculus

Electrophysiologic methods (double plasma filtration or cascade filtration)

Rheohemapheresis

Sponsoring institution

Disease indication

Development phase

Route of delivery

LumiThera

Vision loss associated with AMD Late-stage AMD

Early stage interventional study Phase-1

Light exposure

Vision loss associated with dry AMD

Early stage interventional study

High-risk dry AMD

Phase-4

Second Sight Medical Products The Eye Machine Canada

University Hospital Hradec Kralove

Implantable visual retinal prosthesis Externally applied microcurrent electrical stimulation Cascade filtration

Abbreviations: AMD age-related macular degeneration, GA geographic atrophy, hESC human embryonic stem cell, iPSC induced pluripotent stem cell, mAb monoclonal antibody, MOA mode of action, ROS reactive oxygen species, RPE retinal pigment epithelium

Primary human RPE (ph-RPE) cells represent a better cellular system than the cell line ARPE19. Zhang et al. (2016) demonstrated that ph-RPE cells expressed all three IL-17 receptors and that addition of IL-7A to these cells upregulated the production of IL-1β secretion via the NLRP3 inflammasome activation mechanism. Importantly, these authors found that inhibiting caspase-1 activity and silencing NLRP3 significantly reduced IL-1 β release from RPE cells. Thus, this assay system can be used to find new potent and efficacious blockers of caspase-1 and NLRP3 to help prevent dAMD/GA. Additional work in this area using human patient-derived iPSC-RPE cells is also very encouraging (Galloway et al. 2017, 2018).

Animal Models to Find Anti-dAMD/ Anti-GA Drugs While not truly reflecting the human dAMD/GA disease, a number of animal models have been developed to study the condition and use for potential drug discovery efforts. The light damage models utilize rodents and expose them to very bright light (white or blue) for a number of days/

weeks and the retinal damaged assessed by electroretinograms and histology (Chader 2002). The genetic rodent models include the Royal College of Surgeons (RCS) rats (a recessive genetic defect that prevents phagocytosis of rod-outersegments by RPE cells), P23H rhodopsin defect rat, and an ABCR-1- rat (which has the transporter of 11-trans-retinaldehyde (ABCR-protein) knocked-out). The most appealing of the animal models created thus far is the chemokine receptor2 knockout mouse which exhibits many of the hallmark features of human dAMD/GA (drusen accumulation under RPE, photoreceptor loss followed by CNV; Ambati et al. 2003). How these animal models are exploited to discovery novel therapeutics for treating dAMD/GA remains to be seen.

Cell-Based Assays for Finding New AntiwAMD/Anti-CNV Drugs As described above, wAMD involves poor retinal circulation leading to retinal hypoxia, release of angiogenic factors (e.g., HIF-1/2α; VEGF; ANG-2), and abnormal growth and development of leaky new blood vessels from the choroidal

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Table 8 Recently approved and emerging treatment options being pursued for GA and neovascular AMD Developer/ sponsor Novartis

Targeted pathology wAMD/ Neovascular AMD wAMD/ Neovascular AMD wAMD/ Neovascular AMD wAMD/ Neovascular AMD GA GA

Drug agent Ranibizumab (Lucentis)

Drug class Anti-VEGF antibody

Aflibercept (Eylea)

VEGF-Receptor (VEGF-trap)

Regeneron

Conbercept

VEGF-Receptor (KH-902; Biosimilar)

Chengdu Kanghong

Brolucizumab

Anti-VEGF antibody (RTH-258)

Novartis

Lampalizumab MA09-hRPE

Anti-factor D Fab Cell therapy

Brimonidine tartrate implant Eculizumab

α2-adrenoceptor agonist

Genentech Astellis Pharma Allergan

Anti-C5 mAb

Alexion

GA

Fovista (E10030)

Anti-PDGF aptamer

Ophthotech

Neovascular AMD

Abicipar pegol

Anti-VEGF Aptamer

Neovascular AMD

RBM-007

Anti-FGF aptamer

Allergan/ Molecular Partners Ribomic

PAN-90806

RTK Inhibitor

PanOptica

RXI-109

rX-RNA

RXi Pharma

Sunitinib

RTK Inhibitor

Graybug

APL-2

C3 Inhibitor

Apellis

Faricimab

Bispecific Ab

Chugai

RGX-314

Gene therapy vector (VEGF neutralizer) Gene therapy vector (Angiostatic stimulator)

RegenxBio

Retinostat

Oxford Biomedica

GA

Neovascular AMD Neovascular AMD Neovascular AMD Neovascular AMD Neovascular AMD Neovascular AMD Neovascular AMD Neovascular AMD

Mechanism of action Removes VEGF

Route of administration Intravitreal

Removes VEGF

Intravitreal

Removes VEGF

Intravitreal

Removes VEGF

Intravitreal

Anti-factor D Fab Human umbilical tissue-derived cells Alpha-2-agonist

Intravitreal Subretinal injection Intravitreal implant

mAb against complement factor C5 Anti-PDGF PEGylated aptamer Anti-VEGF

Intravitreal

Kinase inhibitor

Topical ocular

CTGF expression inhibitor Multikinase inhibitor Complement-C3 Inhibitor Anti-VEGF-A/ Ang-2 Ab Anti-VEGF AAV

Intravitreal

Intravitreal

Lentivirus vector

Intravitreal

Intravitreal

Intravitreal injection

Topical Intravitreal Intravitreal

Abbreviations: Ab antibody, Ang-2 angiopoeitin-2, AMD age-related macular degeneration, CTGF connective tissue growth factor, GA geographic atrophy, mAb monoclonal antibody, PDGF platelet-derived growth factor, VEGF vascular endothelial growth factor

system. Such CNV compromises Bruch’s membrane (due to release of MMPs from RPE and Muller cell that digest the latter), retinal interneurons and eventually RGCs as the new blood

vessels branch out into the vitreous and cause local hemorrhages and loss of vision, especially at the macula. Therefore, pathologically relevant cell types (e.g., human micro- and macrovascular

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retinal endothelial cells [REC]; RPE [including primary cells and ARPE-19 cells, and Muller glial cells), surrogate cells (e.g., human umbilical vein endothelial cells (HUVEC)), and co-cultures (Chen et al. 2017), have been utilized to evaluate new drug modalities directed at the neovascular component of wAMD. Functional readouts relevant to the CNV using in vitro assays have involved RPE-stretch-induced release of VEGF (Farjood and Vargis 2018), growth factor-induced REC proliferation/migration/tube formation using a matrigel assay (Chen et al. 2016), cellular permeability, and expression and secretion of various MMPs (Di and Chen 2018).

wAMD/CNV conditions that may lead to discovery and development of new medicines to treat these blinding disorders (Liu et al. 2016; Lambert et al. 2016).

Animal Models to Find Anti-wAMD/ Anti-CNV Drugs Aberrant ocular angiogenesis is the hallmark of CNV/wAMD. Animal models that have been used to study the pathological and drug discovery aspects of these diseases have involved laserinduced and surgically induced CNV in rodents, rabbits, and monkeys (reviewed by Liu et al. 2017). Retinopathy of prematurity (ROI) as induced by high levels of oxygen soon after birth of rodents (OIR) is another useful model of CNV and wAMD (Liu et al. 2017). These models are self-explanatory and have been deployed to study effects of disease-prevention/ reduction using vascular permeability, measurement of VEGF release, and angiogenesis as biomarkers and readouts. In view of the importance of these retinal disorders, a number of transgenic mouse models with spontaneous sub- and intraretinal angiogenesis have also been established for possible screening purposes where other angiogenic signaling molecules may be involved other than or in addition to VEGF (Liu et al. 2017). Due to the aberrant involvement of the complement system in the etiology of wAMD/CNV, an important interaction between the oxidative stress and the latter systems is also being studied (Du et al. 2016), as is the measurement of numerous cytokines and chemokines using multiplex and microarray technologies to find additional targets for

Diabetic Macular Edema and Diabetic Retinopathy Diabetes-induced retinopathy is also a major ocular disease that causes preventable blindness around the world, currently estimated at 93 million worldwide in total. Two forms of diabetic retinopathy (DR) have been described: nonproliferative (NPDR) and proliferative (PDR). Abnormal neovascularization induced by prolonged hyperglycemia and retinal hypoxia causes PDR which results in vision loss (Wang and Lo 2018). Diabetic macular edema (DME) is a hallmark of NPDR and corticosteroids and biologics are used in its treatment (Wang and Lo 2018). Since many of the signs and symptoms of PDR resemble those of wAMD, anti-VEGFs biologies (antibodies (ABs) and small AB fragments [Fabs]) have paved the therapeutic pathways for treatment of PDR in addition to traditional laser photocoagulation of leaky retinal blood vessels. Similarly, the side effects associated with the latter, and the tachyphylactic responses to anti-VEGFs observed in wAMD patients will also necessitate requirement of alternative treatment options for DR and DME. Some novel approaches encompass the explorative use of bispecific antibodies (RO-6867461 [Anti-Ang-2 + Anti-VEGF]), AKB-9778 (Tie-2 activator), EBI-031 and Tocilizumab (IL-6 inhibitors), Luminate (Integrin inhibitor), MTP-131 (cardiolipin inhibitor), Lutein, and ALA (mitochondrion-specific antioxidant) (Wang and Lo 2018). Recently, it has been proposed that not only are the components of aberrant neovascularization and inflammation important in the etiology and ultimate resolution of PDR and NPDR but that degeneration of the neural retinal cells is also a key element leading to blindness resulting from diabetes (Simo et al. 2018). Therefore, interventional and perhaps even prophylactic neuroprotective therapies should be considered for both wAMD and DR.

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Assay Systems and Animal Models for Discovering New Treatments for DR and DME In an effort to explore possible means to ameliorate the effects of DR and DME, researchers have focused attention on identifying possible interventions point of these retinal disorders. Since hyperglycemia is the core of the problem, the effects of high glucose on RPE, Muller glia, vascular endothelial cells, and pericytes have been studied and used for screening compounds for reducing various biomarkers of disease (e.g., secretion of cytokines and angiogenic factors, cell permeability, mitochondrial dysfunction; microglial activation, RPE, and REC tube formation) (e.g., Tien et al. 2017; also see above in section “Cell-Based Assays for Finding New Anti-wAMD/Anti-CNV Drugs”). While none of these in vitro systems truly capitulates the DR/DME, direct effect of potential drugs of benefit in treating the latter have been examined using a variety of animal models (chemically [alloxan/streptozotocin/diet]/surgically [pancreatomy]-induced and genetic) ranging from zebrafish to rodents, to cats, dogs, pigs, and monkey; Olivares et al. 2017). It is worth mentioning that alloxan/streptozotocin/dietinduced retinal disease outcomes resemble human DR/DME, where rodents generally exhibit the major defects in terms of hyperglycemia, damaged pancreatic beta-cells, damaged/ reduced pericytes, increased acellular capillaries, microglial changes, basement membrane thickening, microaneurysms, RGC, and inner retinal cell loss (Olivares et al. 2017). Retinal neovascularization was observed in rodents after systemic hyperglycemia and was most profound under hypoxic conditions in zebrafish, rodents, and monkey (Olivares et al. 2017). Of the genetically induced DR/DME, rodents were the most susceptible showing many of the aforementioned phenotypic changes in their retinal anatomy and pathology. These results emphasize the need to use multiple assays and animal models to assess the therapeutic efficacy of any treatment modalities for DR/DME.

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Ocular Surface Diseases Despite the protection afforded by the blink response, the placement of the eyeball within the orbital socket, and protection provided by the sclera, the eye still remains a target for airborne allergens, pollutants, bacteria, and viruses that fall onto the ocular surface. The tear film covering the cornea and conjunctiva also acts as a barrier but can harbor some of the agents mentioned above.

Allergic Conjunctivitis Seasonal allergic conjunctivitis (SAC) and perennial allergic conjunctivitis (PAC) are allergic reaction of the cornea and conjunctiva to airborne allergens such as pollen, mold, pet dander, and air pollution (Yanni et al. 1999; O’Brien 2013). SAC afflicts millions of patients of all ages every few months and causes debilitating and extremely bothersome excessive tearing, intense itching, grittiness, burning, photophobia, redness and swelling of the eyelids (O’Brien 2013; Gomes 2014). These symptoms are caused by release of histamine, prostaglandins, cytokines, and chemokines from resident mast cells in conjunctiva of the eyelids (Sharif et al. 1996; Yanni et al. 1999). SAC leads to decreased work productivity, increased absenteeism from work and school, limitation of everyday activities, significantly reduced quality of life, including decreased sleep quality. These SAC symptoms combined with seasonal rhinitis cause further ill health and detrimental psychological ill effects leading to impaired social interaction on top of the physical morbidity. Overall, SAC and rhinitis due to their perennial occurrence requires potent and efficacious treatment options. Accordingly, there are now several approved/launched histamine-1 (H1) antagonists that are used in clinical management of SAC. These include emedastine (Sharif et al. 1994), olopatadine (Sharif et al. 1996), epinastine, alcaftadine, and cetirizine. However, by far the most effective agent, with a dual mechanism of action, that provides 24-h relief from SAC upon a single t.o. dose is the H1-antagonist/mast cell

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stabilizer, olopatadine (Patanol (o.1% olopatadine); Pataday (0.2% olopatadine); Pazeo (0.7% olopatadine). Elesion/Elestat [0.05% epinastine) is also quite and effective drug for the treatment of signs and symptoms of SAC. A number of other treatment options for SAC at various stages of development are allergy vaccines (MK-3641; MK-8237; SQ tree SLIT-tablet), an aldehyde scavenger (ADX-102), Syk tyrosine kinase inhibitor (PRT-2761), and an anti-IgE monoclonal antibody (xmab7195) (Gomes 2014).

of compounds following i.v. antigen administration was determined as follows: 30 min prior to i.v. antigen challenge, the animals received 20 μl of the drug solution or saline applied topically to the eye. The animals are then challenged i.v. via the marginal ear vein or lateral tail vein with 1.0 ml of an OA:Evans Blue solution (100 pg: l mg, guinea pigs; 1 mg:2.5 mg, rats). For assessment of the allergic response following topical ocular antigen challenge, 20 μl of ovalbumin (1.0%, w/v) was administered to the sensitized eye 5 min after topical ocular application of the test drug (20 μl). During dose-response studies, the order of compound administration is randomized. Thirty minutes later, the reaction is quantitated, using a scoring of ocular allergic reactions accounting for swelling, discharge, and congestion of the conjunctiva/eye lids. In the histamine-induced vascular permeability model using guinea pigs, animals are injected i.v. via the marginal ear vein with 1.0 ml of Evans Blue dye (1.0 mg/ml). Forty-five minutes post-dye injection, 20 μl of test compound or saline vehicle is applied topically onto one eye of each experimental animal. Thirty minutes following topical drug application, the guinea pigs are anesthetized and challenged subconjunctivally with histamine (300 ng/10 μl). The appearance of blue color on the ocular surface is then quantitated (Yanni et al., 1996).

Assays and Animals Models for Discovering Drugs to Treat Allergic Conjunctivitis Once again, exploiting the knowledge of the possible disease-causing elements, relevant cell types, and animal models have been deployed in the screening for new drugs to treat seasonal and perennial allergic conjunctivitis. Thus, isolated human primary conjunctival epithelial and mast cell (Sharif et al. 1996; Yanni et al. 1997), and corneal epithelial cells (Offord et al. 1999) has been at the forefront of the cell-based assays systems. These cells have been challenged with various allergens and the release of cytokines and other mediators quantified in the presence or absence of test drugs of interest. These assays proved effective in yielding compelling data for advancing H1-antagonists like emedastine (Sharif et al. 1994) and dual pharmacophoric drugs like olopatadine (H1-antagonist and mast cell stabilizer) (Sharif et al. 1996; Yanni et al. 1997) into animal models of allergic conjunctivitis (Yanni et al. 1997). Several different mammalian species have been deployed to test drugs for inflammatory and allergic conjunctivitis including guinea pigs and rodents (Groneberg et al. 2003). The most frequently and preferred models use active immunization sensitization protocols. In the guinea pig model, for instance (Yanni et al. 1997), animals are sensitized with anti-ovalbumin (OA) serum injected subconjunctivally in one eye. Twentyfour hours after passive sensitization, ovalbumin (OA) was administered either intravenously (i.v.) or topically onto the eye. The anti-allergic effect

Dry Eye Disease (DED) Dry eye disease (DED), keratoconjunctivitis sicca, is a multifactorial disease of the tears and ocular surface that results in symptoms of discomfort, visual disturbance, and tear film instability with potential damage to the cornea and conjunctiva (Messmer 2015; Marshall and Roach 2016; Baudouin et al. 2018; Dogru et al. 2018). It is accompanied by increased osmolarity of the tear film and inflammation of the ocular surface. Sjogren’s syndrome is present in ~10% of the total DED patients. Other patients tend to be women who are postmenopausal, pregnant, or who are on hormone replacement therapy or are

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taking oral contraceptives. Defects of the meibomian gland, that normally produces the oily/lipid components of the tear film which limit evaporation of the aqueous fluid of the tears, are also now recognized as key contributors to the development of DED (Baudouin et al. 2018). The signs and symptoms of DED have been difficult to address over the years apart from the use of artificial tears to provide brief and temporary relief. However, there are now four drugs available to the physician to treat DED including various formulations of cyclosporine (Restasis; Ikervis; Cyclokat; all are antiinflammatories that are calcineurin inhibitors), diquafosol tetrasodium (a P2Y2 receptor agonist that stimulates tear production), rebamipide (Mucosta; mucin liberator that works on conjunctival goblet cells), and lifitigrast (Xiidra; an LFA-1 antagonist) (Messmer 2015; Marshall and Roach 2016; Baudouin et al. 2018; Dogru et al. 2018). Several other classes of drugs are currently in early-late-stage research and development that cover diverse mechanisms of action such as thymosin-beta-4 ligand (RGN-259), TRPV1 expression inhibitor (siRNA; SYL-1001), NGF receptor agonist (tavilermide; MIM-D3), ICAM1 expression inhibitor/antioxidant (visomitin; SkQ1), nicotinic receptor agonist (cytisine; OC-02), alpha-4 integrin antagonist (AXR-159), JAK3/Syk Kinase inhibitor (R932348), aldehyde scavenger (reproxalap; ADX-102), multikinase inhibitor (TOP-1630), and RAR-gamma receptor agonist (palovarotene; RG-667) (Messmer 2015; Marshall and Roach 2016). It is hoped that some of these agents will prove to be safe and effective alternative treatment options for the DED patients in the near future.

meibomian gland have been exposed to various treatments to simulate dry eye conditions including hyperosmolarity (400–500 mOsM) (Clouzeau et al. 2012), desiccation Hovakimyan et al. 2012), and inflammation (e.g., exposure to formaldehyde; Vitoux et al. 2018) and the effects on cell viability (lysosomal integrity), cell apoptosis/death (cell membrane permeability and chromatin condensation), secreted cytokines (e.g., IL-1, IL-8), oxidative stress (reactive oxygen species and superoxide anion), and cellular hyperpolarization measured. These indices were then used to determine the potential therapeutic effects of test compounds (e.g., Hagan et al. 2018). Animal models for assessing the impact of test compounds in dry eye conditions have been difficult to establish and correlate with the human disease. Nevertheless, there has been progress made in understanding the DED processes and some level of screening performed using mice subjected to a dry, drafty environment (20% humidity) for 5–10 days after they receive daily subcutaneous injections of scopolamine that inhibits tear secretion. The animals exhibited a desiccating-stress-activated innate immune response resulting in release of cytokines, chemokines and MMPs on the ocular surface, increased intercellular adhesion molecule-1, increased loss of conjunctival epithelial and goblet cells, and CD4 T-cell infiltration (Stern and Pflugfelder 2017). Rabbits have also been employed for dry eye models using desiccation (Gamache et al. 2002) and lacrimal gland inflammation (Negelhout et al. 2005) with limited success.

In Vitro Assays and Animal Models of Dry Eye Disease There are a number of therapeutic intervention points in DED including the ocular surface itself (corneal and conjunctival epithelia and goblet cells), and tear and meibomian glands. Accordingly, in vitro assays have been established that incorporate cells derived from these tissues. Cultured epithelial cells and cell lines of human cornea, conjunctiva (epithelial and goblet cells), and

Even though bacterial infection of the cornea is fairly rare due to increased public awareness and enhanced ocular hygiene, bacterial keratitis is often caused by Staphylococcus aureus and Pseudomonas aeruginosa (Willcox 2011). Infections caused by these bacteria are best treated with the fluroquinolones levofloxacin, moxifloxacin and gatifloxicin, and/or with the aminoglycoside tobramycin. Inflammation of the eyelid hair follicles often caused by bacteria (blepharitis) is a

Bacterial Infection/Ocular Inflammation

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bothersome disorder that causes itching, redness, and irritation. Common treatments include t.o. antibiotic alone, or antibiotic + a corticosteroid. However, due to development of bacterial resistance, other drugs like ciprofloxacin, ofloxacin, and levofloxacin are less prescribed but may still be useful and the only drugs available in less developed world. New broad spectrum antibiotics are eagerly awaited in order that the bacterial resistance can be overcome. Postoperative inflammation and pain are common when patients undergo elective Lasik, cataract or photorefractive keratectomy (PRK) eye surgery. PRK is used to correct mild to moderate nearsightedness, farsightedness, and/or astigmatism and/or mild myopia. Patients often receive nonsteroidal anti-inflammatory drugs such as the prostaglandin synthase inhibitors bromfenac, nepafenac, ketorolac, and/or low-dose steroids such as loteprednol to reduce the pain and inflammation associated with the eye surgeries (Waterbury et al. 2011). Uveitis is caused by inflammatory responses inside the eye as a result of tissue damage, bacterial/viral infection, or due to toxins (Tsirouki et al. 2018). The disease will cause various symptoms, such as decreased vision, pain, light sensitivity, and increased “floaters” in the vitreous, and it causes 10–15% of blindness in the USA. In many cases, the cause is unknown and thus is idiopathic. Anterior uveitis occurs in the front of the eye and is the most common form of uveitis, predominantly occurring in young and middleaged people. Many cases occur in healthy people and may only affect one eye but some are associated with rheumatologic, skin, gastrointestinal, lung, and infectious diseases. Intermediate uveitis is commonly seen in young adults and is observed in the vitreous. It has been linked to several disorders including, sarcoidosis and multiple sclerosis. Posterior uveitis is the least common form of uveitis, and it occurs in the back of the eye, often involving both the retina and the choroid. It is often called choroditis or chorioretinitis (Tsirouki et al. 2018). There are many infectious and noninfectious causes to posterior uveitis. Lastly, panuveitis is a term used when all three major parts of the eye are affected by inflammation.

Behcet’s disease is one of the most well-known forms of panuveitis, and it greatly damages the retina. Intermediate, posterior, and panuveitis are the most severe and highly recurrent forms of uveitis. They often cause blindness if left untreated. Treatment modalities for uveitis primarily try to eliminate inflammation, alleviate pain, prevent further tissue damage, and restore any loss of vision. Treatments depend on the type of uveitis a patient displays. Some, such as using corticosteroid eye drops and injections around the eye or inside the eye, may exclusively target the eye whereas other treatments, such immunosuppressive agents taken by mouth, may be used when the disease is occurring in both eyes, particularly in the back of both eyes (Tsirouki et al. 2018). However, these steroid-based treatments adversely affect the body and can cause glaucoma. A recent development for treating posterior uveitis that overcomes the latter side effect issues associated with corticosteroids centers around ivt injection of the immunosuppressant inhibitor of mammalian target of rapamycin, sirolimus (Nguyen et al. 2018). This drug has shown significant efficacy in a number of patients suffering from noninfectious posterior uveitis (Nguyen et al. 2018).

Screening Assays and Animal Models for Ocular Infectious Diseases and Uveitis Infectious keratitis can be caused by various bacterial strains and fungi like fusarium solani. Obviously bacterial strains and fungi can be grown in vitro and effects of potential antibiotics and antifungal agents determined by direct application to these infectious agents (Jett et al. 1997). Researchers have also developed ex-vivo models using rabbit and human anterior eye segments (Pinnock et al. 2017) and animal models of infectious keratitis using mice and rabbits (Zhang et al. 2017b; Zhu et al. 2017). The study of uveitis and drugs to treat this condition are best studied using animal models of the disease involving rats (Pepple et al. 2018) and mice (Chen et al. 2015). The different methods of inducing experimental autoimmune uveitis, panuveitis, and posterior uveitis are well described and reviewed by Bansal et al. (2015).

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There is also an AIRE knockout mouse model of posterior uveitis and another Tg-knockout mouse model that reflects Birdshot retinochoroidopathy (Bansal et al. 2015).

Refractive Disorders/Errors The most common types of refractive disorders, that result from misalignment of the light focusing on the retina, include nearsightedness (myopia), farsightedness (hyperopia), astigmatism, and presbyopia. While astigmatism results from uneven surface of the cornea, presbyopia develops due to stiffness of the lens. The Latter can lead to formation of cataracts of the lens. While in general refractive disorders can be corrected with eyeglasses, contact lenses, Lasik or PRK, a recent advancement includes the ability of EV-06 (a lipoic acid synthase modulator/S-adenosylmethionine decarboxylase stimulator) to temporarily change the fluidity of the lens to overcome presbyopia. On the other hand, myopia has been far more difficult to address since it primarily affects children, although due to the increasing use of computers/tablets and handheld wireless phones and a reduction in time spent outdoors the incidence and prevalence of myopia is steadily rising. Asian children experience myopia disproportionately greater than Caucasian children. Recent projections indicate that >50% of the world’s population will have myopia by 2050, and more children and young adults will be affected. Since high myopia causes vision loss due to myopic macular degeneration, myopia and it comorbidities (cataracts, retinal detachment, and glaucoma) may become the leading cause of irreversible blindness worldwide. While t.o. dosing of atropine solution/ ointment appears promising for treating myopia, additional modalities are urgently needed to stem the tide of myopia development around the globe.

Animal Models of Myopia For sake of brevity, only myopia will be dealt with here. Several animal models using chicks, rats, mice, guinea pigs, and monkeys have been developed over the years (reviewed by Schaeffel and Feldkaemper 2015). Form deprivation (suturing of eyelids or patching the eye) and lens-induced

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methods are the most common and effective ways to induce myopia, and several classes of drugs have been tested for their efficacy in slowing down and/or preventing myopia. By far the most effective treatment observed in numerous animal species is the use of t.o. antimuscarinic agents (Schaeffel and Feldkaemper 2015; Jiang et al. 2018) that are able to reduce axial length within the globe. A recent study demonstrated that PG FP-receptor agonist, latanoprost (30 μg peribulbarly injected daily for 4 weeks) reduced form-deprivation-induced myopia in guinea pigs by 41% (Yang et al. 2018). Interestingly, peribulbarly injected FP-receptor antagonist, AL-8810 0.5 μg/day for 4 weeks, actually induced myopia in naïve animals (Yang et al. 2018).

Conclusions Of the many ocular disorders, glaucoma, AMD, DR, and myopia represent the major sightthreatening diseases that afflict millions of people on the planet. Even irritating ocular disorders that do not necessarily directly cause blindness such as dry eye and seasonal allergic conjunctivitis affects millions on an annual basis. Since sight is such a precious sense, much effort has been expended to find suitable treatment modalities for the various eye diseases discussed above. It will be evident from the above discourse that while majority of the drugs discovered, developed, and launched for ocular utility represented agonists or antagonist of several GPCRs, that the newer drug classes are antibodies, gene therapeutics, growth factor proteins, cellular therapies, and miniature devices. This agnostic approach to combating ocular diseases is very encouraging and it is hoped that combination products using these foundational elements will be even more productive in helping the patients who suffer from the diseases of the eye. It is also hoped that diagnostics encompassing various biomarkers and devices, and new technologies such as adaptive optics and OCT-coupled with angiography will prove helpful in our deeper understanding of the ocular disease processes and thus lead to superior preventative measures in the future.

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Clinical Pharmacology of Tinnitus: Design and Evaluation Agnieszka J. Szczepek

Contents Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Tinnitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tinnitus Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Influence of Tinnitus on Nonauditory Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multidisciplinary Aspects of Tinnitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

210 210 210 212

Contemporary Studies Involving Pharmacological Interventions for Tinnitus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 The Design of Pharmacological Intervention for Tinnitus . . . . . . . . . . . . . . . . . . . . . . . . . Target Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sample Selection (Inclusion and Exclusion Criteria and Sample Size) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Choice of Methods to Measure the Trial Outcome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

213 215 215 216

References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220

Abstract

Tinnitus – the perception of phantom sound – is estimated to seriously affect the quality of life of about 3% of the entire world population, making it an attractive target for pharmacotherapy. However, none of the so far conducted clinical trials with the use of pharmacological substances could be called a thrilling success. There are multiple reasons for this, which are discussed in this chapter. Moreover, a comprehensive overview of factors that should be

A. J. Szczepek (*) Department of ORL, Head and Neck Surgery, Charité University Hospital, Berlin, Germany e-mail: [email protected]

taken under consideration when designing clinical pharmacological study for tinnitus is presented in an anticipation to help design trials producing meaningful clinical data and identifying clinically relevant substances effective in tinnitus treatment.

Purpose and Rationale The purpose of this chapter was to review and to present contemporary information regarding the design of clinical trials for tinnitus. In addition, the aim was to distinguish the two main different directions that are being developed in tinnitus pharmacology, namely, the treatment of tinnitus percept and the treatment of tinnitus-related distress.

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_61

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Although the American Academy of Otolaryngology-Head and Neck Surgery recommended against the contemporary medial therapy of tinnitus (antidepressants, anticonvulsants, anxiolytics, or intratympanic medications (Tunkel et al. 2014)), the need to explore pharmacological intervention remains. The method: data from clinical studies that were performed between 2006 and 2016 and were analyzed in a recent systematic review (Hall et al. 2015, 2016) were included in the present study. Sixty-five studies that used pharmacological approach for tinnitus treatment were extracted. In these particular 65 studies, the pharmacological substances used, the outcome domains, and the outcome measure instruments were analyzed, presented, and discussed.

Tinnitus Tinnitus Characteristics Tinnitus is a symptom of various diseases that manifests itself as a sound percept without an external source (Jastreboff 1990). Objective tinnitus can be heard by other persons (often only with an aid of amplification) because it is caused by internal bodily noises (e.g., pulse that could be heard if the diseased blood vessel is located in the proximity of the ear). Subjective tinnitus is a phantom sound heard exclusively by the affected person. Illnesses capable of inducing subjective tinnitus include, but are not limited to, the head and neck injuries and all diseases that induce hearing impairment (middle ear inflammation, meningitis, otosclerosis, Meniere’s disease, presbyacusis, ototoxicity, noise-induced hearing loss, vestibular schwannomas, meningioma, intracranial pressure, atherosclerosis, diabetes, and other diseases) (Baguley et al. 2013). The grade of hearing loss has been suggested to correlate with the grade of tinnitus impairment (Mazurek et al. 2010). Recently, cochlear synaptopathies that cause hidden hearing loss are being considered as a possible reason underlying tinnitus (Guest et al. 2017; Liberman and Kujawa 2017). Unfortunately, in many cases, the direct cause of tinnitus remains unknown making causative therapy approaches difficult.

A. J. Szczepek

Box 1

Tinnitus often associates with hearing loss (Henry et al. 2014), but pharmacological intervention for hearing loss has a very small therapeutic time window, as the auditory hair cells are postmitotic, and in mammals, they are unable to regenerate (Seymour and Pereira 2015). Successful attempts of therapy against noise-induced hearing loss included using steroids, magnesium, coenzyme Q10, or D-methionine (Sakat et al. 2016). Importantly, all of the clinical studies were either using protective approach (prior to noise exposure) or an intervention immediately after noise exposure. Non-pharmacological approach of treating hearing loss (hearing aids, implantable hearing aids, and cochlear implants) is effective not only in restoring the ability to hear but also in reducing tinnitus-related distress (Olze et al. 2011; Ramos Macias et al. 2015).

There are several clinical terms used in the descriptive diagnostics of tinnitus that relate to the duration or to the severity of tinnitus – the most common ones are presented in Table 1. Regardless of the specific cause of tinnitus, the common denominator for all types of tinnitus is the activation of auditory cortex under acoustically sterile conditions. This activation results from the stimulation of auditory pathway that may take place in various parts of auditory circuits, starting from the periphery and ending in the auditory cortex.

Influence of Tinnitus on Nonauditory Systems In addition to activating the auditory system, processed acoustic signals stimulate also other structures. A good example is the reaction of a central nervous system to nonverbal acoustic stimuli, such as music (Koelsch 2014). The sound of music activates not only the auditory but also the somatosensory, autonomic, vestibular, and limbic systems (Fig. 1).

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Table 1 Various types of tinnitus used in clinical descriptions. Type of tinnitus Constant Distressing (decompensated) Acute

Intermittent Non-distressing (compensated) Chronic

Peripheral

Central

With mental comorbidities Unilateral

Without mental comorbidities Bilateral

Remarks Refers to the presence of tinnitus (continuous and noncontinuous) Refers to the psychological effect of tinnitus on the affected person Refers to the duration of tinnitus, where tinnitus is considered acute when occurring no longer than 3 months and chronic when longer than 3 months. In some countries or societies, the duration of tinnitus is regarded as chronic when longer than 6 or even 12 months Refers to the anatomical place (but not the cause!) of tinnitus origin, where “peripheral tinnitus” is considered to originate from cochlea and “central tinnitus” from anywhere between the cochlear nucleus and auditory cortex Refers to comorbid mental conditions such as anxiety and depression of phobias Refers to the affected side

Fig. 1 The main pathways underlying autonomic and muscular responses to music. The auditory cortex (AC) also projects to the orbitofrontal cortex (OFC) and the cingulate cortex (projections not shown). Moreover, the amygdala (AMYG), OFC, and cingulate cortex send numerous projections to the hypothalamus (not shown) and thus also exert influence on the endocrine system, including the neuroendocrine motor system. ACC, anterior cingulate cortex; CN, cochlear nuclei; IC, inferior colliculus; M1, primary motor cortex; MCC, middle cingulate cortex; MGB, medial geniculate body; NAc, nucleus accumbens; PMC, premotor cortex; RCZ, rostral cingulate zone; VN, vestibular nuclei (Reprinted with permission from Springer Nature from (Koelsch 2014))

Likewise, simple auditory stimuli such as the sound of chimes or a beep were shown to stimulate several structures in the central nervous system in addition to the auditory brain (Georgiewa et al. 2016). Interestingly, neuroimaging of tinnitus patients determined an

increased cortical activity in the auditory brain of tinnitus patients (Arnold et al. 1996) as well as different reactions to the sound of the nonauditory areas (Georgiewa et al. 2016) suggesting altered neuronal connectivity in the tinnitus brain (Leaver et al. 2016).

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Fig. 2 Tinnitus as stressor that amplifies other mental conditions as well as its own related distress

Box 2

Acoustic signals induce auditory and nonauditory brain stimulation.

Acoustic stimulation can be viewed as an auditory signal that induces neuronal auditory and nonauditory reactions, of which the evoked emotional reactions can be of positive or negative nature. In addition, sound may occasionally induce responses from the autonomic nervous system. In people suffering from tinnitus, the phantom sound perceived by the auditory system comes to be negatively labeled in terms of emotional load. Because of this, the sound of tinnitus becomes a stressor for the affected person. As such, it activates hypothalamus-pituitary-adrenal (HPA) axis inducing the release of stress hormones and provoking tinnitus-specific stress reactions that are referred to as tinnitus-induced

distress. These reactions include but are not limited to nervousness, insomnia, problems with concentrations, and other secondary and tertiary responses to stress. Some of the reactions may induce the development or an aggravation of already existing mental conditions, such as anxiety (Pattyn et al. 2016) or depressive symptoms (Hoare et al. 2011). In turn, these conditions may worsen tinnitus percept and tinnitus-related distress (Fig. 2).

Multidisciplinary Aspects of Tinnitus Tinnitus is a sensation of a sound and as such it often takes the tinnitus sufferers to the office of audiologist. However, the profession of audiologist as a health-care professional is known only in some countries (e.g., the United Kingdom, Sweden, the USA, Canada, Australia, Malaysia, India, or Portugal), whereas in other countries

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(e.g., Germany, Austria, Poland, France, or Czech Republic), people experiencing tinnitus or other hearing problems are examined by the ORL specialists and then optionally referred to other medical professionals. Persons suffering from a long-term (chronic) tinnitus, who in addition to perceiving a phantom sound react to it in a negative emotional way, are often referred to clinical psychologists. In addition, patients with bothersome tinnitus who have comorbid metal conditions may be attended to by psychiatrist, while patients with so-called somatosensoric tinnitus (Haider et al. 2017) will be attended to also by a physiotherapist. As a result, numerous health professionals deal with tinnitus patients: general practitioners, ORL specialists, audiologists, psychologists, psychiatrists, cardiologists, neurologists, physical therapists, and dentists (Tunkel et al. 2014). Because of distinct education and partitioned competences, although focused on tinnitus treatment, these specialists will have different clinical expertise on the subject. During the design of clinical study, one needs to take this under consideration when involving health practitioners.

Contemporary Studies Involving Pharmacological Interventions for Tinnitus Recent systematic review that analyzed outcome domains and instruments used to measure outcome of clinical trials for tinnitus has identified 228 trials meeting the review criteria and performed between the years 2006 and 2016 (Hall et al. 2016). Of these trials, 65 involved pharmacological agents. The drugs could be generally split into two categories (for the precise listing, see Table 2): • Targeting the auditory pathway (substances aiming at the auditory pathway glutamate receptors, substances blocking sodium or potassium pumps) • Targeting the tinnitus-related distress and/ or comorbid disorders (substances aiming at

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serotonin uptake, dopamine receptors, antagonists at the μ-opioid receptor, etc.) Only about a 40% of the drugs used in tinnitus-related clinical trials aim at the tinnitus percept via targeting the auditory pathway, while the remaining 60% are directed against the comorbid diseases and/or tinnitus-related distress (Fig. 3).

The Design of Pharmacological Intervention for Tinnitus The design of clinical intervention for tinnitus depends on several factors, such as sample homogeneity, choice of pharmacological target, or effect measured (Table 3). Sample homogeneity could be achieved by choosing one subtype of tinnitus in an age- and gender-matched group of patients. However, despite the attempts to standardize tinnitus diagnostic and classification procedure (Crummer and Hassan 2004; Langguth et al. 2011), there is still lack of internationally acknowledged and scientifically and clinically verified tinnitus subtypes. Pharmacological trials for tinnitus use standard trial design (parallel, crossover, blinding, etc.). However, several crucial factors need to be taken under consideration when designing tinnitus trial: 1. That tinnitus is a subjective symptom 2. That the acoustic properties of tinnitus are measured with subjective methods 3. That perceiving tinnitus must not mean suffering from tinnitus 4. That all the accepted means to measure the degree of tinnitus-induced distress (or degree of suffer) are subjective 5. That targeting the disease, which presumably caused tinnitus, must not necessarily target tinnitus itself Three further issues are of vast importance when designing the pharmacological trial for tinnitus:

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Table 2 Drugs used in the analyzed pharmacological trials in the period between 2006 and 2016a Drug Acamprosate

Number of trials 1

Alprazolam

2

AM-101

6

Atorvastatin

1

AUT00063 Betahistine dihydrochloride

1 2

BGG492A Cannabis

1 1

Carbamazepine

1

Caroverine

2

Cilostazol

1

Cinnarizine

1

Cyclobenzaprine

1

D-cycloserine

1

Deanxit

1

Dexamethasone Escitalopram

1 1

Fluoxetine

1

Fluvoxamine Gabapentin

1 5

Ginkgo biloba

3

Hangekobokuto

2

Mode of action The mechanism of action of acamprosate is unknown and controversial. Targets NMDA A potent, short-acting anxiolytic of the benzodiazepine class – a minor tranquilizer Esketamine hydrochloride, an N-methyl-D-aspartate (NMDA) receptor antagonist Atorvastatin works by inhibiting HMG-CoA reductase, an enzyme found in the liver tissue that plays a key role in the production of cholesterol in the body Small-molecule modulator of Kv3 potassium channels Betahistine has a very strong affinity as an antagonist for histamine H3 receptors and a weak affinity as an agonist for histamine H1 receptors Competitive antagonist of the AMPA and kainate receptors Cannabinoid is one of a class of diverse chemical compounds that acts on cannabinoid receptors in cells that alter neurotransmitter release in the brain Carbamazepine is a blocker of voltage-gated sodium channels that binds to activated voltage-gated sodium channels, preventing repetitive and sustained firing of an action potential Acts as an N-type calcium channel blocker, competitive AMPA receptor antagonist, and noncompetitive NMDA receptor antagonist (Arnold et al. 1996). It also has potent antioxidant effects (Baguley et al. 2013) Cilostazol is a phosphodiesterase inhibitor with therapeutic focus on cyclic adenosine monophosphate (cAMP) Cinnarizine is an antihistamine and a calcium channel blocker; it is also known to promote cerebral blood flow Cyclobenzaprine is a muscle relaxer medication used to relieve skeletal muscle spasms and associated pain in acute musculoskeletal conditions Is an antibiotic used to treat tuberculosis and target the glycinebinding site of N-methyl-D-aspartate (NMDA) receptors in humans Deanxit is made up of two components: flupentixol 0.5 mg (or flupenthixol, an antipsychotic) and melitracen 10 mg (a tricyclic antidepressant) Corticosteroid medication An antidepressant of the selective serotonin reuptake inhibitor (SSRI) class An antidepressant of the selective serotonin reuptake inhibitor (SSRI) class Selective serotonin reuptake inhibitor (SSRI) and σ1 receptor agonist Mimics the chemical structure of the neurotransmitter gammaaminobutyric acid (GABA) A possible treatment for dementia and Alzheimer’s disease, possibly improving cerebral circulation A lignan isolated from the bark, seed cones, and leaves of trees belonging to the genus Magnolia. Used as analgesic and to treat anxiety and mood disorders (continued)

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Table 2 (continued) Drug Lidocaine

a

Number of trials 2

Lyophilized powder of enzymolyzed honeybee larvae Magnesium Melatonin

1

Memantine Naltrexone

1 1

Neramexane

6

Paroxetine

1

Piribedil Pramipexole Prednisolone Q10 Simvastatin

1 1 1 2 1

Trazodone Vardenafil Vestipitant Zinc

1 1 2 1

1 4

Mode of action Lidocaine alters signal conduction in neurons by blocking the fast voltage-gated Naþ channels in the neuronal cell membrane responsible for signal propagation Unknown NMDA antagonist N-Acetyl-5-methoxy tryptamine: a hormone that is produced by the pineal gland in animals and regulates sleep and wakefulness NMDA antagonist Naltrexone and its active metabolite 6β-naltrexol are antagonists at the μ-opioid receptor A drug related to memantine (Arnold et al. 1996), which acts as an NMDA antagonist (Baguley et al. 2013) and has neuroprotective effects An antidepressant of the selective serotonin reuptake inhibitor (SSRI) class D2 and D3 receptor agonist Dopamine agonist Steroid medication Antioxidant, part of mitochondrial respiratory chain Simvastatin inhibits 3-hydroxy-3-methylglutaryl (HMG) coenzyme A reductase Serotonin antagonist and reuptake inhibitor (SARI) class PDE5 inhibitor Selective antagonist for the NK1 receptor (substance P receptor) NMDA antagonist

Based on the supplementary data from (Hall et al. 2016)

Target Selection General target selection discriminates between tinnitus percept and tinnitus-induced distress. One-third of the previous studies targeted tinnitus-induced distress, and roughly equal number targeted tinnitus percept (Fig. 4). The rest of the studies either have not officially stated their target or targeted other domains, such as quality of life or comorbid symptoms. In addition, the molecular identity of the target needs to be specified. Few genetic studies that were performed to identify possible candidate genes associated with tinnitus rather than doing just that pointed the need of better phenotyping or subtyping of tinnitus (Vona et al. 2017).

Sample Selection (Inclusion and Exclusion Criteria and Sample Size) Stringent inclusion and exclusion criteria supported by up-to-date diagnostic criteria should be set to assure sample homogeneity. The sample should be composed based on gender distribution, age, duration of tinnitus (in the analyzed trials, duration of tinnitus ranged from less than 3 months up to a year; 19 trials either did not report or reported ambiguously the tinnitus duration (Hall et al. 2016)), possible cause of tinnitus, the degree of tinnitus-induced distress (39 of 65 trials in the past did not report this (Hall et al. 2015)), and the degree of hearing loss. In addition, comorbid mental conditions

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Fig. 3 Targets of pharmacotherapy (2006–2016)

Table 3 Factors particularly important during the design of clinical trial for tinnitus Sample homogeneity

Choice of pharmacological target Observed effect

Age Gender Duration of tinnitus Presence of tinnitus Cause of tinnitus (known/ unknown) In case of known cause – central or peripheral origin Comorbid conditions Equal degree of psychological effect of tinnitus on the affected individual The cause of tinnitus Tinnitus-related distress Comorbid conditions Choice of outcome measures (domains measured, tools used for measurement) Clinical significance of the measured changes Time course of the treatment Time course of the follow-up

and other conditions must be taken under account. The method for calculation of sample size should be stated (in the trials analyzed, the sample size varied from 10 to 821 subjects, and the method of calculation was stated only in 9 of 65 trials (Hall et al. 2016)).

Choice of Methods to Measure the Trial Outcome In the pharmacological tinnitus trials analyzed in the past 10 years (2006–2016), various outcome measures were used (Hall et al. 2016) and included psychometric questionnaires, numerical scales, and audiometric measurements (Fig. 5). The psychometric questionnaires were predominantly used when targeting the tinnitus-induced distress, whereas audiometric methods and numerical scales were used when the primary target consisted of tinnitus percept (Meikle et al. 2007).

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Fig. 4 Primary outcome domains used in the past trials (2006–2016)-based on supplementary data from (Hall et al. 2016).

Fig. 5 Primary outcome measures used in the past trials (2006–2016)-based on supplementary data from (Hall et al. 2016).

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Fig. 6 Countries in which the clinical trials were conducted in the past (2006–2016)-based on supplementary data from (Hall et al. 2016).

There are several psychometric instruments that measure tinnitus-related distress. However, one needs caution when choosing the instrument, as they are not identical and often measure different domains with various sensitivities. In addition, the majority of tinnitus-related questionnaires are available in English and not in other languages. The most used tinnitus questionnaires worldwide in the past 10 years (Hall et al. 2016) include English version of Tinnitus Questionnaire (Hallam et al. 1988) and its German version (Hiller and Goebel 1992), Tinnitus Handicap Inventory (Newman et al. 1996) and Tinnitus Functional Index (Meikle et al. 2012). In addition, new questionnaires are being developed to address emerging issues, such as acceptance of tinnitus (Weise et al. 2013) To date, many questionnaires were validated and translated into other languages, e.g., Tinnitus Functional Index is presently available in German (Bruggemann et al. 2017), Swedish (Hoff and Kahari 2017), Polish (Wrzosek et al. 2016), and Dutch (Rabau et al. 2014), but it still remains to be offered in several other tongues, especially considering where the

clinical pharmacological trials are being conducted (Fig. 6). The study design in the past was predominantly randomized controlled (Fig. 6) (Hall et al. 2016), which is a general trend in the clinical research (Fig. 7). Although clinical trials for tinnitus that were conducted in the past have not delivered a breakthrough in medical research (Beebe Palumbo et al. 2015; Plein et al. 2016; Savage and Waddell 2012), they delivered a lot of information that can be used to design an improved and wellfocused trial, in which a variety of tinnitus phenotypes would be recognized (Fig. 8). The therapeutic avenues that have in the past been explored for tinnitus include acupuncture, electromagnetic stimulation, hearing aids, hypnosis, psychotherapy, tinnitus masking devices, cognitive behavioral therapy, and tinnitus retraining therapy (Savage and Waddell 2014). For the design of the future trials, it should not be excluded that a drug therapy could be combined with one or more of the above approaches. In fact, the effectiveness of cognitive behavioral therapy

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Fig. 7 Types of study design used in the past trials (2006–2016)-based on supplementary data from (Hall et al. 2016).

Fig. 8 General design scheme of pharmacological trial for tinnitus

in tinnitus management indicated by Cochran (Martinez-Devesa et al. 2010) and other systematic reviews (Hesser et al. 2011) could be a starting point for such combination therapy. Taken together, the pharmacological trails were in the past anticipated to pinpoint a substance that would universally cure millions of

people suffering from tinnitus. However, rather than providing a quick and uncomplicated solution, pharmacological trials uncovered the enormous diversity among tinnitus sufferers and a consequent need for rigorous tinnitus classification. Introduction of tinnitus taxonomy would improve the choice of inclusion and exclusion

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criteria. This should also have positive impact on the selection of outcome domains and their measurement. Discovery or design of a universal medication against tinnitus, which would be comparable with a pain killer reducing discomfort of a headache as well as a stomachache or a toothache, is a Holy Grail of the clinical tinnitus research. However, improving the design of clinical pharmacological trials for tinnitus may result in obtaining partial answers and putting together step by step the 1000 pieces tinnitus puzzle.

References and Further Reading Arnold W, Bartenstein P, Oestreicher E et al (1996) Focal metabolic activation in the predominant left auditory cortex in patients suffering from tinnitus: a PET study with [18F]deoxyglucose. ORL J Otorhinolaryngol Relat Spec 58:195–199 Baguley D, Mcferran D, Hall D (2013) Tinnitus. Lancet 382:1600–1607 Beebe Palumbo D, Joos K, De Ridder D et al (2015) The management and outcomes of pharmacological treatments for tinnitus. Curr Neuropharmacol 13:692–700 Brüggemann P, Szczepek AJ, Kleinjung T, Ojo M, Mazurek B (2017) [Validation of the German Version of Tinnitus Functional Index (TFI)]. Laryngorhinootologie 96(9):615–619 Crummer RW, Hassan GA (2004) Diagnostic approach to tinnitus. Am Fam Physician 69:120–126 Georgiewa P, Szczepek AJ, Rose M et al (2016) Cerebral processing of emotionally loaded acoustic signals by tinnitus patients. Audiol Neurootol 21:80–87 Guest H, Munro KJ, Prendergast G et al (2017) Tinnitus with a normal audiogram: relation to noise exposure but no evidence for cochlear synaptopathy. Hear Res 344:265–274 Haider HF, Hoare DJ, Costa RFP et al (2017) Pathophysiology, diagnosis and treatment of somatosensory tinnitus: a scoping review. Front Neurosci 11:207 Hall DA, Szczepek AJ, Kennedy V et al (2015) Currentreported outcome domains in studies of adults with a focus on the treatment of tinnitus: protocol for a systematic review. BMJ Open 5:e009091 Hall DA, Haider H, Szczepek AJ et al (2016) Systematic review of outcome domains and instruments used in clinical trials of tinnitus treatments in adults. Trials 17:270 Hallam RS, Jakes SC, Hinchcliffe R (1988) Cognitive variables in tinnitus annoyance. Br J Clin Psychol 27 (Pt 3):213–222 Henry JA, Roberts LE, Caspary DM et al (2014) Underlying mechanisms of tinnitus: review and clinical implications. J Am Acad Audiol 25:5–22. quiz 126

A. J. Szczepek Hesser H, Weise C, Westin VZ et al (2011) A systematic review and meta-analysis of randomized controlled trials of cognitive-behavioral therapy for tinnitus distress. Clin Psychol Rev 31:545–553 Hiller W, Goebel G (1992) A psychometric study of complaints in chronic tinnitus. J Psychosom Res 36:337–348 Hoare DJ, Kowalkowski VL, Kang S et al (2011) Systematic review and meta-analyses of randomized controlled trials examining tinnitus management. Laryngoscope 121:1555–1564 Hoff M, Kahari K (2017) A Swedish cross-cultural adaptation and validation of the Tinnitus Functional Index. Int J Audiol 56:277–285 Jastreboff PJ (1990) Phantom auditory perception (tinnitus): mechanisms of generation and perception. Neurosci Res 8:221–254 Koelsch S (2014) Brain correlates of music-evoked emotions. Nat Rev Neurosci 15:170–180 Langguth B, Biesinger E, Del Bo L et al (2011) Algorithm for the diagnostic and therapeutic management of tinnitus. In: Møller AR, Langguth B, De Ridder D, Kleinjung T (eds) Textbook of tinnitus. Springer, New York, pp 381–385 Leaver AM, Seydell-Greenwald A, Rauschecker JP (2016) Auditory-limbic interactions in chronic tinnitus: challenges for neuroimaging research. Hear Res 334:49–57 Liberman MC, Kujawa SG (2017) Cochlear synaptopathy in acquired sensorineural hearing loss: manifestations and mechanisms. Hear Res 349:138–147 Martinez-Devesa P, Perera R, Theodoulou M et al (2010) Cognitive behavioural therapy for tinnitus. Cochrane Database Syst Rev:CD005233 Mazurek B, Olze H, Haupt H et al (2010) The more the worse: the grade of noise-induced hearing loss associates with the severity of tinnitus. Int J Environ Res Public Health 7:3071–3079 Meikle MB, Stewart BJ, Griest SE et al (2007) Assessment of tinnitus: measurement of treatment outcomes. Prog Brain Res 166:511–521 Meikle MB, Henry JA, Griest SE et al (2012) The tinnitus functional index: development of a new clinical measure for chronic, intrusive tinnitus. Ear Hear 33:153–176 Newman CW, Jacobson GP, Spitzer JB (1996) Development of the tinnitus handicap inventory. Arch Otolaryngol Head Neck Surg 122:143–148 Olze H, Szczepek AJ, Haupt H et al (2011) Cochlear implantation has a positive influence on quality of life, tinnitus, and psychological comorbidity. Laryngoscope 121:2220–2227 Pattyn T, Van Den Eede F, Vanneste S et al (2016) Tinnitus and anxiety disorders: a review. Hear Res 333:255–265 Plein CT, Harounian J, Floyd E et al (2016) A systematic review of eligibility and outcomes in tinnitus trials: reassessment of tinnitus guideline. Otolaryngol Head Neck Surg 154:24–32 Rabau S, Wouters K, Van De Heyning P (2014) Validation and translation of the Dutch tinnitus functional index. B-ENT 10:251–258

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Ramos Macias A, Falcon Gonzalez JC, Manrique M et al (2015) Cochlear implants as a treatment option for unilateral hearing loss, severe tinnitus and hyperacusis. Audiol Neurootol 20(Suppl 1):60–66 Sakat MS, Kilic K, Bercin S (2016) Pharmacological agents used for treatment and prevention in noiseinduced hearing loss. Eur Arch Otorhinolaryngol 273:4089–4101 Savage J, Waddell A (2012) BMJ Clin Evid. (0506) Savage J, Waddell A (2014) BMJ Clin Evid. (0506) Seymour ML, Pereira FA (2015) Survival of auditory hair cells. Cell Tissue Res 361:59–63

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Tunkel DE, Bauer CA, Sun GH et al (2014) Clinical practice guideline: tinnitus executive summary. Otolaryngol Head Neck Surg 151:533–541 Vona B, Nanda I, Shehata-Dieler W et al (2017) Genetics of tinnitus: still in its infancy. Front Neurosci 11:236 Weise C, Kleinstauber M, Hesser H et al (2013) Acceptance of tinnitus: validation of the tinnitus acceptance questionnaire. Cogn Behav Ther 42:100–115 Wrzosek M, Szymiec E, Klemens W et al (2016) Polish translation and validation of the tinnitus handicap inventory and the tinnitus functional index. Front Psychol 7:1871

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Clinical Aspects in Sleep Disorders and Apnea Thomas Penzel and Ingo Fietze

Contents Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 Sleep-Related Breathing Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Treatment of Sleep-Related Breathing Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Questionnaires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polysomnography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Home Sleep Apnea Testing – Polygraphy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

230 230 231 234

Evaluation (Parameters and Statistical Evaluation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Critical Assessment of the Sleep Recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 Alternative Treatments for Sleep Apnea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

Abstract

Sleep disorders are frequently reported complaints. Insomnia and hypersomnolence are symptoms often reported by patients and study participants. Sleep disorders with clinical consequences are not as common as complaints and symptoms might suggest. Sleep medicine is a new discipline which has developed its own curricula and physician specialization. Sleep medicine has developed a classification of sleep disorders with a manual with definitions and severity criteria. This

T. Penzel (*) · I. Fietze Interdisciplinary Sleep Medicine Center, Charitécentrum für Herz- Kresilauf- und Gefäßmedizin CCM11, Charité – Universitätsmedizin Berlin, Berlin, Germany e-mail: [email protected]; ingo.fi[email protected]

classification will become part of the ICD-11 currently developed. The classification defines insomnia, sleep-related breathing disorders, central disorders of hypersomnolence, circadian rhythm sleep-wake disorders, parasomnias, sleep-related movement disorders, and other sleep disorders. Diagnostic procedures include validated questionnaires; daytime testing of alertness and sleepiness; home recording of sleep-wake behavior, activity, and physiological signals; and finally a sleep laboratory investigation, cardiorespiratory polysomnography, with all signals recorded which change during normal and pathological sleep. Quantitative assessment of sleep, sleep stages, arousals from sleep, and vegetative functions during sleep is

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_41

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well-established, and normative values including age as modifier are well described. Sleep disorders are recognized as risk factors for many other medical and mental disorders. Sleep disorders impair performance and may be perceived as early aging. Untreated sleep disorders cause costs at all levels of health care and need to be recognized and treated as appropriate. Sleep disorders are a target to clinical pharmacology by being recognized and potentially excluded in any pharmacological trial. And sleep disorders are subject to drug discovery and development.

Purpose and Rationale Sleep disorders have a high prevalence in the population. Prevalence is reported to be between 10% and 30% in the general population (Ohayon 2011). According to a health survey by the Robert-Koch Institute in 1998, about 14% of the German male population and 27% of the German female population complain about frequent or moderate insomnia (suffering from not sleeping) (Penzel et al. 2005). The survey did not check for sleep disorders according to medical definitions nor did the survey use validated and approved. Only three questions could be related to sleep disorders. These were “complaints about insomnia,” “need too much sleep,” and “being tired.” Questions were rather unspecific. This had been recognized by many sleep researchers, and they initiated their own large sample surveys on sleep disorders (Ohayon and Zulley 2001). Assessment was performed on sleep dissatisfaction and sleep duration and more specific complaints on sleep problems. These extensive computer-driven interviews had a mean duration of 48 min per person. Definitely this is unpractical for general health surveys. A follow-up health survey by the Robert-Koch Institute between 2008 and 2011 contained much more specific questions compared to the first one in 1998 (Schlack et al. 2013). Some new questions were derived from the Pittsburgh Sleep Quality Index (Buysse et al. 1989), a well-validated questionnaire used in

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sleep medicine. The survey assessed the same complaints as previously and in addition more specific questions on sleep duration, problems in initiating and maintaining sleep, frequency of these complaints, perceived quality of sleep, and use of sleeping medication, all during the past 4 weeks. Based on the answers, the authors were able to estimate a prevalence of 5.7% for insomnia in Germany. This survey yielded the most recent and reliable prevalence data for Germany. However in the German health insurance system, sleep disorders play a very small role. Whereas depression, the third most common diagnosis, is responsible for 5.6% of all days on sickness leave, sleep disorders are only listed for 0.26% of days for sickness leave (DAK report 2017). Still days for sickness leave doubled over the last decade. This reflects very well an important fact. Sleep disorders remain not to be a reason for consulting a physician in the first line. Sleep disorders remain not to be a reason for requesting for sick leave. Many patients do not report sleep complaints in the first line but often as a secondary symptom. Still sleep complaints, being difficulties in initiating and maintaining sleep or in suffering from non-refreshing sleep, are very common. Therefore it is important to investigate sleep complains and sleep problems in detail. The first assessment is, both in patients and in presumably healthy subjects, whether they suffer from sleep problems as secondary problems or from a genuine sleep disorder. The field of sleep medicine has developed over the last three decades and has defined its own classification of sleep disorders since 1979. The latest version of the classification is called the International Classification of Sleep Disorders (ICSD-3) version 3 (AASM 2014). These are listed in the following Table 1. One major change from the second edition of ICSD to this third edition is that the many different insomnia subtypes as carefully differentiated in the second edition had now been pooled together to only three definitions. They are now called a disorder. Specifically these are chronic insomnia disorders, short-term insomnia, and other insomnia. A chronic insomnia disorder requires a duration of symptoms for at least

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Table 1 Categories of the International Classification of Sleep Disorders (AASM 2014) together with their subgroups and number of disorder definitions Group Insomnia Sleep-related breathing disorders

Central disorders of hypersomnolence Circadian rhythm sleep-wake disorder Parasomnias

Sleep-related movement disorders Other sleep disorder Appendix A: Sleep-related medical and neurological disorders

3 months. Previously there was a distinction between primary and secondary insomnias. Secondary insomnias were related to a primary psychiatric, medical, or substance abuse disorder. Moreover, the primary insomnias were distinguished in several more subtypes. However it turned out that symptoms and consequences do not allow to distinguish all these subtypes clearly and consistently. The differentiation was difficult, if not impossible to achieve. As a consequence they were pooled together as evidence suggests that untreated insomnia may result in adverse comorbid conditions. Another major change is that many other secondary sleep disorders were taken out of the classification. Only a few very prominent secondary sleep disorders remained in Appendix A as sleep-related medical and neurological disorders. Another change was that all different environmental-induced sleep problems were now forced to be diagnosed as either a fullblown disorder of the other definitions including all defined criteria or the problems remain as “other sleep disorder.” With this, the third edition became more focused and much more condense

Subgroups Isolated symptoms and normal variants Obstructive sleep apnea disorders Central sleep apnea syndromes Sleep-related hypoventilation disorders Sleep-related hypoxemia disorder Isolated symptoms and normal variants Isolated symptoms and normal variants – NREM-related parasomnias REM-related parasomnias Other parasomnias Isolated symptoms and normal variants Isolated symptoms and normal variants – –

Number of definitions 5 19

9 7 15

13 1 6

with clear definitions and severity criteria. A list of all diagnoses defined is presented in Table 2. With this table it becomes clear that the codes being used comes from different sections from ICD 10. Most codes came from G47 and F51 sections. Some other diagnoses were distributed across several other chapters. The new classification, as presented in the ICSD third edition (AASM 2014) is now well based on pathophysiology and is now mature enough to be stable. Finally it will be incorporated in the new ICD 11, which is currently developed, as a chapter of its own, entitled “Sleep-Wake Disorders.” The proposal for the new chapter for ICD 11 follows the structure as described in Table 2 and in the ICSD third edition.

Sleep-Related Breathing Disorders The group of disorders which attracts most attention from the health-care system is sleep-related breathing disorders. Within all types of sleep-related breathing disorders, obstructive sleep apnea

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Table 2 List of all sleep disorders as they are defined and specified with their ICD 10 code. Diagnoses names and codes were taken from AASM coding manual 2014 Group of sleep disorder Insomnia

Sleep-related breathing disorders

Central disorders of hypersomnolence

Circadian rhythm sleep-wake disorder

Diagnosis Chronic insomnia disorder Short-term insomnia disorder Other insomnia disorder Excessive time in bed Short sleeper Obstructive sleep apnea, adult Obstructive sleep apnea, pediatric Central sleep apnea with Cheyne-stokes breathing Central sleep apnea due to a medical disorder without Cheyne-stokes breathing Central sleep apnea due to high-altitude periodic breathing Central sleep apnea due to a medication or substance Primary central sleep apnea Primary central sleep apnea of infancy Primary central sleep apnea of prematurity Treatment-emergent central sleep apnea Obesity hypoventilation syndrome Congenital central alveolar hypoventilation syndrome Late-onset central hypoventilation with hypothalamic dysfunction Idiopathic central alveolar hypoventilation Sleep-related hypoventilation due to a medication or substance Sleep-related hypoventilation due to a medical disorder Sleep-related hypoxemia Snoring Catathrenia Narcolepsy type 1 Narcolepsy type 2 Idiopathic hypersomnia Kleine-Levin syndrome Hypersomnia due to a medical disorder Hypersomnia due to a medication or substance Hypersomnia associated with a psychiatric disorder Insufficient sleep syndrome Long sleeper Delayed sleep-wake phase disorder Advanced sleep-wake phase disorder Irregular sleep-wake rhythm disorder Non-24-hour sleep-wake rhythm disorder Shift work disorder Jet lag disorder Circadian sleep-wake disorder not otherwise specified

ICD 10 code F51.01 F51.02 F51.09 – – G47.33 G47.33 R06.3 G47.37 G47.32 G47.39 G47.31 P28.3 P28.4 G47.39 E66.2 G47.35 G47.36 G47.34 G47.36 G47.36 G47.36 R06.83 – G47.411 G47.419 G47.11 G47.13 G47.14 F11 – F19 F51.13 F51.12 – G47.21 G47.22 G47.23 G47.24 G47.26 G47.25 G47.20 (continued)

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Table 2 (continued) Group of sleep disorder Parasomnias

Sleep-related movement disorders

Other sleep disorders Appendix A: Sleep-related medical and neurological disorders

Diagnosis Disorders of arousal from NREM sleep Confusional arousals Sleepwalking Sleep terrors Sleep-related eating disorder REM sleep behavior disorder Recurrent isolated sleep paralysis Nightmare disorder Exploding head syndrome Sleep-related hallucinations Sleep enuresis Parasomnia due to a medical disorder Parasomnia due to a medication or substance Parasomnia unspecified Sleep talking Restless legs syndrome Periodic limb movement disorder Sleep-related leg cramps Sleep-related bruxism Sleep-related rhythmic movement disorder Benign sleep myoclonus at sleep onset Propriospinal myoclonus at sleep onset Sleep-related movement disorder due to a medical disorder Sleep-related movement disorder due to a medication or substance Sleep-related movement disorder unspecified Excessive fragmentary myoclonus Hypnagogic foot tremor and alternating leg muscle activation Sleep starts (hypnic jerks) Fatal familial insomnia Sleep-related epilepsy Sleep-related headaches Sleep-related laryngospasm Sleep-related gastroesophageal reflux Sleep-related myocardial ischemia

disorders present the highest prevalence and are responsible for the highest direct costs in healthcare systems worldwide, related to sleep disorders. Sleep apnea is a disorder with respiratory cessations during sleep. An apnea is counted if the duration is

ICD 10 code – G47.51 F51.3 F51.4 G47.59 G47.52 G47.53 F51.5 G47.59 H53.16 N39.44 G47.54 F11 – F19 G47.50 – G25.81 G47.61 G47.62 G47.63 G47.69 G47.69 G47.69 G47.69 F11 – F19 G47.69 – – – G47.8 – – – – – –

longer than 10 s. Most apnea events last for 30–50 s, but they may last more than a minute. In parallel with the respiratory cessation, oxygen saturation decreases due to no breathing. Apnea events end with a central nervous activation with parallel

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increase in sympathetic tone resulting in increased heart rate and increased blood pressure during this so-called arousal. Apnea events are called obstructive if they are caused by a collapse of the upper airways when sleeping. Partial obstruction will result in hypopnea events with similar effects as apnea events. Therefore both types of events are counted together and are related to total sleep time; the apnea-hypopnea index (AHI) is the measure for severity. Central apnea events are characterized by a cessation of airflow, but the upper airways remain open. These are observed usually in patients with cardiac problems (i.e., heart failure). Some apnea events may have both an obstructive component with an obstruction of the upper airways and a component with now respiratory efforts, similar to a central apnea. These events are called mixed apnea events. These apnea events are observed in patients who start a sleep apnea therapy, and then this picture is called treatment-emergent apnea – as listed in the above list of diagnoses. While a few apnea events are observed in anybody during sleep, if the number of these events exceeds 5 events/hour of sleep (AHI > 5 events/hour), this is diagnosed as mild sleep apnea. If the number of apneas exceeds 15 events/hour, this is moderate sleep apnea, and if more than 30 events/hour of sleep are found, this is severe sleep apnea. In early epidemiological studies, the prevalence of obstructive sleep apnea (OSA) was estimated to be 4% in men and 2% in women (Young et al. 1993). Many large epidemiological studies including the investigation of sleep apnea were carried out since. Studies were performed in several countries worldwide. Over time these studies reported an increasing prevalence (Peppard et al. 2013). Peppard et al. reported obstructive sleep apnea (AHI > 15 events/hour) in 10–17% of men depending on age and 3–9% in women depending on age. More recent studies have reported even higher prevalence. A population-based cohort study in Lausanne (HypnoLaus) reported moderate to severe sleep apnea (AHI >15 events/hour) in 49% of men and 23% of women in consecutive participants aged 40–85 years (Heinzer et al. 2015). With this high prevalence, a discussion on the definitions of sleep apnea and on the

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medical relevance of sleep apnea has been inaugurated. Because apnea events occur during sleep, affected patients are not aware of this. Patients do not report apneas themselves. A bedpartner may have observed apnea events during sleep or the patient turning blue due to low oxygen when breathing ceases. Most patients with sleep apnea snore as a sign of higher upper airway collapsibility. During an apnea event, when airflow ceases, there is no snoring. Therefore snoring is usually intermittent. Apnea events are terminated by arousals. Arousals are most often visible in the sleep EEG as an increase in EEG frequency and as a shift toward higher sleep stages (Bonnet et al. 2007). Arousals with their central nervous activation open the upper airways to regain breathing and the activation interrupts sleep continuity (Eckert et al. 2014). Therefore patients are not able to reach deep sleep (sleep stage N3) if severely affected. In addition, light sleep (sleep stages N1 and N2) and REM sleep are interrupted by many arousals causing fragmented sleep. As a consequence of fragmented sleep patients experience and report unrefreshing sleep and are often sleepy during daytime (Eckert et al. 2014). Sleep apnea accompanied by excessive daytime sleepiness had been called obstructive sleep apnea syndrome (OSAS) in the past. Today, we know that excessive daytime sleepiness is a typical but not always reported consequence of sleep apnea and sleep physicians avoid the term ‘syndrome’. The numerous hypoxia events and reoxygenation after each apnea during sleep cause a stress to the endothelial system (Lavie 2003). The increase in sympathetic tone with each arousal causes vasoconstriction with increases in heart rate and blood pressure. These periodic changes result in additional stress to the vascular system during sleep. With these rapid and frequent changes, the normal regulation of sleep and sleep recreation becomes severely impaired. The normal lowering of heart rate and blood pressure and the lowering of vascular load cannot take place. The normal hormone secretion pattern during sleep is impaired. As a consequence these changes in physiology impose a risk factor for cardiovascular disorders such as

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hypertension, cardiac arrhythmias, myocardial infarction, and stroke (Shahar et al. 2001). The association with hypertension is most obvious and had been reported early (Young et al. 1997). Sleep apnea is also a risk factor for diabetes and metabolic disorders (Resnick 2003). Patients experience this as early aging and lower performance.

Treatment of Sleep-Related Breathing Disorders The therapy of choice for obstructive sleep apnea is nasal CPAP (continuous positive air pressure) (Mayer et al. 2017). The therapeutic mechanism is mechanical. It is a pneumatic stenting of the upper airways (Sullivan et al. 1981). This treatment is very effective if the nasal mask is tolerated by the patient (Sanders et al. 2008). The upper airways are opened during sleep by a continuous flow of room air resulting in a positive pressure of 4–15 cm H2O in average. The exact pressure needed in a particular patient is titrated in the sleep laboratory. The pressure needed is more or less constant for a particular patient. The pressure needed varies by 1–3 cm H2O depending on sleep stage and body position. Usually higher pressure are needed during REM sleep and sleeping supine. Since couple of years, machines are available which perform a continuous assessment of the upper airway obstruction by a high-frequency oscillation method. High frequency means 20 Hz and the superimposed oscillation is generated by a loudspeaker creating air vibrations on top of the supplied air pressure. Sensing reflecting oscillations allow to determine the collapse of the upper airways. Then pressure is increased automatically until the airways open. These machines determine automatically the required air pressure and are called APAP machines, automatic titrating CPAP (Morgenthaler et al. 2008). For patients who cannot tolerate to exhale against an increased pressure, another group of machines was developed, which lower the air pressure as soon as the patient wants to expire. This lowering of pressure can be set by the sleep physician, and it is usually 4–8 cm H2O lower than the inspiratory pressure. These

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machines are called BPAP or (Bilevel PAP). The time for inspiration and expiration may be set to limit if needed. All these machines do not present a classical ventilation therapy because there is just a nasal mask, there is no volume control, and airflow is continuous. However the distinction between more sophisticated modes, like a timecontrolled BPAP and conventional ventilation, becomes more fuzzy. Due to the high number of patients with obstructive sleep apnea and with prescribed CPAP or APAP devices, this is an economically very important market with all consequences for health-care and health insurance systems. Sleep apnea therapy with all kind of ventilator devices require using the device each night and wearing a mask during each night. Some patients cannot tolerate the nasal mask. Then therapy adherence declines and as a consequence treatment efficiency declines as well. Patients are seeking for alternative treatments. Several alternative therapies for sleep apnea were developed. The most common and popular alternative therapy is the use of mandibular advancement devices (MAD). The oral devices produce a protrusion of the lower jaw by an average of 8 mm up to 15 mm. The devices can be used only if teeth are healthy and stable. The protrusion has to be titrated to reach maximum effectiveness in terms of apnea reduction snoring reduction and while avoiding pain and discomfort during the night and next day. The devices open the upper airways somewhat more. Accordingly they may turn apnea events into hypopnea events and definitely reduce snoring. Depending on the severity of upper airway collapse, this treatment may reduce the number of apnea and hypopnea events to zero. Most often a number of events stay. On average in large patient groups of any sleep apnea severity, a reduction of AHI by 50% is reported. In selected patient groups, results may be better. Today MAD is recommended if the CPAP mask is not tolerated or if the upper airway collapse is not very severe. The upper airway collapse that is not very severe is observed best by checking the effective CPAP pressure during CPAP titration trials. If the effective CPAP

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pressure is low, say 6 cm H2O, then there is a high chance of having a very effective MAD treatment.

Procedures Diagnostic procedures in sleep medicine start first with an assessment of complaints and symptoms regarding sleep behavior and sleep habits. Then follows an assessment of complaints and symptoms. In order to standardize this assessment, a large number of well-established and validated questionnaires are in use. The questionnaires are then followed by clinical investigations to assess clinical features associated with the sleep disorder. A recording of a few characteristic physiological signals at home follow to assess respiration and oxygen saturation during sleep. The last and final procedure in the diagnosis of sleep disorders and sleep-disordered breathing in particular is polysomnography (PSG) in a sleep laboratory or sleep center. This requires trained personnel for the recording and for the interpretation of the recorded data. A sleep center is able to diagnose all disorders as specified above.

Questionnaires A much used questionnaire covering many aspects with a focus on insomnia problems is the Pittsburgh Sleep Quality Index (PSQI) mentioned earlier already (Buysse et al. 1989). Another general and frequently used questionnaire is the Epworth sleepiness scale (ESS) (Johns 1991). This questionnaire consists of eight questions describing situations where one might fall asleep. The situations become increasingly severe and unwanted. Each question is answered with a likelihood of the situation ranging from zero to three. The maximum score is 24 then. Today a threshold of 11 is regarded as being sleepy more than normal. This questionnaire is used much in patients with central disorders of hypersomnolence and in patients with sleep-related breathing disorders. In patients with sleep-related breathing disorders, the ESS is recognized as not being sensitive and not

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being predictive for the diagnosis of sleep apnea. This is mainly due to the fact that sleepiness is not always a sign of sleep-related breathing disorders and that patients with sleep-related breathing disorders are not necessarily sleepy (Qaseem et al. 2014). However the association between sleeprelated breathing disorders and sleepiness is so intuitive that the application of the ESS in these patients remains to be very popular. If a patient complains about problems initiating and maintaining sleep, the Insomnia Severity Index (ISI) is used (Morin et al. 2011). This short questionnaire is now used in many pharmacological studies to assess the severity and interventional improvement of insomnia. In clinical sleep centers, this index is used to document insomnia and its severity. Sleep centers use the ISI often in combination with Beck Depression Inventory (BDI) in order to distinguish between depression and depression-induced insomnia and insomnia as the primary complaint. For the assessment of a periodic leg movement syndrome (PLMS) and restless legs (RLS) as part of the sleep-related movement disorders, a specific questionnaire has been developed. This RLSDI questionnaire is used in many studies and in clinical assessment of PLMS and RLS. This is a very useful and well-validated tool (Walters et al. 2003). Sleep-related breathing disorders have a high prevalence as previously stated (Peppard et al. 2013). There is a body of evidence that sleeprelated breathing disorders have cardiovascular consequences (Shahar et al. 2001; Marin et al. 2005). Therefore there is a clinical need to identify sleep apnea and treat patients if they suffer from moderate and severe sleep apnea (Mayer et al. 2017). Furthermore, evidence showed that surgical interventions of many kinds applied to patients with sleep-related breathing disorders result in much higher postsurgical complication rates. Therefore anesthesia guidelines recommend to assess sleep-related breathing disorders prior to elective surgical interventions. In pharmacological trials, there may be need to exclude sleeprelated breathing disorders on a fast and reliable basis. This assessment should be simple and should have a high sensitivity with adequate

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specificity. Exactly for this purpose, several questionnaires were developed and tested. A systematic review and meta-analysis of questionnaires had been performed and sensitivity and specificity had been listed (Abrishami et al. 2010). A more recent meta-analysis has resulted in a clinical guideline and can be used to review the range of studies using questionnaires and other methods as well (Qaseem et al. 2014). A questionnaire for screening for sleep apnea by anesthesiologists prior to surgical interventions had been presented and validated. This is the STOP-BANG questionnaire (Chung et al. 2008). Each letter stands for a question or finding. The STOP-BANG questionnaire asks for snoring (S), tiredness (T), observed and reported apnea events (O), being treated for high blood pressure (P), BMI higher than 35 kg/ m2 (B), age higher than 50 years (A), neck circumference higher than 40 cm (N), and male gender (G). Each positive answer adds a point. The risk for sleep apnea is increased if the score is 3 and higher (Ong et al. 2010). Sensitivity and specificity are given in Table 3. This questionnaire is regarded as having high sensitivity and specificity compared to other questionnaires. Another questionnaire developed for family physicians is the Berlin questionnaire (Netzer et al. 1999). This questionnaire was developed as a tool for general physicians to assess the risk for sleep apnea (compare Table 3). A very recent development is the NoSAS score which claims to be better than the STOP-BANG and the Berlin questionnaire (Marti-Soler et al. 2016). This questionnaire does not simply use yes/no questions but carefully assigns points to the same items used by the other questionnaires. Other research groups have developed clinical scores which combine clinical findings often seen in sleep apnea and a few questions to one apnea score (Flemons et al. 1994). In principle these scores are similar to the STOP-BANG questionnaire. The questionnaires described above were selected because they have a worldwide distribution and validated translation into many languages. Of course there are more, sometimes regionally popular, questionnaires. In order to become considerably better than questionnaires, which means to achieve a higher specificity, a

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recording of physiological signals over the night is required. This is done with medical devices allowing long-term recording, often denoted as ‘sleep lab’ meaning ‘polysomnography’. A simplified family of devices is denoted as polygraphy or home sleep apnea testing (HSAT). Practically, this method is now very much used in clinical practice to diagnose sleep apnea and to initiate treatment because it does not require a hospital or supervised bed. This method is described with more detail below.

Polysomnography The reference method for the diagnosis of sleeprelated breathing disorders is cardiorespiratory polysomnography. This method includes a quantitative assessment of sleep, respiration, vegetative functions, movements, and behavior. All physiological functions involved in sleep and in sleep disorders are quantitatively assessed. The method and its assessment are well described in the standardized manual of the AASM (Berry et al. 2016). The signals which track a physiological function for the duration of a night are listed in Table 4. Sensor methods were evaluated over the past and are now found to be reliable and satisfactory for good results. Electrode types, sensor types, and number of signals are well standardized, and polysomnographic equipment is available from several companies worldwide. Polysomnography is used both for research and clinical purposes. The biggest differences between systems are found in the flexibility of hardware and the accompanying software for the management of additional sensors and signals. This distinguishes systems which can be used for clinical purpose only and systems which can be used for research as well. Good software provides a good automatic analysis of signals which is used for a pre-evaluation in clinical work and which provides tools for quantitatively assessing signals for research purposes. The sleep recording needs to be evaluated visually according to the rules specified in the AASM manual (Berry et al. 2016). The recorded signals are displayed in 30 s epochs and then evaluated or

ESS >10 Berlin questionnaire STOP-BANG NoSAS Clinical score Home sleep testing

25

42

83 67

90

33 87

Specificity

86

Sensitivity

AHI > = 5

95.0 96.2

93.7

91.7

Positive predictive value

93 79 35 77

Sensitivity 39.0 91

AHI > = 15

28 69 78 95

Specificity 71.4 28 73.9 47 77.5 97.1

Positive predictive value 64.8 73.4

36 50

96

Sensitivity 46.1 89

AHI > = 30

72 93

21

Specificity 70.4 18

50.0 84.8

48.6

Positive predictive value 68.7 45.9

Table 3 Questionnaires for sleep-related breathing disorders. The values are given as percentages and stem from two studies (Silva et al. 2011 und Pereira et al. 2013). The “clinical score” is composed of snoring, age, blood pressure, and male gender (Flemons et al. 1994)

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Table 4 Functions and associated signals assessed during polysomnography Function Sleep

Respiration

Signal Electroencephalogram EEG Electrooculogram EOG Electromyogram mental EMG Airflow

Respiratory effort Oxygen SaO2 or pO2 Carbon dioxide CO2% or pCO2 Snoring Respiratory movement (indirect) Cardiac Movement

Behavior

Additional options

Electrocardiogram Electromyogram of legs and arms Body Video Voice Body position Body core temperature Gastric pH Electrodermal activity Blood pressure Pulse wave

Sensor Electrophysiological electrode: 3–6 standardized positions Electrophysiological electrode: Left and right eyes Electrophysiological electrode: 2 leads Temperature-sensing sensors at the nose and mouth Pressure sensing (nasal prongs) Pneumotachograph with full face mask Inductive plethysmography with two belts Esophageal pressure Pulse oximetry sensor Transcutaneous O2 End-tidal CO2 from expired air with ultrared absorption sensor Transcutaneous CO2 Microphone: Supraglottis or room Radar/microwave technology Matt in the bed or mattress sensors ECG-derived respiration Electrophysiological electrode: 1 lead Electrophysiological electrode Actigraphy or simple movement sensor Room camera Room microphone 3-D acceleration sensor Rectal or ear probe, thermo-pill Antimony sensor probe Resistance probe, no standard Finger photoplethysmography, pulse transit time as surrogate Optical pulse plethysmography/pulse oximetry sensor, no standard sensors

scored (Fig. 1). The sleep EEG with EOG and EMG is used to score sleep stages in categories wake (W) with alpha waves in the EEG; high muscle tone; light sleep (N1), which is transitional sleep with less than 50% of alpha waves lower muscle tone and rolling eye movements; light sleep with specific EEG patterns such as sleep spindles and K-complexes (N2); and slow-wave sleep or deep sleep with more than 20% of time per epoch with delta waves of a frequency between 0.5 Hz and 2 Hz and an amplitude of at least 75 microvolt (N3). The rapid-eye-movement sleep or REM-sleep is denoted as R and is characterized by mixed EEG waves, low muscle tone, and rapid eye movement. This sleep stage is associated with frequent reports of dreaming. The AASM manual sets rules how to score short

awakenings, termed as arousals from sleep. These arousals are scored based on EEG activations and reflect cortical activation only (Bonnet et al. 2007). The AASM manual also sets rules how to classify obstructive and central apnea events, obstructive and central hypopnea events, and hypoventilation. The manual sets rules for scoring movement events to classify leg movements and periodic leg movements. A separate chapter defines all the parameters which are needed for a polysomnography report. These are specified below. Cardiorespiratory polysomnography is attended by trained sleep nurses who can attach or readjust sensors when contact problems occur during the night. Sleep nurses attend all night in a sleep center to be available for patient calls as

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Fig. 1 Example of polysomnography with a 30 s epoch of REM sleep. Rapid eye movements can be observed in the EOG tracings (LOCA2 and ROCA1). A low muscle tone is

visible (CHIN1). The EEG shows in four leads (C3A2, C4A1, O1A2, O2A1) mixed frequency low amplitude activity which is characteristic for REM sleep

well. Even if simpler methods like questionnaires or home sleep apnea testing are used today, as soon as sleep-disordered breathing may be more complex or as soon as comorbidities are present in a particular patient, a polysomnography is required for a full assessment of the disorder (Qaseem et al. 2014; Mayer et al. 2017).

guideline has been released for the management of sleep-disordered breathing as well (Qaseem et al. 2014). The portable devices have achieved high sensitivity and specificity up to an extent that they are used for out-of-center diagnosis of sleep-related breathing disorders in the majority of patients under certain conditions (Collop et al. 2011). There is an ambition to diagnose or at least recognize sleep apnea even simpler than that. Short tests during daytime or questionnaires are desirable. A number of tests had been developed and questionnaires as well. The questionnaires had been described above. The guideline by the American college of physicians (ACP) evaluated the modalities for the diagnosis of sleep apnea and concluded with recommendations in how far portable monitoring can be used and in how far questionnaires, described above, can be used for sleep-related breathing disorders (Qaseem et al.

Home Sleep Apnea Testing – Polygraphy In order to reduce costs and expand the capacities for the diagnosis of sleep-disordered breathing, many portable monitoring devices for sleep apnea were developed over the last two decades. A systematic review and meta-analysis for these devices had been compiled (El Shayeb et al. 2014). A review combined with a clinical

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2014): “The ACP recommends a sleep study for patients with unexplained daytime sleepiness. The ACP recommends polysomnography for diagnostic testing in patients suspected of obstructive sleep apnea. ACP recommends portable sleep monitors in patients without serious comorbidities as an alternative to polysomnography where polysomnography is not available for diagnostic testing.” A more recent position statement of the American Academy of Sleep Medicine states that home sleep apnea testing (HSAT) has to be prescribed by a physician, based on medical history and face-to-face examination (Rosen et al. 2017). The raw data of HSAT must be reviewed by certified sleep physician. An automatic analysis of raw data is not sufficient. A HSAT is not recommended to screen asymptomatic populations. HSAT systems are used worldwide. In some regions of the world, mainly in Europe, they are called Polygraph (PG), a term derived from “polysomnography” but without the “somno” component because polygraphy does not record sleep with EEG, EOG, and EMG. To characterize and classify systems for the home sleep apnea testing, four levels had been introduced first (Flemons et al. 2003). When a cardiorespiratory polysomnography, as defined above, is meant, then this is called a level 1 diagnosis, which is the highest level with highest quality and with most effort made. Due to the requirement of attending personnel, this is performed in a sleep lab setting usually. If the same system is used at home or under experimental conditions outside a sleep lab, without attending personnel, then this is called a level 2 sleep assessment. A level 3 recording device describes a system which can record respiration (respiratory flow, respiratory movement, oxygen saturation), heart rate, and body position with usually 4–6 channels (Flemons et al. 2003). These systems were developed to record sleep apnea at home or elsewhere outside a sleep center. These are called HSAT or PG today. Simpler systems, such as an actigraphy, an oximetry with heart rate, and a respiratory flow recording, are called level 4 systems. They record typically 1–3 channels only. Developments during the last 10 years with sophisticated signal analysis from few signals allow to detect sleep apnea from few signals.

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Therefore another classification system had been introduced, the SCOPER system, which is based on assessment of functions instead of simply counting signals (Collop et al. 2011). The SCOPER system checks the validated assessment of sleep (S), cardiovascular functions (C), oxygen saturation (O), body position (P), respiratory effort (E), and respiratory flow (R). In order to see whether a specific system fulfills the criteria, validation studies are examined. With this it might be possible that a sophisticated analysis of pulse wave recording may reveal sleep stages in terms of wake, non-REM, REM sleep, heart rate, and respiratory effort to detect apnea events, all with one signal. Therefore this new SCOPER system is more appropriate to evaluate and assess usefulness of modern systems to diagnose sleep-related breathing disorders. A class of systems which did profit much from this new SCOPER classification where those making use of arterial peripheral tone based on finger pulse wave recordings (Yalamanchali et al. 2013). These systems use the finger pulse wave with a sophisticated analysis based on pulse amplitude changes and pulse-topulse interval changes over time instead of directly recording airflow or respiratory movement. The Watch-PAT system derives respiration, apnea events, both obstructive and central, and estimates non-REM and REM sleep from the pulse timing behavior. HSAT systems are those who fulfill the requirements to record sleeprelated breathing disorders with sufficient sensitivity and specificity. Using the conditions described above, HSAT systems now become the diagnostic tool of first choice to diagnose sleep apnea unless comorbidities that are present in sleep apnea need to be excluded (Rosen et al. 2017).

Evaluation (Parameters and Statistical Evaluation) The AASM manual explains how to evaluate sleep stages and all-related events. Beside this the manual specifies and defines parameters summarizing the evaluation and which should be included in a polysomnography report (Berry et al. 2016). The parameters and quantitative descriptions derived from polysomnography

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Fig. 2 A 5 min window with five obstructive apnea events is shown. The tracings from top to bottom present nasal pressure, thoracic movements, abdominal movements, oxygen saturation

relate to the following groups: global sleep metric, sleep stages, respiration, cardiovascular assessment, movement assessment, and further observations. Global sleep metrics are total recording time (TRT), the time between lights out and lights on, total sleep time (TST), and percent sleep efficiency, which is the ratio between TRT and TST. The sleep stages are specified by minutes in each sleep stage and percent related to TST, together with sleep latency, stage R latency, and wake after sleep onset. The arousals are quantified by total number and number related to TST, called as arousal index (ArI). Cardiac events are mainly noted as yes or no, and heart rate is given by

highest, lowest, and mean values. Limb movements are given as total number and as an index related to TST, both with and without arousal. The respiratory parameters are a long list. This starts with number of apnea events, obstructive, central, mixed, hypopnea events, obstructive, and central and a sum of all events. An important step in the evaluation is to distinguish the type of respiratory events. Obstructive apnea events are those where a cessation of airflow is detected, but respiratory effort continues as recognized by movements in thoracic and abdominal belt measurements (Fig. 2). Central apnea events are those where a cessation of airflow is detected and no respiratory

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Fig. 3 A 5 min window with central apnea events forming a Cheyne-Stokes breathing pattern is shown. The tracings from top to bottom present nasal pressure, thoracic movements, abdominal movements, oxygen saturation

effort is found as recognized by movements in thoracic and abdominal belt measurement (Fig. 3). However both types of events are accompanied by drops in oxygen saturation (SpO2). Usually obstructive apnea events are longer (30 to 60 seconds) compared to central apnea events (20 to 40 seconds). Correspondingly the drop in oxygen saturation, the desaturation is somewhat less in central apnea events. If the respiratory flow in cases of central sleep apnea forms a spindle like crescendo decrescendo pattern with a cycle length of 40 seconds or more, then this is called CheyneStokes breathing (Fig. 3). This is typically found in patients with heart failure. The apnea-hypopnea

index (AHI) is the sum of all events per TST. For the other event types, equivalent indices are calculated. As such there is an apnea index (AI) for obstructive, mixed, and central apneas. Then respiratory effort-related events and oxygen desaturation events are counted. The occurrence of hypoventilation, Cheyne-Stokes breathing, periodic breathing, and snoring is noted. Possibly additional oxygen saturation statistics can be presented. The complete list of parameters with definitions is specified in the AASM manual for scoring of sleep and associated events (Berry et al. 2016).

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Critical Assessment of the Sleep Recording In the beginning of sleep research and sleep medicine polysomnography was the investigational tool for sleep centers. As sleep research moved into clinical sleep medicine, practical and economic aspects became more and more important. Today sleep apnea is mainly diagnosed using home sleep apnea testing (HSAT) as explained in more detail above. The role of polysomnography in the diagnosis of sleep apnea is decreasing (Mayer et al. 2017). This process is continuing and not finished (Hirshkowitz 2016). With technological developments, home sleep apnea testing becomes more sophisticated and as a consequence more sensitive and specific. This process does not only apply to sleep-related breathing disorders but also to other groups of sleep disorders. With new smartphone applications, it is possible to track sleep. However most smartphone applications were developed intuitively according to the thought: activity correlates to wake and no activity correlates to sleep. The lowest activity might correlate to very deep sleep. This does really reflect our knowledge about slow-wave sleep and REM sleep. Few smartphone apps had been validated. However this technological field is improving quickly. New apps make use of additional sensors like camera, noise, and external bed matts, and external pulse wave sensors. And new apps are validated against polysomnography. These new apps cross the line between simple gadgets for wellness and lifestyle to medical useful devices. It is possible that new apps are able to track sleep and wake and sleep problems over prolonged periods of time with adequate accuracy. However a good validation against polysomnography and considering the specific group of subjects being investigated is always needed. With these recent developments, the role of polysomnography in future may no longer be clinical routine recordings. But polysomnography may become a validation tool and a research tool again (Hirshkowitz 2016). An important discussion is currently ongoing about the role of the AHI as a severity parameter for sleep apnea (Penzel et al. 2015). In view of the

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high prevalence as reported by some epidemiological studies (Heinzer et al. 2015) and in view of not showing any positive effect on cardiovascular mortality when treating patients with CPAP just according to their high AHI (McEvoy et al. 2016), there is some concerns that the AHI may not be the optimal parameter to express the severity of the disease. While this had been assumed for long and while it is clear that the pathophysiology is linked to the collapsibility of the upper airways (Eckert et al. 2014), the AHI was regarded as a simple and reliable surrogate for the severity of the disease. Previous studies did show that survival over 12 years is associated with the AHI and that an effective lowering of AHI by CPAP did result in much lower mortality and morbidity (Marin et al. 2005). Only with the recent studies, this parameter is challenged. Therefore a discussion is in place that previous studies did show such impressive beneficial results, because studies were performed on clinical populations seeking help in a hospital setting. And as soon as subjects without symptoms and without complaints are investigated and if they are treated according to AHI only, then these positive effects may disappear. There are thoughts that the AHI may be an indicator for an increased cardiovascular risk and that the elevated AHI must be seen together with the complete clinical picture with other symptoms and findings together. In order to clarify these different mechanisms, the model currently under development is that there are different phenotypes with obstructive sleep apnea (Penzel et al. 2015). Some subjects may develop sleep apnea as part of normal aging of the upper airways. Other subjects develop obstructive sleep apnea as an accompanying factor with obesity. Another group develops obstructive sleep apnea due to morphological retrognathia. Another group may suffer from a narrow upper airway or a more collapsible upper airway with excessive soft tissue. There may be also a group with neural deficits in respiratory regulation during sleep. A hypersensitivity to CO2 may cause a hyperventilation and then an apnea as a compensation to hyperventilation. This compensation would be physiologic because during sleep, the respiratory system tolerates higher CO2 levels. These concepts may form a

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number of different phenotypes, which, until now, are not well defined and characterized.

Alternative Treatments for Sleep Apnea The first-line treatment for obstructive sleep apnea is CPAP. CPAP with mechanical splinting of the upper airways has a very high effectiveness, if patient compliance with therapy is good. Since many patients cannot tolerate the nasal mask, the second choice for treatment is an individually fitted mandibular advancement device. Oral appliances or mandibular advancement devices are very popular and much better accepted by a majority of patients because they are more comfortable than a nasal mask. However this treatment is less effective (Schwartz et al. 2017). On average the AHI is lowered by 50%. Since the mandibular advancement device causes a protrusion of the lower jaw, a widening of the upper airway space is achieved. This widening is partial and may be enough to overcome the upper airway collapse. However it may be not enough in some subjects, and then apnea events are converted into hypopnea events. Hypopnea events might be converted into snoring. A conversion was not achieved because the widening by oral appliances was by far not enough. Accordingly there is a high variability across patient groups. Unfortunately the selection criteria for finding those patients who benefit most are not clear before treatment initiation. Some patients cannot tolerate an oral appliance in their mouth over the night. Other patients may not wear such a device due to their tooth conditions. If this therapy of second choice cannot be applied, then other alternatives need to be investigated. Shortly after sleep apnea was discovered and about at the same time as CPAP was invented, a concept to open the upper airway by electrical stimulation of the corresponding nerve was patented. A nervus hypoglossus stimulating device was developed and first tested in models and soon thereafter applied to humans with obstructive sleep apnea (Schwartz et al. 2001). This pivotal trial in eight subjects proved the success of the

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concept. A few technological problems were detected when these eight subjects continued to use the nervus hypoglossus stimulation over an extended period of time. The principle was a sensing of inspiratory effort and a stimulation of the hypoglossus nerve during inspiration on one side only. The stimulation causes a protrusion of the tongue, and with this protrusion, the upper airways are widened and tissue becomes stiffer during the stimulation phase. A remote control and a timer in the stimulating device took care that the stimulation period was synchronized with the sleep period. It became clear that the positioning of the stimulating electrodes, the cuff around the nerve, had to be placed carefully on the right position in order to achieve maximum effects. The pivotal trial did show that widening by stimulation could lower AHI by 50% in average. Only a couple of years later, the idea was picked up again, and three competing companies brought devices into clinical practice to be evaluated in large trials in order to achieve FDA approval. Two companies did achieve FDA approval in the meantime. The large studies did fulfill safety, security, and efficiency expectations. If patients were carefully selected according to weight (BMI < 35 kg/m2) and according to a reactive and oval-shaped upper airway, then the effectiveness of the stimulation treatment to lower AHI was somewhat higher than 50% (Strollo et al. 2014). The device is expensive compared to CPAP, and the procedure is invasive compared to CPAP. The titration is similar to CPAP, because during the titration night, the optimal electrodes for stimulation must be identified and the optimal current for stimulation must be set in order to achieve maximal widening and no arousal. Patients with central apnea and Cheyne-Stokes respiration under conditions of heart failure may be treated for their heart failure first. If patients treated for heart failure according to guidelines and if Cheyne-Stokes respiration persists, then a trial of CPAP or other ventilation may be initiated if the LVEF is not below 45%. A new approach in these patients with central sleep apnea is to test pharyngeal nerve stimulation. However studies with this treatment are very small and long-term studies are missing. This phrenic nerve

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stimulation therapy for Cheyne-Stokes respiration is seeking FDA approval now. A considerable number of studies with various kinds of surgical interventions were performed in the past. Maxillofacial surgery was able to widen the upper airways effectively. If patients were carefully selected, then this procedure was highly effective. However only few patients do fulfill the restrictive selection criteria for this intense surgical procedure. Other ENT-related surgical procedures had the upper airways as a target. The therapeutic principle was always to remove obstacles in the upper airways to allow breathing during sleep. A much used method was the uvulopalatopharyngoplasty (UPPP) and also adenotonsillectomy. These surgical procedures, and their variants, had only partial success. The AHI was lowered by 30–50% depending on the study. In these cases only mechanical obstacles can be removed. The neural and functional components of upper airway collapse are impossible to be treated surgical. And unfortunately, a prediction on who might benefit more and who benefits less from surgical procedures could not be established despite many efforts. Positional therapy is useful when sleep apnea has a major positional component. It is estimated that about 10% of patients with obstructive sleep apnea have a largely position dependent sleep apnea with more events or with a higher collapsibility when sleeping supine. These patients may benefit from positional trainers such as pillow vest or electronic devices which train the sleeping subject to avoid the supine position. There are only few pharmacological therapy approaches in place. A good overview is provided by Gautier et al. (2017). Antihypertensive drugs may have some beneficial effects on sleep apnea. A few experimental trials could show only very small effects. Sleep apnea is associated with inflammatory processes and may potentially lead to atherosclerosis following hypoxia stress (Lavie 2003). Anti-inflammatory drug treatment has not been evaluated for effects on sleep apnea. Acetazolamide has been investigated in patients with sleep apnea both at normal and high altitude. A recent meta-analysis has investigated the effect of

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acetazolamide on sleep apnea in high altitude and found that acetazolamide does reduce apnea in terms of AHI but is more beneficial in healthy subjects than in subjects with sleep apnea (Liu et al. 2017). Anti-oxidative drugs may have a beneficial effect because they follow the same concept to prevent inflammatory consequences of the repeated intermittent hypoxia. Several studies are conducted now to systematically evaluate effects of several drugs. Another approach for drug applications is to influence local receptors of the upper airways (Wirth et al. 2013). However human trials have not been presented. In summary pharmacological treatment will have a good chance as another alternative therapy. Especially in view of the change in concept for sleep apnea, there may be phenotypes where sleep apnea is not so severe but still annoying. If sleep apnea is reduced then potentially the corresponding increase in cardiovascular risk is reduced as well. Therefore we can expect beneficial effects of pharmacological treatment, if proven to be successful.

References and Further Reading AASM (2014) International classification of sleep disorders. Third edition (ICSD-3). American Academy of Sleep Medicine, Darien Abrishami A, Khajehdehi A, Chung FA (2010) Systemic review of screening questionnaires for obstructive sleep apnea. Can J Anesth 85:423–438 Berry RB, Brooks R, Gamaldo CE, Harding SM, Lloyd RM, Marcus CL, Vaughn BV, for the American Academy of Sleep Medicine (2016) The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, version 2.3. American Academy of Sleep Medicine, Darien Bonnet MH, Doghramji K, Roehrs T, Stepanski EJ, Sheldon SH, Walters AS, Wise M, Chesson AL Jr (2007) The scoring of arousal in sleep: reliability, validity, and alternatives. J Clin Sleep Med 3:133–145 Buysse DJ, Reynolds ICF, Monk TH et al (1989) The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res 28:193–213 Chung F, Yegneswaran B, Liao P, Vairavanathan S, lslam S, Khajehdehi A, Shapiro CM (2008) STOP questionnaire a tool to screen patients for obstructive sleep apnea. Anesthesiology 108:812–821

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242 disturbances: findings from the Sleep Heart Health Study. Diabetes Care 26:702–709 Rosen IM, Kirsch DB, Chervin RD, Carden KA, Ramar K, Aurora RN, Kristo DA, Malhotra RK, Martin JL, Olson EJ, Rosen CL, Rowley JA, American Academy of Sleep Medicine Board of Directors (2017) Clinical use of a home sleep apnea test: an American Academy of sleep medicine position statement. J Clin Sleep Med 13:1205–1207 Sanders MH, Montserrat JM, Farre R et al (2008) Positive pressure therapy: a perspective on evidence-based outcomes and methods of application. Proc Am Thorac Soc 5:161–172 Schlack R, Hapke U, Maske U, Busch MA, Cohrs S (2013) Häufigkeit und Verteilung von Schlafproblemen und Insomnie in der deutschen Erwachsenenbevölkerung. Bundesgesundheitsblatt 6:740–748 Schwartz AR, Bennett ML, Smith PL, De Backer W, Hedner J, Boudewyns A, Van de Heyning P, Ejnell H, Hochban W, Knaack L, Podszus T, Penzel T, Peter JH, Goding GS, Erickson DJ, Testerman R, Ottenhoff F, Eisele DW (2001) Therapeutic electrical stimulation of the hypoglossal nerve in obstructive sleep apnea. Arch Otolaryngol Head Neck Surg 127:1216–1223 Schwartz M, Acosta L, Hung YL, Padilla M, Enciso R (2017) Effects of CPAP and mandibular advancement device treatment in obstructive sleep apnea patients: a systematic review and meta-analysis. Sleep Breath. https://doi.org/10.1007/s11325-017-1590-6 Shahar E, Whitney CW, Redline S, Lee ET, Newman AB, Nieto FJ, O’Connor GT, Boland LL, Schwartz JE, Samet JM (2001) Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med 163:19–25 Silva GE, Vana KD, Goodwin JL et al (2011) Identification of patients with sleep disordered breathing: comparing

T. Penzel and I. Fietze the four-variable screening tool, STOP, STOP-Bang, and Epworth Sleepiness Scales. J Clin Sleep Med 7:467–472 Strollo PJ Jr, Soose RJ, Maurer JT, de Vries N, Cornelius J, Froymovich O, Hanson RD, Padhya TA, Steward DL, Gillespie MB, Woodson BT, Van de Heyning PH, Goetting MG, Vanderveken OM, Feldman N, Knaack L, Strohl KP, STAR Trial Group (2014) Upper-airway stimulation for obstructive sleep apnea. N Engl J Med 370:139–149 Sullivan CE, Issa FG, Berthon-Jones M, Eves L (1981) Reversal of obstructive sleep apnoea by continuous positive airway pressure applied through the nares. Lancet 1(8225):862–865 Walters AS, LeBrocq C, Dhar A et al (2003) Validation of the international restless legs syndrome study group rating scale for restless legs syndrome. Sleep Med 4:121–132 Wirth KJ, Steinmeyer K, Ruetten H (2013) Sensitization of upper airway mechanoreceptors as a new pharmacologic principle to treat obstructive sleep apnea: investigations with AVE0118 in anesthetized pigs. Sleep 36:699–708 Yalamanchali S, Farajian V, Hamilton C, Pott TR, Samuelson CG, Friedman M (2013) Diagnosis of obstructive sleep apnea by peripheral arterial tonometry: metaanalysis. JAMA Otolaryngol Head Neck Surg 139:1343–1350 Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S (1993) The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 328:1230–1235 Young T, Peppard P, Palta M, Hla KM, Finn L, Morgan B, Skatrud J (1997) Population-based study of sleep-disordered breathing as a risk factor for hypertension. Arch Intern Med 157:1746–1752

Pharmacodynamic Evaluation: Diabetic Methodologies

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Juergen Sandow

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 Clinical Insulin Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Simple Insulin Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248 Glucose Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 Insulin Pumps with Glucose Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250 Clinical Studies with Bihormonal Insulin Pumps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Glucagon (Emergency and Bionic Pumps) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 GLP1 Agonists (Incretin Mimetics, Peptide Analogs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 Exenatide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Liraglutide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Lixisenatide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Dulaglutide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Semaglutide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254 Insulins and GLP1 Agonists (Coformulations) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Summary and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256

J. Sandow (*) Centre of Pharmacology, Frankfurt-Main University, Glashuetten, Germany e-mail: [email protected] © Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_33

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Abstract

Here, the new aspects of diabetes methodology will be reviewed with reference to injection therapy, using analog insulins, GLP1 agonists (incretin mimetics), and the coformulation of incretin mimetics and insulins in the same solution. The options of diabetes therapy with sensor-augmented insulin pumps and the clinical pharmacology of bihormonal insulin pumps dispensing insulin and glucagon as required will be presented (bionic pumps). The specific aspect of advanced insulin pump therapy is the use of algorithms which control the insulin infusion rate and initiate shutdown of insulin infusion when impending hypoglycemia is detected. Bionic pumps (bihormonal) may also provide a stable glucagon solution for fine-tuning of glucose regulation and emergency release, in case of hypoglycemia developing rapidly or inadvertently, in particular during sleep at night time.

Introduction Here, the new aspects of diabetes methodology will be reviewed with reference to injection therapy, using analog insulins, GLP1 agonists (incretin mimetics), and the coformulation of incretin mimetics and insulins in the same solution. The options of diabetes therapy with sensor-augmented insulin pumps and the clinical pharmacology of bihormonal insulin pumps dispensing insulin and glucagon as required will be presented (bionic pumps). The specific aspect of advanced insulin pump therapy is the use of algorithms which control the insulin infusion rate and initiate shutdown of insulin infusion when impending hypoglycemia is detected. Bionic pumps (bihormonal) may also provide a stable glucagon solution for fine tuning of glucose regulation and emergency release, in case of hypoglycemia developing rapidly or inadvertently, in particular during sleep at night time. The insulin therapy in type I diabetic patients is mandatory, and early insulin therapy is now extended to the combination with incretin mimetics. This will decrease the insulin dose, avoid

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hypoglycemia, and reduce body weight reduction when possible. In advanced type II diabetic patients, early insulin therapy is recommended for efficacy during the initial period of restoring glucose control. Injections may be more acceptable and patient friendly when using the combination of incretin mimetics plus insulin. When required for efficacy, this injection therapy is supported by orally active antidiabetic drugs (OADs). Clearly, at advanced stages of type II diabetes, injection therapy is mandatory. It is essential to achieve metabolic control within a clinically acceptable timeframe (target 6 months) and avoid therapeutic inertia. Failure to proceed from prolonged use of orally active antidiabetic drugs (OADs) to injection therapy – at least for the urgent initial correction of glucose control – is often addressed as therapeutic inertia (Harris et al. 2010; Khunti et al. 2013; Khunti and Millar-Jones 2017). The long delay when relying on the procedure of diet and change of lifestyle is harmful for the patient. Learning how to cope with diabetes in an effective manner is an important aspect (patient education). To implement early and effective support by pharmacotherapy is essential (The DCCT Research Group 1993), in particular to start injection therapy when HbA1c is of the order of 8.0 or above. In the previous edition (Becker 2011), clinical pharmacology of insulin preparations was discussed extensively, including attempts to find therapy other than by injection (e.g., insulin therapy by inhalation). Here, we will focus on efficient injection therapy and state-of-the-art insulin pumps. There has been a very significant contribution of clinical pharmacology studies to rapid and highly effective exploration of insulin pens (coformulation of insulin and GLP1 agonists) and sensor-controlled insulin pumps. Glucose concentrations are controlled continuously (CSGM), replacing glucometers and repeated daily finger sticks by glucose sensors for immediate access and storage of 24-h glucose profiles. Insulin pumps are coupled to a sensor for automated adaptation to a target glucose range (hybrid-Aid systems), and “time in range” is an important parameter of system performance when developing new algorithms (Fig. 1).

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Fig. 1 State-of-the-art insulin pump controlled by sensor and guardian link (cablefree transmitter)

The interest of clinical pharmacology is moving from profiling of new antidiabetic compounds (preferably orally active) to patient-friendly injection therapy with glucose sensors and modern insulin pumps (Blak et al. 2012). In simple terms, “fear of injection” which was a major concern of diabetic patients before changing to insulin is no longer of practical relevance. Very extensive clinical pharmacology studies are required for the “intelligent pumps” with algorithms which adapt the insulin infusion rate to changes in monitored glucose concentrations. This is essential for automated dose adaptation. The patient is no longer under stress to perform the necessary changes at frequent intervals. The key concern of insulin-induced hypoglycemia (Briscoe and Davis 2006; Buckingham et al. 2008) is now under better control, in particular when bihormonal pumps become available (e.g., Ilet pump), which deliver fast-acting insulins and a stable glucagon solution at short intervals (mini bolus). Bihormonal pumps will provide solutions for improving dose adaptation in children with diabetes (Patterson et al. 2009) and in adults with rapidly changing insulin requirements, e.g., in sports and strenuous exercise. Automated shutdown of insulin pumps during impending hypoglycemia was an important step forward (e.g., Minimed 670G) enabled by algorithm control. In particular, at nighttime, it was helpful and reassuring, to have a monitoring system alerting parents and relatives (“mySentry”) even when

sleeping in an adjacent room close to the child. An alert function is now part of the Freestyle Libre II glucose sensor system. The key aspect in type II diabetes is the change from basal analog insulin to GLP1 agonists (incretin mimetics, incretin analogs) for injection once or twice per day (QD), and formulations for injection once per week (QW). Treatment is initiated early in type II patients, at an advanced stage. This development was enabled by GLP1 agonists derived from natural gastrointestinal hormones (incretins), modified for prolonged action due to enzyme resistance. The long-term adherence to injection therapy is a very important characteristic (Asche et al. 2011) of the new GLP1 agonists (Chandran et al. 2015), and it represents the major improvement for advanced stages of type II diabetes. The key is to prevent progression of comorbidities associated closely with type I and type II diabetes, in particular cardiovascular comorbidity and end-stage renal disease with dependence on dialysis. A further step forward is to reduce the dose of insulin required for effective and consistent metabolic control. This is achieved by combining injection of GLP1 agonists with analog insulins in the same solution for injection, a pharmaceutical development addressed as coformulation (Kalra and Gupta 2015). Current examples are iDeg-Lira and iGlar-Lixi frequently used in clinical medicine. Here is the most attractive potential for patients with type II diabetes at an advanced stage. The term “fear of

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needle” is disappearing because insulin pens and similar pens for coformulation solutions use small and thin needles which are almost devoid of pain of injection and enable injection at different sites, e.g., arms or abdomen with the same efficiency. In simple terms, extended clinical pharmacology studies have become more acceptable to test persons and diabetic patients because of modern application devices (pain-free injection with longer intervals between injections, e.g., once per week). Practical lifetime of glucose sensors is extended from 3 days to 1 week or longer; there are now advanced sensors for subcutaneous implantation with a practical lifetime of 6 months (Senseonics, Kropff et al. 2017). The sensors provide an alarm in case of impending hypoglycemia (e.g., Freestyle Libre II), which is of particular relevance at nighttime. Current insulin pumps are controlled by glucose sensors to detect the risk of impending hypoglycemia. These sensor-augmented pumps (SAP) shut down for a pre-defined time period when hypoglycemia is detected (e.g., Minimed system) (Fig. 2). Continuous subcutaneous glucose monitoring (CSGM) with sensors provides minute to minute data for the patient to be monitored, e.g., on the glucose meter or recorded continuously on a

Fig. 2 Sensor-augmented pumps (SAP) shut down when hypoglycemia is detected (Minimed system)

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smartphone (Pankowska et al. 2005). In clinical studies, digital recording and storage of profiles is essential for submission to FDA and EMA in product applications.

Clinical Insulin Therapy Within about 10 years, there has been a major development in the aspects of clinical insulin therapy. It is well understood that prolonged treatment with orally active antidiabetic agents may not provide the intended correction of glucose regulation in due time (therapeutic inertia); the associated diabetes-related comorbidities identified by cardiovascular risk factors and lipid profile may persist indefinitely. The frequently invoked “fear of injections “is no longer relevant because insulin pens and more advanced injection devices are patient friendly and easily accepted by patients. Development and progression of comorbidities is under excellent control, provided that the decision for early injection therapy is implemented early on. Adherence to long-term injection therapy is readily established due to once per week (QW) coformulations of GLP1 agonists (Barnett 2013). This review will therefore focus on early injection therapy in type II diabetes patients and on improvements for injection therapy of type I diabetes patients. Correction of glucose profile should be achieved whenever possible within 6 months after starting treatment, preferably with insulins when HbA1c is >8.0 and rising (American Diabetes Association ADA – Standards of Care 2019, European guidelines and related guidelines of International Diabetes Federation IDF). The methods applied in clinical pharmacology have adapted accordingly from characterizing the pharmacokinetic-pharmacodynamic profile of a new compound (PK-PD) toward phase 2 and extended phase 3 trials. Very often there is the need for a comparative design, including frequently used products as comparators (ADA 2019). This development follows the requirements of regulatory agencies (FDA, EMA) to submit comparative trials. The need for early insulin therapy in type I diabetes, in particular in children and adolescents, is well understood. Clinical efficacy is achieved

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with long-acting and short-acting analog insulins for basal insulin support and prandial insulin control, in a similar manner as for biosynthetic human insulin (Sandow et al. 2015; Landgraf and Sandow 2016; Mbanya et al. 2017). Glucose monitoring by parents and family members is much improved by the change to continuous subcutaneous glucose monitoring (CSGM) as an essential element. Clinical pharmacology has focused on the evaluation of suitable devices (sensor-augmented insulin pumps and bihormonal insulin pumps delivering insulin and stable glucagon solution as required) rather than on extending the range of analog insulin products. In particular attempts to replace injections by, e.g., insulin inhalation, were not successful and may have limited relevance in the future. Highly effective but very costly solutions like intraperitoneal insulin pumps with filling by subcutaneous ports are available, but cost and complexity is prohibitive. Most suitable and effective devices for children are insulin pumps, supported by CSGM sensors (sensor-augmented), and automatic shutdown of insulin pumps is enabled when glucose is decreasing rapidly (e.g., Minimed 630G and 670G). The critical factor in clinical safety is hypoglycemia, for instance, under stress and increased glucose requirements in sports, particularly hypoglycemia when it remains unrecognized at nighttime (Briscoe and Davis 2006; Buckingham et al. 2008; Lipska et al. 2017). The critical limitation in patients with type II diabetes is fear of injection which often delays the decision for therapy, at an advanced stage when only insulin therapy is effective to establish and maintain long-term glucose control. Once started, insulin therapy may be combined with orally active antidiabetic drugs (OAD combination therapy), or insulin may be injected together with a GLP1 agonist (insulin plus incretin mimetics as a coformulation). A particular problem for clinical pharmacology studies is the selection of patients at an advanced state of obesity, where results of clinical pharmacology in obesity may differ markedly from those with regular body weight and composition (Vilsbøll et al. 2012). Comorbidities are addressed in the study design, e.g., by selection of patients with cardiovascular disease and

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increased risk factors (Mannucci and Monami 2017; Dalsgaard et al. 2017), patients with impaired kidney function (Mann et al. 2017), and other groups with diabetes-associated comorbidities (e.g., diabetic retinopathy). The current state of clinical pharmacology for diabetes therapy with injectable products has changed markedly during the period from 2005 to 2019. The change was initiated by insulin pumps with controlled subcutaneous delivery, using fastacting insulins to enhanced subcutaneous absorption (Haidar et al. 2013). The most critical problem of insulin-induced hypoglycemia is in part resolved by automatic suspension of pump action when algorithms detect a fast decrease in glucose concentrations and impending hypoglycemia (Hovorka et al. 2010; Kumareswaran et al. 2014). Clinical pharmacology studies are directed toward algorithm controlled sensor-augmented insulin pumps. Clearly, there are essential aspects of patient-friendly therapy, avoiding the dangers of hypoglycemia, providing devices for subcutaneous insulin administration based on highly efficient algorithms for dosage control, at the same time offering a patient-friendly device and long-term patient satisfaction (Elleri et al. 2013; Luijf et al. 2013; Fig. 3). This development critically depends on improving sensors for glucose concentrations (Leelarathna et al. 2014; Tauschmann and Hovorka 2017).

Fig. 3 Small insulin pump (t-slim) with Dexcom sensor and smartphone monitoring

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Fig. 4 Freestyle Libre sensor with reading device, 14 days use period. Hypoglycemia alert available

Sensors are now developed to an amazing level of precision and – at the same time – patient convenience. The route of administration for insulin remains subcutaneous injection or infusion. Intraperitoneal administration requires devices which are very expensive and difficult to handle. Patient acceptance has become the key word of new developments. The starting position is generally a glucose sensor which operates for 2 weeks (e.g., Freestyle Libre I or II) and removes the need for multiple daily finger pricks previously required for effective fine tuning of insulin dosage (Fig. 4). For basal therapy, there is a wide selection of basal insulins to provide 24-h coverage with the single injection and increased insulin concentrations for patients with significant clinical obesity. In general terms, access to insulin in industrialized countries represents the challenge of selecting wisely from a wide range of options. Pricing of insulins and coverage by insurance is a general problem not addressed here. Health economics have clearly established the benefits of early insulin therapy, in particular confirming the durability of effects obtained and the efficient prevention of progression of diabetes-associated comorbidities. The need for hospital admissions due to insulinrelated complications is now markedly reduced. In terms of clinical insulin therapy, the development of biosimilar insulins has contributed to methods of biosynthesis but not to methods of clinical pharmacology evaluation, except that antigenicity has become a more important characteristic. Studies on the antigenicity of biosimilar glargine products are required even for a similar

product from different production sites (North America or Europe). The clinical pharmacology of recombinant human insulin products has been reviewed in detail; they are characterized by clamp studies and in a few instances by direct studies for their PK-PD profiles in healthy subjects (Frank and Chance 1993; Sandow et al. 2015; Landgraf and Sandow 2016). For initial insulin substitution, basal analog insulins are preferred for convenience of handling and superior time-action-profile. Insulin degludec (Heise et al. 2012; Haahr and Heise 2014; Marso et al. 2017) was selected for the first coformulation product in solution together with the GLP1 agonist, liraglutide (Neumiller and Campbell 2009). As an example for the clinical benefit of coformulations, recent studies with iDegLira have confirmed the relevance of this approach in patients with type II diabetes (Buse et al. 2009, 2010, 2013, 2014). The dose of insulin may be reduced in coformulation because of the synergistic action of the GLP1 agonist on basal and prandial glucose secretion, the risk of clinically significant hypoglycemia is reduced, and weight reduction may be established as an additional benefit of the new therapeutic approach (depending on the GLP1 agonist selected in the coformulation).

Simple Insulin Pumps Here is a different approach, how to adapt insulin dosing at short intervals to the rapidly changing glucose requirements in particular of younger people, in sports, in competition of athletes, and to the challenges of modern business life with an

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Fig. 5 OmniPod patch pump with reader and monitoring device, wearing of the patch pump on arm or leg permissible for convenience

increasing stress load and frequent travelling. Simple pumps deliver a steady infusion rate and were first developed for pediatric indications (Maniatis et al. 2001; Ahern et al. 2002; Willi et al. 2003; Weinzimer et al. 2008). They require manual adjustment of the insulin delivery rate, their effect on glucose is monitored by the conventional procedure of frequent finger sticks. More convenient glucose sensors for continuous subcutaneous glucose monitoring (CSGM) offer a reading of the actual subcutaneous glucose concentrations, e.g., on a smartphone or dedicated glucose meter. The current generation of insulin pumps (sensor-augmented) is using glucose sensors to detect the risk of impending and developing hypoglycemia. When signals indicate a rapidly decreasing plasma glucose concentration, the pump shuts down for a pre-defined time period (Minimed 670G). Continuous subcutaneous glucose monitoring (CSGM) with sensors provides minute to minute data for the patient, data to be monitored, e.g., on a glucose meter or view continuously on a smartphone. This procedure also initiates recording of glucose profiles in clinical studies. A specific development has been the technology of patch pumps, which are very small and inconspicuous (Anhalt and Bohannon 2010; Buckingham et al. 2018); they require a minimum of tubing. Access to subcutaneous blood glucose data is directly from the pump, and electronic data transfer to the specific glucometer is very convenient (OmniPod Hybrid Closed-Loop System). Patch pumps are a welcome option for young people because they are inconspicuous and

provide reliable insulin support with an acceptable amount of initial training (Fig. 5).

Glucose Sensors Glucose sensors are currently developed to the stage of being active for 6 months after subcutaneous insertion of the suitable small device (Kropff et al. 2017). This requires currently a brief surgical procedure, which is well accepted by diabetic patients. At a practical level, subcutaneous glucose sensors have been available and very efficiently used for about 20 years. They detect dangers of hypoglycemia, control the rate of devices for subcutaneous insulin administration and provide input for highly efficient algorithms of dosage control. Early warning when hypoglycemia is developing has been the critical and important component in diabetes methodology, in particular during nighttime in children, when devices provide an early alert. A major step forward was the mySentry™ system coupled to an insulin pump (Kaiserman et al. 2013, Cengiz 2013). This system provided a warning for parents and relatives even when children were sleeping in a separate room (MiniMed Paradigm REAL-Time system). Progress of adapting insulin delivery critically depends on coupled sensors for glucose concentrations, because they achieve an amazing level of precision and markedly improve patient convenience (Thabit et al. 2015). This is addressed as sensoraugmented dosage control. The route of insulin administration will remain subcutaneous injection

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or infusion whenever the effective rate control of pumps is required. Intraperitoneal insulin pumps are very expensive and difficult to handle. The concept of an “artificial pancreas” has been fully implemented with subcutaneous infusion and fast-acting insulins. When considering patient acceptance as the key element of new developments, the starting position for improving insulin therapy is to replace or supplement conventional glucose meter by a glucose sensor (Koschinsky and Heinemann 2001) which operates for 2 weeks (e.g., Freestyle Sensor, Weinstein et al. 2007; Garg et al. 2009). This provides immediate information of glucose concentrations (e.g., Freestyle Libre sensor) and enhances effective fine tuning of insulin dosage by the patient. Long-term glucose sensors are currently developed up to six months use (Senseonics); they require subcutaneous insertion of a suitable small device (Kropff et al. 2017, Senseonics). At a practical level, subcutaneous glucose sensors are now very efficiently coupled to insulin pumps with an ever-increasing convenience and reliability (Weinstein et al. 2005; Wilson et al. 2007; Wolpert 2008; O’Connell et al. 2009). With algorithms for subcutaneous insulin dosing, modern pumps apply fast-acting insulins because their insulin absorption profile provides an option for reliable 24-h control, in particular for children with diabetes. Diabetes methodology is concentrating on well-tuned controlled delivery devices. The cost will remain limiting for years to come for reasons of price and lack of reimbursement; extensive clinical studies are required to confirm the long-term advantage and safety for diabetes patients at risk. The most advanced device using aspart insulin and in the second pump dasiglucagon is in the process of applying for an FDA product license (Ilet pump, developed by the group of Damiano based on experience with children and adults, Castle et al. 2019). Briefly, insulin pumps with sensor control are being evaluated in extensive clinical studies for dose adaptation and have been widely introduced into clinical medicine. One overreaching aspect is early detection of hypoglycemia at night and during exercise, the most critical aspect of long-term efficient insulin therapy.

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Insulin Pumps with Glucose Sensors Glucose sensors are coupled to insulin pumps with an ever-increasing convenience and reliability to control insulin dosing (Tamborlane et al. 2008; Bailey et al. 2009; Garg et al. 2009; Aye et al. 2010; Bergenstal et al. 2010; Banerji and Dunn 2013; Castle et al. 2019). Sensor-augmented pumps (SAP) apply fast-acting insulin, e.g., aspart insulin, because the absorption profile is well characterized and provides an option for reliable 24-h control, in particular for children with diabetes (Boyne et al. 2003; Plotnick et al. 2003; Sulli and Shashaj 2003; Swan et al. 2008, 2009; Hirsch et al. 2008a, b; Kordonouri et al. 2010; JDRF 2009). Diabetes methodology is now concentrating to a high extent on well-tuned delivery devices even though the cost will remain limiting for years to come (JDRF 2010). Unfortunately, sensoraugmented pumps (SAP) may not be available in developing countries for many years for reasons of cost and lack of reimbursement (access to insulin has been widely discussed; there is now the issue of access to insulin pumps). Interestingly, clinical pharmacology studies with insulin pumps are easier to perform than the classical glucose clamps studies because the devices comprise subcutaneous glucose sensors which immediately register and show the 24-h glucose profiles. At the time of using the pump, the sensors provide a record of the 24-h profile when suitable software is implemented. The sensors also provide hypoglycemia alert and up-todate online information for the user (e.g., Freestyle Libre II) when required at short intervals, e.g., during exercise (Fig. 6). Pumps controlled by coupled glucose sensors detect the risk of impending and developing hypoglycemia. A rapidly decreasing glucose signal will shut down the pump for a pre-defined time period. Such insulin pumps have been available for some time (Minimed 670G). The glucose sensors for continuous subcutaneous glucose monitoring (CSGM) provide detail for the patient to be viewed immediately on the glucose meter and when coupled to a smartphone (Mastrototaro et al. 2008). They were found particularly effective in patients with type I diabetes and are now

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Fig. 6 Long-term glucose sensor with transmitter and smartphone control (Senseonic)

increasingly also applied in type II diabetes, to provide flexibility of dosage adjustment (Bailey et al. 2009).

Clinical Studies with Bihormonal Insulin Pumps The bihormonal pumps (aka bionic pumps) deliver insulin by controlled infusion and – as a security process – dispense glucagon from a second pump in the case of impending hypoglycemia, which is detected by an algorithm (risk management) and immediately communicated to the patient by an alert signal. Bionic pumps establish a high level of precision, they adapt the insulin infusion rate due to glucose monitoring, and they keep the serum glucose within the pre-defined range. The critical measurement of the efficiency is addressed as “time in range.” On a higher level of precision, bionic pumps continuously adapt the effect of the insulin infusion by small doses of glucagon, and they deliver a bolus dose of glucagon in case of impending hypoglycemia (emergency protocol). Specific studies have been performed about the effect of exercise on glucose control, to confirm that algorithms will adapt the dispensing of glucagon during an acute decrease of plasma glucose caused by targeted exercise and by stressful sports. Technical issues remain to be resolved, mainly the

availability of stable glucagon solutions, e.g., dasiglucagon instead of GlucaGen solution which needs to be prepared freshly every day in a clinical trial. There are two products of stable glucagon solution which will be available shortly after FDA approval. An emergency medication for future use is being developed for nasal application of glucagon powder (Basquimi™). Early clinical studies were performed with two separate t-slim pumps, one for a fast-acting insulin (usually insulin aspart) and the other for the glucagon solution freshly prepared every day. For a very detailed review of the initial studies, refer to El-Khatib et al. (2014). Early studies in adult patients were followed by comparable studies in children. In particular, adaptation of the system to exercise-induced acute changes in insulin secretion and serum glucose is important. This was followed by extended field studies (Castle et al. 2018; El-Khatib et al. 2017).

Glucagon (Emergency and Bionic Pumps) There has been considerable interest in a stable glucagon solution for injection in emergency situations, e.g., diabetic patients with loss of consciousness in particular at night, due to profound hypoglycemia. This is often associated with cramps and convulsions that may resemble neurologic

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Fig. 7 Hypoglycemia emergency set of glucagon for immediate reconstitution

disease (e.g., epilepsy). Currently available emergency medication is a lyophilized glucagon preparation (GlucaGen), which needs to be reconstituted before injection by family members (Fig. 7). In clinical pharmacology studies, the glucagon solution was prepared freshly every day immediately before application in bihormonal insulin pumps. There is a need for a stable glucagon solution which maintains its activity for several weeks, under suitable storage conditions (preferably at room temperature, when used in a bihormonal pump). The clinical pharmacology studies were performed with insulininduced hypoglycemia in the presence of rescue medication, by glucose infusion. The initial configuration for early clinical pharmacology studies was by using two separate pumps, e.g., t-slim pumps (Cengiz et al. 2011; Russell et al. 2016; El-Khatib et al. 2014, 2017; Castle et al. 2018). Glucagon solutions prepared freshly every 24 h were carefully evaluated. The pharmaceutical characteristics of these solutions have been reported in detail (Wilson and Castle 2018). The glucagon analog dasiglucagon with clinical utility confirmed in phase 2 and 3 clinical trials (Hoevelmann et al. 2018) is currently applied as a stable solution in phase 3 trials. An advanced product is the first bihormonal pump system delivering both insulin and glucagon (Ilet pump) by an algorithm based on minute-to-minute data for subcutaneous glucose (Jacobs et al. 2014; Bakhtiani et al. 2014; Russell et al. 2016; El-Khatib et al. 2014, 2017; Castle et al. 2018). The Ilet pump system is in final evaluation by FDA for product licensing (June 2019) (Fig. 8).

Fig. 8 Bihormonal pump with two separate syringes for dispensing insulin and glucagon. Controlled by a CSGM sensor, data are transmitted to the pump and processed by an algorithm to keep glucose concentration within a predetermined range. Glucagon is dispensed when impending hypoglycemia is recognized by the algorithm

GLP1 Agonists (Incretin Mimetics, Peptide Analogs) Here is the next step of diabetes methodology. Pharmacodynamic evaluation has moved forward to characterize peptide hormones analogs (GLP1 agonists) which can be used in type II diabetes for injection therapy and – more conveniently – to be combined with basal analog insulin to reduce the risk of hypoglycemia and enhance weight reduction. The endogenous system (incretin hormones) was established and characterized in detail by extensive research. The critical characteristic of the GLP1 agonists is resistance to inactivation by endogenous enzymes (dipeptidyl-peptidase DPP). Modified DNA biosynthesis is required to obtain clinically relevant prolongation of half-life. Diabetes therapy operates with biosynthetic peptides derived from incretin hormones acting on the gastrointestinal tract in response to food ingestion (Drucker and Nauck 2006; Drucker et al. 2017; Mueller et al. 2017). The GLP1 agonists (incretins) are a new group of drugs for injection in type II diabetes (Zander et al. 2002). They are also applied in type I diabetes as an enhancement of insulin therapy, by preference after suitable development of coformulations with basal insulins, at a reduced dose of insulin. The critical advantage of such coformulations is reduction of the insulin dose and

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low risk of hypoglycemia (Kalra et al. 2016; Kalra and Gupta 2016; Madsbad 2016). For GLP1 agonists, a clinically relevant prolongation of half-life has enabled the clinical use of depot formulations for injection once per week. Two of these coformulations have shown their clinical advantage by improving patient acceptance (dulaglutide and semaglutide). For diabetic patients, there are now regimens for injection once or twice per day injection (QD) and advanced regimens by injections once per week (QW). The group of GLP1 agonists is often addressed as “incretin mimetics.” The response of insulin release and suppression of glucagon release to food ingestion is the characteristic difference when compared with the established insulin therapy. The clinically relevant products for therapy are short-acting GLP1 agonists for once or twice daily s.c. injection (e.g., exenatide, liraglutide, and lixisenatide QD), and the convenient group of GLP1 agonists for injection of 1 week (e.g., dulaglutide and semaglutide QW) . This group is of predominant clinical interest because patients prefer a regimen of infrequent injections supplemented by orally active antidiabetic drugs when required for enhanced efficacy (GLP1-A plus OAD). A very important specific aspect is that GLP1 agonists are used in clinical regimens which reduce the insulin dose and lower the risk of insulin-induced hypoglycemia. The current state of clinical studies with GLP1 agonists has been reviewed (Htike et al. 2017; Sharma et al. 2018) for the formulations for QD and QW injection (including exenatide, liraglutide, lixisenatide, dulaglutide and semaglutide; albiglutide was discontinued.). There is now an interesting and relevant selection of approved products for clinical use by patients and diabetes centers. Access to these products depends on country-specific conditions of availability and reimbursement (health economics); the advantages are clearly documented in several reviews (Banerji and Dunn 2013). One characteristic requirement of the FDA and EMA has been for cardiovascular outcome and diabetes-associated comorbidities (Bethel et al. 2018; Stark Casagrande et al. 2013; Mann et al. 2017; Boyle et al. 2018). This development started with analog insulins and has been extended to the GLP1 agonists group (Marso et al. 2016; Hayward

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et al. 2015; Muskiet et al. 2018; Bahtiyar et al. 2018; Boyle et al. 2018; Bethel et al. 2018; Peterson and Barry 2018; Dalsgaard et al. 2017; Mannucci and Monami 2017; Pfeffer et al. 2015). The large range of clinical studies with GLP1 agonists may be summarized by differentiating single compounds for injection once per day (QD) or once per week (QW) and coformulations which contain the basal insulin together with a matched GLP1 agonist (iDegLira and iGlarLixi).

Exenatide Exenatide is the protagonist of the new development in type II diabetes, to replace basal insulin therapy and reduce the risk of hypoglycemia. Exenatide is applied alone or supported by orally active medication (OADs) (Abdul-Ghani et al. 2015). Exenatide was approved in 2005. Early studies with daily injections were followed by development of once per week depot injection (Drucker et al. 2008; Best et al. 2009, Bydureon™). Clinical pharmacology studies supporting product development and subsequent very extensive clinical studies (Ahren 2011; Buse et al. 2009, 2010a, 2010b, 2011, 2013, 2014) are characterized by extending the range of observations to effects on cardiovascular tolerance and efficacy as well as diabetes-associated comorbidities – with a particular focus on weight reduction in diabetes-associated obesity. These studies follow requirements of regulatory agencies (FDA, EMA). The clinically relevant differences between established products are small, which is reassuring for countries where access is limited. Comparative trials mainly focus on promotional aspects, whereas the level of efficacy is generally of a very similar order, and specific safety issues remain to be monitored in patients with established risk factors.

Liraglutide The clinical pharmacology of liraglutide has been reviewed in terms of pharmacokinetic profile and efficacy and safety when compared with, e.g., exenatide (Heise et al. 2012; Haahr and Heise

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2014). Liraglutide was approved in 2009–2010. Clinical development of liraglutide is characterized by extensive comparative trials (Ahmann et al. 2015; Alatorre et al. 2017; Marso et al. 2016), the comparative clinical studies included important GLP1 agonists (exenatide, lixisenatide) and has prepared the field for coformulation with basal analog insulins (Anderson and Trujillo 2016). The injection once per day was a significant advantage (Victoza™). An interesting characteristic of liraglutide was the focus on weight reduction as a specific indication separate from the insulin field (Saxenda™). Saxenda may be used in patients with clinically significant obesity in the absence of diabetes. The clinical profile of liraglutide was a significant advantage for development of a coformulation with insulin degludec (iDegLira); this coformulation has been leading the field for some time (Gough et al. 2015).

Lixisenatide In a similar manner as with liraglutide, the clinical advantages of lixisenatide (Barnett 2013; Werner 2014) were established and confirmed in comparative trials with established products (Rosenstock et al. 2016a, 2016b, 2016c; Pfeffer et al. 2015; Davies et al. 2017). Lixisenatide was approved in Europe in 2013 and in the United States in 2016 (Okere et al. 2018). The results initiated the coformulation of insulin glargine and lixisenatide. The clinical role of such core formulations is outstanding (Kalra and Gupta 2016; Kalra et al. 2016); there are currently the best application of GLP1 agonist-related peptides (Drucker et al. 2017). The established characteristics are more effective lowering of HbA1c during the test period of 3 months of 6-month, clinical safety with regard to reduced incidence of confirmed hypoglycemia events, and acceptable gastrointestinal tolerance with decreasing symptoms nausea within the first 4 to 8 weeks of application. As with comparable formulations for daily injection, local tolerance and absence of significant local reactions at the injection site is well established (Fonseca et al. 2012) and a wide range for selection of the injection site is well

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documented (e.g. upper arm, leg, abdominal region). Treatment is initiated with a reduced daily dose and up titrated to the final dose depot injections as a general principle for formulations with initial gastrointestinal effects which limit tolerance.

Dulaglutide Dulaglutide currently provides the most convenient once per week depot injection (QW) (Dungan et al. 2014; Kuritzky 2014). Dulaglutide was approved 2014. The critically important characteristic of using once per week (QW) injection has markedly improved adherence to therapy; the treatment is much more readily accepted by the patient (Alatorre et al. 2017) and maintained for prolonged time periods. This has also initiated a clinical development where dulaglutide is selected as the first treatment option of type II patients when their HbA1c control needs to be normalized at an early time, to prevent or reduce diabetic comorbidities. In this respect, patient education has become a very important part of injection therapy; many patients need to go on about the importance of preventing progress of diabetesassociated comorbidities. Interestingly, this is not a question of simply lowering the HbA1c to the normal range, but there have been many specific investigations about diabetes-associated comorbidities, early recognition of prediabetes and e.g. early treatment of lipid disorders (Fig. 9).

Semaglutide Here is currently the next GLP1 agonist for injection once per week, with an extended half-life confirmed by dose escalation pharmacokinetic studies (Kapitza et al. 2017; Sorli et al. 2017). In line with other new antidiabetic medications for injection (FDA guideline), extensive studies on cardiovascular outcome and effects on kidney function were performed (Marso et al. 2016; Monami et al. 2017; Dicembrini et al. 2017;

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Fig. 9 Dulagutide is a GLP1 agonist for subcutaneous injection once per week. Treatment is started with 0.75 mg/ml to adapt for effects on gastrointestinal function

(tolerance) and subsequently continued with the full dose of 1.5 mg/ml. The important clinical advantage may be reduction of body weight

Abdul-Ghani et al. 2017; Jensen et al. 2018; Home 2019). Semaglutide was approved on 2017 for QW injection (Davies et al. 2017). It is the first instance in this group of peptides subsequently developed as a clinical formulation and approved in 2019 for QD oral administration (Granhall et al. 2019)

liraglutide (iDegLira) and insulin glargine – and lixisenatide (iGlarLixi) (Gough et al. 2015; Billings et al. 2018; Vilsboll et al. 2016; Rosenstock et al. 2016a, b). The coformulations are now well established in therapy of type II diabetes (Nuffer et al. 2018, Valentine et al. 2017; Wysham et al. 2018).

Insulins and GLP1 Agonists (Coformulations)

Summary and Outlook

Clinical pharmacology of pharmaceutical formulations became an important aspect in the improving treatment of type II diabetes, development started with premixed insulins (which were more difficult to handle) followed by coformulation of two analog insulins in a single solution, which provides effective glycemic coverage with a minimum of daily injections (Diamant et al. 2014; Alatorre et al. 2017; Frias et al. 2018). Studies were performed with basal insulin degludec and rapid-acting insulin aspart (degludec/aspart, aka iDegAsp) for injection in a single syringe (Kalra 2014; Kalra and Gupta 2015). This coformulation followed the concept of premixed insulins; it was easier to handle, but the risk of hypoglycemia when increasing dosage was not reduced. That situation improved considerably when coformulations became available for type II in a single pen for injection with the solution for injection containing two soluble peptide hormones at a constant ratio of analog insulin and GLP1 agonist (Kalra and Gupta 2016; Kalra et al. 2016). Clinical pharmacology studies were performed with the coformulations of insulin degludec –

Within about 10 years, early therapy of diabetes mellitus type I and type II has improved markedly by injection of coformulations including a GLP1 analog and by the use of algorithm-controlled insulin pumps. The change in therapy and acceptance of more complex technology is due to recognition of the global epidemic that diabetes mellitus has reached. There is now an understanding of advanced early diagnosis and the need for screening. The options for early and effective therapy are particularly obvious for type II diabetes. Diabetes methodology in clinical pharmacology has changed from the assessment of insulin products (analog insulins with fast absorption and long-acting basal insulins) to the clinical assessment of GLP1 agonists (incretin mimetics) and coformulations with basal insulin analogs. Research on diabetes-associated comorbidities has established the potential for prevention and retardation when fully efficient early therapy is initiated (ADA 2018) and maintained (Leite et al. 2014; Merger et al. 2016; Petrosyan et al. 2017; Lehrke and Marx 2017; Wielgosz et al. 2018; Franch Nadal et al. 2017; An et al. 2019; Richter et al. 2018). The approach by diet and

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lifestyle is clearly not sufficient in advanced diabetes I and II; early injection therapy may be the future option for reducing comorbidities in a patient-friendly and acceptable procedure. Progress in the field of insulin pumps within about 10 years is very impressive due to advanced technology, even though access to modern insulin pumps is limited due to cost, and the technical complexity of long-term therapy. Patch pumps have advanced markedly (Omnipod™); they may remain a small albeit useful segment due to restrictions of intensive handling. The critical step forward is introduction of bihormonal (bionic) pumps in patients who are at particular risk or need intensive therapy without the complication of hypoglycemia. Stable glucagon solutions will be available in the near future (dasiglucagon), and it is remarkable that a nasal glucagon single dose preparation for emergency is now available (Baqsimi™). Proof of concept for the bionic insulin pumps is impressive due to recognition and adaptation of exercise induced changes in glucose control which have previously been a problem in particular for adaptation to sports and intensive exercise. Final FDA approval for such pumps is well advanced (Ilet pump). In summary, the most impressive progress has been made with injectable coformulations which may help to overcome therapeutic inertia early after diagnosis of advanced stages of type II diabetes.

References and Further Reading Abdul-Ghani MA et al (2015) Renal sodium-glucose cotransporter inhibition in the management of type 2 diabetes mellitus. Am J Physiol Renal Physiol 309(11): F889–F900 Abdul-Ghani M, DeFronzo RA, Del Prato S et al (2017) Cardiovascular disease and type 2 diabetes: has the dawn of a new era arrived? Diabetes Care 40 (7):813–820 ADA (2019) American Diabetes Association, Standards of Care 2019. https://professional.diabetes.org/contentpage/practice-guidelines-resources Ahern JA, Boland EA, Doane R et al (2002) Insulin pump therapy in pediatrics: a therapeutic alternative to safely lower HbA1c levels across all age groups. Pediatr Diabetes 3(1):10–15 Ahmann AJ, Rodbard HW, Rosenstock J et al (2014) Efficacy and safety of liraglutide vs. placebo when

J. Sandow added to basal insulin analogs in patients with type 2 diabetes (LIRAADD2BASAL). Diabetes 63:A87 Ahren B (2011) The future of incretin-based therapy: novel avenues – novel targets. Diabetes Obes Metab 13:158–166 Alatorre C, Fernández Landó L, Yu M et al (2017) Treatment patterns in patients with type 2 diabetes mellitus treated with glucagon-like peptide-1 receptor agonists: higher adherence and persistence with dulaglutide compared with once-weekly exenatide and liraglutide. Diabetes Obes Metab 19(7):953–961 American Diabetes Association (2018) 3. Comprehensive medical evaluation and assessment of comorbidities: Standards of Medical Care in Diabetes-2018 Diabetes Care 41(Suppl 1):S28–S37 An J, Le QA, Dang T (2019) Association between different types of comorbidity and disease burden in patients with diabetes. J Diabetes 11(1):65–74 Anhalt H, Bohannon NJ (2010) Insulin patch pumps: their development and future in closed-loop systems. Diabetes Technol Ther 12(Suppl 1):S51–S58 Asche C, LaFleur J, Conner C (2011) A review of diabetes treatment adherence and the association with clinical and economic outcomes. Clin Ther 33(1):74–109 Aye T, Block J, Buckingham B (2010) Toward closing the loop: an update on insulin pumps and continuous glucose monitoring systems. Endocrinol Metab Clin N Am 39(3):609–624 Bahtiyar G, Pujals-Kury J, Sacerdote A (2018) Cardiovascular effects of different GLP-1 receptor agonists in patients with type 2 diabetes. Curr Diab Rep 18 (10):92 Bailey T, Zisser H, Chang A (2009) New features and performance of a next-generation SEVEN-day continuous glucose monitoring system with short lag time. Diabetes Technol Ther 11(12):749–755 Banerji MA, Dunn JD (2013) Impact of glycemic control on healthcare resource utilization and costs of type 2 diabetes: current and future pharmacologic approaches to improving outcomes. Am Health Drug Benefits 6(7):382–392 Barnett AH (2013) Complementing insulin therapy to achieve glycemic control. Adv Ther 30(6):557–576 Becker HA (2011) Pharmacodynamic evaluation: diabetes methodologies. In: Vogel HG et al (eds) Drug discovery and evaluation: methods in clinical pharmacology. Springer, Berlin/Heidelberg, pp 457–481 Bergenstal RM, Tamborlane WV, Ahmann A, et al (2010) Effectiveness of sensor-augmented insulin pump therapy in type 1 diabetes Best JH, Boye KS, Rubin RR et al (2009) Improved treatment satisfaction and weight-related quality of life with exenatide once weekly or twice daily. Diabet Med 26:722–728 Bethel MA, Patel RA, Merrill P et al (2018) Cardiovascular outcomes with glucagon-like peptide-1 receptor agonists in patients with type 2 diabetes: a meta-analysis. Lancet Diabetes Endocrinol 6(2):105–113 Billings LK, Doshi A, Gouet D et al (2018) Efficacy and safety of IDegLira versus basal-bolus insulin therapy in patients with type 2 diabetes uncontrolled on

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metformin and basal insulin: the DUAL VII randomized clinical trial. Diabetes Care 41:1009–1016 Blak BT, Smith HT, Hards M et al (2012) Optimization of insulin therapy in patients with type 2 diabetes mellitus: beyond basal insulin. Diabet Med 29:e13–e20 Boyle JG, Livingstone R, Petrie JR (2018) Cardiovascular benefits of GLP-1 agonists in type 2 diabetes: a comparative review. Clin Sci (Lond) 132(15):1699–1709 Boyne MS, Silver DM, Kaplan J et al (2003) Timing of changes in interstitial and venous blood glucose measured with a continuous subcutaneous glucose sensor. Diabetes 52(11):2790–2794 Briscoe VJ, Davis SN (2006) Hypoglycemia in type 1 and type 2 diabetes: physiology, pathophysiology, and management. Clin Diabetes 24:115–121 Buckingham WDM, Lecher T et al (2008) Duration of nocturnal hypoglycemia before seizures. Diabetes Care 31(11):2110–2112 Buckingham BA, Forlenza GP, Pinsker JE et al (2018) Safety and feasibility of the OmniPod hybrid closedloop system in adult, adolescent, and pediatric patients with type 1 diabetes using a personalized model predictive control algorithm. Diabetes Technol Ther 20 (4):257–262 Buse JB, Rosenstock J, Sesti G et al (2009) Liraglutide once a day versus exenatide twice a day for type 2 diabetes: a 26-week randomised, parallel-group, multinational, open-label trial (LEAD-6). Lancet 374:39–47 Buse JB, Sesti G, Schmidt SE et al (2010) Switching to once-daily liraglutide from twice-daily exenatide further improves glycemic control in patients with type 2 diabetes using oral agents. Diabetes Care 33:1300–1303 Buse JB, Drucker DJ, Taylor KL, Kim T et al (2010b) DURATION-1 Study Group. DURATION-1: exenatide once weekly produces sustained glycemic control and weight loss over 52 weeks. Diabetes Care 33(6):1255–1261 Buse JB, Sesti G, Schmidt SE et al (2010c) Switching to once-daily liraglutide from twice-daily exenatide further improves glycemic control in patients with type 2 diabetes using oral agents. Diabetes Care 33:1300– 1303 Buse JB, Nauck M, Forst T et al (2013) Exenatide once weekly versus liraglutide once daily in patients with type 2 diabetes (DURATION-6): a randomised, openlabel study. Lancet 381:117–124 Buse JB, Vilsbøll T, Thurman J et al (2014) Contribution of liraglutide in the fixed-ratio combination of insulin degludec and liraglutide (IDegLira). Diabetes Care 37:2926–2933 Castle JR, Elander M (2019) Long-term safety and tolerability of dasiglucagon, a stable-in-solution glucagon analogue. Diabetes Technol Ther 21(2):94–96 Castle JR, Youssef JE, Wilson LM et al (2018) Randomized outpatient trial of single- and dual-hormone closed-loop systems that adapt to exercise using wearable sensors. Diabetes Care 41(7):1471–1477 Cengiz E (2013) Analysis of a remote system to closely monitor glycemia and insulin pump delivery – is this the beginning of a wireless transformation in diabetes management? J Diabetes Sci Technol 7(2):362–364

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260 Pankowska E, Skorka A, Szypowska A et al (2005) Memory of insulin pumps and their record as a source of information about insulin therapy in children and adolescents with type 1 diabetes. Diabetes Technol Ther 7 (2):308–314 Patterson CC, Dahlquist GG, Gyurus E et al (2009) Incidence trends for childhood type 1 diabetes in Europe during 1989–2003 and predicted new cases 2005–2020: a multicentre prospective registration study. Lancet 373(9680):2027–2033 Peterson SC, Barry AR (2018) Effect of glucagon-like peptide-1 receptor agonists on all-cause mortality and cardiovascular outcomes: a meta-analysis. Curr Diabetes Rev 14(3):273–279 Petrosyan Y, Bai YQ, Koné Pefoyo AJ, Gruneir A, Thavorn K, Maxwell CJ, Bronskill SE, Wodchis WP (2017) The relationship between diabetes care quality and diabetes-related hospitalizations and the modifying role of comorbidity. Can J Diabetes 41(1):17–25 Pfeffer MA, Claggett B, Diaz R et al (2015) Lixisenatide in patients with type 2 diabetes and acute coronary syndrome. N Engl J Med 373(23):2247–2257 Plotnick LP, Clark LM, Brancati FL et al (2003) Safety and effectiveness of insulin pump therapy in children and adolescents with type 1 diabetes. Diabetes Care 26 (4):1142–1146. PMID: 29285508 Richter L, Freisinger E, Lüders F, Gebauer K, Meyborg M, Malyar NM (2018) Impact of diabetes type on treatment and outcome of patients with peripheral artery disease. Diab Vasc Dis Res 15(6):504–510 Rosenstock J, Aronson R, Grunberger G et al (2016a) Benefits of LixiLan, a titratable fixed-ratio combination of insulin glargine plus lixisenatide, versus insulin glargine and lixisenatide monocomponents in type 2 diabetes inadequately controlled on oral agents: the LixiLan-O randomized trial. Diabetes Care 39 (11):2026–2035 Rosenstock J, Diamant M, Aroda VR, Silvestre L et al (2016b) Efficacy and safety of LixiLan, a titratable fixed-ratio combination of lixisenatide and insulin glargine, versus insulin glargine in type 2 diabetes inadequately controlled on metformin monotherapy: the LixiLan proof-of-concept randomized trial. Diabetes Care 39(9):1579–1586 Sandow J, Landgraf W, Becker R, Seipke S (2015) Equivalent recombinant human insulin preparations and their place in therapy. Eur Endocrinol 11(1):10–16 Sharma D, Verma S, Vaidya S, Kalia K, Tiwari V (2018) Recent updates on GLP-1 agonists: current advancements & challenges. Biomed Pharmacother 108:952–962 Sorli C, Harashima SI, Tsoukas GM, Unger J, Karsbøl JD, Hansen T, Bain SC (2017) Efficacy and safety of onceweekly semaglutide monotherapy versus placebo in patients with type 2 diabetes (SUSTAIN 1): a doubleblind, randomised, placebo-controlled, parallel-group, multinational, multicentre phase 3a trial. Lancet Diabetes Endocrinol 5(4):251–260 Stark Casagrande S, Fradkin JE, Saydah SH, Rust KF, Cowie CC (2013) The prevalence of meeting A1C,

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Pharmacodynamic Evaluation: Gastroenterology

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Petar Nikolov, Georgi Banishki, and Milena Nikolova-Vlahova

Contents General Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Receptors in the GIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serotonergic Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cannabinoid Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Opioid Receptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Treatment Routes and Drug Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oral Drug Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parenteral Route of Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Transmucosal Route of Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Local Drug Administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

267 267 268 269 270

Therapeutic Drug Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270 Imaging in Gastroenterology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Positron Emission Tomography (PET) Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contrast Enhanced MRI (CE-MRI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecular Endoscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Oral Vaccines and Oral Tolerogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oral Mucosal Immune Tolerance, Suppression, and Silencing . . . . . . . . . . . . . . . . . . . . . . . . Oral Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oral Tolerogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

279 279 280 281

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282

Abstract P. Nikolov (*) Gastroenterology, Ardea Medical Center, Sofia, Bulgaria e-mail: [email protected] G. Banishki University of Aberdeen, Aberdeen, UK e-mail: [email protected] M. Nikolova-Vlahova Clinic of Nephrology, University Hospital Alexandrovska, Medical University, Sofia, Bulgaria e-mail: [email protected]

Pharmacodynamics aims to explain the complex relationship between the medication’s dose, physiological or pathological response, and the chemical nature of the drug. The human gastrointestinal tract (GIT) is a strictly hierarchic body system with numerous functions and is often a therapeutic target, crosslink, or can even serve as a measurement for drug’s physiologic and biochemical effects. The

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_50

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pharmacological effects and the pharmacodynamics evaluation in the GIT would not have been possible without three distinct receptor families that have been known to have an enormous role in the modulation of the GIT functions: serotonergic, cannabinoid, and opioid receptors. In addition, the route of administration can be of great importance for the absorption and pharmacological response of the drug and there are several main routes of administration in gastroenterology: oral, parenteral, transmucosal, and local. Furthermore, a targeted and individualized approach for drug monitoring was developed that takes into account individual patient variability through careful gathering of pharmacokinetic and pharmacodynamic data: therapeutic drug monitoring, allowing to really individualize patient’s dose. Various novel imaging methods are used in gastroenterology, e.g., PET scan, MRI, and molecular endoscopy, and they all use tracers and contrast agents. They allow for early an accurate detection of various lesions in the GIT. Current understanding of oral tolerance has allowed the development of two groups of medications with a unique pharmacodynamic profile: oral vaccines, inducing immune response and oral tolerogens, initiating immunomodulation with alteration of immune response directed at the development of local and/or systemic immune tolerance.

General Overview Petar Nikolov Pharmacodynamics stands for the study of the physiologic, biochemical and molecular effects of medications on the body and involves receptor binding, postreceptor signaling and biochemical interactions. Pharmacodynamics also aims to explain the complex and multifactorial relationship between the medication’s dose, physiological or pathological response, and the chemical nature of the drug.

P. Nikolov et al.

The human gastrointestinal tract (GIT) is a really complex and yet strictly hierarchic body system with numerous functions: – Provides route and safe passage of food through the body. – Plays a key role in the food’s processing, degradation and utilization. – Acts as an “opened door” to the outside world and thus has a major role in the life adaptation and preservation of the biological individuality of humans – GIT is extremely important for the proper functioning of the innate and acquired immune response. – At the same time plays a pivotal role in the maintenance of oral tolerance. – GIT is a crossroad for all other body systems from a metabolic, regulatory, and signaling standpoint, thus allowing for a therapeutic intervention at many levels. – Small and large intestine host the intestinal flora, which is sometimes regarded as the “forgotten organ” in the human body as it is biochemical activity is only comparable with the one of the liver. – Numerous endocrine and exocrine secreting cells adaptively interact with each other, thus keeping an equilibrium with the other body systems, the nutritional habits, and the outside world. – Oral and rectal drug intake are often the preferred routes of drug administration for many therapies and would not have been possible without the unique and ubiquitous functions of the GIT. – Liver is the most advanced and sophisticated biochemical laboratory in the human body and plays a primordial and diverse role in the drug’s pharmacodynamics. All the above features of the GIT may be a therapeutic target, crosslink or serve as measurement for drug’s physiologic and biochemical effects. With this regard, the human GIT and the science that studies it – gastroenterology – are an important milestone in the pharmacodynamic

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evaluation. Moreover, the human GIT hosts a galore of receptors that are of vital importance for the optimal functioning of the human body as it is a crossroad and often the pharmacological effects of the medications aiming at the GIT may biologically go in their effects beyond the GIT due to its ubiquitous function.

Receptors in the GIT Georgi Banishki Targeting specific receptors has always been one of the most preferred options in drug development and the GIT has been no exception in that area. There have been three different receptor families that have been identified that have been of the greatest interest to researchers due to their role in modulating GI functions and these are the serotonergic (5-HT), cannabinoid, and opioid receptors.

Serotonergic Receptors There have been 12 different serotonin receptors identified to date, all of which are G-protein coupled receptors (GPCRs) and for most time they have been associated with the nervous system and their importance in mood control, depression, anxiety, sleep, etc. However, the greatest store of serotonin in the body is the gut where can be found about 95% of it (Gershon 2013). It was first in the 1950 that was proven that serotonin plays a major role in peristaltic activity as serotonin secreted from enterochromaffin cells in gut mucosa evokes peristaltic activity. Subsequent studies on rats using tryptophan-deficient diet (tryptophan is precursor of serotonin) showed that peristaltic activity was not impaired, but serotonin still has a modulating effect on the gut motility (Gershon 2013). Drugs that target serotonin receptors have been shown to be very effective in patients with IBS. In previously treatment resistant patients, Alosetron, a 5-HT3 antagonist, was shown to be very effective against IBS with

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diarrhea, and Tegaserod, a 5-HT4 agonist, was effective in patient who had IBS with constipation. Both drugs had some severe side effects which have led to their restricted use, but they were a successful proof of concept as it confirmed serotonergic bowel dysfunction to be an important factor in IBS (Gershon 2013). Another major finding regarding serotonin signaling in the gut is its role in inducing inflammatory response. Animal studies using mice which lacked serotonin reuptake transporter (SERT), the activity of serotonin was enhanced and prolonged, and these mice were susceptible to developing trinitrobenzenesulfonic acid (TNBS)-induced or IL-10 KO-associated colitis (Gershon 2013). The exact pathway for this is yet to be understood although there has been evidence suggesting that by stimulating 5-HT7 receptors on dendritic cells to launch the innate immune mechanisms, serotonin can cause inflammation of the bowel (Gershon 2013). Studies using KO mice which are missing the synthesizing enzymes for serotonin, tryptophan hydroxylase 1 and 2 (TPH1 and TPH2), have also confirmed the importance of serotonin in gut inflammation. Most interestingly, mice which have TPH1 gene knocked out had a reduction in inflammation while mice where lacked TPH2 inflammation was increased and thus led to the conclusion that serotonin can act as both the sword and shield of the gut (Gershon 2013). Serotonin has also been confirmed as an important factor in liver regeneration with 5-HT2 receptors found on hepatocytes being involved in promoting DNA synthesis and hepatocytes proliferation (Gershon 2013). Overall, as we understand more about the importance of serotonin in gut function, it is an area that is going to attract even more attention by researchers looking for new therapeutics for different GI disorders.

Cannabinoid Receptors Cannabis has been used to treat different GI ailments for centuries, but only recently with latest scientific discoveries researchers are beginning to

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understand the pharmacologic pathway for this phenomenon. Cannabinoids elicit response through two main types of GPCRs called the cannabinoid 1 and cannabinoid 2 receptors (CB1 and CB2). CB1 receptors are found throughout the whole enteric nervous system (ENS) and the colon epithelium, while CB2 receptors are found primarily in the immune system, and thus they are both involved in multiple processes ranging from GI motility to regulating gastric secretion (Izzo and Sharkey 2010). The main active substance in cannabis, THC, has long been associated with craving of food, and there has been significant evidence accumulated which has directly linked CB1 receptors in the brain and gut with increasing food intake and body weight gain (Izzo and Sharkey 2010). Several antagonists have been developed that target specifically CB1s for promoting weight loss and treating obesity, but only one has reached the market (Rimonabant) and it was later withdrawn due to increased risk of depression (Izzo and Sharkey 2010). Nevertheless, this remains an area with very high potential as rat studies have shown that CB1 expression is upregulated in obesity-prone rats thus confirming the therapeutic potential for future CB1 antagonists that are unable to cross the blood-brain barrier (Izzo and Sharkey 2010). On the other hand, activation of CB1 receptors has also been shown to reduce gastric secretions and decrease gastric ulcers in rodents which is another area that presents exciting new opportunities for future drugs (Izzo and Sharkey 2010). However, probably the greatest interest has been the involvement of CB receptors in controlling inflammation. Cannabis for many years has been used by patients suffering of autoimmune disease including those affecting the GIT such as Crohn’s disease (CD) or ulcerative colitis (UC). Preclinical experiments in humans have shown increased expression of CB receptors and/or enhanced endocannabinoid levels in intestinal biopsies of patients suffering from CD, UC, diverticulitis, and celiac disease (Izzo and Sharkey 2010). Both CB1 and CB2 receptors are possibly involved as in vitro studies have shown them to modulate inflammatory responses and CB1s were

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also shown to promote gut healing (Izzo and Sharkey 2010). In rodent models, both CB1 and CB2 agonists have shown to be effective in reducing inflammation from trinitrobenzene sulfonic acid (TNBS) and oil of mustard induced colitis, and in rodents where CB1 antagonists were applied they were shown to be more susceptible to induced colitis (Izzo and Sharkey 2010). CB2 receptors by being found mainly in immune cells (B cells, killer T cells) has been shown to be involved in suppressing activated macrophages and the secretion of proinflammatory cytokines such as TNFα (Izzo and Sharkey 2010). In addition, as previously stated both CB1 and CB2 play an important role in regulating gut motility and secretion, their activation by an exogenic compound could cause an even greater reduction in gut inflammation through this process as well. All of this accumulated evidence from in vitro and in vivo studies is highly indicative of the huge future potential of CB receptors in the management of diseases such as CD or UC.

Opioid Receptors Just like cannabis, opium and its many different derivatives have found therapeutic applications long before modern medicine. For centuries, it has been used for treatment of pain and diarrhea in instances such as cholera infections. It has been confirmed that opiates act by targeting specific opioid receptors, but unlike serotonin and cannabinoid receptors their function is much better understood and utilized. Opioid receptors are also all GPCRs and can be subdivided into three classes – μ-opioid receptors (MOR), κ-opioid receptors (KOR), and δ-opioid receptors (DOR). All three types are found in the myenteric and submucosal plexus of the ENS and MORs are also found in immune cells in the lamina propria of the gut. All opioid receptors are directly linked to controlling Cl
secretions in the gut and thus water movement, delaying transit from the small intestine to the colon, elevating the resting anal sphincter pressure, and regulating intestinal inflammation (Holzer 2009). The most commonly

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used drug acting on MORs is Loperamide which is the most commonly used drug to treat diarrhea caused by infections of IBS. Loperamide is a MOR agonist; it cannot pass the blood-brain barrier and targets the receptors in the ENS thus causing decreased propulsive motility and intestinal secretion (Holzer 2009). A common side effect as one would expect is constipation, but in overdoses it can also cause fatal arrhythmia and it should be avoided in patients with IBD where it can result in toxic mega colon. There is one other antidiarrheal drug which utilizes the MOR signaling pathway without crossing the blood-brain barrier, but indirectly. Racecadotril or acetorphan inhibits enkephalinases, the enzymes which degrade endogenous opioids, thus increasing their concentration which therefore leads to delayed bowel transit (Holzer 2009). MOR mediated constipation is also quite a common problem for patient treated with opiate analgesics and which suffer from the so-called opioidinduced bowel dysfunction (OBD). Apart from constipation, OBD also included incomplete evacuation, abdominal distention bloating and discomfort, and gastroesophageal reflux, and it persists throughout the whole treatment of the patient and even though he can develop resistance to the analgesic effects of the opioid, the GI effects remain largely the same (Holzer 2009). This can be somehow managed using naloxone, an inverse MOR agonist which could counteract the undesirable effects without compromising the analgesia. This however is limited by its narrow therapeutic range and ability to cross the blood-brain barrier and at higher doses it can greatly reduce analgesia (Holzer 2009). As a result, one approach that has been attempted is by using peripherally restricted opioid receptor antagonists like n-methylnaltrexone, which has both low oral bioavailability and cannot cross the blood-brain barrier. This concept has subsequently been verified in rat, dog, and human studies using both oral and parenteral formulations, and it has now been approved for human use by both the FDA and EMA (Holzer 2009). Nevertheless, its long-term safety and tolerability are not yet known so the recommendation is not to use it for longer than 4 months. Other such antagonists have

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been developed (e.g., alvimopan), but they have found only limited clinical use. Nevertheless, peripheral MOR antagonists remain an area of great interest for the future.

Treatment Routes and Drug Delivery Petar Nikolov The ubiquitous physiological properties of the human GIT along with the various pathology to be found there predispose for a galore of treatment options to be considered in humans. The development of numerous acid suppressing drugs, antiviral agents for viral hepatitis, biologics for inflammatory bowel disease, live biotherapeutics for intestinal disease, etc. has changed the face of gastroenterology forever. These treatment options come with particular drug delivery techniques. In an attempt to improve the efficacy and safety profile of medications, researchers have developed different methods such as individualizing drug therapy, dose titration, therapeutic drug monitoring, delivering drug at controlled rate, targeted delivery, etc. (Tiwari et al. 2012). The most commonly used routes of drug administration in gastroenterology are: – Oral – delivering the drug into the stomach, small or large intestine – Parenteral – subcutaneous, intravenous, intraarterial, intramuscular, intralesional – Transmucosal – transrectal, transnasal, and sublingual – Local – mostly suppositories or enemas

Oral Drug Administration The oral drug administration is probably the most commonly used method of drug administration. It is using oral formulations that could open into the stomach, small or large intestine. There is a great and somewhat unmet need in oral delivery of protein and peptide drugs, suitable devices for delivering the therapeutic agents into

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the systemic circulation. Numerous gelatin capsules, film tablets, sustained release capsules, etc. have been developed in the last decades so to boost the efficacy and tolerability of numerous medications aiming GIT pathology (Tiwari et al. 2012). Oral administration in gastroenterology is only indicated in cases where patients can swallow properly, it is believed that this route of administration would be more beneficial as compared with the others, the drug is not likely to be destroyed or inactivated by stomach acid, pancreatic enzymes, bile acids or colonic bacteria and last but not least the drug would not be inactivated in the intestinal wall and/or the liver (first pass metabolism). Oral medications to be given with a glass of water in an upright position and washed down with a sufficient amount of water. Oral medications should not be given to a recumbent patient due to the risk of aspiration, chocking and also due to the risk of damages to the esophageal mucosa especially by some medications (e.g., tetracyclines, iron salts). To prevent gastric irritation and to achieve the desired concentration, some researchers have developed enteric coated tablets that resist the gastric acid and disintegrate in the intestine alkaline contents. This also helps to achieve the desired concentration of the drug in the small intestine (e.g., in Crohn’s disease) and last but not least to retard the absorption of the drug. Furthermore preparations with colonic release have been developed. Oral formulations can be designed so to release the active substance over different period of time so there is a normal and controlled release oral formulations. The constantly increasing number of peptide and protein drugs being investigated demands the development of novel dosage forms which exhibit also site-specific release. Delivery of drugs into systemic circulation through colonic absorption represents a novel mode of introducing peptide and protein drug molecules and drugs that are poorly absorbed from the upper GIT (PintoAlphandary et al. 2000). Specific targeting of drugs to the colon is recognized to have numerous therapeutic advantages per se and drugs, which are destroyed by the stomach acid and/or metabolized by pancreatic enzymes or affected by bile acids, are slightly

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affected into the colon. Colon targeting is of value for the topical treatment colonic pathology such as Crohn’s disease, ulcerative colitis, amebiasis, and colorectal cancer. Sustained colonic release of medications can be useful also in the treatment of non-GIT conditions. Peptides, proteins, oligonucleotides, colonic diagnostic agents, and even oral vaccines are potential candidates of interest for colon-specific drug delivery. The diverse microflora and numerous enzymes present in the human colon are being exploited to release drugs in the colon (Tiwari et al. 2012); however, some pharmacodynamic obstacles are also involved in the effective local delivery of drugs to the colon due to the artificial bypass of the stomach and small intestine: unpredictable effect of the gut flora that could vary in its composition from person to person (Lagier et al. 2012), differential pH conditions in the colon, differences in the dietary habits, long transit time during the passage from mouth to colon create difficulties in the safe delivery of drugs to the large intestine (Tiwari et al. 2012). Recent technological achievements such as drug coating with pH-sensitive and bacterial degradable polymers, embedding in bacterial degradable matrices and designing into prodrugs are aiming to effectively target drugs to the colon. The use of pH changes is similar to the enteric coating and consists of employing a polymer with an appropriate pH solubility profile. The concept of using pH as a trigger to release the drug in the colon is based on the pH conditions that vary significantly down the GIT. Polysaccharide and azopolymer coating, which is refractory in the stomach and small intestine yet degraded by the colonic bacteria, have been used as carriers for colon-specific targeting. Last but not least, the availability of good preclinical models and clinical methods promoted the quick development and evaluation of colon-specific drug delivery systems for clinical practice (Tiwari et al. 2012).

Parenteral Route of Administration Routes of administration other than the oral are called parenteral. These are used mostly when oral

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therapy is not possible, not well tolerated (e.g., oral therapy triggers vomiting, diarrhea), patients cannot swallow, drug is not absorbed orally, to avoid drug modification by the GIT and when rapid systemic action and dose accuracy are to be ensured. GIT sometimes limits the bioavailability of certain medications because of its protease enzymes and bacteria-rich environment as well as general pH variability from pH 1–7. These extreme conditions make oral delivery particularly challenging for the some medications, e.g., biologics, insulin. A common parenteral route of administration in gastroenterology is the subcutaneous injection. It is used for the application of nonirritant substances (e.g., somatostatin analogues). The drug absorption is slower but the action is sustained and uniform. It often comes with great efficacy and variable tolerability depending on the type of the active substance administered. Overall, the immunogenicity of subcutaneously administered proteins depends upon antigen presentation and processing by lymph nodes and migratory cutaneous dendritic cells in the subcutaneous space (Fathallah et al. 2013). Another parenteral route of administration is the intravenous one. In this case, drugs are given directly into a vein. Normally the drug produces a rapid effect and the target serum concentration can be achieved with lower doses administered. The drug may be given as a bolus, over 5–10 min, or as continuous infusion (e.g., rehydration), over prolonged periods of time. Some medications are considered to have irritant effect when administered intravenously (e.g., iron, cancer chemotherapy, potassium solutions, parental feeding, etc.). Use of intra-arterial administration in gastroenterology is very limited and is used mostly in cases of angiography and embolization therapy (e.g., hepatocellular carcinoma). The intramuscular route of administration allows for the administration of soluble substances, mild irritants, colloids, and suspensions. The volume of injection should not exceed 10 ml. The intramuscular administration of vaccines optimizes the immunogenicity of the vaccine and minimizes adverse reactions at the injection site, e.g., HBV vaccine (Zuckerman 2000). Intralesional injections have features of both parenteral and local drug

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administration. Intralesional injections in gastroenterology are often given under ultrasound control or via endoscope (e.g., endoscopic intralesional steroid injection in refractory esophageal strictures, endoscopic intralesional injection of diluted epinephrine (1:10,000) in the prevention of recurrent bleeding).

Transmucosal Route of Administration The transmucosal route of administration is characterized by several main features: it is normally painless; offers greater flexibility in a variety of clinical situations, including patients who cannot swallow oral medications and/or in cases when it is not possible to establish intravenous access. Additionally it is characterized by a rapid onset of pharmacological effect, which is often preferred for drugs, especially in the treatment of the acute disorders. Human mucosa has rich blood and lymph supply and many drugs can cross the rectal mucosal membrane like any other lipid membrane, meaning that unionized and lipophilic substances are readily absorbed. Many drugs are using the so-called transrectal administration route. The rectum has rich blood and lymph supply: the portion of the drug absorbed from the upper rectal mucosa is carried by the superior hemorrhoidal vein into the portal circulation, whereas the portion absorbed by the lower rectum enters directly into the systemic circulation via the middle and inferior hemorrhoidal vein. Because of that absorption pattern approximately 50% of drug absorbed by the rectum bypasses the liver and additionally CYP3A4 is not present in the lower intestinal segments, meaning that the chances for first pass metabolism are significantly lower as compared with oral drug administration (e.g., indomethacin suppositories used after ERCP for the prevention of post ERCP-pancreatitis). The transnasal and sublingual routes are less commonly used in gastroenterology but again can be really efficient and with a really good safety profile overall (e.g., intranasal fentanyl in procedural and postprocedural pain in children and sublingual nitroglycerin again in the prevention of post ERCP-pancreatitis).

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Local Drug Administration The local drug application in gastroenterology is mostly given in the form of suppositories (e.g., Mesalazine in distal forms of ulcerative colitis) or enemas. The systemic absorption in local application is negligibly low. Enemas can be divided into retention and evacuant enema. In retention enema the fluid containing the drug (e.g., methylprednisolone in ulcerative colitis, mesalazine foam in ulcerative colitis) is usually 100–120 ml. The evacuant enema (e.g., soap water enema before abdominal surgery, X-ray of GIT) aims to remove the fecal matter and flatus. The liquid stimulates bowel movements by distending the bowel wall, whereas soap acts as a softener. The overall quantity of fluid administered is usually up to 600 mg. Fecal microbiota transplantation holds a special place in gastroenterology and could be given as a retention enema (but also in the form of oral capsules) for the local treatment of recurrent Clostridium difficile infection and also ulcerative colitis (Rossen et al. 2015). The development of micelles, liposomes, and even nanoparticles are being currently researched and integrated in oral and parenteral GIT medications. The aim of these sophisticated drug delivery systems is to provide enhanced efficacy for existing and novel medications and/or reduced toxicity for patients. These drug delivery systems may be subjected to even further changes such as PEGylation of liposomes and nanoparticles so to boost their efficacy, formation of nanogels, and solid lipid nanoparticles. These novel drug delivery systems are largely experimental but have also shown some efficacy in some gastrointestinal tumors.

Therapeutic Drug Monitoring Georgi Banishki Management of serious progressive diseases of the GIT offers countless challenges to gastroenterologist from lack of therapeutic response, genetic polymorphisms, drug interactions to adverse drug reactions. This requires a targeted

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approach that takes into account individual patient variability through careful gathering of pharmacokinetic and pharmacodynamic data and this is where therapeutic drug monitoring (TDM) comes into account. TDM has been defined as using laboratory measurements usually a biological matrix of a parameter (e.g., drug metabolites) which after analysis will directly influence patient therapy (Dasgupta 2012). TDM is not applied for all lines of treatment but only in situations when: (1) clinical evidence is deemed insufficient, (2) correlation between serum or whole blood drug concentration and dosage is poor, (3) there is a narrow therapeutic range, (4) drug toxicity may lead to serious adverse events, (5) there is a link between serum or whole blood concentration of the drug and its therapeutic response or toxicity, and (6) there are clinical indications (e.g., toxicity despite no dosage adjustment) which require it (Dasgupta 2012). Naturally, one would expect that chemotherapeutic treatments would be subject to TDM, but that is the case in only a select number of cases. Previously discussed PET imaging has been a huge improvement in monitoring chemotherapy efficacy, but classical pharmacological tests are still rarely used – 5-fluoruracil treatments are one notable exception (Dasgupta 2012). However, in terms of the GIT TDM has started to make a mark in helping choose the best treatment for patients with IBD. In the past, steroids were the preferred choice for IBD management, but nowadays they are primarily used for controlling diseases flares at relapse periods, but for maintaining remission other drugs are now preferred. The main drugs that have been universally accepted for use in IBD maintenance can be subdivided into three classes and these are the aminosalicylates, TNFα inhibitors, and thiopurines. Aminosalicylates (e.g., mesalazine, sulfasalazine) are a class of anti-inflammatories and are the first-line choice for UC, but not so much for CD. These agents have been used for decades without close blood monitoring of metabolites and dosing regimens have been adjusted according to the clinical response and manifestation of symptoms. With anti-TNFs and thiopurines this is not the case due to their narrow therapeutic window and high risk of adverse drug

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reactions, and close TDM is deemed necessary for all patients. This has allowed for selecting the best therapy for the patient and if needs be altering it in order to account for evolving loss of response (LOR) or patient safety concerns. Anti-TNFs are all monoclonal antibodies and are the latest line of drugs used to treat IBD that have come during the last decade. They are the primary recommended therapy for advanced CD or UC and the following drugs have been approved for human use – infliximab, adalimumab, certolizumab (only in the USA), and golimumab. The first concern when giving one of these drugs is the occurrence of primary LOR, or lack of effect after the first phase of treatment which can occur in up to one third of all patients (Kopylov et al. 2014). The second concern is secondary LOR which is also quite common and which is much harder to assess and can occur at any point of a treatment regimen. For some drugs like infliximab, it has been reported to appear in up to two-thirds of all patients in the first year, while in others like adalimumab it has reported as every fourth patient. The main factors that can cause primary LOR are disease progression, age of patient, genetic polymorphisms,

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smoking, and prior exposure to such drugs. For secondary LOR, the main cause has been immunogenicity and the development of antidrug antibodies (ATIs) (Kopylov et al. 2014). ATIs develop for both chimeric and fully humanized anti-TNFs and act by interfering with their binding to TNFα molecules. Consequently, the main tools that have been used for TDM in patients treated with antiTNFs have been evaluation of serum levels of drug metabolites and ATIs as well disease activity measurements. The most common method used is double-antigen ELISA in which the drug molecule (e.g., infliximab) is both the capture antigen and the detection antibody. This has the drawback of being unable to detect ATIs in the presence of the drug in the serum which can be fixed by using antihuman λ antigen detection antibody (AHLC) ELISA which has this capacity (Kopylov et al. 2014). Disease activity is measured by regular monitoring of an inflammatory marker (e.g., CRP/FCP) and endoscopy (Fig. 1). In most cases of primary LOR, this is not due to low levels of drug metabolites but due to increased clearance (e.g., fecal loss in UC) and ATI formation which result in low serum levels (Kopylov et al. 2014). Generally, in such cases increasing the dosing

Therapeutic drug level achieved

CRP/FCP elevated

Presence of ATI

High ATI

No/Low ATI

Switch anti TNF or add immunomodulator

Verify patient adherence, if yes > dose

CRP/FCP not elevated

Verified inflammation (endoscopy)

Verified inflammation (endoscopy)

Mild symptoms

Search for different etiology

Switch anti TNF or add immunomodulator

Symptoms resolve after observation

Keep treatment unchanged

Fig. 1 TDM based algorithm for management of loss of response to TNFα inhibitors (Adapted for use from Kopylov et al. 2014)

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regimen has been ineffective, but switching from one medication to another is something that could have positive results (e.g., infliximab replaced with adalimumab). For cases when the patient experiences secondary LOR, a careful assessment of the level of inflammation and drug metabolites is needed. In case that there is active inflammation and high drug levels, the best option would be to switch to another medication. However, if the inflammation is seen, but drug levels are low there would be two options – first would be to increase the dose and the other, but much less common would be to add an immunomodulator in order to suppress ATI formation. Clinical studies where azathioprine or methotrexate was given in addition to infliximab showed reduction of ATIs and the return of clinical response and improvement in disease management (Kopylov et al. 2014). Thiopurines are immunomodulators or immunosuppressants that act by blocking purine synthesis and thus inhibiting T cell production. The main drug of this class azathioprine was initially developed as an anticancer medication, but was then found out to be extremely effective in transplantations and later became the mainstay in IBD management. In recent years, azathioprine has been gradually replaced as first choice by the monoclonal antibodies as they have been deemed to be more target specific and with less side effects (Kopylov et al. 2014). The most common cause for discontinuation of treatment with thiopurines have been its adverse effects such as myelosuppression due to their interference with DNA synthesis, but there are also about 9% of all patients who do not respond to this line of treatment (Kopylov et al. 2014). Therefore, therapeutic drug monitoring is done by monitoring thiopurine metabolites, but also by monitoring blood counts and doing routine checks on pancreatic enzymes. One other factor that has also been recommended to be taken into account is genetic polymorphisms. Once administered azathioprine is rapidly converted to active metabolite 6-mercaptopurine (6-MP) by a nonenzymatic reaction and after that there are two competing pathways (Kopylov et al. 2014). The first is mediated by the enzyme thiopurine methyltransferase (TPMT) which

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converts 6-MP to the inactive 6-MMP which gets excreted or the other pathway which converts it to 6-thianoguanine (6-TGN). 6-TGN is an active metabolite which causes myelosuppression, and in patients who were shown to have low or intermediate acting, TPMT was at much greater risk of myelosuppression due to 6-TGN accumulation (Kopylov et al. 2014). Three such alleles have been confirmed in Caucasian (TPMT*2, TPMT*3A, or TPMT*3C) and one in AfricanAmerican (TPMT*3C) populations, and the FDA now recommends genotype or phenotype assessment of TPMT prior to initiating azathioprine (Kopylov et al. 2014). In case when patients are found to be homozygous of any of these, azathioprine should be avoided, and in case when they are heterozygous a decrease in recommended dose by 30–70% has been suggested (Kopylov et al. 2014). Nevertheless, even if the patient has been confirmed not to be heterozygous for any of these alleles regular CBCs should be performed as myelosuppression has been seen in patients with normal TPMT after long-term treatment with azathioprine (Kopylov et al. 2014).

Imaging in Gastroenterology Georgi Banishki As in other functional areas, imaging plays a major role in diagnosing GI diseases and even though there were times when it relied on radiological studies it has expanded to include novel technologies which utilize tracers and contrast agents. The imaging techniques used play a great role both in diagnosing and assessing treatment efficacy and can be subdivided into two categories: anatomical and functional. The anatomical techniques such as computer tomographic (CT) scans as their name signifies focus on observation of structural changes and identification of anatomical landmarks induced by GI ailments (e.g., solid tumors), while the functional ones rely on detecting functional and metabolic changes. These techniques can include functional magnetic resonance imaging (fMRI), but also the recently

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Despite of research in the field dating as far as the 1950s, PET scanning has been approved for use in colorectal and esophageal cancer diagnostics and management since 2001 and since then it has revolutionized the field. Several studies have shown that PET results have caused a significant change in cancer management in 25% and in some cases up to 40% of patients (Bailey et al. 2005). PET scanning is a branch of nuclear imaging which uses radioactive isotopes called

nucleotides or tracers aiming to particular organs and structures. The radioactive isotope can be swallowed, injected or inhaled depending on the target of interest. Each radiotracer is specifically designed to be taken by a metabolic pathway at the targeted tissues where it will accumulate. All radionucleotides are designed to have short half-lives and as they are decaying they are emitting positrons (eþ) which travel in the tissue until they meet an electron (e
) which annihilates both and result in the production of photons which then get detected by the scanning device. Once all of these emissions undergo computer reconstruction a 3D image is created and in which the targeted areas will be highlighted and thus may indicate that there are metabolic processes associated with a particular disease in this area (Bailey et al. 2005). PET imaging has found extensive application mainly in neurology and oncology, and it is in the diagnosis and treatment monitoring of different cancers that it has found its greatest use in the GIT (Table 1).

Table 1 UK intercollegiate Committee recommended indications for clinical PET studies in the GIT: (A) supported by randomized controlled clinical trials, meta-

analyses, and systematic reviews, (B) by experimental or observational studies, and (C) other evidence (Adapted from Bailey et al. 2005)

developed molecular imaging techniques such as positron emission tomography (PET). It is important to note that no single technique is superior to another which is why they are always used in connection, but this section will focus on the ones that rely on the use of pharmacological agents and their molecular interaction with biological targets.

Positron Emission Tomography (PET) Imaging

Oncology applications Esophagus

Stomach Small bowel Liver

Indication Staging of primary cancer (B) Assessment of disease recurrence in previously treated cancers (C) No routine indication (C) No routine indication (C)

Assessment of recurrent disease (A) Prior to metastectomy of colorectal cancer (C)

Not indicated

Assessment of gastroesophageal malignancies and local metastases (C) Proven small bowel lymphoma to assess extent of disease (C)

Equivocal diagnostic imaging (CT, MRI, ultrasound) (C) Assessment preand posttherapy intervention (C) Exclude other metastatic disease prior to metastectomy (C)

Pancreas

Colon and rectum

Not indicated routinely Assessment of neoadjuvant chemotherapy (C)

Routine assessment of hepatoma (C)

Staging a known primary (C) Differentiation of chronic pancreatitis from pancreatic carcinoma (C) Assessment of pancreatic masses to determine benign or malignant status (C) Assessment of tumor response (C) Assessment of a mass that is difficult to biopsy (C)

Assessment of polyps (C) Staging a known primary (C)

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Anatomical imaging techniques remain the mainstay in early diagnosing of cancer, but in other aspects such as tumor staging and monitoring treatment outcome and disease progression is where PET being applied to the best benefit. Accurate evaluation of treatment response is critical for optimal treatment decisions in different types of cancer, and Response Evaluation Criteria in Solid Tumors (RECIST) group has come up with criteria on how to evaluate tumor response to treatment (Table 2). RECIST assesses tumor response by the extent of tumor size reduction and in the past this assessment was done primarily using CT scans or MRI, but as these can be sometimes insufficient in visualizing all tumor infiltration points. This has led to the fusion of PET and CT scan technologies and the development of fluorodeoxyglucose-positron emission tomography (FDG-PET/CT). FDG is a glucose analogue radiotracer which is differentially taken up by malignant cells due to their higher glucose metabolism and this can be used to monitor both short- and long-term metabolic response of tumor after chemotherapy (Van Cutsem et al. 2016). This method utilizes the affinity of tumor cells for FDG, which is strongly linked to tumor grade (aggressiveness) and cellularity. Changes in FDG uptake can be detected after a single course of chemotherapy and as early as 24 h after treatment and thus can help

Table 2 Response Evaluation Criteria in Solid Tumors (RECIST) (Adapted from Van Cutsem et al. 2016) Grade Complete response

Partial response Progressive disease

Stable disease

Response criteria Disappearance of all target lesions. Any pathological lymph nodes(whether target or nontarget) must have reduction in short axis to 0.8 mm thickness and 0.8 mm or MIC); (ii) Ratio of maximum/peak antibiotic concentration (Cmax) achieved by a single dose of antibiotic to the MIC of the infectious agent (Cmax/MIC); and (iii) Ratio of the area under the plasma concentration time curve to the MIC of the infectious agent (AUC24/MIC) (see Fig. 1). The prefix, f, is sometimes introduced to indicate that the free, unbound

Fig. 1 Pharmacodynamic indices of antibiotics (MIC: Minimum inhibitory concentration; Cmax: Maximum/peak antibiotic concentration achieved by a single dose of antibiotic; T > MIC: time for which the drug concentration remains above the MIC value of the infectious agent during a given dosing period; AUC/MIC: ratio of the area under the plasma concentration time curve to the MIC of the infectious agent)

fraction of the drug was used in the calculations. When no subscripts are included, it is assumed that the calculations of AUC and T > MIC were based on a 24-h interval at pharmacokinetic steady-state conditions (Mouton et al. 2005; Nielsen and Friberg 2013; Mouton 2014). MIC values are usually determined using either the agar dilution or broth microdilution methods as specified by the CLSI guidelines. Typically, broth dilution methods use liquid medium in which a specified bacterial inoculum [5
105 colony-forming units (CFU)/ml] is exposed to a constant antibiotic concentration generally over an incubation period of 16–20 h. The MIC is defined as the lowest drug concentration that completely inhibits visible growth of the microorganism. The antibiotic concentrations chosen for MIC determinations are typically twofold dilutions of the antibiotic (e.g., 0.5, 1, 2, and 4 concentration units). Depending on the total volume used, the method is either termed macrodilution (1–2 ml) or microdilution ( 500 μl). For agar diffusion methods, an agar plate is inoculated with the target organism and the antibiotic diffuses from a disk or a strip into the agar. The results are generally read off after 24 h. An E-test is a semiautomated agar diffusion test, which contains a strip preimpregnated with an exponential

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gradient of the antibiotic (Nielsen and Friberg 2013; Wayne 2014). Cumulative fraction of response (CFR) can be calculated using the discrete distribution of MIC values. The AUC24 can be obtained directly from previously published literature. In other cases, it can be calculated as follows: AUC24 = Dose/CL. A particular efficacy index better corelates to a given antibiotic/group of antibiotics (Zhanel 2001). Though MIC values are the most studied pharamcodynamic parameter, it gives incomplete information regarding activity of the antibiotic over time. Hence, the parameters mentioned above are most widely used as a measure of the PD index. The PK/PD index for a certain drugbacteria combination is determined by plotting the value of an efficacy endpoint versus the magnitude of each of the three PK/PD indices. It is evident that the pharmacodynamic properties directly depend on the infectious agent, severity of infection, phase of infection, and the MIC value or resistance pattern of the pathogen. This clearly indicates that the local MIC distribution must be taken into consideration in order to achieve the maximum likelihood of a successful antibiotic treatment regimen for an infectious disease (Canut et al. 2012). Extensive and accurate analysis of PD properties is of foremost importance in determining sustained in vivo efficacy of antimicrobial agents.

Optimization of Drug Dosage Regimen Determination of PK/PD parameters is the first step in optimization of dose regimen for treatment of infectious diseases. They increase the likelihood of disease eradication and minimize the probability of exposure-related toxicity (Scaglione and Paraboni 2006). Once the population PK model has been developed and the PD parameters determined, they can be transferred to models to design dosing regimens that aim to achieve the recommended PD values affecting antimicrobial response. Monte Carlo simulations have been widely used to reliably predict the

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probability of achieving the PD parameters in case of antimicrobials. Monte Carlo simulation is a technique that integrates an agent’s in vitro potency distribution with the pharmacokinetic profile to achieve a specifically targeted antimicrobial exposure (Nicolau 2003). Laboratory based in vitro or animal data, preclinical, or clinical PK/PD data are used in Monte Carlo simulations to obtain initial PK/PD breakpoints. The MIC distributions of the target populations based on this values are then determined. The robustness of the Monte Carlo simulation, target population, and dose adjustments are then made to determine the final PK/PD breakpoints for disease eradication (Mouton et al. 2012).

Pharmacokinetic and Pharmacodynamic (PK/PD) Models The determination of PK/PD indices can be carried out using in vitro pharmacodynamic models (IVPM) or animal models. Ethical issues restrict the use of clinical subjects to evaluate PK/PD relationships in the field of anti-infectives. Hence, IVPM are increasingly used to determine PD indices to be utilized as an aid to dose selection and optimization for treatment of infectious diseases. Both preclinical and clinical PK/PD studies are then incorporated into simulations to determine the PK/PD breakpoints for antimicrobials (Mouton et al. 2012). The in vivo or in vitro kill curves (described below) are generally used to build models to estimate PD parameters. It is imperative that different dosage schemes or concentrations of the antiinfective agent are applied in a PK/PD study. Placebo doses are mandatory and it is essential that varying dose intervals, multiple doses, and information on kinetics of absorption, dissimilation, and excretion are incorporated to guarantee the success of a model. PD parameters could be estimated by using nonlinear regression analysis, and it is absolutely crucial that the model takes all possible survival curves of the organisms under study into account (Czock and Keller 2007).

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In Vitro Models The PD in vitro models are broadly divided into static and dynamic models based on whether the concentration of the tested drug is constant or varying over a period of time. The static models usually consist of determination of MIC or continuous monitoring in shake flasks (Vaddady et al. 2010). Static in vitro time-kill studies provide information on killing capacity of a drug and the probability of emergence of antimicrobial resistance, although determination of MIC would be sufficient in clinical practice to determine the efficacy of antibiotics. However, it is of minimal use, as it represents an end-point detection method, therefore, giving no information on pharmacodynamic changes taking place over a period of time. Hence, static models based on MIC determination are of little relevance in development of dosing recommendations. It is therefore imperative that data should be collected using dynamic situations (Czock and Keller 2007; Gloede et al. 2010; Tängdén et al. 2017). Dynamic in vitro models permit continuous adjustment of drug concentrations to mimic the required in vivo PK profile. Target microbes are exposed to the antimicrobials in vessels that are perfused continuously with media to simulate actual conditions during an infection. Sampling to determine PK/PD properties can be done repetitively. This enables the study of various parameters like kill kinetics, drug concentrations, and emergence of resistance. A large number of dynamic models are in vogue, which includes dilution models (i.e., direct contact between infectious agent and the drug) or diffusion/dialysis models (i.e., indirect drug bacteria contact). A detailed description of all models is beyond the scope of this chapter. However, for a comprehensive reading the reader can refer to the published review by Michael et al. 2014, Vaddady et al. 2010, and Czock and Keller 2007. Different models can be used to mimic drug efficacy in humans. These include biofilm models, models for human immune system, multicompartment models, or models to study disease conditions such as otitis media, chronic pneumonia, cystic

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fibrosis, or tuberculosis (Gloede et al. 2010; ParraRuiz et al. 2010; Pawar et al. 2014; Lorenz et al. 2016). In vitro models have some advantages over in vivo animal models as they provide more flexibility and adaptability to the researcher and are comparatively less expensive and resource-intensive. However, they face certain disadvantages like the need of controlled environments and the risk of contamination of the culture vessel. Neither can these models completely mimic all in vivo conditions like the immunological response to and the virulence nor the metabolic behavior of a pathogen. Furthermore, the bacterial growth limits the analysis as it is much faster in vitro than in vivo (Gloede et al. 2010).

In Vivo Models Evaluation of antibiotic therapy using animal models is essential for the evaluation of the therapeutic efficacies of antimicrobial agents. The advantages of animal models over in vitro models are enormous. Animal infection models allow to study drug efficacy with regard to virulence or antimicrobial resistance of an organism. Furthermore, they also relate to the role of the host immune system in response to the infection itself. Thus, it enables to study the antimicrobial effects at the exact site of infection and can mimic/simulate the conditions in humans and thereby human PK/PD profiles in contrast to in vitro models. However, the results from animal models must be interpreted with caution since the antimicrobial PK profile may turn to be extremely different from human subjects. Rodent, rabbit, or more recently porcine hosts are most predominantly used in PK/ PD studies. Mice and rats are preferred due to low cost and handling ease in comparison to other animals (Tängdén et al. 2017). Establishment of infection may require the animals to be rendered neutropenic by prior administration of an immunosuppressant like cyclophosphamide in order to appropriately compare these results to those that might be expected in humans. Murine tumor models have been used to study the efficacy of

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antimicrobials in patients inflicted by cystic fibrosis and burn wound infections caused by Salmonella and Pseudomonas aeruginosa (Crull and Weiss 2011; Pawar et al. 2015). Other common models are the murine thigh infection models, pneumonia model, peritonitis/bacteremia models, skin and soft tissue infection model, meningitis models, and endocarditis models (Andes and Craig 1998; Nielsen and Friberg 2013; Rybtke et al. 2015).

Mathematical Approach to Modeling PK-PD modeling is the mathematical description of the relationships between PK and PD. The choice of the model and the underlying mathematical equation can depend on whether the system under study is in a steady state or an unsteady time-dependant phase. Steady state refers to a condition in that, the concentrations of the active form of the drug at the site of action are constant and the PD parameters are independent of time as in case of long-term intravenous infusions. These models assess how a bacterial culture responds to a constant environment and fixed antibiotic exposure. The growth of the infectious agent is limited by nutrition, space, aeration, and toxic metabolites. When the concentration and response data are in phase or steady state, basic models such as fixed-effect, linear, log-linear, EMAX, and sigmoidal EMAX models are used. When the kinetics and response are out of phase time-variant, pharmacodynamic models which are more complex are applied. These dynamic models utilize time kill curves, where microbial killing is dependent on both time and varying antibiotic concentration. Models can also be referred to as mechanistic, semimechanistic, or nonmechanistic (PérezUrizar et al. 2000; Vaddady et al. 2010). A mechanistic model is a model which takes into account the known or hypothesized mechanisms of behavior of an infectious agent. The parameters are in accordance with PK, physicochemical, biophysical, physiological, and pathophysiological principles of the system under consideration and relate drug concentrations to their observed effect. Nonmechanistic models do not take the underlying biological mechanisms into consideration

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(Vaddady et al. 2010; Felmlee et al. 2012). Semimechanistic models are those in which although mechanistic knowledge is utilized, but are far less complex compared to the mechanistic models. These are also referred to as mechanism-based models. In general, mechanism based PK-PD models contain equations describing microbial growth, effect of antimicrobial drug, and variable drug concentrations (the microorganism submodel, the antimicrobial submodel, and the pharmacokinetic submodel, respectively) (Czock and Keller 2007; Nielson et al. 2011). The final choice of PK/PD model is made based on the pharmacology of the drug and system. Once a model is defined, unknown parameter values are typically estimated using nonlinear regression techniques contained within computer programs such as WinNonlin (Pharsight, Mountain View, CA), Kinetica (Innaphase, Philadelphia, PA), and ADAPT II (Biomedical Simulations Resource, Los Angeles, CA) (Mager et al. 2003). Below we describe some of the commonly applied in vitro models used to rationalize the selection of antibiotics based on the PK/PD characteristics.

Linear Model This model is based on the assumption that a direct proportionality between drug concentration and its effect exists (1). E ¼ S  C þ E0

(1)

Where S is the slope, E0 the intercept. Pharmacodynamically, S represents the effect induced by one unit of C and E0 represents the value in the absence of the drug. The parameter estimations are carried out by linear regression, and this model applies to measured effects with physiological baselines such as blood glucose or blood pressure levels.

Log-Linear Model Log linear model takes into consideration that if the effect of concentration is hyperbolic, the logconcentration-effect relationship would roughly

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be linear in the range of 20–80% of maximal effect. This can be considered as a derivation of Eq. 1 where S represents the change elicited by one unit of log C.

initially for anticancer agents. The model has a first-order rate constant for bacterial multiplication (Kgrowth) and a first-order rate for the death of the bacteria (Kdeath) as shown in Eq. 5.

E ¼ S  log C þ E0

dB ¼ kgrowth  B  kdeath  B dt

(2)

E MAX Model (Hill Equation) This PK-PD model is extensively used to characterize a wide range of pharmacological effects. The model describes the relationship between concentration of a drug and its elicited effect relationship over a wide range of concentrations. Equation 3 assumes that the plasma drug concentration is in rapid equilibrium with the effect site. E¼

Emax  C þ E0 EC50 þ C

(3)

EMAX describes the maximum effect possible, EC50 the concentration required to produce 50% of EMAX, and E0 is the basal value E. This equation is also referred to as the Hill equation.

This model is a derivate of the Emax model and allows convenient fitting of different types of PK/ PD data. This is the most frequently used model due to the fact that the function asymptotes to an upper limit of stimulation or inhibition by a particular drug on the target infectious agent. Here, γ represents the steepness of the curve also referred to as sigmoidicity factor. γ > 1 for steep curve, γ < 1 for a smooth curve, and γ = 1 for a hyperbolic curve, while other parameters are same as in the Emax model. EðtÞ ¼ E0 þ

Emax  CðtÞγ ECγ 50 þ CðtÞγ

The equation accounts for observed exponential growth of bacteria as seen in the time-kill curve experiments in absence of drug (control experiments) as the net result of the growth rate and cell death.

The Logistic Growth Model Most of the modern day antimicrobial models are based on the logistic growth model, which can be used to describe in vitro bacterial population dynamics. It is a very simple yet useful model and is based on the growth rate (r) and the carrying capacity of the environment (K ). N is the bacterial population and N0 the initial bacterial count. It is given by the Eq. 6
 dN N ¼r 1 N dt K

Sigmoidal EMAX Model

(4)

The Bacterial Submodel The simplest mechanism-based is the bacterial submodel and involves a single bacterial compartment. It is adapted from a model developed

(5)

(6)

The logistic growth model is sometimes modified to include the effect of the drug with a new equation as follows
 dN N ¼ kgrowth N 1   f death ðdrugÞ (7) dt N max Where fdeath (drug) is a function that accounting for death of the bacteria due to the antibiotic and Nmax is the maximum number of bacteria.

Pharmacokinetic-Pharmacodynamic Model Pharmacokinetic-pharmacodynamic model is a combination of the bacterial submodel and the PK model. The combined equations characterize the effect that the antibacterial drug has on the bacteria. The effect could be hypothesized to

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either inhibit the bacterial growth rate or enhance the bacterial killing rate.
 Emax  Cγ ðtÞ dB ¼ kgrowth  1  B dt E Cγ 50 þ Cγ ðtÞ  kdeath  B

(8)

Apart from these models, several other models and variants of the basic model or combinations of model are in use for PK/PD analysis. Detailed descriptions of various models have been described by different researchers. For further reading the reader can refer to the publications from Dayneka et al. (1993), Sharma and Jusko (1998), Czock and Keller (2007), Vaddady et al. (2010), Felmlee et al. (2012), and Mouton (2014).

Simulation Aided PK/PD for Infectious Diseases Computer simulations have been widely used in diverse fields. In the field of pharmaceuticals, it is used in the discovery of new drugs, optimizing chemical processes, and, most recently, in designing clinical studies for the treatment of several acute and chronic diseases. Molecular modeling is the best-known example of simulation in drug discovery. In recent years, Monte Carlo simulation of clinical trials is the method of choice for appropriate dosing selection (Mouton et al. 2012). Monte Carlo is a different kind of simulation than the traditional ones by the fact that its model parameters are treated as stochastic or random variables, rather than as fixed values. In other words, the variability of the parameters is included in the model and the long-term impact of that variability is examined. Furthermore, by definition, Monte Carlo simulation is a random number generator that incorporates distributions of variability around PK parameters in a population to simulate drug concentration-time profiles for a large number of conjured individuals. Thus, instead of practically studying different concentrations over different periods of time, Monte Carlo simulation allows intensive analysis of outcome of different trails, even when data from

single doses are coupled to the simulation (Crandon and Nicolau 2011). How is a Monte Carlo simulation performed? First, the underlying structural pharmacokinetic model for the given antimicrobial agent against a particular infection is defined. This can be a done in a single compartment, 2-compartment, or multiple compartment models. One-compartment model assumes that elimination is first order and that PK parameters are independent of the dose and that there is immediate distribution and equilibrium of the drug throughout the body. However, 2-compartment model does not follow linear PK and comprise of absorption, along with the distribution and covariance between the pharmacokinetic parameters in the model. Next, a dose administration model and a compliance model are defined. This includes the number of patients being administered with a particular dose, number of patients who skipped the dose, and other such criteria related to the intake of the drug. Once the conceptual model is defined, it is translated into a computer code using different software programs like MATLAB, GAUSS, or the Pharsight Trial Designer. Once this is done, it is verified for accuracy and the simulation run. The number of replications of the simulation must be defined at the start. The number of replicates depends on the nature of analysis. Larger replicates are used when the variability of an outcome is to be studied. This is followed by the generation of a sequence of independent random numbers having a given distribution with finite mean and variance. Depending on the pharmacokinetic profile, the pharmacodynamic end point is simulated. The end point is assessed again after few days of treatment. Once the inputs are defined and the simulation is performed, the outputs are examined in the form of graphical results or summary statistics of a variable or the relationship between variables. This general protocol is used for simulating any drug to be used in clinical practices. Some of the important components among others to be defined in a clinical trial simulation include structural pharmacokinetic model, dose administration model, distribution and covariance of pharmacokinetic parameters, link between PK and PD, pharmacodynamic model, disease progression

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model, relationship between pharmacodynamic effect, and outcome and survival model (Bonate 2001).

Pharmacodynamics of Antibiotics for Infectious Diseases Gram-Positive Bacterial Infections Large numbers of fatal infections are caused by various Gram-positive bacteria. For instance, Staphylococcus aureus is responsible for a wide range of infections including hospital acquired bacterial pneumonia, ventilator associated bacterial pneumonia, complicated skin and skin structure infections, and severe bacteremia. The most common drug of choice for such concurrent S. aureus bacteremia is Telavancin. Telavancin is a bactericidal lipoglycopeptide, which is also effective against methicillin susceptible and resistant Staphylococcus aureus (MSSA & MRSA) (Wilson et al. 2017). Additionally, Vancomycin and Linezoid have been widely used for the treatment of infections caused by MRSA. Over the years, most bacteria have evolved resistance mechanisms and hence it is imperative that timely preclinical and clinical studies are undertaken to evaluate the efficacy of such antibiotics. The underlying PK data and MIC can either be retrieved from data sets or databases or calculated for the infection under study. In an interesting study, Canut et al. (2012) have evaluated the usefulness of Daptomycin, Tigecycline, and Linezolid for the treatment of MRSA infection and compared it with vancomycin in four western European countries. They have estimated the probability of achieving the recommended value of AUC24/MIC ratio using Monte Carlo simulation technique with 10,000 subjects. They calculated the ƒCmin using Eq. 9. 0:693

f Cmin

τ

f D e t1=2 ¼ u V d 1  e0:693 t1=2 τ

(9)

where fu is the free fraction of drug in plasma, D the administered drug dose, Vd the volume of

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distribution, and t1/2 the half-life elimination. For the analysis, steady-state exposure was evaluated for different intravenous drug dosing regimens, MIC values fixed at a particular concentration and then the probability of target attainment (PTA) calculated from these parameters. A regimen that achieved >90% CFR against bacterial population is considered as optimal. Their studies indicate that 2 g, 3 g, and 4 g daily of vancomycin seem be adequate in Belgium, Spain, and United Kingdom/Ireland, respectively. CFR obtained with 50 mg Tigecycline every 12 h was higher in Spain than in Belgium and the United Kingdom/ Ireland. Additionally, a minimum of 8 mg/kg Daptomycin is necessary in United Kingdom/Ireland, while 4 mg/kg may be sufficient in Spain. The authors concluded that differences in the susceptibility of MRSA strains among countries may be responsible for differences in the antibiotic dose selection and suggest use of local MIC values to achieve success of a PK/PD model to achieve eradication of disease condition. As part of preclinical study, the effect of antimicrobials against infections caused by Staphylococcus aureus and S. epidermidis was evaluated in a novel in vitro PK/PD model of bacterial biofilm by Hall Snyder et al. 2015. Some persistent bacteria that can cause chronic infections are resistant to antibiotics due to their inherent property to form biofilms. Biofilms shield the bacteria against antibiotics and thereby posing an imminent threat especially in chronically infected or immunocompromised patients. Biofilm forming bacteria are difficult to eradicate from severe infection sites such as lungs of cystic fibrosis patients. They are equally notorious and resistant to eradication when associated with medical implants (Taraszkiewicz et al. 2013; Sanchez et al. 2013). Hence, this model by Hall Synder and coworkers could be further extended to study antimicrobials targeting other Gram-positive biofilm forming bacteria. The in vitro model consists of a CDC biofilm reactor (CBR) modified to run PK/PD and simulating human PK in order to evaluate the in vitro activity of antimicrobials. Biofilm conditioning is performed prior to initiation of drug therapy initiation followed by continuous flow with peristaltic pumps in specific media. Upon completion

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of conditioning and continuous flow phases, boluses of antibiotics are injected into the reactor. Free drug concentrations were used, and simulated regimens were included. The model was used to study effect of high doses of Daptomycin versus Vancomycin either alone or in combination with Clarithromycin or Rifampin to treat infections caused by Staphylococcus species. Biofilmembedded cell concentrations (mean and standard deviation in CFU/cm2) were then computed. Time kill curves were plotted to determine total reduction in CFU counts and the therapeutic enhancement of combination regimens was calculated statistically. Pharmacokinetic samples were obtained through the injection port of each model. PK and PD parameters to verify target antibiotic concentrations were obtained at the same time points. PK parameters were estimated using routine procedures. The half-life (t1/2), area under the curve (AUC), and fCmax were determined by the trapezoidal method utilizing software tools like the PK Analyst software (Hall Snyder et al. 2015). It is essential to maintain appropriate growth controls and replicates for any set of antibiotics to be tested in such model systems. Their study highlights that combinations of Daptomycin þ Rifampin and Vancomycin þ Rifampin were the most effective against biofilmassociated staphylococcal infections. Daptomycin þ rifampin with the best activity emerged as a promising drug combination that could be used as a future regimen to treat resistant biofilm-associated staphylococcal infections. A similar but novel in vitro biofilm model has also been developed by Parra-Ruiz et al. in 2010. The authors used their model to assess the in vitro activities of several antimicrobials alone or in combination against Staphylococcus aureus isolates. Daptomycin, Vancomycin, and Moxifloxacin were evaluated either alone or in combination with Clarithromycin or Rifampin. This study was also performed using an in vitro model, which consisted of a CDC biofilm reactor wherein the concentrations of biofilm-embedded bacteria were computed and plotted to graph time-kill curves. The results clearly indicate that combinations of Daptomycin, Moxifloxacin with Clarithromycin were the most effective

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(P < 0.01) regimens. This may represent future regimen to treat persistent infections of biofilm forming Gram-positive bacteria such as S. aureus and Streptococcus pneumoniae.

Gram-Negative Bacterial Infections Antimicrobial resistance in Gram-negative bacteria is increasing at an alarming rate. Additionally, the lack of new antibiotics limited treatment option to reappraise of currently available antibiotics. Cefepime, Ceftriaxone, Imipenem, and Piperacillin-Tazobactam antibiotics have been extensively used worldwide during the last three decades to treat acute/chronic hospital acquired infections. These antibiotics are resourceful in treatment of infections caused by Gram-negative bacteria like Pseudomonas, Acinetobacter, Klebsiella, Enterobacter, Serratia, Stenothermophilus, Proteus, and Citrobacter (Zervos and Nelson 1998; Drago and De Vecchi 2008; Saltoglu et al. 2010). In the past decades, antibiotic resistance is on the surge, and PK/PD modeling based on Monte Carlo simulations can be used reliably to predict the efficacy of antimicrobial regimes against an array of Gram-negative bacteria. This can foster microbial eradication and speed up the recovery rates of infectious diseases (Bonate 2001; Eagye et al. 2007). A two compartment multiple dose model was used by Eagye et al. in 2007 to determine 24-h concentration-time profile at steady-state conditions for different drugs against Gram-negative bacteria causing fatal infections. Patient-derived pharmacokinetic values combined with 5000 trial Monte Carlo simulation was used to determine the predicted cumulative fraction of response (CFR) values. The probability that the selected antibiotic regime will either meet or exceed a predefined pharmacodynamic target at a given MIC dilution (PTA) was calculated for different antibiotics. The target index f T > MIC was selected as the PD property of the drugs. The results of the study indicate that for Carbapenems, 40% f T > MIC was considered bactericidal; 50% f T > MIC for PiperacillinTazobactam and Cephalosporins. A CFR of 90% was the set threshold for reliable empirical

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therapy. A p-value of  0.05 indicated a statistically significant outcome. The confidence intervals were calculated at α = 0.05 using Newcomb Wilson method without correction for continuity. A novel standardized time kill-curve assay has been developed by Foerster et al. 2016. This assay and the subsequent pharmacodynamic modeling can be used to evaluate existing and novel antimicrobials against different strains of Neisseria, which have developed resistance to first-line empirical monotherapy. GraverWade (GW) medium, which supports the growth of a wide range of N. gonorrhoeae auxotypes and clinical isolates, was used. The time kill assay is a useful assay as described earlier and can be used for any antibiotic-pathogen combination. For time-kill curve analyses, N. gonorrhoeae was grown in GW medium in the presence of the desired antibiotics, covering a range of dilutions. A 0.5 McFarland inoculum of the test strains has to be prepared. Following this, 30 μl of the inoculum was diluted in 15 ml prewarmed (37  C) antimicrobial-free GW medium and 90 μl per well was dispensed in round bottom 96-well microtiter plates. Plates were preincubated and 10 μl of the antimicrobial concentrations (or PBS in case of drug free control) was added to each well containing the reincubated bacteria. The growth rate was estimated as the coefficient of a linear regression from the logarithm of the colony counts. Pharmacodynamic model with Eq. 10 as described by Regoes et al. 2004 was used in the study. ψ ðaÞ ¼ ψ max h i ðψ max  ψ min Þða=zMICÞk i h ða=zMICÞk  ðψ max =ψ min Þ

(10)

ψ max describes the maximal growth rate of the bacteria in the absence of antimicrobial agent, while ψ min represents the minimal bacterial growth rate at high antimicrobial concentrations. zMIC is the pharmacodynamic MIC value where the bacterial growth rate is zero (ψ (zMIC) = 0). k is the Hill coefficient, describing the steepness of the sigmoidal relationship between bacterial

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growth and antimicrobial concentration. All parameters were estimated using the R software package and bacterial growth rates are estimated from the time-kill curves by linear regression. The pharmacodynamic model was finally fitted to the estimated growth rates at different antimicrobial concentrations. The authors suggest that PD parameters based on a wide range of concentrations below and above the MIC can provide valuable information that could be useful for improving future dosing strategies. PD parameters obtained using time kill analysis coupled to evaluate the potential of Ciprofloxacin as an anti-infective agent (Schuck et al. 2005). Ciprofloxacin has been widely used for the treatment of a variety of nosocomial infections, especially in intensive care units (ICUs). Ciprofloxacin is a second-generation fluoroquinolone, which exhibits rapid concentration-dependent bactericidal activity against most Gramnegative aerobic species like Pseudomonas aeruginosa and Acinetobacter baumannii. A recent investigation by Khachman et al. (2011) provides an extensive and simplified methodology for optimizing ciprofloxacin dosing in ICU patients mediated by the use of population pharmacokinetic–pharmacodynamic analysis and Monte Carlo simulations. This model can be used as a guideline for studying various other antibiotics active against Gram-negative pathogens. The population PK model was based on a clinical study employing 102 ICU patients. Two sets of PK/PD simulations were carried out with each 10,000 patients simulated per dosage regimen investigated. The first set included complete distribution of MIC values including susceptible and resistant strains, while the second set was carried out across each MIC value according to a geometric progression from 0.002 mg/L to 2 mg/L. This model was also a predictor to determine possible continued usage of ciprofloxacin for the treatment of Gram-negative infections in ICU patients due to their potential to develop resistances. Such population PK/PD modeling could be of great value for studying efficacy of existing and novel antimicrobials against infectious diseases. Colistin or its inactive prodrug Colistin methanesulfonate (CMS) is increasingly used as

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a last-line therapy to treat infections caused by multidrug-resistant (MDR) Gram-negative pathogens (Dudhani et al. 2010a). With significant improvements in the understanding of the chemistry, PK/PD characteristics and their interrelationship, substantial progress has been made in optimizing use of CMS in clinics. This includes the first scientifically based dosing algorithm for critically ill patients receiving CMS to generate a desired target steady-state plasma concentration of formed Colistin. Most PD data on Colistin has been generated using in vitro models. Colisitin exhibits a strong concentration-dependent killing against P. aeruginosa, A. baumannii, and K. pneumoniae and their MDR strains as indicated time-kill studies in static and dynamic systems. A consistent finding of both in vitro and in vivo studies is regrowth with Colistin monotherapy, even with concentrations above those which can be safely achieved clinically (Nation et al. 2014). Although owing to increased resistance as indicated by regrowth with Colistin monotherapy in in vitro and in vivo studies, combination therapy may be beneficial. It still appears that Colistin is the most promising antimicrobial for severe infections caused by Gram-negative bacteria. Studies have been performed employing dose-fractionation design to investigate the PK/PD index, which best correlates with Colistin efficacy. The overall killing effect was best correlated with ƒAUC/MIC followed by ƒT > MIC (Nation et al. 2014). Studies in neutropenic thigh and lung mouse infection models again indicated that the PK/PD index that best correlated with Colistin efficacy was fAUC/MIC. Dose-fractionation studies with Colistin were conducted against P. aeruginosa strains, its MDR clinical strain, and a strain from cystic fibrosis patient to determine this index. The relationships between antibacterial effects and PD parameters were examined using an inhibitory sigmoid maximum-effect model (Dudhani et al. 2010a). Similar studies for other antimicrobials can be conducted to define optimum dosage regimens in humans. The models of antimicrobial therapy designed for planktonic infections are often of little importance in treating infections by biofilm forming bacteria. Additionally, models derived from two compartment

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analysis simulating the blood and various tissues as two compartments may also not be completely mimicking the exact conditions in biofilms. In 2015, Cao et al. have designed a new in vitro model which could mimic the conditions during the biofilm formation by bacteria. This seaweed alginate-embedded biofilm model allows the simultaneous measurement of antibiotics within the matrix and parallel bacterial killing. The authors hypothesized that biofilms may be considered as independent compartments with particular pharmacokinetics. The biofilm model was used to study the antibiotic penetration and concomitant killing of Pseudomonas aeruginosa by Tobramycin. Tobramycin is the drug of choice for treating biofilm-associated infections of P. aeruginosa like cystic fibrosis (Herrmann et al. 2010). Additionally, Pawar et al. 2015 showed the biofilm associated tolerance toward clinical antibiotics in murine tumor model. Such in vivo biofilm models will be able to simulate the clinical pharmacokinetics of antibiotics and could be an ideal model for testing new treatment strategies.

Pharmacodynamic Indices for Optimal Therapy As described earlier, for any antimicrobial agent a particular PD parameter correlates best with the successful eradication of an infectious agent. Time above MIC (T > MIC) correlates best with the activity of β lactams. On the hand, AUC24/ MIC is adjudged the best for aminoglycosides and fluoroquinolones. However, sometimes in case of these two classes of antibiotic Cpmax/MIC is used (Zhanel 2001). A detailed account of PK/PD indices for all antibiotics is beyond the scope of this chapter, so here we describe representatives of the most widely studied values. Additionally, Table 1 briefly describes few antibiotics and their pharmacodynamic parameters. For β lactams, a minimum T > MIC of 40–50% of the dosing interval is required to exercise bactericidal effects and a corresponding bacteriological cure of greater than 85%. Maximal bactericidal effects are achieved when T > MIC ranges between 60 and 70% of the dosing interval

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Table 1 Pharmacodynamics of clinically available antibiotics

Antibiotic Colistin

Effective against/ Treatment of Acinetobacter baumannii

Relevant pharmacodynamic parameter fAUC/MIC

Colistin

Pseudomonas aeruginosa

fAUC/MIC

Setting Murine thigh and lung infection model In vitro model

Tobramycin

Pseudomonas aeruginosa

fAUC/MIC, T > MIC

In patient studies

Levofloxacin

Pseudomonas aeruginosa

Amikacin

Gram-negative bacteria

AUC24/MIC, Cpmax/MIC T > MIC & log AUC

Cefepime, Ceftriaxone, Imipene, PiperacillinTazobactam

Gram-negative bacteria like Pseudomonas, Acinetobacter, Klebsiella, Enterobacter, Serratia, Stenothermophilus, Proteus, Citrobacter Staphylococcus aureus

Phase IV clinical trial Thigh infection and pneumonia in mice models In vitro two compartment model

Trovafloxacin, Ciprofloxacin Daptomycin, Vancomycin, Tigecycline, Linezolid Ciprofloxacin

Telavancin, vancomycin plus Aztreonam or PiperacillinTazobactam CeftriaxoneSublactam

T > MIC

AUC/MIC

Bergen et al. 2010 Mouton et al. 2005 Lee et al. 2007 Craig et al. 1991

Eagye et al. 2007

In vitro dynamic model In vitro PK/PD model of bacterial biofilm Healthy human volunteers

Firsov et al. 1999

Reduction in log10 CFU/ml

Clinical trials

Yim et al. 2016

T > MIC

Clinical trials

Sharma et al. 2016

Invasive MRSA infections including S. aureus bacteremia

AUC24/MIC

Broad spectrum, treatment of acute uncomplicated and complicated urinary tract infections and uncomplicated pyelonephritis Mixed infection by Pseudomonas aeruginosa, Escherichia coli, and methicillin-resistant Staphylococcus aureus

AUC24/MIC

Bacteria causing complicated urinary tract infections

Reference Dudhani et al. 2010b

Hall Snyder et al. 2015 Schuck et al. 2005

AUC24 – area under the curve at 24 h; Cpmax – maximum serum concentration; MIC – minimal inhibitory concentration; T – time; T > MIC – time over MIC; CFU – colony forming units; ml – milliliter

as suggested by animal studies and clinical trials in otitis media (Craig and Andes 1996; Andes and Craig 1998). Additionally, animal studies indicate that for stasis, a f T/MIC of 20% is required for Carbapenams, 30% for Penicillins, and 40% for Cephalosporins. Maximal efficiency also described as 2 log reduction in CFU requires f T/ MIC 40% for Carbapenams, 50% for Penicillins, and 50–70% for Cephalosporins (Crandon and

Nicolau 2011). In a murine thigh infection model to evaluate the effect of Amoxicillin and Amoxicillin-Clavulanate against Streptococcus pneumoniae, highest mortality rates (80–100%) were seen when serum levels exceeded the MIC for less than 20% of the dosing interval. Maximal survival was approached when serum levels exceeded the MIC for 40% of the 8-h dosing interval (Andes and Craig 1998). Their studies

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demonstrate that a reduction of 1 log10 or greater in CFU/thigh at 24 h is consistently observed when amoxicillin levels exceed the MIC for 25–30% of the dosing interval. The Aminoglycosides and Fluoroquinolones exhibit a concentration dependent killing. For Fluoroquinolones, an AUC24/MIC ratio of at least 125 is required to successfully treat respiratory tract infections caused by Gram-negative bacteria in terminally ill, elderly patients (Schuck et al. 2005). Based on in vitro, in vivo, and clinical data, it has been suggested that the PK/PD parameter AUC24/MIC describes the activity of Vancomycin the best, therefore giving an exact idea about time related bacteriological outcome in patients with respiratory tract infections caused by S. aureus. An AUC24/MIC value  400 is required to improve the outcome of patients with severe staphylococcal infections (Moise-Broder et al. 2004). However, this value is relatively high when compared with other antibiotics.

Pathophysiological Conditions Leading to Treatment Failures In spite of the in depth analysis of PK/PD parameters for comprehensive assessment of drug efficacy and undue toxic effects, most of the treatment regimen do not result in the optimal outcome often and thus lead to treatment failures. This is a major concern especially in critically ill patients and must be carefully assessed and addressed to realize successful treatment of acute and chronic infectious diseases. There are often different reasons contributing to reduced or incomplete cure in critically ill patients (Bamberger 1997). The major reason is attributed to the fact that PK/PD parameters are mostly studied in infection models/animals. Laboratory based in vitro and animal data are further used to determine antimicrobial PD, which determines the initial dosing regimens to be used in clinical practice. However, it is observed that the PK/PD of antimicrobials in critically ill patients/patients with severe infections differ significantly from the patient groups from whose data the prospective dosing regimens had been developed. This often

S. Bhuyan et al.

leads to entirely different/inadequate antimicrobial concentrations at the site of infection in terminally ill patients and thus to poor outcomes. Such data give a general regimen to be used in clinical scenario, but often fail in individual patients due to differences in immunity and other health parameters. The vast array of pathophysiological changes occurring in patients suffering from infectious disease with varying degree of severity can complicate antibiotic dosing. Pharmacokinetics may be vastly altered in case of critical illness, hepatic dysfunction, sepsis, burns, pregnancy, cystic fibrosis, or other primary and secondary bacterial infections. In such case, dosing may fail and individualized therapy may be necessary. For example, in case of sepsis low plasma concentration of drugs can be a result of either an increase in CL or Vd. This may be a result of increased cardiac output or increased capillary permeability (McKinnon and Davis 2004; Udy et al. 2008). Myocardial depression leading to decrease in organ perfusion and ultimately microvascular circulation failure adds to inefficiency of antibiotic regimen. In other cases, sepsis can induce multiple organ dysfunction, including renal and/or hepatic dysfunction, leading to drastic decrease in antibacterial clearance (Roberts and Lipman 2009). In case of severe burns, serum concentrations of antibiotics may fall below the MICs of infecting pathogens, implicating an increased requirement of the drug or in some cases frequent dosing of the antibiotic (Dryden et al. 2011). Therefore, there is a potential for patients who are critically ill with sepsis or burns to have subinhibitory serum concentrations of Linezolid with standard dosing regimens. These altered PK factors in critically ill patients can have a significant effect attainment of adequate antibiotic doses. Moreover, considerable interpatient variability in drug absorption, distribution, metabolism, and elimination; protein binding; and tissue absorption can have a profound effect on the ability to achieve pharmacodynamic targets at conventional doses (McKinnon and Davis 2004; Vincent et al. 2016). Additionally, it is seen that the antimicrobial PK profile of small animal models can be extremely different to that in humans. The drug

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retention and/or clearance may be substantially different. Therefore, careful dosing strategies must be designed with care. Most importantly, bacteria embedded in biofilms pose a serious challenge for antimicrobial action and is the major reason for failure of dosage regimens in fatal infections. Similarly, most of the chronic infections are due to mixed bacterial species or super-infections which possess an even greater threat. Such diseases are often critical and have extremely low survival rates. Hospital/ ventilator-associated pneumonia, sepsis, diabetes foot infection are few such cases which warrant attention. Under such situations, often optimal PD targets cannot be achieved using single antibiotic and combination therapy may be required (McKinnon and Davis 2004; Tamma et al. 2012; Yim et al. 2016).

Overcoming Treatment Failures To overcome the issue of treatment failures, several factors affecting PK/PD characteristics should be evaluated to achieve the desired pharmacodynamic targets in antimicrobial selection. A mere consideration of MIC values may not be sufficient. Additional consideration of patientspecific pharmacokinetic variation must be made. Concentration–time data of antimicrobials must be studied in terminally ill patients when developing population PK models. These will give a realistic dosing regimen that will account for drastically altered drug concentrations in patients (McKinnon and Davis 2004; Tängdén et al. 2017). Success can be achieved by employing therapeutic drug monitoring (TDM). TDM involves measurement of drug concentrations and dose adjustment based on the observed concentration in relation to a target drug exposure. Antimicrobial TDM can not only minimize drug toxicity, but also maximize drug efficacy leading to an overall therapeutic effect. TDM thus can be used for infected critically ill patients where early dose adaptation to the needs of the individual patient can give better outcome. Furthermore, TDM can be of great importance for such patients where prompt appropriate antibiotic therapy is

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crucial. Additionally, nomograms can be designed where PK characteristics derived from more than one patient can be used in models to determine dosing regimens (Tängdén et al. 2017). In case of tuberculosis patients with a greater risk of treatment failure, TDM ensures appropriate serum drug concentrations and may assist in the clinical decision-making process (Nuermberger and Grosset 2004). A good outcome of an infection episode may be achieved by early institution of appropriate antimicrobial therapy. Depending on the nature of the etiological agent, either a specific antimicrobial agent or combination of agents can be effective in achieving targeted infection clearance especially in critically ill patients (Bush and Levison 1988). Depending on the type of infection, severity of illness, or possible immunosupression optimized dosing regimens can be designed for different combination of antibiotics. When studying the combination of drugs and their time–kill curves in vitro PK/PD models, differences in their PK properties can be taken into account. Prediction of PD target attainment can be made based on this study and specifically designed dosing regimens can be effective over normal regimens. However, these should be should be validated in clinical studies before they can be used in general practice. Combination therapy involving two or more antibiotics can be used to treat complicated fatal/ chronic diseases by mixed infections of Grampositive and Gram-negative bacteria. However, a forehand analysis of the additive, synergistic, or antagonistic effect of the drugs is essential before such regimens can be introduced. In case of TB treatment, combination therapy is in vogue. It benefits from the additive and/or synergistic effects of antimycobacterial agents. This is practiced as there is a high potential for the development of drug resistance using monotherapy. Models similar to the in vitro PK/PD models for simultaneous simulation of serum kinetics of two or more drugs with different half-lives developed against Streptococcus pneumonia and Staphylococcus aureus could be used for development of anti-TB agents (Nuermberger and Grosset 2004; Vaddady et al. 2010). Combination therapy using Piperacillin/Tazobactam or Imipenem/Cilastatin

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can be used to treat mixed bacterial foot infections in diabetic patients (Saltoglu et al. 2010). It has also been found useful in the treatment of chronic biofilm infection by Pseudomonas aeruginosa in murine tumor model (Herrmann et al. 2010; Pawar et al. 2015). Clinical trials have been made to compare the efficacy of combination therapy for treatment of hospital-acquired pneumonia. Pharmacodynamic interactions between Telavancin and Aztreonam or Piperacillin/Tazobactam were evaluated against P. aeruginosa, E. coli, and S. aureus using an in vitro one compartment PK/PD model (Yim et al. 2016).

Conclusion To counter the antimicrobial resistance and the persistence of bacteria in chronic infections, PK/ PD properties should be considered from the point of selection of antibiotics. Additionally, it is of high importance to understand the nature of bacterial infection, the antibiotic resistance profile, and biofilm formation ability of the bacteria. An in depth analysis of PK/PD of antibiotics and use of right models and simulation data is essential for revamping existing treatment regimens. The same procedure should be considered while developing/evaluating new strategies against fatal infectious diseases. Incorporation of region or patient specific PK/PD values would be key factor determining successful outcome. Additionally, choice of particular model or antibiotic/s against infectious diseases should be governed by an extensive investigation of the causative agent/s, resistance mechanisms, underlying patho-physiological stage, and PK/PD parameters.

References and Further Reading Andes D, Craig WA (1998) In vivo activities of amoxicillin and amoxicillin-clavulanate against Streptococcus pneumoniae: application to breakpoint determinations. Antimicrob Agents Chemother 42:2375–2379 Bamberger D (1997) Antibiotics: why they fail in patients who are critically ill. Crit Care Nurs Q 20:60–68

S. Bhuyan et al. Bergen PJ, Bulitta JB, Forrest A, Tsuji BT, Li J, Nation RL (2010) Pharmacokinetic/pharmacodynamic investigation of colistin against pseudomonas aeruginosa using an in vitro model. Antimicrob Agents Chemother 54:3783–3789 Bonate PL (2001) A brief introduction to Monte Carlo simulation. Clin Pharmacokinetic 40:15–22 Bush LM, Levison ME (1988) Antibiotic selection and pharmacokinetics in the critically ill. Crit Care Clin 4:299–324 Canut A, Isla A, Betriu C, Gascón AR (2012) Pharmacokinetic – pharmacodynamic evaluation of daptomycin, tigecycline, and linezolid versus vancomycin for the treatment of MRSA infections in four western European countries. Eur J Clin Microbiol Infect Dis 31(9):2227–2235 Cao B, Christophersen L, Thomsen K, Sønderholm M, Bjarnsholt T, Jensen PØ, Høiby N, Moser C (2015) Antibiotic penetration and bacterial killing in a Pseudomonas aeruginosa biofilm model. J Antimicrob Chemother 70:2057–2063 Carlton KK Lee, Michael P Boyle, Marie Diener-West, Lois Brass-Ernst, Michelle Noschese, Pamela L Zeitlin (2007) Levofloxacin Pharmacokinetics in Adult Cystic Fibrosis. Chest 131 (3):796-802 Craig WA, Andes D (1996) Pharmacokinetics and pharmacodynamics of antibiotics in otitis media. Pediatr Infect Dis J 15:944–948 Craig, WA, Redington, J, Ebert, SC Pharmacodynamics of amikacin in vitro and in mouse thigh and lung infections. J. Antimicrob. Chemother. 27 (Suppl.) S29–S40 (1991). Crandon JL, Nicolau DP (2011) Pharmacodynamic approaches to optimizing beta-lactam therapy. Crit Care Clin 27:77–93 Crull K, Weiss S (2011) Antibiotic control of tumor-colonizing Salmonella enterica serovar Typhimurium. Exp Biol Med 236:1282–1290 Czock D, Keller F (2007) Mechanism-based pharmacokinetic–pharmacodynamic modeling of antimicrobial drug effects. J Pharmacokinet Pharmacodyn 34:727–751 Dayneka NL, Garg V, Jusko WJ (1993) Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 21:457–478 Drago L, De Vecchi E (2008) The safety of cefepime in the treatment of infection. Expert Opin Drug Saf 7:377–387 Dryden M, Johnson AP, Ashiru-Oredope D, Sharland M (2011) Using antibiotics responsibly: right drug, right time, right dose, right duration. J Antimicrob Chemother 66:2441–2443 Dudhani RV, Turnidge JD, Coulthard K, Milne RW, Rayner CR, Li J, Nation RL (2010a) Elucidation of the pharmacokinetic/pharmacodynamic determinant of colistin activity against Pseudomonas aeruginosa in murine thigh and lung infection models. Antimicrob Agents Chemother 54:1117–1124

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Dudhani RV, Turnidge JD, Nation RL, Li J (2010b) fAUC/ MIC is the most predictive pharmacokinetic/pharmacodynamic index of colistin against Acinetobacter baumannii in murine thigh and lung infection models. J Antimicrob Chemother 65:1984–1990 Eagye KJ, Nicolau DP, Lockhart SR, Quinn JP, Doern GV, Gallagher G, Abramson MA (2007) A pharmacodynamic analysis of resistance trends in pathogens from patients with infection in intensive care units in the United States between 1993 and 2004. Ann Clin Microbiol Antimicrob 6:11 Felmlee MA, Morris ME, Mager DE (2012) Mechanismbased pharmacodynamic modeling BT. In: Reisfeld B, Mayeno AN (eds) Computational toxicology, vol I. Humana Press, Totowa, pp 583–600 Firsov AA, RG Vasilov, SN Vostrov, OV Kononenko, I Yu Lubenko, S H Zinner, (1999) Prediction of the antimicrobial effects of trovafloxacin and ciprofloxacin on staphylococci using an in-vitro dynamic model. Journal of Antimicrobial Chemotherapy 43 (4):483-490 Foerster S, Unemo M, Hathaway LJ, Low N, Althaus CL (2016) Time-kill curve analysis and pharmacodynamic modelling for in vitro evaluation of antimicrobials against Neisseria gonorrhoeae. BMC Microbiol 16(1):1–11 Gloede J, Scheerans C, Derendorf H, Kloft C (2010) In vitro pharmacodynamic models to determine the effect of antibacterial drugs. J Antimicrob Chemother 65:186–201 Hall Snyder AD, Vidaillac C, Rose W, McRoberts JP, Rybak MJ (2015) Evaluation of high-dose daptomycin versus vancomycin alone or combined with clarithromycin or rifampin against Staphylococcus aureus and S. epidermidis in a novel in vitro PK/PD model of bacterial biofilm. Infect Dis Ther 4:51–65 Herrmann G, Yang L, Wu H, Song Z, Wang H, Høiby N, Ulrich M, Molin S, Riethmüller J, Döring G (2010) Colistin-tobramycin combinations are superior to monotherapy concerning the killing of biofilm Pseudomonas aeruginosa. J Infect Dis 202:1585–1592 José Pérez-Urizar, Vinicio Granados-Soto, Francisco J Flores-Murrieta, Gilberto Castañeda-Hernández, (2000) Pharmacokinetic-Pharmacodynamic Modeling. Archives of Medical Research 31 (6):539-545 Lorenz A, Pawar V, Häussler S, Weiss S (2016) Insights into host–pathogen interactions from state-of-the-art animal models of respiratory Pseudomonas aeruginosa infections. FEBS Lett 590:3941–3959 Mager DE, Diversity of Mechanism-Based Pharmacodynamic Models. Drug Metabolism and Disposition 31 (5):510-518 McKinnon PS, Davis SL (2004) Pharmacokinetic and pharmacodynamic issues in the treatment of bacterial infectious diseases. Eur J Clin Microbiol Infect Dis 23:271–288 Meibohm B, Derendorf H (2002) Pharmacokinetic/pharmacodynamic studies in drug product development. J Pharm Sci 91:18–31

341 Michael J, Barth A, Kloft C, Derendorf H (2014) Pharmacodynamic in vitro models to determine the effect of antibiotics. In: Vinks AA, Derendorf H, Mouton JW (eds) Fundamentals of antimicrobial pharmacokinetics and pharmacodynamics. Springer New York, New York, pp 81–112 Moise-Broder PA, Forrest A, Birmingham MC, Schentag JJ (2004) Pharmacodynamics of vancomycin and other antimicrobials in patients with Staphylococcus aureus lower respiratory tract infections. Clin Pharmacokinet 43:925–942 Mouton JW (2014) Setting clinical MIC breakpoints from a PK/PD point of view: it is the dose that matters. In: Vinks AA, Derendorf H, Mouton JW (eds) Fundamentals of antimicrobial pharmacokinetics and pharmacodynamics. Springer New York, New York, pp 45–61 Mouton J, Dudley M, Cars O, Derendorf H, Drusano G (2005) Standardization of pharmacokinetic/pharmacodynamic (PK/PD) terminology for anti-infective drugs: an update. J Antimicrob Chemother 55:601–607 Mouton JW, Brown DFJ, Apfalter P, Cantón R, Giske CG, Ivanova M, MacGowan AP, Rodloff A, Soussy C-J, Steinbakk M, Kahlmeter G (2012) The role of pharmacokinetics/pharmacodynamics in setting clinical MIC breakpoints: the EUCAST approach. Clin Microbiol Infect 18:E37–E45 Nation RL, Bergen PJ, Li J (2014) Pharmacokinetics and pharmacodynamics of colistin BT. In: Vinks AA, Derendorf H, Mouton JW (eds) Fundamentals of antimicrobial pharmacokinetics and pharmacodynamics. Springer New York, New York, pp 351–380 Nicolau DP (2003) Optimizing outcomes with antimicrobial therapy through pharmacodynamic profiling. J Infect Chemother 9:292–296 Nielsen EI, Friberg LE (2013) Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol Rev 65:1053–1090 Nuermberger E, Grosset J (2004) Pharmacokinetic and pharmacodynamic issues in the treatment of mycobacterial infections. Eur J Clin Microbiol Infect Dis 23:243–255 Parra-Ruiz J, Vidaillac C, Rose WE, Rybak MJ (2010) Activities of high-dose daptomycin, vancomycin, and moxifloxacin alone or in combination with clarithromycin or rifampin in a novel in vitro model of Staphylococcus aureus biofilm. Antimicrob Agents Chemother 54:4329–4334 Pawar V, Crull K, Komor U, Kasnitz N, Frahm M, Kocijancic D, Westphal K, Leschner S, Wolf K, Loessner H, Rohde M, Häussler S, Weiss S (2014) Murine solid tumours as a novel model to study bacterial biofilm formation in vivo. J Intern Med 276:130–139 Pawar V, Komor U, Kasnitz N, Bielecki P, Pils MC, Gocht B, Moter A, Rohde M, Weiss S, Häussler S (2015) In vivo efficacy of antimicrobials against biofilm producing Pseudomonas aeruginosa. Antimicrob Agents Chemother 59:AAC.00194-15

342 Regoes RR, Wiuff C, Zappala RM, Garner KN, Baquero F, Levin BR (2004) Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob Agents Chemothe 48:3670–3676 Roberts J, Lipman J (2009) Pharmacokinetic issues for antibiotics in the critically ill patient pharmacokinetic issues for antibiotics in the critically ill patient. Crit Care Med 37:840–851 Rybtke M, Hultqvist LD, Givskov M, Tolker-Nielsen T (2015) Pseudomonas aeruginosa biofilm infections: community structure, antimicrobial tolerance and immune response. J Mol Biol 427:3628–3645 Saltoglu N, Dalkiran A, Tetiker T, Bayram H, Tasova Y, Dalay C, Sert M (2010) Piperacillin/tazobactam versus imipenem/cilastatin for severe diabetic foot infections: a prospective, randomized clinical trial in a university hospital. Eur Soc Clin Infect Dis 16:1252–1257 Sanchez CJ, Mende K, Beckius ML, Akers KS, Romano DR, Wenke JC, Murray CK (2013) Biofilm formation by clinical isolates and the implications in chronic infections. BMC Infect Dis 13:47 Scaglione F, Paraboni L (2006) Influence of pharmacokinetics/pharmacodynamics of antibacterials in their dosing regimen selection. Expert Rev Anti-Infect Ther 4:479–490 Schuck EL, Dalhoff A, Stass H, Derendorf H (2005) Pharmacokinetic/pharmacodynamic (PK/PD) evaluation of a once-daily treatment using ciprofloxacin in an extended-release dosage form. Infection 33:22–28 Sharma A, Jusko WJ (1998) Characteristics of indirect pharmacodynamic models and applications to clinical drug responses. Br J Clin Pharmacol 45:229–239 Tamma PD, Cosgrove SE, Maragakis LL (2012) Combination therapy for treatment of infections with gramnegative bacteria. Clin Microbiol Rev 25:450–470 Tängdén T, Martín VR, Felton TW, Nielsen EI, Marchand S, Brüggemann RJ, Bulitta JB (2017) The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections. Intensive Care Med 43:1021–1032 Taraszkiewicz A, Fila G, Grinholc M, Nakonieczna J (2013) Innovative strategies to overcome biofilm resistance. Biomed Res Int 2013:1–13

S. Bhuyan et al. Udy A, Roberts J, Boots R, Lipman J (2008) Dose adjustment and pharmacodynamic considerations for antibiotics in severe sepsis and septic shock BT. In: Rello J, Restrepo MI (eds) Sepsis: new strategies for management. Springer Berlin Heidelberg, Berlin/Heidelberg, pp 97–136 Vaddady PK, Lee RE, Meibohm B (2010) In vitro pharmacokinetic/pharmacodynamic models in anti-infective drug development: focus on TB. Future Med Chem 2:1355–1369 Vincent J, Bassetti M, François B, Karam G, Chastre J, Torres A, Roberts JA, Taccone FS, Rello J, Calandra T, De Backer D, Welte T (2016) Advances in antibiotic therapy in the critically ill. Crit Care 20:1–13 Vishnu Dutt Sharma, Aman Singla, Manu Chaudhary, Manish Taneja, (2016) Population Pharmacokinetics of Fixed Dose Combination of Ceftriaxone and Sulbactam in Healthy and Infected Subjects. AAPS PharmSciTech 17 (5):1192-1203 Wayne (2014) Performance standards for antimicrobial susceptibility testing; Twenty-fourth informational supplement, CLSI document M100–S24. In: Clinical and laboratory standards institute report. Clinical and Laboratory Standards Institute, Wayne (PA), pp 1–230 Wilson SE, Graham DR, Wang W (2017) Telavancin in the treatment of concurrent Staphylococcus aureus bacteremia: a retrospective analysis of ATLAS and ATTAIN studies. Infect Dis Ther 6(3):413–422 Yim J, Smith JR, Barber KE, Hallesy JA, Rybak MJ (2016) Evaluation of pharmacodynamic interactions between telavancin and aztreonam or piperacillin/tazobactam against Pseudomonas aeruginosa, Escherichia coli and methicillin-resistant Staphylococcus aureus. Infect Dis Ther 5:367–377 Zervos M, Nelson M (1998) Cefepime versus ceftriaxone for empiric treatment of hospitalized patients with community-acquired pneumonia. The Cefepime study group. Antimicrob Agents Chemother 42: 729–733 Zhanel GG (2001) Influence of pharmacokinetic and pharmacodynamic principles on antibiotic selection. Curr Infect Dis Rep 3:29–34

Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer

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Fatih M. Uckun and Sanjive Qazi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344 Role of PD Studies in Translational Oncology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Role of PD Studies in Early Oncology Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 Role of PD Studies in Defining Optimized Treatments Regimens with a New Therapeutic Intervention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Role of PD for Identification and Development of Combined Treatment Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Pharmacodynamics Modeling for Development of Oncology Drugs . . . . . . . . . . . . . . 353 Emerging Role of Quantitative Systems Pharmacology for Model-Informed Drug Discovery and Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355

Abstract

Pharmacodynamics (PD) has been integral to the design of rational drug dosing regimens. Detailed PD studies during both the preclinical F. M. Uckun (*) AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA e-mail: [email protected]; [email protected] S. Qazi AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA Bioinformatics Program, Gustavus Adolphus College, St. Peter, MN, USA e-mail: [email protected]

and clinical stages of the drug development process can also contribute to lead optimization or the selection of the optimal “best-inclass” compound, improve clinical potency estimates, and help predict the drug exposure needed to achieve meaningful clinical responses. There has been a substantial and continued increase in the number of clinical oncology trials with integrated PD studies since 2002. Notably, a significant portion of all interventional clinical trials with PD components are initiated for evaluation of oncology drugs. PD studies frequently play a pivotal role in determining the initial dose level for first-inhuman clinical studies of immunooncology drugs. The integration of PD data into the dose safety modeling in early oncology studies

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_37

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may provide accurate predictions of the doseeffect relationships by advancing the understanding of target engagement as well as exposure response and therefore has the potential to improve the decision making regarding the optimal dose and schedules as well as riskbenefit assessments for later stages in clinical development. PD studies also have the potential to provide early clinical proof of concept when drugs with complementary activity profiles are combined in cancer therapy. In recent years, population pharmacokinetics–pharmacodynamics (PK-PD) modeling has become a key tool towards streamlining and optimizing oncologic drug development through early understanding, identification, and quantification of various dose–response relationships in the context of other patient characteristics as well as risk-benefit of different dosing schedules. Finally, a new and exciting strategy known as “Quantitative Systems Pharmacology” is emerging that advances systems level, multiscale models for disease progression and treatment to better characterize the hierarchical, non-linear, dynamic responses at the network level of drug action that may affect both efficacy and toxicity in clinical settings.

Introduction An improved understanding of the molecular pathology of cancers combined with the development of targeted therapeutics and immuno-oncology drugs that activate the host immunity against cancer cells (Turan et al. 2018; Socinski et al. 2018; Valla et al. 2018) has caused a paradigmshift in drug development and clinical trial methods by providing the foundation for personalized medicine strategies with specific roadmaps for the patient-tailored rational deployment of specific drugs in biomarker-enriched patient populations (Biankin et al. 2015; Tsimberidou et al. 2017; Phelan et al. 2018; DiNardo et al. 2018; Alsharedi et al. 2018; Drilon et al. 2018; Hidalgo et al. 2018; Mutti et al. 2018; Lu et al. 2016; Torres-Ayuso et al. 2018; Palmirotta et al.

F. M. Uckun and S. Qazi

2018; Peck 2015; Jamal et al. 2017; Krebs et al. 2016; Laetsch et al. 2017). Pharmacodynamics (PD) has been integral to the design of rational drug dosing regimens (FDA 2016). PD is the study of the relationship between drug concentration and its effects at the subcellular, cellular, tissue, organ system, or whole-body level, including all of the pharmacological actions, pathophysiological effects, and therapeutic activities, and adverse side effects of the active drug ingredient, therapeutic moiety, and/or its metabolite(s) (de Man et al. 2018; Derendorf et al. 2000; de Vries et al. 2018). While some PD studies require tissue biopsies, others use surrogate tissues such as blood cells or noninvasive methods such as anatomic or functional imaging. Some drugs result in activation of gene expression which can be leveraged in PD studies. For example, omaveloxolone is a semisynthetic oleanane triterpenoid that potently activates Nrf2 with subsequent antioxidant function. In a recently reported Phase 1 study (NCT02029729), downstream Nrf2 activation was assessed in peripheral blood mononuclear cells by quantification of target gene mRNA expression (Creelan et al. 2017). An increase in select Nrf2 target gene expression was observed during the course of treatment, across multiple dose levels. Mutant IDH1 produces high levels of 2-hydroxyglurate (2HG), thought to initiate oncogenesis through epigenetic modifications of gene expression. Inhibitors of the mutant isocitrate dehydrogenase 1 (IDH1) are being evaluated in patients with brain tumors. Recently, Andronesi et al. described an elegant noninvasive 3D MR spectroscopic neuroimaging method for rapid and easy detection of 2HG to study the PD of IDH305, an orally available, brain penetrant, mutant-selective allosteric high affinity IDH1 inhibitor that acts on both canonical (R132H) and noncanonical (R132C) mutated enzymes (Andronesi et al. 2018). The authors demonstrated the feasibility of image-based 2HG PD serial assessments and demonstrated that the IDH305 treatments of glioma patients during the NCT02381886 Phase 1 clinical study caused a rapid decline of 2HG levels by 70% as expected from an inhibitor of mutant IDH1.

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Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer

The purpose of this chapter is to discuss the role of PD studies in the drug development process with a focus on the integration of PD studies in contemporary clinical trials of oncology drugs.

Role of PD Studies in Translational Oncology The drug development strategies should encompass both the nonclinical and clinical stages of the life cycle of a promising new drug candidate. The quality of the nonclinical development, including the identification of robust biomarkers, noninvasive PD assays, well-defined relationships between the PD parameters and pharmacokinetics (PK) parameters, development of laboratory tests for predictive biomarkers, and PD analyses amenable to validation, has a direct and differentiating impact on the success of the early phase clinical development (Brennan et al. 2018). Most drug makers seek earlier decision making about go or no-go plans on the basis of PK and PD characteristics of their promising drug candidates. Detailed PK-PD studies during both the preclinical and clinical stages of the drug development process can also contribute to lead optimization or the selection of the optimal “best-in-class” compound, improve clinical potency estimates and help predict the drug exposure needed to achieve meaningful clinical responses. PD studies frequently play a pivotal role in determining the initial dose level for firstin-human clinical studies of immunostimulatory drugs according to the minimal anticipated biologic effect level (MABEL) approach by integrating all of the available in vitro and in vivo information by PK/PD modeling. It is also important to note that the insights and lessons learned from nonclinical PD studies often provide the foundation for highly promising combined modality regimens. For example, nonclinical PD studies demonstrated that the FDAapproved 2nd-line anti-chronic lymphocytic leukemia (CLL) drug Venetoclax targeting the antiapoptotic protein BCL2 potentiated/complemented the activities of and sometimes synergized with the Bruton’s tyrosine kinase (BTK) inhibitors

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Acalabrutinib and Ibrutinib. Tam et al. recently reported the results of a clinical study (NCT02471391) which demonstrated that dual targeting of BTK and BCL2 with Ibrutinib plus Venetoclax as part of an innovative treatment regimen results in significantly improved response rates and treatment outcomes in patients with mantle-cell lymphoma (Tam et al. 2018). The complete response rate at week 16 was 42%, which was markedly higher than the historical result of 9% at this time point with Ibrutinib monotherapy (Tam et al. 2018). According to the clinicaltrials.gov data repository, a total of 1,930 interventional clinical oncology trials with integrated PD studies were initiated between August 1994 and July 2018. Of these, only 33 (1.7%) were started between August 1994 and July 2002. There has been a substantial and continued increase in the number of clinical oncology trials with integrated PD studies since 2002 (Fig. 1): 224 trials (8.6-fold increase from previous 4 years) were initiated between August 2002 and July 2006, 474 trials (2.1-fold increase from previous 4-years) between August 2006 and July 2010, 564 trials (~19% increase from previous 4 years) between August 2010 and July 2014, and 635 (~13% increase from previous 4 years) between August 2014 and July 2018. Notably, ~62% of the clinical PD studies in oncology were initiated within the last 8 years. Hence, PD studies are playing an increasingly important role in the clinical development path of oncology drugs. Notably, a significant portion of all interventional clinical trials with PD components are initiated for evaluation of oncology drugs. Whereas 635 of the 2,076 clinical PD studies (30.6%) that were initiated between August 2014 and July 2018 were in patients with cancer, only 178 studies (8.6%) were in patients with neurological disorders (Neuro), 177 studies (8.5%) in patients with cardiovascular diseases (CVD), 136 studies (6.6%) in patients with pulmonary disease (PD), 54 studies (2.6%) in patients with allergic disorders (AD), and 129 studies (6.2%) in patients with autoimmune disorders (AI) (Fig. 2). There were more studies in cancer patients (viz.: 635 studies) than in patients with CVD, PD, AD,

F. M. Uckun and S. Qazi

Clinical Trials with Integrated PD Study

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Time Interval of Initiation for Clinical Trails

Fig. 1 Clinical trials with integrated PD studies in oncology. We interrogated the clinicaltrials.gov data repository (https://clinicaltrials.gov/) to determine the number of interventional trials that employed pharmacodynamic methods to characterize anti-cancer therapies from 1994

PD

to 2018 in 4 year increments. All Interventional trials that were started over the 4-year period were included in the totals. There was a total of 1,930 trials counted from 1994 to 2018. Search terms to identify the trials were “Pharmacodynamic,” “Interventional studies,” and “Cancer”

AI/AD

CANCER

OTHER

NEURO

AI/AD

CANCER

CVD

Fig. 2 Patient populations of clinical PD studies initiated between 2014 and 2018. We interrogated the clinicaltrials. gov data repository (https://clinicaltrials.gov/) to determine the number of interventional trials with integrated PD studies that were initiated between August 2014 and July 2018. All Interventional trials that were started over the 4-

CVD

NEURO

OTHER

PD

year period were included in the totals. Search terms to identify the trials were “Pharmacodynamic,” “Interventional studies,” and either “Cancer,” “Cardiovascular Diseases (CVD),” “Pulmonary Disease (PD),” “Autoimmune Diseases (AI),” “Allergic disorders (AD),” or “Neurological Disorder (Neuro)” to stratify the disease types

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Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer

and AD combined (viz.: 545 studies). Table 1 depicts a select list of actively recruiting clinical oncology trials with integrated PD components and estimated primary completion dates ranging from 01/01/2017 to 07/31/2018.

Role of PD Studies in Early Oncology Trials The primary objective of Phase I oncology trials is to determine the optimal dose of an agent or combination of agents that can be used as the recommended phase 2 dose (RP2D) (Cook et al. 2015; Caimi et al. 2017). The RP2D levels of anticancer agents are traditionally determined by dose-limiting toxicities (DLT) and correspond to the maximum tolerated dose (MTD) , which is the highest clinically-safe dose that is derived from DLT data obtained most commonly during the first few treatment cycles. Identification of the MTD is still the most commonly used method to identify the RP2D for oncology drugs. There is a need to reconsider the assessment of MTD for some medicinal products as a need for dose reduction is discovered in a high percentage of patients in Phase III trials, despite the absence of doselimiting toxicity (DLT) conventionally defined by Grade 3 and 4 events (Lavezzi et al. 2018). A recent workshop demonstrated that many FDA-approved anticancer drugs with molecular targets were subject to dose reductions in latestage registration trials to improve their tolerability (Jänne et al. 2016). As Phase I trials of molecularly targeted agents often do not use toxicity data beyond the first two cycles of treatment to determine the RP2D, it has been suggested that longitudinal relative dose intensity evaluations may be warranted to obtain more robust RP2D levels (Hirakawa et al. 2018). The comparisons of the on-target PD profiles of targeted therapeutics may help identify the best in class compounds and contribute to the proof of concept obtained early oncology trials involving in biomarker-enriched patient populations. For example, Acalabrutinib and Ibrutinib exhibited comparable on-target PD in regard to changes in CCL3/CCL4 chemokine production, migration

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assays, and changes in B-cell receptor signaling pathway proteins, and both are associated with high overall response rates and durable remissions in previously treated CLL (Patel et al. 2018). There is a growing consensus and enthusiasm among oncologists that a more comprehensive evaluation of the drug activity profile including its PD and PK features should be used to identify the RP2D levels. It has been reported that only one third of the studies used toxicity endpoints alone to determine the RP2D (Hansen et al. 2017). That is in part because new-generation targeted anticancer agents exhibit clinically meaningful activity at levels 25% of the MTD (Jain et al. 2010). The potential advantages of including multiple nontoxicity endpoints such as PD, PK, and efficacy with or without toxicity to define RP2D as an alternative to toxicity alone include the identification of better tolerable effective dose levels. For example, using an accelerated titration, 3 + 3 dose-escalation, open-label Phase I trial (NCT01940133) of continuous once-daily dosing (OD), Wicki et al. evaluated the safety, pharmacokinetics (PK), and pharmacodynamics of PQR309 in patients with advanced solid tumors. PQR309 is an orally bioavailable, balanced pan-phosphatidylinositol-3-kinase (PI3K), mammalian target of rapamycin (mTOR) C1, and mTORC2 inhibitor (Wicki et al. 2018). The MTD and RP2D of PQR309 was 80 mg of orally OD. PK was dose-proportional and PD showed PI3K pathway phosphoprotein downregulation in paired tumor biopsies (Wicki et al. 2018). Notably, “nonclassically” defined RP2Ds were associated with a statistically significant five-fold higher rate of FDA drug approval for individual anticancer drugs (Hansen et al. 2017). The commonly used Phase I/II designs with an expansion phase after determination of the MTD during a dose escalation phase allow for early evaluation of clinical activity across multiple MTD-based vs. PD-based RP2D levels. Adaptive trial designs with randomized evaluation of multiple RP2Ds provide the opportunity to select the “best” RP2D. There is general consensus among stakeholders that the first-in-human Phase I studies should be designed with focus on pharmacometrics tools and PK/PD-based nonsafety

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F. M. Uckun and S. Qazi

Table 1 Select list of currently recruiting interventional clinical studies with integrated PD components NCT study # NCT02448589

NCT03008018

NCT03450109

NCT02303028

NCT02619162

NCT02503709

NCT02679196

NCT02350868

Official title A Phase I, open-label, nonrandomized, dose-escalating safety, tolerability, pharmacokinetic and pharmacodynamic study of TAS-119 in patients with advanced solid tumors (https:// ClinicalTrials.gov/show/NCT02448589) An open-label ascending dose study evaluating the safety/tolerability, pharmacokinetic and pharmacodynamic effects of KA2507 in patients with solid tumors (https://ClinicalTrials.gov/show/ NCT03008018) A Randomized and Open-label Study to Assess Pharmacokinetics, Pharmacodynamics and safety of LY01005 versus goserelin comparator (ZOLADEX ®) following a single administration in patients with prostate cancer (https://ClinicalTrials.gov/show/ NCT03450109) A Phase I and enrichment study of lowdose metronomic Topotecan and Pazopanib in pediatric patients with recurrent or refractory solid tumors including CNS tumors (https:// ClinicalTrials.gov/show/NCT02303028) Nintedanib plus Letrozole in postmenopausal women with breast cancer: clinical trial phase 0/1 safety and pharmacodynamics (https:// ClinicalTrials.gov/show/NCT02619162) A Phase 1 trial of the combination of the heat shock protein-90 (HSP90) inhibitor Onalespib (AT13387) and the cyclindependent kinase (CDK) inhibitor AT7519M in patients with advanced solid tumors (https://ClinicalTrials.gov/ show/NCT02503709) An open-label ascending dose study evaluating the safety/tolerability, pharmacokinetic and pharmacodynamic effects of KA2237 In patients with B Cell lymphoma (https://ClinicalTrials.gov/ show/NCT02679196) A Phase 1, first-in-human, dose-seeking study evaluating the safety, pharmacokinetics, and pharmacodynamics of orally administered MPT0E028 in subjects with advanced solid malignancies without standard treatment (https://ClinicalTrials. gov/show/NCT02350868)

Sponsor Taiho Oncology

PD/PK study PD/PK of TAS119 (Aurora A KI)

Patient population Advanced solid tumors

Kaxus Therapeutics, Ltd

PD/PK of KA2507 (HDACi)

Advanced solid tumors

Luye Pharma Group, Ltd

PD/PK of LY01005 (Goserelin acetate microspheres)

Prostate cancer

The Hospital for Sick Children

PD/PK of Pazopanib (TKI)

Pediatric R/ R solid tumors and CNS tumors

Centro Nacional de Investigaciones Carlos III

PD/PK of Nintedanib (TKI)

Breast cancer

NCI

PD/PK of Onalespib (Hsp90i) and CDKI AT7519 (CDK1,2,4,6,9 i)

Advanced solid tumors

Karus Therapeutics

PD/PK of KA2237 (PI3Ki)

B-cell lymphoma

Taipei Medical University

PD/PK of MPT0E028 (HDACi)

Advanced solid tumors

(continued)

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Table 1 (continued) NCT study # NCT01977638

NCT02514239

NCT02605746

NCT02510001

NCT02501902

NCT02940132

Official title Phase 1 study to assess safety, tolerability, pharmacokinetics, and pharmacodynamics of CXD101 given orally (twice-daily dosing for 5 consecutive days in a 21-day period) in patients with advanced malignancies expressing the biomarker HR23B (https://ClinicalTrials.gov/show/ NCT01977638) An open-label, Phase I, dose escalation study to characterize the safety, tolerability, pharmacokinetics, and pharmacodynamics of intravenous doses of BI 836909 in relapsed and/or refractory multiple myeloma patients (https://ClinicalTrials.gov/show/ NCT02514239) A Phase 0/II study of Ceritinib (LDK378) in preoperative glioblastoma multiforme (GBM) and CNS metastasis patients scheduled for resection to evaluate central nervous system (CNS) penetration (https://ClinicalTrials.gov/ show/NCT02605746) A sequential Phase I study of MEK1/2 inhibitors PD-0325901 or Binimetinib combined With cMET inhibitor PF02341066 in patients with RAS mutant and RAS wild type (with aberrant cMET) colorectal cancer (https:// ClinicalTrials.gov/show/NCT02510001) An open-label Phase Ib study of Palbociclib (Oral Cdk 4/6 Inhibitor) plus Abraxane (registered) (nab-paclitaxel) in patients with metastatic pancreatic ductal adenocarcinoma (https://ClinicalTrials. gov/show/NCT02501902) Phase 1 study to assess the safety, tolerability, pharmacokinetics/ pharmacodynamics and preliminary efficacy of SC10914 in patients with advanced solid tumors (https:// ClinicalTrials.gov/show/NCT02940132)

endpoints to establish a more rationale dose finding paradigm in oncology drug development. The integration of PK/PD data into the dose safety modeling in early oncology studies may provide accurate predictions of the dose–effect relationships by advancing the understanding of target engagement as well as exposure response and

Patient population Advanced solid tumors, lymphoma, MM

Sponsor Oxford University Hospitals

PD/PK study PD/PK of CXD101 (HDACi)

Boehringer Ingelheim

PD/PK of BI836909 (antiBCMAxCD3 BiTE)

R/R MM

St Joseph Hospital Med Center

PD/PK of Ceritinib (ALKi)

GBM/CNS mets

University Oxford

PD/PK of Binimetinib (MEKi) and Crizotinib (ALKi/ ROS-1i)

Colorectal cancer

Pfizer

PD/PK of Palbociclib (CDK4/6 i) and Abraxane

Metastatic pancreatic ductal carcinoma

Jiangxi Qingfeng Pharmaceutical Co Ltd

PD/PK of SC10914 (PARPi)

Advanced solid tumors

therefore has the potential to improve the decision making regarding the optimal dose and schedules as well as risk-benefit assessments for later stages in clinical development (Grisafi et al. 2018). They may also help optimize the benefit–risk profile of oncology drugs through dose adaptation strategies for individualized dosing.

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Role of PD Studies in Defining Optimized Treatments Regimens with a New Therapeutic Intervention PD studies combined with PK and safety evaluations can provide actionable information regarding the alternative dose and schedule to realize the full clinical potential of a new therapeutic intervention. It was discovered that higher systemic exposures of the histone deacetylase inhibitor vorinostat were required than achieved in pediatric Phase I trials with continuous daily dosing for in vivo increased histone acetylation and cytotoxic activity. Consistent histone acetylation in peripheral blood mononuclear cells (PBMC) was only seen at the highest continuous dose level of vorinostat (300 mg/m2/dose), a dose determined to be too toxic in combination with isotretinoin in a study performed by the Children’s Oncology Group (COG) (NCT00217412) (Fouladi et al. 2010). At the continuous vorinostat MTD (230 mg/m2/dose), only transient histone acetylation was observed. Consequently, Pinto et al. conducted a Phase I trial in children with relapsed/refractory neuroblastoma to determine the MTD of vorinostat on an interrupted schedule, escalating beyond the previously identified pediatric MTD. The maximum intended dose of vorinostat (430 mg/m2/day) was tolerable when it was combined with isotretinoin. This dose led to increased vorinostat exposures and increased histone acetylation in surrogate tissues (viz., PBMC) when compared to lower doses of vorinostat (Pinto et al. 2018). Overall, the percent change from baseline in histone acetylation levels at 1 h post treatment was significantly greater in dose level 5 compared to dose levels 1–4 and this difference persisted for 24 h. The metabolism of drugs and therefore the pharmacogenomics (PG) has a substantial impact on systemic exposure levels of the parent compound as well as its metabolites and the risk/ severity of toxicities associated with them. The histone deacetylase inhibitors such as abexinostat, panobinostat, romidepsin, and vorinostat are eliminated through glucuronidation by UGT1A1. PD studies combined with PK and PG have demonstrated that polymorphisms (e.g.,

F. M. Uckun and S. Qazi

UGT1A1*28 and UGT1A1*60) that reduce UGT1A1 function cause increased systemic exposure, increased global protein lysine acetylation, and toxicities (e.g., thrombocytopenia) (Goey et al. 2016). Multiparameter modeling combining a population pharmacokinetic (PPK) model and a PD model describing the change in platelet levels in patients with cancer administered belinostat as a 48-h continuous intravenous infusion, along with cisplatin and etoposide, has been employed to optimize the treatment schedule and revealed that a q3week schedule of belinostat allows for sufficient platelet recovery before the next belinostat infusion is optimal (Peer et al. 2018). Many targeted therapeutics, especially tyrosine kinase inhibitors (TKI), inhibit multiple kinases even if they have been labeled as highly selective inhibitors of one particular tyrosine kinase (Uckun et al. 2002, 2007, 2010; Uckun and Qazi 2010). For example, the BTK inhibitor Ibrutinib has been shown to inhibit other tyrosine kinases, including SRC, LYN, FYN, HCK, LCK, YES1, and FGR at nanomolar concentrations (Uckun and Qazi 2010; Honigberg et al. 2010). Therefore, PD evaluations of such compounds should not be limited to a single target kinase occupancy or inhibition in order to better understand its on-target and off-target effects and design appropriate and data-driven risk mitigation strategies. Ilorasertib (ABT-348) inhibits Aurora and VEGF receptor (VEGFR) kinases. In patients with advanced solid tumors, PD studies indicated that ilorasertib treatment engages both of these intended targets, but with maximum inhibition of VEGFR family kinases occur at lower exposures than typically required for inhibition of Aurora B in tissue. In agreement with the PD data, the DLTs in the NCT01110486 clinical trial were predominantly related to VEGFR inhibition (Maitland et al. 2018). The combined PK and PD evaluations help determine clinical strategies for effective treatment of target patient populations (Stein et al. 2018; Tan et al. 2018; Tham et al. 2008). For example, for the BTK inhibitor Acalabrutinib that has an elimination half-life of 1 h., a twice daily (BID) dosing is used because it has been

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Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer

shown to maintain plasma concentrations that are associated with >95% target BTK occupancy over the treatment interval and inhibition of BTK phosphorylation and activity in peripheral blood circulating CLL cells (Byrd et al. 2016). Likewise, recent analyses of the quantitative relationship between duration of severe neutropenia (the efficacy endpoint) and area under effect curve of absolute neutrophil counts, the PD endpoint, based on data from filgrastim products, a human granulocyte colony-stimulating factor (G-CSF), have provided useful information regarding the relationship between ANC and duration of severe neutropenia that can be used for dose selection and optimization of clinical trial design for G-CSF (Li et al. 2018). Pegfilgrastim is a long-acting G-CSF indicated for prevention of febrile neutropenia in patients receiving myelosuppressive chemotherapy by promoting neutrophil recovery. In a Phase I, randomized, double-blind, three-way crossover trial in healthy volunteers, Waller et al. evaluated the PK, PD, safety, and tolerability of the proposed biosimilar, comparing MYL-1401H, reference pegfilgrastim (Neulasta®, Amgen Inc., Thousand Oaks, CA, USA) sourced from the European Union, and reference pegfilgrastim sourced from the USA. The primary PK and PD end points were similar across all groups. MYL-1401H demonstrated similar PK, PD, and safety to reference pegfilgrastim in healthy volunteers and may be an equivalent option for the prevention of febrile neutropenia (Waller et al. 2018). Likewise, combined PK/PD studies are often critical in determining if different administration routes of the same compound are equally effective and safe. For example, PK and PD (viz.: 20S proteasome inhibition) parameters of the proteasome inhibitor bortezomib following subcutaneous versus intravenous administration were very similar and this information together with the similar efficacy of subcutaneous versus intravenous bortezomib supports the approved routes of administration for bortezomib (i.e., intravenous and subcutaneous injection) (Moreau et al. 2012). That being said, there are multiple challenges in incorporating PK and PD endpoints, including but not limited to increased labor, resource

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utilization, and trial complexity; increased burden of multiple blood draws, tumor biopsies, and imaging for the patient populations with advanced cancer; and commonly the absence of validated robust assays that can be used to obtain reliable PD endpoints in clinical settings.

Role of PD for Identification and Development of Combined Treatment Modalities PD studies have the potential to provide early clinical proof of concept when drugs with complementary activity profiles are combined in cancer therapy (Rocchetti et al. 2009). The addition of the base excision repair inhibitor methoxyamine to fludarabine increases DNA double-strand breaks (Bulgar et al. 2010). Caimi et al. determined the safety, PK, PD, and RP2D of the base excision repair blocker methoxyamine combined with fludarabine in adult patients with relapsed/ refractory hematologic malignancies (Caimi et al. 2017). They reported that this drug combination resulted in increased DNA damage measured with the Comet assay, as documented by cumulative increases in comet tail length throughout the first week of the combined methoxyamine + fludarabine therapy, indicating progressive DNA damage. The highly significant correlation between decreases in circulating malignant lymphocytes and comet tail length highlighted the relevance of DNA double-strand break measurements as a surrogate PD marker of the antineoplastic effect of methoxyamine and fludarabine. Notably, methoxyamine combined with fludarabine was safe and well tolerated. Hematologic toxicity was comparable to single agent fludarabine. The PD studies therefore demonstrated the potential of this combination as part of conditioning regimens of stem cell transplant and use of methoxyamine as fludarabine dosesparing agent. Sometimes, combined use of multiple anticancer drugs results in excellent treatment outcomes, and the question arises if the therapeutic benefits could be further improved by reducing toxicities with fewer cycles of therapy. Functional/

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metabolic imaging using PET scans has been frequently applied as a PD measure of the activity of the multiagent regimen. For example, the intensive polychemotherapy regimen eBEACOPP (bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone in escalated doses) is highly active in patients with advanced-stage Hodgkin’s lymphoma, but it is also associated with toxicities. Borchmann et al. investigated in a randomized Phase 3 study whether metabolic tumor response as determined by PET after two cycles of standard regimen eBEACOPP would allow responsedirected adjustments of treatment intensity, increasing it for PET-positive patients or reducing it for PET-2-negative patients (NCT00515554) (Borchmann et al. 2018). PET negativity after two cycles allowed reduction to only four cycles of eBEACOPP without loss of tumor control. PET-guided eBEACOPP provided outstanding efficacy for all patients and increased the overall survival by reducing treatment-related risks for patients with excellent PET responses. PETguided personalized reduced intensity treatment strategies should be considered in patients undergoing treatment with highly active regimens for metabolically active tumors such as nonHodgkin’s lymphoma, Hodgkin’s lymphoma, NSCLC, breast cancer. Based on clinical safety and activity data in the NCT01063816 trial, poly-ADP-ribose polymerase (PARP) inhibitors like Veliparib (Niu et al. 2017) or Rucaparib will likely be used in combination with standard chemotherapy drugs especially for treatment of ovarian cancer (Gray et al. 2018). It will be important to obtain detailed PK and PD data using parent-metabolite PK modeling and PD of these targeted therapeutics to gain insights into drug–drug interactions in order to optimize the administration schedules for the various components of the treatment regimens. It is also important to evaluate the impact of food on the PK and PD of these drugs. For example, Rucaparib can be taken with or without food but has different PK parameters when taken with food (versus fasting) probably due to solubility in the small intestine (Dal Molin et al. 2018).

F. M. Uckun and S. Qazi

Tegafur/gimeracil/oteracil (S-1) and irinotecan combination is attractive for breast cancer refractory to anthracyclines and taxanes. A reduction in circulating endothelial cell progenitors (CEPs) used to monitor the PD of S-1 is strongly correlated with antiangiogenic effects. Because vascular endothelial growth factor-A-driven tumor angiogenesis for the formation of a functional vascular bed and the subsequent tumor growth partly depend on the mobilization of CEPs, a change in the CEP level may be a predictive marker for antiangiogenesis therapy. The CD34+ circulating endothelial cell (CEC) level was closely associated with the treatment response to chemotherapy, including S-1. Pharmacokinetics and reductions of CD34+ CECs as pharmacodynamics were also analyzed. There was an association between clinical benefit and reduction in baseline CD34+ CECs (4,6-diamino-2phenylindole (DAPI)+, CD45 , CD146+, or CD105+ and CD34+) by S-1. These results provided the foundation for combined use of irinotecan and S-1 in advanced GI malignancies (Ishiguro et al. 2017). PD studies in early oncology trials also provide the first clinical proof of concept for further development of a new clinical strategy for difficult-totreat cancers. Pelareorep, an oncolytic virus and an isolate of reovirus Type 3 Dearing showed single-agent antitumor activity. A recent PD study in patients with advanced pancreatic cancer demonstrated reovirus replication within pancreatic tumor and associated apoptosis, thereby providing the first proof of concept that the high frequency of RAS mutations in pancreas cancer would promote selective reovirus replication in pancreatic tumors and enhance the anticancer activity of gemcitabine (Mahalingam et al. 2018). Likewise, it will be very important to emphasize PK/PD analyses in clinical trials involving older adults with cancer, especially those over age 75. The careful analysis of both chronological and functional age and comorbidities on PK/PD of new drugs in relationship to the safety- as well as efficacy-related clinical outcome parameters will help identify subsets of older adults who are likely to benefit from specific therapeutic interventions

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Role of Clinical Pharmacodynamics Studies in the Era of Precision Medicines Against Cancer

as well as those who are most vulnerable to morbidity and/or mortality (Nightingale et al. 2018).

Pharmacodynamics Modeling for Development of Oncology Drugs In recent years, population pharmacokinetics–pharmacodynamics (PK-PD) modelling has become a key tool toward streamlining and optimizing oncologic drug development through early understanding, identification, and quantification of various dose–response relationships in the context of other patient characteristics as well as riskbenefit of different dosing schedules (Garralda et al. 2017; Nightingale et al. 2018; Owonikoko et al. 2018; Sato et al. 2017). The development of nonclinical models that can predict the clinical toxicities of immuno-oncology drugs, including immune checkpoint inhibitors and stimulators, is a focal point of emphasis in contemporary translational cancer research and regulatory science/ policy workshops (e.g., FDA-AACR Workshop on nonclinical Models for Safety Assessment of Immuno-oncology Products. September 6th, 2018 Marriott Wardman Park, Washington, DC). PK/PD modeling is a useful tool throughout all stages of drug development, and applications differ during the preclinical and clinical stage. Modeling strategies can accelerate the clinical development process by (i) providing the foundation for an early analysis of the safety and tolerability profile of drug candidates, (ii) early definition of the risk-benefit ratio and the therapeutic index and (ii) supporting the design of optimal treatment regimens (Meille et al. 2017; Zamboni et al. 2001; Zhou and Gallo 2011). Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal and sometimes personalized dosing regimens (Ait-Oudhia and Mager 2016; Block 2015; Buil-Bruna et al. 2016; Ciccolini et al. 2017; Claret et al. 2009, 2015). Early understanding of toxicities and PK determination of the oral pan-histone deacetylase inhibitor Abexinostat allowed Fouliard et al.

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to build a PK/PD model of thrombocytopenia, which predicted the optimal administration schedule allowing higher doses with minimal thrombocytopenia (Fouliard et al. 2013). This optimized schedule is currently used in the trials in solid tumors with abexinostat. Exposure to anthracycline and trastuzumab was simulated based on available dosing records and by using a kinetic-pharmacodynamics (K-PD) and a fixed PK model from literature, respectively. PD models for troponin T and LVEF were successfully developed, identifying maximum troponin T concentration after anthracycline treatment as a significant determinant for trastuzumab-induced LVEF decline. These models can help identify patients at risk of drug-induced cardiotoxicity and optimize cardiac-monitoring strategies. One of the contributing factors to the high attrition rate for developmental therapeutics in oncology is the inadequate dose and regimen selection combined with an insufficient understanding of the pharmacology to design an optimal drug development program (Postel-Vinay et al. 2016). The US Food and Drug Administration (FDA), European Medicines Agency (EMA), as well as Japan’s PMDA consider quantitative modeling and simulation (M&S), including population PK analyses, population PK and PD model analyses, exposure–response analyses, and physiologically based pharmacokinetic (PBPK) model analyses, as useful tools that can provide actionable insights that inform the decision-making process in early-stage as well as late-stage oncology drug development programs. PK/PD models for anticancer agents have been developed and successfully applied to: (1) provide insights into fundamental mechanisms implicated in tumor growth, (2) assist in dose selection for first-inhuman phase I studies (e.g., effective dose, escalating doses, and maximal tolerated doses), (3) design and optimize combination drug regimens, (4) design clinical trials, and (5) establish links between drug efficacy and safety and the concentrations of measured biomarkers (Eigenmann et al. 2017; Garralda et al. 2017; Gallo and Birtwistle 2015). The emergent field of pharmacometrics, defined as “the science of

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developing and applying mathematical and statistical methods to (a) characterize, understand, and predict a drug’s pharmacokinetic and PD behavior, (b) quantify uncertainty of information about that behavior, and (c) rationalize data-driven decision making in drug development process and pharmacotherapy,” combines principles from pharmacology (PK and PD), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity (Buil-Bruna et al. 2016; Musuamba et al. 2017; Manolis et al. 2017). The importance of PK-PD modeling for the drug development process is best illustrated in the example of pembrolizumab (Turner et al. 2018; Elassaiss-Schaap et al. 2017; Freshwater et al. 2017; Patnaik et al. 2015). Modeling helped identify the FDA-approved 2 mg/kg every 3 weeks dose schedule for nonsmall cell lung cancer therapy by predicting that this dose level which is much lower than the 10 mg/kg dose level studied in patients would have robust clinical activity due to intratumor drug exposure estimations. Notably, pembrolizumab was approved just 4 years after the phase I clinical trial started, through breakthrough designation by the FDA. This timeframe clearly contrasts with the 10 or greater years that former drugs traditionally took to be approved. Model-informed drug development (MIDD) employs mathematical and statistical models to describe disease progression, PK, and PD to improve the clinical trial design and clinically relevant predictions. Advancing MIDD in oncology and identifying regulatory-acceptable best practices pertaining to MIDD will require close collaboration between drug makers and regulatory agencies as well as multistakeholder workshops, such as the 2014 EMA/European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Dose Finding and the 2018 public FDA-International Society of Pharmacometrics (ISOP) Workshop on

F. M. Uckun and S. Qazi

Model-Informed Drug Development (https:// www.fda.gov/Drugs/NewsEvents/ucm589449.htm) co-sponsored by the FDA’s Center for Drug Evaluation and Research (CDER) and ISOP (Musuamba et al. 2017; Manolis et al. 2017; Schindler et al. 2018). The EMA Modelling and Simulation Working Group (MSWG) in collaboration with the FDA-Office of Clinical Pharmacology (OCP) pharmacometrics group strive to facilitate the much-needed harmonization on good M&S/MIDD practices through dialog and collaboration across all stakeholders. The desired goal is to develop “best practices in integrating PK, PD, efficacy, and safety data into models to best inform oncology drug development, evaluate disease- and mechanism-specific early endpoints to predict long-term efficacy, and discuss potential regulatory implications of model-informed decisions in drug development.”

Emerging Role of Quantitative Systems Pharmacology for ModelInformed Drug Discovery and Development Traditional PD models attempt to determine drug effects by integrating specific and confirmatory similar datasets and then predicting results in related scenarios. In this paradigm, the models are parsimonious, the parameters of the models can be identified, and therefore the models can incorporate population variability and define parameter uncertainties. An emerging new approach is termed “Quantitative Systems Pharmacology” that advances systems level, multiscale models for disease progression and treatment (Iyengar et al. 2012; Lai et al. 2018; Musante et al. 2017; Ribba et al. 2017). Systems level consideration of drug responses in these models attempt to better characterize the hierarchical, nonlinear, dynamic responses at the network level of drug action that may affect both efficacy and toxicity in clinical settings. These PD models are highly mechanistic and take into consideration the effects of drug actions spanning from the scale of molecular interactions to organlevel responses. Since these interactions are

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nonlinear at the multiscale levels, the effects of drugs exhibit emergent behaviors relating to pronounced on-target and off-target PD actions of drug treatments. These models strive to integrate data from diverse datasets and hence have required the development of model exchange platforms such as PharmML (Bizzotto et al. 2017) and sophisticated toolboxes to perform multiscale simulations and apply nonlinear statistical analyses (Cheng et al. 2017; Eissing et al. 2011). These types of models prioritize biological detail over parameter identifiability and the simulations enable rich exploration of mechanistic variabilities to better identify on-target and off-target PD effects of drug action. An important aspect of these models relating to precision medicine goals is to explain differences in drug efficacy and toxicity in heterogeneous populations that display genetic or biomarker profile differences.

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Pharmacodynamic Evaluation: Gene Therapy

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Nicolas Grandchamp

Contents Gene Therapy: Concept and Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362 Genome Editing and Genic Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Genome Editing Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 Vector Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366 Clinical Trials in Gene Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Seminal Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 370 Type of Diseases Treated by Gene Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biosafety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bioproduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Legislation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

380 380 381 381 381

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381

Abstract

Gene therapy is based on the genome modifications by introducing nucleic acids in the cells of the patient to treat diseases. There are different gene therapy strategies, the most common of which is to add a healthy copy of the disease-inducing gene. Other strategies consist of replacing the gene which is responsible for the disease for a healthy copy, or to inactivate a gene that is functioning improperly. Gene therapy is designed for untreatable diseases or with a heavy treatment.

N. Grandchamp (*) Biosource / GEG Tech, Paris, France e-mail: [email protected]

To modify the genome of patients, different gene vectors are used. There are two major classes of them, synthetic vectors which are complexes between nucleic acids (DNA, RNA, or protein) and synthetic molecules and viral vectors which are derived from viruses. Each of them has their advantages and limits. The vectors can be administered directly in vivo or used for ex vivo approaches which consist to genetically modify the patient cells in vitro before being reintroduced into the patient. This chapter gives a statement of the art of gene therapy. It describes the different strategies and tools used in this area before describing the seminal clinical trials and the range of diseases for which gene therapy could be

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_51

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relevant. This chapter concludes by considering the issues and challenges of the gene therapy.

Gene Therapy: Concept and Strategies There are several ways and contexts to genetically modify cells with different implications in terms of technology, biosafety, and ethics. Firstly, the origin of the cells used somatic or germline cells. In the first case, the genome modifications of patient cells cannot be passed on to subsequent generations of children, which is not the case with germline cells. Therefore, although the genetic modification of germline cells can be attractive in the field of transgenesis, this way is not explored to design gene therapy given the ethical and societal implications. Somatic gene therapy can be broadly split into two categories: in vivo and ex vivo gene therapy (Fig. 1). Ex vivo gene therapy consists of modifying the patient’s cell outside his body. The cells are cultured and genetically modified in vitro in labs, and then they are selected and reintroduced into the patient to treat the disease. Ex vivo gene therapy can be applied only to some cell types or selected tissues and is frequently used for diseases involving bone marrow cells. Compared to the in vivo gene therapy, ex vivo gene therapy is less likely to induce adverse immunological reactions

Fig. 1 Strategies of in vivo gene therapy and ex vivo gene therapy

N. Grandchamp

in the patient’s body since the genetic correction is done in vitro. The success depends on stable genic modification after reintroduction into the patient’s body and the severity rate of side effects which could be induced by the genic modification. In vivo gene therapy consists of directly modifying the genome of the cells inside the patient’s body to treat genetic diseases. It can be administered directly in the blood by systemic injection or applied specifically in a tissue such as the liver, muscle, skin, lung, spleen, eye, or brain. The success depends on several factors, such as efficient delivery of the vector to the target cells, extracellular and intracellular degradation of the vector, the stability of the genic modification, and the level of toxicity or severity rate of side effects induced by the vector or the genic modification. The side effects which may be observed directly depend on the vector type used and the genic modification induced. There are different types of gene modifications, the choice being guided by the diseases and the experimental constraints. The common way is to add a therapeutic gene to restore a function of the body that is working improperly. The strategy is to add a transgene, which is integrated in the genome of the patient, to be expressed over a long period. However, side effects can be observed such as genotoxicity due to the locus of integration which can interfere with the expression of other genes such as oncogenes (Hacein-Bey-Abina

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et al. 2003). To avoid that, strategies allowing the targeting of the integration of the therapeutic gene in a safe locus are developed. Another strategy consists of replacing the defective gene for a healthy copy thanks to the homologous recombination (HR) process of the cells. For this purpose, genome editing tools are used to induce a DNA double-strand break which will promote RH mechanisms of the cell (Fig. 2). This strategy allows to avoid side effects induced by the genome modification. Indeed, replacing, and not adding, a defective gene by a healthy copy avoids genotoxic effects induced by an unsafe locus of integration. Furthermore, this strategy allows to have a physiologic expression of the gene which will regulate as the wild type, restoring the phenotype in recessive and dominant genetic disease. The issue is to very accurately design genome editing tools able to precisely and efficiently perform genomic recombination into the desired locus. Another way for some diseases is to inhibit or inactivate the defective gene when that provides a therapeutic effect. This type of strategy

has been firstly explored by introducing repressive RNA specific sequences, such as miRNA or shRNA, to inhibit the expression of the target gene (Moroni et al. 1992). However, the efficiency of this approach is often too low to observe a therapeutic effect. Another way has been explored developing the strategy of exon skipping (Benchaouir et al. 2007). This smart approach consists of introducing small DNA probes which interfere in splicing to eliminate the mutated part of the protein. However, this strategy can be used only in very specific contexts, when truncated proteins can have a therapeutic effect. A third way to inactivate a gene, which is very attractive due to its high efficiency, is to cut the gene sequence to induce nonhomologous end joining (NHEJ) DNA repair mechanisms which have the potential to introduce mutations inactivating the gene (Fig. 2). All those strategies are very attractive, but integration of the vector and/or the use of genome editing tools poses the principal issue of avoiding side effects by controlling the locus of genomic modification. As described in the next chapter, several tools are available;

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Fig. 2 Double-stranded breaks (DSBs) induce endogenous DNA repair mechanisms. DSBs can be repaired by nonhomologous end joining (NHEJ) or homology-directed repair (HDR). NHEJ often leads to deleterious insertions or

deletions (indels), while HDR leads to high-fidelity DNA repair using the homologous chromosome or exogenously introduced DNA as a template

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however, none have shown any off-target effects which could depend on the genomic context and the target (Pattanayak et al. 2011; Wang et al. 2015). For this reason, the research is ongoing to find and design new generations of genome editing tools to outperform those existing. Awaiting the next generations, to design gene therapy strategies based on existing genome editing tools, the biosafety level must be carefully evaluated, and an accurate risk/benefit balance must be established. Another strategy avoiding potential side effects caused by the genomic integration of the vector is to use non-integrating vectors. In this case, the gene encoding the therapeutic factor is not inserted in the genome of the patient but is in a DNA molecule which is extrachromosomic. This type of strategy has a higher level of biosafety but has a restricted scope because the therapeutic factor is, in most of the cases, expressed transiently. Indeed, extrachromosomic DNA molecules are lost during the cellular divisions. Consequently, these vectors are used to design gene therapy strategies needing transient expression of therapeutic factors such as in vaccination (Negri et al. 2007; Tatsis and Ertl 2004) or to transiently promote or inhibit physiologic mechanisms such as angiogenesis (Gounis et al. 2005). In some cases where the target cells are nondividing cells, there is no dilution of the extrachromosomic DNA molecules, and the expression of the transferred gene can be observed for several years. Non-integrating vectors can be used to induce long-term expression in the muscle (Xiao et al. 1996), brain (Ahmed et al. 2018; Gray et al. 2010; Yáñez-Muñoz et al. 2006), or eyes (Li et al. 1994; Philippe et al. 2006) and address genetic diseases involving these types of structures. To implement the discussed gene therapy strategies, the key tool is the vector combined or not with a genome editing tool. In this area, the perfect vector does not exist. Each of them has their advantages and limits in terms of targets, efficiency, or level of biosafety, and the choice of the vector is done according the therapeutic context.

N. Grandchamp

Genome Editing and Genic Vectors In the field of gene therapy, vectors are vehicles for delivering foreign DNA or RNA into patient cells. The vectors used in gene therapy are divided into two distinct categories: synthetic and viral vectors. In the first case, the nucleic acid of interest is complexed with synthetic molecules to be able to enter in cells. In the second case, the viruses are modified to be non-replicative and non-pathogen. The nucleic acid of interest is included in a virus, replacing its viral genome and allowing its delivery into the cells. These two classes of vectors can be non-integrating, inducing transient modifications in most of the cells or long-term modifications in nondividing cells, or integrating, allowing to permanently modify the genome of patients. The viral vectors integrate, either they naturally have a system of integration or they are combined with an ectopic genome editing system, while synthetic vectors must be combined with a genome editing system to be integrating.

Genome Editing Systems Genome editing systems allow to permanently modify the genome of cells. There are nonspecific systems such as transposons (Ding et al. 2005; Ivics et al. 1997), which randomly integrate the therapeutic DNA, and specific systems allowing to target or to choose a genomic locus of integration. The most suitable editing systems are systems which allow the choosing of a targeted genome locus to inactive a gene or to insert a therapeutic DNA. In human cells, the first system used, allowing to efficiently choose the target genomic locus for genetic engineering, is based on ZFN (Urnov et al. 2005). This system combines zinc finger domains able to recognize a DNA sequence, combining with the nuclease FokI, which can induce DNA double-strand breaks (Fig. 3). This system can be combining with synthetic and viral vectors and is very attractive to design gene

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therapy. For example, Sangamo Biosciences, which is an American firm, has used ZFNs to provide a therapeutic solution to X-linked severe immunodeficiency syndrome (Urnov et al. 2005). During their investigations, the authors demonstrate the possibility of making gene repair with a ZFN specific mutated il2rγ gene through the stimulation of RH mechanisms. In this way, it is possible to replace the mutated sequence with the wild-type sequence of the gene. However, engineering of ZFN is complex, and Sangamo has locked the technology by filing several major patents in the field. Consequently, research laboratories have a very restrictive access to this technology, avoiding the development of its potential. At the beginning of 2009, an alternative technology has emerged, the transcription activator-like effector nucleases (TALENs, Fig. 3) (Boch et al. 2009). This genome editing system is easier to design than ZFN, allowing research laboratories themselves to design the tools to develop gene therapy strategies. However, this technology is limited by the very large size of the proteins and the many repeat sequences

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making its DNA synthesis and its combination with vector systems complex. Recently a new genome editing tool has emerged which is revolutionizing the field of genome editing and thus gene therapy, the CRISPR system (clustered regularly interspaced short palindromic repeats) (Cong et al. 2013). This system is based on adaptable immune mechanisms used by many bacteria to protect themselves from foreign nucleic acids, such as viruses or plasmids. It is made of two components: a guide RNA (gRNA) which targets a DNA sequence and the Cas9 nuclease allowing a DNA double-strand break (Fig. 3). This system is very easy to design and cheap to produce; any molecular laboratory has the capabilities to produce their own CRISPR/Cas9 systems, democratizing genome editing areas and accelerating research in many fields. Now, a wide range of gene therapy strategies are based on CRISPR/Cas9 system, and several companies have emerged in this area, such as CRISPR Therapeutics or Editas. All genome editing systems, as efficient and as accurate as they are, require a vector to be delivered into a cell to be able to edit a genome.

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Fig. 3 Mechanism of ZFN, TALEN, and CRISPR/ Cas9. ZFN, TALEN, and CRISPR/Cas9 achieve genome modification by inducing targeted DNA double-stranded breaks (DSBs), which would be corrected by NHEJ and

point mutation

donor DNA

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HR repair mechanisms. NHEJ-mediated repair leads to the introduction of variable length insertion or deletion. HRmediated repair could lead to point mutation and gene replacement, in the present of donor DNA

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Vector Systems Synthetic Vectors Synthetic vectors have the advantage of being easy and inexpensive to produce. In addition, they are not immunogenic which is a definite asset for the development of gene therapies. To allow an optimal transport system, a synthetic vector must have several characteristics and be able to: – Form a complex with the nucleic acid to vectorize and compact it – Protect the nucleic acid from various sources of degradation such as nucleases – Obtain homogeneous synthetic vector/nucleic acid particles of small size (1E9 TU/mL), and they have a large cargo capacity allowing to deliver a wide range therapeutic transgene and, in some strategies, several of them. However, the main limitation of these vectors is, as for the MLV, their uncontrolled integration even the lentiviral vector genotoxic potential is lower. In order to overcome this problem, advances in the biology of the HIV virus have made it possible to consider modifying the viral integrase to make it specific to a desired site. Several strategies have been tested but, for now, none has given acceptable results to go ahead in therapy. Another way explored is to inactivate the wild-type integrase of the vector either to design a therapeutic strategy based on non-integrating vector with the advantages of lentiviruses or to replace it by an ectopic genome editing tools such as ZFN or CRISPR. This generation of vector is very attractive

N. Grandchamp

because, as the integrating version, it can be producing with very high concentration to be very efficient; it has a large cargo capacity and offers a wide range of design to target specific cell populations without the side effects due to the uncontrollable integration. However, for the moment, all the types of integrase mutant tested have shown a background of integration, in the range of 0.1–0.5%. This limit has to be taken into consideration in the designing of gene therapy strategies. Recently, a new generation of lentiviral vectors has emerged with the particularity of being nonintegrating without background and having an unprecedented level of biosafety (Sarkis et al. 2014). This new generation of lentiviral vector is mutated for the reverse transcription, and then the RNA genome of the vector cannot be converted in DNA and is taken charge by the cells as a mRNA. This type of vector induces only transient expression in all type of cells (dividing and nondividing) and is perfectly suitable to express genome editing tools or to design a strategy needing a transient expression of a therapeutic factor, such as in the field of vaccination, vascularization, or oncology. Although RNA lentiviral vector keeps several advantages of the integrating version, such as large cargo capacity or high flexibility of design, its use is limited by its efficiency which is too low for some applications. Now, several ways are being explored to overcome this limit, and it is probable that this new generation of vectors offering new standards in terms of biosafety will be the basis for a wide range of the next gene therapy strategies.

Clinical Trials in Gene Therapy Seminal Trials The concept of gene therapy is already old, since it was born in the early 1970s when the scientists Rogers and then Friedmann and Roblin raised the possibility of using exogenous DNA to replace defective DNA in people affected by genetic defects (Friedmann and Roblin 1972). This idea materialized experimentally in 1989 by French

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Anderson. He conducted the first human gene therapy safety test where a harmless marker was injected into a 52-year-old man. The test succeeded, and the following year he treated a 4-year-old girl, Ashanti DeSilva, who was suffering from an immune disorder caused by a defective ADA gene (Blaese et al. 1995). At the time the only solution was to treat the patient with an artificial PEG-ADA supplement, though F. Anderson set out to make a more permanent cure. The strategy has been to take T cells out the body and to genetically modify them by the introduction of a healthy functioning copy of the ADA gene. Currently DeSilva is alive and well but still taking her PEG-ADA supplements; the issue is still not clear as to whether it was the gene therapy treatment carried out by F. Anderson or the supplement itself that has been keeping her going all these years. The trial received a mixed reception, but nonetheless it was a shot in the arm for gene therapy enthusiasts all over the world. However, in 1999, the death of a patient, Jesse Gelsinger, 18 years old and participating in a gene therapy trial, triggered a huge reaction (Science 2000). Since then, the sector has alternated between enthusiasm and reserve for gene therapy. Jesse Gelsinger joined a clinical trial which included 18 patients who suffered from ornithine transcarbamylase (OTC) deficiency, an X-linked genetic disease of the liver induced by a dysfunctional OTC gene, the symptoms of which include an inability to metabolize ammonia. This clinical trial has been derived by the Dr. James M. Wilson’s team of the Pennsylvania University. The protocol validated by the FDA consisted of a systemic injection of an adenoviral vector containing a healthy version of the OTC gene. Before the vector injection, the control results of Jesse Gelsinger indicated that he had neutralizing antibodies to adenovirus, and thus CD4 T cells activated against it as well. These results were satisfactory for continuing the study on him. Twelve hours after injection, Jesse Gelsinger had the same symptoms as all subjects: fever, myalgia, and biochemical consequences such as anemia and thrombocytopenia. However, 18 h after the injection, he began to show signs of mental

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deterioration and jaundice that the other 17 subjects did not have. As the hours went by, his condition worsened, doctors had to place him under artificial respiration to control his air since he found himself in compensated respiratory alkalosis caused by a high level of ammonia in the blood. Similarly, to lower his ammonia, they did a hemodialysis; however, all these organs worsened, starting with the lungs. Finally, 98 h after the injection, Jesse Gelsinger died. The findings from his autopsy reveal that Jesse died as a result of systemic activation of his innate immune system. A very high concentration of the vectors has been detected in the liver but also in organs such as lymph nodes, bone marrow, and spleen. Pennsylvania University highlighted the need for further research on gene therapy to understand and promote the success of future trials to treat this disease. It took until 2000 to observe therapeutic effects in the field of gene therapy with Alain Fischer at the Necker Hospital for Sick Children in Paris who has led the SCID-X (severe combined immunodeficiency X-linked) gene therapy trial (Cavazzana-Calvo et al. 2000). This clinical trial involved children who presented X-linked SCID which is a faulty copy of a gene on the X chromosome that makes the immune protein interleukin-2. As a result, they have no resistance to infection, and they must live in a sterile environment to not die. A total of 15 patients, have been treated so far – 11 in Paris and 4 in London. The gene therapy strategy has been to modify ex vivo the HSC of the children with an MLV vector carrying the healthy version of the SCID gene and to reinject them. For the first time, the trial was a success as all the children showed a significant health improvement. However, the second time five children developed leukemia and one of them died. The clinical trial was stopped in an emergency, and the analyses showed that leukemia was induced by the insertion of the MLV vector near the LIM domain-only 2 (LMO2) proto-oncogene inducing its deregulation (Hacein-Bey-Abina et al. 2003). This dramatic side effects show that gene therapy needs to be optimized, involving vector design, and the balance risk/benefit must be accurately

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evaluated. Since this failure, other trials for SCID-X have started taking these points into consideration. This proof of concept has opened the way for several clinical trials all around the world Fig. 4 Geographical distribution of gene therapy clinical trials (by country)

Fig. 5 Vectors used in gene therapy clinical trials.  Other than lentivirus

Fig. 6 Indications addressed by gene therapy clinical trials

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(Fig. 4) using different vectors (Fig. 5), to treat different diseases (Fig. 6) and in different phases (Fig. 7). Recently, for some of them, a gene therapy product has obtained a marketing authorization (Table 1).

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Fig. 7 Phases of gene therapy clinical trials

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Phases of Gene Therapy Clinical Trials WILEY

Phase I 56.8% (n=1476) Phase I/II 20.9%(n=544) Phase II 17.1% (n=445) Phase II/III 1% (n=25) Phase III 3.8% (n=98) Phase IV 0.1% (n=3) Single subject 0.2% (n=6)

The Journal of Gene Medicine, © 2017 John Wiley and Sons Ltd

www.wiley.co.uk/genmed/clinical

Table 1 Approved gene therapy products

Name

Date of approval

First Approving agency

Disease

Type of vector used

Manufacturer

Gendicine

October 2003

State Food and Drug Administration of China

Head and neck squamous cell carcinoma

Adenoviral Vector

Shenzhen SiBiono GeneTech (Shenzhen, China)

Glybera®

November 2012

European Marketing Authorization (EMA)

Lipoprotein lipase deficiency

AAV1

UniQure (Amsterdam, Netherlands)

Strimvelis™

June 2016

EMA

Adenosine deaminase deficiency (ADA-SCID)

Lentiviral Vector

GlaxoSmithKline (Middlesex, United Kingdom)

Kymriah™

August 2017

FDA

Acute lymphoblastic leukaemia

Lentiviral Vector

Novartis Pharmaceuticals (Basel, Switzerland)

Yescarta™

October 2017

FDA

B-cell lymphoma

Lentiviral Vector

Kite Pharma, Incorporated (Santa Monica, California, USA)

Luxturna™

December 2017

FDA

Retinal dystrophy (biallelic RPE65 mutation)

AAV2

Spark Therapeutics, Inc. (Philadelphia, Pennsylvania, USA)

Zolgensma™

May 2019

FDA

Spinal muscular atrophy

AAV9

Novartis Pharmaceuticals (Basel, Switzerland)

Type of Diseases Treated by Gene Therapy Neuromuscular Diseases Inherited neuromuscular disorders encompass a broad group of genetic conditions, and the discovery of these underlying genes has expanded

greatly in the past three decades. The discovery of such genes has enabled more precise diagnosis of these disorders and the development of specific gene therapeutic approaches. Duchenne muscular dystrophy and spinal muscular atrophy are the most common debilitating neuromuscular disorders affecting children. The development of such

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therapeutic approaches can be categorized into two broad strategies. The first strategy, initiated in the late 2000s, relates to the correction of mutant RNA processing, using either antisense oligonucleotides or small molecules that can modify mutant RNA splicing (Lu et al. 2003; Magee et al. 2006; Benchaouir et al. 2007; Kinali et al. 2009). The second strategy involves the use of AAVs to deliver a functional or partially functional gene copy to the affected cells and tissues. Such translational research has led to the approval of two genetic therapies by the US Food and Drug Administration: eteplirsen for Duchenne muscular dystrophy (Railroading at the FDA 2016) and nusinersen for spinal muscular atrophy, which are both antisense oligonucleotides that modify pre-mRNA splicing (Khorkova and Wahlestedt 2017). Very recently, in May 2019, Novartis received US approval for its spinal muscular atrophy gene therapy Zolgensma which provides through AAV infusion a normal copy of the SMN1 gene to babies born with a defective gene. The one-time treatment costs $2.1M

Ocular Diseases The eye is an easily accessible, highly compartmentalized, and immune-privileged organ that offers unique advantages as a gene therapy target. Proof-of-concept for vector-based gene therapies has also been established in several experimental models of human ocular diseases involving photoreceptor, ganglion cell, and optic nerve degeneration. Gene therapy has also been explored as a potential therapy for red-green color blindness, corneal neovascularization, and a variety of other corneal diseases, including allograft rejection, optic nerve trauma, autoimmune uveitis, and melanoma (Williams et al. 2017; Cavalieri et al. 2018; Sahel and Dalkara 2019). In 2017, the US Food and Drug Administration allowed the first gene therapy to be placed on the market, Luxturna, which consists of direct injection of the vector into the patient’s body to treat Leber congenital amaurosis which is an inherited retinal disease (Darrow 2019). The vector used derives from the AAV and contains a healthy copy of the RPE65 gene. The cost of the treatment

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is $425K per eyes. Following this first authorization, this approach is also tested in several other monogenic ophthalmic diseases such as Leber’s optic neuropathy or Stargardt’s disease.

Blood Diseases Blood disorders comprise of a wide range of diseases involving red blood cell and iron disorders, white blood cell disorders, bone marrow failure syndromes, thrombosis and anticoagulation disorders, bleeding disorders, and autoimmune blood cell disorders. Many of them are inherited disorders involving genetic dysfunction. Gene therapy holds great potential as a cure for blood, which has limited treatment options (Kohn 2019). As discussed previously, gene therapy strategies are designed to address several immune diseases SCID-X, but there are also other immune disorders for which gene therapy strategies have been designed. There is, for example, ADA-DICS, a severe immunodeficiency characterized by the absence of the ADA protein, necessary to produce lymphocytes (Aiuti et al. 2002, 2009). Results are also encouraging for the treatment of Wiskott-Aldrich syndrome (WAS) (Aiuti et al. 2013). This disease, resulting from a mutation on the WAS gene, is characterized by dysfunctions of the blood cells and the absence of platelets. Trials conducted in Europe and the USA confirm the effectiveness of the approach to improve the health status of those treated, including adults. Approaches targeting other immunodeficiencies are currently being tested, including X-linked chronic septic granulomatous disease. In 2016, Strimvelis™ was the first ex vivo gene therapy approved for the treatment of ADA-SCID (Stirnadel-Farrant et al. 2018). This therapy was evaluated in 18 patients who are still alive with no major side effects, but the price of the treatment is about $700K. Hemoglobin disorders are inherited diseases, such as thalassemia and sickle cell anemia, and are the most common monogenic disorders worldwide, causing significant morbidity, mortality, and healthcare expenditures. Gene therapy strategy for β-globinopathies is to add a healthy copy of the β- or γ-globin

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Pharmacodynamic Evaluation: Gene Therapy

genes and elements of the locus control region in hematopoietic stem cells (HSCs) with a lentiviral vector (Perumbeti and Malik 2010). In contrast to immune disease where a relatively small proportion of HSCs and a relatively small amount of transgene protein expression are adequate to produce a phenotypic correction, β-globinopathies requires high amounts of gene expression in individual red blood cells. Furthermore, engraftment of a large proportion of gene-corrected HSCs that repopulate the bone marrow is necessary in order to affect significant clinical effects. Therefore, the clinical trials in this field use the lentiviral vector which currently offers potential for delivering high expression and transducing a high proportion of HSCs, allowing genetic therapy approaches for β-globinopathies. In France, a 13-year-old boy became the first sickle cell disease patient to be treated with gene therapy. In October 2014, the boy received LentiGlobin BB305 gene therapy (Thompson et al. 2018). After receiving the modified stem cells, the boy kept getting red blood cell transfusions until researchers could measure adequate levels of the modified hemoglobin. The patient received the last transfusion on day 88 after the cell transfer. Six months after the procedure, his total hemoglobin levels were stable. He had some side effects from the chemotherapy used to get rid of his blood cells, but all issues seem resolved, and the gene therapy itself has not, as of yet, caused any side effects. However, caution should be observed because several past clinical trials have already shown late side effects. Hemophilia A and B are X-linked bleeding disorders, respectively, caused by mutations in the gene coding for FVIII or gene coding for FIX, resulting in deficient and/or defective coagulation, leading to spontaneous or traumatic bleeding into joints, muscles, or body cavities (Perrin et al. 2019). Gene therapy strategies have the aim of bringing the healthy version of the gene to hepatocytes which are the cells producing FVIII and FIX. At the beginning, for most clinical trials, AAV vector was chosen for its high efficiency to modify hepatocytes and non-integrating feature which makes a safer vector than those

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derived from retroviruses. However, some studies have shown that in some cases, AAV vectors can be integrated, and, in the meantime, several studies have upgraded the design of lentiviral vectors to be safer. Furthermore, pre-existing neutralizing antibodies to AAV following natural exposure to the wild-type virus may inhibit gene transfer with AAV vectors restricting its use. For these reasons, more and more gene therapy strategies based on lentiviral vectors are being designed. Fanconi anemia is an inherited bone marrow failure disorder characterized by aplastic anemia and an enhanced risk for the development of leukemia. The syndrome may occur as a consequence of a defect in 1 out of at least 15 genes. The development of gene therapy approaches for this disorder has focused on the protein encoded by the FANCA gene, 95,96 which is most commonly mutated. An international working group has been established to chart the path and facilitate the development of gene therapy for Fanconi anemia. 95,96 gene therapy for patients with Fanconi anemia is particularly challenging because of low numbers of hematopoietic stem cells and sensitivity to myelosuppressive regimens. Metachromatic leukodystrophy (MLD) is an inherited autosomal recessive disorder secondary to a deficiency of the lysosomal enzyme arylsulfatase A. Massive accumulation of nonmetabolite sulfatides damages both the central and peripheral nervous systems. A mouse knockout model of MLD has been developed and used for the exploration of gene therapy approaches (Hess et al. 1996). Stem cell-targeted gene transfer followed by autologous transplantation is one approach (Matzner et al. 2002). An alternative that is also being explored in the mouse model is the direct injection of AAV vectors encoding ARSA into the CNS (Sevin et al. 2006). Early correction is essential because the most common form of MLD develops in the second year of life with rapid, progressive CNS dysfunction. Thus, the direct introduction of AAV vectors into the brain seems preferable than stem cell-targeted gene transfer in that the correction of the phenotype with the latter approach requires several

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months, during which the patients continue to deteriorate.

Neurodegenerative Diseases Neurodegenerative disease processes often involve the progressive accumulation of dysfunctional proteins within cells, leading to cell death. For now, there are no existing therapies that correct underlying neurodegenerative disease processes; current therapies provide merely symptomatic relief. Gene therapy is a promising strategy by altering or inducing the expression of specific proteins for neuroprotection, neurorestoration, and, ultimately, correction of the underlying pathogenic mechanism (Deverman et al. 2018). For example, in adrenoleukodystrophy, a demyelinating disease of the central nervous system, patients’ blood stem cells are corrected ex vivo with a lentivirus and then reinjected. Twenty nine patients were treated, and the results show a stabilization or improvement of their condition in most cases (Duncan et al. 2019). Promising results have also been obtained with a similar approach in children with metachromatic leukodystrophy (Sessa et al. 2016), and other work is underway in Sanfilippo’s disease. A French trial involving four children with this disease is ongoing. The gene therapy strategy was to inject AAV vector into different areas of the patient’s brain to induce the production of the missing enzyme by the brain cells (Tardieu et al. 2017). No noticeable side effects were noted during the 30 months following treatment, and an improvement in intellectual and behavioral development was observed, paving the way for a phase III trial. Gene therapy approaches are also designed to address other more common neurological diseases such as Parkinson’s disease or Alzheimer’s disease. Skin Diseases There is a wide range of skin diseases involving gene malfunctioning such as epidermolysis bullosa (EB), pachyonychia congenita, melanoma, ichthyosis, xeroderma pigmentosum, wound healing, or Netherton syndrome.

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Junctional epidermolysis bullosa (JEB) is an EB disease for which there are the most advanced results. In 2017, a 7-year-old child suffering from this rare and serious disease received several autologous transplants of skin cells genetically modified with a retrovirus vector to correct the mutation causing the disease, affecting the LAMB3 gene (Hirsch et al. 2017). This mutation prevents the junction between the dermis and the epidermis. Faced with a vital emergency, German and Italian doctors performed this procedure successfully on 80% of the body surface. In another form of the disease, a clinical trial is underway in the USA to produce genetically corrected epidermal leaflets to treat the skin lesions that appear in dystrophic epidermal necrolysis.

Cardiovascular Diseases In the cardiovascular field, researchers are developing gene therapies to promote the regeneration of vascular tissues in the case of arterial ischemia. Other strategies aim to fight against restenosis (narrowing of an artery occurring after stent implantation), using genes coding for proteins that slow down or, on the contrary, stimulate these processes. No significant results have been reported in the treatment of chronic heart failure with an adeno-associated vector encoding Serca2; however, efforts are underway to improve the cardiac tropism of the vectors (Greenberg et al. 2016). Cancer Research in gene therapy for cancer is currently focused in multiple areas, including genetically engineered viruses that directly kill cancer cells, gene transfer to offset the abnormal functioning of cancer cells, and immunotherapy, which stimulate the immune system to recognize and kill tumor cells. Oncolytic viruses are defined as genetically engineered (or in some cases naturally) occurring viruses that selectively replicate in and kill cancer cells without harming the normal tissues (Bommareddy et al. 2018). In addition to the direct lysis of tumor cells, viral infection can induce acute devascularization of the tumor,

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Pharmacodynamic Evaluation: Gene Therapy

causing the formation of large necrotic regions, such as those created by antiangiogenic agents. In addition, tumor infection by oncolytic viruses stimulates the antitumor immune response, which in turn contributes to the elimination of even uninfected tumor cells. Among the most currently studied oncolytic viruses are herpes simplex virus 1 (HSV-1), vaccinia virus (VV), serotype 5 adenovirus, serotype 3 reovirus, Newcastle disease virus (NDV), measles virus, and vesicular stomatitis virus (VSV). Directing the virus to the tumor in sufficient quantity remains a major challenge for effective oncolytic virotherapy in a clinical setting. In addition, exploiting the antitumor immune response induced by oncolytic viruses while limiting the antiviral response remains another obstacle to overcome. New generations of oncolytic viruses including transgene are developed to be more aggressive and to induce better anticancer activity. Linked to these innovative developments, the marriage of virotherapy and chemotherapy leads to better control of the efficacy of these biotherapeutic agents. These developments point to a promising future for this multimodal therapeutic platform, with remarkable flexibility and specificity. As proof, several oncolytic viruses are in the advanced phase of clinical evaluation and could be approved soon for the treatment of certain tumors (Ribas et al. 2017; Hirooka et al. 2018). In the same time, other strategies based on gene transfer to offset the abnormal functioning of cancer cells are explored. Although most cancers harbor multiple oncogenic mutations, accumulating preclinical and clinical data now supports the fact that many cancers are sensitive to inhibition of single oncogenes, a concept named oncogene addiction. Consequently, one way to restore the functions of tumor suppressors is to reintroduce wild-type tumor suppressor gene into target cancer cells for expression such as TP53, PTEN, or BRCA1 with a vector. Given the high-mutation frequency of the tumor suppressor p53 in human cancers, restoration of wild-type p53 function is the most explored strategy in this approach of cancer therapy. The first vector used in this aim has

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been the adenoviral vector (Adp53). Preclinical studies have shown that Adp53 induces tumor regression in various cancers, including head and neck cancer, colorectal cancer, lung cancer, ovarian cancer, bladder cancer, and prostate cancer (Roth et al. 1998; Zhang et al. 2005). In 2003, the outcome of these promising results was the first commercialization of the world’s first gene therapy product, the Gendicine, approved by the State Food and Drug Administration of China for the treatment of head and neck squamous cell carcinoma (Wilson 2005). Despite its use for 15 years in more than 30,000 patients and several studies showing a good safety and a significantly better responses compared to standard therapies, SCH-58500 and Advexin, which are the versions developed in the USA, have not been approved by the FDA, owing to their ineffectiveness (Guo and Xin 2006). No Adp53 therapies have been approved in the USA and Europe for now. Although the strategies based on oncogene addiction are attractive, the principal weakness of using these approaches is that they do not address the problem of cancer progression as selected by the recessive phenotypes of genetic instability and apoptotic resistance that arise from loss-of-function defects of tumor suppressors. Other strategies that could answer these issues are based on the immunotherapy. The immune system interacts closely with tumors during the disease development and progression to metastasis. The complex communication between the immune system and the tumor cells can prevent or promote tumor growth. Based on this observation, several strategies of immunotherapy cancer are born. The principle is to enable the immune system to recognize tumor cells to kill them. Initial strategies did not involve gene therapy factor; they used the T-cell growth factor interleukin 2 (IL-2) or monoclonal antibodies to empower the immune system in killing cancer cells. Although encouraging results have been observed is some cases, these strategies have several limitations and specifically the accessibility to the tumor. Cancer immunotherapy has been greatly upgraded by its combination with gene therapy allowing to genetically modify immune cells such

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a

Tumour cell

β2 T cell Tumour-specific T cell either naturally occurring or from a transgenic mouse

b

Tumour cell

Tumour antigen VH

MHCI

TCR

α β

Tumour peptide

scFv

B cell Tumour-speicfic antibody from B cells T cell

γ ε

εδ ζ CD3 complex

VL CD8

CD3ζ or FcεRlγ

T cell

MH. Kershaw et al. 2013

Fig. 8 Derivation of TCRs and CARs for the genetic modification of T cells. (a) T-cell receptor (TCR) genes, made up of α- and β-chains, can be derived from tumorspecific T cells, which can naturally occur in humans, or from the immunization of human leukocyte antigen (HLA)-transgenic mice. Alternatively, they can be derived from screening bacteriophage libraries of antibodies. The α- and β-chains associate with the γ-, δ-, ε-, and ζchains of the CD3 complex. When the TCR encounters a processed tumor antigen peptide fragment displayed on the major histocompatibility complex (MHC) of the tumor cell, phosphorylation of immunoreceptor tyrosine-base activation motifs (ITAMs) occurs, leading to a cascade of

intracellular signaling that results in the release of cytokines and cytotoxic compounds from T cells. (b) Chimeric antigen receptors (CARs) are composed of a single-chain antibody variable fragment (scFv) extracellular domain linked through hinge and transmembrane domains to a cytoplasmic signaling region. Genes encoding the scFv are derived from a B cell that produces a tumor-specific antibody. An scFv is shown linked by a CD8 hinge to transmembrane cytoplasmic signaling regions derived from CD3ζ. CARs usually exist as a dimer, and they recognize tumor antigen directly (with no requirement for MHC) on the surface of a tumor cell. MHCI MHC class I

as T cells. First strategies have been based on ex vivo gene transfer of T-cell receptor (TCR, Fig. 8). After identification of a TCR specific to the tumor patient, their T cells are collected, modified with a lentiviral vector containing the TCR, and reinjected into the patient. In this way, modified T cells are able to recognize tumor cells to kill them. This type of strategy appeared very efficient in some cases but was often limited by the fact that TCR is constrained by the HLA restriction. Consequently, TCR immunotherapy fits only with patients who express the particular HLA type (similar to organ or bone marrow transplantation). In addition, tumors can lose their antigen by downregulation of HLA. Recently a new strategy based on the same principle as TCR but non-HLA restricted is revolutionizing the field of the cancer immunotherapy. This type of strategy used chimeric antigen receptors (CARs) which consist of a tumor antigen-binding domain of a single-chain antibody (scFv) fused to intracellular signaling domains

capable of activating T cells upon antigen stimulation (Fig. 8) (Curran et al. 2012). In the same way as the strategy used TCR, T cells are removed from the patient, genetically modify with a lentiviral vector containing the appropriate CAR, and reinjected to the patient. Thus, the modified T cells recognize cancer cells and kill them, independently of the HLA restriction. CAR T-cell strategy rapidly showed very promising results for lymphoma with moderate side effects such as cytokine release syndrome (CRS), neurologic events (encephalopathy, confusion, aphasia, or agitation), and low white or red blood cell count. Most of the side effects can be managed with drugs or resolved on their own without the need for treatment. CRS is the most common and severe side effect, but the improvement of the clinical protocol highly reduces it. Consequently, two products, Kymriah and Yescarta, have been rapidly approved for commercialization in the USA in 2017 and in Europe in 2018, but they are expensive, about $400K (Sheridan

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Pharmacodynamic Evaluation: Gene Therapy

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Fig. 9 Autologous versus allogenic CAR cells. (a) Autologous strategy: Cells are collected from the patient, genetically modified to express CAR, and reinjected into the patient. Donor and recipient are the same person. (b)

Allogenic strategy: Cells are collected from donors, genetically modified to express CAR, and used to establish offthe-shelf CAR products, usable to treat patients. Donor and recipient are different

2017, FDA 2018). This is due to the production process which is complex and needs lentiviral vectors produced in GMP grade and requires the use of the patient cells (autologous context, Fig. 9a), avoiding the generation of T-cell universal banks. Indeed, the T cells distinguish between what “belongs” and “does not belong” to the body and induce immune response as soon as they detect a foreign antigen. Consequently, it is not possible to use CAR T cells in an allogenic context without inducing graft versus host disease (GVHD). Two principal strategies are being explored to address this issue and generate universal chimeric antigen receptor T cells which will be “off-the-shelf” allogeneic treatments (Fig. 9b). The first consists of using immune cells which does not induce immune response if they interact with a foreign antigen such as natural killer (Hu et al. 2018) or gamma delta T cells which is a particular subpopulation of T cells (Fisher and Anderson 2018). The principal issues are the efficacy of collecting the cells and genetically modifying them. The second uses CAR T cells genetically modified with genome editing tools such as CRISPR or TALEN to inactivate genes responsible for the foreign antigen recognizing. UCAR19 is one

of the most advanced therapies in the field, it is a genetically modified CAR T-cell product (antiCD19 scFv-41BB-CD3ζ) manufactured from healthy donor cells, in which TRAC and CD52 genes have been disrupted to allow administration in non-HLA-matched patients (Qasim et al. 2017). UCAR19 is designed for relapsed/refractory acute lymphoblastic leukemia. In June 2015 at Great Ormond Street Hospital in London, the first baby, an 11-month-old girl called Layla, benefited from compassionate care using UCAR19 although it was a phase I trial. Another compassionate care has been realized with the same treatment in December 2015 with a 16month-old girl. For now, they are healthy. However, themselves and other patients receiving CAR treatments or involving in CAR clinical trials must be followed up extremely closely. Indeed, this type of therapeutic strategy is very new and dramatic recent events impose great prudence. In 2018, University of Pennsylvania’s Abramson Cancer Centre announced the death of one patient participating in the clinical trial which had allowed the validation of Kymriah. The researchers reported an unexpected mechanism, the accidental genetic engineering of a single leukemic cell during the early manufacturing of CAR T-cell therapy which induced a relapse

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and the death of the patient (Ruella et al. 2018). This unanticipated event shows clearly that we have the necessary background knowledge to be able to evaluate precisely the safety of this type of therapy. The balance risk/benefit has to be carefully evaluated. On the other hand, the design of the therapeutic strategy has to be upgraded at both the clinical protocol and the design of the lentiviral vector. In this regard, the new generation of vector including switch systems allowing to inactivate modify cells reinjected is a major issue of the moment.

Infectious Diseases A cure by gene therapy could be considered for a case of HIV infection. Several approaches are being studied. One of these is to make the CD4-T, the HIV-targeted cells, of patients resistant to the virus: HSC are removed from the patient and genetically modified with a genome editing tool to inactivate the gene coding for a CCR5 surface receptor which is the entry receptor of the HIV. Consequently, the absence of this receptor prevents the entry of the virus into the cells. Reinjected into the patient, these modified cells multiply and differentiate into immune cells resistant to the virus, making it possible to restore the subject’s immune system (Gupta et al. 2019). A phase I/II trial is underway in the USA. Recently, there was a tremendous upheaval in the scientific community in this area. Jiankui He, a scientist from a university in Shenzhen (China) claims he has succeeded in helping create the world’s first genetically edited babies. At the Second International Summit on Human Genome Editing, that took place in November 2018, he presented his results about twin girls who were born earlier this month after he edited their embryos using CRISPR technology to remove the CCR5 gene which would provide them protection against HIV. Most of researchers condemned the irresponsible approach of Jiankui He, invoking a serious violation of laws, regulations, and ethical standards (Krimsky 2019; Wang et al. 2019a). Furthermore, for many of them, in addition to regulatory and ethical transgressions, this experience exposes children to risks

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without any real benefit; there are already safe and effective ways to prevent the transmission of the AIDS virus. Furthermore CCR5 has been also identified as a suppressor for cortical plasticity in brain regions involved in learning and memory processes; its inactivation in the whole body could induce cognitive side effects (Zhou et al. 2016). The summit’s president, biologist David Baltimore, Nobel Laureate, denounced for his part “a lack of self-regulation by the scientific community due to a lack of transparency.” The World Health Organization is currently forming an expert committee on human genome editing, which will meet soon. It will be tasked with examining “scientific, ethical, social and legal challenges” in this area.

Challenges Biosafety Despite the encouraging results obtained through several clinical trials and the approving of more and more products in gene therapy in both the USA and Europe, some severe side effects have been observed even going as far to the death of the patient in some cases. Consequently, researchers have to remain cautious about the use of gene therapy and the possible occurrence of adverse effects over time. Followups of treated patients over several years will provide more information on the safety and efficacy of these drugs. The multiplication of clinical trials in various fields should allow us to learn a lot more in the years to come to further improve the processes. Research must go ahead to design new vectors with higher levels of biosafety. The next generations should address the problem of genotoxicity with controlled integration in safe locus without off targets or, when it is possible, induce a therapeutic effect from non-integrating forms. Regarding this, a new generation of lentiviral vectors remaining in RNA could meet these challenges, either to safely bring genome editing tools or to efficiently express a therapeutic factor. Another challenge in vectorology is to

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Pharmacodynamic Evaluation: Gene Therapy

answer the problem of the immune response which can develop in patients, in particular with AAV vectors, and the impossibility of reinjecting the treatment a second time. The development of new AAV capsids could answer this challenge.

Bioproduction Another field of action is the bioproduction. Indeed, the production of vectors or genetically modified cells on an industrial scale remains a major obstacle for the development of innovative gene therapy drugs. The processes derive from research labs at an early stage and are not always suitable for large-scale deployment according to good manufacturing practices in pharmaceutical production plans. Technological and industrial innovations are still needed to improve production yields. Indeed, the doses required to treat a patient are often very large and the validation of clinical trials need high numbers of people, showing an important step to take for going to the market.

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Legislation Another issue is about the legislation. Indeed, the development of these innovative therapies raises new questions and requires adaptation of the laws. Some precautionary principles remain unclear when a patient is treated by gene therapy; what about the risk of dissemination of the vector? The solution in transgenesis is clear; genetically modified organisms must remain confined. Nevertheless, such a measure would have dramatic consequences in gene therapy. In this case, which regulations apply if extracellular detection of the vector persists in the treated patient? It seems that the legislation differs from one continent to another; in Japan, for example, the legislation provides for the holding of a patient treated by gene therapy in quarantine until the demonstration of the absence of dissemination of the vector in biological fluids is demonstrated. Knowing that in some clinical trials, AAV vector dissemination was found several years after vector injection, it is easy to imagine the consequences for the patient under Japanese law. It remains up for debate as to the relevance of European legislation compared to that of other countries.

Pricing The price of these drugs is also a new topic of public health thinking. The Glybera costed about one million dollars causing its commercial failure, the Strimvelis™ more than $700K per treatment, and the Spinraza is announced at several hundred thousand euros per year, for life. If these pricings can be understood for the actual benefit and the reduction of the costs of continuing care given to individuals suffering from rare genetic diseases, full-cost economic studies remain to be carried out in order to discuss emerging issues. Who could afford these types of therapies? How will the health insurance or the health systems in some countries manage these costs? How do you make gene therapy drugs available to disadvantaged populations is also one of the questions to start thinking about?

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Pharmacological Therapy in Inborn Errors of Metabolism

18

Anibh M. Das and Sabine Illsinger

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 Therapeutic Principles of Treatment in IEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Substrate Reduction by Diet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacological Substrate Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Supplementation of a Missing Cofactor/Vitamin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Activation of Alternative Pathways for the Elimination of Toxic Compounds . . . . . . . . Enzyme Augmentation by Chaperones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enzyme Replacement Therapy (ERT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

386 387 388 391 392 394 395

Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Abstract

Different therapeutic principles can improve clinical outcome in patients suffering from inborn errors of metabolism (IEM). IEMs are inherited disorders which are based on a primary enzyme defect or deficiency of a cellular transporter. Substrate reduction by diet (exogenous substrate), pharmacological substrate reduction (endogenous substrate), supplementation of a missing cofactor/vitamin, activation

of alternative pathways for the elimination of toxic compounds, augmentation of enzyme activity by chaperones, and enzyme replacement therapy (in selected diseases like lysosomal storage diseases) are therapeutic options for alimentary and pharmacological treatment in IEM. In this chapter we will discuss options for pharmacological and/or dietary therapy in selected prototype IEM.

Introduction A. M. Das (*) Department of Paediatrics, Hannover Medical School, Hannover, Germany e-mail: [email protected] S. Illsinger Department of Neuropaediatrics, Children’s Hospital Oldenburg, Oldenburg, Germany e-mail: [email protected]

Inborn errors of metabolism (IEMs) are inherited disorders which are either due to a primary enzyme defect or deficiency of a transporter. In enzyme deficiencies, the physiological substrate of the enzyme reaction accumulates, while the product of this reaction (distal to the reaction) is

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_62

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decreased. Accumulation of substrates can occur quickly, especially in catabolic states or during dietary indiscretions, thus intoxicating the organism. Therefore, this group of IEM is called “intoxication-type” IEM. If an intracellular transporter is compromised, the substrate to be transported accumulates in one cell compartment and is low in others (storage disease, e.g., lysosomal storage disorders (LSD)) (Fig. 1). As it takes some time until substantial levels of substrate accumulate, these disorders mostly have a more chronic course, with the exception of M. Niemann-Pick type C where neonatal liver failure occurs in some patients. All IEMs are rare diseases, so-called orphan diseases. In Europe, these are defined as diseases affecting fewer than 5 in 10,000 inhabitants, in the USA a disease affecting less than 200,000 inhabitants ( 1:0, CLhuman ¼ a
ðBrWanimal
CLanimal Þb =BrWhuman When b > 1:3, CL may be overpredicted When b < 0:55, CL may be underpredicted where MLP represents maximum lifespan potential and BrW indicates brain weight (Mahmood and Brian 1996).

Allometric Scaling of Hepatically Eliminated Drugs The interspecies scaling of biliary and renal CL is not discussed within the chapter and the reader is referred to respective publications. The Liver Blood Flow Method For hepatically eliminated drugs, a common approach is to extrapolate human CL by the hepatic blood flow (LBF) ratio between humans and animals (Zou et al. 2012a). CLhuman ¼ CLanimal
ðLBFhuman =LBFanimal Þ It has been found that the mouse and monkey LBF methods were more accurate approach for human CL prediction than rat and dog LBF methods (Stoner et al. 2004; Ward and Smith 2004).

673

Normalization by In Vitro CL The in vitro results from hepatic metabolism studies using microsomes, hepatocytes, and liver slices can be extrapolated in order to incorporate intrinsic CL. Combining the in vitro intrinsic with the in vivo CL of animals, Lave et al. (1997) predicted the human CL using allometric scaling. The in vivo CL of each species was normalized by multiplying the ratio of CL in human hepatocytes or microsomes versus CL in animal hepatocytes or microsomes. The method seemed to have not been widely used and may not show superiority over other allometric scalings.

Allometric Scaling of Unbound Drug CL Plasma protein binding of many drugs varies considerably among animal species and only unbound drug can be eliminated. Therefore, protein binding has been considered to potentially influence the distribution and elimination of drugs (Zou et al. 2012a). Unbound CL ¼ CL=f u Unbound CL ¼ a
ðBWÞb where fu is the unbound fraction in plasma. Although the fu in rats is observed to be representative of the average fu in animals, correction for protein binding in each animal species would be more favorable than just considering only rats and humans (Tang and Mayersohn 2005). Tang and Mayersohn (2005) proposed the unbound fraction-corrected intercept method (FCIM) using the ratio of unbound fraction in plasma (fu) between rats and human (Rfu), based on 61 sets of CL values in animal species: CL ðmL=minÞ ¼ 33:35
ða=Rf u Þ0:77 where a is a coefficient obtained from allometric scaling. It has been shown that, practically, unbound CL cannot be predicted more accurate than total CL, except for some drugs. Typically, protein binding corrections will not be made unless the ratio of fu between rats and humans is tenfold or more.

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Computational (In Silico) Approaches Wajima et al. (2003) proposed an approach to predict human oral CL (mL/min/kg) by using experimental data for oral CL of the rat and dog, molecular weight (MW), clogP, and the number of hydrogen bond acceptors (Ha): logðCLÞ ¼  0:5927 þ 0:7386logðCLratÞ þ 0:5040 logðCLdogÞ þ 0:06014 c logP  0:1862 logðCLdogÞ
c logP þ 0:02893 MW
c logP þ 0:02551 logðCLratÞ
logðCLratÞ
c logP  0:03029 logðCLratÞ
logðCLratÞ
Ha  0:03051 logðCLratÞ
MW
c logP þ 0:08461 logðCLdogÞ
logðCLdogÞ
logðCLdogÞ  0:2510 logðCLdogÞ  0:2510 logðCLdogÞ
logðCLdogÞ
MW þ 0:04607 logðCLdogÞ
c logP
c logP  0:003596 c logP
c logP
Ha þ 0:0005963 c logP
Ha
Ha

Unlike other allometric scaling approaches, the Wajima method incorporates molecular structure parameters and is not dependent on the BW. The authors obtained clearance for 68 drugs either eliminated from renal excretion as unchanged drugs or extensively metabolized. The method gave a very good prediction of CL for the drugs studies.

predicting F, it is necessary to predict all three parameters: Fa, Fg, and Fh. Assuming that total CL is equal to hepatic CL, it is possible to predict Fh using allometric scaling. On the other hand, to predict Fg and Fh, in vitro-in vivo extrapolation methods (IVIVE) using hepatic microsomes, hepatocytes, and intestinal microsomes have been actively investigated. These approaches are out of the scope of this chapter, and the reader is referred to respective publications.

Interspecies Scaling of Vd Vd is commonly extrapolated pharmacokinetic parameter from animals. Vd of the central compartment (Vc) is most important in establishing the safety or toxicity for FIH studies by providing initial estimate of the plasma concentration following intravenous administration, and can be predicted with more accuracy than Vd at steady state (Vss) or Vd by area (Vβ). For majority of drugs, the exponents of the allometry for Vd revolve around 1.0. It has been suggested that if exponents of the allometry are > 1.1, then the Vd may be dramatically overestimated (Mahmood 2005).

Interspecies Scaling of Oral Bioavailability Interspecies Scaling of t1/2 The described above allometric scaling approaches predict human systemic CL. For orally administered drugs, it is vital to predict human oral bioavailability (F). F can be predicted based on preclinical in vivo estimates. One method is to use the average of all preclinical species. For example, if mouse, rat, and dog are 40%, 50%, and 60%, the human estimate is 50%. This method, however, is a rule of thumb and should be used carefully. Since F can be expressed as: F ¼ Fa
F g
F h where Fa denotes the fraction of the compound absorbed, Fg denotes intestinal availability, and Fh denotes hepatic availability, ideally for

Unlike CL and Vd, the t1/2 is not directly related to the physiological body function, and thus, the correlation between BW and t1/2 across species is poor (Mahmood 2005). Caldwell et al. (2004) proposed a simple allometric scaling for human t1/2 approximately = 4
t1/2 (rat) (hr), using 145 drugs. Another approach is to indirectly predict t1/2 from CL and Vc. Alternatively, Mahmood (1998) suggested use of the allometry of mean residence time (MRT) versus BW to predict first MRT. t1/2 can be then predicted by dividing the predicted MRT by 1.44. It is suggested that different approaches should be used to provide a range of predicted human t1/2 before scientific judgment is used to select an appropriate estimate (Mahmood 2005).

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Dose Finding in Single Dose Studies by Allometric Scaling

Interspecies Scaling for Protein Therapeutics It has been commonly believed that the nonhuman primate, usually the cynomolgus monkey, is the most relevant species for conducting preclinical PK studies for therapeutic monoclonal antibodies (mAbs). Human CL of mAbs can be reasonably projected based on monkey CL alone, by simple allometry with a fixed exponent of 0.85 for soluble antigen targets or 0.90 for membrane-bound targets. The dosage range for PK parameter determination was assumed to be linear (Deng et al. 2011).

Evaluation Two tragic stories, of Tusko (West et al. 1962) which occurred 55 years ago, and of TGN1412 which occurred in 2006, are used as classical examples. Both fatal cases were due to overdoses in first trials. Tusko, a 14-year-old Indian male elephant, died after intramuscularly dosing of 0.1 mg/kg which was a total dose of 297 mg lysergic acid diethylamide (LSD) on its body weight of 2970 kg. The trial tried to mimic a temporary form of madness in a zoo elephant. The dose was selected based on the observation that the rage in cats was produced with intravenous dose of 0.15 mg/kg LSD. Later, based on the calculations using the allometric approach, the actual dose of LSD to Tusko should have been much less and was in the range of 3–56 mg from different allometric approaches. TGN1412 was intended to be used to treat leukemia and autoimmune disease such as rheumatoid arthritis. It is an agonistic monoclonal antibody which can bypass the requirement for T cell antigen receptor signaling and activates human T cells by only stimulating co-stimulatory receptor CD28 in the immune system. In the FIH clinical trial, it was dosed at 0.1 mg/kg to six healthy volunteers (Expert scientific group 2006). The dose selection was based on the no observed adverse effect level (NOAEL) which is considered as 50 mg/kg from the repeated dose toxicity study in cynomolgous monkeys

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(as described in the draft USA Food and Drug Administration (FDA) guideline “Estimating the Safe Starting Dose in Clinical Trials for Therapeutics in Adult Healthy Volunteers,” 2002) with an additional safety factor of 160. However, extra precautions were not taken when antibodies are used to stimulate rather than neutralize components of the immune system. More than 90% of the CD28 receptors were bound by TGN1412 with proposed FIH dose of 0.1 mg/kg based on later calculations. Without any knowledge on the behavior of this compound in humans, the receptor occupancy of more than 90% was too high and induced massive production of cytokines and uncontrolled inflammatory responses which were observed in all six healthy volunteers in this trial. In conclusion, the preclinical development studies that were performed with TGN1412 did not predict a safe dose for use in humans, even though current regulatory requirement were met. Although the above two stories represented the failed evaluations of allometric scaling for dose finding in single dose studies, the importance of allometric scaling for the selection of “first time dose” in a species appears to be of immense significance. In addition, an understanding of the pharmacokinetic-pharmacodynamic relationship contributes to a much improved judgment. The most widely used method for FIH dose estimation are “dose-by-factor” approach which is based on the NOAELs in multiple species and the “pharmacokinetically guided” approach. Both of these approaches rely on allometric scaling either of the dose itself or of drug clearance. For the NOAEL-based approach, the following case is used: The NOAEL in the 4-week rat toxicity study was 10 mg/kg/day and 3 mg/kg/day in the 4-week dog toxicity study, the human equivalent doses (HED) using body surface area conversion factor (BSA-CF), 0.16 for rats and 0.54 for dogs, were calculated as 1.6 mg/kg/day, then the maximum recommended starting dose (MRSD) in the FIH clinical trial is estimated as 9.7 mg/man by applying the default safety factor of 10 and based on a 60 kg body weight for a man. Basically, the dose by factor approach applies an exponent for body surface area (0.67), which account for differences in metabolic rate, to convert doses between

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animals and humans. Thus, HED is determined by the below equation: HEDðmg=kgÞ ¼ Animal NOAELðmg=kgÞ
ðBWanimal ðkgÞ=BWhuman ðkgÞÞ0:33

However, the dose by factor approach based on NOAEL does not take into account of systemic exposure (AUC) and the safety factor applied in the calculation of MRSD is very empirical. In the pharmacokinetically guided approach, systemic exposure instead of dose is extrapolated from animal to human, and difference in potency, free fraction in plasma and bioavailability between animals and humans should be also taken into account for the extrapolation. FIH dose from pharmacokinetically guided approach is calculated by the below equation: D ¼ AUC
CL=F in which AUC is extrapolated human AUC based on the animal AUC corresponding to NOAEL or lowest animal AUC if a NOAEL and its corresponding AUC are available from more than one animal species. Or by the equation: D ¼ Css
τ
CL=F in which Css is extrapolated human steady-state plasma concentration based on the animal Css and τ is the dosing interval. With extrapolated AUC or Css, the key elements left to project a dose in humans to produce a target AUC or Css are CL and absolute oral F based on above two equations. Besides predicted from in vitro data, the human oral F is predicted in some practice based on in vivo estimates using the average of all preclinical species. This method, however, is a rule of thumb and should be used with caution. The methods of allometric scaling of CL and F from animals to humans have been extensively discussed in the other sections of this chapter. In another practice of interspecies allometric scaling to predict human PK parameters and FIH dose, oral plasma PK of ST-246 (Amantana et al. 2013) smallpox therapeutic was evaluated in mice, rabbits, monkeys, and dogs. Simple

allometry relating animal oral plasma CL (CL/F) to animal BW was used to determine human CL/F. Using a 70 kg body weight, the human CL/F was predicted as 254 L/h from the approach of simple allometry (point estimate). Based on the ROE, the CL/F was predicted by using the MLP correction, since the scaling exponent was approximately 1.0. The point estimate of human CL/F was predicted as 51.4 L/h from the approach of MLP-corrected allometry. In order to establish good safety margin in a FIH study, a relatively lower CL/F was considered in this practice to determine a safe dose. With a pharmacokineticguided approach, the starting oral dose of 485 mg is the product of the lowest observed systemic exposure value (AUC) among the species utilized in this study which is 9.43 h  μg/mL in dog and the scaled human CL/F which is 51.4 L/h based on the approach of MLP-corrected allometry. The trial was conducted from the low and median dose levels of 400 mg and 600mg to 800mg and the observed CL from these three levels of dosing are in close proximity to the predicted human CL/F from the approach of MLP-corrected allometry. Hence, this evaluation shows that allometric scaling of animal PK is useful in dose selection for FIH trials. The similar approach was used to predict a FIH dose of 7-O-succinyl macrolactin A (SMA) (Keumhan et al. 2017), based on allometric scaling of PK data from mice, rats, and dogs. The human CL of SMA was first predicted by both simple allometric scaling and MLP-corrected allometric scaling of estimated CL from mice, rats, and dogs. The first-in-man dose of SMA was calculated by multiplying the efficacious exposure (AUC) with the predicted human CL from MLP-corrected allometry, which predicted a lower value of human CL of SMA. Interspecies allometric scaling, including simple allometric scaling and allometric scaling with correction factors of MLP or BrW, has been increasingly applied in recent years to predict human PK properties of mAbs from preclinical data. However, PK allometric scaling across species with above allometric scaling fails in some cases of nonlinear PK and qualitative and quantitative difference in disposition pathways which

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Dose Finding in Single Dose Studies by Allometric Scaling

are typical for mAbs. Because the PK profiles of mAbs can be affected by their antigen through a target-mediated drug disposition, allometric scaling with correction factor of antigen concentration (AC) was evaluated for human CL estimation of four types of mAbs, including bevacizumab, etanercept, infliximab, and adalimumab (Wang et al. 2016b). In this evaluation, the plasma concentration of vascular endothelial growth factor (VEGF), which is the antigen of bevacizumab, was detected by enzyme-linked immunosorbent assay (ELISA) kits. The concentrations of tumor necrosis factor α which is the antigen of the rest three mAbs were obtained from published studies. The mean CLs of the mAbs in rabbit and dog were divided by the AC of the species and the product plotted as a function of BW on a log-log scale as in the below equation: CL=AC ¼ a
BWb The predicted human CL of 4.05 mL/day/kg of bevacizumab was close to the observed human CL of 5.73 mL/day/kg based on AC-corrected allometry and allometric scaling having the best prediction of human CL of etanercept and infliximab in comparison with other approaches including simple allometric scaling. Scaling with correction factors of MLP or BrW has equivalent good prediction of human CL of adalimumab with simple allometric scaling and scaling with a correction factor of BrW. These results indicated that AC has reasonably corrected the additional PK differences among the species besides the BW for mAbs. Although further evaluations AC-corrected allometry need to be conducted in the multiple species scaling of mAbs that showed nonlinear PK profiles, it may provide us a new perspective to estimate human PK parameters of mAbs from preclinical data to better find the dose of FIH trial of mAbs. The allometric scaling for pegylated liposomal and nanoparticle anticancer drugs was first evaluated with the PK of CKD-602(S-CKD602), doxorubicin (Doxil®), and cisplatin (SPI-077) which were all available from mice, rats, dogs, and phase I clinical studies (Caron et al. 2011). Because proposed CL pathways for nanoparticles and liposomes

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are the monocytes and macrophages of the mononuclear phagocytel system (MPS), liver weight, spleen weight, monocyte count, spleen blood flow, and liver blood flow, and potential factors associated with the MPS were evaluated to determine if parameters other than BW can best allometrically scale the disposition of these anticancer agents. The variable with the strongest relationship to liposomal clearance across all agents was total monocyte count. A  20.4%, 186%, and 78.2% difference existed between predicted and actual CL for S-CKD602, Doxil®, and SPI-077, respectively, when adding monocyte count into the allometric equation. This evaluation provided the preliminary evidence that factors associated with the MPS, such as monocyte count, may improve the prediction of CL in humans of drugs with liposomal formulations.

Critical Assessment of the Method Human Systemic CL The performance of prediction of human systemic clearance by allometric scaling was investigated for nearly 400 compounds (in addition with respect to charge class) by Lombardo et al. (2013a), and followed the assessment by the PhRMA initiative (Ring et al. 2011). In the first, the lowest mean-fold error, as well as frequency within twofold from predicted to observed CL, was observed for the following methods: (a) SSS using monkey, directly, or (b) including a correction of differences in liver blood flow, (c) FCIM, and (d) multiple linear regression (MLR) rat-dog. MLR is based on a logarithmic scaling of two species (log(CL human) = 0.4
log (CL rat) + 0.4
log(CL dog) – 0.4, Lombardo et al. 2013a). The FCIM performed very well with a GMFE (geometric mean-fold error) of 1.9 with 62% of compounds 30,000 U/day) to overcome UFH's low bioavailability; therefore, i.v. administration is generally preferred, as therapeutic plasma concentrations can be quickly achieved and effectively monitored (Alquwaizani et al. 2013). Several clinical trials have been published suggesting that modified dosing regimens in obese patients may be necessary to rapidly achieve the desired PD effect. This is done by measuring the activated partial thromboplastin time (aPTT) when administering UFH (Shank and Zimmerman 2015). Several studies suggest that larger than normal standard dosing of UFH may be warranted in these cases to provide the desired therapeutic aPTT levels (Freeman et al. 2010). Many of these same studies have also suggested that this can be done without risking excessive anticoagulation leading to bleeding. However, while this may allow the target aPTT level to be reached quicker, administering a higher dose of UFH to obese patients may not lead to additional efficacy in reducing the incidence of VTE, and the potential increase in the risk for bleeding should not be ignored (Joy et al. 2016). Because of the unpredictable bioavailability and inconsistent anticoagulant effects of UFH, low molecular weight heparins (LMWHs) with their predictable dose response (peak anti-Xa activity occurring 3–5 h after injection) have replaced UFH in many treatment paradigms. LMWHs are derived by depolymerization of UFH, with isolation and extraction of low molecular weight fragments. The most consistent and widely used laboratory test for LMWH has been the anti-FXa activity assay, although monitoring is typically not used, it remains an option for highrisk patients (renal insufficiency, obesity, pregnancy, noncompliance) where dosing adjustments may be required. In these cases, anti-Xa plasma levels are typically drawn 4 h after administration, and subsequent dosing adjusted to target levels (Alquwaizani et al. 2013). The enoxaparin package insert recommends a 1 mg/kg dose; however, many times obese

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Special Populations: Profiling the Effect of Obesity on Drug Disposition and Pharmacodynamics

patients receive an arbitrary lower dose in practice. This seems to be out of a concern that if the recommended dose is followed in obese patients, there is an increased likelihood of supratherapeutic anti-Xa levels and therefore an increased risk of bleeding. However, it is important to note that anti-Xa levels have not been prospectively correlated with any clinical outcomes (Thompson-Moore et al. 2015). That said, a recent study assessed the i.v. administration of enoxaparin sodium (1.5 mg/kg Total Body Weight) as a 6-h infusion and resulted in higher observed maximum PD activity (Emax) and overall systemic PD activity (effect)-time curve from time zero to infinity (AUEC1) values for both antiXa and anti-IIa levels in the obese patients. The absolute CL and Vd for anti-Xa activity were significantly increased in obese subjects (0.99 L/h vs. 0.74 L/h and 5.77 L vs. 4.37 L, respectively) (Hanley et al. 2010). Another study conducted by Thompson-Moore et al. prospectively assessed enoxaparin dosing in hospitalized morbidly obese patients (ThompsonMoore et al. 2015). The dosing practices observed in the hospital setting seemed to be reflective of other studies reported in the literature. Approximately 53% of patients received less than the recommended 1.0 mg/kg dose of enoxaparin. While 15 patients weighed >150 kg, only 1 patient was dosed with 1.0 mg/kg, again, reflective of an arbitrary dosing limit for these types of patients. Interestingly, despite this under-dosing, greater than 50% of the patients still had supratherapeutic anti-Xa levels. The actual median dose that produced a therapeutic anti-Xa level was 0.83 mg/kg actual body weight) (Thompson-Moore et al. 2015). The increased PD activity observed in this study is likely reflective of the drug’s poor distribution into adipose tissue and corresponding increased Vd (Thompson-Moore et al. 2015). While no recommendations are provided in the enoxaparin package insert for dosing in obese patients, considering this increase in PD activity, it is generally recommended that initiation of therapy should occur at a lower initial dose and anti-Xa levels should be monitored and used for dose adjustments as needed (Thompson-Moore et al. 2015).

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Warfarin is still the most commonly prescribed anticoagulant globally. Warfarin’s mechanism of action involves the inhibition of the vitamin K epoxide reductase complex in the liver thereby depleting the body’s vitamin K dependent coagulation Factors II, VII, IX, and X and anticoagulant proteins C and S (Brunton et al. 2006). Warfarin is dosed to a therapeutic PD effect by measuring the international normalized ratio (INR), a value derived from the patient’s prothrombin time (PT) laboratory value. Compared to normal weight patients, obese and morbidly obese patients have been found to take a significantly longer median time to achieve therapeutic INR (8 and 10 days vs. 6 days) values and higher average daily doses (6.6  0.3 and 7.6  0.5 vs. 5  0.3 mg) and mean discharge doses (6.7  0.5 and 6.7  0.7 vs. 4.4  0.5 mg). In summary, compared to normal weight patients, obese and morbidly obese patients had a lower initial response to warfarin (Wallace et al. 2013). Rivaroxaban is an oral, direct Factor Xa inhibitor that targets free and clot-bound Factor Xa and Factor Xa in the prothrombinase complex. Rivaroxaban is the first of a new class of compounds termed direct oral anticoagulants that specifically target a single coagulation factor (such as Factor Xa or thrombin). These compounds were developed in recent years to overcome the limitations of established anticoagulants, particularly warfarin. Factor Xa plays a central role in blood coagulation as it is activated by both the intrinsic and common coagulation pathways. Factor Xa directly converts prothrombin to thrombin via the prothrombinase complex, leading to fibrin clot formation and activation of platelets by thrombin (Mueck et al. 2014). Rivaroxaban does not require routine PD monitoring; however, there is a linear relationship between PT and rivaroxaban concentrations when using a sensitive PT reagent (e.g., Neoplastin+). Additionally, antifactor Xa values derived from an assay using rivaroxaban calibrators can be used to indirectly measure rivaroxaban concentrations. In a clinical pharmacology study conducted during drug development, there was no clinically significant difference between rivaroxaban PK and PD observed when assessed in obese vs.

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nonobese individuals. The Cmax and AUC values were unaffected in subjects weighing >120 kg. Additionally, rivaroxaban inhibited FXa activity to a similar extent in obese and nonobese individuals. FXa maximum values (Emax) occurred 3 to 4 h after rivaroxaban administration for both groups. However, the Emax for prolongation of PT decreased significantly (P 30 kg/m2 and 35 kg/m2; and Group 3 >35 kg/m2) (Westhoff et al. 2014). Each participant received a transdermal patch containing 0.55-mg EE and 2.1-mg gestodene (GSD). Each patch was used weekly for three 28-day cycles and its PD effect was measured by the Hoogland score, which is a composite score that comprises of transvaginal ultrasound and estradiol (E2) and progesterone levels every 3 days in Cycles 2 and 3. Additionally, PK and EE, GSD, and sex hormone-binding globulin were assessed (Westhoff et al. 2014). Study results reported that only six ovulations occurred during the study, and no participant ovulated in both study cycles. The ovulations observed occurred across the different weight groups and was unaffected by differences in BMI. A majority of participants had Hoogland scores of 1 or 2 regardless of BMI grouping. Follicle-like structures 3-fold for obese women compared to nonobese women whichever ECP was taken. However, this risk was greater for those taking LNG than for those taking UPA. Interestingly, women who had unprotected intercourse after using ECP were more likely to get pregnant than those who did not, regardless of type used (Glasier et al. 2011). Consistent with Glasier et al., a small clinical pharmacology study conducted by Edelman et al. assessed the PK parameters of nonobese (median 22.8 kg/m2) and obese women (median 39.5 kg/m2) dosed with both a single and double dose of LNG ECP. The single dose of LNG ECP in obese women resulted in a significantly lower Cmax (Cmax-obese = 5.57 ng/mL) than that observed in normal weight women (Cmax-non-obese = 10.30 ng/mL), approximately 50% lower. Doubling the dose of LNG ECP increased the Cmax significantly (Cmax-obese = 10.52 ng/mL) essentially

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Special Populations: Profiling the Effect of Obesity on Drug Disposition and Pharmacodynamics

normalizing the Cmax level to that of the normal BMI subjects receiving a single ECP dose (Edelman et al. 2016).

Invading Organisms Antimicrobials Antimicrobial agents are classified based on chemical structure and proposed mechanism of action. There are those agents that: (1) inhibit synthesis of bacterial cell walls; (2) act directly on the cell membrane, increasing permeability and compromising the structure of the microorganism; (3) disrupt ribosomal function to inhibit protein synthesis and are bacteriostatic; (4) disrupt ribosomal function to alter protein synthesis and are bactericidal; (5) affect bacterial nucleic acid metabolism; and (6) are antimetabolites that block essential enzymes of folate metabolism (Brunton et al. 2006). Additionally, these mechanisms of action can be further divided into time-dependent or concentration-dependent effects and each antimicrobial class has a unique PK/PD target. For example, β-lactam antibiotics are time-dependent, they produce the most effective PD response when the concentration of the free drug remains above the minimum inhibitory concentration (MIC). Aminoglycosides are concentration-dependent, with the most effective PD response occurring when the Cmax of the drug is over the MIC (Cmax/MIC) (3). Then there are those compounds that are both time- and concentration-dependent, therefore when the AUC of the antimicrobial from 0–24 h is over the MIC (AUC0–24/MIC) thereby drives bacterial killing (Alobaid et al. 2016). The β-lactam antibiotics fall into the first class of agents described that inhibit the synthesis of bacterial cell walls. They include the penicillins, cephalosporins, β-Lactamase inhibitors, and Carbapenems (Brunton et al. 2006). A prospective study conducted by Hites et al. assessed the PK and PD parameters of infected obese patients (BMI
30 kgm2) who received either meropenem (MEM), piperacillin-tazobactam (TZP), or cefepime/ceftazidime (CEF) β-lactam antibiotics. The primary PD parameter in this

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study was defined as the “clinical breakpoint” of the microorganisms assessed for different antimicrobial therapies (Hites et al. 2014). These breakpoints, as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST), have been fixed to ensure a good probability of therapeutic success. The breakpoint for β-lactams used by EUCAST is defined as the drug’s free fraction ( fT) >1 MIC [fT>MIC] during 40–50%, 50–60%, and 60–70% of the dosage interval for MEM, TZP, and CEF, respectively. It should be noted that a higher PD target is frequently used when clinical situations arise where patients are suffering from conditions like septic shock or are neutropenic due to oncological treatment. These situations may justify the use of a total fraction (T)>4MIC [T>4MIC] for 40%, 50%, and 70% of the dosage interval for MEM, TZP, and CEF, respectively (Hites et al. 2014). Different pathogens have different PD targets. For example, for infections caused by Enterobacteriaceae spp. and P. aeruginosa, the EUCAST’s clinical breakpoints for these pathogens are: 2 mg/L for MEM, 8 mg/L and 16 mg/L for TZP, and 1 mg/L and 8 mg/L for CEF, respectively. Total serum concentrations and fT at 40%, 50%, and 70% of the dosage intervals for MEM, TZP, and CEF, respectively, were as follows: 6.2 mg/L and 6.1 mg/L for MEM, 36.7 mg/L and 25.7 mg/L for TZP, and 16.1 mg/L and 12.5 mg/L for CEF (Hites et al. 2014). When evaluating the fT>MIC for Enterobacteriacae spp., adequate serum concentrations were obtained in 93% of patients receiving MEM, 84% of patients receiving TZP, and 91% of patients receiving CEF. For infections due to P. aeruginosa, adequate fT>MIC were obtained in 93% of patients receiving MEM, 68% of patients receiving TZP, and 73% of patients receiving CEF. According to the EUCAST criteria, any percentage of PD target that is 4MIC) for infections from Enterobacteriacae spp. and P. aeruginosa, much fewer patients reached the PD target. Adequate serum concentrations were only reached for 21% for MEM, 55% for TZP, and 91% for CEF with Enterobacteriacae spp. infections and for 21% for MEM, 19% for TZP, and 18% for CEF P. aeruginosa infections. Therefore, only CEF with 91% of patients infected with Enterobacteriacae spp. met the EUCAST criteria (Hites et al. 2014). The decreased serum concentrations observed in this study is likely caused from an increase in Vd and CL for all three study drugs, when compared to non-obese individuals. A CrCL of >150 mL/min was observed in approximately 25% of those in the obese cohort. This augmented renal CL appears to be the major risk factor for failure to reach therapeutic concentrations. Together, these data suggest that standard dosage regimens, particularly for TZP, are insufficient in obese, noncritically ill patients (Hites et al. 2014). Increased drug CL has been described in critically ill patients and has been termed augmented renal clearance (ARC), a condition where renal elimination of circulating solutes is increased. ARC is defined as a CrCL of
130 mL/min/ 1.73 m2. ARC is associated with subtherapeutic antimicrobial concentrations and worse clinical outcomes in critically ill patients receiving standard doses of antimicrobial therapy. An increase in renal CL has been observed in obese individuals who have normal kidney function, most likely due to the increased kidney size and renal blood flow associated with obesity. While this altered physiology may result in lower antimicrobial concentrations, higher concentrations may be observed in obese patients with co-morbidities such as diabetic nephropathy. Obese individuals are more likely to have pathologies that cause hepatic dysfunction, such as hepatic steatosis, possibly resulting in decreased drug metabolism. Obesity may also have an impact on different hepatic enzyme systems

K. T. Moore

causing increased (e.g., CYP2E1) or decreased (e.g., CYP3A4) activity. In another study conducted by Sturm et al., the use of piperacillin/tazobactam was examined in critically ill morbidly obese patients (BMI >40 kg/m2). All patients achieved the PK/PD target of 100% fT>MIC for pathogens with an MIC of 16 mg/L using a piperacillin/tazobactam dose of 4.5 g i.v. every 6 h. Morbidly obese patients had a higher piperacillin Vd (31.0 L vs. 22.4 L) and lower CL (6.0 L/h vs. 13.7 L/h) compared with nonobese patients (Atkinson et al. 2007). The net result was a t1/2 of 3.7 h compared with 1 h reported in other populations. This longer t1/2 likely contributed to an extended % fT>MIC for susceptible pathogens. Based on these results, it would appear that the tested 4.5 g i.v. dose every 6 h would be sufficient to attain the desired PK/PD target of %fT>MIC (Sturm et al. 2014). While the effects of obesity on penicillin PK and PD parameters are sparse to nonexistent in the literature, compounds like ampicillin, penicillin, ticarcillin would likely exhibit similar changes in their PK and PD to that of piperacillin. Cefoxitin is a second-generation cephamycin antibiotic and classified as a semisynthetic, broadspectrum, cephalosporin. Cefoxitin is commonly used for perioperative parenteral surgical prophylaxis. Moine et al. studied the PK and PD of a 40 mg/kg i.v. dose in morbidly obese individuals (Moine et al. 2016). Although the dose used in this study was substantially higher than the standard cefoxitin doses typically used, the Cmax observed in this study was similar to those previously reported for nonobese populations receiving a much lower weight-based dose. This lower than expected Cmax along with a prolonged t1/2 is consistent with the approximate twofold higher Vd values observed in this study compared to nonobese populations. Additionally, despite the use of these higher doses, tissue concentrations were poor, with an average tissue/serum ratio of 8%, and below the Clinical and Laboratory Standards Institute (CLSI) breakpoint for anaerobes targeted by this antibiotic. As with the other β-lactams, the time during which unbound drug concentrations are greater than the pathogen MICs ( fT>MIC) is

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the PD parameter best correlated with clinical efficacy (Moine et al. 2016). The authors noted that the PD target for surgical prophylaxis is largely undefined. Considering that surgical contamination may occur at any point during the procedure, a f TMIC of 100% was suggested as an ideal target for cefoxitin and β-lactam antibiotics in general, during surgery. Even though the authors calculated a more conservative f TMIC of 70%, tissue concentrations failed to reach the needed level for efficacy. Concentrations were below the susceptibility breakpoint for S. aureus and E. coli, suggesting inadequate coverage if an intraoperative contamination would have occurred. The authors suggest that although the weight-based dose at 40 mg/kg performed better than a standard 2-g dose, it would likely be inadequate to prevent infection (Moine et al. 2016). Aminoglycosides are primarily used to treat infections caused by aerobic gram-negative bacteria. They are considered bactericidal as they disrupt ribosomal function to alter protein synthesis. This class of antibiotic includes gentamicin, tobramycin, amikacin, kanamycin, netilmicin, streptomycin, and neomycin (Brunton et al. 2006). The aminoglycoside antimicrobials are hydrophilic weak bases and have a corresponding low Vd (closer to blood volume). These compounds have optimal bactericidal activity when achieving peak concentrations that are 8x to 10x the MIC of the targeted pathogen (Hanley et al. 2010). A study conducted by Bauer et al. assessed the steady-state PK of three aminoglycosides – gentamicin, tobramycin, and amikacin – in morbidly obese subjects (Bauer et al. 1983). The investigators observed that the mean Vd values were substantially larger for the morbidly obese subjects compared to the nonobese subjects: gentamicin (Vd-obese = 23.31 L vs. Vd-non-obese = 17.01 L), tobramycin (Vd-obese = 29.01 L vs. Vd-non-obese = 18.31 L), and amikacin (Vdobese = 26.81 L vs. Vd-non-obese = 18.61 L). Similarly, CL values were larger in the morbidly obese subjects compared to the nonobese subjects: gentamicin (CLobese = 135.8 mL/min vs. CLnonobese = 95.9 mL/min), tobramycin (CLobese = 162.4 mL/min vs. CLnon-obese = 101.3 mL/min),

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and amikacin (CLobese = 157.3 mL/min vs. CLnonobese = 99.2 mL/min). Albeit the significant increases in Vd and CL, there was no significant difference between predicted and measured steady-state concentrations as determined by Cmax and Cmin values. This is likely attributed to the larger creatinine clearance values secondary to hyperfiltration commonly observed in obese individuals (Bauer et al. 1983). Daptomycin is a lipopeptide antibiotic used in the treatment of systemic and life-threatening infections caused by gram-positive organisms (i. e., enterococci, staphylococci, streptococci). The MIC of daptomycin (MIC90) is typically 1 μg/ mL for staphylococci and streptococci and 2 to 4 μg/mL for enterococcal species. This compound is both time- and concentration-dependent, in which bacterial eradication is dependent upon the ratio of AUC0–24h to the MIC (AUC0–24h/MIC). Dvorchik and Damphousse conducted a study assessing the PK of daptomycin in moderately obese (BMI between 25 and 39.9 kg/m2) or morbidly obese (BMI
40 kg/m2) compared to matched nonobese (BMI between 18.5 and 24.9 kg/m2) subjects (Dvorchik and Damphousse 2005). After administration of a 4-mg/kg total body weight dose, the Cmax and AUC values for both obese groups were higher compared to their respective matched nonobese controls: Moderately Obese [Cmax-obese = 57.75 μg/mL vs. Cmaxnon-obese = 46.28 μg/mL and AUC(0-1)obese = 420.53 μgh/mL vs. AUC(0-1)non-obese = 322.37 μgh/mL] and Morbidly obese [Cmax-obese = 67.00 μg/mL vs. Cmax-non-obese = 53.22 μg/mL and AUC(0-1)obese = 547.78 μgh/mL vs. AUC(0-1) non-obese = 418.76 μgh/mL]. These differences equate to mean Cmax values that were ~25% higher in the obese groups than in their matched controls and AUC values were ~30% to 35% greater in the obese groups compared to their matched controls. Additionally, significant differences in daptomycin Vd were observed between obese and nonobese groups. The obese group saw increases in absolute Vd that were ~25% larger in the moderately obese subjects and ~55% in the morbidly obese subjects compared to the

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respective controls. In a similar trend, CL values were also increased in obese vs. nonobese subjects: moderately obese CLobese = 855.80 mL/h vs. CLnon-obese = 732.80 mL/h and morbidly obese CLobese = 1015.83 mL/h vs. CLnon-obese = 696.41 mL/h. Interestingly, renal CL was not substantially different between obese and nonobese subjects. The authors speculate that this may be a consequence of all subjects having an estimated creatinine clearance
70 mL/min, however did not provide any information on whether obese subjects had CrCL values representative of abnormally high glomerular filtration rate (GFR) or hyperfiltration (Dvorchik and Damphousse 2005). Vancomycin is a tricyclic glycopeptide antibiotic that is commonly used to treat Gram positive pathogens, for example, severe staphylococcal and enterococcal infections. Vancomycin exerts a time-dependent antibacterial effect, and its clinical response is a function of the AUC0–24h and MIC (AUC0–24h/MIC). Current guidelines recommend maintaining vancomycin trough concentrations >10 μg/mL to avoid Staphylococcus aureus resistance and between 15 and 20 μg/mL in complicated infections such as bacteremia, endocarditis, and osteomyelitis. Guidance is also provided for treatment of methicillin-resistant staphylococcus aureus (MRSA) in which a dosage of 15–20 mg/kg every 8–12 h without exceeding 2000 mg of vancomycin/dose. This in turn creates a challenge since obese patients would, however, typically require doses that would exceed 2000 mg/dose due to the initial weight-based dosing paradigm. A study conducted by Adane et al. assessed the PK of vancomycin in obese subjects. For the treatment of S. aureus-associated lower respiratory tract infections, clinical success was found with an AUC0–24h/MIC >315, whereas a successful microbiologic response required a AUC0–24h/ MIC >866. Clinical practice guidelines state that an AUC0–24h/MIC
400 is needed for clinical effectiveness. At this level, the likelihood of S. aureus resistance is low and ensures adequate penetration in tissues such as the lung with minimizing potential nephrotoxicity. Those enrolled into the study had a median weight of 147.9 kg,

K. T. Moore

BMI of 49.5 kg/m2 a ClCr of 124.8 mL/min/ 1.73m2 and received a median vancomycin dose of 4000 mg/day, resulting in median AUC0–24h that was 582.9 mgh/L. The mean Vd was 0.51 L/kg, and CL was 6.54 L/h. Simulations indicated that 4000–5000 mg/day of vancomycin in this population provided
93% probability of a AUC0–24h /MIC ratio of
400 leading to an MIC of 1 μg/mL and therapeutic effectiveness (Adane et al. 2015). Ciprofloxacin is a broad-spectrum fluoroquinolone antibiotic. Quinolones function by inhibiting DNA gyrase and topoisomerase ultimately inhibiting cell division. Its PD effect is both concentration-dependent and time-dependent, with clinical efficacy best described using an AUC0–24/MIC ratio. Ciprofloxacin is active against both gram-positive and gram-negative bacteria. A study conducted by Allard et al. assessed the PK of ciprofloxacin and its primary metabolite (desethyleneciprofloxacin) in both obese subjects (mean weight = 110.7 kg; mean BMI = 36.4 kg/m2) and normal weight subjects (mean weight = 71.8 kg; mean BMI = 23.3 kg/m2) (Allard et al. 1993). After receiving a single 400 mg iv dose of ciprofloxacin infused over 1 h, ciprofloxacin CL was significantly increased in obese subjects (897.44 mL/min) compared with nonobese subjects (744.44 mL/min), additionally CLrenal in obese subjects was 29% higher than in nonobese subjects. Lastly, ciprofloxacin Vd was larger in obese subjects (Vd-obese = 269.17 L) than nonobese subjects (Vd-non-obese = 219.03 L) (Allard et al. 1993).

Antifungals The azole antifungals include the imidazole and triazole classes, which share the same antifungal spectrum and mechanism of action. Azoles inhibit the fungal cytochrome P450 enzyme 14α-sterol demethylase thereby inhibiting fungal growth (Brunton et al. 2006). Fluconazole is active against a variety of Candida spp., with a PK/PD target of AUC0–24h/MIC >25. There is scarcity of literature that addresses the effects of obesity on the PK and PD of fluconazole. Of the few publications available, most report data from case studies.

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Special Populations: Profiling the Effect of Obesity on Drug Disposition and Pharmacodynamics

Lopez and Phillips described a case report of a critically ill morbidly obese patient (BMI = 84 kg/m2) receiving renal replacement therapy and who was being treated with fluconazole at a dose of 600 mg (Atkinson et al. 2007). The investigators calculated a Vd = 163.3 L and CL = 3.25 L/h, for this patient, which was significantly larger than those previously reported for critically ill nonobese patients with acute renal failure (Vd = 65.6 L and CL = 1.9 L/h) (Atkinson et al. 2007).

References and Further Reading Abernethy DR, Schwartz JB (1988) Verapamil pharmacodynamics and disposition in obese hypertensive patients. J Cardiovasc Pharmacol 11(2):209–215 Abernethy DR, Greenblatt DJ, Smith TW (1981) Digoxin disposition in obesity: clinical pharmacokinetic investigation. Am Heart J 102(4):740–744 Abernethy DR, Greenblatt DJ, Divoll M et al (1984) The influence of obesity on the pharmacokinetics of oral alprazolam and triazolam. Clin Pharmacokinet 9(2):177–183 Adane ED, Herald M, Koura F (2015) Pharmacokinetics of vancomycin in extremely obese patients with suspected or confirmed Staphylococcus aureus infections. Pharmacotherapy 35(2):127–139 Allard S, Kinzig M, Boivin G et al (1993) Intravenous ciprofloxacin disposition in obesity. Clin Pharmacol Ther 54(4):368–373 Alobaid AS, Hites M, Lipman J et al (2016) Effect of obesity on the pharmacokinetics of antimicrobials in critically ill patients: a structured review. Int J Antimicrob Agents 47(4):259–268 Alquwaizani M, Buckley L, Adams C, Fanikos J (2013) Anticoagulants: a review of the pharmacology, dosing, and complications. Curr Emerg Hosp Med Rep 1:83–97 Anderson WJ, Lipworth BJ (2012) Does body mass index influence responsiveness to inhaled corticosteroids in persistent asthma? Ann Allergy Asthma Immunol 108(4):237–242 Atkinson AJ, Abernethy DR, Daniels CE, Dedrick RL, Markey SP (2007) Principles of clinical pharmacology, 2nd edn. Academic, Amsterdam Bauer LA, Drew Edwards WA, Patchen Dellinger E et al (1983) Influence of weight on aminoglycoside pharmacokinetics in normal weight and morbidly obese patients. Eur J Clin Pharmacol 24:643–647 Beavers CJ, Heron P, Smyth SS et al (2015) Obesity and antiplatelets-does one size fit all? Thromb Res 136:712–716 Blouin RA, Warren GW (1999) Pharmacokinetic considerations in obesity. J Pharm Sci 88(1):1–7

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Bordeaux BC, Qayyum R, Yanek LR et al (2010) Effect of obesity on platelet reactivity and response to low-dose aspirin. Prev Cardiol 13:56–62 Bowman SL, Hudson SA, Simpson G et al (1986) A comparison of the pharmacokinetics of propranolol in obese and normal volunteers. Br J Clin Pharmacol 21:529–532 Brill MJ, Diepstraten J, van Rongen A et al (2012) Impact of obesity on drug metabolism and elimination in adults and children. Clin Pharmacokinet 51(5):277–304 Brill MJ, van Rongen A, Houwink AP et al (2014a) Midazolam pharmacokinetics in morbidly obese patients following semi-simultaneous oral and intravenous administration: a comparison with healthy volunteers. Clin Pharmacokinet 53:931–941 Brill MJ, Houwink AP, Schmidt S et al (2014b) Reduced subcutaneous tissue distribution of cefazolin in morbidly obese versus non-obese patients determined using clinical microdialysis. J Antimicrob Chemother 69:715–723 Brunton LL, Lazo JS, Parker KL (eds) (2006) Goodman and Gilman’s The pharmacological basis of therapeutics, 11th edn. McGraw-Hill, New York Casati A, Putzu M (2005) Anesthesia in the obese patient: pharmacokinetic considerations. J Clin Anesth 17(2): 134–145 Cataldi M, di Geronimo O, Trio R, Scotti A et al (2016) Utilization of antihypertensive drugs in obesity-related hypertension: a retrospective observational study in a cohort of patients from southern Italy. BMC Pharmacol Toxicol 17:9 Cheymol G (2000) Effects of obesity on pharmacokinetics implications for drug therapy. Clin Pharmacokinet 39(3):215–231 Cheymol G, Poirier J-M, Barre J, Pradalier A, Dry J (1987) Comparative pharmacokinetics of intravenous propranolol in obese and normal volunteers. J Clin Pharmacol 27(11):874–879 Cheymol G, Woestenborghs R, Snoeck E et al (1997) Pharmacokinetic study and cardiovascular monitoring of nebivolol in normal and obese subjects. Eur J Clin Pharmacol 51(6):493–498 Cho S-J, Yoon I-S, Kim D-D (2013) Obesity-related physiological changes and their pharmacokinetic consequences. J Pharm Investig 43:161–169 Christoff PB, Conti DR, Naylor C, Jusko WJ (1983) Procainamide disposition in obesity. Drug Intell Clin Pharm 17(7–8):516–522 de la Peña A, Yeo KP, Linnebjerg H et al (2015) Subcutaneous injection depth does not affect the pharmacokinetics or glucodynamics of insulin lispro in normal weight or healthy obese subjects. J Diabetes Sci Technol 9(4):824–830 Dunn TE, Ludwig EA, Slaughter RL et al (1991) Pharmacokinetics and pharmacodynamics of methylprednisolone in obesity. Clin Pharmacol Ther 49(5):536–549 Dvorchik BH, Damphousse D (2005) The pharmacokinetics of daptomycin in moderately obese, morbidly obese, and matched non-obese subjects. J Clin Pharmacol 45:48–56

746 Edelman AB, Cherala G, Blue SW et al (2016) Impact of obesity on the pharmacokinetics of levonorgestrelbased emergency contraception: single and double dosing. Contraception 94(1):52–57 Fan J, de Lannoy IA (2014) Pharmacokinetics. Biochem Pharmacol 87:93–120 Farrell GC, Teoh N, McCuskey R (2008) Hepatic microcirculation in fatty liver disease. Anat Rec Adv Integr Anat Evol Biol 291(6):684–692 Freeman AL, Pendleton RC, Rondina MT (2010) Prevention of venous thromboembolism in obesity. Expert Rev Cardiovasc Ther 8(12):1711–1721 Fukuchi H, Nakashima M, Araki R et al (2009) Effect of obesity on serum amiodarone concentration in Japanese patients: population pharmacokinetic investigation by multiple trough screen analysis. J Clin Pharm Ther 34(3):329–336 Galletti F, Fasano ML, Ferrara LA et al (1989) Obesity and beta-blockers: influence of body fat on their kinetics and cardiovascular effects. J Clin Pharmacol 29 (3):212–216 Gandhi A, Moorthy B, Ghose R (2012) Drug disposition in pathophysiological conditions. Curr Drug Metab 13 (9):1327–1344 Glasier A (2013) Emergency contraception: clinical outcomes. Contraception 87(3):309–313 Glasier A, Cameron ST, Blithe D et al (2011) Can we identify women at risk of pregnancy despite using emergency contraception? Data from randomized trials of ulipristal acetate and levonorgestrel. Contraception 84(4):363–367 Hanley MJ, Abernethy DR, Greenblatt DJ (2010) Effect of obesity on the pharmacokinetics of drugs in humans. Clin Pharmacokinet 49(2):71–87 Hites M, Taccone FS, Wolff F et al (2014) Broad-spectrum β-lactams in obese non-critically ill patients. Nutr Diabetes 4(6):e119 Jain R, Chung SM, Jain L et al (2011) Implications of obesity for drug therapy: limitations and challenges. Clin Pharmacol Ther 90(1):77–89 Jensen MD, Ryan DH, Apovian CM et al (2014) 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults. J Am Coll Cardiol 63:2985–3023 Jiang X-L, Samant S, Lesko LJ, Schmidt S (2015) Clinical pharmacokinetics and pharmacodynamics of clopidogrel. Clin Pharmacokinet 54:147–166 Joy M, Tharp E, Hartman H et al (2016) Safety and efficacy of high-dose unfractionated heparin for prevention of venous thromboembolism in overweight and obese patients. Pharmacotherapy 36(7):740–748 Jusko WJ (2017) Clarification of contraceptive drug pharmacokinetics in obesity. Contraception 95: 10–16 Kees MG, Weber S, Kees F, Horbach T (2011) Pharmacokinetics of moxifloxacin in plasma and tissue of morbidly obese patients. J Antimicrob Chemother 66:2330–2335

K. T. Moore Knibbe CA, Brill MJ, van Rongen A et al (2015) Drug disposition in obesity: toward evidence-based dosing. Annu Rev Pharmacol Toxicol 55:149–167 Kubitza D, Becka M, Zuehlsdorf M, Mueck W (2007) Body weight has limited influence on the safety, tolerability, pharmacokinetics, pharmacodynamics of rivaroxaban (BAY 59-7939) in healthy subjects. J Clin Pharmacol 47:218 Landsberg L, Aronne LJ, Beilin LJ et al (2013) Obesityrelated hypertension: pathogenesis, cardiovascular risk, and treatment. A position paper of the obesity society and the American Society of Hypertension. J Clin Hypertens (Greenwich) 15:14–33 Lentz SR (2016) Thrombosis in the setting of obesity or inflammatory bowel disease. Blood 128(20): 2388–2394 Levy BI, Schiffrin EL, Mourad JJ et al (2008) Impaired tissue perfusion a pathology common to hypertension, obesity, and diabetes mellitus. Circulation 118 (9):968–976 Leykin Y, Miotto L, Pellis T (2011) Pharmacokinetic considerations in the obese. Best Pract Res Clin Anaesthesiol 25:27–36 Martin JH, Saleem M, Looke D (2012) Therapeutic drug monitoring to adjust dosing in morbid obesity – a new use for an old methodology. Br J Clin Pharmacol 73 (5):685–690 Michalaki MA, Gkotsina MI, Mamali I et al (2011) Impaired pharmacokinetics of levothyroxine in severely obese volunteers. Thyroid 21(5):477–481 Moine P, Mueller SW, Schoen JA et al (2016) Pharmacokinetic and pharmacodynamic evaluation of a weightbased dosing regimen of cefoxitin for perioperative surgical prophylaxis in obese and morbidly obese patients. Antimicrob Agents Chemother 60(10): 5885–5893 Mornar S, Chan LN, Mistretta S et al (2012) Pharmacokinetics of the etonogestrel contraceptive implant in obese women. Am J Obstet Gynecol 207(2):110. e1–110.e6 Morrish GA, Pai MP, Green B (2011) The effects of obesity on drug pharmacokinetics in humans. Expert Opin Drug Metab Toxicol 7(6):697–706 Mueck W, Stampfuss J, Kubitza D, Becka M (2014) Clinical pharmacokinetic and pharmacodynamic profile of rivaroxaban. Clin Pharmacokinet 53:1–16 Munjal S, Gautam A, Rapoport AM, Fisher DM (2016) The effect of weight, body mass index, age, sex, and race on plasma concentrations of subcutaneous sumatriptan: a pooled analysis. Clin Pharmacol Adv Appl 8:109–116 Reflection paper on investigation of pharmacokinetics and pharmacodynamics in the obese population 25 January 2018 1, EMA/CHMP/535116/2016 2, Committee for Human Medicinal Products (CHMP) Robinson JA, Burke AE (2013) Obesity and hormonal contraceptive efficacy. Women’s Health (Lond Engl) 9(5):453–466

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Rowland M, Tozer TN (1995) Clinical pharmacokinetics, concepts and applications, 3rd edn. Lippincott Williams & Wilkins, Philadelphia Shah DK, Missmer SA, Correia KF, Ginsburg ES (2014) Pharmacokinetics of human chorionic gonadotropin injection in obese and normal-weight women. J Clin Endocrinol Metab 99(4):1314–1321 Shank BR, Zimmerman DE (2015) Demystifying drug dosing in obese patients. American Society of Health System Pharmacists. eBook Simmons KB, Edelman AB (2016) Hormonal contraception and obesity. Fertil Steril 106(6):1282–1288 Smit C, De Hoogd S, Brüggemann RJM, Knibbe CAJ (2018) Obesity and drug pharmacology: a review of the influence of obesity on pharmacokinetic and pharmacodynamic parameters. Expert Opin Drug Metab Toxicol 14(3):275–285 Steinkampf MP, Hammond KR, Nichols JE, Slayden SH (2003) Effect of obesity on recombinant follicle stimulating hormone absorption: subcutaneous versus intramuscular administration. Fertil Steril 80(1):99–102 Sturm AW, Allen N, Rafferty KD et al (2014) Pharmacokinetic analysis of piperacillin administered with tazobactam in critically ill, morbidly obese surgical patients. Pharmacotherapy 34(1):28–35 Thompson-Moore NR, Wanat MA, Putney DR et al (2015) Evaluation and pharmacokinetics of treatment dose enoxaparin in hospitalized patients with morbid obesity. Clin Appl Thromb Hemost 21(6):513–520 Tucker GT (1981) Measurement of the renal clearance of drugs. Br J Clin Pharmacol 12:761–770 van Kralingen S, Diepstraten J, Peeters MY et al (2011) Population pharmacokinetics and pharmacodynamics

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of propofol in morbidly obese patients. Clin Pharmacokinet 50:739–750 van Rongen A, Välitalo PAJ, Peeters MYM et al (2016) Morbidly obese patients exhibit increased CYP2E1mediated oxidation of acetaminophen. Clin Pharmacokinet 55:833–847 Wallace JL, Reaves AB, Tolley EA et al (2013) Comparison of initial warfarin response in obese patients versus non-obese patients. J Thromb Thrombolysis 36(1):96–101 Westhoff CL, Reinecke I, Bangerter K, Merz M (2014) Impact of body mass index on suppression of follicular development and ovulation using a transdermal patch containing 0.55-mg ethinyl estradiol/2.1-mg gestodene: a multicenter, open-label, uncontrolled study over three treatment cycles. Contraception 90(3):272–279 Wójcicki J, Jaroszynska M, Droździk M et al (2003) Comparative pharmacokinetics and pharmacodynamics of propranolol and atenolol in normolipaemic and hyperlipidaemic obese subjects. Biopharm Drug Dispos 24 (5):211–218 World Health Organization (2018) Obesity and overweight. http://www.who.int/en/news-room/fact-sheets/ detail/obesity-and-overweight. Accessed 8 Oct 2018 Wu B (2016) Morbid obesity alters both pharmacokinetics and pharmacodynamics of propofol: dosing recommendation for anesthesia induction (Short communication). Drug Metab Dispos 44: 1579–1583 Zuckerman M, Greller HA, Babu KM (2015) A review of the toxicologic implications of obesity. J Med Toxicol 11:342–354

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Gerard Sanderink and Andreas Kovar

Contents Purpose and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 Procedure and Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

751 751 752 752 753

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . PK Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Population PK Analysis of Sparse Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dose Adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 757

Abstract

Kidney and liver are the main organs involved in the elimination of drugs. In general, the elimination capacity of the kidney is lower than of the liver, because of the smaller organ size and associated blood flow. Renal excretion can be limited by glomerular filtration rate in case of passive excretion or transporter capacity by total renal blood flow and in case of active secretion. Impaired renal function is a

G. Sanderink (*) Translational Medicine and Early Development, Sanofi-Aventis R&D, Vitry-sur-Seine, France e-mail: Gerard.Sanderink@sanofi-aventis.com A. Kovar Translational Medicine and Early Development, Sanofi, Frankfurt, Germany e-mail: andreas.kovar@sanofi.com

rather common condition in patients. Therefore, both dedicated studies in patients with renal impairment and pharmacokinetic investigations via means of population pharmacokinetics are routinely used in drug development to investigate if a dose adjustment needs to be applied in this vulnerable population.

Purpose and Rationale Kidney and liver are the main organs involved in the elimination of drugs. Both have a metabolic and a direct excretory capacity, although the first is predominant for drugs eliminated by the liver, while the most frequent mechanism of renal clearance is direct excretion of the unchanged drug or its circulating metabolites. In general, the elimination capacity of the kidney is lower than of the

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_8

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liver, because of the smaller organ size and associated blood flow. Renal excretion can be limited by glomerular filtration rate in case of passive excretion or transporter capacity by total renal blood flow and in case of active secretion. Impaired renal function is a rather common condition, with an estimated 30 million people in the USA having chronic kidney damage (CDC 2018). Kidney function is well known to decrease with age and in our experience mild or moderate renal impairment is the norm in study populations above 75 years. It is therefore necessary to evaluate the potential impact of changes in renal function on drug pharmacokinetics in many cases, and not only for prescribing information including dose adjustment, but also in order to conduct a clinical development program in large patient populations with adequate exclusion criteria guaranteeing safe drug administration. The main rationale to evaluate the impact of renal impairment is when a drug or its active metabolites are mainly eliminated by renal excretion, especially when a drug is intended to be given in patients with decreased renal function (e.g., elderly patients) or augmented renal function (e.g., critically ill patients) and exhibits a narrow therapeutic margin. In such situations, dose adjustment may be warranted to reduce the risk of adverse drug reactions/toxicity and therapy failure, respectively. However, impaired renal function can also affect nonrenal drug elimination and has also been associated with other changes, such as changes in absorption, transport, and tissue distribution. Also plasma protein binding might play a role through decreased drug protein binding due to low albumin levels. This is of relevance for drugs with high plasma protein binding (>90%) and that have a high hepatic extraction ratio (>0.7). Another indirect mechanism of impaired renal function is uremic plasma that inhibits enzyme or transporter activity. For most drugs, the evaluation will focus on the effect of decreased glomerular filtration, but it should be kept in mind that renal (drug) transporters, expressed in the basolateral and apical membrane of renal proximal tubules, play an important role in tubular secretion and

G. Sanderink and A. Kovar

reabsorption of drug molecules in the kidney as well. Thus, in case of active renal secretion of a transporter substrate, there is also a potential for clinical significant drug-drug interactions with perpetrators that inhibit those transporters like cimetidine (OCT) or probenecid (OAT and OATP). Finally, the possibility that a drug is eliminated during hemodialysis needs also to be considered. As a consequence, for most drugs that are likely to be administered to patients with renal impairment – including drugs that are not primarily excreted by the kidney – the respective pharmacokinetics should be assessed in patients with renal impairment to provide appropriate dosing recommendations. Both guidance documents on renal impairment of the FDA and the EMA (US FDA Guidance for Industry 2010; EMA 2015) outline several approaches to study the effect of renal impairment on drug exposure. One approach is a population PK analysis of sparse data in large scale clinical trials, which allows to compare patients with reduced renal function with the typical patient for a given indication. Some limitations of this approach are that it does not address patients that are voluntary excluded from such studies, which is generally the case for severe and end-stage renal impairment. Also some specific parameters like unbound drug fraction and circulating metabolites may need to be included in the evaluation, and the sample size should provide sufficient sensitivity. However, the approach may be very useful to confirm the absence of a treatment risk for patients with renal disease, especially when it is not easily feasible to conduct a specific study in renally impaired subjects without the clinical indication. When a specific pharmacokinetic study in subjects with decreased renal function is conducted, it can be done according to a “full” design or according to a “reduced” or “staged” design. In the first case, all degrees of renal impairment are included in the study. In the reduced design, the effect of severely decreased renal function in comparison to normal renal function is investigated first. If the results indicate that also other degrees of renal impairment may alter the pharmacokinetics of the study drug to a clinically relevant extent,

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the study should be expanded (staged design). This reduced design approach is considered to be useful only if no relevant effect is expected. For a full-range study in subjects with renal impairment, participants are classified according to their glomerular filtration rate (GFR) which is considered the best indicator of overall kidney function. The current classification is as follows: Normal/control: GFR
90 ml/min Mild impairment: 6095

90 to 95

>95

75%)

39%

23%

Recovery by Route of Excretion 100 90 80 70 60 50 40 30 20 10 0 Urine (>75%)

75%)

Fecal (>75%)

No primary route

Major Route of Excretion

Fig. 3 Recoveries pre- and post- MIST

• The PK/ADME compound

properties

of

the

test

Metabolite Quantification and Identification The hADME study provides valuable samples of urine, feces, and plasma which can provide useful information on the metabolic fate of the test compound in man. While the samples are initially assayed for radioactive content, with the aim of obtaining a mass balance, the main utility of the samples is in the quantification and identification of drug metabolites.

Due to the low quantities of plasma that can be taken during the hADME study, initial method development is usually performed using samples of urine and feces. This enables establishment of optimal chromatographic conditions prior to analyzing the precious plasma extracts. Excreta samples containing notable quantities of radioactivity (usually >90% excreted radioactivity) are selected for analysis and can be pooled by timepoint and subject as appropriate. The standard analytical method for metabolite investigations is liquid chromatography with radioactivity detection. The use of radioactivity makes no assumptions as to the fate of the drug entity and as the response is independent of structure relative

788

proportions of all components in the sample is achieved immediately. The key objective of the hADME study is quantification and identification of the radioactive components in systemic circulation in order to meet the regulatory requirements of the ICH and FDA. As well as the limited amount of sample available, these investigations are also challenging due to fact that concentrations of radioactivity are generally much lower in plasma than in any other matrix. During the preclinical studies performed early in the drug development process, relatively high doses of radioactivity can be administered (50–100 μCi/kg) and the sample volumes are much lower. In the hADME study, the amount of radioactivity that can be administered is determined by the dosimetry assessment and for [14C] is usually around 50–100 μCi. Where drugs exhibit poor bioavailability, extensive metabolism, or high volumes of distribution, this can result in extremely low concentrations of drug and thus radioactivity in circulation. The analytical challenge is compounded by the definition what constitutes a major radioactive component requiring identification. The initial regulatory guidance provided by the FDA indicated that a major radioactive component would be one that accounts for >10% AUC of parent AUC, the subsequent guideline produced by the ICH indicated that a major radioactive component was one that accounted for >10% total radioactivity AUC. For some time, this led to confusion and uncertainty with an unstated belief that the ICH guideline would be the standard that took precedence. The issue was resolved by the publication of an updated guidance from the FDA (FDA 2016) in which the definition of a major radioactive component was confirmed as one that accounted for >10% total radioactivity AUC. In contrast to the determination of the mass balance of the test compound where measurements are expressed as % dose administered, the detection limits in plasma are dependent upon the specific activity of the test material. A standard bioanalytical assay developed for plasma will generally rely upon the amount of chemical present in the matrix, whereas for radioactive studies the detection limit increases as the chemical dose

A. McEwen

increases. This situation is shown graphically for different radioactive doses in Fig. 4. Taking 100 mg as the proposed human dose, the detection limit decreases as the dose given to the volunteers is increased. For a standard radioactive dose of 100 μCi, the detection limit increases as the proposed chemical dose increases. This should be borne in mind when reviewing the dosimetry data – will the proposed radioactive dose meet the objectives of the study – and can be used as a justification when moving from ICRP category I dose to a category IIa dose. It is also worth noting that low radioactive doses do not necessarily result in low specific activity material. Doses prepared for studies supported by accelerator mass spectrometry (AMS, discussed later in the chapter) may contain a low radioactive dose but also tend to be administered as a low chemical dose, thus resulting in a high specific activity. This has knock on effects on assessing the potential stability of the dose material. Plasma samples can be prepared for quantification in a number of ways but are generally either across subjects at specific timepoints or more commonly by preparation of AUC pools using the methods published by Hamilton (Hamilton et al. 1981) or Hop (Hop et al. 1998). The AUC method provides information on the relative exposure of each metabolite compared to circulating total radioactivity, but can result in dilution of the radioactive response. In general, a mixed approach would be employed with an AUC pool supplemented by a couple of timepoint-specific analyses. AUC pools can be generated in a timeand volume-dependent manner across subject. The gold standard approach to metabolite investigations using plasma samples from hADME studies has been the use of high performance liquid chromatography (HPLC) combined with radiodetection. In combination with modern high resolution mass spectrometers, the eluent from the column can be split (usually 10:90 or 20:80) to enable simultaneous quantification and identification of drug metabolites. A major limitation to the use of radioflow detection methods is the poor sensitivity obtained. There are two main types of radioflow cell: those using a solid scintillant and those using a liquid

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The Human ADME Study

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Fig. 4 Detection limits in plasma based on dose administered

flow cell where eluent is mixed with scintillator in the detector and counted. Of the two cell types, the flow through cell provides the best performance, but the key limiting factor for both detectors is the short residence time in the cell. Components in samples containing low concentrations of radioactivity can pass through the detector without providing sufficient counts for detection. Recent detectors such as the BetaRam5 detector (LabLogic) use “active counting” (ACMTM) to provide improvements in the signal observed. An evaluation of the technique was reported by Attwood et al. (2010) and indicated consistent retention times and compatibility with UPLC. The use of ACMTM avoids the requirement for the sample to be fraction collected and counted off-line thereby eliminating the possible loss of volatile metabolites during sample processing. Given the low concentrations of radioactivity in the critical plasma samples, the chances of success can be enhanced by employing good chromatographic practice. System refinements such as reducing dead volumes and shortening

the distance between column and detector will increase the sensitivity, though it should be borne in mind that sharpening the chromatographic peak will shorten the residence time in the detector, potentially reducing the signal. Another consideration during the chromatographic analyses is the “quenching” effect that is often associated with radioactive measurements. Most investigators assume this to be constant throughout the chromatographic run although this is rarely checked. In addition to the radioactive components of interest, the samples will also contain a large number of co-eluting components present in the sample matrix. The presence or absence of quenching effects can be checked by running a blank sample and adding test material to the eluate. This can be achieved by a variety of methods such as direct infusion postcolumn, or postcolumn by collecting fractions and spiking. During standard method establishment investigations, the suitability of the system is often established using parent material, but once a complex mixture is injected for analysis, the relative

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recovery of components can vary. The use of radiolabeled materials in the hADME study allows column recovery to be determined easily. This can alleviate concerns that material has been retained on the column, the stainless steel tubing, or the radiodetector cell. By measuring the column recovery, the system performance can be optimized as part of the method establishment process. In an attempt to overcome the limitations associated with the radioflow systems, alternative methods were investigated and these can be divided into two main categories: a) stop-flow and b) dynamic flow methods. The stop-flow methods are as the name suggests based on stopping the flow once a radioactive peak is detected, thus providing longer detection times and therefore higher detection efficiencies. The peak is held within the detector as opposed to passing through therefore increasing the signal to noise ratio. The technology was originally developed for investigations performed in the agrochemical industry where samples routinely contain low levels of radioactivity. The advantages of stop-flow technology have been discussed in the literature (Nassar et al. 2004), but as yet the stop-flow technique has not been widely adopted in the drug development process. The major drawbacks associated with stop-flow technology are that subsequent analyses can result in inconsistent retention times and the detector is incompatible with LC/MS, meaning the samples need to be analyzed twice. Stop-flow has a lower throughput than alternative methods as counting occurs during the chromatographic run and therefore extends the chromatographic run times. The use of a modified “dynamic flow” radiodetector was described by Cuyckens et al. (2008). Improvements in sensitivity were achieved by a modification to the standard online radiochemical detection system introducing the capability to provide variable scintillation fluid flow. Further improvements were achieved by reducing the internal diameter of the tubing, resulting in better peak shape, increased sensitivity, and higher resolution. When compared to conventional radioHPLC using [3H]- and [14C]-labelled compounds, the method was reported to have comparable

A. McEwen

sensitivity to conventional techniques, was compatible with UPLC (thus shortening the chromatographic run times), and was suitable for hyphenation with mass spectrometers. The detection limits obtained using the radiochromatographic method can be significantly improved by performing the quantitation offline. Eluent from the HPLC column can be fraction collected directly into scintillation vials, appropriate scintillation cocktail added, and the samples counted using standard liquid scintillation counting (LSC) methods. Each sample (fraction) can be counted for longer time periods, thus dramatically improving the sensitivity. On standard liquid scintillation counters, the samples are typically counted one vial at a time. If the chromatographic run is 30 min, with fractions collected at 15 s intervals, then counting each vial for 4 min would result in a total counting time of over 4 h. The utility of off-line counting was significantly improved by the introduction of microplate scintillation counting (MSC) plates (Dear et al. 2006; Krauser et al. 2012). When performing chromatographic analysis with MSC counting, the eluent is collected directly into microplates, 96 or 384 well, using accurate fraction collectors. The technique has the advantage that several plates can be selected per run and the process can be automated thus improving the throughput. Two types of plate are commercially available, one with a solid scintillant base and another employing liquid scintillant. Use of solid scintillant plates results in a slightly lower sensitivity but allows recovery of notable fractions from the plate postcounting for further characterization using mass spectrometric techniques. Both types of plate give a notable improvement in sensitivity when directly compared to the results obtained using traditional fraction collection-liquid scintillation counting methods. Unlike traditional liquid scintillation counters, microplate scintillation counters are able to count multiple wells simultaneously (12–16 dependent on counter); therefore, the throughput is much higher. The technique should however be used with care as one of the key steps in sample analysis is evaporation of the eluent from the plate at which point there is

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The Human ADME Study

791

Table 6 Comparison radiodetector sensitivity

Radiodetection HPLC-RFD HPLC-LSC HPLC-MSC Stop-flow HPLC-AMS

Background (CPM) 15 25 2 15

Counting Efficiency (%) 70 90 70 70

Counting time (min) 5–10 s 10 10 1

Limit of Detection (DPM) 250–500 10 5 25–50 0.0001

Limit of Quantification (DPM) 750–1500 31 15 75–150

Taken from Zhu et al. 2005

the possibility to lose volatile components (parent drug or metabolites). It is therefore good practice to perform a system suitability check before committing the precious samples (especially plasma) for analysis. A good system suitability check would include a recovery check for radioactivity from plates spiked with parent compound and would ideally compare radioprofiles obtained from other biological matrices such as urine using both radioflow detection and microplate scintillation counting. An improved method for quantifying the radioactive content of microplate fractions has subsequently been reported (Dear et al. 2008). The method was essentially an imaging technique and was reported to shorten counting times required for analysis. The counting methods detailed above were compared by Zhu et al. (2005) and the relative limits of detection discussed. The data are reproduced in Table 6 and show that of all the commonly used methods of radiometric detection the microplate scintillation counter provides the lowest limit of detection, with the exception of accelerator mass spectrometry. Whilst AMS has an extremely low detection, it is not a true radiometric method as quantitation is based on graphitization and measurement of [14C] atoms. The relative sensitivity of the common techniques used for quantification of drug metabolites (radioflow, standard fraction collection, and microplate scintillation counting) was assessed in a comparative study using the same sample (McEwen et al. 2014). In general good agreement was observed using all three counting techniques, but overall the microplate scintillation counting provided greater resolution of the chromatographic peaks and a lower background. This

consideration assumes great importance when selecting the analytical method for the analysis of clinical plasma samples where concentrations of drug-related material, and therefore concentrations of radioactivity, are generally low.

Alternatives to Carbon-14 NMR The development of high field NMR machines in the 1980s led researchers to explore the use of NMR for analysis of plasma and urine samples. The technique was suitable for the study of both endogenous compounds (biomarkers) and drug metabolites. The technique requires limited sample preparation and is nondestructive, meaning that the sample can be retained for use in additional experiments. The use of NMR for quantification and identification of drug metabolites in clinical and preclinical samples was developed by Nicholson (Nicholson et al. 1983, 1984a, 1985; Bales et al. 1984a, b, 1985). NMR is an inherently insensitive technique but has the advantage that rapid multicomponent analyses can be performed with limited sample preparation and with no prior assumptions as to the sample identity. The technique also provides structural information sometimes absent from mass spectrometric data such as the specific site of hydroxylation. Analyte concentrations need to be >50 μM for effective detection and the molecule requires suitable proton groups such as CH3-, -CH2-, or CH. The early promise faded as researchers turned to mass spectrometric techniques, but recently NMR has been used to

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generate quantitative data to answer regulatory questions (Dear et al. 2008; Caceres-Cortes and Reilly 2010). NMR has also been used to investigate likely routes of metabolism and provide an estimate of renal clearance (Nedderman et al. 2011) in the absence of traditional radioactive tracers. Initial studies were conducted using proton NMR as most drugs contain hydrogen atoms, and this also provides structural information. For compounds containing fluorine, 19F NMR provides the opportunity to obtain quantitative data, although structural information obtained is limited. The low levels of fluorine in biological systems and the fact that fluorine 19 has a 100% isotopic abundance mean that all signals obtained can be related to drug or drug related material. This has led to many researchers using the technique in early drug metabolism (Dear et al 2000, Desmoulin et al. 2002, Ismail et al. 2002, Lenz et al. 2002, Malet-Martino et al. 2006). The technique continues to generate interest and recently two groups have published papers (James et al. 2017, Haitao et al. 2017) comparing the results obtained using F19 NMR with those obtained using the radiolabeled compounds.

Inductively Coupled Plasma Mass Spectrometry (ICP-MS) ICP-MS is a technique that can detect a wide range of elements (metals and nonmetals) at extremely low concentrations, in the order of on part in 1015 (ppq). While the technique generally covers elements not found in the pharmaceutical compounds, for drugs containing halogen atoms, especially bromine and iodine, ICP-MS offers an alternative method for detection and quantification of drug-related material. The advantage of the technique is that detection involves atomization and ionization of the compound, meaning that quantification is independent of chemical structure and can be performed without the requirement for synthetic standards. The technique can be used to determine several elements simultaneously and can be combined with both normal and reverse phase HPLC to separate and quantify

A. McEwen

drug metabolites in biological fluids (Axelsson et al. 2001; Duckett et al. 2002). While ADME studies are currently not conducted for in the development of “biologicals,” it is interesting to note that the use of ICP-MS has recently been reported in the study of large molecule metabolism using a test compound labelled with iodine127 (Lim et al 2014).

AMS The concept of accelerator mass spectrometry (AMS) can be traced back to a cyclotron experiment conducted in the 1930s to measure 3H and 4He (Alvarez and Cornog 1939). The technique remained relatively underutilized until the publication of a paper by Richard A Muller in 1977. His paper in Science showed how particle accelerators (cyclotrons and linear) could be used for detection of tritium, carbon-14, and several other isotopes of scientific interest including beryllium10, widely used in geology. He also published the first radiodating determination using tritium. Within month other groups (Nelson et al. 1977; Bennett et al. 1977) published further data using linear particle accelerators. These measurements were made using relatively large accelerators operating with terminal voltages of 7 and 8 MV. Since these early experiments, smaller instruments have been introduced and the trend toward smaller and smaller machines continues. Modern instruments operating at low accelerator voltages such as 200 kV for determination of 14C are being designed and built by companies such as ETH Zurich (Suter 2010). Originally used in academic institutions as a radiocarbon dating technique, the ability to determine extremely low concentrations of radioactivity associated with carbon-14 and tritium was of great interest to scientists involved in the drug development process, especially those involved in the safety assessment of drug metabolites. The application of AMS in the drug development process has been the subject of several general reviews (Lappin and Garner 2005; Lappin and Stevens 2008). Despite being described in some quarters as a low level counting technique, AMS

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The Human ADME Study

does not directly count radioactive decay particles but instead provides C12:C14 isotope ratios for the sample. AMS was first applied to biomedical studies in the 1980s (Litherland 1980). Since then further reviews detailing the biomedical applications of AMS have appeared (Lappin et al. 2006; Young and Ellis 2007; Young et al. 2008; Vogel et al. 2010; Seymour 2011). Specific examples of AMS investigations in the drug development process were also reported by Swart et al. (2016) and Bloomer et al. (2016). One of the common misconceptions surrounding AMS is that it is a nonradioactive technique. While the amount of radioactivity involved in AMS studies is extremely low, such that the samples can be treated as nonradioactive and the usual human dosimetry is not required, the material administered to the volunteers is still radioactive and will require radiosynthesis in the usual way. Terms such as “lightly labelled” have been used, but the drug material should be prepared under accepted quality standards with a defined specific activity and structural characterization to ensure the study fulfils the regulatory objectives. While the regulatory requirements associated with administration of radioactive material to human volunteers may be eliminated, it is still good practice in the drug development process to obtain good quality data on the ADME properties of the investigational product in the preclinical species used in the toxicology assessments. The gold standard for these investigations is still the use of radiolabeled material. If these investigations are conducted around the same time as the hADME study, then contemporaneous analysis can be performed to compare the nature of circulating metabolites in preclinical and clinical samples. While AMS provides exceptional limits of detection, it is not a technique that enables structural elucidation of the drug metabolites under analysis. Prior to analysis using AMS, all samples must be converted to elemental carbon, a process known as graphitization (Young et al. 2008). Samples of urine, fecal extracts, and/or plasma must be separated using chromatography, fractionated, and each fraction graphitized for analysis by AMS, thus generating a radioprofile similar to HPLC-LSC (Young and Ellis 2007; Young et al.

793

2008). Currently this is an inherently slow process requiring manual processing, and while progress in designing a system capable of transforming liquid inlet to a gaseous CO2 output has been reported (Liberman et al. 2004, 2007), this has yet to become commercially available. Equally valuable information can be obtained using modern high resolution mass spectrometers using early clinical samples from the single ascending dose and multiple ascending dose (SAD/MAD) studies. In the hADME study, hyphenated LC-MS/MS-RAD analysis can help optimize the separation of components while obtaining good quality quantitative data. It could be argued therefore that while AMS can undoubtedly provide a tool to solve specific problems associated with low bioavailability and/ or extensive metabolism, adoption of AMS as the gold standard is still some way off and will ultimately depend upon further advances in AMS technology.

Summary The hADME study is a pivotal study in the drug development process, providing a bridge between the preclinical toxicology findings and the clinical studies. The standard design involves administration of radiolabeled to human volunteers, collection of excreta and plasma, determination of a mass balance, and quantification and identification of the components in systemic circulation.

Examples Example A: Dosimetry One of the key activities in the planning of a hADME study is synthesis of the radiolabeled drug material, and this requires two pieces of information (1) the amount of radioactivity that can be administered to the volunteers and (2) the proposed chemical dose. The dosimetry study is therefore critical to providing this information forming the basis for the risk assessment in man.

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Fig. 5 Binding to melanin – uveal tract

Pigmented rats are used to assess binding of the drug material to melanin which is assessed from concentrations present in the uveal tract. Concentrations in pigmented skin can be measured, but there may be some confusion as to whether the observed radioactivity is bound to pigmented skin or the melanin present in the fur. An example image showing binding of test compound to the uveal tract is provided in Fig. 5. Tissue distribution studies in support of dosimetry calculations are generally designed to provide information on the distribution and kinetics of the radiolabeled material following dosing. A typical set of images obtained is provided in Fig. 6. In this example, it can be seen that the radioactivity distributes throughout the animal by 2 h following administration and is then seen to be eliminated through the kidney and gastrointestinal tract so that at 168 h following dosing radioactivity is seen only in the liver, caecum, and uveal tract. Inclusion of an additional timepoint at 504 h shows that radioactivity has been almost completely eliminated from the animal with the exception of the uveal tract. In the example, provided tissues (with the exception of the uveal tract) were clear of radioactivity by 504 h. As a general rule the earlier complete elimination is observed, the higher will be the

A. McEwen

radioactive dose that can be administered to man. To provide a realistic estimate of the risk associated with radioactivity present in the eye, the data can be analyzed to provide a biological halflife for modelling purposes. The tissue concentration data are provided Table 7 and the kinetic analysis is reproduced in Fig. 7. The data in this case indicate a half-life of around 213 h. One further factor affecting the result will be the relative routes of elimination of radioactivity. In this case, radioactivity was eliminated by both fecal and urinary routes. Dosimetry assessment for compounds showing high fecal elimination usually result in lower values than those with high urinary elimination. This is due to the longer residence time of radioactivity that passes through the gastrointestinal tract which results in greater internal exposure for this organ.

Example B: Mass Balance A typical hADME study usually runs for a fixed period (nominally 7 days) with radioactive content in urine and feces measured to assess the mass balance recovery. It should be stressed that the primary objective is to assess the routes and rates of excretion of radioactivity and while a full mass balance is welcomed it is not always achievable. Daily measurement of the radioactivity excreted in urine and feces allows the elimination to be followed in real time and can provide useful information on the rates of elimination as well as predicting the time to reach 90%. A useful method is to plot “body burden graphs” as shown in the examples below.

Compound A The hADME study for compound A was designed on the basis that the metabolism was well understood and a fixed term of 9 days was assigned for residence in the clinic. Urine and feces were collected on a daily basis and radioactive content determined. The elimination was then characterized using a body burden calculation as described earlier. The data obtained are provided in Table 8 and the kinetic analysis is shown in Fig. 8.

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The Human ADME Study

2 Hours

Eye

795

Intra-orbital lachrymal gland

Uveal tract

Stomach

Liver

168 Hours

Muscle

Kidney

Lung

Liver

24 Hours

Testis

Caecum

Liver

8 Hours Uveal tract

Adrenal gland

Lung

Small intestine Testis

Kidney

Caecum

Uveal tract

Liver

504 Hours

Caecum

Uveal tract

Fig. 6 Typical QWBA experiment for dosimetry assessment

Mean recovery obtained in this study was 87.5% (75.4% urinary, 12.1% fecal), thus below an arbitrary cut-off of 90% for a good recovery. The standard release criteria ( 25% of CLtot)

FDA

EMA

PMDA

CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 3A [2A5, 2 J12,4F2, 2E1] UGT1A1, 1A3, 1A4, 1A6, 1A9, 2B7, 2B15b MAO, FMO, XO, ALDH, ADH

CYP1A2, 2B5, 2C8, 2C9, 2C19, 2D6, 3A

CYP1A2, 2B5, 2C8, 2C9, 2C19, 2D6, 3A [2A5, 2 J12,4F2, 2E1] UGT1A1, 1A3, 1A4, 1A5, 1A9, 2B7, 2B15b MAO, FMO, XO, AO, ALDH, ADH

Not specified Not specified

P-gp, BCRP

Not specified CATP1B1/1B3a

P-gp, BCRP

OCT2, OAT1/3

Not specified

OCT2, OAT1/3, MATE1/2K

a

In case the autoradiography (ARG) in animal shows significant accumulation in the liver (PMDA) Require the identification of UGT subtype c In case the drug is not metabolized by major CYPs b

Table 2 Targets to be examined: whether a drug is an inhibitor or not Metabolic enzymes CYPs UGTs Transporters Gut and systemic Hepatic (CLH > 25% of CLtot) [recommendation] Renal (CLR, secretion > 25% of CLtot) [recommendation]

FDA

EMA

PMDA

CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 3A UGT1A1, 1A3, 1A4, 1A6, 1A9, 2B7, 2B15

CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 3A* UGT1A1, 2B7

CYP1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 3A* UGT1A1, 2B7

P-gp, BCRP OA1P1B1/1B3 [MRPs, BSEP]

OATP1B1/1B3, [BSEP, OCT1]

OATP1B1/1B3, [MRP2, BSEP, OCT1]

OC12, OAT1/3 [MRPs, MA1E1/2-K]

OCT2,OAT1/3 [MATE1/2-K]

OCT2,OAT1/3, MATE1/ 2-K, [MRP2, 4]




For CYP3A4, investigation with multiple substrate with different binding site are required

CYP3A in vitro. If the in vitro induction results are positive according to predefined thresholds using basic models, the investigational drug is considered an enzyme inducer and further in vivo evaluation may be warranted An overview as regards which metabolic enzymes to be examined based on FDA, EMA, and PMDA guideline, e.g., whether a drug is a

substrate, inhibitor, or inducer or not is given in Tables 1, 2, and 3.

Transporter-Based DDIs Although less well-recognized than metabolizing enzymes, membrane transporters can have

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P. Stopfer

Table 3 Targets to be examined: whether the drug is an inducer or not FDA Metabolic enzymes Enzyme CYP3A4/5 (PXR),[if positive, CYP2C8, (transcriptional 2C9, 2C19]CYP1A2 (AhR),CYP2B6 factor) (CAR)

BVIA

PMDA

CYP3A4/5 (PXR), CYP1A2 (AhR), CYP2B6 (CAR)

CYP3A4/5 (PXR), [if positive, CYP2C9 et al] CYP1A2 (AhR), CYP2B6 (CAR)

P-gp (in case PXR and/or CAR mediated induction observed)

Not mentioned

Transporter “Methods for in vitro evaluation are not well understood” “Should consult with FDA about studying induction in vivo”

important effects on pharmacokinetics and drug exposure. To date, most identified transporters belong to one of two superfamilies: ATP-binding cassette (ABC) and solute carrier (SLC). Transporters govern the transport of solutes (e.g., drugs and other xenobiotics) in and out of cells. In contrast to metabolizing enzymes, which are largely concentrated in the liver and intestine, transporters are present with varying abundance in all tissues in the body and play important roles in drug distribution, tissue-specific drug targeting, drug absorption, and elimination. Transporters can also work in concert with metabolizing enzymes (see also ▶ Chap. 31, “Pharmacodynamic Drug–Drug Interactions”). A number of transporter-based interactions have been documented in recent years. Analogous to drug interactions mediated by P450 enzymes, coadministration of a drug that is an inhibitor or an inducer of a drug transporter may affect the pharmacokinetics of a drug that is a substrate for that transporter. Transporters can affect the safety profile of a drug by affecting the concentration of a drug or its metabolites in various tissues. Transporter-based drug interactions and the potential effect of drug transporters on safety make it important to determine whether transporters affect the absorption and disposition of an investigational drug and whether the investigational drug can affect the absorption and disposition of other drugs through an effect on transporters. The effect of a compound on drug transporter function will be investigated, e.g., in bidirectional transport experiments in tissue cultures. Results of these experiments show whether the investigated

compound is a drug transporter substrate (by determining the net transport rate, efflux ratio, or Michaelis constant (Km)) or an inhibitor of the transporter (by IC50 or Ki values). Because of the lack of a validated in vitro system to study transporter induction, the definitive determination of induction potential of an investigational drug on transporters is based on in vivo induction studies. An overview as regards which drugs transporters to be examined based on FDA, EMA and PMDA guideline, e.g., whether a drug is a substrate, inhibitor, or inducer or not is given in Tables 1, 2, and 3.

General Strategies for the Planning and Conduct of DDI Trials The evaluation of a DDI potential for a compound is at first based on all in vitro data collected for a compound to whether the drug is a substrate, inhibitor, or inducer or a metabolic enzyme or drug transporter in relation to the (expected) in vivo plasma concentrations (e.g., maximum plasma concentrations). The respective cut offs, which have to be considered, are given based on a “basic model” for reversible, time-dependent inhibition and also induction. Respective overviews as regards specific recommendations (e.g., use of unbound or total drug concentrations) and cut off values (as to whether a clinical DDI trial needs to be performed) of the guidelines from different authorities are given in Tables 4, 5, and 6. Mechanistic static models and/or more comprehensive dynamic models (e.g., physiologically based PK

45

Drug–Drug Interaction Studies

833

Table 4 Basic model: for reversible inhibition (R = 1 þ [I]/Ki or IC50) FDA EMA Metabolic enzyme: Reversible inhibition Systemic [I] = total Cmax [I] = unbound Cmax R > 1.1 R  1.02 Gut [l]G = Dose/250 mL, R > () 11 Transporter: Reversible inhibition Systemic P-gp, BCRP [I] = total Cmax [I] = unbound Cmax R  1.1 R  1.02 CAT1,3, OCT2 [I] = unbound Cmax [I] = unbound Cmax MATE1, 2-K(PMDA) R  1.1 R  1.02 [I] = unbound Cmax, inleta CA7P1B1,3 [I] = total Cmax R  1.1 and R  1.04 [I] = unbound Cmax, inleta R  1.25 Gut P-gp, BCRP [I]G = Dose/250 mL, R > () 11 a

PMDA [I] = total Cmax R > 1.1

[I] = total Cmax R  1.1 [I] = unbound Cmax R  1.25 [I] = unbound Cmax, inleta R  1.25

Cmax,inlet is calculated as fu,b x ([I]max,b þ Fa x Fg x ka x Dose/Qh)

Table 5 Basic model: time-dependent inhibition (TDI) FDA Time-dependent inhibition (TDI) Systemic [I] = total Cmax R > 1.1 Gut [l]G = Dose/250 mL R > 11

EMA

PMDA

[I] = unbound Cmax R  1.25 [I]G = Dose/250 mL R  1.25

[I] = total Cmax R > 1.1 [I]G = Dose/250 mL R > 11

Table 6 Basic model: induction [R = 1 þ Emax  [I]/(EC50 þ [I])] FDA Metabolic enzyme induction mRNA change > predefined threshold [I] = total Cmax R < 0.9 Transporter induction Not mentioned

Cut off value

EMA

PMDA

>2-fold (concentration dependent increase) or 20% increase of the increase in positive controla [I] = unbound Cmax [I] = total Cmax R: Not defined R < 0.9

a

It is acceptable to use the enzyme activity as a measure, in case that the inhibition of enzyme can be clearly denied (PMDA)

(PBPK) models) may be used additionally, and specific recommendations can be found in the guidelines from FDA, EMA, and PMDA. It should be noted that currently only the FDA provides a dedicated flowchart how to explore DDI potential with PBPK models. The recommended cases to use PBPK (EMA/FDA) are to predict DDI’s worst-case scenarios (additive “multiple DDI mechanisms” combined with, e.g., organ

impairment), dose-dependent DDIs, the effect of a less potent inhibitor, or the impact of a DDI in subpopulations. The initial approach to assessing clinical DDIs is the evaluation of underlying mechanisms with probe compounds. Examples of appropriate probe compounds that are considered to be specific and representative for a defined metabolic pathway or drug transporter are defined in current regulatory

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Table 7 Overview as regards inhibitor, inducer, and substrate lists provided by authorities Metabolic enzyme (in vitro/in vivo) Inhibitor Inducer Substrate Transporter (in vitro/in vivo) Inhibitor Inducer Substrate

FDA

EMA

PMDA

/◎ ◎/◎ /○

○/◎ / ○/○

○/◎ ○/◎ ○/◎

/○ /○ /○

/ / /

◎/○ ◎/○ ◎/○

◎: listed with intensity for each drug (i.e., classification [weak, moderate, or strong] or Ki value), ○: listed, : not listed

guidelines (e.g., (1,2,3)). A brief overview, which inhibitors, inducers, and substrates are specified in the respective guidelines, can be found in Table 7. If a clinical relevant DDI cannot be excluded through screening with a probe compound, further clinical DDI evaluations may become necessary (see Figs. 1 and 2). The demonstration of a relevant DDI in a study with a probe compound can result in the need of further studies with concomitant medications for the NME (Figs. 1 and 2) to determine: 1. Whether additional studies are needed to better quantify the effect and to examine the effects of weaker inhibitors (early studies usually examine strong inhibitors) on the investigational drugs as substrates and effects of investigational drugs (as inhibitors) on a range of substrates. 2. Whether dosage adjustments or other prescribing modifications (e.g., additional safety monitoring or contraindications) are needed based on the identified interaction(s) to avoid undesired consequences. Drug interaction information is used along with information about exposure-response relationships in the general population and specific populations, to help predict the clinical consequences of DDIs.

drug transporter pathways, the timing of DDI in vivo clinical studies depends on the safety range and on the frequency of co-medications that would act as inhibitor on the compound especially during Phase II or III. Independently of underlying mechanisms for potential DDIs, it has to be evaluated whether compounds with narrow therapeutic windows (relevant co-medications) in the targeted therapeutic area need to be evaluated in addition to the before mentioned studies which use probe drugs. It should be noted that DDI studies with specific co-medications might be necessary in order to have a specific label of no clinically relevant DDI. In general, the described principles of nonclinical and clinical assessment of DDI are also valid for oncological NCEs. However, studies in healthy volunteers might not be possible due to low tolerability of the compound. Furthermore, study design options might be limited due to a reduced clinical state of the patients. In general, all “combination trials” of standard chemotherapy together with a NME should be designed to investigate possible DDIs between the different compounds in light of this document (approaches to be discussed on a case-by-case basis).

Practical Considerations for DDI Trials Should in vitro data show that a drug is an inhibitor or inducer of enzymes, it may be recommended to conduct a DDI study as early as possible in clinical development in order to exclude any possible liability of this interaction. For substrates of specific drug metabolizing or

When testing an investigational drug for the possibility that its metabolism is inhibited or induced (i.e., as a substrate), selection of the interacting drugs should be based on in vitro or in vivo studies identifying the enzyme systems that

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Drug–Drug Interaction Studies

metabolize the investigational drug. The choice of the interacting drug can then be based on known, important inhibitors and inducers of the pathway under investigation. Strong inhibitors and inducers provide the most sensitive assessment and should generally be tested first

Study Design In vivo DDI studies generally are designed to compare substrate concentrations with and without the interacting drug. Because a specific study can address a number of questions and clinical objectives, many study designs for investigating DDI can be considered. In general, crossover designs in which the same subjects receive substrate with and without the interacting drug are more efficient. A study can use a randomized crossover (e.g., Substrate (S) followed by Sþ Inhibitor (I), S þ I followed by S), one-sequence crossover (e.g., S followed by S þ I), or a parallel (S in one group of subjects and S þ I in another group) design, and there may be reasons to have another period when the I is removed to assess effect duration. The following possible dosing regimen combinations for a substrate and interacting drug can also be used: single dose/ single dose, single dose/multiple dose, multiple dose/single dose, and multiple dose/multiple dose. Additional factors include consideration of the sequence of administration and the time interval between dosing of substrate and inhibitor/ inducer. The selection of a study design depends on a number of factors for both the substrate and interacting drug, including: 1. Whether the substrate and/or interacting drug is used acutely or chronically 2. Safety considerations, including whether a substrate is a NTR drug (NTR drugs are defined as those drugs for which there is little separation between therapeutic and toxic doses or the associated blood or plasma concentrations) or non-NTR drug 3. Pharmacokinetic and pharmacodynamic characteristics of the substrate and interacting drugs

835

4. Whether there is a desire to assess induction as well as inhibition 5. Whether the inhibition is delayed 6. Whether there is a need to assess persistence of inhibition or induction after withdrawal of the interacting drug The interacting drugs and the substrates should be dosed so that the exposures of both drugs are relevant to their clinical use, including the highest doses likely to be used in clinical practice, and plasma levels of both drugs should be obtained to show this. The following considerations may be useful: • When attainment of steady state is important (especially for the drug being the perpetrator drug), and either the substrate or interacting drug or their metabolites have long half-lives, one or both periods of a crossover study should be long, but several other approaches can be considered, depending on pharmacokinetic characteristics of the drug and metabolites. For example, if the substrate has a long halflife, a loading dose could be used to reach steady-state concentrations earlier in a onesequence crossover followed by an S þ I period long enough to allow I to reach steady state (here too, using a loading dose could shorten that period). • When it is important that a substrate and/or an interacting drug be studied at steady state for a long duration because the effect of an interacting drug is delayed, as is the case for inducers and time-dependent inhibition (TDI), documentation that near steady state has been attained for the pertinent substrate drug and metabolites as well as the interacting drug is critical, and both S and I should be present long enough to allow the full effect to be seen. This documentation can be accomplished by sampling over several days prior to the periods when test samples are collected. This information is important for metabolites and the parent drug, particularly when the half-life of the metabolite is longer than the parent. It is also important when the interacting drug and metabolites both are metabolic inhibitors (or

836

•

•

•

•

•

inducers). Finally, it is critical to evaluate the time it takes for the enzyme activities to return to normal when induction or TDI is involved so that a third crossover period in which the interacting drug (I) is removed will generally be recommended. Studies can usually be open label (unblinded), unless pharmacodynamic endpoints (e.g., adverse events that are subject to bias) are critical to the assessment of the interaction. For a rapidly reversible inhibitor, administration of the interacting drug either just before or simultaneously with the substrate on the test day might increase sensitivity by ensuring maximum exposure to the two drugs together. For a mechanism-based inhibitor (a drug that requires metabolism before it can inactivate the enzyme; an example is erythromycin), administration of the inhibitor prior to the administration of the substrate drug can maximize the effect. If the absorption of an interacting drug may be affected by other factors (e.g., the gastric pH), it may be appropriate to control the variables or confirm the absorption through plasma level measurements of the interacting drug. Timing of administration may be critical in situations of concurrent inhibition and induction. For example, if the investigational drug is a substrate for both enzymes and OATP, and rifampin is used as an enzyme inducer, the simultaneous administration of the drug with rifampin (an OATP inhibitor) may underestimate enzyme induction, so delayed administration of the substrate is recommended. The optimal delayed time should be determined. In addition, it is critical to evaluate the duration of the interaction effect after the interacting drug has been removed. When the effects of two drugs on one another are of interest, the potential for interactions can be evaluated in a single study or two separate studies. Some design options are randomized three-period crossover, parallel group, and one-sequence crossover. To avoid variable study results because of uncontrolled use of dietary/nutritional supplements, tobacco, alcohol, juices, or other foods

P. Stopfer

that may affect various metabolizing enzymes and transporters during in vivo studies, it is important to exclude, when appropriate, subjects who used prescription or over-the-counter medications, dietary/nutritional supplements, tobacco, or alcohol within 1 week prior to enrollment. In addition, investigators should explain to subjects that for at least 1 week prior to the start of the study until its conclusion. • Because interactions might differ in subgroups of different pharmacogenetic genotypes, genotyping for the enzymes and transporters involved in the interaction should be carried out when appropriate. • Detailed information on the dose given and time of administration should be documented for the coadministered drugs.

Study Population In most situations, clinical DDI studies can be performed using healthy volunteers, and findings in healthy volunteers will predict findings in the patient population for which the drug is intended. Safety considerations, however, may preclude the use of healthy subjects in studies of certain drugs. In addition, there are circumstances in which subjects drawn from the intended patient population offer advantages, including the opportunity to study pharmacodynamic endpoints not present in or relevant to healthy subjects. The extent of drug interactions (inhibition or induction) may be different depending on the subjects’ genotype for the specific enzyme or transporter being evaluated. For example, subjects lacking the major polymorphic clearance pathway will show reduced total metabolism or transport. However, alternative pathways can become quantitatively more important in these subjects. In such cases, the alternative pathways should be understood and studied appropriately. Thus, phenotype or genotype determinations to identify genetically determined metabolic or transporter polymorphisms are important when evaluating effects on enzymes or transporters with polymorphisms, such as CYP2D6,

45

Drug–Drug Interaction Studies

CYP2C19, CYP2C9, UGT1A1, and OATP1B1 (SLCO1B1). In addition, it is valuable to specify the need for stratifying the population based on genotype while conducting the DDI studies. Another alternative is to consider powering the study for the genotype status that is likely to have the highest potential for interaction.

Choice of Substrate and Interacting Drugs CYP-Mediated Interactions The Investigational Drug as a Substrate of CYP Enzymes – Effect of Other Drugs on Investigational Drugs When testing an investigational drug for the possibility that its metabolism is inhibited or induced (i.e., being the victim drug of a DDI as a substrate), selection of the interacting drug can then be based on known, important inhibitors and inducers of the pathway under investigation. Strong inhibitors and inducers provide the most sensitive assessment and should generally be tested first. Consider, for example, an investigational drug metabolized by CYP3A with the contribution of this enzyme to the overall elimination of this drug that is either substantial ( 25% of the clearance pathway) or unknown. In this case, the inhibitor and inducer can be itraconazole and rifampin, a strong inhibitor and a strong inducer, respectively. Respective strong inhibitors or inducers should be looked after in the respective sections of the guidelines from FDA, EMA, and PMDA or in the most current literature. If the study results are negative, then absence of a clinically important DDI for the metabolic pathway is demonstrated. If the clinical study of the strong inhibitor or inducer is positive, effects through in vivo studies of other less potent specific inhibitors or inducers may be needed to be evaluated. If the investigational drug is metabolized by CYP3A and its plasma AUC (Area under the plasma concentration time curve) is increased fivefold or higher by strong CYP3A inhibitors, it is considered a sensitive substrate of CYP3A. The labeling would indicate that the drug is a “sensitive

837

CYP3A substrate” and that its use with strong or moderate inhibitors may call for caution, depending on the drug’s exposure-response relationship. For further information as regards the labeling of respective DDI effects, please look at the respective section of the DDI guidelines from EMA, FDA, and PMDA. If a drug is metabolized by a polymorphic enzyme (such as CYP2D6, CYP2C9, 1327 CYP2C19, or UGT1A1), the comparison of pharmacokinetic parameters of this drug in poor metabolizers and extensive metabolizers may substitute for an interaction study for that particular pathway, as the PK in the poor metabolizers will indicate the effect of a strong inhibitor. When the study suggests the presence of a significant interaction with strong inhibitors or in poor metabolizers, further clinical DDI evaluation, e.g., with weaker inhibitors or intermediate metabolizers, may be recommended (additionally also mechanistic modeling approaches may be used supporting respective investigations). The Investigational Drug as an Inhibitor or an Inducer of CYP Enzymes: Effect of Investigational Drugs on Other Drugs When studying an investigational drug as the interacting drug (being the perpetrator drug), the choice of substrates (approved drugs) for initial in vivo studies depends on the P450 enzymes affected by the interacting drug. When testing inhibition, the substrate selected should generally be one whose pharmacokinetics are markedly altered by the coadministration of known specific inhibitors of the enzyme systems (sensitive substrates) to see the largest impact of the interacting investigational drug. Examples of such substrates include (refer also to the respective section in the EMA, FDA, and PMDA DDI guidelines and most recent literature): 1. 2. 3. 4.

Midazolam for CYP3A Theophylline for CYP1A2 Bupropion for CYP2B6 Repaglinide for CYP2C8

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5. Warfarin for CYP2C9 (with the evaluation of S-warfarin) 6. Omeprazole for CYP2C19 7. Desipramine for CYP2D6 If the initial study determines that an investigational drug either inhibits or induces metabolism of sensitive substrates, further studies using other substrates, representing a range of therapeutic classes, based on the likelihood of coadministration, may be useful. If the initial study with the most sensitive substrates is negative, it can be presumed that less sensitive substrates also will be unaffected. It should be noted that several of the substrates recommended for drug interaction studies are not specific because they are substrates for more than one CYP enzyme or may be substrates for drug transporters. While a given substrate may not be metabolized by a single enzyme (e.g., dextromethorphan elimination is carried out primarily by CYP2D6 but other enzymes also contribute in a minor way), its use in an interaction study is appropriate if the inhibitor (the investigational drug) to be evaluated is selective for the CYP enzyme of interest. If an investigational drug is a CYP inhibitor, it may be classified as a strong, moderate, or weak inhibitor based on its effect on a sensitive CYP substrate. For example, CYP3A inhibitors can be classified based on the magnitude of the change in plasma AUC of oral midazolam or other CYP3A substrates that are similar in characteristics (e.g., fm (%clearance contributed by CYP3A), half-life, not subject to transporter effect) as midazolam, when the substrate is given concomitantly with the inhibitor. If the investigational drug increases the AUC of oral midazolam or other CYP3A substrate by fivefold or higher ( fivefold), it can be considered a strong CYP3A inhibitor. If the investigational drug, when given at its highest dose, increases the AUC of oral midazolam or other sensitive CYP3A substrates by between two- and fivefold ( two- and > fu*CLint) are dependent upon both the fu and the intrinsic clearance for activity. CL ffi f u  CLint

Quantitative Prediction of Clinical DDIs

(17)

For drugs with more than one pathway of clearance, Eq. 12 can be rewritten as CLint ¼

V max, 1 V max, 2 þ K m, 1 K m, 2

¼ fm1  CLint þ ð1  fm1 Þ  CLint

(22)

where fm is the fraction metabolized by a specific pathway (Ito 2005). If only one pathway is inhibited, then

46

In Vitro/In Vivo Correlation for Drug-Drug Interactions

859

Fig. 8 Impact of fm and inhibition potency on expected DDI magnitude

CLint, I ¼

¼

V max, 1 V   þ max, 2 ½I  K m, 2 K m, 1 1 þ Ki fm1  CLint þ ð1  fm1 Þ  CLint ½I  1þ Ki

concentration of the inhibitor at the site of inhibition.

(23)

To obtain a ratio in the uninhibited and inhibited state, this becomes CLint, I fm1 þ ð1  fm1 Þ ¼ CLint 1 þ ½I =Ki

(24)

Equation 24 can be converted from intrinsic clearance to AUC ratio to give

Regulatory Guidance and DDIs Both the EMA and FDA Guidance propose a tiered approach for DDI prediction using basic models, mechanistic static models, and then complex models such as physiologically based pharmacokinetic modeling (PBPK). For basic models, a decision tree scheme for inhibition in line with FDA and EMA Regulatory Guidance is shown in Fig. 9. For reversible inhibition, the following equation holds: AUCR ¼ 1 þ ½I =K i

AUCR ¼

1 fm1 þ ð1  fm1 Þ 1 þ ½I =K i

(25)

Equation 25 summarizes the key drivers to observed inhibition in the clinic. The magnitude of DDI effect is dependent upon characteristics of both the perpetrator and victim. For the victim drug, the fraction metabolized (fm) by the inhibited pathway is a key driver of the magnitude of the DDI effect; the higher the fm, the larger the potential magnitude of the DDI (Fig. 8). Key features of the perpetrator include the inhibition potency for the particular DME, the unbound fraction, and the free

(26)

where AUCR is the AUC ratio in the presence and absence of an inhibitor and the inhibitor concentration [I] is the unbound Cmax value for liverbased DDIs or is calculated using the relationship of dose/250 mL for gut-based DDIs. Exceeding recommended cutoff values (AUCR >1.02 for liver-based DDI and AUCR > 11 for gut-based DDIs) triggers a potential clinically relevant DDI result and a move down the decision tree to more complex mechanistic static models (MSM) or PBPK. A similar decision tree for TDI is also shown in Fig. 9; the following equation is used to estimate DDI risk, where Kdeg is the degradation rate of the

860

J. Wahlstrom and L. Wienkers

Fig. 9 Basic model decision tree for inhibitory DDIs

Characterize in vitro inhibition (Reversible and TDI)

Reversible inhibiton Is AUCRliver ≥ 1.02 or AUCRgut ≥ 11? Based on: AUCRreversible = 1 + [I]/Ki Where [I]liver = unbound Cmax and [I]gut = dose/250 mL, or Time-dependent inhibition Is AUCR ≥ 1.25? Based on: AUCRme-dependent = (Kobs + Kdeg)/Kdeg Where kobs = kinact•[I]/(KI+[I]) (EMA) or kinact•50•[I]/(KI+[I]) (FDA)

If no, then NCE is not an inhibitor If so, then estimate AUCR using: 1)Mechanistic static model (MSM) or 2)Dynamic model (PBPK) If AUCR ≥ 1.25 using MSM or PBPK, Guidance recommend clinical study

enzyme of interest. The same [I] definitions apply for this equation as for reversible inhibition, but the cutoff is AUCR 1.25.  AUCR ¼

kobs þ K deg K deg

threshold

1þ

(≥2-fold increase in mRNA), or

1 ½I organ

(30)

Ki

0.8 < AUCR? Based on:

Inactivorgan

Threshold established with known inducers or 1/(1+ScalingFactor*Emax*[I]/EC50+[I])

K deg, organ ½I organ  kinact K deg, organ þ ½I organ þ K I

dose  Emax  ½I organ  Inductorgan ¼ 1 þ
EC50 þ ½I organ

If no, then the NCE is not an inducer If so, then estimate AUCR using:

½I gut ¼

2)Dynamic model (PBPK)

½I liver

be constant and maximal throughout the dose interval. This is a conservative assumption that may not accurately represent the clinical situation for many drugs. Estimation of the overall DDI magnitude is accomplished using Eq. 29 below, which integrates both the expected gut and liver contributions to DDI, as well as the potential

(32)

The concentrations in the gut and liver are defined by the following equations, where [I]max, b is the maximum concentration of the inhibitor in blood.

1)Mechanistic static model and/or

Fig. 10 Basic model decision tree for inductionbased DDIs

(31)

Fa ka Dose Qenterocyte

(33)

  Fa ka Dose ¼ fu, b ½I max, b þ (34) Qliver 

An additional option is PBPK modeling. PBPK is a modeling technology that has seen recent emergence both for internal decisionmaking for compound progression through drug discovery and development and for regulatory applications (Jamei et al. 2009). PBPK models have three main types of input parameters: demographic and genetic information (i.e., age or gender), physiological information (i.e., organ blood

862

flow and enzyme levels), and drug-specific parameters (i.e., pKa, logP, solubility). Differential equations and Monte Carlo-based simulations integrate the inputs together to simulate a variety of outcomes, including plasma concentrationtime profiles, enzyme activity profiles, and drugtissue concentrations. Because of the types of inputs and the modeling technique used, PBPK is well-suited for modeling where changes in physiology or populations may impact PK, changes in physicochemical properties or formulations may impact PK, or where dynamic simulations such as DDIs are desired. PBPK may be used to answer fundamental clinical pharmacology based questions such as (1) what are the intrinsic factors that may influence exposure, (2) what are the extrinsic factors that may influence exposure, and (3) what are situations in which dosing may need to be adjusted due to intrinsic and extrinsic factors. Because physiologically relevant parameters are included, PBPK may more closely represent the clinical situation than basic or MSM models. PBPK may also provide information on expected variability in studies based on demographic factors. The potency of an inhibitor or inducer is determined based on the magnitude of its interaction with a sensitive probe substrate for a specific enzyme pathway. Strong inhibitors increase AUC  fivefold, moderate inhibitors increase AUC two-  to < fivefold, and weak inhibitors increase AUC  1.25- to < twofold. Strong inducers reduce AUC  80%, moderate inducers reduce AUC by 50 to 85%/15 min) than for class I drugs (>85%/30 min) in order to be on the safe side with this extension. There are, however, findings from simulation experiments indicating that the risk of a false-positive conclusion on bioequivalence based on the BCS concept is considerably lower in case of class III than in class I compounds. On the other hand, compounds with incomplete – and often site-dependent – absorption may be more sensitive to interactions with excipients which might impact GI transit, intestinal absorption, and metabolism in the gut wall. Thus, the potential influence of differences in excipients used in both investigational products (test and reference) needs to be carefully considered and justified.

Regulatory Requirements and Need for Harmonization The scientific basis for regulatory requirements in bioequivalence has continuously been developed and further optimized during the last four decades. On the other hand, deviations between the regions – e.g., Europe, North America, and Japan – still exist in several details, and this makes global development of medicinal products, especially generics, difficult. As consequence more

49

Bioequivalence

than one BE study may be necessary to fulfill the regional requirements. With the intention to fill this gap and to harmonize the divergent regulations, several initiatives have been started, in particular the series of BIOinternational conferences in the 1990s and currently the EUFEPS Global Bioequivalence Harmonization Initiative (GBHI).

The BIO-International Conferences: Toward Science-Driven Regulations Primary intention of the BIO-international conferences was originally to discuss open issues in bioequivalence and to support the development of science-driven regulations. Scientist from academia and industry contributed to this process and important progress was achieved in exchange with experts from regulatory authorities. Examples for essential advancements were, among many other detailed achievements, the development of the Biopharmaceutics Classification System or the scaling procedure for the investigation of highly variable drugs. Results and conclusions of the discussions have been summarized in conference reports (McGilveray et al. 1990; Blume and Midha 1993; Blume et al. 1995; Midha et al. 1996, 2005) and, even more important, were incorporated in new or revised guidelines. Considering that this implementation was not identical in all cases, discussions on harmonization were started more recently in smaller BIO-international conferences held in the 2000s in London.

The EUFEPS Global Bioequivalence Harmonization Initiative A more systematic approach in harmonization of BE requirements was started by EUFEPS, the European Federation for Pharmaceutical Sciences, with its network on biopharmaceutics and bioavailability. Essential for the success of this Global Bioequivalence Harmonization Initiative is that key regulatory scientists from EMA

901

as well as the US-FDA were prepared to support this process significantly from the very beginning. This harmonization initiative is structured by means of specific conferences which take place every 18 months alternatively in Amsterdam, the Netherlands, or Rockville, USA. Meanwhile three conferences have been held in March 2015, September 2016, and April 2018. Results and conclusions from the discussions are summarized in conference reports (Chen et al. 2018, 2019; The global bioequivalence harmonization initiative: report of EUFEPS/AAPS third conference Manuscript in preparation) in order to give the scientific community the chance to further contribute to those issues still open even after the intensive debate during the meetings. It is desirable that these activities will continue further on and will achieve essential success in harmonization. For this purpose significant contribution by all relevant jurisdictions globally is aspired. This initiative might also be supportive for the ICH process which obviously plans taking up BE issues as well, e.g., as first topic the BCSbased biowaiver concept.

Conclusions and Future Perspective Concepts and requirements for BE assessment have been developed and further optimized during the previous decades. Relevant open issues have been identified and systematically resolved. Meanwhile, most of the international guidelines include appropriate requirements and suggestions for the majority of relevant BE issues. Nonetheless, the scientific community should continue their effort in contributing to further improvement of the current regulations.

Therapeutic Equivalence and Interchangeability For the clinical use of medicinal products approved based on BE assessments, in particular generic alternatives, the question of their proven

902

therapeutic equivalence in comparison to the innovator product is of essential relevance. Considering that assessment of BE is generally understood as confirmation of therapeutic equivalence, all generic products may be declared as therapeutic alternatives of the approved reference products. On the other hand, comparisons between the generic products are normally missing, and thus, therapeutic equivalence between all of them may be questionable. In this context a discussion on “individual bioequivalence” in the 1990s in the USA may be reflected which concluded that BE assessment confirmed the “prescribability” of the generic products, while their “interchangeability” has not been reliably demonstrated. The therapeutic experience with generic substitution in all major jurisdictions did, however, not discover major clinical problems. Nonetheless, every exchange of products during maintenance treatment should be handled with care.

Approval Policy and Reimbursement Decision In most jurisdictions different committees are responsible for the decisions on marketing authorization and reimbursement. Therefore, marketing authorization for medicinal products does not automatically include reimbursement. In increasing number of countries, the latter depends on the outcome of price negotiations between pharmaceutical industry and health insurance companies. In other countries, e.g., Germany, specific fixed-price limits have been defined for the reimbursement of generic medicinal products. These fixed-price limits are set case by case on a compound- and dose-strength-specific basis by a national reimbursement committee. This system, at the same time, supports the general understanding in the public that all medicinal products listed in such a fixed-price group are

H. H. Blume

interchangeable and, thus, may be used for generic substitution.

References and Further Reading Amidon GL, Lennernäs H, Shah VP, Crison JR (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res 12:413–420 Blume H, Midha KK (1993) Bio-international ‘92, conference on bioavailability/bioequivalence and pharmacokinetic studies. J Pharm Sci 82:1186–1189 Blume HH, Schug BS (1999) Class III drugs – better candidates for BA/BE waiver. Eur J Pharm Sci 9:117–121 Blume HH, McGilveray IJ, Midha KK (1995) BIO-international ‘94, conference report on bioavailability, bioequivalence and pharmacokinetic studies. Eur J Drug Metab Pharmacokinet 20:3–13 Chen M-L, Blume H, Beuerle G, Davit B, Mehta M, Potthast H, Schug B, Tsang YC, Wedemeyer R-S, Weitschies W, Welink J (2018) The global bioequivalence harmonization initiative: summary report for EUFEPS international conference. Eur J Pharm Sci 111:153–157 Chen M-L, Blume H, Beuerle G, Mehta M, Potthast H, Brandt A, Schug B, Ducharme M, Endrenyi L, Gallicano K, Schuirmann D, Welink J (2019) The global bioequivalence harmonization initiative: report of EUFEPS/AAPS second conference. Eur J Pharm Sci 127: 24–28 Karim A (1986) Effects of food on the bioavailability from controlled-release products in adults. J Allergy Clin Immunol 78:695–70 McGilveray IJ, Midha KK, Skelly JP, Dighe S, Doluisio JT, French IW, Karim A, Burford R (1990) Consensus report from bio international ‘89: issues in the evaluation of bioavailability data. J Pharm Sci 79:945–946 Midha KK, Nagai T, Blume HH, Hubbard JW, McGilveray IJ, Williams R (1996) Conference report of bio-international ‘96, conference on bioavailability, bioequivalence and pharmacokinetic studies Midha KK, Shah VP, Singh GJP, Patnaik R (2005) Conference report: bio-international 2005. J Pharm Sci 96:747–754 Schug BS, Wolf D, Nilius R, Martin W, Schall R, Blume HH (2000) Product related food effects for nifedipine once daily solid oral dosage forms. Poster at the APVWorld Conference, Berlin The global bioequivalence harmonization initiative: report of EUFEPS/AAPS third conference. Manuscript in preparation

Population Pharmacokinetics and Pharmacokinetic-Pharmacodynamics in Clinical Pharmacology

50

Daniel F. B. Wright, Chihiro Hasegawa, and Hesham S. Al-Sallami

Contents Part 1: General Concepts and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Optimizing the Dose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Biomarkers for Physiological Response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

904 905 905 906

Part 2: Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Models . . . . . . What Is a Model? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacokinetic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacodynamic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pharmacokinetic-Pharmacodynamic Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Immediate Effects PKPD Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Delayed Effect PKPD Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Statistical Models for Uncertainty in Drug Response Measurement . . . . . . . . . . . . . . . . . . . Statistical Models for Variability Between Individuals (Heterogeneity) . . . . . . . . . . . . . . .

906 906 907 908 909 909 910 912 912

Part 3: Methodology (Nonlinear Mixed Effects Modeling) . . . . . . . . . . . . . . . . . . . . . . . . . 914 Part 4: Example of Population PK and PKPD Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

916 916 916 916 919

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 924 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 927

D. F. B. Wright (*) · H. S. Al-Sallami School of Pharmacy, University of Otago, Dunedin, New Zealand e-mail: [email protected]; [email protected] C. Hasegawa School of Pharmacy, University of Otago, Dunedin, New Zealand Translational Medicine Center, Ono Pharmaceutical Co., Ltd., Osaka, Japan e-mail: [email protected]

Abstract

Clinical pharmacology is a broad professional and scientific discipline concerned with all aspects of drug use in humans. One of the primary goals of this field is to improve health outcomes by supporting the development, rational use, and safety of medicines. Clinical pharmacology and pharmacometrics are closely related and share common goals and research

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_18

903

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themes. Notable amongst these are pharmacokinetics and pharmacodynamics. In the pharmaceutical industry, population pharmacokinetic and pharmacokinetic-pharmacodynamic studies aid dose selection, assess links between drug exposure and efficacy and safety metrics, and inform the dosing information that will be presented on the drug label. In the clinical environment, population pharmacokinetic and pharmacokinetic-pharmacodynamic studies are conducted to aid dose optimization for an individual patient. The aim of this chapter is to present an overview of population pharmacokinetic and pharmacokinetic-pharmacodynamic concepts and methodology as they apply in the industrial and clinical setting. The chapter is divided into four parts: Part 1 will provide a board overview of general concepts and definitions related to population pharmacokinetic and pharmacokinetic-pharmacodynamic analyses; Part 2 will look at commonly used models; Part 3 will explore methodology; primarily nonlinear mixed effects modeling, and Part 4 will present examples of pharmacokinetic and pharmackinetic-pharmacodynamic analyses, presented in the style that is typical for a regulatory submission involving phase I data.

In the pharmaceutical industry, population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) studies play an important role in the analysis of Phase 1, 2, and 3 data, and to a lesser extent, data that arises from preclinical and post-marketing trials. The goals of PK and PKPD analyses align with those of the broader clinical pharmacology team, i.e., to aid decisions about dose selection for the prospective drug, to assess efficacy and safety data, and ultimately, to inform the dosing information that will be presented on the drug label. Post-marketing, in the clinical setting, PK and PKPD are often viewed as an applied field within the medical discipline of clinical pharmacology, focused primarily on optimizing drug therapy for individual patients. In both cases, the types of analyses conducted may extend beyond population PK and PKPD to include exposureresponse (ER) analyses, quantitative systems pharmacology modeling (QSP), physiological-

D. F. B. Wright et al.

pharmacokinetic (PBPK) modeling, and optimal design work, amongst others. These approaches are bound together by the application of mathematical and statistical methodologies, referred to collectively as “pharmacometrics.” In this chapter, we assume that pharmacometric analyses performed in the industrial or clinical setting will share broadly similar goals, particularly with regards to optimizing drug doses. The only assumed difference is that the research conducted in the drug industry will tend to focus on the optimization of dose to the level of regulatory requirement (e.g., dosing guidance for the drug label) while in the clinical environment, the focus is usually dose optimization for an individual patient. The aim of this chapter is to present an overview of population pharmacokinetic and pharmacokinetic-pharmacodynamic concepts and methodology as they apply in the industrial or clinical setting. The chapter is divided into four parts: Part 1 will provide a board overview of general concepts and definitions related to population PK and PKPD analyses, Part 2 will look at commonly used PK and PKPD models, Part 3 will explore methodology, primarily nonlinear mixed effects modeling, and Part 4 will present an example of PK and PKPD analyses, presented in the style that might be expected for a regulatory submission involving phase I data.

Part 1: General Concepts and Definitions Clinical pharmacology is a broad professional and scientific discipline concerned with all aspects of drug use in humans. One of the primary goals of this field is to improve health outcomes by supporting the development, efficacious use, and safety of medicines. While a diverse discipline, this chapter will focus primarily on two central themes in clinical pharmacology research, pharmacokinetics and pharmacodynamics. Here pharmacokinetics is defined as the time course of drug concentrations in the body, a science focused on the relationship between the drug dose and exposure (Fig. 1a). The generic term “drug exposure” will be used to refer to drug concentration at any time point, i.e., C(t). Note that it is convenient and

Population Pharmacokinetics and Pharmacokinetic-Pharmacodynamics in Clinical Pharmacology

Fig. 1 Conceptual framework for PK, PD, and PKPD data (Adapted from Wright et al. 2011, used with permission from John Wiley and Sons licence number 4277280083455)

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a concentration

50

time

effect

b

concentration

effect

c

time

common to summarize C(t) as a time-invariant measure such as the maximum, minimum, or steady-state average plasma concentration post dose (C pmax , C pmin , and C pss, ave , respectively), as well as the area under the plasma concentration time curve (AUC). Pharmacodynamics is concerned with the relationship between drug exposure and response (Fig. 1b) and is independent of time. Pharmacokinetics-pharmacodynamics (PKPD) is a combination of PK and PD that allows us to explore the time course and magnitude of drug response (Fig. 1c). In this chapter, we will consider pharmacodynamic responses that are quantified by measuring a biomarker or surrogate (e.g., blood pressure, prothrombin time to measure anticoagulation), rather than dichotomous measures of drug effect (e.g., seizure or no seizure) or other clinical endpoints (e.g., death, hospitalization, etc.). A definition of a pharmacodynamic biomarker will be presented below.

Pharmacometrics Pharmacometrics is concerned with the analysis and interpretation of data that arises from drug studies. The discipline can be traced to the seminal work of Louis Sheiner (Sheiner 1977) and, in

particular, his collaboration with Stuart Beal to develop nonlinear mixed effects modeling methodology for drug studies (Sheiner and Beal 1980). Pharmacometrics involves the use of mathematical and statistical models to predict drug exposure, physiological response, and clinical outcomes, and to describe the variability in these measures between (and within) individuals. As such, pharmacometric analyses play an important role in drug development as well as clinical practice. The terms “population analysis,” “population approach,” and “population modeling” are also commonly used to refer to pharmacometric analyses.

Optimizing the Dose All things are poison and not without poison; only the dose makes a thing not a poison Paracelsus (physician and botanist, 1493–1541)

The notion that a relationship exists between the amount of drug given to an individual and the intensity of the resulting response (desired or adverse) is intuitive and has likely been understood since antiquity. It was certainly recognized by Paracelsus in the sixteenth century who noted, in the above quote, that a poison and a therapeutic agent differ only in the dose that is administered.

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However, it was not until the last century that it was possible to quantify the relationship between drug dose and physiological response. Research intended to inform dose selection or optimization, whether to support drug labeling decisions for regulatory submission or to individualize therapy in the clinic, requires a quantitative understanding of drug exposure and physiological response, and how these vary between and within individuals. The optimal dose is expected to have the highest probability of achieving a desired physiological response in an individual patient or a population of patients while carrying a minimal risk of adverse effects. The general concept is that the drug will achieve the desired physiological response in most people once a threshold exposure (or steady-state plasma concentration) has been reached. Increasing the exposure may increase the magnitude of the response in some cases, depending on the shape of the exposure-response curve (see Fig. 3), but may also increase the risk of adverse effects and toxicity. This concept underpins the clinical practice of therapeutic drug monitoring, where plasma concentrations are measured and doses adjusted to achieve a specified target range. TDM is particularly useful for drugs with a narrow therapeutic range, i.e., where the desired physiological response and toxicity can occur within a narrow exposure range, and where the physiological response is difficult to measure, i.e., cases where the clinical endpoint is to prevent an adverse event (e.g., seizure).

Biomarkers for Physiological Response The ideal measure of drug effectiveness is the consistent achievement of the clinical outcome of interest in the intended patient population. However, when the outcome is the long-term prevention of an adverse clinical event (e.g., stroke with anticoagulant therapy or cardiovascular disease with lipid-lowering drugs) or requires long-term observation (e.g., the cessation of gouty attacks with urate-lowering therapy), a biomarker for the physiological response is required.

D. F. B. Wright et al.

A biomarker is an indicator of the biological, pathological, or pharmacological response to drug therapy (Biomarkers Definitions Working Group 2001). The biomarker may be on the causal pathway between the disease and the clinical outcome or may be correlated in some way with the outcome. The drug may alter the underlying disease which in turn will alter the biomarker or the drug may act directly on the biomarker itself which, in turn, will be correlated with the clinical outcome (e.g., symptomatic treatment). The term biomarker can be distinguished from “biomeasure” and “surrogate.” A biomeasure is any physiological measurement that can be obtained clinically (e.g., blood pressure). A biomarker is therefore a special case of a biomeasure. A surrogate is a biomarker that is intended to substitute for a clinical outcome. A surrogate is usually approved by a regulatory agency and can be used in the drug approval process.

Part 2: Pharmacokinetic and Pharmacokinetic-Pharmacodynamic Models What Is a Model? Essentially, all models are wrong, but some are useful George E. P. Box (statistician, 1918–2013)

A model is a construct that allow us to simplify reality. A toy car is a trite example. It is small enough to fit into a child’s hand yet, to a child, it retains all of the essential features of the real thing such as the right body shape, wheels, and perhaps a flashy paint job. Similarly, a pharmacometric model is a mathematical representation of the complex interaction between a drug and a biological system. It is used to describe the relationship between input variables, such as drug dose and time, and output variables including plasma drug concentration and drug response. For a pharmacometric model to be useful, it must be simple enough for practical use yet retain the essential mathematical features that allow us to understand the relationship between input (e.g.,

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drug dose, time) and output (e.g., plasma drug concentrations, drug response). This concept is embodied in the often-used quote above from George Box. All models are wrong because a model cannot recreate reality; however, a model can serve a useful purpose if it retains a close relationship with important aspects of reality. The term “population model” is commonly used in pharmacometrics and may seem misleading. In the context of a pharmacometric analysis, “population” refers to the group (population) of individuals who are the intended recipients of the medicine. This may also include healthy volunteers who may be recruited, for example, in a Phase I pharmacokinetic study. A population model will, therefore, provide typical drug exposure and response information, estimates of random between subject variance, predictable variance in drug exposure or response associated with measurable patient factors (e.g., weight), and random within subject variance. Hence, the information contributed by an individual patient is retained and contributes to the overall understanding of drug behavior.

Pharmacokinetic Models Pharmacokinetics is the science that relates the dose administered to the time course of measured drug concentrations in the body (usually the plasma) and therefore provides information about drug exposure. A PK model can predict the typical time course of drug concentration C (t) as a function of the administered dose (D) over time (t) and is dependent on unknown pharmacokinetic parameters (θpk). The primary PK parameters of interest are clearance (CL), the apparent volume of distribution (V ), the absorption rate constant (ka) (for extravascular administration), systemic availability (F) (for extravascular administration), and the secondary parameter elimination rate constant (ke), which is given by CL/V. CL is a constant that relates the rate of elimination to the measured drug concentration and is related to the functional capacity of the body. V is the apparent volume into which the drug distributes and is related to body

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composition. F is the fraction of drug that reaches the systemic circulation. A PK model can be constructed using a compartmental structure with an input model and a disposition model (Fig. 2). The input model describes the time course of drug movement from the site of administration (e.g., the gut) to the site of drug measurement (e.g., the plasma). The disposition model describes the time course of drug distribution, metabolism, and elimination from the body and can be depicted as single or multiple compartments. Input, distribution, and elimination occur simultaneously and the relative time course of each will determine the pharmacokinetic behavior of the drug. Figure 2 depicts one and two compartment PK models for an orally administered drug. The body is represented as a series of discrete units into which the drug distributes and from which drug elimination occurs. The compartments do not represent true physiological spaces, and it is assumed that drug behavior is similar within each compartment. In a one compartment model (Fig. 2a), the drug is assumed to distribute evenly throughout a single compartment. In a two compartment model (Fig. 2b), the drug distributes into an additional peripheral compartment. For a one compartment extravascular administration model, the concentration of drug in the plasma at any time (after a single dose) is given by Equation 1 The equation for a one compartment PK model with first-order absorption and elimination C ðt Þ ¼

D  F  ka V  ðk a  k e Þ
  expðk e tÞ  expðk a tÞ

By convention the number of compartments in a PK model is defined by the number of exponential terms needed to describe the disposition of the data. Therefore, the model above, while having two exponential terms, has only one term related to disposition and hence is considered a one compartment model.

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Fig. 2 Schematic of a one compartment PK model (a) and a two compartment PK model (b) for an orally administered drug with first-order absorption and elimination

Pharmacodynamic Models Pharmacodynamics relates drug concentrations to the observed pharmacological response. Since in vitro pharmacodynamic experiments are often conducted in equilibrium conditions, the relationship between concentration and response is typically independent of time. A PD model can predict the typical response (denoted E for effect) of a drug as a function of the drug concentration (C) and unknown pharmacodynamic parameters (θpd). Note that we use the terms 'drug effect' and 'drug response' interchangeably in this chapter. The relationship between drug concentration and response is usually characterized by a hyperbolic function, which yields a nonlinear relationship between effect and concentration. Hence, doubling the concentration (or dose) will not necessarily result in a doubling of drug response and the drug response will asymptote to a maximum despite increased concentrations.

Much of the underpinning theory, and the models used to describe the exposure-response relationship, is based on receptor-binding theory. However, in pharmacometrics, we are primarily concerned with the magnitude of drug response that will result from a given dose or exposure. . Therefore, a model that will predict drug response, not drug binding is required. The relationship between receptor occupancy and drug effect can be described using a proportionality constant known as ‘intrinsic activity’, which can range from 1 for a full agonist to 0 for an antagonist. This enables the relationship between concentration and effect to be expressed using an empirical version of the Emax model: Equation 2 The Emax model E ¼ E max 

C C 50 þ C

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Fig. 3 The Emax model showing the change in shape with different Hill coefficients, which will dictate the shape of the curve. In this model Emax = 1, C50 = 0.025 units/L

where Emax is the maximum effect of the drug, C is the drug concentration, and C50 is the drug concentration which results in 50% of maximum response.

absence of drug. This model can be used to predict the change in this status (e.g., blood pressure) after drug administration.

The Emax model has been widely applied in pharmacodynamics and is the basis for many models in pharmacometrics. There are two parameters of interest: Emax and C50. A generalization of the Emax model, called the sigmoidal Emax, includes an empirical exponent termed the Hill coefficient (λ) which changes the shape of the exposure-response curve (Fig. 3):

Equation 4 The Emaxmodel with a constant baseline status (S0) E ¼ S 0 þ Emax 

C C 50 þ C

Pharmacokinetic-Pharmacodynamic Models

Equation 3 The sigmoidal Emaxmodel Cλ E ¼ E max  C 50 λ þ C λ Values of λ greater than 1 produce a steep exposure-response curve and predict that relatively small changes in drug concentration will produce a rapid change in effect which, at the extreme, can be observed as an “on-off” phenomenon (e.g., anti-arrhythmic agents). Values of λ less than 1 produce a shallow concentrationresponse curve where response increases rapidly at low concentrations but approaches the Emax asymptote slowly at high concentrations (Fig. 3). The Emax model can be further modified to include baseline physiological status (S0) in the

A PKPD model can predict the typical response (E) of a drug as a function of the dose (D) and time (t) dependent on pharmacokinetic (θpk) and pharmacodynamic parameters (θpd). PKPD models conventionally include two basic subtypes: • Immediate effects model • Delayed effects model

Immediate Effects PKPD Model Under nonequilibrium conditions, an immediate effects PKPD model predicts that drug effects will reach a maximum at approximately the same time as the maximum plasma concentration of the drug

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Fig. 4 The time course of drug concentrations (blue line) and drug effects (green line) for an immediate effects model. In this model Emax = 1, C50 = 0.1 units/L. The concentrations were generated using a one compartment model with first-order input. Dose = 1 unit, CL = 1 L/hour, V = 1 L, and ka = 0.5 h

(see Fig. 4). The PK model provides the timedependent plasma concentration (C(t)) so that the immediate drugs effects model can be described by the Emax model (equation 5).

E ¼ E max 

C ss, ave C 50 þ C ss, ave

Delayed Effect PKPD Models Equation 5 Immediate effects PKPD model C ðt Þ E ðt Þ ¼ E max  C 50 þ C ðt Þ By contrast, under equilibrium conditions, it is assumed that the steady state average concentration (Css, ave) is sufficient to describe the important pharmacokinetic characteristics of the drug. This is given by Equation 6 A model for Css,ave under equilibrium conditions C ss, ave ¼

dose rate CL

where dose rate is the maintenance dose (e.g., mg/day). This greatly reduces the complexity of the PK model, but it will only predict the magnitude of drug effects not the onset or duration. The PK model can be substituted into a PD model to provide a steady-state PKPD model:

Equation 7 Immediate effects PKPD model under equilibrium conditions

An important limitation of many PKPD models is that the data used to develop the model will usually lack detail concerning underlying PK and PD mechanisms, i.e., they are empirical in nature. In the absence of this mechanistic information it will only be possible to model the rate-limiting step in the time course of drug response. Three mechanisms for delayed effects will be considered as possible rate-limiting processes in the time course of drug effects: drug-receptor binding, distribution to the biophase, and delay related to biological systems (e.g., secondary messengers). Delay in drug-receptor binding. If the ratelimiting step in the time course of drug effects is driven by the drug-receptor interaction, the onset of drug effects will be related to the dissociation constant, koff (Wright et al. 2011). If the koff value is large, providing a short equilibration half-life, then the drug will be seen to behave as if it has an immediate effect (also termed an “immediate effect” or “direct action”). Delay in distribution to the biophase. For most drugs the site of action is distal to the venous compartment. A model can therefore be constructed to account for the delay in drug distribution to the so-called “effect compartment” or

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“biophase.” The distinction between the terms “biophase” and “effect compartment” is one of semantics. The biophase is the supposed true site of action. The effect compartment is a theoretical space that appears to have the same distributional properties as the biophase. The effect compartment is therefore not a true model for the biophase but rather a model for the delay in effect due to drug distribution to the biophase. This distribution is essentially a pharmacokinetic phenomenon, but it is described entirely and empirically by the observed delay in pharmacodynamic behavior. An effect compartment model includes a link function between the PK model and PD model (Holford and Sheiner 1982). If the distribution to the biophase is a first-order process, the delay between peak plasma concentration and peak drug effect will be independent of dose and clearance but dependent only on the rate constant of elimination from the effect compartment. The model assumes that only a small amount of drug distributes into the biophase so that the overall impact on mass-balance is negligible. While the volume of the effects compartment cannot be determined, the rate constant for the loss of drug from the effect compartment can be estimated during the modeling analysis. The full effect

compartment model therefore includes a PK model, a link model for the effect compartment, and a simple Emax model driven by the effect compartment concentration (Ce):

Fig. 5 Schematic of four turnover models including inhibition or stimulation of the intermediary production or inhibition or stimulation of the intermediary elimination

(Adapted from Wright et al. 2011, used with permission from John Wiley and Sons licence number 4277311151819)

Equation 8 Effect compartment PKPD model. ke0is the equilibrium rate constant for the effect compartment dC e ¼ k e0  ðC ðt Þ  C e Þ; C e ðt ¼ 0Þ ¼ 0 dt Eðt Þ ¼ Emax 

C e ðt Þ C 50 þ C e ðt Þ

Delay in system response. The mechanism of action for many drugs involves the activation or blockade of a receptor which, in turn, initiates a physiological response mediated by a series of biological processes (e.g., second messengers) (Fig. 5) (Dayneka et al. 1993; Jusko and Ho 1994; Sharma and Jusko 1998). These processes have a time course of their own and often constitute the rate-limiting step in the time course of drug effects. Like effect compartment models, these models are characterized by a delay in the observed effect with respect to the measured plasma

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Table 1 Models for the turnover of a single biological intermediate (I ). Rin is the zero-order production rate of I, kout is the elimination rate constant for I Mechanism Inhibition of input

Model

A B

Stimulation of input

dI dt

C

Inhibition of loss

dI dt

D

Stimulation of loss

dI dt

concentration and are commonly referred to as “turnover models” (and sometimes “indirectresponse models”) (Jusko and Ho 1994). An important difference between models based on physiological intermediaries and those based on an effect compartment is that the time of maximum effect for the turnover model will be dose dependent (Fig. 5). There are four proposed mechanisms for drugs acting on a single biological intermediary (I ) resulting in four turnover PKPD models (Dayneka et al. 1993; Jusko and Ho 1994; Sharma and Jusko 1998). These are presented in Table 1, while the typical concentration-effect profiles for each model are shown in Fig. 6. Selection of the most appropriate PKPD model should be guided by prior knowledge of the drug pharmacology. Data from more than one dose level greatly helps to distinguish between models (immediate or delayed action and, if delayed, an effect compartment or a turnover model, see Fig. 6). For a direct effect model, the time of the peak concentration is also the time of peak effect for all dose levels. The effect compartment model introduces a delay between the peak concentration and peak effect; this could be due to drug transport to the site of action. If this transport is a first-order process (e.g., diffusion), the time delay is independent of dose. All four turnover models exhibit a dose-dependent delay between peak concentration and peak effects.

dI dt

h i ðt Þ  ¼ Rin  1  E max  C50CþC ðt Þ  k out I h i ðt Þ  ¼ Rin  1 þ E max  C50CþC ðt Þ  k out I h i ðt Þ ¼ Rin  k out  1  Emax  C50CþC ðtÞ  I h i ðt Þ ¼ Rin  k out  1 þ Emax  C50CþC ðtÞ  I

“uncertainty.” It is assumed that uncertainty arises typically from four sources: 1. Process error – where the dose or timing of dose or timing of blood samples are not conducted at the times that they are recorded 2. Measurement error – where the response is not measured exactly due to assay error 3. Model misspecification 4. Moment to moment variability within a patient In mathematical terms, a model for one individual can be described as: Equation 9 The mathematical form of a model with residual error   yj ¼ f D, xj , θ þ ej where the jth observation (e.g., drug concentration) for the individual yj is a function of the administered dose (D) and time, and θ is an np-by-1 vector of unknown mean parameters for the individual. In this model, the jth observation deviates from the model prediction by an error, ej, which is assumed to be normally distributed with a mean of zero and a variance of σ2: Equation 10 The distribution of ej   ej  N 0, σ2

Statistical Models for Uncertainty in Drug Response Measurement A population model includes a statistical model to describe the variability between the model predictions and the observations. This is termed residual unexplained variability (RUV) or

Statistical Models for Variability Between Individuals (Heterogeneity) An expansion of a model for a single subject to a population of subjects includes an additional

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Fig. 6 Concentration-effect profiles using the four turnover PKPD models and three dose levels: 50 mg (dotted curve), 500 mg (dashed curve), and 2000 mg (solid curve). CL = 10, V = 20, Emax = 1,C50 = 1, E0 = 1

consideration of the variability between people. This yields the well-known population approach model. An important goal of population modeling is to establish the relationship between pharmacokinetic and pharmacodynamics parameters of interest and covariates (i.e., observable patient characteristics such as sex, age, weight, height, organ function indices, and concomitant drugs). Genetic covariates may also predict differences in PKPD parameters for some drugs. Covariates explain some of the variability between individuals and therefore provide the basis for decisions about individualized dosing in clinical practice. In population analyses, variability between individuals is also called heterogeneity, between subject variability (BSV), intra-individual variability (IIV), and population parameter variability (PPV).

In mathematical terms, a population model for repeated measures in a series of individuals can be generalized as: Equation 11 The mathematical form of a population model with between-subject variability and residual error   yij ¼ f Di , xij , θ; ηi þ eij where the jth observed concentration for the ith individual (yij) is a function of the administered dose (Di) and time(xij) and ηi is an np-by-1 vector of the difference between the parameter estimates for the ith individual and the typical values (geometric mean values) for the population. The distribution of ηi for all subjects in the study population is often assumed to be normally

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distributed (although transformations can be applied to account for other distributions) with a mean of zero and a variance-covariance given by Ω: Equation 12 The distribution of ηi ηi  N ð0, ΩÞ BSV can be described by two distinct models. Firstly, a model is developed to describe predictable reasons why individuals are different (BSVP, where “P” means predictable) and then a model is developed to quantify random variability (BSVR, where “R” means random) (Holford and Buclin 2012). Simply put, BSVP is the variability that can be explained by covariates while BSVR is the remaining random component. By quantifying both BSVP and BSVR in a population it is possible to predict the likely range of PKPD responses that may occur. BSVP can be reduced by accounting for influential covariates (e.g., size, organ function, disease state, or genetics) on parameter estimates. However, random variability in PK parameters across a patient population still remains even after accounting for patient covariates. A recent review showed that the average CV% for clearance (based on 181 population PK studies) was about 40% (IQR 26–48) (Al-Sallami et al. 2014). This corresponds to a fivefold variability in steady state average concentration (Css, ave) which clinically necessities a fivefold difference in dose-requirements to achieve a target Css across the population (Fig. 7). The authors suggest a recalibration of current perception of what constitutes normal PK variability. Traditionally, between-subject variability in PK parameters was considered “low” for CV%  10%, “medium” for CV% of around 25%, and “high” for CV% > 40% (Rowland and Tozer 2011). We propose that a CV of 25–50% for clearance should be considered normal variability for most drugs.

Part 3: Methodology (Nonlinear Mixed Effects Modeling) The following is a brief overview of nonlinear mixed effects modeling. It is not intended to be an exhaustive review of technical details or of the

statistical methodology. For further details, please refer to Bonate 2011, Davidian and Giltinan 1995, and Vonesh and ChinChilli 1997. Population analyses have traditionally employed three approaches: the naïve pooled approach; the two stage analysis; and the population nonlinear mixed effects modeling. The naïve pooled method, in essence, assumes that all observations arise from a single individual and hence differences between individuals cannot be quantitated (Sheiner and Beal 1980). For the two stage approach, the parameters of interest are estimated for each individual in the data set separately using ordinary least squares or a similar estimation method. The population parameters are then determined by calculating the arithmetic or geometric mean of the parameter values across all of the subjects. While a simple method for estimating population parameter values and BSV, the “twostage” method requires rich data from each individual and may result in inflated and/or biased estimates of BSV. In nonlinear mixed effects modeling, the population parameters and variance terms are estimated simultaneously for all individuals. Sparse data and unbalanced sampling designs can potentially be accommodated (although see Siripuram et al. 2017 for limitations in this regard), and both population and individual parameter values can be estimated. The term “mixed effects” refers to the combination of fixed effect parameters (e.g., mean drug CL in a patient population) and random effect parameters (e.g., between-subject variance for CL in a patient population). Nonlinear mixed effects modeling provides a means of assessing the probability of the data arising from particular structural and variance models, given the parameters θ, Ω, and σ 2. In other words, the parameter estimation will often involve the computation of the likelihood of the observed data arising from the given model. It is usual to search for the best set of parameter values (often maximum likelihood estimates) via iterative algorithms. There are several nonlinear mixed effects modeling software packages, but NONMEM ® is commonly used in pharmacometrics. It was originally developed by Lewis Sheiner and Stuart

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Fig. 7 Illustration of variability and fold-difference in Css as a result of BSV in CL of a hypothetical drug with PK described by a one compartment model with a CL of 1% and 100% bioavailability. Css calculated for a dose of 1 unit

Beal (Beal et al. 2011) and was the first software developed specifically for analyzing pharmacokinetic and pharmacodynamic data. Because nonlinear mixed effects models are nonlinear with respect to the random effects parameters, no closed-form solutions are available to solve the integrals for the expectation and variance of the likelihood. This problem has been addressed by using a linearization process during parameter estimation. The first-order (FO) method for approximating the likelihood in NONMEM ® was first proposed by Sheiner and Beal and uses a first-order Taylor series approximation around the random effects (evaluated at ηi = 0). The firstorder conditional estimation (FOCE) method uses a similar principle, but the expansion is evaluated at each iteration based on a conditional estimate of ηi, i.e., the empirical Bayes estimates (EBEs) of the BSV. An interaction term can be added (FOCE-I) for heteroscedastic error models to account for interaction between η and e. The Laplacian approximation method uses a second-

order Taylor series around the conditional estimates of ηi (Davidian and Giltinan 1995). For NONMEM the objective function value (OFV) is proportional to minus twice the log likelihood (2LL). Once a population model is fitted to the data, the model performance is evaluated based on statistical significance, predictive performance, and biological plausibility. To show statistically whether one model performs better than another, the likelihood ratio test is usually used. As the OFV is proportional to –2LL, and the likelihood ratio is asymptotically and approximately chisquared distributed, a decrease in OFV between two nested models of 3.84 points denotes a pvalue 10% of the data were BLQ (Beal 2001). If 25%

Other organs

No recommendations

Uptake by OCT2, OAT1/3, efflux by MATEs

Case-by-case

Efflux by P-gp and BCRP

Substrates for P-gp and BCRP Efflux Transporters In cases when intestinal wall secretion exceeds 25% of the total clearance for orally administered drug, in vitro testing should be performed to identify the transporter involved and to qualitatively or semiquantitatively describe the interaction. The involvement of gut secretion is assessed when intestinal drug transport is clinically relevant (e. g., for oral, nasal, or inhalation delivery route), and it can be estimated based on drug PK and mass balance studies. In practice, substrate potential for P-gp and BCRP efflux transporters is assessed for all (or most) orally administered investigational drugs (Fig. 2). Standard systems for determination of whether a drug is a substrate for P-gp or BCRP efflux transporters include Caco-2 cells, but other cell cultures (e.g., MDCK, LLC-PK1) or membrane vesicles overexpressing the transporter of interest can also be used. To confirm adequate expression of transporters, experiments with known P-gp or BCRP probe substrates are performed as positive control. Routinely used cell-based test to identify substrates for efflux transporters is bidirectional transport assay. The test system comprises two compartments (donor and acceptor) divided by a cell monolayer. The conditions are usually kept the same in both compartments (pH 7.4). Transport of a drug (expressed as apparent

permeability coefficient, Papp) is determined in both directions (B–A and A–B) using the following equation: Papp ¼

ðdQ=dtÞ
V A
C0

(1)

Here, dQ/dt is the permeability rate, C0 the initial concentration in donor compartment, V the volume of receiver compartment, and A surface area of the monolayer. Based on these values, the efflux ratio (RE) is calculated as: RE ¼

Papp ðB  AÞ Papp ðA  BÞ

(2)

In cases when transfected cell lines are used, efflux ratio is calculated for both transfected (RT) and wild-type cells (RW), and the resulting ratio is expressed as: RE ¼

RT RW

(3)

RE value higher than 2 indicates that a drug is a substrate for the tested efflux transporter. In contrast, RE around 1 implies that there is minimal or no drug efflux. Low RE values, i.e., RE < 0.5, suggest the involvement of uptake transporters.

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Bidirectional transport assay should be performed with various concentrations of the test drug (at least four different concentrations) covering the range of clinically relevant concentrations (concentration at the luminal intestinal membrane), except when tested concentration of a drug is limited by its solubility or cytotoxicity. Also, transport rate should be linear over the employed concentration range and under the selected experimental conditions. To confirm that investigational drug is a substrate for the efflux transporter of interest, additional study with P-gp or BCRP inhibitor should be performed. If possible, a selective inhibitor should be used, at concentration of at least ten times its Ki value. At least 50% reduction in RE value in the presence of the inhibitor in comparison with RE in the absence of inhibitor or RE equal to unity in the presence of inhibitor confirms that a drug is a substrate for the tested transporter. In such a case, additional in vivo interaction studies should be considered. Other cutoff values may also be used if they are adequately justified and validated with known drug substrates. The problem with the identification of P-gp substrates with the addition of inhibitor lies with the fact that no specific P-gp inhibitor have been identified so far. Therefore, when using Caco-2 cells, which express various transporters, the study should be performed with two or more P-gp inhibitors to insulate specific contribution of this transporter. Inverted membrane vesicles can also be used to identify drug substrates for efflux transporters. Here the drug-binding site of the transporter is located at the outer membrane surface, so the transported substrate is accumulated within the vesicles. Vesicles are subsequently filtered, and the amount of drug is quantified using a suitable method (as noted before). Transport activity is determined by subtracting the amount of drug accumulated in control vesicles incubated with AMP from the transporter-overexpressing vesicles incubated with ATP. Substrate uptake rates determined in membrane vesicles are calculated based on mg protein, so they represent relative values in relation to the

S. Cvijic

specific vesicle system. In order to calculate absolute values, i.e., Michaelis-Menten constant (Km), a range of substrate concentrations should be tested. The ITC group (Brouwer et al. 2013) suggests to test at least seven substrate concentrations, whereas the highest one should correspond to at least 90% of the maximum transport velocity, along with a control (zero concentration) probe. Subsequently, kinetic parameters can be obtained from the Michaelis-Menten equation (Eq. 4) by applying regression analysis:

V¼

Vmax
½S þ CLpassive
½S K m þ ½ S

(4)

In Eq. 4, V is the uptake rate, Vmax the maximum uptake rate, CLpassive passive diffusion clearance, and [S] substrate concentration. Membrane vesicle-based test may not be appropriate for highly permeable lipophilic compounds because extensive passive diffusion in both directions may overrun the efflux process (i.e., highly permeable drugs may escape the vesicle via passive diffusion through the lipid bilayer). An alternative way to evaluate whether a drug is a substrate for efflux transporters is ATPase assay, which is especially useful for highly permeable drugs. The test is performed on transporter-rich membranes obtained from mammalian cells or baculovirus-insect cells. The activity of transporter is reflected in changes in ATPase activity, which can be estimated by quantifying the amount of the generated inorganic phosphate. This test is useful in identifying highly permeable drug substrates for ABC transporters, but since this is an indirect assay, it is recommended to confirm the results by an additional assay.

Substrates for SLC Transporters In vitro uptake studies are routinely used to investigate whether a drug is a substrate for the key transporters from the SLC family, namely, OATP transporters in hepatocytes and OCT, OAT, and MATE transporters in renal tubules. According to the regulatory recommendations, OATP-mediated

53

In/In Vivo Correlation for Transporters

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hepatic uptake studies should be performed when hepatic transformation is major or one of the major routes of drug transformation, i.e., when hepatic metabolism or biliary secretion contributes to more than 25% of the total drug clearance (Fig. 2) or when uptake of a drug into the liver is clinically important (e.g., to exhibit pharmacological effect). In a similar manner, uptake by renal transporters (OCT, OAT, and MATE) should be assessed when renal secretion exceeds 25% of the total drug clearance and when in vivo renal clearance (in preclinical species) is higher than glomerular filtration rate (GFR). Assuming there is no reabsorption of a drug, active renal secretion can be estimated based on renal clearance (CLr), GFR, and unbound fraction of drug in plasma (fu,p) using Eq. 5. In addition, renal clearance mechanisms should also be elucidated for drugs that exhibit pharmacological or toxic effect in kidneys. Active renal
secretion  ¼ CLr  f u,p
GFR

(5)

Commonly used in vitro systems for the assessment of OATP-mediated transport include hepatocyte cultures and transfected cell lines (CHO, HEK293, MDCK). Substrates for renal transporters (OCT, OAT, and MATE) are usually assayed on transfected cell lines (CHO, HEK293, MDCK), although MATE-mediated uptake can also be investigated in membrane vesicles. A note herein is that, when estimating MATE substrates, pH in the test system should be adjusted because activity of this transporter is affected by proton gradient. When conducting an uptake study, a substrate can rapidly accumulate inside the cell or vesicle forcing the uptake process to deviate from linearity. For this reason, it is important to select early time points (in the linear phase) to estimate the uptake rate and calculate relevant kinetic parameters. The initial uptake rate can be estimated using linear or dynamic regression analysis. Active uptake clearance (CLuptake) is then determined using Eq. 6, based on the data from the initial linear uptake phase:

CLuptake ¼

Amountt2  Amountt1 ðt2  t1Þ
Cmedium

(6)

In Eq. 6, Amountt1 and Amountt2 are cumulative amounts of drug over time t1 and t2, respectively, and Cmedium concentration of drug in medium. In addition, percentage of the active uptake (in relation to passive diffusion) can be estimated from the slope of the initial uptake phase determined in the presence and absence of a known inhibitor: %Active uptake ¼

  Slope with inhibitor 1
100 Slope without inhibitor

(7) When a drug is identified as a substrate for the uptake transporter, kinetic parameters (Km, Vmax, CLint) are calculated to characterize the transport process. Uptake kinetic parameters can be estimated using Eq. 4 and regression analysis of the experimental data. Then active uptake clearance (or unbound active uptake clearance, CLactive,u) can be expressed as: CLactive,u ¼

Vmax Km

(8)

whereas the total unbound uptake clearance of a drug (CLuptake,u) is the sum of passive (CLpassive,u) and active clearances (CLactive,u): CLuptake,u ¼ CLpassive,u þ CLactive,u

(9)

A drug is considered as OATB substrate if uptake ratio, in terms of drug uptake in transfected cells in comparison to drug uptake in empty vector-transfectant cells, is equal to or higher than 2. In addition, the test should be performed with a selective inhibitor of the transporter of interest (e. g., rifampin for OATP1B1/OATP1B3), at concentration of at least ten times its Ki or IC50 value. This test confirms that a drug is a substrate for the tested transporter if drug uptake in the presence of selective inhibitor is reduced for at least 50% in comparison to the uptake in the absence of

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inhibitor. Other cutoff values may also be used if properly justified and validated with known probe substrates.

Hepatobiliary Transport Drug transport from the blood, through the hepatocytes, into the bile requires the activity of both uptake transporters located at the basolateral (sinusoidal) membrane of hepatocytes and efflux transporters on the canalicular hepatocyte membrane. Therefore, this kind of transport cannot be assessed in cell systems expressing only uptake or efflux transporters. Up to now, the most suitable in vitro assay to assess the interplay between uptake and efflux hepatocyte transporters and estimate biliary clearance of a drug has been SCH assay (Nakakariya et al. 2012; De Bruyn et al. 2013; Yang et al. 2016). Based on SCH-generated data, it is possible to calculate biliary efflux clearance and biliary excretion index (BEI) of a drug using the following equations (Liu et al. 1999a): CLbile,app

Amountcellþbile  Amountcell ¼ (10) t
Cmedium

CLbile,int ¼ BEI ¼

Amountcellþbile  Amountcell t
Ccell

(11)

Amountcellþbile  Amountcell Amountcellþbile
100% Ccell ¼

(12) Amountcell Vintracell

(13)

Depending on whether the drug concentration in medium (Cmedium) or drug intracellular concentration (Ccell) is used, in vitro apparent biliary clearance from medium to bile (CLbile,app) and in vitro intrinsic biliary clearance from hepatocytes to bile (CLbile,int) can be estimated, respectively. Here, Cmedium is the initial drug concentration in the incubation medium, Ccell is concentration of a drug in hepatocytes which can be calculated using Eq. 13, and t is the incubation time. Vintracell in Eq. 13 is the volume of intracellular space per mg protein. Amountcell is the amount of drug

accumulated in the cells, and Amountcell+bile is the sum of drug amount in the cells and bile canaliculi. These values can be estimated experimentally by modulating opening of tight junctions to bile canaliculi using incubation media with and without Ca2+/Mg2+. The principle is described within the patented B-Clear ® technology (Liu et al. 1999b). BEI is a measure of drug accumulation in the bile and serves as a qualitative indicator of biliary excretion of drug. Another method to calculate biliary clearance of a drug (CLbile) was proposed by Cantrill and Houston (2017) (Eq. 14), whereas hepatocyte-tomedia unbound concentration ratio (Kpu) is used to reflect drug partition between hepatocytes and external medium and hence the contribution of active uptake process (Eq. 15): CLbile ¼

Amountcellþbile  Amountcell t
ðKpu
Cmedium Þ Kpu ¼

CLuptake CLpassive

(14)

(15)

In vitro obtained hepatic drug clearance values are usually expressed in μl/min/mg protein, and for further scaling to in vivo estimates, these values should be converted to ml/h/kg using the available data on liver weight and protein content in liver tissue of the species of interest. The converted values can also be used as inputs in physiologically based pharmacokinetic (PBPK) models to predict PK of a drug in humans or in preclinical species.

Inhibition Studies Inhibitors affect transporter activity by noncompetitive binding to the receptor site, thus hindering other drugs to interact with the transporter, or they can obstruct the process that generates the required energy for active transport (e.g., P-gp inhibitors that block ATP hydrolysis). According to the regulatory guidelines (EMA 2012; PMDA 2014; FDA 2017), the potential of a drug to inhibit a transporter should be accessed for

53

In/In Vivo Correlation for Transporters

transporters that are known to be involved in clinically relevant drug interactions. There are certain differences between the regulatory documents, but the inclusive list of recommended target transporters comprises P-gp, BCRP, OATP1B1, OATP1B3, OCT2, OAT1, OAT3, MATEs, OCT1, and BSEP. An inhibition study can be performed as unidirectional or bidirectional transport assay with an annotation that monolayer cell cultures are not suitable for efflux inhibition tests because there is no accepted method to calculate Ki value. Preferable method to estimate drug inhibition potential for an efflux transporter includes testing with membrane vesicles. Also, due to the pronounced variability regarding P-gp inhibition parameters between laboratories, EMA (2012) suggests to use at least two systems to test a drug inhibition potential for this transporter. If justified, this approach can be used for other transporters as well. An alternative to detect a drug inhibition effect on P-gp and MRP1 transporters is to use whole cell-type calcein assay. This is an indirect type of test that measures fluorescence of free calcein trapped inside the cell due to the inhibition of P-gp and MRP1 activity. The test is based on the fact that hydrophobic calcein ester (calcein AM), added to the cell culture, can be actively transported out of the cell. But if efflux transporters are blocked, hydrolyzed calcein derivative (fluorescent) will accumulate within the cell. So, if fluorescence in the presence of different concentrations of the investigational drug increases, this indicates that a drug is an inhibitor of the tested transporter (Glavinas et al. 2011). The inhibitory potential of a drug is assessed in the presence of a known probe substrate for the transporter of interest. Since inhibitory potential of a drug can be substrate-specific, the best option for the in vitro study is to use the same probe substrate as in the prospective clinical study. If this is not an option, a preferred in vitro probe substrate is the one that generates lower IC50 value for the known inhibitors (to minimize the chance for false-negative results). Also, it should be noted that investigation of a drug inhibition potential for OATP1B1/OATP1B3 transporters requires an

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additional preincubation step for at least 30 min to detect possible time-dependent inhibition which could reduce a drug IC50 value, e.g., as observed for cyclosporine (Gertz et al. 2013). The test should be performed with a range of substrate concentrations (at or preferably below its Km, taking care that the transport is linear over the employed concentration range) and the range of concentration for the investigational drug, starting from the highest concentration (higher than clinically expected at the site of interaction) to potentiate inhibitory effect, but not to exceed drug solubility and cytotoxic concentrations. Clinically relevant concentration of the tested inhibitor (I) depends upon the localization of the interacting transporter: I. Intestinal luminal concentration (Igut) for luminal intestinal transporters (P-gp, BCRP): Igut ¼

Dose 250 ml

(16)

II. Maximal unbound hepatic inlet concentration (Iu,in,max) for hepatic uptake transporters (OATP1B): Iu,in,max ¼ f u,p   Cmax þ Fa
Fg
ka
Dose
Qh
Rbp (17) In Eq. 17, Fa is fraction of drug absorbed, Fg fraction of drug that escapes the intestine unchanged, ka absorption rate constant, Qh hepatic blood flow, and Rbp blood-to-plasma concentration ratio. Certain approximations can be made when Fa, Fg, and ka are unknown, and then a worst-case scenario is assumed with Fa
Fg = 1 and ka = 0.1. Also, fu,p = 1% can be used when experimentally determined value is lower than 1% (due to uncertainties in the measurements). III. Maximal unbound plasma concentration (Imax,u) for renal transporters (OAT, OCT, MATE):

974

S. Cvijic

Iu ¼ Cmax,u

(18)

If a drug shows inhibition effect, the test is repeated with additional drug concentrations to calculate IC50 and Ki values. According to EMA (2012), it is preferable to use Ki to assess a drug potential to inhibit a transporter. IC50 is suggested only as an alternative when Ki cannot be determined, if linear conditions are maintained, and there is no time-dependent inhibition. IC50 can be estimated using several methods (Balimane et al. 2008; Volpe et al. 2014). In general, the calculated Papp values for a probe substrate in the presence of different inhibitor concentrations are plotted against inhibitor concentrations, and the obtained curve is fitted by regression analysis to determine the inhibitor concentration that corresponds to 50% reduction in the probe substrate Papp. The final value is usually expressed as a mean for at least three separate measurements. There are also different methods to calculate Ki. One of the approaches is to convert IC50 to Ki using the following equation: Ki ¼

IC50 S 1þ Km

ðS  substrate concentrationÞ (19)

Several aspects need to be considered when determining IC50 and Ki values, as highlighted by the ITC group (Brouwer et al. 2013). One thing is that the obtained values may vary depending on the method employed, and this may complicate comparison of the results obtained in different laboratories. Moreover, IC50 does not provide information on the type of inhibition (competitive, noncompetitive, uncompetitive). Also, in case of competitive inhibition, IC50 depends upon the substrate concentration (in oppose to Ki). This annotation should be taken into account if IC50 is used for in vitro-in vivo extrapolation (IVIVE). IC50 is generally determined with a single probe substrate concentration, and when the substrate concentration is far below Km, IC50 will be equivalent to Ki value. On the other hand, substrate concentration above its Km

(>50% Km) leads to erroneous Ki estimates based on Eq. 19. Another approach to analyze inhibition data refers to the use of Dixon plots (distinguishes between competitive and noncompetitive or uncompetitive inhibition) or, e.g., “quotient velocity plot” (distinguishes between all types of inhibition, including competitive, noncompetitive, and mixed-type inhibition) (Yoshino and Murakami 2009). These graphical methods enable determination of the inhibition type and Ki which can be considered as a more robust parameter than IC50. In vitro determined IC50 and Ki values are subsequently used to estimate a drug potential to in vivo inhibit transporters of interest, which will be discussed in section “In Vitro/In Vivo Extrapolation.”

Induction Studies Although it is known that certain drugs may induce membrane transporters, up to date there are no official recommendations on how to conduct in vitro transporter induction studies. The only suggestion given so far concerns investigation of potential P-gp inducers in cases when an investigational drug is identified as CYP inducer (due to similar mechanism of inducing CYP enzymes and P-gp transporter by interacting with PXR and CAR nuclear receptors). In case a drug is a CYP inducer, an induction study should be conducted using the same assumptions and test conditions as suggested for the investigation of CYP induction potential.

Interaction with Metabolites Metabolites are hydrophilic in nature and usually not able to passively cross lipid membrane barrier, so they require active transport. Inhibition of these processes may lead to accumulation of potentially toxic or active metabolites in tissues, which can lead to potentially dangerous clinical consequences. For this reason, investigation of metabolite-transporter interaction should be included in

53

In/In Vivo Correlation for Transporters

drug development pipeline. Regulatory recommendation is that metabolite should be tested as a substrate for transporter(s) when a metabolite contributes to 50% of the total drug activity (EMA 2012; FDA 2017). Inhibition potential of metabolites is usually assessed in vivo, along with the parent drug. But in the cases when parent drug is not identified as inhibitor, in vitro inhibition studies with metabolites should still be performed when a metabolite is less polar than the parent drug and exposure to metabolite exceeds 25% of parent drug exposure or when a metabolite is more polar than the parent drug and exposure to metabolite exceeds 100% of parent drug exposure (FDA 2017). Still, these studies can only be planned in later stages of drug development, after the metabolites have been identified in the relevant in vivo studies. Additionally, in silico quantitative structureactivity relationship (QSAR) may contribute to early investigation of the presumed metabolites of NDE and their elimination routes.

Critical Assessment of In Vitro Methods In vitro studies are, with no doubt, important part of the overall assessment of transporters’ effect on drug absorption, disposition, and DDIs. However, almost all of these studies have certain limitations, depending on the in vitro system and type of the study. Most of the suggested probe substrates and inhibitors are interacting with multiple transporters, meaning that they usually lack sensitivity (e.g., rosuvastatin is suggested as OATP1B1 substrate, but it is also a substrate for, e.g., BCRP, MRP2, P-gp, OAT3; metformin is substrate for OCT2, but it also interacts with MATE1/MATE2 and OCT1). The criteria for the selection of appropriate probe substrates and inhibitors are also lacking, except for P-gp, and need to be established in the near future. Additional matter to consider in the in vitro studies is whether qualitative information on the involvement of a single transporter in drug absorption and disposition is enough to decide upon the need to conduct clinical interaction

975

studies. Namely, neglecting the role of multiple transporters on drug interactions may lead to false-positive (implying unnecessary clinical studies) or false-negative results (when necessary clinical testing is left out). Also, for drugs whose PK is influenced by the combined effect of transporter(s) and metabolic enzymes, a standard transporter in vitro assay might not be able to indicate clinical relevance of the tested transporter for a particular drug. The use of multiple tests with different systems might be the most accurate option to predict the transporter-mediated drug interactions. As noted above, for certain types of transporter studies, various in vitro test systems and methods can be used, so different laboratories can obtain different results. And comparison of such results can be challenging. Even with a single method, and the same type of material, the results may vary (Cantrill and Houston 2017). In case of cell-based studies, certain drugs cannot be tested in clinically relevant concentration ranges because of the limited solubility or high cytotoxicity. Organic solvents might be added to increase drug solubility, but in limited concentration (less than 1% vol/vol) due to potential effect on cell integrity and transporter activity. As for cytotoxic drugs, an alternative option is testing in membrane vesicle-based systems. Nonspecific binding to the system components (cells, apparatus) is also a potential problem because it may reduce effective drug concentration. Therefore, the concomitant mass balance tests (percent recovery) are routinely done to evaluate nonspecific binding. Percent recovery is expressed as the sum amount of substrate remaining in donor and acceptor compartments at the end of test in relation to the initial substrate amount in the donor compartment. The test is considered valid if percent recovery is more than 70%. A drug can also bind to membrane vesicles, and therefore its binding affinity should be checked prior to experiments. Moreover, a drug can bind to the filter used to separate membrane, and this phenomenon may be obviated by using nonreactive filter materials or by preincubation of filter with excess unlabeled substrate (Brouwer et al. 2013).

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Membrane vesicles assays have many advantages (suitable system for different types of transporter studies, can express various proteins, the estimated unbound substrate concentration is useful to calculate relevant kinetic parameters), but they have some drawbacks. One of them concerns lipophilic compounds that may penetrate the membrane by passive diffusion or bind to the membrane, indicating false-negative results in drug-transporter interaction studies. In these cases, alternative tests (e.g., cell monolayers) are preferable to study transporter interactions. As for suspended hepatocyte cultures, their functionality may be altered due to cryopreservation and membrane leakage (Yang et al. 2016). In addition, possible bias in cells polarity may happen during isolation of the cells. When it comes to the use of SCH, one of the limitations observed in rat SCH is downregulation of uptake transporters. However, this has not been an issue with human hepatocytes when cultured under appropriate conditions (Kotani et al. 2011). Reduced expression of uptake transporters in hepatocytes can further limit concentration of a drug substrate available for efflux transporters and lead to underprediction of in vitro obtained biliary efflux values. In order to predict relevant in vivo values based on these data, scaling factors (SFs) have to be used. But the problem is that these factors are drug- and transporter-specific, and evaluation of the predictive value of such in vitro data is difficult (Cantrill and Houston 2017). Looking at the type of study, certain approximations are often made in the estimation of kinetic parameters describing transporter-mediated drug permeability in a bidirectional in vitro assay. This particularly concerns Km value which is assumed to be similar in both directions if the experimental conditions (buffer pH) on both sides are the same. However, some observations indicate that Km estimates can be direction-dependent (Harwood et al. 2013). General problem with the inhibition studies is that decreased transport of probe substrate in the presence of the investigational drug does not provide information on the nature of the interaction (investigational drug can be either inhibitor or competitive substrate). However, subsequent

S. Cvijic

substrate studies can indicate whether a drug is a substrate. For this reason, substrate and inhibition tests should be performed in combination.

In Vitro/In Vivo Extrapolation Quantitative prediction of the influence of transporters on drug disposition, tissue exposure, and DDIs is challenging. Based on the current knowledge and proposed theories on drug-transporter interaction mechanisms, several approaches to link in vitro data to the expected in vivo outcomes (in vitro-in vivo correlation (IVIVC) or IVIVE) have been suggested, and they will be described in this section. ITC group and regulatory agencies have provided recommendation on whether to conduct follow-up clinical transporter studies, whereas the decision is made based on in vitro results and IVIVE. To make a decision, data from multiple in vitro assays are needed, along with supportive data from preclinical species and first-inhuman studies (if available). Other data such as patient population, drug therapeutic index, safety profile, and therapeutic indications indicating likely co-administration of other drugs that are substrates or inhibitors for the same transporter pathways should also be considered when planning in vivo interaction studies. To exemplify, if in vitro experiments suggest that a drug is a substrate for renal tubule transporters, this finding should be confirmed in the in vivo study. Moreover, if the investigational drug is likely to be co-administered with a drug that is a known substrate or inhibitor of OCT, OAT, or MATE transporters, potential interaction should be assessed before phase III (preferably before phase II) clinical trials. In vitro obtained Km and Vmax values are preferable transporter kinetic parameters to be used for IVIVE. Clearance value can also be considered, but it should be kept in mind that this parameter does not take into account saturable kinetics of drug transport. A major problem when using in vitro kinetic data for IVIVE is that these values depend upon the applied method, so the experimentally obtained values may vary considerably.

53

In/In Vivo Correlation for Transporters

As for the in vivo parameters, drug plasma levels have been traditionally used for IVIVE to assess the interaction of transporters in vivo. However, there are cases when plasma levels do not follow changes in drug tissue exposure, and in these cases, IVIVE may lead to erroneous estimates regarding the interactions with transporters. Technological achievements in imaging techniques may help to overcome these issues. Namely, imaging techniques such as positron-emission tomography, single-photon emission computed tomography, magnetic resonance imaging, etc. can provide valuable information on drug-transporter interactions in vivo, including tracking of dynamic changes in tissue drug concentration and estimation of critical transporter-related kinetic parameters. These data can eventually be correlated to the in vitro findings. Due to the differences in the expression and activity of transporters in the in vitro systems and in vivo environment, quantitative prediction of transporter-mediated drug PK, tissue exposure, and DDI based on in vitro data requires the use of scaling factors. A scaling factor is defined by dividing fitted in vivo clearance value with the in vivo clearance predicted based on in vitro measurements. One of the suggested approaches is to estimate the transporter-specific scaling factor as the average (geometric mean) factor for different drug substrates, assuming that in vitro measurements for different drugs used to calibrate the SF are done in the same system (Jones et al. 2012). Another approach concerns estimation of the compound-specific SF (Poirier et al. 2009). This empirical value can be obtained by fitting with the animal PK data, and then the same factor can be used to predict human PK based on preclinical data (e.g., in human hepatocytes). An ideal approach to correlate in vitro to in vivo data is to take into account the effect of all the important transporters enrolled in the disposition of a drug. Such a complex in vitro system that accounts for the effect of all the transporters in different tissues does not exist, nor is likely to be established. SCH model can be used to gain knowledge on the involvement of multiple transporters in liver. Alternatively, multiple assays can be combined (e.g., cell-based systems, membrane

977

vesicles, substrate and inhibition studies) to assess the contribution of each pathway separately. In general practice, IVIVE approaches for transporter-mediated drug processes can be regarded as static or dynamic. Static approaches are based on drug clearance data (e.g., “Qgut model” for intestinal bioavailability, “well-stirred model,” and “extended clearance model” for hepatic drug clearance, “well-stirred renal model”) and generally do not take into account changes in drug (substrate or inhibitor) concentration during dosing interval. Also, plasma or luminal concentrations are often used as a substitute for intracellular drug concentration. Moreover, this approach may be suitable to evaluate the effect of a single transporter, but assessing the interaction of multiple transporters or enzymetransporter interplay is rather difficult. Although this approach has limitations, it is widely used in the initial phase of transporter studies to provide preliminary information on possible drug-transporter interaction before conducting additional in silico and/or in vivo studies. On the other hand, dynamic approaches usually refer to the application of more complex PBPK simulations and modeling tools that can track changes in drug concentration in plasma and tissues as a result of parallel processes a drug undergoes in the organism, including interaction with multiple transporters and/or enzymes. Basic principles of both approaches will be described in the following text.

IVIVE for Oral Absorption Drugs that are substrates for intestinal absorption may display dose-dependent nonlinear pharmacokinetics, whereas increased drug concentrations (exceeding saturation capacity of transporter) lead to nonproportional decrease in drug absorption (for apical influx transporters) and, in opposite, increase in drug absorption (for apical efflux transporters). For drugs that are substrates for both apical influx and efflux transporters (e.g., quinidine), the situation can be more complicated. Although it is not a rule that substrates for intestinal transporters will exhibit erratic absorption, in vitro screening of possible interactions with

978

S. Cvijic

intestinal transporters in early drug development phase, and concomitant IVIVE, may help to detect problems with poor oral drug bioavailability. The influence of transporter interference on drug absorption depends upon its expression in the intestinal tissue and activity for a certain drug (expressed as Km). In order to establish correlation between in vitro and in vivo data for drugs that are substrates for intestinal transporters, several aspects need to be considered. First, the in vitro component of IVIVC comprises both drug-related and system-related parameters. Drug-related parameters are addressed in the expression for in vitro intestinal drug clearance: CLint,active ¼

Vmax Km þ C

(20)

where CLint,active is drug intrinsic clearance due to active transport and C unbound drug concentration at the transporter binding site. Furthermore, by applying the concept of the Qgut model, whereas “drug flow” through the enterocytes (Qgut) encompasses both perfusion and permeability drug transport (Eq. 21), intestinal drug bioavailability (Fg) can be predicted based on drug permeability and intrinsic clearance (CLint,gut) in the enterocytes (Eq. 22). Qgut ¼

Fg ¼

CLperm
Qent CLperm þ Qent

Qgut
 Qgut þ f u,gut þ CLint,gut

(21)

also depend on the experimental system (e.g., direction-dependent Km value in certain systems, Vmax value dependent on the expression of transporter in the system) and employed conditions. To overcome these issues, more complex multi-compartment mathematical models have been proposed to estimate intrinsic kinetic parameters of active drug transport (Harwood et al. 2013).

IVIVE for Hepatobiliary Transport Several successful attempts to establish a relationship between in vitro and in vivo biliary clearance data have been reported in literature and among them (Nakakariya et al. 2012). Still, the predictive power of the proposed extrapolation methods is hard to determine, since available data on the rate and extent of biliary elimination of drugs from clinical studies are rather limited. I. One of the approaches to estimate in vivo biliary clearance of a drug is scaling of in vitro obtained values (Eq. 23). But the problem with this approach is that scaling factors seem to be both drug- and transporter-specific (Cantrill and Houston 2017). Also, the overall intrinsic clearance in vivo for OATP substrates can be underestimated if hepatic uptake intrinsic clearance from SCH or plated hepatocytes is used for prediction and very large scaling factor have to be used to bridge the discrepancy (Li et al. 2014):

(22)

Here, Qent is blood flow through the enterocytes, CLperm drug clearance defining permeability through the enterocytes, and fu,gut the fraction of unbound drug in the enterocytes. This model seems to be well predictive for drugs with relatively high Fg (Fg > 0.5), but not for drugs with high intestinal clearance because saturation phenomena and nonlinear processes are not taken into account (Zamek-Gliszczynski et al. 2013). As for the system-related parameters, it has been shown that drug-transporter kinetic determinants

CLbile,int,pred ¼ CLbile,int,in vitro
SF

(23)

In Eq. 23, CLbile,int,pred is the predicted in vivo intrinsic biliary clearance and CLbile,int,in vitro intrinsic biliary clearance determined in vitro. II. On the other hand, biliary clearance of a drug can be estimated based on a well-stirred hepatic model. To estimate drug intrinsic biliary clearance in vivo (CLbile,int,in vivo) from in vivo clearance data (CLbile,in vivo), the following equation applies:

53

In/In Vivo Correlation for Transporters

Qp
CLbile,in vivo ¼ Qp  CLbile,in vivo

CLbile,int,in vivo

979

CLbile,int,pred,g ¼ (24)

where Qp represents liver blood flow. This equation can be modified to account solely for the fraction of drug unbound in plasma (Fukuda et al. 2008): CLbile,int,in vivo ¼

Qp
CLbile,in vivo 1
(25) CLbile,in vivo f u,p Qp  Rbp

where Rbp is blood-to-plasma concentration ratio. In a similar manner, biliary clearance of a drug in vivo can be estimated using in vitro data: CLbile,app,pred ¼

Qp
CLbile,app,in vitro Qp þ CLbile,app,in vitro

(26)

Or if the fraction of inbound drug is taken into account: CLbile,app,pred ¼

Qp
f u,p
CLbile,app,in vitro Qp þ f u,p
CLbile,app,in vitro (27)

In Eqs. 26 and 27, CLbile,app,pred is the predicted in vivo apparent biliary clearance and CLbile,app,in vitro apparent biliary clearance determined in vitro. There are speculations about whether it is better to use CLbile,app,in vitro or CLbile,int,in vitro to predict biliary clearance of a drug in vivo, but generally the data go in favor of intrinsic clearance values (Nakakariya et al. 2012). Also, the correlation is improved if unbound plasma concentration is used in the estimation of in vivo values (Fukuda et al. 2008). Further modification of the method to estimate drug biliary clearance in vivo was proposed by Li et al. (2010), who introduced the so-called g factor that incorporates quantitative data on the amount of hepatobiliary transporters (MRP2, BCRP, and BSEP) in rat SCH:

Qp
g
CLbile,int,pred Qp þ g
CLbile,int,pred

(28)

III. The third approach to estimate hepatic drug clearance in vivo is based on the extended clearance concept which takes into account multiple transports and/or metabolic processes when predicting overall drug hepatic intrinsic clearance. Transporter-enzyme-mediated overall hepatic intrinsic clearance (CLint,H) is mathematically described as:
 CLint,H ¼ CLint,met þ CLint,bile   PSuptake
PSefflux þ CLint,met þ CLint,bile
 ¼ CLint,met þ CLint,bile
Kp,uu (29) where: PSuptake ¼ PSactive þ PSpassive

(30)

PSefflux ¼ PSbasal-active þ PSpassive

(31)

In Eqs. 29, 30, and 31, CLint,met is metabolic intrinsic clearance, CLint,bile biliary intrinsic clearance, PSactive active uptake clearance, PSbasal-active active efflux, PSpassive passive diffusion clearance on either direction across the sinusoidal membrane, and Kp,uu the unbound drug concentration in the liver relative to plasma at steady state. Kp,uu is difficult to measure in vivo, and an alternative in vitro approach is to calculate ratio of drug uptake (in human hepatocyte suspension) at 37  C and on ice (when PSpassive  (CLint,met + CLint,bile)), as described in Eqs. 32 and 33. Different methods to estimate Kp,uu are described in the review of Shitara et al. (2013).

Kp,uu ¼

PSactive þ PSpassive PSuptake ¼ PSpassive PSpassive

(32)

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S. Cvijic

Kp,uu ¼

Kp ð37 CÞ Kp ðon iceÞ

(33)

In cases when hepatic uptake is the rate-determining step for drug elimination (CLint,met + CLint, bile  PSpassive and PSefflux = 0), the overall hepatic drug clearance can be expressed as:
 CLint,H ¼ PSactive þ PSpassive

(34)

Izumi et al. (2017) evaluated different IVIVE approaches for the prediction of human hepatic clearance of OATP substrates and suggested that the best approach, that yielded reasonable correlation with the overall hepatic intrinsic clearance in vivo, is based on the in vitro parameter that describes the rate-determining step for drug hepatic elimination, i.e., uptake intrinsic clearance (PSuptake) while neglecting hepatic metabolism. In addition, it was shown that introducing Kp,uu value to account for the difference between drug concentration in hepatocytes and plasma improves correlation between the predicted and in vivo observed clearance values. PSuptake can be estimated from the initial slope of linear equation that illustrates changes in the ratio of hepatocytes to buffer drug concentration during time in relation to the ratio of drug exposure and concentration in the buffer, as shown in Eq. 35: AUCð0tÞ,buffer XH ¼ PSuptake
þ V0 Cbuffer Cbuffer

(35)

In Eq. 35, XH is the amount of drug in hepatocytes, Cbuffer concentration of drug in the incubation buffer, AUC(0t),buffer area under the drug concentration-time curve in the incubation buffer, and V0 initial distribution volume that represents instantaneous drug adsorption to the surface of hepatocytes. PSuptake determined in vitro at 37  C can be transposed to the corresponding in vivo value (using hepatocellularity for the human liver and relevant SFs). However, PSuptake values obtained in human hepatocyte suspensions are subjected to inter-batch variability, so using batch-specific SF can improve the predictions (Izumi et al. 2017).

IVIVE for Renal Clearance In vitro-in vivo prediction methods for transporter-mediated renal drug clearance have not been well established, and the confidence in the prediction is rather low. Namely, data on transporter-mediated renal drug clearance is difficult to obtain in vitro, due to current inability to design a system that will reproduce physiology and functionality of nephron. Also, there are no available methods to assess renal drug clearance in humans directly. Renal clearance of drugs (CLrenal) is comprised of three processes (glomerular filtration, active tubular secretion, and tubular reabsorption), which is represented in the following equation:
 CLrenal ¼ f u,b
GFR þ CLsec
ð1  Freabs Þ

(36)

where CLsec is renal secretory clearance and Freabs fraction of reabsorbed drug. If the well-stirred renal model is assumed, secretory clearance can be expressed as: CLsec ¼ Qr


f u,b
CLint, sec Qr þ f u,b
CLint, sec

(37)

Here Qr is the renal blood flow and CLint,sec intrinsic secretion clearance which depends upon the activity of uptake and efflux transporters as illustrated in the following equation: CLint,sec ¼

PSinf lux,b
PSef f lux,a PSinf lux,b þ PSef f lux,a

(38)

In Eq. 38, PSinflux,b, and PSefflux,a represent relevant influx and efflux intrinsic clearances through basolateral (b) and apical (a) membranes of proximal tubules. A common approach to estimate renal active clearance of drugs includes empirical assessment based on GFR and scaling of animal data. However, allometric scaling based on animal data may not be the best option because of the variations in transporters expression and activity between species. Also, there is a risk of upregulation or

53

In/In Vivo Correlation for Transporters

downregulation of other transporters in transporter-knockout animals which may tangle interpretation of the results. An additional option is to use SFs to translate in vitro (e.g., from kidney slices) to in vivo data. Several studies demonstrated that clearance data obtained in kidney slices can correlate to the in vivo clearance values for some drugs, but SFs needed to be applied (Watanabe et al. 2011). Alternative approach to predict renal drug clearance in humans is to use PBPK modeling, but this approach also has some drawbacks, including uncertainties regarding the expression levels of renal transporters.

IVIVE for Inhibition Studies In case of transporter inhibition studies, in vitro-generated Ki or IC50 values for the investigational drug are correlated to the in vivo drug concentration (intestinal, liver, or plasma concentration, depending on the localization of the transporter of interest), and the outcome is used to estimate potential clinically significant interactions (Fig. 3). The criteria to evaluate a drug inhibition potential are not harmonized between the leading regulatory authorities, and in addition recently proposed FDA draft guideline (FDA 2017) introduced certain changes in comparison to the FDA guideline from 2012 (FDA 2012). In summary, the following criteria and cutoff values are recommended, depending on the regulatory document: I. Orally administered investigational drug has a potential to inhibit P-gp or BCRP transporters in vivo if: Igut/IC50  10 (FDA 2017) I1/IC50  0.1 or Igut/IC50  10 (FDA 2012; PMDA 2014) I1u/(Ki or IC50)  0.02 or Igut/(Ki or IC50)  10 (EMA 2012) Here I1 and I1u are total (unbound and bound) and unbound systemic concentrations of the inhibitor, respectively. Comparison of the prediction performance of different criteria indicated superiority of

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the criterion suggested in the novel FDA draft guideline (Zhou et al. 2016). However, one clear disadvantage of using IC50 value for the assessment of P-gp inhibition potential of a drug is that it can vary considerably between the laboratories, which may lead to inconsistency in the interpretation of the results. To minimize false results, it is advisable to run inhibition studies with a number of wellknown inhibitors (Zamek-Gliszczynski et al. 2013). II. Investigational drug has a potential to inhibit OATP1B1/OATP1B3 transporters in vivo if: R = 1 + Iu,in,max/IC50  1.1 (FDA 2017) Cmax/(Ki or IC50)  0.1 and R  1.25 (FDA 2012) R  1.04 (EMA 2012) R  1.25 (PMDA 2014) where R represents the ratio of victim drug AUC in the presence and absence of inhibitor (investigational drug). Again, criterion indicated in the novel FDA draft guidance seems to be the most appropriate (Vaidyanathan et al. 2016). As in the case of P-gp inhibition, IC50 values also show large variability depending on the substrate drug and experimental conditions (e.g., with or without preincubation step), and this may influence the validity of the suggested cutoff criteria. Additional problem with the proposed R criteria is that it treats hepatic drug transport solely as OATP-mediated process and neglects the contribution of passive diffusion and/or efflux and hepatic drug metabolism. This often leads to overestimation of the contribution of active hepatic uptake. A way to overpass this limitation is to use relative activity factors (RAFs) when estimating contribution of a specific transporter in the overall hepatic uptake based on the in vitro data. III. Investigational drug has a potential to inhibit OAT transporters in vivo if: Cmax,u/IC50  0.1 (FDA 2012, 2017) Cmax,u/(Ki or IC50)  0.02 (EMA 2012) Cmax,u/IC50  0.25 (PMDA 2014) According to the results of Dong et al. (2016), the most suitable criterion for OAT

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IVIVE

Intestine

Liver

Cut-off criteria based on Igut/IC50 (or Ki) or I1/IC50 (or Ki)

Cut-off criteria based on Cmax/IC50 (or Ki) and/or R

Inhibition of P-gp or BCRP

Inhibition of OATP1/3

Kidney

Cut-off criteria Cut-off criteria based on based on Cmax,u/IC50 (or Ki) Cmax,u/IC50 (or Ki)

Inhibition of OAT1/3

Inhibition of OCT2 and/or MATEs

Other organs

No recommendations

Case-by-case

Fig. 3 Recommendations on the in vivo investigation of a drug inhibition potential for major transporters based on in vitro data

transporters appear to be the one proposed by FDA and PMDA. IV. Investigational drug has a potential to inhibit OCT2 and/or MATE transporters in vivo if: Cmax,u/IC50  0.1 (OCT2) or Cmax,u/ IC50  0.02 (MATE) (FDA 2017) Cmax,u/IC50  0.1 (OCT2) (FDA 2012) Cmax,u/(Ki or IC50)  0.02 (OCT2) or Cmax,u/ (Ki or IC50)  0.02 (MATEs) (EMA 2012) Cmax,u/IC50  0.25 (OCT2) or Cmax,u/ IC50  0.25 (MATEs) (PMDA 2014) Based on the findings of Dong et al. (2016), it seems the best to apply different cutoffs for OCT2 and MATEs, as proposed in the new FDA guideline. In each of the abovementioned cases, other cutoff values may also be considered if properly justified using an in vitro system calibrated with known inhibitors and non-inhibitors. But to highlight again, for each investigational drug and interacting transporter, the final decision on whether to conduct clinical DDI studies will depend on the therapeutic indication of the investigational drug and likeliness of co-medication with drugs that are known substrates for a particular transporter.

PBPK Modeling In vivo systems are quite complex, and their correct representation in the vitro environment is not feasible. Therefore, in silico tools that represent some of the complexity of the in vivo conditions provide a valuable mean to assess bioperformance of drugs and elucidate contribution of different mechanisms on drug pharmacokinetics. PBPK models are physiologically based mathematical models that use a series of differential equations to simulate drug transit and bioperformance in the body. These models usually comprise a set of compartments (organs, tissues, and their substructures) linked by the vascular system. They integrate various physiological data (e.g., characteristics of different cells and spatial structures, blood flow rates) and are able to simulate numerous physiological and biochemical processes including transporter-mediated drug disposition and enzyme-mediated metabolism. PBPK models go even beyond feasible experiments since they are able to test mechanical hypotheses and assess parameters that are difficult or impossible to measure in vivo. Also, simulation and modeling may indicate

53

In/In Vivo Correlation for Transporters

the involvement of processes and mechanisms affecting drug bioperformance that have not been identified in vitro or in vivo. Some of the commercially available software packages for PBPK modeling have integrated data on the localization and expression levels of certain human transporters. Data on some animal transporters are also provided. If information on the suspected transporter is not included in the software database, these data have to be input manually, and they should preferably be experimentally obtained. Relative expression of a transporter in organs and tissues can be determined by traditional methods such as immunoblotting and RNA-based methods. More recently, advances in quantitative proteomics enabled determination of absolute transporter abundances in human tissues and commonly used in vitro systems. But these data should be used with caution because obtained expression levels can vary depending on, e.g., age, gender, disease state, and especially drug history, along with specificities related to the applied methodology. PBPK modeling tools have been accepted by the regulatory authorities in the USA and EU, and nowadays labeling information for several marketed drugs include information on potential DDIs based solely on in silico modeling results. But, these examples are mostly related to simulations regarding the influence of metabolic enzymes on DDIs. As yet, PBPK modeling has not been exploited much to support regulatory submission that address issues related to transporter-mediated interactions, and examples of such submissions are rather scarce. Some of the examples have been listed by Pan et al. (2016) and include the employment of PBPK modeling to understand the role of (i) P-gp on intestinal absorption and possible DDIs of naloxegol and ceritinib as P-gp substrates and ibrutinib as P-gp inhibitor and (ii) hepatic OATP on possible DDIs with simeprevir as OATP substrate.

Tissue Models In a PBPK model, each tissue is displayed as a series of separate compartments. A common tissue structure is composed of vascular, intracellular, and extracellular compartment. The intestine,

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liver, and kidney (and other organs such as the lungs, brain) models are usually more complex. Drug partitioning between extra- and intracellular space depends on passive diffusion through the cellular membrane and active transit mediated by membrane transporters (influx and/or efflux). In line with this, tissues can be treated as perfusionlimited (drug transfer is limited by blood flow rate through the tissue) or permeability-limited (drug transport is a saturable process partly governed by the expression and activity of membrane transporters, besides passive diffusion) (Fig. 4). Passive diffusion is characterized by the PSt factor (permeability-surface area for the given tissue) (Eq. 39), while carrier-mediated transport is described by Michaelis-Menten equation and transporter-specific kinetic parameters (Km and Vmax). The PSt for each tissue can be scaled from the PSt for the liver, using the cell volume (Vcell) in each tissue (Eq. 40). Liver PSt can be estimated from drug passive permeability determined in hepatocyte culture. Km and Vmax values are usually determined in the in vitro experiments, and, based on the assay type, they can be scaled to

arterial blood

vascular

blood flow (Q)

venous blood

Kp extracellular

Vmax Km

PSt

saturable metabolism intracellular

Cint,u

Fig. 4 Schematic representation of permeability-limited tissue in the PBPK model

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S. Cvijic

the relevant in vivo values. In vitro intrinsic clearance can also be scaled to the relevant in vivo value. Alternatively, Km and Vmax might be obtained by fitting PBPK results to the in vivo PK data, when in vivo data are available (after clinical studies). PSt ¼ Papp
S

ðS  exchange surface areaÞ (39)

PStðtissue1Þ ¼ PStðtissue2Þ


Vcellðtissue1Þ Vcellðtissue2Þ

(40)

Knowledge of the expression levels of transporters in different tissues is necessary to enable adequate scaling from in vitro and preclinical studies to human data. Namely, if relative expression levels of a transporter (relative amount in each compartment/tissue compared to its Vmax measurement environment) are known, then in vitro Vmax can be scaled to the in vivo value. In order to do so, knowledge on the expression of transporter in the in vitro system used for Vmax determination is also required (e.g., if hepatocytes are used, it can be assumed that the expression level of transporter in vitro is equal to its expression level in the liver). If the expression level in vitro is not known, the obtained Vmax needs fitting to the in vivo data. To extrapolate in vitro Km to the in vivo value, it is necessary to know relevant drug concentration in vitro (e.g., unbound intracellular concentration for the efflux transporters). Herein, unbound concentration of drug in a tissue depends upon unbound drug concentration in plasma, plasma/tissue partitioning, tissue binding, cellular membrane permeability, and degradation via cellular metabolic pathways. There are several methods to estimate drug partitioning into tissues (expressed as Kp value), but the choice of equations depends upon the processes a drug is assumed to undergo in the organism. For instance, lipophilic drugs with high passive permeability will most likely pass cellular membranes by simple passive diffusion, and distribution into body tissues will be perfusion-limited. In these cases, Kp can be calculated from drug physicochemical

properties (e.g., molecular weight, lipophilicity, dissociation constant(s), blood-to-plasma concentration ratio, fraction unbound in plasma and tissue) (Kuepfer et al. 2016). A combined equation that defines drug entry into extracellular tissue compartment, considering three-compartment tissue model (vascular-extracellular-intracellular), and combination of passive diffusion, active transport, and tissue metabolism are given in a following form: 

 Vv
Rbp dCec
Kp dt   Cec
Rbp ¼ QT
Cart   PSt Kp
 XnInfTr
Cec,u  Cic,u  i¼1

Vec þ




Vimax
Cec,u XnEffTr þ j¼1 Kim þ Cec,u




Vjmax
Cic,u Kjm þ Cic,u

(41)

In a similar manner, change in intracellular drug concentration over time can be expressed as: Vic



 dCic ¼ PSt
Cec,u  Cic,u dt XnInfTr Vi
Cec,u max þ i¼1 Kim þ Cec,u XnEffTr Vj
Cic,u max  j¼1 Kjm þ Cic,u XnEnz Vk
Cic,u max  k¼1 K k þ C ic,u m
  CLic,u
Cic,u

(42)

In Eqs. 41 and 42, Cec and Cic are drug concentrations in extracellular and intracellular compartment, respectively (Cec,u and Cic,u relevant unbound drug concentrations); Vec and Vic volumes of extracellular and intracellular compartment, respectively; Vv volume of vascular compartment; QT tissue blood flow; Cart arterial blood concentration; CLic,u unbound intrinsic clearance in tissue; and nInfTr, nEffTr, and nEnz

53

In/In Vivo Correlation for Transporters

numbers of influx transporters, efflux transporters, and enzymes, respectively. Decision on whether to use perfusion- or permeability-limited model for a certain organ might be difficult to make, since both passive and active diffusion contribute to the overall drug transport. With PBPK modeling, it might be the best to test different hypotheses and analyze the outcomes, to eventually choose the most suitable drug-specific model. As noted before, PBPK models for certain organs/tissues can be more specific to capture the complexity of physiological structure and processes. In novel PBPK models, gastrointestinal tract (GIT) is represented as a series of separated compartments defined by a number of physiological parameters, including the expression of some major transporters and metabolic enzymes. Based on the relevant drug-related and physiological parameters, a series of differential equations is used to describe drug transport, dissolution, and absorption in various segments of the GIT. Kidney model comprises an additional kidney tubule compartment to account for drug active and passive secretion and reabsorption, in addition to filtration and metabolic clearance. Kidney filtration can be estimated based on GFR, while the other transport processes (perfusion- or permeability-limited) can be simulated based on the input data from in vitro or animal studies (as for other tissues). Hepatic PBPK models include the additional gall bladder compartment to account for biliary excretion of a drug. The simulated processes include emptying of the gallbladder over a defined period of time and reabsorption of a drug (enterohepatic circulation model). Thereof, the necessary input kinetic parameters regarding hepatic drug transport include intrinsic passive diffusion clearance (CLint,pass), intrinsic uptake clearance (CLint,uptake), and intrinsic biliary clearance (CLint,bile), while basolateral efflux is usually neglected. Input hepatic clearance values for PBPK are usually obtained using SCH, and in this case, linear kinetics is assumed. An alternative is to use Km and Vmax values to simulate nonlinear hepatic drug clearance. These values

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can be input directly in a PBPK model or transformed into in vivo hepatic clearance based on a well-stirred hepatic model (described in “In Vitro/ In Vivo Extrapolation” section). There are different approaches to scale transporter-mediated intrinsic clearance obtained in vitro to the relevant in vivo parameters, some of them reviewed by Yang et al. (2016). I. Simple scaling based on physiological parameters (e.g., liver weight and number of hepatocytes per g liver) often lead to overprediction or underprediction of the relevant in vivo values, and therefore, transporter (or compound)-specific SFs should be used. These empirical SFs can be estimated by comparing in vitro data to the estimated in vivo clearance from intravenous data (human or animal). II. Another way to estimate in vivo clearance values from in vitro data is based on the quantitative data on transporter(s) expression/ abundance. In addition, relative expression factor (REF) and RAF were proposed as correction factors to bridge the gap between data parameters obtained in different systems (e.g., human hepatocytes vs. recombinant systems or in vitro transport clearance vs. in vivo secretory clearance) and facilitate scaling of in vitro to in vivo data. These factors were initially introduced for metabolic enzymes, but the same concept has been applied to transporters. RAF refers to the difference in activity between the in vitro and in vivo system (based on intrinsic drug clearance in the tissue or Vmax), while REF describes the difference in transporter expression between in vitro and in vivo system (based on relative mRNA or protein quantification). Absolute scaling factor that describes the difference in “functional transporter expression” (i.e., transporter expression based on absolute protein quantitation and activity) between in vitro and in vivo system can also be used, but kinetic data that link transporter expression with its function are scarce. There are numerous examples of successful application of PBPK modeling approach to estimate the impact of transporters on drug

986

disposition and DDIs. PubMed database search on terms “PBPK” and “transporters,” performed on August 28, 2018, retrieved 133 results, whereas more than 60% of these publications emerged in the last 5 years.

Critical Assessment of IVIVC Methods Despite the widespread use of IVIVE and in silico predictions to project ADME behavior of drugs, there are still many challenges associated with translation of in vitro to in vivo data. One of them is lack of precise data on transporter expression and activity in the in vitro systems, preclinical species, and humans. Furthermore, ethnic variability and polymorphism in the transporter-encoding genes may cause variations in transporter expression in different tissues and, consequently, distinct effects on a drug PK and DDIs. Statins are a typical example for the influence of transporter (OATP1B1) polymorphism on drug exposure to central and peripheral tissues, i.e., increased exposure to peripheral tissues may lead to myopathy. Prediction of drug-transporter interactions for patients with organ impairment or for specific population groups (pediatrics, geriatrics, pregnant women) is also challenging. It is well known that the expression and activity of transporters changes during aging and in certain physiological and pathological states. But due to the ethical reason, in it is not always feasible to assess the impact of transporters on drug disposition in vivo. Here, PBPK modeling might be a helpful alternative. Another issue regarding IVIVE is related to the use of blood or plasma drug concentrations as a substitute for the unbound drug concentration in tissues to assess transporter-mediated drug interaction in vivo, which may be a reason for false results, e.g., if DDI is reflected on drug tissue concentration, but not on plasma concentration. In these cases, PBPK modeling may be a useful alternative to in vivo studies to estimate drug tissue concentration. Another available option is to determine drug distribution in different organs and tissues using imaging techniques such as positron-emission tomography.

S. Cvijic

PBPK modeling undoubtedly offers numerous advantages in addressing a wide range of PK issues. But, no matter how advanced, PBPK models are not self-sufficient, and they require input and/or validation data from in vitro and in vivo studies. Namely, due to the gaps in our current knowledge about human physiology and transporter expression and function, modeling strategy is often driven by assumptions. Also, there are issues regarding the validity of the in vitro data used as inputs or the conformance of the applied modeling approach. This has been elaborated in the review of Yang et al. (2016). In brief, scaling of transporter-mediated in vitro clearance values (or relevant Km and Vmax values), based on physiological parameters, may lead to underestimation or overestimation of the in vivo clearance values. In fact, most of the published examples regarding PBPK modeling of transporters’ effect relay on the use of scaling factors to bridge the differences in the expression and activity of transporters between in vitro systems and preclinical species and humans. But as mentioned before, these values are compound-specific, system-specific, and species-specific, and their introduction in a PBPK model brings some distrust in the prediction results. But despite these limitations, it is rational to believe that new knowledge, and consequently, updates in PBPK models (e.g., regarding model components and organ substructure), will enhance predictability of these tools and lead to increased confidence in in silico results and more wider use of modeling tools.

Concluding Remarks In recent years, considerable scientific progress, advanced technologies, and new knowledge have enabled deeper understanding of the role of transporter in drug disposition and DDIs. Huge efforts of a number of scientists, research groups, working parties, and regulatory organizations resulted in established methods for in vitro and in vivo assessment of transporter-mediated drug interactions. In addition, development of predictive mathematical models enabled translation of in

53

In/In Vivo Correlation for Transporters

vitro to in vivo data through the means of IVIVC/ IVIVE. As a step upward, in silico PBPK models provided an integrative in vitro-in vivo-in silico platform to mechanistically explain drug interference with transporters. However, transporter science is evolving, and information presented in this chapter, including in vitro setups, decision criteria, IVIVE methods, etc., reflects current thinking on specific transporter issues, which may change when more transporter-related data become available.

References and Further Reading Balimane PV, Marino A, Chong S (2008) P-gp inhibition potential in cell-based models: which “calculation” method is the most accurate? AAPS J 10:577–586 Bircsak KM, Gibson CJ, Robey RW et al (2013) Assessment of drug transporter function using fluorescent cell imaging. Curr Protoc Toxicol 57:23–26 Brouwer KL, Keppler D, Hoffmaster KA et al (2013) In vitro methods to support transporter evaluation in drug discovery and development. Clin Pharmacol Ther 94:95–112 Camenisch G, Riede J, Kunze A et al (2015) The extended clearance model and its use for the interpretation of hepatobiliary elimination data. ADMET DMPK 3:1–14 Cantrill C, Houston JB (2017) Understanding the interplay between uptake and efflux transporters within in vitro systems in defining hepatocellular drug concentrations. J Pharm Sci 106:2815–2825 De Bruyn T, Chatterjee S, Fattah S et al (2013) Sandwichcultured hepatocytes: utility for in vitro exploration of hepatobiliary drug disposition and drug-induced hepatotoxicity. Expert Opin Drug Metab Toxicol 9:589–616 Dong Z, Yang X, Arya Vet al (2016) Comparing various in vitro prediction criteria to assess the potential of a new molecular entity (NME) to inhibit organic anion transporter 1 AND 3 (OAT1 and OAT3) in vivo. Clin Pharmacol Ther 99(Suppl 1):S94–S95 El-Kattan AF, Varma MV (2018) Navigating transporter sciences in pharmacokinetics characterization using the extended clearance classification system. Drug Metab Dispos 46:729–739 European Medicine Agency (EMA), Committee for Human Medical Products (CHMP) (2012) Guideline on the investigation of drug interactions. https://www. ema.europa.eu/documents/scientific-guideline/guidelineinvestigation-drug-interactions_en.pdf. Accessed 29 Sept 2018 Fan PW, Song Y, Berezhkovskiy LM et al (2014) Practical permeability-based hepatic clearance classification system (HepCCS) in drug discovery. Future Med Chem 6:1995–2012

987 FDA Guidance for Industry (2012) Drug interaction studies – study design, data analysis, implications for dosing, and labeling recommendations (draft guidance). https:// www.xenotech.com/regulatory-documents/2012/2012_ guidance.aspx. Accessed 29 Sept 2018 FDA Guidance for Industry (2017) In vitro metabolism and transporter-mediated drug-drug interaction studies (draft guidance). https://www.fda.gov/downloads/Drugs/Guid ances/UCM581965.pdf. Accessed 29 Sept 2018 Fukuda H, Ohashi R, Tsuda-Tsukimoto M et al (2008) Effect of plasma protein binding on in vitro-in vivo correlation of biliary excretion of drugs evaluated by sandwich-cultured rat hepatocytes. Drug Metab Dispos 36:1275–1282 Gertz M, Cartwright CM, Hobbs MJ et al (2013) Application of PBPK modeling in the assessment of the interaction potential of cyclosporine against hepatic and intestinal uptake and efflux transporters and CYP3A4. Pharm Res 30:761–780 Glavinas H, von Richter O, Vojnits K et al (2011) Calcein assay: a high-throughput method to assess P-gp inhibition. Xenobiotica 41:712–719 Gozalpour E, Fenner KS (2018) Current state of in vitro cell-based renal models. Curr Drug Metab 19:310–326 Harwood MD, Neuhoff S, Carlson GL et al (2013) Absolute abundance and function of intestinal drug transporters: a prerequisite for fully mechanistic in vitro-in vivo extrapolation of oral drug absorption. Biopharm Drug Dispos 34:2–28 Izumi S, Nozaki Y, Komori T et al (2017) Comparison of the predictability of human hepatic clearance for organic anion transporting polypeptide substrate drugs between different in vitro-in vivo extrapolation approaches. J Pharm Sci 106:2678–2687 Jones HM, Barton HA, Lai Y et al (2012) Mechanistic pharmacokinetic modeling for the prediction of transporter-mediated disposition in humans from sandwich culture human hepatocyte data. Drug Metab Dispos 40:1007–1017 Kotani N, Maeda K, Watanabe T et al (2011) Culture period-dependent changes in the uptake of transporter substrates in sandwich-cultured rat and human hepatocytes. Drug Metab Dispos 39:1503–1510 Kuepfer L, Niederalt C, Wendl T et al (2016) Applied concepts in PBPK modeling: how to build a PBPK/ PD model. CPT Pharmacometrics Syst Pharmacol 5:516–531 Li N, Singh P, Mandrell KM et al (2010) Improved extrapolation of hepatobiliary clearance from in vitro sandwich cultured rat hepatocytes through absolute quantification of hepatobiliary transporters. Mol Pharm 7:630–641 Li R, Barton HA, Varma MV (2014) Prediction of pharmacokinetics and drug-drug interactions when hepatic transporters are involved. Clin Pharmacokinet 53:659–678 Liu X, Chism JP, LeCluyse EL et al (1999a) Correlation of biliary excretion in sandwich-cultured rat hepatocytes and in vivo in rats. Drug Metab Dispos 27:637–644

988 Liu X, LeCluyse EL, Brouwer KR et al (1999b) Biliary excretion in primary rat hepatocytes cultured in a collagen-sandwich configuration. Am J Phys 277: G12–G21 Momper JD, Tsunoda SM, Ma JD (2016) Evaluation of proposed in vivo probe substrates and inhibitors for phenotyping transporter activity in humans. J Clin Pharmacol 56(Suppl 7):S82–S98 Nakakariya M, Ono M, Amano N et al (2012) In vivo biliary clearance should be predicted by intrinsic biliary clearance in sandwich-cultured hepatocytes. Drug Metab Dispos 40:602–609 Pan Y, Hsu V, Grimstein M et al (2016) The application of physiologically based pharmacokinetic modeling to predict the role of drug transporters: scientific and regulatory perspectives. J Clin Pharmacol 56(Suppl 7):S122–S131 Pfeifer ND, Yang K, Brouwer KL (2013) Hepatic basolateral efflux contributes significantly to rosuvastatin disposition I: characterization of basolateral versus biliary clearance using a novel protocol in sandwich-cultured hepatocytes. J Pharmacol Exp Ther 347:727–736 PMDA Japan (2014) Guideline on the investigation of drug interactions. http://www.nihs.go.jp/mss/T140710jimu.pdf. Accessed 29 Sept 2018 Poirier A, Cascais AC, Funk C et al (2009) Prediction of pharmacokinetic profile of valsartan in human based on in vitro uptake transport data. J Pharmacokinet Pharmacodyn 36(6):585–611 Sarmento B, Andrade F, da Silva SB et al (2012) Cellbased in vitro models for predicting drug permeability. Expert Opin Drug Metab Toxicol 8:607–621 Shitara Y, Maeda K, Ikejiri K et al (2013) Clinical significance of organic anion transporting polypeptides (OATPs) in drug disposition: their roles in hepatic clearance and intestinal absorption. Biopharm Drug Dispos 34:45–78

S. Cvijic Vaidyanathan J, Yoshida K, Arya Vet al (2016) Comparing various in vitro prediction criteria to assess the potential of a new molecular entity to inhibit organic anion transporting polypeptide 1B1. J Clin Pharmacol 56 (Suppl 7):S59–S72 Volpe DA, Hamed SS, Zhang LK (2014) Use of different parameters and equations for calculation of IC50 values in efflux assays: potential sources of variability in IC50 determination. AAPS J 16:172–180 Watanabe T, Kusuhara H, Watanabe T et al (2011) Prediction of the overall renal tubular secretion and hepatic clearance of anionic drugs and a renal drug-drug interaction involving organic anion transporter 3 in humans by in vitro uptake experiments. Drug Metab Dispos 39:1031–1038 Wu CY, Benet LZ (2005) Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm Res 22:11–23 Yang K, Guo C, Woodhead JL et al (2016) Sandwichcultured hepatocytes as a tool to study drug disposition and drug-induced liver injury. J Pharm Sci 105:443–459 Yoshino M, Murakami K (2009) A graphical method for determining inhibition constants. J Enzyme Inhib Med Chem 24:1288–1290 Zamek-Gliszczynski MJ, Lee CA, Poirier A et al (2013) International Transporter Consortium (ITC) recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans. Clin Pharmacol Ther 94:64–79 Zhou T, Arya V, Zhang L (2016) Comparing various in vitro prediction criteria to assess the potential of a new molecular entity (NME) to inhibit P-glycoprotein (P-GP) in vivo. Clin Pharmacol Ther 99(Suppl 1): S89–S90

Relevance of Transporters in Clinical Studies

54

Bruno Hagenbuch

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Major Drug Transporters with Clinical Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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ATP-Binding Cassette (ABC) Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Multidrug Resistance Protein 1 (MDR1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Bile Salt Export Pump (BSEP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Multidrug Resistance-Associated Protein 2 (MRP2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Breast Cancer Resistance Protein (BCRP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Solute Carrier (SLC) Transporters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Organic Cation Transporters (OCTs) of the SLC22 Family . . . . . . . . . . . . . . . . . . . . . . The Organic Anion Transporters (OATs) of the SLC22 Family . . . . . . . . . . . . . . . . . . . . . . The Multidrug and Toxin Extrusion (MATE) Proteins of the SLC47 Family . . . . . . . . The Organic Anion Transporting Polypeptides (OATPs) of the SLCO Family . . . . . .

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Summary and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1001

Abstract

It has become clear that drug disposition is not just a result of passive diffusion and metabolizing enzymes. Numerous transporters were identified in recent years to be involved in the absorption, distribution, and excretion of essentially all drugs. While transporters of the solute carrier (SLC) family are mainly involved in the uptake of drugs into cells,

B. Hagenbuch (*) Department of Pharmacology, Toxicology and Therapeutics, The University of Kansas Medical Center, Kansas City, KS, USA e-mail: [email protected]

ATP-binding cassette (ABC) transporters are responsible for their efflux. Among the more than 420 SLC and 47 ABC transporters, only about 25 seem to be important for the disposition of over-the-counter and prescription drugs. Among these the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) have identified seven transporters which need to be tested for investigational drugs and an additional five transporters that are considered to be important. Two of the seven transporters, the multidrug resistance protein 1 (MDR1) and the breast cancer

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_23

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resistance protein (BCRP), are ABC transporters. The other five, the organic cation transporter 2 (OCT2), the organic anion transporter 1 (OAT1) and 3 (OAT3), and the organic anion transporting polypeptide 1B1 (OATP1B1) and 1B3 (OATP1B3), are SLC transporters. If additional transporters become clinically relevant, they may be added by the regulatory agencies to the list or required transporters.

Introduction Initially, drug disposition, which includes absorption, distribution, metabolism, and excretion, was thought to be mainly the result of passive diffusion and metabolizing enzymes. More recently, however, it became clear that drug transporters play an important role in absorption, distribution, and excretion of drugs and their metabolites (Giacomini et al. 2010; Hillgren et al. 2013). Most drugs are administered orally and have to cross initially a barrier built of polarized epithelial cells in the intestine, the enterocytes (Fig. 1a) (Drozdzik et al. 2014). Once in the portal blood, drugs first reach the liver where they can be removed by what is called first-pass metabolism, a combination of drug uptake into hepatocytes (Stieger and Hagenbuch 2016), metabolism by drug-metabolizing enzymes, and excretion into bile (Fig. 1b) (Pfeifer et al. 2014). Drugs that are not (completely) cleared by this first-pass effect will reach the systemic circulation and will be carried to their target organ where they bind to a receptor or are taken up into the cells to affect their drug target. To act in the brain, drugs also have to cross the blood-brain barrier, another polarized epithelial layer that contains drug uptake and efflux transporters in the plasma membranes facing the blood or the brain (Fig. 1c) (Abdullahi et al. 2017). Eventually, most drugs not cleared by the liver will be excreted via the kidneys by either filtration or secretion. Renal secretion also involves the transport of the drug across the basolateral membrane into the cell and then across the brush-border membrane into the tubule (Fig. 1d) (Liu et al. 2016).

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In 2010 the International Transporter Consortium published the first recommendations on which transporters it considered to be important for drug absorption and disposition and extended this list in 2013 (Giacomini et al. 2010; Hillgren et al. 2013). In the meantime, the Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA) have published guidelines for the pharmaceutical industry listing which drug transporters need to be tested when evaluating an investigational drug (Table 1). This chapter will review the clinical importance of drug uptake and efflux transporters with an emphasis on those transporters that are highlighted by the regulatory agencies for investigational drugs.

Major Drug Transporters with Clinical Relevance About 10% of all human genes are transporter related. Among these are ATP-binding cassette (ABC) transporters and solute carrier (SLC) transporters. ABC transporters are primary active efflux transporters that utilize the energy derived from the hydrolysis of ATP to transport their substrates against electrochemical concentration gradients. There are 7 ABC families that contain 47 members and 3 pseudogenes (https://www. genenames.org/cgi-bin/genefamilies/set/417). All the important drug efflux transporters are classified within three ABC families: in family ABCB there are the multidrug resistance protein 1 (MDR1; gene symbol ABCB1) and the bile salt export pump (BSEP; ABCB11); family ABCC contains the multidrug resistance-associated protein 2 (MRP2; ABCC1); and in family ABCG there is the breast cancer resistance protein (BCRP; ABCG2) (Table 1 and Fig. 1). The SLC transporters are in general uptake transporters, but some may also mediate the efflux of substrates out of the cell. SCL transporters can be secondary active, transporting their substrates against an electrochemical gradient, or facilitated transporters, transporting their substrates along an

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Relevance of Transporters in Clinical Studies

a

b

Enterocyte Blood

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Lumen

Hepatocyte

Blood

MDR1

NTCP

BCRP

OATP1B1

MRP4 MRP6 BSEP MDR1

OATP1B3

MRP2

OATP2B1

MRP3 OCT1

PEPT1

OATP2B1

OCT3

OCT1

MRP2

OCT3 OAT2 OAT7

OSTα/β

Bile

BCRP

MRP3

MATE1

OSTα/β

c

d

Blood-brain barrier Blood luminal

Brain abluminal

OATP1A2

OATP1A2

OATP2B1

OATP2B1

OCT3

OCT3

MATE1

MATE1 MDR1 BCRP

Proximal tubule cell Blood

MDR1

Urine

MRP2 OCT2

MRP4

OAT1 OAT2 OAT3

MATE1/2-K OAT4 OCTN1/2 URAT1

OATP4C1

MRP1

OATP1A2 OCT1

MRP4 MRP5

Fig. 1 Schematic localization of transporters involved in the disposition of drugs in the small intestine (a), hepatocytes (b), the blood-brain barrier (c), and the proximal tubule of the kidney (d)

electrochemical gradient. There are currently 65 SLC families that contain over 420 members and more than 20 pseudogenes (http://slc. bioparadigms.org/). All of the important drug uptake transporters that are recommended or considered by the regulatory agencies are classified within three SLC families: in the SLC22 family, there are the organic cation transporter 1 (OCT1; SLC22A1) and OCT2 (SLC22A2), as well as the organic anion transporter 1 (OAT1; SLC22A6) and OAT3 (SLC22A8); the SLC47 family consists of the multidrug and toxin extrusion protein 1 (MATE1; SLC47A1) and MATE2 (SLC47A2); and in the SLCO family, there are the organic anion transporting polypeptide 1B1 (OATP1B1; SLCO1B1) and OATP1B3 (SLCO1B3) (Table 1 and Fig. 1).

ATP-Binding Cassette (ABC) Transporters The Multidrug Resistance Protein 1 (MDR1) The gene for the human multidrug resistance protein 1 (MDR1; gene symbol ABCB1) was originally identified from multidrug-resistant carcinoma cells in 1986 (Roninson et al. 1986) as the gene encoding P-glycoprotein (Ueda et al. 1986), a glycoprotein of 170 kDa that was originally discovered in 1976 (Juliano and Ling 1976). Since then it has become clear that MDR1 is also expressed in normal cells of the body, in particular in epithelial cells (Schinkel and Jonker 2003).

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Table 1 Human transporters involved in the uptake and efflux of endo- and xenobiotics Protein name MDR1 BSEP MRP1 MRP2 MRP3 MRP4 BCRP NTCP ASBT PEPT1 PEPT2 OCT1 OCT2 OAT1 OAT3 ENT1 ENT2 MATE1 MATE2 OSTα OSTβ OATP1A2 OATP1B1 OATP1B3 OATP2B1

Gene symbol ABCB1 ABCB11 ABCC1 ABCC2 ABCC3 ABCC4 ABCG2 SLC10A1 SLC10A2 SLC15A1 SLC15A2 SLC22A1 SLC22A2 SLC22A6 SLC22A8 SLC29A1 SLC29A2 SLC47A1 SLC47A2 SLC51A SLC51B SLCO1A2 SLCO1B1 SLCO1B3 SLCO2B1

Recommended or considered by regulatory agencies FDA, EMA, PMDA Considered by FDA, EMA, PMDA Considered by PMDA

FDA, EMA, PMDA

Considered by EMA and PMDA FDA, EMA, PMDA FDA, EMA, PMDA FDA, EMA, PMDA

PMDA, considered by FDA and EMA PMDA, considered by FDA and EMA

FDA, EMA, PMDA FDA, EMA, PMDA

Transporters are classified either as ATP-binding cassette (ABC) transporters or as solute carrier (SLC) transporters EMA European Medicines Agency, FDA Food and Drug Administration, PMDA Pharmaceuticals and Medical Devices Agency

In enterocytes, MDR1 is expressed in the apical membrane (Fig. 1a) where it protects the body from toxic xenobiotics. Pumping its substrates out of the cells, MDR1 can restrict substrate uptake and thus affect the bioavailability of numerous drugs. In general, MDR1 substrates are hydrophobic and amphipathic. They can be between 200 and over 4000 Da and are mostly uncharged or positively charged. They include numerous anticancer agents, antimicrobials, several HIV protease inhibitors, immunosuppressant drugs like cyclosporine A, and various cardiovascular drugs including calcium channel blockers and digoxin (Schinkel and Jonker 2003; Terada and Hira 2015; Saidijam et al. 2018). See Lund et al. (2017) for a compilation of drugs that modulate MDR1 activity and can lead to adverse drug-drug interactions. Given the

broad substrate spectrum and the fact that several of these drugs often are prescribed together, there is a real possibility for adverse drug-drug interactions either because of inhibition or induction of MDR1. The quinidine-digoxin drug-drug interaction, e.g., could be explained by inhibition of MDR1-mediated efflux of digoxin from enterocytes by quinidine, resulting in increased absorption and thus increased plasma concentration of digoxin. Similar effects due to MDR1 inhibition were reported for amiodarone, dronedarone, and propafenone, three antiarrhythmics that are likely prescribed to patients that also take oral digoxin (Wessler et al. 2013). In contrast, rifampicin administration induced the expression of MDR1 in the duodenum and decreased absorption and thus bioavailability of digoxin (Wessler et al. 2013).

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MDR1 is also a key player in the protection of the brain from toxic xenobiotics. It is expressed in the luminal membrane (Fig. 1c) where it prevents the uptake of its substrates from blood into the endothelial cells of the blood-brain barrier (Schinkel and Jonker 2003). As a consequence, the brain penetration of numerous drugs is rather low but can be increased in the presence of MDR1 inhibitors or in the absence of a functional MDR1. This was nicely demonstrated using mdr1a (
/
) mice (Schinkel et al. 1996). These mice are lacking the mouse homolog of the human MDR1 protein, and therefore they accumulated drugs in the brain 10- to 100-fold above the levels of their wild-type controls (Schinkel and Jonker 2003). In the liver (Fig. 1b), MDR1 is expressed at the canalicular membrane of hepatocytes and can impact the excretion of mainly cationic xenobiotics that have been taken up into hepatocytes via OCT1 and OCT3. In proximal tubule cells (Fig. 1d), MDR1 is expressed at the brush-border membrane and can affect the secretion of substrates that have been taken up into the tubular cells via OCT2. Given that mutations or polymorphisms can lead to an inactive (or overactive) MDR1, it is also important to search for and characterize such gene variants. At least 390 sequence variants have been identified in the coding region of the ABCB1 gene, but the majority of these variants only occur at low frequencies ( T (G412G), c.2677G > T/A (A893S/T), and c.3435C > T (I1145I) with different drug substrates have been performed. Some of them have been associated with effects on drug disposition, response, and toxicity, but overall the findings were conflicting and had limited clinical implications. These findings indicate that in the case of MDR1, inhibition due to drugdrug interactions and induction of expression are predominant and clinically more relevant than polymorphisms. Because MDR1 can affect the oral bioavailability, the distribution, and the excretion of drugs, the regulatory agencies expect that

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investigational drugs are tested in vitro whether they are substrates of MDR1. Class I drugs (i.e., highly soluble and highly permeable) according to the biopharmaceutical classification system (Amidon et al. 1995) only need to be tested as MDR1 substrates if there are potential safety concerns regarding distribution into the brain or the kidneys. The in vitro test should either be performed by measuring the transepithelial flux, e.g., using Caco-2 cell layers or by inhibiting the transepithelial flux with at least one known MDR1 inhibitor at a concentration of more than ten times its Ki value. An efflux ratio of at least two or inhibition of the efflux ratio by more than 50% by the known inhibitor indicates that the investigational drug is an MDR1 substrate. If the drug is a substrate, an in vivo study might be necessary based on the safety margin of the drug, on its therapeutic index, and on the fact that a likely co-medication is an MDR1 inhibitor (Lund et al. 2017).

The Bile Salt Export Pump (BSEP) The human bile salt export pump (BSEP; gene symbol ABCB11), a glycoprotein of 170 kDa, is expressed almost exclusively in the canalicular membrane of hepatocytes (Fig. 1b) where it is responsible for the export of mainly conjugated bile acids (Stieger 2011). Normal BSEP function is required because mutations in the ABCB11 gene that result in either reduced or completely absent BSEP function result in cholestatic liver disease that can be mild (benign recurrent intrahepatic cholestasis type 2) or life threatening (progressive familial intrahepatic cholestasis type 2) (Stieger 2011). Numerous drugs and xenobiotics inhibit BSEP function in vitro, and a correlation of their IC50 values with reported hepatotoxicity revealed that if the IC50 value is below 25 μM, chances for drug-induced liver injury increased (Morgan et al. 2010; Stieger 2011). However, no correlation between maximal free plasma concentration and BSEP inhibition or liver injury could be established. This is likely due to the fact that the uptake step for these drugs, transport across the basolateral membrane

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into hepatocytes, is rate limiting (Hillgren et al. 2013). Sequencing of the ABCB11 gene revealed numerous mutations and polymorphisms. For example, the c.1331T>C variant leading to an alanine at position 444 instead of a valine (V444A) is associated with low BSEP expression (Stieger 2011) and was overrepresented in a population with cholestatic liver disease, suggesting that it can contribute to or predispose for liver disease (Droge et al. 2017). Although not required by the regulatory agencies, the International Transporter Consortium recommends that BSEP function should be tested under certain conditions. The European Medicines Agency states that BSEP inhibitory potential should be considered in particular if plasma bile acid levels are increased in animal studies. Furthermore, if a BSEP inhibitor is given to humans, their serum bile acid levels should be monitored along with liver serum markers because of the potential of drug-induced liver injury (Hillgren et al. 2013).

The Multidrug Resistance-Associated Protein 2 (MRP2) The multidrug resistance-associated protein 2 (MRP2; gene symbol ABCC2) is a 190 kDa glycoprotein expressed in the canalicular membrane of hepatocytes, as well as in the apical membrane of enterocytes and proximal tubular cells (Fig. 1). It was originally identified as a canalicular multispecific organic anion transporter mediating the efflux of conjugated anionic substrates including bilirubin glucuronides and numerous drug conjugates into bile. Its functional absence leads in humans to the Dubin-Johnson syndrome, a rare benign disorder characterized by conjugated hyperbilirubinemia (Schinkel and Jonker 2003). Numerous studies have characterized the role MRP2 plays in the hepatobiliary excretion of drugs, and with its strategic localization in the apical membrane of enterocytes, MRP2 and its inhibition could affect the bioavailability of its drug substrates. Furthermore, it also can contribute directly to the renal excretion of drugs. It

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seems that both MDR1 and MRP2 play an important role in protecting the human body from potentially toxic xenobiotics. Given its broad substrate specificity, drug-drug interactions seem possible. However, only a few of these interactions have been described so far. In a recent screening of 124 natural compounds, only 3.2% were inhibitors of MRP2, while the breast cancer resistance protein (BCRP) was inhibited by 36% of the compounds (Sjostedt et al. 2017). Besides inhibitors also stimulators of MRP2 have been characterized in vitro, but so far only a few studies have investigated the stimulatory effect on MRP2 in vivo, and it could be verified in a rat model (Heredi-Szabo et al. 2009). Numerous genetic polymorphisms have been identified in the ABCC2 gene, but only a few lead to decreased MRP2 function, and conjugated hyperbilirubinemia is a possible consequence. Because hyperbilirubinemia could be a sign of hepatotoxicity, the International Transporter Consortium recommends that in cases of druginduced hyperbilirubinemia, inhibition of MRP2 should be tested (Hillgren et al. 2013). Furthermore, the Japanese Pharmaceuticals and Medical Devices Agency suggests that inhibition of MRP2 could lead to increased drug concentrations in hepatocytes or drug-induced increases in plasma concentrations of endogenous compounds.

The Breast Cancer Resistance Protein (BCRP) The breast cancer resistance protein (BCRP; gene symbol ABCG2) is an ABC half-transporter of 75 kDa that probably functions as a dimer. It was originally identified in the multidrug-resistant human breast cancer MCG-7 cell line. Initial characterization demonstrated that expression of BCRP conferred resistance to several anticancer agents (Saidijam et al. 2018). Later studies discovered that similar to MDR1, BCRP is expressed in the apical membrane of many epithelia including the enterocytes, hepatocytes, and endothelial cells of the blood-brain barrier (Fig. 1) and protects the organism from numerous xenobiotics (Terada and Hira 2015).

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Relevance of Transporters in Clinical Studies

Functional characterization revealed that besides anticancer agents, BCRP transports numerous drugs from many different classes including antivirals, antibiotics, tyrosine kinase inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs), and statins (Lee et al. 2015). Although BCRP transports such a wide variety of drugs, drug-drug interactions exclusively due to BCRP are rare, except for limiting oral absorption in the intestine, because of an overlap in substrate specificity with other transporters and drugmetabolizing enzymes. For example, curcumin, a natural polyphenol and the main curcuminoid of turmeric, increased AUC of the NSAID sulfasalazine in healthy volunteers between 2and 3.2-fold (Lee et al. 2015). Similarly, the AUC of rosuvastatin increased in healthy volunteers in the presence of the immunosuppressant cyclosporine A or the protease inhibitors tipranavir and ritonavir. A suggested mechanism includes inhibition of uptake into hepatocytes via OATP1B1 (see below) and increased absorption due to BCRP inhibition in the intestine (Lee et al. 2015). BCRP expressed at the blood-brain barrier, together with MDR1, protects the brain from potentially toxic xenobiotics. While this is good for the normal function of the brain, it also limits the brain penetration of drugs that have their target in the brain. One example is imatinib mesylate, for which there is some in vitro evidence that it could be used to treat malignant gliomas. However, a clinical study showed that imatinib mesylate had minimal activity in malignant gliomas, probably because it is a substrate of BCRP (Urquhart and Kim 2009). Therefore, inhibitors of BCRP, and potentially dual BCRP/MDR1 inhibitors, could be useful tools to increase drug delivery to the brain. More than 80 polymorphisms have been documented in the ABCG2 gene. However, most of these are rare and found in less than 1% of the population. The most frequent polymorphism, c.421C>A, results in a lysine at position 141 instead of a glutamine (Q141K). This mutation leads to a protein that is less stable than the wildtype and results in a reduced BCRP function, due to reduced plasma membrane expression levels.

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The allele frequency of this polymorphism is about 5–10% in Caucasians and African-Americans but between 30% and 60% in East Asians (Hira and Terada 2018). In addition, this polymorphism has been identified in a genome-wide association study as being associated with increased serum urate concentrations and an increased risk of gout. This demonstrates another important role BCRP plays in the elimination of uric acid in the intestine (Cleophas et al. 2017). Because BCRP has the potential to affect the oral bioavailability, the tissue distribution, and the hepatic excretion of drugs, the regulatory agencies treat BCRP similar as MDR1 and expect that investigational drugs are tested in vitro whether they are substrates of BCRP following the same principles as outlined for MDR1 above. Like for MDR1, class I drugs according to the biopharmaceutical classification system are excluded.

Solute Carrier (SLC) Transporters The Organic Cation Transporters (OCTs) of the SLC22 Family Organic Cation Transporter 1 (OCT1) The organic cation transporter 1 (OCT1; gene symbol SLC22A1) is a glycoprotein of approximately 70 kDa expressed mainly at the sinusoidal or basolateral membrane of human hepatocytes (Fig. 1b). In addition, OCT1 is expressed at the basolateral membrane of human enterocytes (Fig. 1a), at the apical membrane of proximal tubule cells (Fig. 1d), and in several additional tissues including the lung, heart, skeletal muscle, brain, mammary and adrenal gland, eye, adipose tissue, and immune cells (Koepsell 2013). OCT1 transports a wide variety of substrates that in general have a molecular weight of less than 500 Da, are mainly hydrophobic, and carry a positive charge. Common model substrates for in vitro studies are 1-methyl-4phenylpyridinium (MPP+), N-methylquinine, and tetraethylammonium (TEA) (Koepsell 2013). Besides these model substrates, numerous endogenous cationic substrates including the

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neurotransmitters acetylcholine, dopamine, norepinephrine, and serotonin are transported by OCT1. Among the drug substrates are antiarrhythmics, antibiotics, anticholinergics, the antidiabetic metformin, antihypertensives, anticancer agents, antivirals, β2-agonists, diuretics, and H2antagonists (Nies et al. 2011). The importance of OCT1 for metformin uptake into its target cells, human hepatocytes, was demonstrated when in healthy volunteers metformin was coadministered with verapamil, an OCT1 inhibitor. After co-administration with verapamil, metformin did not reduce maximal blood glucose concentrations to the same degree as when the volunteers only received metformin (Patel et al. 2016). In contrast, metformin exhibited a larger reduction in blood glucose levels after healthy volunteers were treated with rifampicin, an agonist of the pregnane X receptor (PXR). As a result OCT1 mRNA levels were increased about fourfold in peripheral blood cells. This study suggests that increased expression of OCT1 resulted in increased hepatic uptake and activity of metformin (Patel et al. 2016). Numerous single nucleotide polymorphisms were identified in the SLC22A1 gene, several of which affect metformin efficacy. Reduced function polymorphisms c.181C>T (R81C), c.1201G>A (G401S), c.1260GAT>del (420del), and c.1393G>A (G465R) resulted in higher metformin AUC, higher maximal plasma concentrations, and reduced glucose-lowering effects during oral glucose tolerance testing (Wagner et al. 2016). These findings are consistent with reduced uptake of metformin into hepatocytes. OCT1 is not only important for metformin uptake but mediates the uptake of many additional clinical important drugs. For example, lamivudine, a drug used to treat HIV infection and a substrate of OCT1, was transported less efficiently by the above polymorphic variants, and carriers of these polymorphisms would have lower drug efficiency. The International Transporter Consortium has listed OCT1 as a clinical relevant transporter because OCT1 activity positively correlates with how patients with chronic myeloid leukemia respond to imatinib and because OCT1 seems to

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be involved in the interindividual response to metformin (Giacomini et al. 2010). However, so far the regulatory agencies do not require OCT1 to be tested for drug interactions. The European Medicines Agency and the Japanese Pharmaceuticals and Medical Devices Agency consider to add OCT1 to the required drug transporters to be tested in the future.

Organic Cation Transporter 2 (OCT2) The organic cation transporter 2 (OCT2; gene symbol SLC22A2) is a glycoprotein of 555 amino acids and is mainly expressed at the basolateral membrane of proximal tubule cells (Fig. 1d) (Koepsell 2013). There it plays a crucial role in the secretion of organic cations by mediating the first step, uptake across the basolateral membrane into the tubular cells, before these cations are excreted across the brushborder membranes by MATE1 and MATE2K (see below). Additional tissues with minor OCT2 expression are the small intestine, lung, placenta, thymus, brain, and inner ear. Similar to OCT1, also OCT2 has a broad substrate specificity and transports numerous OCT1 substrates but also some distinct compounds (Nies et al. 2011). Model OCT2 substrates include 1-methyl-4phenylpyridinium (MPP+); tetraethylammonium (TEA); endogenous monoamines like norepinephrine, dopamine, and serotonin; the antineoplastic drug oxaliplatin; the antiviral lamivudine; the antidiabetic drug metformin; and the antihypertensive atenolol (Yin and Wang 2016). The combined uptake by OCT2 across the basolateral membrane and the secretion by MATE1/2-K across the brush-border membrane are crucial functions for the renal elimination of metformin. Several drug-drug interactions at OCT2 affect renal metformin secretion. Cimetidine, a histamine H2-receptor antagonist, is well known to reduce renal clearance of metformin. Early studies in healthy volunteers suggested that the OCT2 substrate and inhibitor cimetidine would inhibit OCT2-mediated uptake of metformin into tubular cells and thus reduce renal clearance (Yin and Wang 2016). Similarly, the antiviral dolutegravir, when co-administered with metformin, increased metformin AUC by 2.5-fold.

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Based on in vitro studies where dolutegravir was identified as a weak OCT2 inhibitor, its effect is probably only partially due to OCT2 inhibition (Yin and Wang 2016). The anticancer drug cisplatin leads to dose-limiting nephrotoxicity because the kidney accumulates this drug to a higher degree than other organs. In vitro studies demonstrated that cisplatin is a good substrate of OCT2. In addition, cancer patients with the polymorphism c.808G>T (S270A) which results in lower OCT2-mediated uptake had reduced cisplatin-induced nephrotoxicity (Yin and Wang 2016). Thus, selective OCT2 inhibitors might be useful in protecting cancer patients from cisplatin toxicity. Several polymorphisms have been identified in the SLC22A2 gene with four of the nonsynonymous variants showing ethnic-specific allele frequencies of more than 1%. These polymorphisms are c.495G>A (M165I), c.808G>T (A270S), c.1198C>T (R400C), and c.1294A>C (K432Q) (Fujita et al. 2006). When these four variants where tested in vitro using the Xenopus laevis oocyte expression system, all four variants were able to transport MPP+, but differences in kinetics and in inhibition studies were observed, suggesting substrate-dependent effects. Such substrate-dependent inhibition was further characterized by comparing IC50 values obtained for the inhibition of the fluorescent substrate, N,N,N-trimethyl-2-[methyl(7-nitrobenzo[c][l,2,5] oxadiazol-4-yl)amino]ethanaminium iodide (NBD-MTMA), of MPP+ and of metformin (Belzer et al. 2013). The results demonstrated that inhibition of OCT2-mediated metformin transport was about ten times more effective than for OCT2-mediated MPP+ uptake. Thus, when drug-drug interactions at OCT2 are investigated, it might be more predictive if more than one transport substrate is used and ideally the most likely co-medication is included. The regulatory agencies recommend that investigational drugs with significant renal secretion (active secretion of parent drug by the kidney is 25%) should be tested as substrates for OCT2. If the in vitro results show that the investigational drug is an OCT2 substrate (transport of at least twofold above the negative control) and an OCT2

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inhibitor, i.e., inhibits OCT2-mediated transport of the investigational drug by 50% at a concentration of at least ten times the Ki value, in vivo studies might be necessary (Giacomini et al. 2010; Hillgren et al. 2013).

The Organic Anion Transporters (OATs) of the SLC22 Family Organic Anion Transporter 1 (OAT1) and 3 (OAT3) The organic anion transporter 1 (OAT1, gene symbol SLC22A6) is a glycoprotein of 550 amino acids and is mainly expressed at the basolateral membrane of the proximal tubule cells (Fig. 1d) (Koepsell 2013). It plays an important role in the renal secretion of numerous organic anions. OAT1 seems to work as an organic anion/α-ketoglutarate exchanger, exchanging the intracellular α-ketoglutarate for mainly hydrophilic and small (less than 300 kDa) organic anion. Besides various endogenous compounds including different dicarboxylates and prostaglandins, OAT1 mediates the basolateral uptake of numerous drugs such as angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, antibiotics, antivirals, histamine H2-receptor antagonists, and NSAIDs (Burckhardt and Burckhardt 2011). In in vitro assays, the model organic anion p-aminohippurate (PAH) is frequently used to characterize OAT1. The prototypical inhibitor of OAT1 is probenecid, although it is not an OAT1-selective inhibitor. Besides probenecid several NSAIDs inhibit OAT1-mediated transport (Burckhardt and Burckhardt 2011). The organic anion transporter 3 (OAT3, gene symbol SLC22A8) is a 542-amino acid, and similar to OAT1, it is mainly expressed at the basolateral membrane of the proximal tubule cells (Fig. 1d) (Koepsell 2013). Together with OAT1 it is involved in the renal secretion of various endogenous and exogenous organic anions. Like OAT1, OAT3 seems to work as an organic anion/α-ketoglutarate exchanger. Thus, both OATs are expressed in the same basolateral membrane and use the same mode of transport.

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Similarly, the substrate specificities of the two transporters overlap but are not identical. While OAT1 transports mainly small and hydrophilic organic anions, OAT3 substrates are in general larger and more hydrophobic. Endogenous OAT3 substrates include bile acids like cholate and taurocholate, sulfated hormones like dehydroepiandrosterone sulfate and estrone3-sulfate, prostaglandins, and urate. In addition, OAT3 transports numerous drugs including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, antibiotics, histamine H2-receptor antagonists, and several statins (Burckhardt and Burckhardt 2011). In in vitro assays, the model organic anion used to characterize OAT3 function is estrone-3-sulfate. Probenecid is also an inhibitor of OAT3. In addition, NSAIDs, cimetidine, bumetanide, and some dicarboxylates were reported to inhibit OAT3. Because both, OAT1 and OAT3, handle a broad range of substrates, several drug-drug interactions have been described. The organic anion transport inhibitor probenecid inhibits both OAT1 and OAT3. Probenecid is a uricosuric drug which inhibits the reabsorption of uric acid in the tubules and is used to treat hyperuricemia associated with gout. Probenecid can also be used to prolong penicillin serum levels because both OAT1 and OAT3 can transport penicillin and are involved in the secretion of this β-lactam antibiotic. However, based on in vitro data, it seems that the major effect is due to inhibition of OAT3, while the effect on cephalosporins is probably due to inhibition of both OATs (Burckhardt and Burckhardt 2011). This drugdrug interaction actually is used as a beneficial side effect of the drug and not an adverse effect. Similarly, probenecid is used to prevent nephrotoxicity of cidofovir, an antiviral drug used to treat cytomegalovirus-induced eye infections in people with AIDS. The underlying mechanism is inhibition of OAT1-mediated accumulation of cidofovir in the proximal tubule cells (Yin and Wang 2016). In the SLC22A6 gene encoding OAT1 and the SLC22A8 gene encoding OAT3, only very few amino acid changing polymorphisms have been identified. For OAT1, the polymorphism

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c.149G>A (R50H) resulted in increased affinities of the mutated protein for adefovir, cidofovir, and tenofovir. This might lead to increased nephrotoxicity for patients carrying this polymorphism (Burckhardt and Burckhardt 2011). For OAT3, the polymorphism c.913A>T (I305F) which is found at a frequency of 3.5% in Asian-Americans showed reduced estrone-3-sulfate transport compared to the wild-type OAT3 (Burckhardt and Burckhardt 2011). In a recent report, it was shown that Asians with this variant had reduced renal secretion and clearance of the cephalosporin antibiotic cefotaxime (Yee et al. 2013). All of the other identified reduced-function variants are found at less than 3% allele frequency. Thus, the R50H mutation in OAT1 and the I305F mutation in OAT3 could potentially impact renal drug elimination and increase drug concentrations in subjects carrying these mutations. Regarding regulatory agencies, OAT1 and OAT3 are treated similar as OCT2 because they also play a role in renal secretion of numerous drugs. Thus, the recommendations described above for OCT2 are the same for OAT1 and OAT3 (Giacomini et al. 2010; Hillgren et al. 2013).

The Multidrug and Toxin Extrusion (MATE) Proteins of the SLC47 Family Multidrug and Toxin Extrusion 1 (MATE1) and 2 (MATE2) The SLC47 family contains two genes, SLC47A1 and SLC47A2 encoding multidrug and toxin extrusion 1 (MATE1) and MATE2 as well as the splice variant MATE2-K. MATE1 is composed of 570 amino acids, MATE2 has 602 amino acids, and the splice variant MATE2-K which is missing part of exon 7 has 566 amino acids. MATE1 is expressed at the apical membrane of the proximal and distal tubule and at the canalicular membrane of hepatocytes. MATE2-K is also expressed in the apical membrane of the proximal tubule, while MATE2 mRNA was not detected in the kidney. Both MATE1 and MATE2-K are involved in the tubular secretion of organic cations that were transported into tubular cells by OCT2. A potentially similar mechanism is

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proposed for hepatocytes where the basolateral OCT1 takes up cationic substrates into the cells and the canalicular MATE1 secretes them into bile. The transport mechanism for MATE-mediated secretion is H+-coupled electroneutral organic cation exchange (Motohashi and Inui 2013). Substrates transported by MATE1/2-K include well-known OCT substrates including TEA, MPP+, cimetidine, metformin, guanidine, procainamide, quinine, topotecan, cisplatin, oxaliplatin, but also some anionic compounds like estrone-3-sulfate, acyclovir, and ganciclovir. While some of these compounds including TEA, MPP+, and quinidine had similar affinities for both, MATE1 and MATE2-K, affinities for choline and cimetidine were very different for the two transporters, suggesting that they are multispecific transporters with overlapping but also distinct substrate specificities. Initially drug interactions with metformin, such as the above-described cimetidine-metformin interaction, were explained as cimetidine inhibition of OCT2-mediated metformin uptake across the basolateral membrane. However, in vitro studies revealed that cimetidine is a much stronger inhibitor of MATE1/2-K than of OCT2 with a Ki value more than 20-fold lower, suggesting that the main inhibitory effect is at the secretory step mediated by MATE1/2-K (Motohashi and Inui 2013). Pyrimethamine, an antiparasitic compound, is a selective inhibitor of MATE1/2-K. When co-administered with metformin, increased metformin AUC and decreased renal clearance of metformin were reported, demonstrating that inhibition of MAT1/2-K could lead to clinically relevant drug-drug interactions (Yin and Wang 2016). The genetic variant c.-66T>C in the 50 UTR and the intronic c.922-158G>A in the SLC47A1 gene are associated with a higher glucose-lowering effect of metformin, suggesting that they result in lower expression levels MATE1. The variant c.-130G>A in the 50 UTR of the SLC47A2 gene has been associated with a decreased glucose-lowering effect of metformin (Staud et al. 2013). So far no non-synonymous polymorphisms in MATE1/2-K have been reported.

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Because of observed drug-drug interactions and the potential for adverse effects of new drugs, the regulatory agencies recommend the same procedures for MATE1/2-K as for OCT2 and OAT1/OAT3 described above (Giacomini et al. 2010; Hillgren et al. 2013).

The Organic Anion Transporting Polypeptides (OATPs) of the SLCO Family Organic Anion Transporting Polypeptide 1B1 (OATP1B1) and 1B3 (OATP1B3) The organic anion transporting polypeptide 1B1 (OATP1B1, gene symbol SLCO1B1) is a glycoprotein of 691 amino acids with a molecular weight of about 85 kDa. It is exclusively and evenly expressed at the basolateral (or sinusoidal) membrane of human hepatocytes throughout the liver lobule. OATP1B3 (SLCO1B3) is also a glycoprotein with 702 amino acids and a molecular weight of about 120 kDa. In the liver, OATP1B3 is expressed at the basolateral membrane mainly around the central vein with less expression toward the portal vein (Hagenbuch and Stieger 2013). Besides the liver, OATP1B3 has also been documented in several cancers, but it seems that outside the liver the cancer-type OATP1B3 is mainly expressed, which is missing the N-terminal 28 amino acids and is hardly expressed at the plasma membrane. As a consequence its transport activity is strongly reduced (Chun et al. 2017). OATP1B1 and OATP1B3 have a broad and partially overlapping substrate specificity. They transport various endogenous compounds including bile acids, bilirubin and its conjugates, thyroid hormones, and several steroid conjugates (Hagenbuch and Stieger 2013). Besides these endogenous substrates, both OATPs also transport numerous drugs including statins, antihypertensives, antibiotics, and anticancer agents (Roth et al. 2012). Given that the two proteins share 80% amino acid identity, it is not astonishing that they share most of the substrates. However, there are some substrates that are specifically transported only or mainly by one of the

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two liver OATPs. Estrone-3-sulfate at low concentrations (nanomolar range) is preferentially transported by OATP1B1, but at higher concentrations the low affinity high capacity OATP1B3 can take over. For OATP1B3 at least two selective substrates have been documented: cholecystokinin-8 (CCK-8) and telmisartan (Roth et al. 2012). In addition, fluorescein-containing substrates like fluorescein-methotrexate or 8-fluorescein-cAMP are in general better substrates of OATP1B3 (Bednarczyk 2010; Gui et al. 2010). There are a number of clinically relevant drugdrug interactions that involve the liver-specific OATPs. Because polymorphisms that result in an inactive or less active transporter mainly in the SLCO1B1 gene have been linked to altered drug disposition, it is in general assumed that OATP1B1 plays the more important role than OATP1B3 for the disposition of most drug substrates. In 2008, the SEARCH collaborative group was able to link the SLCO1B1variant c.521T>C (V174A, also known as OATP1B1*5) to an increased risk of statin-induced myopathy (Link et al. 2008). Several studies demonstrated that the immunosuppressant cyclosporine A, a known inhibitor of several OATPs, affected drug bioavailability in transplant patients that were treated with statins, repaglinide or bosentan (Patel et al. 2016). In several studies, statin AUC was increased in the presence of cyclosporine A between 3.5 and almost tenfold. Similar increases in AUCs for statins were also observed in patients treated with gemfibrozil, rifampicin, and HIV protease inhibitors. One of the common properties of all these drugs is that they are known inhibitors of OATP1B1 and OATP1B3. Because increased plasma levels of statins have been associated with rhabdomyolysis, any drug-drug interactions that can increase statin plasma concentrations have to be carefully monitored (Patel et al. 2016). As indicated above, polymorphisms that lead to an inactive or less active transporter have been identified in the SLCO1B1 gene. Reports regarding the variant c.388A>G (N130D, OATP1B1*1b) are conflicting with increased, decreased, or unchanged effects, also depending on the drug substrate (Gong and Kim 2013). When assessed in vitro, OATP1B1*5 expression

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at the plasma membrane is reduced to about 35% of wild-type OATP1B1. Consistent with this reduced expression level, the majority of the studies reported an increase in AUC for patients with this polymorphism. Similarly, OATP1B1*15 which consists of c.388A>G plus c.521T>C is associated with increased AUCs for several drugs including pravastatin, pitavastatin, and rosuvastatin as well as some antihypertensives, anticancer drugs, and the cholesterol-lowering ezetimibe (Gong and Kim 2013). For OATP1B3, only a few polymorphisms have been identified. The most frequent variants are c.334T>G (S112A) and c.699G>A (M233I), both showing similar expression levels in in vitro experiments. OATP1B3-M233I was associated with reduce uptake of CCK-8 and rosuvastatin (Gong and Kim 2013). The AUC of mycophenolic acid glucuronide in renal transplant patients was increased in patients with the c.334T>G and c.699G>A haplotype although in addition conflicting results were reported. Given that only a few studies are available that investigated OATP1B3 polymorphisms, additional studies are required to see whether, e.g., OATP1B1 function could compensate for decreased OATP1B3 activity. Patients with Rotor syndrome, a disorder with conjugated hyperbilirubinemia and coproporphyrinuria, were compared to their normal family members. In addition, studies in OATP1A/OATP1B knockout mice were performed. These studies revealed that only a combination of a defective OATP1B1 with a defective OATP1B3 lead to the disease, demonstrating that either a functional OATP1B1 or functional OATP1B3 was sufficient for normal bilirubin disposition (van de Steeg et al. 2012). With regard to OATP1B1/OATP1B3 inhibitors, several compounds have been identified that inhibit uptake mediated by both transporters. The most frequently used inhibitors are rifampicin, cyclosporine A, rifamycin SV, bromosulfophthalein, and MK-571. However, many of these inhibitors also interact with other drug transporters. For example, MK-571 was originally established as an MRP2 inhibitor, but later experiments revealed that it also inhibited uptake mediated by both, OATP1B1 and OATP1B3 (Brouwer et al. 2013). Estropipate (also known as piperazine

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estrone sulfate) is a somewhat selective inhibitor for OATP1B1 with an IC50 of 0.06 μM as compared to 19.3 μM for OATP1B3. In contrast, ursolic acid was identified as a somewhat selective OATP1B3 inhibitor with an IC50 of 2.3 μM as compared to 12.5 μM for OATP1B1 (Gui et al. 2010). However, given that both OATPs have shown substrate-dependent inhibition patterns, more detailed research is needed to elucidate the underlying mechanisms and to hopefully identify really selective OATP1B1 and OATP1B3 inhibitors. Such selective inhibitors might be useful as co-medication with new chemical entities that are good drugs but have a low bioavailability due to extensive liver first-pass metabolism. OATP1B1 and OATP1B3 are key uptake transporters for numerous drugs and are expressed at the basolateral membrane of human hepatocytes. The regulatory agencies expect that investigational drugs are tested in vitro to examine whether they are substrates of OATP1B1 and/or OATP1B3 in cases where ADME studies indicate that the hepatic uptake or elimination of the investigational drug is significant (25% of total drug clearance) or if the hepatic uptake is clinically important (for biotransformation or if the drug target is in the liver). If the in vitro results show that the investigational drug is an OATP1B1 or OATP1B3 substrate (transport of at least twofold above the negative control), and a known OATP inhibitor such as rifampicin can decrease OATP1B1 and/or OATP1B3-mediated uptake by more than 50% at a concentration of at least ten times the Ki value, the investigational drug is considered an OATP1B1 or OATP1B3 substrate. If the investigational drug is a substrate, in vivo studies might be necessary (Giacomini et al. 2010; Hillgren et al. 2013).

Summary and Outlook So far seven transporters, which are known to be involved in adverse drug-drug interactions due to inhibition of the transporters or due to polymorphisms, are required by the regulatory agencies to be tested for investigational drugs. Five additional transporters either are already required by certain

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agencies or are considered by some or all of the three major agencies. However, because some of the drug-drug interactions involve multiple transporters, and because there are at least another 13 transporters that are known to be involved in drug disposition, the list of required transporters can increase, and it is even possible that certain transporters that are currently not considered will be required to be tested in the future. The most likely candidates are listed in Table 1, but several other transporters that are drug targets or mediate the transport of only a few drugs with so far no reported significant adverse effects could become relevant in the future. Acknowledgments The author would like to acknowledge the National Institutes of Health grant GM077336.

References and Further Reading Abdullahi W, Davis TP, Ronaldson PT (2017) Functional expression of P-glycoprotein and organic anion transporting polypeptides at the blood-brain barrier: understanding transport mechanisms for improved CNS drug delivery? AAPS J 19:931–939 Amidon GL, Lennernas H, Shah VP et al (1995) A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm Res 12:413–420 Bednarczyk D (2010) Fluorescence-based assays for the assessment of drug interaction with the human transporters OATP1B1 and OATP1B3. Anal Biochem 405:50–58 Belzer M, Morales M, Jagadish B et al (2013) Substratedependent ligand inhibition of the human organic cation transporter OCT2. J Pharmacol Exp Ther 346:300–310 Brouwer KL, Keppler D, Hoffmaster KA et al (2013) In vitro methods to support transporter evaluation in drug discovery and development. Clin Pharmacol Ther 94:95–112 Burckhardt G, Burckhardt BC (2011) In vitro and in vivo evidence of the importance of organic anion transporters (OATs) in drug therapy. Handb Exp Pharmacol 201:29–104 Chun SE, Thakkar N, Oh Y et al (2017) The N-terminal region of organic anion transporting polypeptide 1B3 (OATP1B3) plays an essential role in regulating its plasma membrane trafficking. Biochem Pharmacol 131:98–105 Cleophas MC, Joosten LA, Stamp LK et al (2017) ABCG2 polymorphisms in gout: insights into disease susceptibility and treatment approaches. Pharmgenomics Pers Med 10:129–142

1002 Droge C, Bonus M, Baumann U et al (2017) Sequencing of FIC1, BSEP and MDR3 in a large cohort of patients with cholestasis revealed a high number of different genetic variants. J Hepatol 67:1253–1264 Drozdzik M, Groer C, Penski J et al (2014) Protein abundance of clinically relevant multidrug transporters along the entire length of the human intestine. Mol Pharm 11:3547–3555 Fujita T, Urban TJ, Leabman MK et al (2006) Transport of drugs in the kidney by the human organic cation transporter, OCT2 and its genetic variants. J Pharm Sci 95:25–36 Giacomini KM, Huang SM, Tweedie DJ et al (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9:215–236 Gong IY, Kim RB (2013) Impact of genetic variation in OATP transporters to drug disposition and response. Drug Metab Pharmacokinet 28:4–18 Gui C, Obaidat A, Chaguturu R et al (2010) Development of a cell-based high-throughput assay to screen for inhibitors of organic anion transporting polypeptides 1B1 and 1B3. Curr Chem Genomics 4:1–8 Hagenbuch B, Stieger B (2013) The SLCO (former SLC21) superfamily of transporters. Mol Asp Med 34:396–412 Heredi-Szabo K, Glavinas H, Kis E et al (2009) Multidrug resistance protein 2-mediated estradiol-17beta-D-glucuronide transport potentiation: in vitro-in vivo correlation and species specificity. Drug Metab Dispos 37:794–801 Hillgren KM, Keppler D, Zur AA et al (2013) Emerging transporters of clinical importance: an update from the International Transporter Consortium. Clin Pharmacol Ther 94:52–63 Hira D, Terada T (2018) BCRP/ABCG2 and high-alert medications: biochemical, pharmacokinetic, pharmacogenetic, and clinical implications. Biochem Pharmacol 147:201–210 Juliano RL, Ling V (1976) A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. Biochim Biophys Acta 455:152–162 Koepsell H (2013) The SLC22 family with transporters of organic cations, anions and zwitterions. Mol Asp Med 34:413–435 Lee CA, O’Connor MA, Ritchie TK et al (2015) Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design. Drug Metab Dispos 43:490–509 Link E, Parish S, Armitage J et al (2008) SLCO1B1 variants and statin-induced myopathy – a genomewide study. N Engl J Med 359:789–799 Liu Y, Zheng X, Yu Q et al (2016) Epigenetic activation of the drug transporter OCT2 sensitizes renal cell carcinoma to oxaliplatin. Sci Transl Med 8:348ra397 Lund M, Petersen TS, Dalhoff KP (2017) Clinical implications of P-glycoprotein modulation in drug-drug interactions. Drugs 77:859–883

B. Hagenbuch Morgan RE, Trauner M, van Staden CJ et al (2010) Interference with bile salt export pump function is a susceptibility factor for human liver injury in drug development. Toxicol Sci 118:485–500 Motohashi H, Inui K (2013) Multidrug and toxin extrusion family SLC47: physiological, pharmacokinetic and toxicokinetic importance of MATE1 and MATE2-K. Mol Asp Med 34:661–668 Nies AT, Koepsell H, Damme K et al (2011) Organic cation transporters (OCTs, MATEs), in vitro and in vivo evidence for the importance in drug therapy. Handb Exp Pharmacol 201:105–167 Patel M, Taskar KS, Zamek-Gliszczynski MJ (2016) Importance of hepatic transporters in clinical disposition of drugs and their metabolites. J Clin Pharmacol 56(Suppl 7):S23–S39 Pfeifer ND, Hardwick RN, Brouwer KL (2014) Role of hepatic efflux transporters in regulating systemic and hepatocyte exposure to xenobiotics. Annu Rev Pharmacol Toxicol 54:509–535 Roninson IB, Chin JE, Choi KG et al (1986) Isolation of human mdr DNA sequences amplified in multidrugresistant KB carcinoma cells. Proc Natl Acad Sci U S A 83:4538–4542 Roth M, Obaidat A, Hagenbuch B (2012) OATPs, OATs and OCTs: the organic anion and cation transporters of the SLCO and SLC22A gene superfamilies. Br J Pharmacol 165:1260–1287 Saidijam M, Karimi Dermani F, Sohrabi S et al (2018) Efflux proteins at the blood-brain barrier: review and bioinformatics analysis. Xenobiotica 48: 506–532 Schinkel AH, Jonker JW (2003) Mammalian drug efflux transporters of the ATP binding cassette (ABC) family: an overview. Adv Drug Deliv Rev 55:3–29 Schinkel AH, Wagenaar E, Mol CA et al (1996) P-glycoprotein in the blood-brain barrier of mice influences the brain penetration and pharmacological activity of many drugs. J Clin Invest 97:2517–2524 Sjostedt N, Holvikari K, Tammela P et al (2017) Inhibition of breast cancer resistance protein and multidrug resistance associated protein 2 by natural compounds and their derivatives. Mol Pharm 14:135–146 Staud F, Cerveny L, Ahmadimoghaddam D et al (2013) Multidrug and toxin extrusion proteins (MATE/ SLC47); role in pharmacokinetics. Int J Biochem Cell Biol 45:2007–2011 Stieger B (2011) The role of the sodium-taurocholate cotransporting polypeptide (NTCP) and of the bile salt export pump (BSEP) in physiology and pathophysiology of bile formation. Handb Exp Pharmacol 201:205–259 Stieger B, Hagenbuch B (2016) Recent advances in understanding hepatic drug transport. F1000Res 5:2465 Terada T, Hira D (2015) Intestinal and hepatic drug transporters: pharmacokinetic, pathophysiological, and pharmacogenetic roles. J Gastroenterol 50:508–519 Ueda K, Cornwell MM, Gottesman MM et al (1986) The mdr1 gene, responsible for multidrug-resistance, codes

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for P-glycoprotein. Biochem Biophys Res Commun 141:956–962 Urquhart BL, Kim RB (2009) Blood-brain barrier transporters and response to CNS-active drugs. Eur J Clin Pharmacol 65:1063–1070 van de Steeg E, Stranecky V, Hartmannova H et al (2012) Complete OATP1B1 and OATP1B3 deficiency causes human Rotor syndrome by interrupting conjugated bilirubin reuptake into the liver. J Clin Invest 122:519–528 Wagner DJ, Hu T, Wang J (2016) Polyspecific organic cation transporters and their impact on drug intracellular levels and pharmacodynamics. Pharmacol Res 111:237–246

1003 Wessler JD, Grip LT, Mendell J et al (2013) The P-glycoprotein transport system and cardiovascular drugs. J Am Coll Cardiol 61:2495–2502 Wolking S, Schaeffeler E, Lerche H et al (2015) Impact of genetic polymorphisms of ABCB1 (MDR1, P-glycoprotein) on drug disposition and potential clinical implications: update of the literature. Clin Pharmacokinet 54:709–735 Yee SW, Nguyen AN, Brown C et al (2013) Reduced renal clearance of cefotaxime in Asians with a low-frequency polymorphism of OAT3 (SLC22A8). J Pharm Sci 102:3451–3457 Yin J, Wang J (2016) Renal drug transporters and their significance in drug-drug interactions. Acta Pharm Sin B 6:363–373

Role of Clinical Pharmacokinetics Studies in Contemporary Oncology Drug Development

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Fatih M. Uckun and Sanjive Qazi

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 Contribution of Pharmacokinetics to Clinical Development of Oncology Drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 The Impact of Hepatic and/or Renal Impairment on PK of Oncology Drugs and Patient-Tailored Dosing Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1009 Importance of PK in the Changing Regulatory Landscape Regarding Access of Pediatric and Young Adult Patient Populations to New Treatment Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1010 Multiscale Mechanistic PK Modeling Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013

Abstract

Pharmacokinetics (PK) studies enable drug developers to elucidate the relationship of dose to blood concentrations of drugs in various patient populations and determine the need

F. M. Uckun (*) AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA e-mail: [email protected]; [email protected] S. Qazi AresMIT Biomedical Computational Strategies (ABCS), Minneapolis, MN, USA Ares Pharmaceuticals, St. Paul, MN, USA Bioinformatics Program, Gustavus Adolphus College, St. Peter, MN, USA e-mail: [email protected]

for dose adjustment based on PK differences among demographic subgroups or subgroups with impaired elimination. PK studies also provide the basis for therapeutic drug monitoring in rare patient populations or when effective drugs with very narrow safe therapeutic windows must be used. Population PK studies are aimed at optimizing the dose and schedule by identifying the factors that alter the dose-concentration relationship and determining if such alterations change the therapeutic index using a data-driven approach and integrated sources of information. The clinical importance of identifying and implementing optimum dosing strategies has led to increased application of the population PK strategies in early oncology clinical trials. Multi-scale mechanistic PK models have been developed in an attempt to

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_24

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better predict the clinical performance of the oncology drug candidates. Over the last two decades PK studies have increasingly become an integral part of early clinical development of promising oncology drugs entering the clinical space. Of the total of 4,481 interventional clinical oncology trials with integrated PK studies registered in the clinicaltrials.gov data repository that were initiated within the 24-year time interval between 1994 and 2018, ~60% of the clinical PK studies were initiated within the last 8 years.

Introduction Pharmacokinetics (PK) is the study of the drug concentrations in the body during a period of time, and it includes the processes by which the drug is absorbed, distributed, metabolized, and excreted (ADME). An ideal drug should have high absolute bioavailability with low variability and exhibit linear PK over therapeutic dose range without significant modulation of the PK by concomitant food or pH-altering medications. An ideal drug should also reach the target site(s) of action promptly at effective/nontoxic concentrations, should not accumulate in nontarget organs, and should not have a narrow therapeutic index. Furthermore, it should not be extensively metabolized by a liver enzyme so that its clearance would not be significantly affected by hepatic dysfunction or by concomitant use of other drugs that affect one or more metabolizing enzymes. However, the PK profiles of most drugs are influenced by their physicochemical properties, product/formulation, administration route, patient’s intrinsic and extrinsic factors (e.g., organ dysfunction, diseases, concomitant medications, food). PK studies enable drug developers to elucidate the relationship of dose to blood concentrations of drugs in various patient populations and determining the need for dose adjustment based on PK differences among demographic subgroups or subgroups with impaired elimination (e.g., hepatic or renal disease). Defining the optimum dosing strategy for a population, subgroup, or individual patient requires resolution

F. M. Uckun and S. Qazi

of the interindividual, kinetic, as well as random variability (Turner et al. 2015; Undevia et al. 2005). Population PK studies are aimed at optimizing the dose and schedule by identifying the factors that alter the dose-concentration relationship and determining if such alterations change the therapeutic index using a data-driven approach and integrated sources of information, as detailed in a 1999 FDA Guidance for Industry that was prepared by the Population Pharmacokinetic Working Group of the Clinical Pharmacology Section of the Medical Policy Coordinating Committee in the Center for Drug Evaluation and Research (CDER) in cooperation with the Center for Biologics Evaluation and Research (CBER) at the FDA (1999). In 2003, the FDA ExposureResponse Working Group under the Medical Policy Coordinating Committee, Center for Drug Evaluation and Research (CDER), in cooperation with the Center for Biologics Evaluation and Research (CBER) at the FDA issued another guidance for industry regarding study design, data analysis, and regulatory applications related to exposure-response relationships (FDA 2003a; Overgaard et al. 2015). The clinical importance of identifying and implementing optimum dosing strategies has led to increased application of the population PK strategies in early oncology clinical trials. Population PK studies provide actionable safety, efficacy, and dosage optimization information for the drug label because of their early integration with clinical oncology trials. The purpose of this chapter is to review and discuss the increasing role of PK studies in the oncology drug development process.

Contribution of Pharmacokinetics to Clinical Development of Oncology Drugs Over the last two decades, PK studies have increasingly become an integral part of early clinical development of promising oncology drugs entering the clinical space (Chen et al. 1999; Uckun et al. 1995, 2013, 2015; Ursino et al. 2017; Waller et al. 2018; Wicki et al. 2018). As

Role of Clinical Pharmacokinetics Studies in Contemporary Oncology Drug Development

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Clinical Trials with Integrated PK Study

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Time Interval of Initiation for Clinical Trials

Fig. 1 Clinical trials with integrated PK studies in oncology. We interrogated the clinicaltrials.gov data repository (https://clinicaltrials.gov/) to determine the number of interventional clinical trials that employed PK studies from 1994 to 2018 in 4 year increments. All Interventional trials that were started over the 4-year period were included

in the totals. There were a total of 4481 trials counted from 1994 to 2018. A pronounced increase in the use of PK studies was observed after the year 2002. Search terms to identify the trials were “pharmacokinetics,” “interventional studies,” and “cancer”

shown in Fig. 1, a pronounced and continued increase in the use of integrated PK analyses was observed in clinical trials that started after the year of 1998. Of the total of 4481 interventional clinical oncology trials with integrated PK studies registered in the clinicaltrials.gov data repository that were initiated within the 24-year time interval between August 1994 and July 2018, only 30 (0.7%) were started between August 1994 and July 1998, 121 (2.7%; 3.8-fold increase from previous 4 years) between August 1998 and July 2002, 532 (11.9%; 4.4-fold increase from previous 4 years) between August 2002 and July 2006, 1135 (25.3%; 2.1fold increase from previous 4-years) between August 2006 and July 2010, 1244 (27.8%; 9.6% increase from previous 4 years) between August 2010 and July 2014, and 1420 (31.7%; 14.1% increase from previous 4 years) between August 2014 and July 2018. Notably, ~60% of the clinical

PK studies were initiated within the last 8 years. Hence, PK studies are playing an increasingly important role in the clinical development path of oncology drugs. PK studies often combined with integrated pharmacodynamics (PD) components play a pivotal role in clinical comparisons of different formulations, prodrugs and dosing schedules aimed at identifying the best way of using a promising new drug at a nontoxic dose level. For example, DTS-201 is a doxorubicin (Dox) prodrug that shows encouraging data in experimental models in terms of both efficacy and safety compared with conventional Dox. Notably, a high equivalent dose of Dox could be delivered without severe drug-related cardiac events. DTS-201 was administered at four dose levels ranging from 80 to 400 mg/m2, which is equivalent to 45–225 mg/m2 of conventional Dox (Schöffski et al. 2017). The recommended phase II dose (RP2D) was 400 mg/m2.

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PK modeling and model-informed precision dosing (MIPD) have been explored as tools to optimize treatment outcomes in oncology by maximizing patient safety via overdose protection and by avoiding treatment failures caused by suboptimal drug exposures (Barbolosi et al. 2015). NCT02732275 is a first-in-human phase I study of the epigenetic modulator DS-3201b, a dual inhibitor of enhancer of zeste homolog 1 (EZH1) and EZH2 in patients with relapsed/ refractory lymphomas. Recently, a population PK model was developed using the integrated PK data from the study to define the dose-exposure relationships and reported that a 2-compartment PK model with first-order elimination and absorption lag-time best characterized the plasma concentration-time profile of DS-3201a (Atsumi et al. 2017). Physiologically based pharmacokinetic (PBPK) modeling is a potential tool which can be effectively applied throughout all phases of oncology drug development and allows a more granular prediction of tissue drug exposures (Schwenger et al. 2018; Schultze-Mosgau et al. 2018; Cheeti et al. 2013; Ferl et al. 2016; Saeheng et al. 2018; Rowland 2013; Sager et al. 2015). The number of experimental animals and human participants enrolled in the studies can be reduced using PBPK modeling and PBPK-population-PK modeling. For example, Tsukamoto et al. studied the kinetics of capecitabine and its metabolites. Their PBPK model integrated tissue-specific information about metabolic enzyme activity between tumor and normal cells from in vitro data and enabled the prediction of the therapeutic index in terms of exposure in target organs and toxicity in off-target organs (i.e., gastrointestinal tract toxicity) (Tsukamoto et al. 2001). Besides the systemic exposure levels of the parent compound and/or its metabolites, several baseline characteristics, including but not limited to age, gender, and race as well as comorbidities of the host also affect the risk of severe side effects and tolerability as well as efficacy of drugs at optimized dose levels (Owonikoko et al. 2018). Therefore, it is very important to identify biomarkers that (i) allow the rational assignment of individual patients to those treatments that are

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most likely to benefit them and ensure maximized patient safety as well as best survival outcome and likewise and (ii) enroll into a particular study a biomarker-enriched population that is most likely to benefit from the treatment program (Jamal et al. 2017). For example, inhibition of Janus-kinase 1/2 (JAK1/2) is an innovative strategy to treat myeloproliferative neoplasms, but recently this exciting new treatment approach has been shown to be associated with a 15-fold higher risk of development of aggressive B-cell lymphomas. Lymphomas occurring during JAK1/2 inhibitor treatment were preceded by a preexisting B-cell clone in all patients tested. Therefore, detection of a preexisting B-cell clone may identify individuals at risk (Porpaczy et al. 2018). Unlike small molecules which bind to their molecular targets without significantly affecting the systemic exposure levels, biotherapeutic agents, such as monoclonal antibodies (e.g., the anti-PD1 monoclonal antibody pembrolizumab), bind to their targets with much higher affinity and display a nonlinear “target-mediated drug disposition” (TMDD). The disposition of the drug-target molecular complexes can influence the systemic exposure levels (Ahamadi et al. 2017; Moreau et al. 2012). In addition, the lack of a relationship of pembrolizumab PK and overall survival (OS) in patients with advanced melanoma and non-small cell carcinoma (NSCLC) demonstrates the challenges in determining the RP2D and optimal dosing for monoclonal antibodies and immune-oncology drugs (Turner et al. 2018; Freshwater et al. 2017; Chatterjee et al. 2016; Turan et al. 2018). It is also important to take into consideration the circadian fluctuations of the ADME of oncology drugs (VérennoneauVeilleux and Bélair 2017). Importantly, PK studies provide the basis for therapeutic drug monitoring in rare patient populations or when effective drugs with very narrow safe therapeutic windows must be used (Thomas et al. 2018a). Therapeutic drug monitoring is particularly important for optimized clinical use for certain therapeutics, such as oral antihormonal drugs are essential in the treatment of breast and prostate cancer, that display a high interpatient PK variability, when the treatments

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employ fixed doses, which has the associated risks of underdosing as well as overdosing (Groenland et al. 2018; Paci et al. 2014). When there is compelling evidence from nonclinical studies for an association between systemic exposure levels of a drug or its metabolite and the desired treatment outcomes, PK-guided dose escalation studies utilizing real-time PK measurements to determine the dose cohorts based on the systemic exposure levels could provide the opportunity to determine the maximum tolerated systemic exposure (MTSE) levels and how they compare to the systemic exposure levels proven effective in nonclinical studies.

The Impact of Hepatic and/or Renal Impairment on PK of Oncology Drugs and Patient-Tailored Dosing Schedules The Cancer Therapy Evaluation Program (CTEP) at the NCI prioritized study of special patient populations with hepatic dysfunction phase I clinical trials (HDCT) to determine safe administration parameters of oncology drugs for subjects with varying degrees of liver dysfunction. HDCT sponsored by CTEP and others have provided clinically useful information on the optimal dosing of oncology drugs in subjects with different degrees of liver test abnormalities that have provided administration guidance in the labels for patients with abnormal organ function. Hepatic dysfunction phase I clinical trials (HDCT) provide safe administration parameters of oncology drugs for subjects with varying degrees of liver dysfunction (Mansfield et al. 2016). The elimination of several oncology drugs, such as the proteasome inhibitor bortezomib, occurs through metabolism by liver enzymes (Tan et al. 2018). The change in liver function may potentially change the inhibitory and/or inducing potential of the liver metabolizing enzymes, thus the PK and PD in patients with hepatic impairment may differ from patients with normal hepatic function. As cancer patients often have alterations in their liver function due to disease-related reasons (e.g., liver metastases), hepatotoxic treatments (chemotherapy, radiation

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therapy, treatment with immuno-oncology drugs), and/or other comorbidities they may have, it is important to determine the effects of hepatic impairment on the PK and safety of drugs metabolized by liver enzymes and also, if possible, determine whether dose modification would be necessary in such patients. Patients in these studies are typically assigned to different groups according to their liver function as per NCI and FDA guidance (FDA 2003b). The primary objective of such studies is to evaluate the effect of hepatic impairment on the steady state PK of the respective therapeutic agents in advanced cancer patients. The secondary objectives are to evaluate the effect of hepatic impairment on the safety and antitumor activity of the respective therapeutic agent in advanced cancer patients. Some of the ongoing studies evaluating the effects of hepatic impairment on the PK and safety of targeted therapeutics include among others NCT01767623 (A Study of The Impact of Severe Hepatic Impairment on the Pharmacokinetics and Safety of Vemurafenib – a BRAF kinase inhibitor – in BRAF V600 Mutation-Positive Cancer Participants), NCT02894385 (Effect of Hepatic and Renal Impairment on the Pharmacokinetics, Safety and Tolerability of BAY1841788/ daralutamide – a nonsteroidal antiandrogen), NCT03092999 (Effect of Hepatic Impairment on the Pharmacokinetics, Safety and Tolerability of BAY1002670/Vilaprisan – a steroidal selective progesterone receptor modulator/SPRM), NCT03359850 (Pharmacokinetic and Safety Study of Niraparib – a PARP inhibitor – With Normal or Moderate Hepatic Impairment Patients), NCT03282513 (A Study of AG-120 (Ivosidenib) – an IDH1 inhibitor in Subjects With Mild or Moderate Hepatic Impairment or Normal Hepatic Function), and NCT01429337 (Pharmacokinetics and Safety of Midostaurin – FLT3 inhibitor – in Subjects With Impaired Hepatic Function and Subjects With Normal Hepatic Function). In a recent Pfizer study (NCT01576406), the effect of hepatic impairment was evaluated on the pharmacokinetics and safety of the ALK-inhibitor crizotinib in patients with advanced cancer. No adjustment to the approved 250 mg twice daily

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(BID) dose of crizotinib was recommended for patients with mild hepatic impairment. The recommended dose was 200 mg BID for patients with moderate hepatic impairment, and the dose was recommended not to exceed 250 mg daily for patients with severe hepatic impairment. Adverse events appeared consistent among the hepatic impairment groups (El-Khoueiry et al. 2018). Sonidegib is a potent, selective, and orally bioavailable inhibitor of the Hedgehog signaling pathway, primarily metabolized by the liver. Horsmans et al. assessed the PK and safety of sonidegib in subjects with varying degrees of hepatic function. Sonidegib exposures were similar or decreased in the hepatic impairment groups compared with the normal group, and sonidegib was generally well-tolerated in all subjects. Dose adjustment was not considered necessary for subjects with mild, moderate, or severe hepatic impairment (Horsmans et al. 2018). By comparison, the analysis of the impact of hepatic impairment on the PK and PD of the alkylating agent Trabectedin, that is metabolized by the liver and has been associated with liver toxicities, including including hepatic failure, revealed that Trabectedin treatment of patients with hepatic impairment results in higher plasma exposures but hepatotoxicity in patients with normal liver function can be effectively addressed through dose reductions and delays (Calvo et al. 2018). It is generally known that renal impairment can affect not only the disposition of drugs that are cleared primarily through the kidney but also other drugs with minimal renal elimination because of the effects of kidney disease on drugmetabolizing enzymes, transporters, and drugbinding proteins. Some drugs such as Udenafil, a phosphodiesterase-5 inhibitor, used to treat erectile dysfunction, are not predominantly eliminated by the kidney but renal impairment can alter its secretion/transport pathways. Significant correlations were observed among the creatinine clearance, oral clearance, and maximum concentration of Udenafil and a dose adjustment of Udenafil would seem warranted in subjects with moderate or severe renal impairment (Cho et al. 2018). Drug PK and safety of oncology drugs must therefore be assessed in subjects with a renal impairment.

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PK studies in combination with model-based strategies, including population PK and physiologically based PK (PBPK) modeling, have been used to evaluate the impact of renal impairment on dose-exposure relationships and optimize dosing for patients with various degrees of renal impairment (Xiao et al. 2017; Beumer et al. 2016; Tortorici et al. 2012). The insights gained from these studies are used for dose selection/dose adjustment in patient populations with renal impairment to improve the therapeutic index of anti-cancer treatments (EMA 2015; FDA 2010).

Importance of PK in the Changing Regulatory Landscape Regarding Access of Pediatric and Young Adult Patient Populations to New Treatment Platforms The clinical trial landscape in oncology has traditionally been associated with significant delays in the evaluation of promising new therapies in poor prognosis pediatric cancer patients who are in urgent need for therapeutic innovations (FDA 2018; Freyer et al. 2013; Bleyer et al. 2018; Burke et al. 2007; Uckun and Kenny 2018; Vassal et al. 2015; Veal et al. 2010; Beaver et al. 2017; Thomas et al. 2018b; Chuk et al. 2017; Fern and Taylor 2018). There is growing consensus among pediatric hematologists-oncologists, US Food and Drug Administration (FDA), European Medicines Agency (EMA), coalitions of subject matter experts, support groups, and other stakeholders that these delays have contributed to the unsatisfactory progress in improving the survival outcomes of adolescents with cancer (Kim et al. 2017; FDA 2018; Gaspar et al. 2018; Stark et al. 2016). Both FDA and EMA launched new regulatory initiatives aimed at improving the access of pediatric cancer patients to novel therapies developed for adults with cancer. The European Pediatric Medicine Regulation [(EC)No1901/2006)] mandated the establishment of the EMA’s Pediatric Committee to provide guidance to pharmaceutical companies regarding their Pediatric Investigation Plans (PIPs) for their drugs in pipeline (EC 2006). The multistakeholder

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platform ACCELERATE (http://www.accelerateplatform.eu) presented a consensus expert opinion in support of early drug access for adolescents with cancer indicating that enrollment of adolescents of 12 years and over in adult early-phase clinical drug trials would represent a safe and efficient strategy in drug development (Gaspar et al. 2018). Several changes were proposed by ACCELERATE to facilitate that adolescents have access to early drug development programs, including that (i) there should be no set upper or lower age limit criteria for phase II and phase III trials for cancers that are present in both pediatric and adult populations with similar biology and (ii) adolescents over 12 years of age should be included from the onset of the cancer drug development process in adults (Gaspar et al. 2018). In June 2018, FDA issued a draft guidance entitled “Considerations for the Inclusion of Adolescent Patients in Adult Oncology Clinical Trials” (FDA 2018) emphasizing that pediatric oncology drug development should be coordinated with oncology drug development for adults as part of an overall drug development plan and detailing a series of recommendations regarding inclusion of pediatric patients in adult oncology trials in the USA which was based on a previous FDA publication (Chuk et al. 2017) and can be viewed as a strong endorsement of the recent ACCELERATE proposal (Gaspar et al. 2018) in Europe. The new FDA recommendations would certainly expand the options available for adolescent cancer patients who have relapsed after or are refractory to standard therapeutic strategies with no curative options, or for whom no standard therapies with curative intent exist. The draft guidance suggesting that adolescent patients may be enrolled in first-in-human clinical trials after initial adult PK and toxicity data are obtained is aimed at providing significant risk mitigation for adolescents (FDA 2018). Furthermore, the important provisions of the Race for Children Act, which is incorporated as Title V of the FDA Reauthorization Act (FDARA) that was enacted on August 18, 2017 (FD&C Act Sec. 505B (a)(3), 21 USC 355c (a)(3), Public Law 115-52), has created a mechanism to expedite the evaluation of novel medicines with the potential to address

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the unmet need in the pediatric population by requiring pediatric investigation of appropriate new drugs intended for adults with cancer (Reaman 2018). Specifically, Title V requires evaluation of new molecularly targeted drugs and biologics “intended for the treatment of adult cancers and directed at a molecular target substantially relevant to the growth or progression of a pediatric cancer” in molecularly targeted pediatric cancer investigation to generate clinically meaningful study data, “using appropriate formulations, regarding dosing, safety and preliminary efficacy to inform potential pediatric labeling” by designing and executing earlier rational dose finding and signal seeking trials (Reaman 2018). The Alliance for Childhood Cancer, representing more than 30 national patient advocacy groups and professional medical and scientific organizations invested in advancing the interests of children with cancer, applauded the inclusion of the RACE for Children Act in the FDA Reauthorization Act of 2017 (FDARA) legislation, passed in the Senate and in the House in July of 2017. These new regulatory initiatives by EMA and FDA combined with umbrella clinical trial initiatives aimed at allowing children and adolescents with relapsed or refractory pediatric cancers early access to promising targeted precision medicines have the potential to significantly alter the therapeutic landscape for difficult-to-treat pediatric/adolescent cancers for the benefit of current and future pediatric cancer patients (Uckun and Kenny 2018). The FDA recommendations in the draft guidance are based in part on the observed similarities in disposition and PK of drugs in adolescents and adults (Thai et al. 2015; Fern and Taylor 2018; Smith et al. 2016; Freyer et al. 2013; FDA 2018; Gaspar et al. 2018; Fouladi et al. 2010; Forrest et al. 2018; Paoletti et al. 2013). Sometimes, the adult PK exposure can be used as target for dose finding in pediatrics. For example, the pediatric sunitinib PK data were adequately predicted from adult data with a mean prediction error of 1.80% (Janssen et al. 2017). It should be noted, however, that the cited similarities were based on single agent studies with the inherent limitation that a careful

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consideration of the PK and PD features of the major metabolites were not included in relationship to other cancer drugs that are typically used in combination trials or comorbidities of patients. The single agent trials have traditionally not included pharmacometabolomics, pharmacogenetics, and pharmacogenomics studies for the parent drug or its metabolites. Modifications of critical proteins through reactive metabolites are thought to be responsible for a number of adverse drug reactions (Krauss et al. 2012; Niu et al. 2017; Kalgutkar and Dalvie 2015; Reis-Mendes et al. 2015; Han et al. 2017; van Andel et al. 2018; Chavan et al. 2018; Sun et al. 2018). Therefore, besides the levels of drug exposure, the generation of chemically reactive metabolites also contributes to drug side effects. The metabolism of some of the anticancer drugs is highly complex due to the engagement of multiple enzymes and transporters and is therefore prone to unintended drug-drug interactions. For example, the standard anticancer drug Irinotecan serves as the prodrug for the 2–3 logs more potent topoisomerase I inhibitor SN-38 that is responsible for the doselimiting toxicities (DLTs) associated with irinotecan. Single nucleotide polymorphisms in several drug metabolizing enzymes (e.g., uridine diphosphate glucuronosyltransferase [UGT] 1A1, UGT1A7, UGT1A9) and drug transporters (e.g., ATP-binding cassette [ABC] B1, ABCC1) are associated with irinotecan toxicity (de Man et al. 2018). Fluoroacetate is considered one of the major metabolites of 5-fluorouracil responsible for its cardiotoxicity (Reis-Mendes et al. 2015). Several therapeutic and toxic effects of cyclophosphamide are the result of the actions of its active metabolites formed by the hepatic microsomal cytochrome P450 mixed function oxidase system: The active cyclophosphamide metabolites hydroxycyclophosphamide and acrolein are shown to be more cardiotoxic than the parent drug. In human autopsy cardiac tissues of previously doxorubicin (Dox)-treated patients, the cardiac levels of the metabolite doxorubicinol were almost double of the parent compound doxorubicin (Reis-Mendes et al. 2015). Although Paclitaxel cardiotoxicity is usually low and does not seem to be related with the formation of reactive

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metabolites, its concomitant use with Dox results in significantly increased cardiotoxicity because the pharmacokinetic interactions between paclitaxel and DOX and also because paclitaxel stimulates the NADPH-dependent reduction of Dox into doxorubicinol. The schedule paclitaxel followed by Dox is more cardiotoxic with an incidence of 18–20% of congestive heart failure than in patients with breast cancer given Dox followed by paclitaxel at standard dose levels (Reis-Mendes et al. 2015). Ponatinib is an orally available pan-BCR-ABL tyrosine kinase inhibitor that has been approved for treatment of resistant chronic myeloid leukemia (CML) and Philadelphia chromosome-positive ALL. However, it can cause severe side effects including cardiovascular toxicity with both arterial and venous thromboembolism and severe systemic hypertension, vascular occlusions as well as pancreatitis, and liver toxicity. Although the initial work had suggested CYP3A4 as a major pathway of ponatinib disposition, Lin et al. recently reported that CYP1A1, a highly inducible enzyme that unlike many other P450s can be expressed in most tissues such as lung and lung tumors, is highly active toward this compound and metabolism by CYP1A1 results in the formation of reactive epoxides from ponatinib that likely contribute to the side effects associated with its clinical use (Lin et al. 2017). Epoxides are chemically reactive and can react covalently with both DNA and proteins to cause mutations and toxicity. CYP1A1 levels are constitutively very low but are highly inducible on activation of the aryl hydrocarbon receptor by compounds including polycyclic aromatic hydrocarbons found in cigarette smoke. Notably, hypertension or vasoocclusive disease observed in ponatinib-treated patients has been associated with smoking. Therefore, it is of vital importance that hybrid adolescent-adult studies incorporate detailed analyses aimed at characterizing the drug-drug interaction at the level of the parent compounds as well as their metabolites both in adolescents and adult populations. Risk mitigation measures aimed at maximizing the safety of adolescent patients enrolled on the hybrid studies should take the data from such analyses into consideration. In view of these regulatory changes, we anticipate a

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growing emphasis on PK studies in future clinical trials of oncology drugs with a major focus on the PK profiles of precision medicines in the pediatric/adolescent and young adult patient populations.

Multiscale Mechanistic PK Modeling Platforms The majority of oncology drug candidates fail in early oncology clinical trials due to excessive toxicity and/or disappointing efficacy (Krauss et al. 2012; Turner et al. 2015; Gadkar et al. 2016). Multiscale mechanistic PK models have been developed in an attempt to better predict the clinical performance of the oncology drug candidates (Rousseau and Marquet 2012; Smith et al. 2017; Darwich et al. 2017; Barbolosi et al. 2015; Bizzotto et al. 2017a, b; Wilkins et al. 2017; Yankeelov et al. 2016). These models take into consideration the complexity of the host response, flux of the drug through different compartments of the host body, nonlinear treatment-emergent responses through drug-induced pertubation of a complex system and account for patient-to-patient differences in regard to drug metabolism and transportation. Systems level consideration of drug responses in these models attempt to better characterize the hierarchical, nonlinear, dynamic responses at the network level of drug action that may affect both efficacy and toxicity in clinical settings. Systems PK also aims to explain the variations in drug uptake and metabolism by considering (i) drugspecific factors such as physiochemical properties and drug regimens, (ii) patient-specific factors that account for individual differences in the quality (e.g., affinity, catalytic activity of transporters and metabolic enzymes) and quantity of interactions (e.g., synthesis and degradation of drugs entering the body), (iii) epigenetic factors that regulate expression of transporters and metabolism of drug, (iv) tissue organ variabilities in anatomy, size of parenchymal cell numbers, and fluid volumes; and (v) environmental factors that modify the PK via food uptake and nutrition. The software platforms available as analytical tools for

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these models have also evolved to evaluate a large number of variables, perform clinical simulations along with databases allowing the integration of different knowledge environments. An example of a modeling platform includes “The Drug Disease Model Resources” (DDMoRe; http://www.ddmore.eu/) consortium that aims to improve the accessibility and cost effectiveness of model-informed drug discovery and development (Wilkins et al. 2017) by providing a curated model repository and an interoperability framework to integrate infrastructure for efficient exchange and integration of models across modeling languages (e.g., PFIM, Onolix, Simulx, R, NONMEM7, PsN, WinBUGS, MATLAB, SimCYP). In this contemporary modeling environment, the user is able to interact with the “Interoperability Framework” (IIF) via a graphical front-end interface (MDL-IDE; Bizzotto et al. 2017b; Smith et al. 2017) to enable editing of models written in HTML exchange formats such as PharmML (Swat et al. 2015; Bizzotto et al. 2017a). The advantage of the MDL-IDE workflows is realized by use of scripting in the R statistical computing language which enables full access to thousands of statistical and simulation packages. The IIF can be fully customized for speed, consistency, and fit-for-purpose modeling to better predict the toxicity and efficacy of the drug candidates.

References and Further Reading Ahamadi M, Freshwater T, Prohn M, Li CH, de Alwis DP, de Greef R, Elassaiss-Schaap J, Kondic A, Stone JA (2017) Model-based characterization of the pharmacokinetics of pembrolizumab: a humanized anti-pd-1 monoclonal antibody in advanced solid tumors. CPT Pharmacometrics Syst Pharmacol 6:49–57 Alsharedi M, Bukamur H, Elhamdani A (2018) Osimertinib for the treatment of patients with EGFR mutation-positive non-small cell lung cancer. Drugs Today (Barc) 54:369–379. https://doi.org/10.1358/ dot.2018.54.6.2817668 Atsumi R, Yoshiba S, Maruyama D, Tobinai K, Ishida T, Ishitsuka K, Imaizumi Y, Takeuchi S, Tsukasaki K, Adachi N, Fujitani S, Tachibana M, Yoshihara K, Ishizuka H (2017) Population pharmacokinetic and exposure-response modeling for the EZH1/2 dual inhibitor DS-3201b in patients with non-Hodgkin lymphomas. Blood 130(Suppl 1):2544

1014 Barbolosi D, Ciccolini J, Lacarelle B, Barlési F, André N (2015) Computational oncology – mathematical modelling of drug regimens for precision medicine. Nat Rev Clin Oncol 13:242–254 Beaver JA, Ison G, Pazdur R (2017) Reevaluating eligibility criteria – balancing patient protection and participation in oncology trials. N Engl J Med 376:1504–1505 Beumer JH, Ding F, Tawbi H, Lin Y, Viluh D, Chatterjee I, Rinker M, Chow SL, Ivy SP (2016) Effect of renal dysfunction on toxicity in three decades of cancer therapy evaluation program-sponsored single-agent phase I studies. J Clin Oncol 34:110–116 Bizzotto R, Comets E, Smith G, Yvon F, Kristensen NR, Swat MJ (2017a) PharmML in action: an interoperable language for modeling and simulation. CPT Pharmacometrics Syst Pharmacol 6:651–665. https:// doi.org/10.1002/psp4.12213 Bizzotto R, Blaudez E, Borella E, Carrara L, Chan P, Chenel M, Comets E, Gieschke R, Harling K, Harnisch L, Hartung N, Hooker AC, Karlsson MO, Kaye R, Kloft C, Kokash N, Lavielle M, Lestini G, Magni P, Mari A, Mentré F, Muselle C, Nordgren R, Nyberg HB, ParraGuillén ZP, Pasotti L, Rode-Kristensen N, Sardu ML, Smith GR, Swat MJ, Terranova N, Yngman G, Yvon F, Holford N, DDMoRe Consortium (2017b) Model description language (MDL): a standard for modeling and simulation. CPT Pharmacometrics Syst Pharmacol 6:647–650. https://doi.org/10.1002/psp4.12222 Bleyer A, Tai E, Siegel S (2018) Role of clinical trials in survival progress of American adolescents and young adults with cancer – and lack thereof. Pediatr Blood Cancer 65:e27074. https://doi.org/10.1002/pbc.27074 Burke ME, Albritton K, Marina N (2007) Challenges in the recruitment of adolescents and young adults to cancer clinical trials. Cancer 110:2385–2393 Calvo E, Azaro A, Rodon J, Dirix L, Huizing M, Senecal FM, LoRusso P, Yee L, Poggesi I, de Jong J, Triantos S, Park YC, Knoblauch RE, Parekh TV, Demetri GD, von Mehren M (2018) Hepatic safety analysis of trabectedin: results of a pharmacokinetic study with trabectedin in patients with hepatic impairment and experience from a phase 3 clinical. Trial 36:476–486. https://doi.org/10.1007/s10637-017-0546-9 Chatterjee M, Turner DC, Felip E, Lena H, Cappuzzo F, Horn L, Garon EB, Hui R, Arkenau HT, Gubens MA, Hellmann MD, Dong D, Li C, Mayawala K, Freshwater T, Ahamadi M, Stone J, Lubiniecki GM, Zhang J, Im E, De Alwis DP, Kondic AG, Flotten O (2016) Systematic evaluation of pembrolizumab dosing in patients with advanced non-small-cell lung cancer. Ann Oncol 27:1291–1298. https://doi.org/10.1093/annonc/mdw174 Chavan BB, Tiwari S, Nimbalkar RD, Garg P, R S, Talluri MVNK (2018) In vitro and in vivo metabolic investigation of the Palbociclib by UHPLC-Q-TOF/MS/MS and in silico toxicity studies of its metabolites. J Pharm Biomed Anal 157:59–74. https://doi.org/10.1016/j. jpba.2018.05.008 Cheeti S, Budha NR, Rajan S, Dresser MJ, Jin JY (2013) A physiologically based pharmacokinetic (PBPK)

F. M. Uckun and S. Qazi approach to evaluate pharmacokinetics in patients with cancer. Biopharm Drug Dispos 34:141–154. https://doi.org/10.1002/bdd.1830 Chen CL, Levine A, Rao A, O’Neill K, Messinger Y, Myers DE, Goldman F, Hurvitz C, Casper JT, Uckun FM (1999) Clinical pharmacokinetics of the CD19 receptor-directed tyrosine kinase inhibitor B43-Genistein in patients with B-lineage lymphoid malignancies. J Clin Pharmacol 39:1248–1255 Cho YS, Noh YH, Lim HS, Cho SH, Ghim JL, Choe S, Kim SB, Park JS, Lee SK, Yang WS, Chang JW, Bahng MY, Bae KS (2018) Effects of renal impairment on the pharmacokinetics and safety of udenafil. J Clin Pharmacol 58:905–912. https://doi.org/10.1002/jcph.1095 Chuk MH, Mulugeta Y, Cline MR, Mehrotra N, Reaman GH (2017) Enrolling adolescents in disease/targetappropriate adult oncology clinical trials of investigational agents. Clin Cancer Res 23:9–12. https://doi.org/ 10.1158/1078-0432.CCR-16-1367 Darwich A, Ogungbenro K, Hatley OJ, Rostami-Hodjegan A (2017) Role of pharmacokinetic modeling and simulation in precision dosing of anticancer drugs. Transl Cancer Res 6:S1512–S1529. https://doi.org/10.21037/ tcr.2017.09.14 de Man FM, Goey AKL, van Schaik RHN, Mathijssen RHJ, Bins S (2018) Individualization of Irinotecan treatment: a review of pharmacokinetics, pharmacodynamics, and pharmacogenetics. Clin Pharmacokinet. https://doi.org/10.1007/s40262-018-0644-7. [Epub ahead of print] EC Regulation No 1901/2006 of the European Parliament and of the Council of 12 December 2006 on medicinal products for pediatric use and amending Regulation (EEC) No 1768/92, Directive 2001/20/EC, Directive 2001/83/EC and Regulation (EC) No 726/2004. https:// ec.europa.eu/health/sites/health/files/files/eudralex/vol-1/ reg_2006_1901/reg_2006_1901_en.pdf El-Khoueiry AB, Sarantopoulos J, Ciombor KK, Xu H, O’Gorman M, Chakrabarti J, Usari T, El-Rayes BF (2018) Evaluation of hepatic impairment on pharmacokinetics and safety of crizotinib in patients with advanced cancer. Cancer Chemother Pharmacol 81:659–670. https://doi.org/10.1007/s00280-018-3517-8 EMA – European Medicines Agency (2015) Guideline on the evaluation of the pharmacokinetics of medicinal products in patients with decreased renal function. Available at: http://www.ema.europa.eu/docs/en_GB/ document_library/Scientific_guideline/2016/02/WC50 0200841.pdf. Accessed 17 Dec 2015 FDA Guidance for Industry (1999) Population pharmacokinetics. https://www.fda.gov/downloads/drugs/guid ances/UCM072137.pdf FDA Guidance for Industry (2003a) Exposure-response relationships: study design, data analysis, and regulatory applications. https://www.fda.gov/downloads/ drugs/guidancecomplianceregulatoryinformation/guid ances/ucm072109.pdf FDA Guidance for Industry (2003b) Pharmacokinetics in patients with impaired hepatic function: study design,

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Contents Phase I Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP1A2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2C9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2C19 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2D6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP3A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1020 1020 1023 1025 1027 1029

Phenotyping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Critical Assessment of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Modifications of the Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Other CYPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2A6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2B6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2C8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . CYP2E1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1030 1030 1031 1031 1031 1032 1032 1033 1033 1034

Phase II Enzymes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . N-Acetyltransferases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Uridine Diphosphate Glucuronosyltransferases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methyltransferases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glutathione S-transferases and Sulfotransferases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1035 1035 1038 1039 1040

References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1040

Abstract

G. Montay (*) GMPK/PK Sanofi-Aventis, Vitry-Sur-Seine, France e-mail: Guy.Montay@sanofi-aventis.com J. Maas · R. Wesch R&D Metabolism and PK Germany, Sanofi-Aventis Deutschland GmbH, Frankfurt am Main, Germany e-mail: Jochen.Maas@sanofi-aventis.com; Roland. Wesch@sanofi-aventis.com

CYP1A2 is involved to a major extent in the metabolism of several drugs (imipramine, clozapine, fluvoxamine, olanzapine, theophylline, acetaminophen, propranolol, and tacrine) as well as of diet components (methylxanthines), endogenous substrates (estrogens), numerous aryl, aromatic and heterocyclic amines, and polycyclic aromatic hydrocarbons. It is

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_25

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inducible, notably by cigarette smoking, diet habits such as consumption of cruciferous vegetables (e.g., broccoli, watercress, collard greens, Brussels sprouts, and mustard) and of charbroiled meats, some drugs (omeprazole, phenytoin, and rifampicin) and is a target enzyme for the development of some cancers. Up to now, more than 25 CYP1A2 alleles have been detected. Probe drugs for CYP1A2 phenotyping are caffeine and theophylline. For safety concerns and drug availability, the preferred probe is caffeine. Caffeine 3-demethylation is mediated by CYP1A2, and accounts for 80% of caffeine clearance. Caffeine is also a probe drug for N-acetyltransferase and xanthine oxidase (Clin Pharmacol Ther 53:203–514, 1993). II.T.1 II.T.1.1 II.T.1.2 II.T.1.3 II.T.1.4 II.T.1.5 II.T.1.6

Phase I enzymes CYP1A2 CYP2C9 CYP2C19 CYP2D6 CYP3A Other CYPS CYP2A6

II.T.1.6.1 CYP2B6 II.T.1.6.2 CYP2C8 II.T.1.6.3 CYP2E1 II.T.1.6.4 II.T.2 II.T.2.1 II.T.2.2 II.T.2.3 II.T.2.4

Phase II enzymes N-acetyltransferases Uridine diphosphate glucuronosyltransferases Methyltransferases Glutathione S-transferases and sulfotransferases

The understanding of the role of pharmacogenetics in drug metabolism expanded greatly in the 1990s. This is mainly due to technological improvements in gene scanning and gene variant identification. The number of variant alleles identified for genes coding for drug metabolizing enzymes (DME) considerably increased in the early 2000s, and continues to increase. The clinical consequences – or at

least genotyping–phenotyping relationships – of DME polymorphisms have not been demonstrated for all variants. In the text below, only those DME allele variants will be mentioned for which significant changes in enzyme activity have been found using probe drugs. Comprehensive information on the nomenclature of cytochrome P450 (CYP) alleles can be found at www.imm.ki.se/CYPalleles and Phase I and Phase II DMEs at www. pharmgkb.org/index.jsp.

Phase I Enzymes CYP1A2 Purpose and Rationale CYP1A2 is involved to a major extent in the metabolism of several drugs (imipramine, clozapine, fluvoxamine, olanzapine, theophylline, acetaminophen, propranolol, and tacrine) as well as of diet components (methylxanthines), endogenous substrates (estrogens), numerous aryl, aromatic and heterocyclic amines, and polycyclic aromatic hydrocarbons. It is inducible, notably by cigarette smoking, diet habits such as consumption of cruciferous vegetables (e.g., broccoli, watercress, collard greens, Brussels sprouts, and mustard) and of charbroiled meats, some drugs (omeprazole, phenytoin, and rifampicin) and is a target enzyme for the development of some cancers. Up to now, more than 25 CYP1A2 alleles have been detected. Probe drugs for CYP1A2 phenotyping are caffeine and theophylline. For safety concerns and drug availability, the preferred probe is caffeine. Caffeine 3-demethylation is mediated by CYP1A2, and accounts for 80% of caffeine clearance. Caffeine is also a probe drug for N-acetyltransferase and xanthine oxidase (Kalow and Tang 1993). Procedure Phenotyping: A fixed or weight-adjusted dose of caffeine (solution, tablet, and coffee) ranging from 1 to 3 mg/kg is administered. Diet requirements have to be respected (stable xanthine-free diet avoiding beverages such as coffee, tea, cola,

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chocolate, no food component with CYP1A2inducing properties) during the test period. As smoking is known to induce CYP1A2, control of stable smoking status is mandatory. There are two commonly used and robust methods for phenotyping. The first one measures caffeine (1,3,7-methylxanthine) and its N-demethylated metabolite 1,7-dimethylxanthine (paraxanthine) in a plasma or saliva sample collected within 5–7 h post-caffeine dosing (Fuhr and Rost 1994). The second one uses the assay of the metabolites 1-methylurate (1 U), 1-methylxanthine (1X), 5-acetylamino-6formylamino-3-methyluracil (AFMU), and 1,7-dimethylurate (17 U) levels in urine collected at least for 8 h post-dosing (Campbell et al. 1987; Rostami-Hodjegan et al. 1996). Commonly used methods for caffeine and metabolite(s) assay in plasma or urine involve an extraction step followed by HPLC with UV detection (Krul and Hageman 1998a; Rasmussen and Bosen 1996; Schreiber-Deturmeny and Bruguerolle 1996). Urine needs to be acidified (pH 3.0–3.5) before sample freezing. Genotyping: Reduced activity has been reported for CYP1A2*1C and CYP1A2*1F alleles in smoking subjects. Induction of CYP1A2 activity has been associated with these alleles, but the effect of CYP1A2*1F mutation on CYP1A2 activity has not been confirmed (Nordmark et al. 2002). In Caucasians, frequency of the CYP1A2*1C and CYP1A2*1F variants is about 1% and 33%, respectively (Sachse et al. 2003).

Evaluation Metabolic ratios (MR) used are plasma 17X/137X and urinary (1 U + 1X + AFMU)/17 U. In controlled conditions, in nonsmoking young and elderly subjects, intraindividual and interindividual variability in 17X/137X MR was about 17% and 47%, respectively, with no effect of age (Simon et al. 2003). A 70-fold range in MR has been observed in smoking and nonsmoking female Caucasian subjects using the urinary MR (Nordmark et al. 1999). Up to 200-fold differences were found using the urinary test. Lower variability is expected using the plasma caffeine test.

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Higher CYP1A2 activity in men versus women has been reported, though inconsistently, and in children. Higher MR is usually observed in smokers versus nonsmokers, when population sample size is large. Pregnancy and oral contraceptives intake were found to decrease CYP1A2 activity (Abernathy and Todd 1985; Caubet et al. 2004; Kalow and Tang 1993). CYP1A2 activity was found lower in colorectal patients versus controls (Sachse et al. 2003). Large variability in CYP1A2 activity explains that its distribution has been described unimodal, bimodal, or trimodal. Poor metabolizers (PM, characterized with a MR 0.3 are PM. Subjects with DM/DX 28%) of NAT2*5 alleles has been observed in Caucasians and Africans, and of NAT2*7 in Asians (>10%) and of NAT2*14 in Africans (>8%), this last one being 0) by drug concentrations C > 0. For a linear system, the

with effect E, the slope of the line S, drug concentration C, and the effect in the absence of drug E0. However, the relationship between concentration and effect may not be linear for the whole concentration range in most cases, especially at very low or very high drug concentrations. The linearly increase in drug effect also contradicts with the actual biologic response where a maximum response will be reached with ever-increasing drug concentration. Since many effects are nonlinear, so this simple additive procedure cannot be applied in most situations. A well-known model that can simply and adequately describe the concentration-effect relationship is the Emax model:

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E¼

Emax • C EC50 þ C

(7)

where Emax is the maximum drug effect and EC50 is the concentration yields 50% of the maximum effect. This model can mimic the increasing trend of effect as drug concentration increased and is also able to predict the saturation effect at very high concentration levels. It is also important to notice that this model can be simplified to a linear relationship for small C  EC50 such that S = Emax/EC50 and to an inverse proportional relationship for   largeC  EC50 such that E ¼ Emax  1  ECC50 . In order to adjust the shape of effect-concentration relationship, a Hill factor is often incorporated into the Emax model that becomes the socalled sigmoid Emax model: E¼

Emax • C γ EC50 γ þ C γ

(8)

where γ affects the slope of the middle part of the curve with flattened curve if γ < 1 and steeper curve if γ > 1. As γ increases, the effect becomes more sensitive to the change of drug concentration. When γ is very large (>5), the effect E turns out to be 0 if below the threshold EC50 and Emax if above EC50. The concentration-effect relationship is almost like an on-off switch in such threshold model. Although this parameter rarely has mechanistic meanings, this value may help characterize and classify drug effect (Goutelle et al. 2008). For example, it has been shown that a high Hill coefficient and low maximum kill rate are observed for time-dependent antibiotics, while a low Hill coefficient and high maximum kill rate are seen among concentration-dependent antibiotics (Czock and Keller 2007).

Types of Pharmacokinetic Models The most important component of model-based drug development is the pharmacokinetic model, which has different types: non-compartmental analysis, compartmental models, and physiological models. Non-compartmental analysis uses

concentration-time data to estimate essential PK parameters such as AUC, CL, t1/2, Cmax, Tmax, etc. with fewer assumptions than other model-based approaches. The accuracy of non-compartmental analysis increases with more time points in concentration-time data. Even though non-compartmental analysis has less predictability of PK profiles with different dosing regimen that has not been investigated, this approach has frequently been applied for allometric scaling and is also acceptable for bioequivalence studies. The majority of PK models are compartmental models which rely on linear or nonlinear differential equations derived from mass balance to describe drug kinetics. Instead of assuming the body as one homogenous compartment, compartmental modeling divides the whole body into several interconnected compartments with each consisting of organs or tissues that are kinetically homogenous. It also assumes that the rate of drug distribution between compartments follows firstorder kinetics. This approach is critically important since it can predict concentration-time profiles of alternative dosing regimen from simulation. Thus this simple modeling technique is quite useful in PK/PD approach to relate drug response mechanism with drug delivery mechanism. The most widely used modeling technique in PK/PD approach is population modeling, which will be discussed with more details later in this chapter. The biggest limitation of conventional compartmental models is that different compartments may not have a clear physiological significance but are abstract mathematical constructs. To overcome this limitation and extrapolate PK to different physiological conditions or alternatively to drugs has similar property; physiologically based pharmacokinetic (PBPK) models were developed based on actual physiological and biological meanings for drug and are expected to be a simple and direct approach to relate the observed drug response to target tissue exposure. Both organ physiology (weight, blood flow, enzyme expression, etc.) and drug-specific physical-chemical property (solubility, protein binding, tissue to plasma partition coefficient, etc.) are critical prior information for robust PBPK model development. A validated PBPK model can predict the

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quantitative behavior of similar drugs or extrapolate PK with altered physiology which is beyond the range of investigated experimental conditions.

Types of Pharmacodynamic Models Limited conclusions can be drawn from PK alone; PK/PD approach bridges the gap between the time course of drug concentration and therapeutic response. PD response usually has several dependent variables as clinical endpoints, surrogates, and biomarkers. In most cases, only one is modeled at a time. It is an important decision to choose an appropriate dependent variable which is meaningful, measurable, and appropriate for modeling. Dependent variables may be continuous (blood pressure), categorical (several score levels), or binary (alive, dead).

Continuous Response Variables The simplest PD model is a direct response model where the effect is directly related to the concentration in central compartment. Theoretically, the therapeutic response is directly triggered by drug concentration at effect site. The direct correlation between response and plasma concentration may result from rapid drug distribution and instant pharmacological response or the equilibrium between plasma and effect site achieved at steady state (Derendorf et al. 2000). This type of model can often be expressed using Eq. 8. A delayed effect may be observed when there is a temporal dissociation between the time course of blood/plasma concentration and the effect of the pharmacological agent. This dissociation may occur due to the delayed distribution between central compartment and target site, or indirect mechanisms such as time-consuming synthesis or degradation of endogenous substance, or a more complex receptor-mediated effect (Derendorf et al. 2000). Indirect models are often used to describe this type of model where the relationship between response and concentration is not one-to-one but rather shown in a counterclockwise hysteresis loop. In case of delayed effect, it is often that either an effect compartment model or an indirect response model can be used to describe it. An

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effect compartment is a hypothetical compartment characterized by the time course of concentration at the effect site. It is linked to the PK compartment but with no intercompartment mass transfer. The indirect response mechanism can be described using indirect response models where the drug affects a precursor and subsequently influences the PD response. The rate of change of the response over time in the absence of drug can be expressed as: dR ¼ k in  k out R dt

(9)

where R is the measured response, kin is the apparent zero-order rate constant for response production, and kout is the first-order rate constant for response dissipation. Four basic models were proposed that the drug can either inhibit or stimulate the production or loss of the response (Sharma and Jusko 1996). Both the inhibition and stimulation functions can be incorporated kin or kout in   into   I • Cp Eq. 9 with expression of I C p ¼ 1  ICmax 50    þCp E • Cp for inhibition and S C p ¼ 1 þ ECmax50 þC for p stimulation. One of the possible reasons for the lag of drug effects may come from signal transduction controlled by secondary messengers (Mager et al. 2003). Other mechanistic PD models based on irreversible effects (e.g., cell or target inactivation, enzyme inactivation) and tolerance mechanism (e. g., counter-regulation, precursor pool depletion) are also important components and can play a big role in PK/PD approach.

Noncontinuous Response Variables The majority of PD models describe continuous response variables. However, clinical data are not always continuous but sometimes categorical variables such as the severity of a disease (ordinal scaled), or binary variables (dead, alive), or timeto-event (censored) data. Such responses are more clinically relevant to drug efficacy and safety and can be described as the probability of an event occur using logistic models or survival models. Logistic regression is suitable to use for the prediction of probability change with predictors when the outcome is a binary response. The logit

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transformation, the link function in logistic regression, is defined as the logarithm of the odds (the probability of an effect occurs divided by the probability of the effect not occur):
logitðpÞ ¼ ln

p 1p

the lifetimes T. The hazard function h(t) is the probability of an event occur immediately following time t given that the event has not occurred at time t:

 ¼ Lð x Þ

(10)

where L(x) is a linear function of predictors such as L(x) = β0 + β1 x1 + β2 x2 + . . . + βk xk where xi represents the predictors (e.g., drug concentration, exposure, time). The logit transformation expands the ends of scale by mapping the interval (0, 1) onto (1, 1), such that the small difference in p would reflect as a larger difference on logit scale. Instead of just p, logit( p) is used as the response in this regression. Its inverse function, also known as the logistic function, is as following: expðlogitÞ expðLðxÞÞ ¼ or 1 þ expðlogitÞ 1 þ expðLðxÞÞ 1 1 ¼ pð x Þ ¼ 1 þ expðlogitÞ 1 þ expðLðxÞÞ (11)

pð x Þ ¼

Both the logit and its inverse functions are monotonically increasing; the confidence interval for the probability of an event occurs ( p) can be extrapolated from the confidence interval for logit ( p). The application of logistic regression can also be extended in cases where the dependent variable has more than two outcome categories or when the multiple categories are ordered. For the analysis of time-to-event data (such as time from origin of treatment to disease progression or duration of survival), they are often “censored” due to loss of follow-up (right censoring) or delayed entry (left censoring). Survival analysis aims to avoid this bias of incomplete longitudinal data. The survival function is defined as the probability that the event has not occurred by certain duration such that S(t) = Pr(T > t), where T denotes the time of the event occur. Also, notice that F(t) = 1  S(t) where F(t) is the cumulative distribution function; this leads to the relationship of S0(t) = f(t), the probability density function of

hðt Þ ¼ lim

Δt!0

¼

Prðt < T < t þ Δtj T > t Þ Δt

f ðt Þ S ðt Þ

(12)

The cumulative hazard H(t) is the area under the hazard function which relates to the survival function in the relationship as: H ðt Þ ¼ lnSðt Þ or S ðt Þ ¼ exp½H ðt Þ

(13)

The survival function S(t) is monotonically decreasing, while the cumulative hazard function H(t) is monotonically increasing. When no event time is censored, the survival and hazard can be easily estimated by assuming parametric survival distributions (e.g., exponential, Weibull, loglogistic, etc.) In case of data with right censoring or left truncation, nonparametric method should be used. Kaplan-Meier estimator and Nelson-Aalen estimator provide nonparametric estimates for survival function and cumulative hazard rate function, respectively. The Cox regression model, also known as the proportional hazards regression, is a semi-parametric model with an unspecified baseline hazard function (nonparametric) and a parametric component: hðtjX Þ ¼ h0 ðt ÞexpðβX Þ

(14)

where X = (X1, . . ., Xn) is the covariate vector and β = (β1, . . ., βk)0 is the coefficient vector with each βi the log hazard ratio associated with oneunit change in Xi with the rest of covariates remaining constant. The baseline hazard only depends on t but not on any covariates Xi, while the hazard ratio exp(βX) only depends on the covariates but not on time t (independent of time, so-called proportional hazard). However, time-dependent covariates are more commonly seen in pharmacometrics setting since the hazard

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is associated with drug concentration which varies over time. In this case, the Cox regression model can still be used but the hazard ratio depends on time as well: hðtjX Þ ¼ h0 ðt ÞexpðβX ðt ÞÞ

(15)

Gieschke et al. applied both logistic regression and Cox regression model to explore the relationship between systemic capecitabine exposure and its safety/efficacy outcomes (Gieschke et al. 2003). Holford discussed the details of the link between basic concepts of PK/PD and time-toevent analysis and how this approach can reveal more information for the prediction of therapeutic effects (Holford 2013). Gong et al. proposed the application of machine learning method for timeto-event data analysis, and their results indicated that machine learning-based methods provide better performance than the traditional Cox model (Gong et al. 2018).

Evaluation Plotting effects versus various covariates like time, concentrations, and demographic variables will help to generate hypothesis about a future model. Is the effect time invariant? Is a hysteresis observed? It is Clockwise or counterclockwise? A strategy to decide about using an effect compartment or a direct response model has been discussed by Felmlee et al. (2012). Knowledge about the underlying process can also help to decide on this question and about the related physiology and will also help to explore more mechanistic models. The following items should be clearly addressed in a PK/PD model development: 1. Problem and purpose of the model 2. Assumptions in the modeling process (explicit and implicit assumptions) 3. Rationale of model development (Why is a model considered to be better than another?) 4. Validation strategy using internal or external data

General Approaches of Pharmacometrics All models are mathematical representations of the data. Often, the main objective of developing a PK/PD model is to describe the data, to predict unknown situations, and to explain underlying mechanisms. The application of using mathematics and statistics to understand data during drug development originates from simple descriptive summary of data. With the fast development in this field for the last 30 years, a lot of improvements have been seen in model efficiency and capability, especially after the inception of population modeling.

Traditional Approach The simplest modeling approach to evaluate data from multiple subjects or animals is “naïve pooled approach,” where data from all individuals are pooled first and then fit. The mean response estimated using this approach is always biased, and the interindividual differences in exposure and response are also ignored. Consequently, this is rarely used for PK/PD data analysis. Before the inception of population approach, the traditional method that modelers used is “two-stage approach.” The individual’s data are fit separately first. Subsequently, individual parameter estimates are combined, and descriptive summary statistics are calculated including mean, variance, and covariates on each parameter. The mean estimates of parameters obtained from this approach are generally good, but the estimates of interindividual variability are biased and imprecise (Sheiner and Beal 1980). In addition, the twostage approach highly relies on the richness of data for each individual. Hence this approach may not be applicable in the situation of sparse data where individual parameters are not easy to estimate. To overcome the limitation of both earlier approaches, Sheiner et al. developed a new approach which allowed dealing with sparse data to estimate population mean parameters and interindividual variability and incorporate covariate effects (Sheiner et al. 1972). With the advancement in computing power, this valid and robust

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population approach has been widely used in PK/ PD modeling to facilitate current drug development.

Population Approach The population approach, also known as the mixed-effect modeling, consists of both fixed effect parameters (population mean values) and random effect parameters (variability within the population). In general, all PK and PD parameters have certain distributions in population. Nonlinear mixed-effect modeling can simultaneously calculate parameters and their distributions from the full set of individual data. In this way, information from each individual are gathered and contributed to the covariate determination and the corresponding variability quantification. A general population model consists of three components: a structural model, a covariate model, and a stochastic model (Mould and Upton 2012). Structural model adopts the classical compartmental model to describe the time course of concentration profile (PK) or measured response (PD). Covariate model characterizes the relationship between PK/PD response to demographic covariates (such as weight, height, gender, etc.). The covariate identification and covariate model development are very critical as it supports labeling for special population based on their demographic information such as kidney/liver function, metabolic status, etc. Stochastic model describes the unexplainable variability in the data which includes inter- and intraindividual variability, residual, etc. More detailed discussion of population PK can be found in a separated chapter in this book. The application of population approach in PK/PD modeling provides clinical pharmacologist with better understanding of underlining mechanisms and assists rational dose adjustment for subpopulations of patients. Learning and Confirming Circle Drug development usually involves several iterations of model-informed learning and confirming where learning answers “how much/what” questions and confirming answers “yes/no” questions. Classical clinical studies are self-consistent, that is, a hypothesis is accepted or refused with the

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information coming from the study and nothing else. The learn and confirm approach (Sheiner 1997) consists of alternating learning and confirming cycles: a study to generate a hypothesis and a subsequent study to confirm the hypothesis (or to improve the model), followed by further hypothesis generating based on the previous finding and confirming (or not confirming) studies. Phase 1 and Phase 2B/3 are considered as learning stages, while Phase 2A and Phase 3/4 are seen as confirming stages based on the objectives in each stage. However, learning is still a very important subsidiary in the confirmation stages since learning while confirming would help keep the knowledge updated for confirming questions.

Software Given that pharmacometrics is an interdisciplinary science fused with pharmacology, physiology, computer science, and mathematical/statistical modeling, PK/PD modeling and simulation heavily relies on software to make clinical trials more efficient. With the rapid evolving computation efficiency, a variety of efficient, flexible, and user-friendly software are available recently. For non-compartmental analysis, WinNonlin (Phoenix) has been widely used in pharmaceutical industry. Other software such as PK packages in R, Kinetica, Scientist, and PKSolver can also perform non-compartmental analysis. As for nonlinear mixed-effect modeling, NONMEM was the first and most commonly used software for population PK and PK/PD modeling and simulation. Ever since the release of the first version, NONMEM has been continuously updated with new statistical methods and estimation algorithms. Because NONMEM does not have a very user-friendly interface, “front-end” and “backend” software (such as Pirana, R, SAS) have been incorporated with NONMEM to overcome this limitation. Other software which has been widely used for mixed-effect modeling include MONOLIX, a software based on the stochastic approximation expectation maximization (SAEM) algorithm for reliable convergence and ADAPT, which is based on Monte Carlo

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parametric expectation maximization. Mixedeffect modeling is also possible in R with packages such as NLME, RxODE, mrgsolve, saemix, etc. The Pmetrics package in R allows nonparametric modeling of population PK using nonparametric adaptive grid (NPAG) algorithm. In addition, packages like PKgraph and ggplot provide graphical user interface for model diagnosis. The biggest advantages of R are the various statistical packages available and the possibilities of data management and visualization and that many scientists working in population PK/PD are programming with R and are publishing solutions for various problems. Berkeley Madonna also serves as a fast ordinary differential equation solver with a visualization interface. Besides NONMEM, Bayesian pharmacometrics modeling can also be performed in BUGS and Stan. SimCyp, PK-Sim, and GastroPlus are commonly used for PBPK modeling. Versions and owners of software change rapidly. Actual information about these commercial or free software packages can be found on the corresponding web sites on the Internet.

Critical Assessment of the Method PK/PD Concepts in Antimicrobials Establishing PK/PD relationship of drug candidates is critical during the whole process of drug development. Bridging the PK/PD information from preclinical to clinical studies and optimizing dosage regimen for special population is very crucial to ensure drug efficacy and to minimize toxicity. In this section, we will introduce important concepts of PK/PD approach in antimicrobial drug development since the PK/PD approach in this area has been well developed. The determination of correct dose and dosing interval can be evaluated via PK/PD approach (Sy et al. 2016). The discussion of PK/PD approach in antibiotic dose optimization is mainly separated into three approaches, namely, the minimum inhibitory concentration (MIC)-based, the time course-based, and in vivo animal model-based approaches. The PK/PD concepts and theories presented for

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antibiotics are also broadly applicable to drug development in other therapeutic areas.

MIC-Based Approaches MIC, defined as the minimum inhibitory concentration, is a PD surrogate index of the susceptibility of certain pathogen in the presence of a specific antimicrobial treatment. This simple index does not reflect the time course of pharmacological effect but provides very useful information on antibiotic efficacy. From in vitro experiment, MIC can be easily determined as the lowest antibiotic concentration that inhibits the visible growth of microorganism in the end of a 16–20-h incubation period (Sy and Derendorf 2016). The most common MIC-based PD indices (Fig. 2) that quantitatively link the drug exposure and microbiological outcomes for in vivo efficacy prediction are the percentage of a dosing interval in which the drug concentration is above the MIC (T > MIC, time above MIC), the ratio between the peak concentration and MIC (Cmax/MIC), and the 24-h area under the concentration-time curve divide by MIC (AUC/MIC). Since only the unbound proportion of drug is pharmacologically active, an italicized prefix f, implying the free drug concentration, is often seen with these indices. The PK/PD index that associated with specific antibiotic agent is determined from either the dose fractionation study in the rodent or the time-kill kinetic studies (Sy et al. 2016). The microbiological outcome, which is quantified by the change in log10 colony-forming unit (CFU), usually correlated very well with at least one of these MICbased PD surrogate indices. If the PK/PD relationship is best characterized by T > MIC, implying time-dependent bacterial killing, i.e., once the free drug concentration is above the MIC, increasing the antibiotic concentration will not further increase the killing rate, but extending this period would ensure drug efficacy. One classic example of “time-dependent” killing is β-lactam antibiotics as the efficacy is enhanced with longer exposure times at which the free concentration maintained above MIC. In the case of antibiotics exhibiting concentration-dependent killing, the antimicrobial killing rate increases as the concentration of antibiotic increases. Therefore, the aim for this

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PK/PD indices Cmax

Concentration

Fig. 2 Pharmacokineticpharmacodynamic indices as targets for achieving antimicrobial efficacy. T > MIC, the percentage of a dosing interval in which the drug concentration is above the MIC; Cmax/MIC, the ratio of the maximal drug concentration to the MIC; AUC/MIC, the ratio of the area under the drug concentration-time curve to the MIC

Y. Yu et al.

• T>MIC • Cmax/MIC • AUC/MIC

Cmax/MIC

AUC/MIC

AUC

MIC

T>MIC

type of antibiotic is to attain the magnitude of ƒC max/MIC ratio, which is proportional to the initial killing rate. The killing pattern of aminoglycosides correlates well with this ratio. For drugs which have concentration-independent killing but with persistent effects after drug exposure, their killing can typically be characterized by ƒAUC/MIC ratio. The bacterial killing rate for this type is related to both the duration when free drug concentration is above the MIC and the total exposure of the antibiotic. A good example that belongs to this group is vancomycin due to its extended postantibiotic effects that inhibit bacteria regrowth even free drug concentration dropped below MIC.

Time Course-Based Approaches One of the limitations with the simple MIC-based approach is that the complex interactions between the host, the pathogen, and the drug itself are difficult to be captured by this static in vitro parameter. Different combinations of bacteria growth and killing kinetics in response of antimicrobial agent may lead to the same MIC in the end of incubation period. In addition, the MIC determined by twofold dilution is a relatively crude index, rather than an accurate estimation. Furthermore, the antibiotic concentration can remain constant during the time course of in vitro MIC measurement, or keep changing to mimic the dynamic change of free concentration at the actual target site in vivo . Therefore, another approach

Time

that provides more detailed PK/PD information to evaluate time course of bacterial response with dynamic exposure of the anti-infective agent is the in vitro time-kill assays. The time-kill experiments can be either static (fixed antibiotic concentration) or dynamic (constantly changing antibiotic concentration). During static time-kill study, a sample was taken at each prespecified time, and bacterial colonies were counted after incubation. Although this experiment is simple, static studies are labor intensive as the drug concentration ranges from 0.25-fold up to 16-fold based on twofold dilution, and the duration of each experiment varies from 6 to 72 h (Sy et al. 2016). For dynamic time-kill study, the drug concentration in the medium is changing by replacing with the fresh ones without antibiotic contained, rather than having the same medium throughout the experiment. The dynamic time-kill experiments are usually conducted via a hollowfiber system where the model is continuously flushed with the fresh medium using a flow rate which is similar as the half-life of the investigated agent. Therefore, the in vivo drug kinetics can be simulated by this in vitro system.

Animal Models The in vitro setting conditions, for either MIC determination or time-kill assays, are still highly distinct from the kinetic situation in vivo at the site of infection. Instead of aerobic and protein-free in vitro environment, the in vivo condition is rather

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anaerobic, acidic, and prone to protein binding (Levison 2004). Also, one of the drawbacks of hollow-fiber model is that the diffusion blockage may occur due to bacteria clusters adhering along the capillary walls, which may ultimately alter the flow rate in the system (Sy and Derendorf 2016). To overcome the limitation of in vitro PK/PD evaluation, such as lack of immune system and limited nutrients available, in vivo evaluation of PK/PD relationship is also feasible. One possibility would be to use animal models such as rats or mice. Drug distribution at the target site are most commonly studied in thigh or lung infection models usually carried out in neutropenic rodents to avoid the variability of different immunity levels. Neutropenia can be induced by administration of cyclophosphamide (Zuluaga et al. 2006). Due to faster drug elimination in animals than humans, uranyl nitrate administration was performed before treatment to induce transient renal impairment, therefore delaying drug elimination in animals (Craig et al. 1991). PK samples were collected using microdialysis technique to measure the unbound drug concentration in the ISF at the target tissue. The bacterial density change from the starting inoculum was evaluated at the end of treatment period. These PD results were correlated with the PK/PD indices (i.e., ƒT > MIC, ƒCmax > MIC, and ƒAUC/MIC) in dose fractionation studies. Animal models provide a more pertinent approach to evaluate humanlike PK/PD; however, one of the biggest disadvantages is this procedure is not able to mimic human PK for drugs with extensive hepatic metabolism (Sy et al. 2016).

Model-Based Drug Development Establishing PK/PD relationship using modeling and simulation is very critical for dosing regimen selection of antimicrobial agents, as the delineation of such relationship can greatly help selection of dosing regimen that has a high probability to overcome bacterial resistance and optimize clinical outcome (Drusano 2004). These model-based approaches provide useful information on the PK/

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PD index that best characterize the antimicrobial activity, as well as the translational value based on in vitro time-kill kinetics or animal PK data. In this section, we will discuss and illustrate the application of pharmacometrics in antimicrobial drug development. For MIC-based PK/PD indices, the dose and dosing interval can be determined through simulation of human PK and the desired target value of PK/PD index. When it comes to time-kill-based or animal PK/PD data, different dosing regimens can be evaluated via simulation of bacterial responses based on semimechanistic models.

Monte Carlo PK/PD Simulations Given robust descriptive PK model and PD index determined from preclinical experiments, simulation of virtual clinical trials with different dosing regimens and targets can be done for dose optimization. Monte Carlo simulation is a computerbased mathematical technique that helps answer many “what if” questions before conducting expensive clinical trials in patient population (Roberts et al. 2011). Utilizing results from PK/ PD simulation provides better confidence to the dosing regimen selected for clinical trials. The major components of Monte Carlo simulation include essential PK parameters and their corresponding interindividual variability, predefined antibiotic PD index, or parameters from semi-mechanistic PD models. For MIC-based approach, the simulated individual concentrationtime profile can be evaluated against the prespecified PK/PD index. If PK/PD model was used for simulation, optimal dose selection would be based on simulated data of bacterial response over time. However, simulation results may be invalid due to potential confounding factors or small sample size in PK data used for model development, or model development is based on total concentration rather than free concentration. In these situations, the simulation results should be interpreted with caution. Probability of Target Attainment For MIC-based approach, the percentage of the virtual subjects that achieve the target PK/PD index under certain dosing regimen can be

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computed from simulated individual PK profiles. This is defined as the probability of target attainment (PTA). PTA is usually determined from 1000 to 10,000 individual concentration-time profiles simulated from population PK model, considering the interindividual viability. Under each dosing regimen, the likelihood of achieving target attainment at a prespecified MIC value was calculated based on the distribution of PK/ PD index (e.g., ƒT > MIC, ƒCmax/MIC, ƒAUC/ MIC) from simulated PK profiles. This specific target is often associated with 1- or 2- log10 reduction of bacterial count from animal studies. After repeating the above likelihood calculation with a range of increasing MIC values, PTA and MIC can be plotted with x-axis as the MIC values and y-axis as the probability. This plot indicates the trend of less probability of successful antimicrobial achievement with increasing MIC. A probability greater or equal to 90% is usually accepted. To illustrate the application of this approach, the example used here is from Singh and his colleagues (2017). PTA analysis of tigecycline was performed on traditional PK/PD target AUC/MIC >6.96 h and the new target of ƒAUC/MIC >2.05 h, accounting for atypical nonlinear plasma protein binding. The simulation was based on 10,000 individuals with body weight, creatinine clearance, and gender incorporated into the individual clearance. Individual AUC at steady state for dosing intervals (AUCss(0–24h)) was calculated from daily doses of 100, 150, 200, 250, and 300 mg intravenously. At each MIC level in a range of 0.064–64 mg/L, individual AUC/MIC ratio was calculated using each AUCss(0–24h) divided by the corresponding MIC value. If the virtual subject has an AUC/MIC ratio greater than 6.96 h, the clinical outcome on this subject is considered as successful. Subsequently, the overall percentage of virtual subjects was computed at each MIC level under each dosing regimen (Fig. 3). Applying similar approach but combined with a protein binding model, ƒAUC/MIC ratio of each individual was calculated, and the percentage of virtual subjects achieving ƒAUC/MIC at least 2.05 h at different MICs with different tigecycline daily doses is also shown in Fig. 3.

Y. Yu et al.

If the target is based on AUC/MIC, a significant increase of target achievement is clearly shown with increasing daily dose. In contrast, dose increment does not significantly alter target attainment if it is based on ƒAUC/MIC. This finding implies the importance of using free concentration and exposure in PK/PD index for drug efficacy and the impact of protein binding on clinical breakpoints. PTA analysis was also used in this example to evaluate the effect of plasma protein binding on tigecycline clinical breakpoint selection. As Fig. 4 shows, the PTA analysis results at a daily dose of 100 mg; the clinical breakpoint for tigecycline against E. coli is 0.5 mg/L without consideration of protein binding but is 0.25 mg/L regarding the free AUC. Since clinical breakpoint is a very critical criterion to stratify patients into different susceptibility phenotypes, ignoring the protein binding could put more patients into risk of clinical failure. Another approach to utilize Monte Carlo simulation results for clinical outcome prediction is to compute the expected PTA for a given microorganism population with a specific dosing regimen (Asin-Prieto et al. 2015; Mouton et al. 2005). This is the so-called cumulative fraction of response (CFR), which can be calculated using the previous PTA values and MIC distributionin
n P the equation of CFRð%Þ ¼ PTAi  F i , i¼1

where PTAi is the PTA at specific MIC and Fi is the bacterial isolate frequency at that corresponding MIC level. In this case, CFR is particularly useful when the exact pathogen susceptibility is unknown because the success of clinical outcome is predicted based on the whole population rather than a single MIC value. The population MIC distribution can be obtained from European Committee on Antimicrobial Susceptibility Testing (EUCAST) or from a particular healthcare facility. It is important to notice that the pathogen susceptibility can vary between different locations and over time (Roberts et al. 2011; Asin-Prieto et al. 2015). Therefore, CFR is often specific as its calculation is based on the MIC distribution in a specific facility at a particular time.

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Fig. 3 Probability of target attainment as a function of minimum inhibitory concentration (MIC) of tigecycline against Escherichia coli at different doses with target

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of MICs was obtained from the European Committee on Antimicrobial Susceptibility Testing (EUCAST). (Image adapted from Singh et al. (2017) and used with permission)

Semi-mechanistic PK/PD Model If the PD data is from time-kill curve experiments, semi-mechanistic PK/PD model is commonly used to establish the relationship between

the bacterial colony population change and antimicrobial concentration over time. The information used for PD model development is often borrowed from static time-kill

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experiments, while the dynamic time-kill data provides additional information on PK/PD relationship and thus can be used for model validation. Rather than complicated mechanistic models considering the bacterial growth cycle, states of bacterial susceptibility, drug-receptor information, and the mechanisms of drug action, semi-mechanistic models are empirical models with simpler terms but still able to capture the bacterial response on antibiotics and monitor the development of bacterial resistance (Sy et al. 2016). In the absence of antimicrobial intervention, the population number of bacteria in an inoculum over time represents the net result of bacterial natural self-replication and degradation (Jusko 1971): dN ¼ k growth N  k death N dt

(16)

where N is the bacterial count and kgrowth and kdeath are the first-order rate constant for bacterial growth and death, respectively. This model assumes constant growth and death rate; however, it is difficult to separately define each constant as the observations are often based on knet = kgrowth  kdeath. Also notice that in both static and dynamic time-kill curve studies, bacteria grow exponentially but then reach a plateau or stationary level when the net growth is zero (Nielsen et al. 2011b). A logistic growth model (Tam et al. 2005) can better describe this selflimiting growth pattern:
 dN N ¼ k net 1  N dt N max

(17)

where Nmax is the carrying capacity or the maximum population size in the system. From the analytical solution of Eq. 17, one important characteristic of this model is that bacteria population approaches the carrying capacity astime goes to  infinity, i.e., lim N ðt Þ ¼ N max . In other n!1

words, bacterial population achieves stationary phase when N approaches Nmax.

When the in vitro system is exposed with antimicrobial agents, the drug effect can be incorporated into the logistic growth model such that:
 dN N ¼ k net 1  N  f kill ðdrugÞ dt N max

(18)

where the fkill(drug) is the drug effect (Nolting et al. 1996; Mouton and Vinks 2005; Liu et al. 2005; Treyaprasert et al. 2007), which is often represented by a sigmoid Emax or simple Emax model as f kill ðdrugÞ ¼

E max C γ N EC50 γ þ C γ

(19)

where C is the drug concentration, Emax is the maximum killing effect, and EC50 is the concentration at which half of the maximum drug effect is achieved. The shape parameter γ, also known as the Hill factor, is 1 in simple Emax model. Modification on Eq. 18, such as incorporating an adaptation or delay function, allows the logistic growth model to adapt to bacterial regrowth. With an adaptation function on EC50, the drug effect   is E max C γ modified as f kill ðdrugÞ ¼ ðα • EC50 Þγ þCγ N ,

wherein α = 1 + β(1  eCτt) with β the maximal adaptation and τ the rate of adaptation factor (Tam et al. 2008). By using this adaptation function, the decline in the kill rate over time was successfully modeled. Subsequently, the model can well predict the microbial responses to both gentamicin and amikacin. A delay term, usually expressed as 1  ekt, ranges between 0 and 1 from time zero to infinity. This function can be incorporated on the bacterial growth phase and/or anti-infective drug effect (Nolting et al. 1996; Liu et al. 2005; Treyaprasert et al. 2007). Treyaprasert et al. tried different types of delay function in PD model of azithromycin against four bacterial strains and found that incorporating delay term on both term can best describe the antibiotic response (Treyaprasert et al. 2007). An alternative method to describe the self-limiting growth capacity is to implement the idea of bacterial phenotypic switching between

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Fig. 5 Schematic illustration of the pharmacokinetic/ pharmacodynamic model. C central compartment; P peripheral compartment; S proliferating and drug-sensitive bacteria; R resting and drug-insensitive bacteria; k0 drug infusion rate constant; ka drug absorption rate constant; ke drug elimination rate constant; kgrowth and kdeath rate

constants for multiplication and degradation of bacteria, respectively; kSR rate constant for transformation from the growing, sensitive stage to the resting stage; kRS rate constant for transformation from the resting stage to sensitive stage

susceptible (S) normally growing cells and persistent resting (R) cells with reduced growth rates (Balaban et al. 2004). In this case, the total number of bacteria would be the sum of both subpopulations (S + R) as it is illustrated in Fig. 5. In this model, assumptions were made as the majority of the bacteria in the early growth phase are in susceptible state and bacteria in persistent state do not respond to antimicrobials. As the bacteria population in the system increases, bacteria in growing stage are gradually transformed into a resting stage, leading to the stationary phase. The kinetic behavior of each subpopulation without antibiotic exposure can be described in the following equations:

death rate and incorporated as an additive or proportional effect:

dS ¼ k growth S  k death S  k SR S þ k RS R dt dR ¼ k SR S  k RS R  k death R dt

(20)

(21)

where kSR and kRS are the transfer rates between susceptible population and resting population. Since persistent population is unlikely to return it back to susceptible state, thus the term kRS can often be set as 0. The antimicrobial effect, with same sigmoid Emax model in Eq. 19, can either decrease bacterial growth rate or increase bacterial

dS ¼ k growth ð1  f ðdrugÞÞS  k death S dt  k SR S þ k RS R

(22)

dS ¼ k growth S  ðk death þ f ðdrugÞÞS dt  k SR S þ k RS R

(23)

dS ¼ k growth S  k death ð1 þ f ðdrugÞÞS dt  k SR S þ k RS R

(24)

Nielsen et al. used this semi-mechanistic model to simultaneously fit in vitro static and dynamic time-kill data of Streptococcus pyogenes exposed to five different antibiotics (Nielsen et al. 2007, 2011b). The model was modified with an addition of an effect compartment to describe the time delay of drug effect. The investigators later on extended this model with a binding model implemented to describe the on and off adaptive resistance for gentamicin (Nielsen et al. 2011a). Simulation was performed on this model. The dose fractionation study indicated that the PK/

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PD indices can be identified from in silico predictions based on this semi-mechanistic PK/PD model. This study implied the power of applying PK/PD model derived from in vitro studies to describe the antimicrobial effect and select the optimal dosing regimen for clinical studies.

Models of Combination Therapies Combination therapy, a drug intervention consisting of at least two therapeutic agents for the treatment of same condition, has gain popularity in recent years to overcome the emergence of bacterial resistance. The effect of combination therapy can also be characterized by logistic growth model utilizing Loewe additivity to evaluate synergistic effect of drug combination when each component has its intrinsic antimicrobial activity (Greco et al. 1995). Under the assumption that each therapeutic agent cannot interact with itself, the Emax model to evaluate the drug combination of two agents can be expressed as:  E max ¼

k max 1þ



C1 α1 EC50,1

γC 1 C 2 C2 þ α2 EC þ α1 α2 EC 50,2 50,1 EC50,2

C1 α1 EC50,1

γC 1 C 2 C2 þ α2 EC þ α1 α2 EC 50,2 50,1 EC50,2

k k

(25) where kmax is the initial killing rate, αi refers to the same adaptation function defined previously, and γ indicated the Loewe synergism (γ > 0) or Loewe antagonism (γ < 0). A semi-mechanistic

PK/PD model incorporating Loewe additivity has been used to successfully describe the combination effect of vertilmicin (an aminoglycoside) and ceftazidime (a β-lactam) against Pseudomonas aeruginosa (Zhuang et al. 2015). In this case, both vertilmicin and ceftazidime have antimicrobial activities with their own mechanism of action. Characterizing the potential synergistic effect in PK/PD modeling and simulation would assist dose selection of this therapeutic combination. When it comes to the combination of β-lactam (BL) and β-lactamase inhibitor (BLI), such as avibactam and aztreonam, ceftazidime and avibactam, and meropenem and vaborbactam, the BLI has limited intrinsic antimicrobial activity but restores the antimicrobial activity of BL, leading to an enhanced spectrum of activity with this type of drug combination. Given that the microorganism’s susceptibility to BL enhanced with increasing concentration of BLI and the degradation of BL also depends on bacterial density, it is reasonable to incorporate a drug degradation model into the semi-mechanistic PK/PD model. Sy et al. have developed such models for ceftazidime/avibactam combination to predict the exposure-response behavior of both agents (Sy et al. 2017, 2018). The schematic representation of the model structure is illustrated in Fig. 6. Similar as other semi-mechanistic models of logistic growth equation, the antimicrobial effect of BL can be described in a sigmoidal Emax model. However, instead of using a simple kgrowth

BL

S

kSR

kdeath kdeg

Fig. 6 Schematic representation of the combined effects of β-lactam antibiotic (BL) and β-lactamase inhibitor (BLI) on the growth and killing of pathogens in a closed system

R kdeath

BLI

including bacterial density-dependent BL degradation. (Image adapted from Sy et al. (2018) and used with permission)

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EC50 parameter for drug potency, a bi-exponential function of BLI concentration is applied in Eq. 19 such that BL EC50 shifts toward lower values with increasing BLI concentrations. Therefore, the function of drug effect has been modified to: f kill ðdrugÞ ¼

E max • BLγ N ðAeα • BLI þ Beβ • BLI Þγ þ BLγ (26)

where A and B are constants with their sum determines the initial value of EC50 when BLI is absent. α and β are the exponents that describe the relationship of BLI concentration and BL potency. The biexponential model was used since BL MIC drops rapidly from monotherapy to combination therapy with even the lowest BLI concentration, and then BL MIC continues decreasing but in a slower pace as BLI concentration increases. Given that MIC correlated to EC50 in the logistic growth model (Mouton and Vinks 2005), this bi-exponentially decreasing function is suitable to model the mechanism that BLI restores BL susceptibility to bacteria. As previously discussed, a delay function can also be implemented on the growth and/or kill term in this semi-mechanistic model to modulate the initial decline of bacteria and delay in later regrowth in the presence of BLI. During the time-kill studied by Sy et al., degradation of both BL and BLI was also monitored. A drastic degradation rate was seen when bacterial population is high in the system and BL degraded in a concentration-dependent manner of BLI. Hence, a saturable Michaelis-Mententype equation was applied to BL degradation model with degradation rate depending on bacteria population as well as the concentration of both BL and BLI:
 dBL Degmax • N φ BLI ¼ 1  BL dt IC50 þ BLI Kmφ þ N φ (27) where Degmax is the maximum degradation rate constant, Km is the bacteria density at which degradation rate is half of the maximum value,

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φ is the Hill coefficient that determines the shape of the function, and IC50 is the BLI concentration that yields 50% decrease of the BL degradation rate. By applying this semi-mechanistic model, considering bacteria-mediated BL degradation and inhibition of degradation of BLI, Sy et al. were able to develop the PK/PD model from static time-kill data to simultaneously describe the dynamic change of multidrug resistant Pseudomonas aeruginosa, ceftazidime degradation, and the inhibition effect from avibactam (Sy et al. 2018). This model was further validated using dynamic time-kill data as well as data from animal models. By incorporating the mechanism of drug resistance, the model was able to give more detailed prediction of the bacterial dynamics in response to BL/BLI combination. It has the potential to expedite the BL/BLI combination drug development by confident simulation of clinical trials.

Special Population Since most of the time, the initial PK model development is based on data from healthy adult volunteers in Phase 1. Drug PK may not be the same for patients or special populations such as obese population, geriatric population, pediatric population, etc. due to the different ontogeny, physiology, and pathophysiology conditions in these special populations. Elderly patients are particularly subject to drug toxicity including antibiotics due to their diminished physiology reserve and the frequent polypharmacy, therefore requiring dose adjustment of some antimicrobial agents (Benson 2017). For pediatric population, allometric extrapolation of clearance can give reasonable pediatric dose for similar exposure in children if using a drug-specific allometric exponent rather than using the fixed exponent of 0.75 (Mahmood 2007). It was also found that an age-dependent exponent in allometric scaling model can well predict the first-in-children dose (Mahmood et al. 2014). Dosing in obesity is also complicated not only due to the physiological alternation and comorbidity but also the lack of standardized measure of creatinine clearance and the

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variability of types of body weight used for dose adjustment (Meng et al. 2017). Renal impairment has a big impact on the PK of drugs which are extensively eliminated by kidney (30%). Particularly, for patients with endstage renal disease (ESRD, renal function is less than 10% of the normal capacity), whether dose needs to be adjusted and how the dose should be adjusted becomes a critical issue as ESRD patients also routinely receive hemodialysis to assist drug removal. Pharmacometrics is critically valuable for dose optimization in special population. The dosing finding can be based on both approaches as we discussed previously: (1) PTA based on PK modeling and simulation and PK/PD indices and (2) simulation based on semi-mechanistic PK/PD model. An example is illustrated here on the application of PK/PD approach for gentamicin dosing strategy in ESRD patients receiving hemodialysis (Zhuang et al. 2016). Similar approach can also be utilized for dose adjustment in other special populations. A one-compartment model was able to describe gentamicin PK adequately. An additional clearance during hemodialysis was incorporated into the model as CLTOT = CLNR + CL R + HEMO ∙ CLHD such that the total clearance is the sum of nonrenal clearance, renal clearance, and hemodialysis clearance when dialysis is on (HEMO is an indicator with value 1 when hemodialysis is on and 0 when hemodialysis is off). PD model was developed based on data from in vitro static and dynamic time-kill studies against three bacterial strains. An adaptive factor was incorporated onto EC50 as previously described. Based on this semi-mechanistic PK/PD model, Monte Carlo simulation was performed with two dosing regimens: (i) 120 mg after hemodialysis, which is the recommendation from gentamicin label, and (ii) 240 mg 1 h before hemodialysis, which has been used in several literatures. The concentration-time profiles for 1000 subjects are showing in Fig. 7 (upper) with the lines indicating mean values and shaded bands implying the 50% (dark) and 95% CI (light). The bacterial killing over time in ESRD patients undergoing both dosing regimens was also predicted (Fig. 7 lower).

Y. Yu et al.

Simulation results suggested that PTA of fCmax/ MIC > 8 are 10% and 100% for the first and second dosing regimen, respectively. Since gentamicin toxicity is associated with the trough concentration, therefore PTA of fCtrough/MIC < 2 was also calculated for both dose regimens, yielding 80% and 25%, respectively. PTA results implied the first dosing regimen has better safety but lower efficacy than the second. Predictions of bacterial response from the semi-mechanistic PK/PD model suggested that the second dosing regimen is only slightly better than the first one with both dosing regimens displaying bacterial density reduction of >1  102. Therefore, the author concludes that the first dosing regimen provides a well-balanced benefit/risk profile than the second regimen in ESRD patients.

Modifications of the Method Pharmacometrics has become a very important component in drug development to maximize the clinical potential of drugs. During the recent 30 years, PK/PD approaches have been rapidly evolving and widely spread in academia, pharmaceutical industry, and regulatory agency. The principles and applications presented here illustrate the opportunities in model-informed drug development. Books from Ette and Williams (2007) and Schmidt and Derendorf (2014) provide excellent overview on the basic principles of pharmacometrics and numerous examples of its applications to expedite successful drug development. FDA and EMA issued guidelines on population PK (FDA 1999; EMA 2007) and other related topics such as pediatrics (EMA 2006), exposure-response relationships (FDA 2003), or QTc interval prolongation (FDA 2005). Both agencies have emphasized the power of PK/PD approach in effective data leveraging and risk/ benefit balancing for rational dose selection and response prediction. Currently, FDA is conducting model-informed drug development pilot program to facilitate decision-making and improve trial success probability for drug development.

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Fig. 7 Prediction of antibacterial activity of gentamicin against P. aeruginosa under two dosing regimens. (a) Concentration-time profile with 120 mg at the end of hemodialysis; (b) concentration-time profile with 240 mg 1 h before hemodialysis; (c) bacterial response-time profile with 120 mg at the end of hemodialysis; (d) bacterial

References and Further Reading Asin-Prieto E, Soraluce A, Troconiz IF, Campo Cimarras E, Saenz de Ugarte Sobron J, Rodriguez-Gascon A, Isla A (2015) Population pharmacokinetic models for cefuroxime and metronidazole used in combination as prophylactic agents in colorectal surgery: model-based evaluation of standard dosing regimens. Int J Antimicrob Agents 45(5):504–511 Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S (2004) Bacterial persistence as a phenotypic switch. Science 305(5690):1622–1625

response-time profile with 240 mg 1 h before hemodialysis. Dashed lines represent the safety threshold (2 mg/L). Arrows represent dosing time. Tick error bars represent the hemodialysis time interval. (Image adapted from Zhuang et al. (2016) and used with permission)

Benson JM (2017) Antimicrobial pharmacokinetics and pharmacodynamics in older adults. Infect Dis Clin N Am 31(4):609–617 Chaurasia CS, Muller M, Bashaw ED, Benfeldt E, Bolinder J, Bullock R, Bungay PM, DeLange EC, Derendorf H, Elmquist WF, Hammarlund-Udenaes M, Joukhadar C, Kellogg DL Jr, Lunte CE, Nordstrom CH, Rollema H, Sawchuk RJ, Cheung BW, Shah VP, Stahle L, Ungerstedt U, Welty DF, Yeo H (2007) AAPS-FDA workshop white paper: microdialysis principles, application, and regulatory perspectives. J Clin Pharmacol 47(5):589–603 Craig WA, Redington J, Ebert SC (1991) Pharmacodynamics of amikacin in vitro and in mouse thigh and lung

1068 infections. J Antimicrob Chemother 27(Suppl C):29–40 Csajka C, Verotta D (2006) Pharmacokinetic-pharmacodynamic modelling: history and perspectives. J Pharmacokinet Pharmacodyn 33(3):227–279 Czock D, Keller F (2007) Mechanism-based pharmacokinetic-pharmacodynamic modeling of antimicrobial drug effects. J Pharmacokinet Pharmacodyn 34(6):727–751 Derendorf H, Meibohm B (1999) Modeling of pharmacokinetic/pharmacodynamic (PK/PD) relationships: concepts and perspectives. Pharm Res 16(2):176–185 Derendorf H, Lesko LJ, Chaikin P, Colburn WA, Lee P, Miller R, Powell R, Rhodes G, Stanski D, Venitz J (2000) Pharmacokinetic/pharmacodynamic modeling in drug research and development. J Clin Pharmacol 40(12 Pt 2):1399–1418 Drusano GL (2004) Antimicrobial pharmacodynamics: critical interactions of ‘bug and drug’. Nat Rev Microbiol 2(4):289–300 EMA (2006) Guidance on the role of pharmacokinetics in the development of medicinal products in the pediatric population. http://www.ema.europa.eu/docs/en_GB/ document_library/Scientific_guideline/2009/09/WC5 00003066.pdf EMA (2007) Guidance on reporting the results of population pharmacokinetic analyses. http://www.ema. europa.eu/docs/en_GB/document_library/Scientific_ guideline/2009/09/WC500003067.pdf Ette EI, Williams PJ (2007) Pharmacometrics: the science of quantitative pharmacology. Wiley, Hoboken FDA (1999) Guidance for industry population pharmacokinetics. https://www.fda.gov/downloads/drugs/guid ances/UCM072137.pdf FDA (2003) Guidance for industry exposure-response relationships – study design, data analysis, and regulatory applications. https://www.fda.gov/downloads/ drugs/guidancecomplianceregulatoryinformation/guid ances/ucm072109.pdf FDA (2005) Guidance for industry E14 clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for nonantiarrhythmic drugs. https://www. fda.gov/downloads/Drugs/GuidanceComplianceRe gulatoryInformation/Guidances/ucm073153.pdf Felmlee MA, Morris ME, Mager DE (2012) Mechanismbased pharmacodynamic modeling. Methods Mol Biol 929:583–600 Gabrielsson J, Weiner D (2016) Pharmacokinetic and pharmacodynamic data analysis: concepts and applications, 5th edn., revised and expanded. Apotekarsocieteten, Stockholm Gieschke R, Burger HU, Reigner B, Blesch KS, Steimer JL (2003) Population pharmacokinetics and concentration-effect relationships of capecitabine metabolites in colorectal cancer patients. Br J Clin Pharmacol 55 (3):252–263 Gong X, Hu M, Zhao L (2018) Big data toolsets to pharmacometrics: application of machine learning for time-to-event analysis. Clin Transl Sci 11:305–311

Y. Yu et al. Goutelle S, Maurin M, Rougier F, Barbaut X, Bourguignon L, Ducher M, Maire P (2008) The Hill equation: a review of its capabilities in pharmacological modelling. Fundam Clin Pharmacol 22(6):633–648 Greco WR, Bravo G, Parsons JC (1995) The search for synergy: a critical review from a response surface perspective. Pharmacol Rev 47(2):331–385 Holford N (2013) A time to event tutorial for pharmacometricians. CPT Pharmacometrics Syst Pharmacol 2: e43 Jusko WJ (1971) Pharmacodynamics of chemotherapeutic effects: dose-time-response relationships for phasenonspecific agents. J Pharm Sci 60(6):892–895 Levison ME (2004) Pharmacodynamics of antimicrobial drugs. Infect Dis Clin N Am 18(3):451–465. vii Liu P, Rand KH, Obermann B, Derendorf H (2005) Pharmacokinetic-pharmacodynamic modelling of antibacterial activity of cefpodoxime and cefixime in in vitro kinetic models. Int J Antimicrob Agents 25(2):120–129 Mager DE, Wyska E, Jusko WJ (2003) Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos 31(5):510–518 Mahmood I (2007) Prediction of drug clearance in children: impact of allometric exponents, body weight, and age. Ther Drug Monit 29(3):271–278 Mahmood I, Staschen CM, Goteti K (2014) Prediction of drug clearance in children: an evaluation of the predictive performance of several models. AAPS J 16(6):1334–1343 Meng L, Mui E, Holubar MK, Deresinski SC (2017) Comprehensive guidance for antibiotic dosing in obese adults. Pharmacotherapy 37(11):1415–1431 Mould DR, Upton RN (2012) Basic concepts in population modeling, simulation, and model-based drug development. CPT Pharmacometrics Syst Pharmacol 1:e6 Mouton JW, Vinks AA (2005) Pharmacokinetic/pharmacodynamic modelling of antibacterials in vitro and in vivo using bacterial growth and kill kinetics: the minimum inhibitory concentration versus stationary concentration. Clin Pharmacokinet 44(2):201–210 Mouton JW, Dudley MN, Cars O, Derendorf H, Drusano GL (2005) Standardization of pharmacokinetic/pharmacodynamic (PK/PD) terminology for anti-infective drugs: an update. J Antimicrob Chemother 55(5):601–607 Nielsen EI, Viberg A, Lowdin E, Cars O, Karlsson MO, Sandstrom M (2007) Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments. Antimicrob Agents Chemother 51(1):128–136 Nielsen EI, Cars O, Friberg LE (2011a) Pharmacokinetic/ pharmacodynamic (PK/PD) indices of antibiotics predicted by a semimechanistic PKPD model: a step toward model-based dose optimization. Antimicrob Agents Chemother 55(10):4619–4630 Nielsen EI, Cars O, Friberg LE (2011b) Predicting in vitro antibacterial efficacy across experimental designs with

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a semimechanistic pharmacokinetic-pharmacodynamic model. Antimicrob Agents Chemother 55(4):1571–1579 Nolting A, Dalla Costa T, Rand KH, Derendorf H (1996) Pharmacokinetic-pharmacodynamic modeling of the antibiotic effect of piperacillin in vitro. Pharm Res 13(1):91–96 Roberts JA, Kirkpatrick CM, Lipman J (2011) Monte Carlo simulations: maximizing antibiotic pharmacokinetic data to optimize clinical practice for critically ill patients. J Antimicrob Chemother 66(2):227–231 Schmidt S, Derendorf H (2014) Applied pharmacometrics. AAPS advances in the pharmaceutical sciences series: 14. Springer, New York Schuck EL, Grant M, Derendorf H (2005) Effect of simulated microgravity on the disposition and tissue penetration of ciprofloxacin in healthy volunteers. J Clin Pharmacol 45(7):822–831 Sharma A, Jusko WJ (1996) Characterization of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm 24(6):611–635 Sheiner LB (1997) Learning versus confirming in clinical drug development. Clin Pharmacol Ther 61(3):275–291 Sheiner LB, Beal SL (1980) Evaluation of methods for estimating population pharmacokinetics parameters. I. Michaelis-Menten model: routine clinical pharmacokinetic data. J Pharmacokinet Biopharm 8(6):553–571 Sheiner LB, Rosenberg B, Melmon KL (1972) Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput Biomed Res 5(5):411–459 Singh RSP, Mukker JK, Drescher SK, Deitchman AN, Derendorf H (2017) A need to revisit clinical breakpoints of tigecycline: effect of atypical non-linear plasma protein binding. Int J Antimicrob Agents 49(4):449–455 Sy SKB, Derendorf H (2016) Pharmacokinetics I: PK-PD approach, the case of antibiotic drug development. In: Müller M (ed) Clinical pharmacology: current topics and case studies, 2nd edn. Springer, Cham, pp 185–217 Sy SK, Zhuang L, Derendorf H (2016) Pharmacokinetics and pharmacodynamics in antibiotic dose optimization. Expert Opin Drug Metab Toxicol 12(1):93–114

1069 Sy S, Zhuang L, Xia H, Beaudoin ME, Schuck VJ, Derendorf H (2017) Prediction of in vivo and in vitro infection model results using a semimechanistic model of avibactam and aztreonam combination against multidrug resistant organisms. CPT Pharmacometrics Syst Pharmacol 6(3):197–207 Sy SKB, Zhuang L, Xia H, Beaudoin ME, Schuck VJ, Nichols WW, Derendorf H (2018) A mathematical model-based analysis of the time-kill kinetics of ceftazidime/avibactam against Pseudomonas aeruginosa. J Antimicrob Chemother 73(5):1295–1304 Tam VH, Schilling AN, Nikolaou M (2005) Modelling time-kill studies to discern the pharmacodynamics of meropenem. J Antimicrob Chemother 55(5):699–706 Tam VH, Ledesma KR, Vo G, Kabbara S, Lim TP, Nikolaou M (2008) Pharmacodynamic modeling of aminoglycosides against Pseudomonas aeruginosa and Acinetobacter baumannii: identifying dosing regimens to suppress resistance development. Antimicrob Agents Chemother 52(11):3987–3993 Treyaprasert W, Schmidt S, Rand KH, Suvanakoot U, Derendorf H (2007) Pharmacokinetic/pharmacodynamic modeling of in vitro activity of azithromycin against four different bacterial strains. Int J Antimicrob Agents 29(3):263–270 Zhuang L, Sy SK, Xia H, Singh RP, Mulder MB, Liu C, Derendorf H (2015) Evaluation of in vitro synergy between vertilmicin and ceftazidime against Pseudomonas aeruginosa using a semi-mechanistic pharmacokinetic/pharmacodynamic model. Int J Antimicrob Agents 45(2):151–160 Zhuang L, He Y, Xia H, Liu Y, Sy SK, Derendorf H (2016) Gentamicin dosing strategy in patients with end-stage renal disease receiving haemodialysis: evaluation using a semi-mechanistic pharmacokinetic/pharmacodynamic model. J Antimicrob Chemother 71(4):1012–1021 Zuluaga AF, Salazar BE, Rodriguez CA, Zapata AX, Agudelo M, Vesga O (2006) Neutropenia induced in outbred mice by a simplified low-dose cyclophosphamide regimen: characterization and applicability to diverse experimental models of infectious diseases. BMC Infect Dis 6:55

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Contents Introduction and Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 The Interests of the Individual Patient in the Study Have Precedence over the Interests of Society at Large . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1073 Regulatory Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Key Definitions (ICH E2A) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adverse Event or Adverse Experience (AE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Adverse Drug Reaction (ADR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Unexpected Adverse Drug Reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serious Adverse Event (SAE) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serious Unexpected Suspected Adverse Reactions (SUSARs) . . . . . . . . . . . . . . . . . . . . . . .

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Planning of a Clinical Trial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Investigator’s Brochure (IB) is the Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Informed Consent Form to Include All Relevant Safety Information . . . . . . . . . . . . Routine Safety Data Collection: Adverse Events/Serious Adverse Events . . . . . . . . . . Outcome Events/Unblinding of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of Expected Events/Adverse Events of Special Interest . . . . . . . . . . . . . . . . . . . Standardized Data Collection for Later Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data and Safety Monitoring Boards (DSMB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endpoint Adjudication Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Collection and Reporting of Adverse Events/Serious Adverse Events . . . . . . . . . . . 1077 Collection of Adverse Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077 Reporting of Serious Adverse Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1077

R. Heissing (*) Heissing Pharmacovigilance Consulting GmbH, Partenheim, Germany e-mail: [email protected] A.-R. van Troostenburg Gilead Sciences International Ltd, Cambridge, UK © Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_63

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R. Heissing and A.-R. van Troostenburg Monitoring of Patient’s Safety and Actions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Continuous Monitoring of Patient’s Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development Safety Update Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Actions and Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1080

Abstract

Pharmacovigilance is a broad field that spans across all stages of the life cycle from preclinical drug development, clinical development, marketing approval, and post-marketing use. This chapter will focus on the pharmacovigilance aspects of interventional clinical trials. It provides a brief overview over the key elements of protecting patients in clinical trials as well as collecting and reporting safety information for the purposes of developing the safety profile of an investigational medicinal product. Regulations and requirements across the globe are complex and national, while certain international standards through the ICH guidelines form a common basic platform through which multinational clinical trials can harmonize.

Introduction and Scope Pharmacovigilance / Drug safety is a broad field that spans across all stages of the life cycle from preclinical drug development, clinical development, marketing approval, and post-marketing use. This chapter will focus on the drug safety aspects of interventional clinical trials; other studies such as observational or epidemiological studies and non-study pharmacovigilance of drug use after marketing approval are beyond the scope. The following pages will initially look at the regulatory requirements – taking the ICH guidelines (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, ICH) first and then adding a brief view on the legal framework and guidance in three major regions in the world: the US Food and Drug Administration (FDA), the European Union’s

European Medicines Agency (EMA), and the Japanese Pharmaceuticals and Medical Devices Agency (PMDA). Thereafter key definitions and terminology are introduced to provide the basis for understanding of the following discussions around planning a clinical trial, the collection and reporting of adverse events and the tools and requirements for monitoring of patient safety in clinical trials, and, lastly, actions to take to actively protect patients. The rules and regulations that govern drug safety in clinical trials are complex and, as they are within the jurisdiction of each individual country’s health authority, differ across the world. Therefore, this chapter will focus on the underlying fundamental principles and rely on the definitions and basic rules agreed in the ICH guidelines, which form the basis from which individual country regulations have further evolved. Wherever and whenever an actual clinical trial is being planned, it is critical to ensure that the specific rules of the country or countries where this clinical trial will be conducted are followed. When designing a clinical trial, a clear research question needs to translate into defined objectives for the study and specific data collected to meet the primary (and secondary) objectives of the trial. In the typical efficacy clinical trial, these will be a small and very well-defined set of data points. However, the purpose of drug safety in clinical trials is broader and beyond the narrowly defined efficacy data set, and the vast majority of data collected is in support of the drug safety requirements. Drug safety in clinical trials has a twofold purpose and in the order listed firstly to protect the subject/patient who is participating in the clinical trial and secondly to understand the general drug safety and tolerability of the drug being studied for the protection of patients who would be exposed to the drug after its approval in general

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use and under less well-controlled conditions than those within a clinical trial. The purpose and priorities have their origin in the Declaration of Helsinki and form the fundamental basis for clinical trials and the ethics governing scientific research.

The Interests of the Individual Patient in the Study Have Precedence over the Interests of Society at Large This principle is taken forward in the practical guidelines on GCP (ICH E6) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2016).

Regulatory Requirements ICH The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Guideline E6 “Good Clinical Practice” (GCP) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2016) was finalized in 1996. It is the international basis describing the responsibilities and expectations of all stakeholders in the conduct of clinical trials in Europe, Japan, the USA, and beyond. The clinical safety-related guidelines (as opposed to the ICH safety guidelines, which concern preclinical requirements) are presented in ICH E2A-F and the Medical Dictionary for Regulatory Affairs (MedDRA) under the ICH multidisciplinary guidelines. Those specific to safety in clinical trials are: • E2A – Clinical Safety Data Management: Definitions and Standards for Expedited Reporting (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 1994) • E2B(R3) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2013a)

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and E2B(R3) IWG (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2013b) – Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports (ICSR) and Implementation: Electronic Transmission of ICSRs • E2F – Developmental Safety Update Report (DSUR) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2010)

USA The Food and Drug Administration (FDA) is the supervising authority for investigational new drugs and interventional clinical trials in the USA. Interventional clinical trials performed in the USA are regulated by the code of federal regulations CFR (Code of Federal Regulations (CFR) – Title 21 food and drugs – Chapter I food and drug administration (FDA), Department of health and human services – Subchapter D – Drugs for human use – Part 312 – Investigational new drug application (n.d.)). In CFR Title 21, Chapter I, subchapter D, part 312 “Investigational New Drug Application,” the general rules and detailed requirements are described, from setup and application until the closure of a clinical trial. Definitions and specifics about the collection, ongoing analysis, and reporting of safety information are outlined in §312.32 “IND safety reporting.” Additional information can be found in the “Final rule” guidance document on 21 CFR Parts 312 and 320 “Investigational New Drug Safety Reporting Requirements for Human Drug and Biological Products and Safety Reporting Requirements for Bioavailability and Bioequivalence Studies in Humans.”

EU The regulations that currently govern interventional clinical trials in the EU are complex.

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EU Directive 2001/20/EC is the underlying basis, but legally binding is its adoption into local legislation in the individual EU member states (e.g., German “Arzneimittelgesetz”), which in parts vary from country to country. Details on the collection and reporting of safety information can be found in EU “CT-3” guidance document “Detailed guidance on the collection, verification and presentation of adverse event/ reaction reports arising from clinical trials on medicinal products for human use” (2011/C 172/01) (European Commission 2011). EU Clinical Trial Regulation 536/2014 was adopted and entered into force in 2014 and will replace in the second half of 2019 the existing EU Clinical Trial Directive 2001/20/EC and all national legislation that was put in place to implement this Directive. But there will be an interim period of 3 years, where clinical trials can still be run according to the national legislation.

necessarily have to have a causal relationship with this treatment.

Japan

Serious Adverse Event (SAE)

The Pharmaceuticals and Medical Devices Agency (PMDA) is the supervising authority for the conduct of interventional clinical trials in Japan. Clinical trials in Japan are carried out in accordance with the Japanese Ordinance on Standards for Conduct of Clinical Trials (GCP) (Enforcement Regulations of the Law on Securing Quality, Efficacy and Safety of Pharmaceuticals, Medical Devices, Regenerative and Cellular Therapy Products, Gene Therapy Products, and Cosmetics 1961; Pharmaceutical Administration and Regulations in Japan 2017; J-GCP Ordinance of the Ministry of Health and Welfare 1997).

Any untoward medical occurrence that results in death, is life-threatening, requires inpatient hospitalization or prolongation of existing hospitalization, results in persistent or significant disability/ incapacity, or is a congenital anomaly/birth defect.

Key Definitions (ICH E2A)

Planning of a Clinical Trial

Adverse Event or Adverse Experience (AE)

The Investigator’s Brochure (IB) is the Reference

Any untoward medical occurrence in a patient or clinical investigation subject administered a pharmaceutical product and which does not

This is the key document for any clinical trial; it forms the core of the submission package when applying for approval to conduct the clinical trial

Adverse Drug Reaction (ADR) All noxious and unintended responses to a medicinal product related to any dose should be considered adverse drug reactions. The phrase “responses to a medicinal product” means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility, i.e., there are facts (evidence) or arguments to suggest a causal relationship.

Unexpected Adverse Drug Reaction An unexpected adverse reaction is one, the nature or severity of which is not consistent with the applicable investigator’s brochure.

Serious Unexpected Suspected Adverse Reactions (SUSARs) Any adverse event that is serious, is unexpected, and is suspected to be causally related to the investigational drug.

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and as a living document will evolve during the conduct of a clinical trial program, as more information becomes available over time. Each clinical trial is supported by the IB, containing all information on the study drug available at the time and providing a broad overview of the known safety and tolerability of the investigational medicinal product (IMP). The majority of the IB will be taken up by detailed descriptions of studies and research results available and displaying both the growing knowledge of the targeted efficacy and the gained safety information to inform the investigators, ethics committees (ECs)/institutional review boards (IRBs), and authorities approving the trial. The safety information will be further summarized in a concise section of the IB of a highly technical nature, known as the Reference Safety Information (RSI). The RSI presents a list of any events identified in previous clinical trials as expected serious adverse drug reactions (SADR). Of note, this means that events which are not listed in the RSI are “unexpected” and therefore potentially reportable: the RSI guides the selection of individual safety reports that have to be reported to authorities, other investigators, and ethics committees during the conduct of the trial – because they are new information that has not previously been observed. The requirements for selecting appropriate events for the RSI is guided by detailed rules, particularly clearly laid out within the European regulations.

The Informed Consent Form to Include All Relevant Safety Information While the IB is a technical, scientific document that is used for reviewing authorities, ethics committees, and investigators, the subject/patient in the study also needs to receive transparent and comprehensive information about the clinical trial, the demands of the protocol, and what is known about the study drug. The relevant safety information available – and what is not yet known about the drug – needs to be summarized in a concise, readable fashion that allows a layperson to understand the possible benefits but

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importantly the possible risks of participation in the clinical trial.

Routine Safety Data Collection: Adverse Events/Serious Adverse Events As part of the planning/designing of a clinical trial, the protocol will have to define very clearly what data will be collected in the context of the trial. For interventional clinical trials, the standard set of data to be collected in (almost) all instances are adverse events and serious adverse events, regardless of causality – furthermore, the investigator and study sponsor are required to make an assessment as to whether or not an event observed in a study subject is possibly related to the study drug or not. Where the investigator – or the receiving sponsor of the clinical trial – considers an event possibly related to the study drug, the adverse event is considered a possible adverse drug reaction (ADR). The study protocol defines not only what safety data will be collected, but it will also define timelines by which the investigator has to communicate with the sponsor and send certain reports in a more expedited manner than simply collecting the information in the case report form (CRF) of the trial subject. Typically events that have to be sent faster to the sponsor of the clinical trial are those that may require onward reporting in a quick turnaround to authorities, ethics committees, and other investigators – these will be the SAEs but may also include certain events of special interest and some so-called special situation reports (SSRs) like pregnancies, or medication errors (see below).

Outcome Events/Unblinding of Data The adverse event information collected in certain clinical trials may serve a purpose beyond the general understanding of the safety of study subjects, but specific events may also constitute part of the data collected for answering the efficacy objective of the clinical trial. Such trials are named clinical outcome trials and study the impact of an intervention (in most cases the administration of a drug) on

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defined clinical outcomes. For example, cardiovascular outcome trials investigate strokes, heart attacks, and death – all of such events would clearly be collected as adverse event safety data but at the same time are the key “endpoint events” for answering the efficacy question whether a certain medical intervention may prevent or reduce such cardiovascular outcomes. In such clinical outcome trials, the planning of collection, data handling, assessment, and adjudication of defined “outcome events” is a key point and requires a complex system setup to standardize events as much as possible. Beyond the investigator reporting an event, such events routinely go to a separate adjudication committee, who reviews the available information and applies preset definitions and algorithms to maximize homogeneity of diagnosis. In case of clinical outcome trials, the outcome events often constitute serious unexpected events In case of a suspected causal association these events would meet the definition of a SUSAR and require unblinding and reporting to Authorities and Ethics Committees. It is therefore important in a clinical outcome trial protocol, that the protocol clearly identifies the specific outcome events. The study sponsor will have to obtain agreement with the supervising authorities and ethics committees for special conditions for the reporting or unblinding of outcome events. Global guidelines allow for clearly defined, case-by-case agreements on reporting conditions for outcome events to avoid routine and systematic unblinding of such events, which may threaten the data integrity of the clinical trial.

Definition of Expected Events/ Adverse Events of Special Interest A primary part of the planning for a clinical trial and indeed a whole clinical development program is to determine (and update regularly) the RSI in the IB but also consider whether there are any adverse events of special interest (AESI). These AESIs may be nonserious in nature, but perhaps the preclinical program suggested that there may be a possible issue with a certain type of event (e.g., skin reactions or diarrhea) or they are part of the symptoms

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of the treated disease but may be of particular importance to determine whether the study drug is a contributing factor or modifies the event in any way and requires an intensive additional diagnostic pathway to ensure full understanding (e.g., hepatic or neurologic events of a certain nature). Adverse events of special interests are then identified in the IB and the specific study protocol and require the investigator to perform more detailed data collection, possibly perform a set of predefined mandatory additional diagnostic tests and also expedited communication to the study sponsor – often aligned with the timelines for reporting SAEs. Therefore AESIs will undergo special and fast scrutiny by both investigators and sponsors, and the information on such events will be maximized – this may over the course of a development program disprove a preclinical concern or elucidate specific treatment pathways that may be helpful for later use of the drug in the post-approval era (such as effective treatment of drug-induced diarrhea).

Standardized Data Collection for Later Pooling When planning a drug development program, the overall aim is to develop clinical data to the purpose of marketing authorization. From the very outset, the different clinical trials to be conducted need to be designed in a fashion that will allow them to be used not just in isolation, but the data should be standardized so that pooling of data from multiple studies becomes possible. In order to support analysis and understanding of emerging safety profiles, the data from different studies should be combinable as well as separating different subgroups and populations from across studies.

Data and Safety Monitoring Boards (DSMB) When planning a clinical trial or a whole program, the setting up of a DSMB is a complex and important task to ensure effective oversight over the clinical study or program. It requires the identification of the appropriate membership and

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development of communication routes and mechanisms to supply data, charters, and rules of communication among the members as well as with the sponsor, meeting frequency, review principles, and standards of assessment. The DSMB is set up independently from the conduct or sponsor of a clinical trial with the aim to perform ongoing unblinded data monitoring while the study is ongoing. DSMBs are often set up to support a number of studies in the same program and provide ongoing guidance to the study sponsor as to whether the trial may continue as it is, requires modification, or should be terminated. The sponsor remains blind and will not receive the detailed reviews of the DSMB, but only the final recommendation. The full DSMB materials, minutes, analyses and documentation will be added to the Trial Master File after end of study.

Endpoint Adjudication Committee Where an endpoint of a clinical trial is not an objective measure (such as a blood pressure or a particular laboratory value) it is important to ensure standardization of the measure and remove subjectivity and variance as much as possible. Particularly for clinical outcome trials, where the endpoints may be composites of multiple adverse events – the standardization of diagnosis of each of the contributing events is critical and should be agreed upon beyond the individual investigator. An endpoint adjudication committee is a way to ensure that the defined endpoint events are diagnosed to a common standard and context independent, based on preset data elements, clinical tests, and diagnostics that allow a central diagnosis to be made.

Collection and Reporting of Adverse Events/Serious Adverse Events

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informed consent and until leaving the clinical trial (protocol defined end of data collection). All AEs experienced by a patient at a certain point in time form an adverse event case report, an ICSR (individual case safety report). The ICSR forms the smallest unit of reporting – it may contain more than one event in the patient. The investigator uses an AE/SAE form, paper or electronic, to document the AE(s) the study participant has experienced. The AE/SAE form can be integrated into the electronic Case Report Form (eCRF) of the trial or be a separate, loose form. All adverse events have to be evaluated by the investigator and sponsor concerning seriousness, causal relationship, and expectedness. The assessments given by the investigator should not be downgraded by the sponsor. Usually all nonserious AEs are entered into the clinical trial database and all serious AEs into the clinical trial and safety database. Consistency of the two databases has to be ensured by either the way of collecting AEs (simultaneously and electronically into both databases) or later reconciliation between the databases. Case reports containing a serious unexpected suspected adverse reaction (SUSAR) event are usually unblinded, unless they are defined as exempted outcome events by the clinical trial protocol. The investigational drug given (verum, comparator, or placebo) is then documented in the case report in the safety database. Adverse events are coded using the Medical Dictionary for Regulatory Affairs (MedDRA). Special situation reports without an associated adverse event (e.g., pregnancy, overdose, medication error, etc.) might not qualify for individual case reporting but nevertheless need to be recorded by the sponsor for continuous and cumulative safety analysis and presentation and discussion in periodic aggregate safety reports such as the Development Safety Update Report.

Collection of Adverse Events Reporting of Serious Adverse Events Adverse Events and Serious Adverse Events are actively collected by the investigator and sponsor starting from the moment a participant signs the

The obligations on the collection and reporting of safety information of a clinical trial are directly

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conferred with the approval to perform a clinical trial and lie with the study sponsor (the applicant for the clinical trial authorization). The safety information collected in the clinical trial is reported by the investigators to the study sponsor – in the (e)CRF or on special forms and there is a subset of data that requires prompt (expedited) reporting to the sponsor – these are mainly serious events. Case reports containing a serious unexpected adverse reaction then have to be reported further by the sponsor. When a case is initially reported to a sponsor, the report may be incomplete – for reasons where there is still a lack of knowledge, as the situation of the patient is still evolving or because a form has not been effectively completed. Only cases that contain a minimum set of data should be reported onward – these are considered “valid” cases for the purposes of reporting. For the purpose of reporting cases of suspected adverse reactions, the minimum data elements for a valid case are: • • • •

An identifiable reporter An identifiable patient An adverse reaction A suspect medicinal product (see Annex IV, ICH-E2D Guideline (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2003))

If any of these elements are missing, the case is considered “nonvalid” for expedited reporting purposes, and while at that time therefore not reportable, it is incumbent on the sponsor of the clinical trial to follow up the case intensively and urgently to ensure that at least the minimal criteria are all met. In the setting of a clinical trial, there should not really be any nonvalid cases, as the structured and tight control over the treatment and data collection on all patients should ensure that all four minimal criteria are always available even at the first instance. Depending on national legislation, the individual case reports have to be as a minimum reported in an expedited manner to national competent authorities. There are strict timelines defined for

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the reporting, usually 7 calendar days after first awareness, if the case report is classified as fatal or life-threatening, and 15 days for all other serious case reports. Based on the applicable legislation, these case reports might have to be reported, if the adverse reaction not only is causally associated to verum, but to a comparator drug or even placebo (although this is rare, if there is a suspicion of the ADR being related to an excipient). The identification of potentially reportable cases in a timely manner as they are received by the sponsor of the clinical trial is a key activity in the ongoing support of a clinical trial. Particularly where a clinical trial is conducted in a doubleblind fashion, the regulatory requirements of the EU require unblinding of the treatment code for the patient, unless there is a prior documented agreement with the authority who approved the clinical trial that certain events do not require unblinding (such as in the case of endpoint events in a clinical outcome trial). In the context of international clinical trials conducted in several countries worldwide, the sponsor will usually apply a common standard to reporting to authorities – so that all authorities receive the same information and therefore may report cases in an unblinded manner to authorities who do not require it. Figure 1 presents an algorithmic decision tree to determine whether a case requires unblinding and potentially reporting. This is taking general principles only and would need to be adjusted and supported by detailed reporting requirements for each country, ethics committee and site, as any of these may have additional requirements for other events to be reported than SUSARs. The challenge with unblinding of SUSARs for the purposes of expedited reporting is to ensure that only a few select individuals in the sponsor responsible for reporting the event (typically a part of the drug safety personnel) receive the information on the treatment allocation of a patient and that this information is not shared with personnel otherwise involved in the conduct of the clinical trial or investigators. In addition to reporting of SUSARs to supervising national competent authorities, individual countries may require reporting of individual case

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Fig. 1 Assessing reportability in blinded clinical trials (by ARvanTroostenburg)

reports to ethics committees and to participating investigators. Timelines and report format vary from 7/15 days individual case reports to, e.g., quarterly batch reports with cases included in form of a line listing. Also, while reports to ethics committees will be of the same standard as the reports going to the national competent authority – i.e., in an unblinded fashion, reporting to investigators is done blinded, so as not to affect the integrity of the blinded study design and bias the investigators. Only in the case of a major safety concern arising from received safety information is consideration given to unblinding investigators at the same time as all other personnel – this is in general only when major actions for the safety of the patients in the clinical trial may have to be considered (see further below for Urgent Safety Measures). If required or possible, expedited reporting can be done electronically using the ICH defined E2B standard. Reporting requirements for serious unexpected adverse reactions continue even after the end of the clinical trial. Therefore collection, processing (only in the safety database), and reporting of respective case reports received after the end of the trial continue. Events relevant for patient safety may occur during a clinical trial which do not fall within the definition of a serious unexpected adverse reaction and thus are not subject to the reporting requirements described above, e.g. a major safety finding from a newly completed animal study,

such as carcinogenicity. Sponsors are also obliged to inform the national competent authority, ethics committee, and investigators of findings that could adversely affect the safety of subjects, impact the conduct of the trial, and might materially alter the current benefit-risk assessment for an investigational drug.

Monitoring of Patient’s Safety and Actions Continuous Monitoring of Patient’s Safety The sponsor is responsible for the ongoing safety evaluation of the investigational drug. Ongoing safety evaluation consists of various layers of safety monitoring activities. Evaluation and assessment of individual case reports are performed during case processing. An important aspect is the causality assessment. To decide about a potential causal association between drug treatment and adverse event a variety of aspects can be taken into account, e.g., timely relationship, pharmacological plausibility, de-challenge and rechallenge, concurrent diseases, and concomitant medication. Cumulative interim safety analyses are performed at specified time points. It may include a comparison of adverse event rates for verum against comparator/placebo or a comparison of adverse event rates against predefined expected

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rates based on epidemiological data, or identification of trigger events to detect cases of interest, e.g. Hy’s Law cases and drug-induced liver injury. In case of blinded clinical trials, the support of an external Data and Safety Monitoring Board for unblinded analysis is needed to keep staff of the sponsor involved in the conduct of the clinical trial blind. Another tool for a cumulative safety evaluation is the Development Safety Update Report.

Development Safety Update Report The ICH E2F Guideline (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) 2010) defines format and content of the Development Safety Update Report (DSUR). It provides periodic (typically annually) analysis of clinical trial safety for an investigational drug. The DSUR summarizes identified and potential risks, describes new safety issues that arose during the period of the report, determines if reporting period safety information is in accordance with prior product safety knowledge, and provides an update on the clinical development program. The main focus is on interventional clinical trials, ongoing or completed during the reporting interval. The investigator’s brochure is the reference document for the DSUR. Included in the DSUR is a line listing of serious adverse reactions arising in the reporting interval and a cumulative tabulation of all serious adverse events organized by MedDRA System Organ Class, from the start of development to date, for verum, comparator, placebo, and still blinded cases.

Actions and Measures New relevant safety information, such as newly identified safety issues, changed product safety knowledge, or DSMB recommendations, may warrant actions to be taken in a clinical trial or even across a whole clinical program.

R. Heissing and A.-R. van Troostenburg

A basic measure is the update of the investigator’s brochure. New product safety knowledge is included and newly identified side effects added to the Reference Safety Information (RSI). Updates of the IB are usually performed on a frequent basis (e.g., yearly) to include and summarize the increasing knowledge about the developmental drug. Safety issues having a significant impact on the safety of the subjects may require a substantial protocol amendment, e.g. changing exclusion criteria or introducing a new monitoring procedure and need to be submitted to national competent authorities and ethics committees for approval. Safety issues requiring urgent safety measures to protect subjects against any immediate hazard, such as temporarily halting of the clinical trial, may be taken immediately without prior authorization from the competent authority in form of an urgent amendment. The sponsor must inform the competent authority and the ethics committee concerned as soon as possible. Some countries have strict timelines in place for the reporting of Urgent Safety Measures, and there may be only a few hours from the information being available to the study sponsor to the need to inform competent authorities. Therefore a rapid assessment and decision-making system needs to be in place at the sponsor to be able to respond to major safety issues with all due haste for the protection of patients in the clinical trial – or also possibly a whole development program of many clinical trials.

References and Further Reading Code of Federal Regulations (CFR) – Title 21 food and drugs – Chapter I food and drug administration (FDA), Department of health and human services – Subchapter D – Drugs for human use – Part 312 – Investigational new drug application Directive 2001/20/EC of the European Parliament and of the council of 4 April 2001 on the approximation of the laws, regulations and administrative provisions of the Member States relating to the implementation of good clinical practice in the conduct of clinical trials on medicinal products for human use

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Enforcement Regulations of the Law on Securing Quality, Efficacy and Safety of Pharmaceuticals, Medical Devices, Regenerative and Cellular Therapy Products, Gene Therapy Products, and Cosmetics (MHW ordinance no. 1, February 1, 1961). Final revision: MHLW ordinance no.82, April 10, 2015 and MHLW ordinance no.92, July 31, 2014 (to be enforced on June 12, 2017) article 273 European Commission – Communication from the Commission – Detailed guidance on the collection, verification and presentation of adverse event/reaction reports arising from clinical trials on medicinal products for human use (‘CT-3’) (2011/C 172/01) June 2011 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) – Guideline E2A – Clinical safety data management: definitions & standards for expedited reporting October 1994 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) – Guideline E2D – Post-approval safety data management: definitions and standards for expedited reporting November 2003 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) – Guideline E2F – Developmental safety update report (DSUR) August 2010 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use

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(ICH) – Guideline E2B (R3) clinical safety data management: data elements for transmission of individual case safety reports July 2013a International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) – Guideline E2B (R3IWG implementation: electronic transmission of individual case safety reports July 2013b International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) – Guideline E6 ‘good clinical practice’ (GCP) – R1 June 1996 and integrated addendum R2 November 2016 J-GCP Ordinance of the Ministry of Health and Welfare No. 28 of March 27, 1997 (As last amended by the ordinance of ministry of health, labour and welfare No. 161 of December 28, 2012) and guidance on the ministerial ordinance on the standards for the conduct of clinical trials of medicinal products (PFSB/ELD notification no. 1228/7 dated 28 December 2012) Pharmaceutical Administration and Regulations in Japan (2017) (1.3 safety information on adverse reactions and infections during the study) Regulation (EU) no 536/2014 of the European Parliament and of the council of 16 April 2014 on clinical trials on medicinal products for human use, and repealing directive 2001/20/EC

Part III Regulations

Regulatory Guidance: ICH, EMA, FDA

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Contents General Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1086 Activities for ICH Conferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 Objectives of the ICH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 Overview of Guidelines for Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 Overview of Guidelines for Efficacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 ICH Topics E2 A to F . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Detailed Overview for Guidelines for Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carcinogenicity S 1 A-C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Genotoxicity Guidelines (ICH/S2A, S2B and S2(R1)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toxicokinetics/Pharmacokinetics (ICH/S3A and S3B) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICH/S4A (20) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reproductive Studies (ICH/S5 A+B) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICH/S6: Preclinical Safety Evaluation of Biotechnology-Derived Products . . . . . . . . . Safety Pharmacology (ICH/S7 A+B) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Immunotoxicology Studies (ICH/S8) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICH S9: Nonclinical Evaluation of Anticancer Pharmaceuticals . . . . . . . . . . . . . . . . . . . . . ICH Guideline S10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S11 Nonclinical Pediatric Safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multidisciplinary Guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ICH Multidisciplinary Guidelines M3 (Timing) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Common Technical Document (ICH/M4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1097 1097 1104 1105 1107 1108 1110 1112 1114 1118 1120 1125 1125 1127 1131

Outlook and future of ICH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1134 Outlook on ICH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136

Abstract G. Bode (*) Institute for Pharmacology and Toxicology, University Medical Center, University of Goettingen, Goettingen, Germany e-mail: [email protected]

Two revolutionary breakthroughs have favored the international development of pharmaceutical compounds: on one hand the International Conferences on Harmonization (ICH), on the

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_58

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other hand the computer sciences. ICH provides tremendous support for the strategies in general by creating global guidelines, and these recommend the basis for research, for preclinical and clinical development. A great number of solutions for detailed pending issues in the areas of Quality, Safety, and Efficacy are offered. The enormous amount of data is then organized for the Common Technical Dossier by teams of experimental researchers, physicians, computer scientists, and statisticians for submission to Regulatory Agencies for the final evaluations necessary before the market authorization. Thus, the pharmaceutical field is passing through a major period of transformation. Many changes are driven by information technology but also by enormous progress in medical scientific research. Therefore, it has to be recognized that many breakthroughs, made in labs as well as on laptops, have changed and facilitated the pharmaceutical world. This chapter highlights the progress in the International Conferences on Harmonization during the last decades; the focus is on preclinical Safety (Pharmacology and Regulatory Toxicology) with the objectives to identify risks for drugs in development by in silico, in vitro, and in vivo methods to prevent, treat, and cure diseases. When needed, then also regional guidances, e.g., from US Food and Drug Administration (FDA) or European Medicines Agency (EMA), are referred to. This global, complex procedure reveals: we are now living in the age of big data and success is only possible by an enthusiastic multidisciplinary communication and transparent cooperation.

General Considerations The International Conferences for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use is now rebaptized into the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH continuous in bringing together the regulatory authorities and pharmaceutical industry to discuss scientific and technical aspects of drug development

G. Bode

and registration. This was an important decision, because in the 1980s a great number of differing discrepancies came into existence for the drug development in different areas of the world with the result that many studies for quality, safety, and efficacy had to be repeated. Figure 1 illustrates one example from 1986, where more than 12 months studies were requested, even when clinical treatment was only intended for 1 week. Since its inception in 1990, ICH has gradually evolved to respond to the increasingly global face of drug development. ICH’s mission is to achieve greater comparable requirements worldwide to ensure that safe, effective, and high-quality medicines are developed and registered in the most resource-efficient manner. The International Conferences was jointly supported and organized by the Commission of the European Communities (CEC), the US Food and Drug Administration (FDA), and the Japanese Ministry of Health and Welfare (MHW), together with the pharmaceutical industry, as represented by the International Federation of Pharmaceutical Manufacturers Associations (IFPMA), the European Federation of Pharmaceutical Industry Associations (EFPIA), the US Pharmaceutical Manufacturers Association (PMA), and the Japanese Pharmaceutical Manufacturers Association (PMA) (Table 1). Many important initiatives have been undertaken between regulatory authorities and industry associations, particularly on a bilateral basis first, to promote harmonization of regulatory requirements between the three regions Japan, the USA, and the European Community. ICH owes much to these initiatives of experts during international symposia. ICH has been very successful in this harmonization process; its expansion from a threeparty movement to an internationally active process is supported worldwide. ICH marks today 25 years of successful transparent cooperation. Its reduction from huge international conferences to a more counseling organization reflects the fact that many of the major issues in quality, safety, and efficacy have been agreed upon and today focuses on special problems. The organization for the process of creating guidances has been clarified; the ICH Steering

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20

EC

Animal exposure (months)

Japan 15

Canada USA

10

5

0 1 week to month

Up to 3 months

3 months and longer

Proposed human exposure R. Walker, DIA Symposium, Tokyo, October 1990

Fig. 1 Durations of animal studies for proposed clinical trials (1986) Table 1 Organization of International Conferences on Harmonization (ICH) Organization Trade associations EFPIA European Federation of Pharmaceutical Industries and Associations JPMA Japanese Pharmaceutical Manufacturers Association PhRMA Pharmaceutical Research and Manufacturers Association (USA) Regulatory authorities European Commission and appropriate “parties” Union (CHMP, QWP, BQWP, EWP, SWP, PhVWP) Japan Ministry of health and welfare and appropriate expert support (MHLW, NIHS, universities) USA Food and drug administration and appropriate expert support (regulatory communication, efficacy, safety, quality – review staff, expert leads – CDER/CBER chief officers) Observers Canada, EFTA, WHO Interested World self-medication industry – WSMI, parties international generic pharmaceutical alliance – IGPA “Umbrella” organization, ICH secretariat IFPMA International Federation of Pharmaceutical Manufacturers Associations

Committee governs and controls the objectives and their progress on every step (Fig. 2). The groups of experts are competent and self-critical,

knowing that often special expertise is needed. ICH then allows specialists to be invited as consultants to the discussions to lift conclusions up to the state-of-the art levels. In addition, the ICH Step 2 Consensus papers are published and submitted to comments for a period of 6 months. Hereby all levels of information and recommendation need to be respected for harmonization development to optimize the design of the individual tests and supported the endeavor to avoid redundant studies. ICH guidelines represent always the highest level of scientific and regulatory recommendations. Regional guidelines like those from the EMA or FDA refine the ICH guidances by adding important details which also have to be taken into consideration. As an example, the ICH guidelines on carcinogenicity (S1 A-C) are supplemented by additional information via the EMA guideline on carcinogenicity on which organs need to be sampled and assessed by microscopic evaluation, or that this work should preferably be done by one board certified pathologist, or that historical data are only acceptable if these are not older than 5 years, due to a possible genetic drift in the number of generations during breeding. Thereafter, on the level of OECD guidelines, all details of the individual studies can be considered. Accordingly, complete regulatory guidance can only be gained when all specific guidelines are respected (Table 2).

1088 Fig. 2 Five steps of ICH before implementation

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5 Implementaon 4 Finalized Text 3 Formal Consultaon 2 Consensus Achieved 1 Technical Discussions in EWG

Table 2 Different levels of regulatory guidances Levels of recommendations for regulatory toxicology ICH Highest level of information, accepted by regulatory agencies, focus on the need and timing of studies. Example: Carcinogenicity ICH S1 A-C EMA or FDA Additional details helpful for strategies and individual tests Example: EMA (January 2003) – note for guidance on carcinogenic potential FDA (May 2002): Carcinogenicity studies protocol submissions OECD Study details, accepted internationally as standard methods for safety testing, regularly updated with the assistance of thousands of national experts from OECD member countries. Example: Test no. 451 carcinogenicity studies

This chapter focuses on safety. Safety means in the ICH language: Nonclinical safety investigations like toxicology or safety pharmacology studies, which identify undesirable and often unexpected adverse effects induced by pharmaceutical or chemical compounds. In vitro or in vivo investigations describe the type and degree of toxicity and assess the risks first for nonclinical models. The results support the subsequent management of the risks in humans, when participating in clinical trials or later as patients after market authorization, and attempt to analyze the mechanisms behind the alterations; they extrapolate preclinical hazards to humans and finally help to communicate these risks to populations concerned with exposure of that particular substance.

Test strategies for toxicological investigations have been refined and the extrapolation of preclinical results to humans improved. Soon after the finalization of important guidelines it became visible, that unnecessary studies were avoided, and as a result a number of animals could be saved for experiments as shown in 1997 (Table 3). The assessments of all properties should be conducted using high-quality scientific standards with data collection records readily available and in compliance with good laboratory practices (GLP) regulations stressing the requirement to document all results and provide safe conditions for archiving. The safety of drugs is of public interest; therefore, Ethical Committees take care of the safety of volunteers and patients as long as clinical investigations are running. Regulatory agencies have become partners for Industry and offer scientific advice, especially when the development reaches critical steps as, e.g., the decisions for administering the new drug for the first time to humans or later at the end of Phase II when an enormous extension in the number of patient enrolments takes place or finally for the MAA (Market Authorization Application). With the creation of the International Conferences/Council of Harmonisation (ICH), many of international initiatives were channeled into a global process, which continuous with the running revisions and updates of all considerations. The World Health Organization (WHO) is highly active in distributing existing knowledge, also in countries

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Table 3 Animal usage before and after the influence of ICH Advantages Animal use for toxicological studies Single-dose toxicity (2 routes) Repeated-dose sub-chronic

Repeated-dose chronic

Reproduction

Carcinogenicity

2 rodent 1 non-rodent 1st duration (e.g., 1 month) Rodent Non-rodent 2nd duration (e.g., 3 months) Rodent Non-rodent Recovery Rodent Non-rodent 1st duration (e.g., 6 or 9 months) Rodent Non-rodent 2nd duration (e.g., 12 months) Rodent Non-rodent Segment I rat Japanese style US/EU style Segment II rat Japanese style US/EU style Segment II rabbit Segment III rat 1st species (e.g., rat) 2nd species (e.g., mouse) Medium- or short-term study

Total

Before ICH1 200–300 16–32

After ICH4 50–100 0

80 24

160a 32

160 32

0 0

200 40

0 40

160 32

160 32

160 32

160 32

192 96

192 0

96 + 48 96 60 96 400–500 500 0 2720–2936

0 96 60 96 400–500 500 160 1478–1583

Lumley and van Cauteren (1997) a Excludes offspring and dose-range finding studies

of development. There are a number of regional societies supporting this process (Table 4). Global Cooperation Groups (GCG) address increasing interest by non-ICH parties in ICH guidelines and operations. These groups facilitate dissemination of information on ICH activities, guidelines, and their use. The underlying principle is that ICH will not seek to impose its views, but the GCG will serve as resource for information. Four brochures have been published on ICH and GCG, available at ICH website www.ich.org. The ICH focuses on guidances for quality, preclinical safety (= toxicology), and efficacy (clinical

Table 4 Regional initiatives Regional harmonization initiatives APEC Asia-Pacific Economic Cooperation ASEAN Association of the Southeast Asian Nations GCC Gulf Cooperation Council PANDRH Pan American Network for Drug Regulatory Harmonization SADC Southern African Development Community

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effective substances) of pharmaceutical compounds. This section illustrates all ICH guidelines important for the safe use of drugs worldwide. Under the heading of S are all guidelines collected which contribute to the preclinical safety of pharmaceutical compounds. These can be in vitro experiments or classical toxicity studies in animals identifying unexpected adverse reactions or safety pharmacology investigations focusing on damages of physiological functions. Besides the general GLP requirements, all safety studies should always be supported by kinetic information; there is a need to know at which exposure levels which undesirable effects appear and if these reactions have any clinical importance or if thresholds for reactivity can be denominated for human conditions and therefore are acceptable if used with care.

Activities for ICH Conferences A Steering Committee was appointed with members from EU, FDA, MHW, EFPIA, JPMA, PMA, and IFPMA, with observers from the WHO and from the regulatory authorities of Canada and Switzerland (for EFTA). The Steering Committee sets up three joint industry/regulatory Expert Working Groups to deal with the technical aspects of the three main subject areas – quality, safety, and efficacy – which were discussed in three parallel workshops during the conferences. Each of the Expert Working Groups has members, representing EU, FDA, MHW, EFPIA, PMA, and JPMA. With advice from these technical Working Groups, the Steering Committee was responsible for the selection and prioritization of the topics discussed at the Workshops at the ICH conferences (Table 1). The International Conference on Harmonization differs from many other harmonization initiatives in that it has a recognized status and is backed by a commitment on the part of both industry and regulators, to facilitate greater harmonization of technical requirements in the three

G. Bode

regions and today worldwide. Commitment to these objectives, set out in the terms of reference, was reaffirmed by the Steering Committee in a statement issued first following the meeting held in Tokyo, October 1990.

Objectives of the ICH The objectives of ICH have been set out from the beginning and continue to be respected in all subsequent activities: 1. To provide a forum for a constructive dialogue between regulatory authorities and the pharmaceutical industry on the real and perceived differences in the technical requirements for product registration in the CEC, USA, and Japan 2. To identify areas where modifications in technical requirements or greater mutual acceptance of research and development procedures could lead to a more economical use of human, animal, and material resources, without compromising safety 3. To make recommendations on practical ways to achieve greater harmonization in the interpretation and application of technical guidelines and requirements for registration There is considerable success of the international discussions and constructive solutions documented in the guidelines.

Overview of Guidelines for Quality Quality: There are more than 20 guidelines focusing on issues concerning the quality of compounds. These deal with issues like stability, analytical validation, detailed solutions how to handle impurities, further aspects of pharmacopeias, quality of biotechnology-derived, specifications, manufacturing practices, pharmaceutical development, quality risk assessments, pharmaceutical quality systems, development and manufacture of drug substances, and life cycle

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management. In addition there are a number of issues with translateral importance, many of these can be found under the headings of multidisciplinary guidelines and sections.

Overview of Guidelines for Efficacy Efficacy: There are more than 30 guidelines dealing with problems of efficacy during clinical trials or after marketing authorization. Among them are as follows: Population Exposure: The Extent of Population Exposure to Assess Clinical Safety Good Clinical Safety Data Management: Definitions and Standards for Expedited Reporting Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports Clinical Safety Data Management: Periodic Safety Update Reports for Marketed Drugs Structure and Content of Clinical Study Reports Dose Response Information to support Drug Registration Ethnic Factors in the Acceptability of Foreign Clinical Data Good Clinical Practice Studies in support of Special Populations: Geriatrics General Considerations for Clinical Trials Statistical Principles for Clinical Trials Choice of Control Group for Clinical Trials Clinical Investigation of Medicinal Products in the Pediatric Population Principles for Clinical Evaluation of New Antihypertensive Drugs Clinical QT; Definitions in Pharmacogenetics, Pharmacogenomics Quantification of Genomic Biomarkers Multiregional Clinical Trials, Genomic Sampling and Safety Data Collection And in addition, like in quality and safety, there are a number of multidisciplinary guidelines, clarifying the interrelationships of efficacy with quality and safety.

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The essential objectives, scopes, and principles of the clinical guidelines are summarized in the following. ICH Topic E1 the Extent of Population Exposure to Assess Clinical Safety for Drugs Intended for Long-Term Treatment of Nonlife-Threatening Conditions (Step 5 in 1995) E1 guideline was finalized under Step 4 in October 1994. This document gives recommendations on the numbers of patients and duration of exposure for the safety evaluation of drugs intended for the long-term treatment of non-life-threatening conditions.

ICH Topics E2 A to F See ▶ Chap. 58, “General Principles of Pharmacovigilance in Clinical Development” ICH Topic E3 Structure and Content of Clinical Study Reports (Step 5 in 1996) The overall safety evaluation of the test drug(s)/ investigational product(s) should be reviewed, with particular attention to events resulting in changes of dose or need for concomitant medication, serious adverse events, events resulting in withdrawal, and deaths. Any patients or patient groups at increased risk should be identified and particular attention paid to potentially vulnerable patients who may be present in small numbers, e.g., children, pregnant women, frail elderly, people with marked abnormalities of drug metabolism or excretion, etc. The implication of the safety evaluation for the possible uses of the drug should be described. The discussion and conclusions should clearly identify any new or unexpected findings, comment on their significance, and discuss any potential problems such as inconsistencies between related measures. The clinical relevance and importance of the results should also be discussed in the light of other existing data. Any specific benefits or special precautions required for individual subjects or at-risk groups and any implications for the conduct of future studies should be identified.

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ICH Topic E4 Dose Response Information to Support Drug Registration (Step 5 in 1994) Dose-response data are desirable for almost all new chemical entities entering the market. These data should be derived from study designs that are sound and scientifically based; a variety of different designs can give valid information. The studies should be well-controlled, using accepted approaches to minimize bias. In addition to carrying out formal dose-response studies, sponsors should examine the entire database for possible dose-response information. Dose-response data should be explored also for possible differences in subsets based on demographic characteristics, such as age, gender, or race. Knowledge whether there are pharmacokinetic differences among these groups, e.g., due to metabolic differences, differences in body habitus, or composition will be helpful. Informative dose-response data, like information on responses in special populations, on longterm use, on potential drug-drug and drug-disease interactions, is expected but might, in the face of a major therapeutic benefit or urgent need, or very low levels of observed toxicity, become a deferred requirement. ICH Topic E5 (R1) Ethnic Factors in the Acceptability of Foreign Clinical Data (Step 5 in 1998) This guidance describes how a sponsor developing a medicine for a new region can deal with the possibility that ethnic factors could influence the effects (safety and efficacy) of medicines and the risk/benefit assessment in different populations. Results from the foreign clinical trials could comprise most, or in some cases, all of the clinical data package for approval in the new region, so long as they are carried out according to the requirements of the new region. Acceptance in the new region of such foreign clinical data may be achieved by generating “bridging” data in order to extrapolate the safety and efficacy data from the population in the foreign region(s) to the population in the new region.

G. Bode

ICH Topic E6(R2) Guideline for Good Clinical Practice (Step 5 in 2017) Clinical trials should be conducted in accordance with the ethical principles that have their origin in the Declaration of Helsinki and that are consistent with GCP and the applicable regulatory requirement(s). Before a trial is initiated, foreseeable risks and inconveniences should be weighed against the anticipated benefit for the individual trial subject and society. A trial should be initiated and continued only if the anticipated benefits justify the risks. The rights, safety, and well-being of the trial subjects are the most important considerations and should prevail over interests of science and society. The available nonclinical and clinical information of an investigational product should be adequate to support the proposed clinical trial. Clinical trials should be scientifically sound, and described in a clear, detailed protocol, which has received acceptance prior institutional review board (IRB)/independent ethics committee (IEC) approval/favorable opinion. The medical care given to, and medical decisions made on behalf of, subjects should always be the responsibility of a qualified physician or of a qualified dentist. Each individual involved in conducting a trial should be qualified by education, training, and experience to perform his or her respective task(s). Freely given informed consent should be obtained from every subject prior to clinical trial participation. Guideline for good clinical practice E6(R2) EMA/CHMP/ICH/135/1995 Page 15/70 2.10. All clinical trial information should be recorded, handled, and stored in a way that allows its accurate reporting, interpretation, and verification. Addendum: The confidentiality of records that could identify subjects should be protected, respecting the privacy and confidentiality rules in accordance with the applicable regulatory requirement(s). Investigational products should be manufactured, handled, and stored in accordance with applicable good manufacturing practice

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(GMP). They should be used in accordance with the approved protocol.

these patients should be appropriately represented in clinical trials.

ICH Topic E7 Studies in Support of Special Populations: Geriatrics Questions and Answers (Step 5 in 2010) This guideline provides answers to a number of questions. As an example: Why do we need an adequate representation of geriatric patients in the clinical database? Geriatric patients can respond differently from younger patients to drug therapy in a number of ways, and such differences can be greater in patients 75 years and older:

ICH Topic E8 General Considerations for Clinical Trials (Step 5 in 1998) Objectives: In the three ICH regions, the evolution of drug development strategies and evaluation processes has led to the establishment of regional guidances on general considerations for clinical trials and the process of clinical development of pharmaceuticals for human use. This harmonized guideline is derived from those regional documents as well as from ICH guidelines. The ICH document “General Considerations for Clinical Trials” is intended to:

(a) The geriatric population has age-related physiological changes that can affect the pharmacokinetics of the drug, and the pharmacodynamic response to the drug, both of which can influence the drug-response and the dose-response relationship. (b) Geriatric patients are more prone to adverse effects since they often have comorbidities and are taking concomitant therapies that could interact with the investigational drug. The adverse effects can be more severe, or less tolerated, and have more serious consequences than in the non-geriatric population. With the increasing size of the geriatric population (including patients 75 and older) and in view of the recent advances in pharmacokinetics and pharmacodynamics since the ICH E7 guideline was established in 1993, the importance of geriatric data (from the entire spectrum of the geriatric patient population) in a drug evaluation program has increased. Not all potential differences in pharmacokinetics, pharmacodynamics, disease-drug and drug-drug interactions, and clinical response that can occur in the geriatric population can be predicted from non-geriatric populations, as the geriatric patients are far more likely to have multiple illnesses and to be receiving multiple drugs. Therefore, to assess the benefit/risk balance of a drug that will be used in the geriatric population,

(a) Describe internationally accepted principles and practices in the conduct of both individual clinical trials and overall development strategy for new medicinal products. (b) Facilitate the evaluation and acceptance of foreign clinical trial data by promoting common understanding of general principles, general approaches, and the definition of relevant terms. (c) Present an overview of the ICH clinical safety and efficacy documents and facilitate the user’s access to guidance pertinent to clinical trials within these documents. (d) Provide a separate glossary of terms used in the ICH clinical safety and efficacy related documents that pertain to clinical trials and indicate which documents contain them. The term “drug” should be considered synonymous with “investigational (medicinal) product,” “medicinal product,” and “pharmaceutical product” including vaccines and other biological products. The principles established in this guideline may also be applied to other clinical investigations (e.g., radiotherapy, psychotherapy, surgery, medical devices, and alternative therapies). ICH Topic E9 Statistical Principles for Clinical Trials (Step 5 in 1998) Scope and direction: The focus of this guidance is on statistical principles. It does not address the use

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of specific statistical procedures or methods. Specific procedural steps to ensure that principles are implemented properly are the responsibility of the sponsor. This guidance should be of interest to individuals from a broad range of scientific disciplines. However, it is assumed that the actual responsibility for all statistical work associated with clinical trials will lie with an appropriately qualified and experienced statistician, as indicated in ICH E6. The role and responsibility of the trial statistician, in collaboration with other clinical trial professionals, is to ensure that statistical principles are applied appropriately in clinical trials supporting drug development. For each clinical trial contributing to a marketing application, all important details of its design and conduct and the principal features of its proposed statistical analysis should be clearly specified in a protocol written before the trial begins. The protocol and subsequent amendments should be approved by the responsible personnel, including the trial statistician. The trial statistician should ensure that the protocol and any amendments cover all relevant statistical issues clearly and accurately, using technical terminology as appropriate. The principles outlined in this guidance are primarily relevant to clinical trials conducted in the later phases of development, many of which are confirmatory trials of efficacy. In addition to efficacy, confirmatory trials may have as their primary variable a safety variable (e.g., an adverse event, a clinical laboratory variable, or an electrocardiographic measure) and a pharmacodynamic or a pharmacokinetic variable (as in a confirmatory bioequivalence trial). Although the early phases of drug development consist mainly of clinical trials that are exploratory in nature, statistical principles are also relevant to these clinical trials. The main principle is to minimizing bias and maximizing precision. It is important to identify potential sources of bias as completely as possible to draw valid conclusions from clinical trials. Bias can occur in subtle or unknown ways and its effect is not measurable directly.

G. Bode

ICH Topic E10 Choice of Control Group in Clinical Trials (Step 5 in 2001) Control groups have one major purpose: to allow discrimination of patient outcomes (e.g., changes in symptoms, signs, or other morbidity) caused by the test treatment from outcomes caused by other factors, such as the natural progression of the disease, observer or patient expectations, or other treatment. The control group experience tells us what would have happened to patients if they had not received the test treatment or if they had received a different treatment known to be effective. The choice of control group is therefore always a critical decision in designing a clinical trial. That choice affects the inferences that can be drawn from the trial, the ethical acceptability of the trial, the degree to which bias in conducting and analyzing the study can be minimized, the types of subjects that can be recruited and the pace of recruitment, the kind of endpoints that can be studied, the public and scientific credibility of the results, the acceptability of the results by regulatory authorities, and many other features of the study, its conduct, and its interpretation. The general principles described in this guideline are relevant to any controlled trial, but the choice of control group is of particularly critical importance to clinical trials carried out during drug development to demonstrate efficacy. The choice of the control group should be considered in the context of available standard therapies, the adequacy of the evidence to support the chosen design, and ethical considerations. ICH Topic E11 Clinical Investigation of Medicinal Products in the Pediatric Population (Step in 2001) Objectives of the guidance: The number of medicinal products currently labeled for pediatric use is limited. It is the goal of this guidance to encourage and facilitate timely pediatric medicinal product development internationally. The guidance provides an outline of critical issues in pediatric drug development and approaches to the safe, efficient, and ethical study of medicinal products in the pediatric population.

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Scope of the guidance: Specific clinical study issues addressed include: 1. Considerations when initiating a pediatric program for a medicinal product 2. Timing of initiation of pediatric studies during medicinal product development 3. Types of studies (pharmacokinetic, pharmacokinetic/pharmacodynamic (PK/PD), efficacy, safety) 4. Age categories 5. Ethics of pediatric clinical investigation General principles: Pediatric patients should be given medicines that have been appropriately evaluated for their use. Safe and effective pharmacotherapy in pediatric patients requires the timely development of information on the proper use of medicinal products in pediatric patients of various ages and, often, the development of pediatric formulations of those products. Advances in formulation chemistry and in pediatric study design will help facilitate the development of medicinal products for pediatric use. Drug development programs should usually include the pediatric patient population when a product is being developed for a disease or condition in adults and if it is anticipated that the product will be used in the pediatric population. Obtaining knowledge of the effects of medicinal products in pediatric patients is an important goal. However, this should be done without compromising the well-being of pediatric patients participating in clinical studies. This responsibility is shared by companies, regulatory authorities, health professionals, and society as a whole. ICH Topic E12 Principles for Clinical Evaluation of New Antihypertensive Drugs (Step 5 in 2000) This document provides general principles for the clinical evaluation of new antihypertensive drugs. It describes accepted principles in the three ICH regions versus some region-specific differences. These differences may be harmonized in future but may require discussions with regional regulatory authorities.

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General principles for the assessment of efficacy: The primary goal is the effect of the drug on systolic and diastolic blood pressures. In the past the primary endpoint of most studies was diastolic blood pressure. Although all drugs to date have reduced both systolic and diastolic blood pressures, the recognition of isolated or predominant systolic hypertension as a significant and remediable risk factor demands explicit evaluation of the effect of a drug on systolic blood pressure. Many clinical trials of many interventions (including low- and high-dose diuretics, reserpine, and beta-blockers, usually as part of combination therapy) have shown consistent beneficial effects on long-term mortality and morbidity, most clearly on stroke and less consistently on cardiovascular events. Whether some drugs or combinations have better effects than others on overall outcomes or on particular outcomes is not yet known. Formal mortality and morbidity outcome studies are not ordinarily required for approval of antihypertensive drugs, and the kind of active control mortality and morbidity studies that would be convincing is not well defined. Results of a large number of ongoing outcome studies could affect this policy and modify requirements. It should be noted that, even if an antihypertensive effect has been proven, a significant concern about a detrimental effect on mortality and/or cardiovascular morbidity might lead to a need for outcome studies. ICH Topic E14 the Clinical Evaluation of QT/ QTc Interval Prolongation and Proarrhythmic Potential for Non-antiarrhythmic Drugs (Step 5 in 2005) An undesirable property of some non-antiarrhythmic drugs is their ability to delay cardiac repolarization, an effect that can be measured as prolongation of the QT interval on the surface electrocardiogram (ECG). The QT interval represents the duration of ventricular depolarization and subsequent repolarization and is measured from the beginning of the QRS complex to the end of the T wave. A delay in cardiac repolarization creates an electrophysiological environment that favors the

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development of cardiac arrhythmias, most clearly torsade de pointes (TdP) but possibly other ventricular tachyarrhythmias as well. TdP is a polymorphic ventricular tachyarrhythmia that appears on the ECG as continuous twisting of the vector of the QRS complex around the isoelectric baseline. A feature of TdP is pronounced prolongation of the QT interval in the supraventricular beat preceding the arrhythmia. TdP can degenerate into ventricular fibrillation, leading to sudden death. While the degree of QT prolongation is recognized as an imperfect biomarker for proarrhythmic risk, in general there is a qualitative relationship between QT prolongation and the risk of TdP, especially for drugs that cause substantial prolongation of the QT interval. Because of its inverse relationship to heart rate, the measured QT interval is routinely corrected by means of various formulae to a less heart ratedependent value known as the QTc interval. It is not clear, however, whether arrhythmia development is more closely related to an increase in the absolute QT interval or QTc. Most drugs that have caused TdP clearly increase both the absolute QT and the QTc (hereafter called QT/QTc). Documented cases of TdP (fatal and nonfatal) associated with the use of a drug have resulted in the withdrawal from the market of several drugs and relegation of other drugs to second-line status. Because prolongation of the QT/QTc interval is the ECG finding associated with the increased susceptibility to these arrhythmias, an adequate premarketing investigation of the safety of a new pharmaceutical agent should include rigorous characterization of its effects on the QT/QTc interval. Objectives: This document provides recommendations to sponsors concerning the design, conduct, analysis, and interpretation of clinical studies to assess the potential of a drug to delay cardiac repolarization. This assessment should include testing the effects of new agents on the QT/QTc interval as well as the collection of cardiovascular adverse events. The assessment of the effects of drugs on cardiac repolarization is the subject of active investigation. When additional data (nonclinical and clinical) are accumulated in the future, this document may be reevaluated and revised.

G. Bode

Scope: The recommendations of this document are generally applicable to new drugs having systemic bioavailability but may not apply to products with highly localized distribution and those administered topically and not absorbed. The focus is on agents being developed for uses other than the control of arrhythmias, as antiarrhythmic drugs can prolong the QT/QTc interval as a part of their mechanism of clinical efficacy. While this document is concerned primarily with the development of novel agents, the recommendations might also be applicable to approved drugs when a new dose or route of administration is being developed that results in significantly higher exposure (i.e., Cmax or AUC). Additional ECG data might also be considered appropriate if a new indication or patient population was being pursued. The evaluation of the effect of a drug on the QT interval would also be considered important if the drug or members of its chemical or pharmacological class have been associated with QT/QTc interval prolongation, TdP, or sudden cardiac death during post-marketing surveillance. This guideline should be read together with the preclinical guideline ICH S7 B. The Expert Groups of E14 and S7 B have and will continue to cooperate to create strategies which will provide greater safety for patients.

The ICH guidelines E15, E16, and E18 ICH Topic E15 Definitions for genomic biomarkers, pharmacogenomics, pharmacogenetics, genomic data, and sample coding categories (Step 5 in 2008) ICH Topic E16 Genomic Biomarkers Related to Drug Response: Context, Structure and Format of Qualification Submissions (Step 3 in 2009) ICH guideline E18 on genomic sampling and management of genomic data (Step 5 in 2018) ICH Guideline E17 on General Principles for Planning and Design of Multiregional Clinical Trials (Step 5 Coming into Effect in June 2018) Objectives of the guideline: With the increasing globalization of drug development, it has become

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important that data from multiregional clinical trials (MRCTs) can be accepted by regulatory authorities across regions and countries as the primary source of evidence, to support marketing approval of drugs (medicinal products). The purpose of this guideline is to describe general principles for the planning and design of MRCTs with the aim of increasing the acceptability of MRCTs in global regulatory submissions. Background: Data from MRCTs are often submitted to multiple regulatory authorities without a previously harmonized regulatory view on the development program. There are currently no ICH guidelines that deal specifically with the planning and design of MRCTs, although the ICH E5 guideline covers issues relating to the bridging of results from one region to another. MRCTs conducted according to the present guideline will allow investigation of treatment effects including safety evaluations in the overall population as well as investigations of the potential impact of intrinsic and extrinsic factors, thus offering an earlier access to new drugs worldwide. Scope of the guideline: MRCT is defined in the present guideline as a clinical trial conducted in more than one region under a single protocol. Such data will be submitted to multiple regulatory authorities for drug approval (including approval of additional indications, new formulations, and new dosing regimens) and for studies conducted to satisfy post-marketing requirements.

with greater efficiency, studies could be conducted with greater global participation, and the public health would be better served. The proposed guideline would be consistent with risk-based approaches and quality-by-design principles.

ICH E19 Optimization of Safety Data Collection (Concept Paper of ICH in August 2017) This new guideline is proposed to provide harmonized guidance on when it would be appropriate to use a targeted approach to safety data collection in some late-stage premarketing or post-marketing studies and how such an approach would be implemented. Recognizing that protection of patient welfare during drug development is critically important, unnecessary data collection may be burdensome to patients and serve as a disincentive to participation in clinical research. By tailoring safety data collection in some circumstances, the burden to patients would be reduced, a larger number of informative clinical studies could be carried out

General Regulatory Background Treatment with compounds associated with carcinogenic potential is unacceptable for banal indications; for severe indications like life-threatening cancer diseases, the treatment with carcinogenic compounds often does not increase the overall risk of the underlying disease the patient suffers from. Tumors are usually the result of a multihit, multistep long-term process which needs in humans many years. These long durations are reflected in animal testing: carcinogenicity studies are often lifelong studies; the duration in these models is mostly 24 months in rats and today also in mice (former duration was 18 months in mice). Carcinogenicity studies are therefore the most expensive (approximately 1.5–4 million dollars or euros)

Detailed Overview for Guidelines for Safety In the following a survey is given in regard to the guidelines on preclinical safety (Toxicology or Safety Pharmacology).

Carcinogenicity S 1 A-C There are a number of endpoints which should not be tested in humans; these are mainly the assessment of genotoxicity, teratogenicity, and cancerogenicity. Especially the cancerogenic risk can usually not be investigated in humans; it is ethically forbidden and such a risk by new drugs for patients is unacceptable. The potential to induce tumors, therefore, can only be evaluated by nonclinical studies. The nonclinical testing for carcinogenic potential today employs short-, mid-, and long-term studies in rodents. Such studies are considered to have a relatively high power of predictivity for the carcinogenic risk in humans.

1098 Table 5 Usual strategies for cancerogenicity studies Carcinogenicity testing: the usual way Usually in rodents (rats, mice) Usually starts with 3 month dose-range-finding study followed by the main study Usually over a treatment period of 2 years Usually 3 treatment groups + control Usually needed for MAA/NDA(marketing authorization application, new drug application)

G. Bode Table 7 When are bioassays not necessary? No need for carcinogenicity studies? (ICH S1A) Short-term indications For example, anesthetics, diagnostics Unequivocally genotoxic compounds Assumption of trans-species carcinogens Low life expectancy of (tumor)patients (2–3 years) Prolongation of survival may trigger carcinogenicity testing Topical use drugs without systemic exposure Unless there is cause for concern

Table 6 When are carcinogenicity studies needed? Need for carcinogenicity studies? (ICH S1A) Clinical use for >6 months(continuous or intermittent) Known carcinogenic potential for product class Structure-activity relationship suggesting carcinogenic risk Preneoplastic lesions in repeated dose toxicity studies Long-term tissue retention resulting in local tissue reactions or other pathophysiological responses Equivocal genotoxicity tests

preclinical studies. Accordingly, they should be well designed and conducted in such a way that they clearly indicate any risk involved. The basis of the strategies is always literature research, what is known from the chemical class of the new drug, which pharmacodynamic effects can be expected, detail by detail is collected for a weight-of-evidence approach before the experimental part starts with dose-range finding tests (Tables 5 and 6).

Guideline on the Need for Carcinogenicity Studies of Pharmaceuticals (S1A) (2) This guideline was adopted in 1997 and implemented in all regions. The objectives of carcinogenicity studies are: • To identify a tumorigenic potential in animals • To assess the relevance of these identified risks for humans Any cause for concern derived from laboratory investigations, animal toxicology studies, and data in humans may lead to a need for carcinogenicity studies.

Carcinogenicity studies should be performed for any pharmaceutical whose expected clinical use is continuous for at least 6 months. Certain classes of compounds may not be used continuously over a minimum of 6 months but may be expected to be administered repeatedly in an intermittent manner. For pharmaceuticals used frequently in an intermittent manner during the treatment of chronic or recurrent conditions, carcinogenicity studies are generally also needed. Examples of such conditions include allergic rhinitis, depression, and anxiety. Pharmaceuticals administered infrequently or for short duration of exposure (e.g., anesthetics and radiolabel imaging agents) do not need carcinogenicity studies (Table 7). The time after consumption is too short to cause any clinically relevant damages leading to neoplasms. But on the other hand, there may be causes for concern. Examples are: • Previous demonstration of carcinogenic potential in the product class that is considered relevant to humans • Structure-activity relationship suggesting carcinogenic risk • Evidence of preneoplastic lesions in repeated dose toxicity studies • Long-term tissue retention of parent compound or metabolite(s) resulting in local tissue reactions or other pathophysiological responses Unequivocally genotoxic compounds need not to be subjected to long-term carcinogenicity studies. It is assumed that these products, damaging

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the DNA, will cause neoplasias. The S1A guideline offers in addition an alternative: if a drug is intended to be administered chronically to humans, a chronic toxicity study (up to 1 year) may be necessary to detect early tumorigenic effects. In practice, this option has not been used since 1997. Main concern against this type of studies by industry is the lack of historical comparison data. The tumor evaluation was always based on 2-year data. Other conditions which justify not conducting carcinogenicity assays are summarized in (Table 7). • In instances, where the life expectancy in the indicated population is short (i.e., less than 2–3 years), no long-term carcinogenicity studies may be required. For example, oncolytic agents intended for treatment of advanced systemic disease do not generally need carcinogenicity studies. • In cases where the therapeutic agent for cancer is generally successful and life is significantly prolonged, there may be requirements to provide knowledge about the tumorigenic risk. • When such pharmaceuticals are intended for adjuvant therapy in tumor-free patients or for prolonged use in non-cancer indications, carcinogenicity studies are usually needed. • Pharmaceuticals showing poor systemic exposure from topical routes in humans may not need studies by the oral route to assess the carcinogenic potential to internal organs. • Carcinogenicity studies are not generally needed for endogenous substances, when given essentially as replacement therapy (i.e., physiological levels), particularly, where there is previous clinical experience with similar products (e.g., animal insulins, pituitary hormones). In general, the need for the 2-year rodent assay to assess a carcinogenic potential continues to be questioned because of too many positive outcomes. In addition, retrospective analyses of various datasets (PhRMA, FDA, JPMA, and EU) concluded that based on genotoxicity and nongenotoxic mechanisms, detectable in pharmacology and chronic toxicity data (usually present at

1099 Table 8 Conditions of cancerogenicity studies

a

negative

outcome

of

Negative prediction, initiative of PhRMA Rat chronic toxicology studies = ! Good predictors of negative outcome in 2-year rat carcinogenicity studies: (a) Analysis of 182 compounds: If No preneoplasia in chronic toxicity studies No genotoxicity No hormonal perturbation signals No immunosuppression ! No value added from 2-year rat carc. study Sistare FD et al. (2011), Toxico- Pathol;39(4):716–744

the end of phase 2 in the development of a new pharmaceutical), the outcome of the 2-year rat carcinogenicity study could be predicted with reasonable assurance (Table 8). There are two extremes of the spectrum: Negative predictions can be made when predictive carcinogenic signals are absent, and positive predictions are possible when such signals are present. In between a category of compounds still remain for which the outcome cannot be predicted with sufficient certainty and where experimental studies may add value to identify real hazards. These hypotheses stimulated ICH to test in an ongoing common exercise by Drug Regulatory Agencies and Industry if there are chances to reduce the number of rodent bioassays. Such prospective evaluation is necessary to justify any revision of the present recommendations of the ICH guideline S1 (EMA 2016). Sponsors are strongly encouraged to submit Carcinogenicity Assessment Documents (CADs) to Drug Regulatory Agencies (DRAs) for all investigational pharmaceuticals with ongoing or planned 2-year rat carcinogenicity studies. The CAD would address the overall carcinogenic risk of the investigational drug as predicted by the available knowledge on pharmacology and toxicology and a rationale for why the conduct of long-term studies would or would not add value to that assessment, in the latter case by a request of a “virtual” waiver. DRAs independently review the submitted documents and evaluate the degree of concordance with sponsors. During this prospective evaluation period, waiver requests will not be granted, but the data

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are intended solely for collecting real-world experience. Submitted CADs will finely be compared to the real outcome of the 2-year carcinogenicity studies and/or any other factors of a weight-ofevidence evaluation. Main objective will be the assessment of accuracy of the predictions, with emphasis on the “virtual” waivers. The final conclusion was originally intended to be based on 50 examples; by the end of 2017, 46 cases were available; they are considered sufficient to modify any conclusions. Their prospective assessments by industry and agencies are now compared to the final outcome of the real longterm studies. There seems hope that a revision of the guideline S1 A may be justified. The competent ICH Expert Groups will come to a conclusion in 2019.

Testing for Carcinogenicity of Pharmaceuticals (S1B) S1B was adopted and implemented in 1997. Historically, the regulatory requirements for the assessment of the carcinogenic potential were the conduct of long-term carcinogenicity studies in two rodent species, usually the rat and the mouse. It was the mission of ICH to examine whether this practice could be reduced without compromising human safety. The discussion in the Expert Working Group soon revealed that the rule of testing in two species had to be resumed Fig. 3 Options for assessment of carcinogenic potential

and the American consumer societies demanded continuation with the same standard of safety as before, based on two species testing. But more flexibility in the strategies was developed. As a new experimental approach to test for carcinogenic potential, a basic scheme was set up to comprise one long-term rodent carcinogenicity study, plus one other study of the type that supplements the long-term carcinogenicity study and provides additional information that is not readily available from the long-term assay: S1B: Basic Principle for Testing the Carcinogenic Potential (Fig. 3): • One long-term rodent carcinogenicity study plus • One short- or medium-term study that supplements the long-term carcinogenicity study and provides additional information not readily available from the long-term assay

The species selected should be appropriate, based on considerations on pharmacology, repeated-dose toxicology data, metabolism (see also guidelines S1C and S3A), toxicokinetics (see also guidelines S1C, S3A, and S3B), and route of administration (e.g., less common routes such as dermal and inhalation). In the absence of clear evidence favoring one species, it is

Cancer Testing Strategies (ICH S1B) Principle: 2 species 1 long-term rodent carcinogenicity study (2 years in rats)

+ 1 short or mediumterm rodent study, 6 months tg mice

or

1 long-term carcinogenicity study in a second rodent species, 2 year wildtype mice

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recommended that the rat be selected for the longterm study, because data collections by the European, American, and Japanese colleagues resulted in the conclusion that the rat was the more sensitive species and historical data were more meaningful in this species. Additional tests may either be short- or medium-term in vivo rodent test systems, usually selecting the mouse. According to the guideline, these models of carcinogenesis may use transgenic or neonatal rodents or may include models of initiation and promotion in rodents (but the latter option is today considered to be useful models for hepatic carcinogenesis or adequate mechanistic studies, but not as assays appropriate as general screen for drug-induced potential for carcinogenesis). Also neonatal animals are hardly used any longer. All these models were assessed with approximately 40 different compounds in a strenuous study by the International Life Science Institute (ILSI/HESI) (Cohen et al. 2001). The most appropriate models today are transgenic mouse models with either activated oncogenes, like Tg.rasH2 model or Tg.AC skin model

Table 9 Transgenic mouse models in practice 2018 Transgenic mouse models Activated oncogenes = TgrasH2 model (Japan) Tg.AC skin model (USA); also gavage Inactivated tumor suppressor gene = p53 “knock out” (=p53+/) model (USA)

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(favored especially in the USA) or a transgenic model with inactivated tumor suppressor gene, the p53 “knock out” (=p53+/) model (Table 9). The Food and Drug Administration (FDA) supports the selection of these models and considers that P53+/is the right choice, if a compound is clearly or equivocally genotoxic. The Tg.AC model is adequate for dermally applied products, neonatal mice have been used when the drug is clearly or equivocally genotoxic, and the Tg.Hras2 can be recommended for genotoxic or non-genotoxic products. The EU/EMA concluded in addition that p53 and Tg.rasH2 are equally sensitive to genotoxic compounds (although some false positives or false negatives have been identified). Tg.rasH2 is more sensitive to peroxisome proliferators, and altogether p53 and Tg.rasH2 are acceptable in a regulatory context as alternatives. The guideline describes in the “Notes” important information about the new models. Note 1 informs about the Syrian hamster embryo (SHE) cell-transformation assay (Fig. 4) Mauthe et al. 2001 reviewed the SHE cell-transformation assay and described the following: The Syrian hamster embryo (SHE) cell-transformation assay represents a short-term in vitro assay capable of predicting rodent carcinogenicity of chemicals with a high degree of concordance (LeBoeuf et al. 1996). The SHE assay model identifies the earliest identifiable stage in carcinogenicity, morphological cell transformation. In contrast to other short-term in vitro assays, both genotoxic and epigenetic

Fig. 4 Negative control and positive result of Syrian hamster embryo test

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carcinogens are detected. The SHE assay, originally developed by Berwald and Sachs (1965) and modified as described by LeBoeuf and Kerckaert 1986, was included in the International Life Sciences Institute, Health and Environmental Sciences Institute (ILSI/HESI). Alternative carcinogenicity testing (ACT) collaboration provides additional information on the use of shortterm in vitro tests in predicting carcinogenic potential. A total of 19 ILSI compounds have been tested in the SHE assay: 15 were tested for this project, whereas clofibrate, methapyrilene, reserpine, and di(2-ethylhexyl)phalate (DEHP) were tested previously. Of the three noncarcinogenic compounds tested, two were negative in the SHE assay, whereas ampicillin was tested positive. The remaining 16 compounds tested were either known rodent carcinogens and/or human carcinogens. From this group, 15 tested positive in the SHE assay, whereas phenacetin, a genotoxic carcinogen, was tested negative. Therefore, overall concordance between the SHE assay and rodent bioassay was 89% (17/ 19), whereas concordance with known or predicted human carcinogens was 37% (7/19). Based on these data, it is concluded that the SHE cell-transformation assay has utility for predicting the results of the rodent carcinogenesis bioassay but lacks the selectivity to distinguish between rodent and human carcinogens. Note 2 on conditions to limit testing with one species only: if the findings of a short- or longterm carcinogenicity study and of genotoxicity tests and other data indicate that a pharmaceutical clearly poses a carcinogenic hazard to humans, a second carcinogenicity study would not usually be useful. Note 3 provides details about the short- or midterm models. Evidence of tumorigenic effects of the drug in rodent models should be evaluated in light of the tumor incidence and latency, the pharmacokinetics of the drug in the rodent models as compared to humans, and data from any ancillary or mechanistic studies that are informative with respect to the relevance of the observed effects to clinical conditions.

G. Bode Table 10 Types of cancerogenicity studies for MAA. Categorization of active substances with carcinogenicity data according to type and number of studies performed Active substances with carcinogenicity data 2 long-term carcinogenicity studies 1 long-term carcinogenicity study in rats +1 transgenic mouse study 1 long-term carcinogenicity study in mice or rats 1 transgenic mouse model No carcinogenicity studies performed Total

Number 116 8

% 80.5 5.5

13

9

1 6

1 4

144

100

Source: Friedrich and Olejniczak (2011)

The results from any tests cited above should be considered as part of the overall “weight-ofevidence” taking into account the scientific status of the test systems. It needs finally to be stressed that a long-term carcinogenicity study in a second rodent species (e.g., mice) is still considered acceptable. When looking back at recent applications, these options of life span studies in the mouse have not been replaced by transgenic models. This is the result of the analyses of Friedrich and Olejniczak in 2011 reflecting the situation until 2009 (Table 10). It looks that there is a trend in experimental strategies that transgenic mice are increasingly part of the routine testing paradigm. Further observations should be performed.

Dose Selection for Carcinogenicity Studies of Pharmaceuticals (S1C) International Conference on harmonization (1997): Guideline on dose Selection for carcinogenicity studies of Pharmaceuticals This guideline was adopted and implemented in the different regions in 1997. Traditionally, carcinogenicity studies for chemical agents have relied upon the maximally tolerated dose (MTD) as the standard method for high-dose selection. The MTD is generally chosen based on data derived from toxicity studies of 3 months’

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duration. Testing options for dose-range-finding studies are as follows: • Usually, 3 months for long-term studies or 1 month for neonatal or transgenic mice with a range of different dose levels, often 5. • There is a focus on toxicity endpoints, determination of MTD with profiling of AUC (e.g., 1 and 3 months for rats or 1 and 4 weeks for alternatives). • Toxicokinetics: In many cases it is acceptable by agencies to use instead of transgenic animals their wild type associates. Ideally, the doses selected for rodent bioassays for non-genotoxic pharmaceuticals should provide an exposure to the agent, and an adequate margin of safety, without any significant severe chronic dysfunction and compatible with good survival of up to 2 years. The guideline calls for a flexible approach to dose selection. The guideline proposes five different approaches (Tables 11 and 12):

Table 11 Criteria for high-dose selection Criteria of high-dose selection (ICH S1C) Maximum-tolerated dose (MTD) (10% of weight loss compared to controls) 25-fold AUC ratio (rodent/human) Saturation of absorption Dose-limiting pharmacodynamic effects (e.g., hypotension, inhibition of blood clotting) Maximum feasible dose, limit dose: 1500 mg/kg

Table 12 Examples for recommendation in the EU guidance for cancerogenicity assays EMA, CPMP (2003) note for guidance on carcinogenic potential Historical control data not older than 5 years, same strain and testing facility Histopathological terms according to well-defined classifications (e.g., (ILSI, StP, IARC, Reni) Ideally, one pathologist with board certification responsible for histological evaluation Peer-review (slides) required for 10% of all tumors

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1. Toxicity-based endpoints (MTD = maximumtolerated dose or minimum toxic dose) 2. Pharmacokinetic endpoints (25 times the human AUC) 3. Saturation of absorption 4. Pharmacodynamic endpoints 5. Maximum feasible dose

Ad 1) The ICH Expert Working Group on safety has agreed to continue the use of the MTD as an acceptable toxicity-based endpoint for highdose selection for carcinogenicity studies. The MTD is defined as the top dose or maximum-tolerated dose that produces a minimum toxic effect over the course of the carcinogenicity study. Factors to consider are alterations in physiological functions, which would alter the animal’s normal life span or interfere with interpretation of the study. Such factors include no more than 10% decrease in body weight gain relative to controls and target organ toxicity or significant alterations in clinical pathological parameters. Ad 2) For relatively well-tolerated drugs, the alternative of a systemic exposure representing 25 times multiple of the human AUC (at the maximum recommended daily dose) may be an appropriate endpoint for dose selection for carcinogenicity studies for non-genotoxic pharmaceuticals, as a pragmatic solution without any clear-cut scientific justification but supported by data indicating that such a compound does not induce severe toxicities at lower exposure levels. Ad 3) Limitation of absorption: High-dose selection based on saturation of absorption measured by systemic availability of drug-related substances is acceptable. The mid- and low-doses selected for the carcinogenicity study should take into account saturation of metabolic and elimination pathways. If such saturation is identified, then there is no need to raise the dose levels, since exposure will not increase. Ad 4) Pharmacological properties: Pharmacodynamic endpoints for high-dose selection will be highly compound-specific. The high-dose

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selected should produce a pharmacodynamic response in dosed animals of such magnitude as would preclude further dose escalation. However, the dose should not produce disturbances of physiology or homeostasis. Examples include hypotension, inhibition of blood clotting, or insulin-like effects. Too high exposure would exclude survival up to 2 years and jeopardize the study conclusion. Ad 5) The maximum feasible dose by dietary administration was considered 5% of diet. By many scientists this amount of drug is considered to be too high. Therefore, a new and more reasonable solution was formulated in the guideline ICH/S1C(R), which follows.

Addendum to Dose Selection for Carcinogenicity Studies of Pharmaceuticals Addition of a limit dose and related notes of pharmaceuticals. This addendum S1C (R) was adopted in 1998. In determining the high dose for carcinogenicity studies, it may not be necessary to exceed a dose of 1500 mg/kg/day. This limit dose applies only in cases where there is no evidence of genotoxicity and where the maximum recommended human dose does not exceed 500 mg/day. This limit dose helps to calculate the amount of compound needed for such long-term assays. Compared to the upper options, it is not applied very often, since in most cases drug-induced toxicity can be identified.

Genotoxicity Guidelines (ICH/S2A, S2B and S2(R1)) A permanent alteration of genes or chromosomes can cause heritable effects leading to malformations and dysfunctions in the next generation or inducing tumors in the individual patient. The former genotoxicity guidelines: Genotoxicity Guidelines (ICH/S2A and S2B), Genotoxicity: Guidance on Specific Aspects of Regulatory Genotoxicity Tests for Pharmaceuticals, and Genotoxicity: A Standard Battery for Genotoxicity Testing of Pharmaceuticals have been replaced in

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2011 by the integrated Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use S2(R1) (Current Step 4 version dated 9 November, 2011). The main reasons to revise the previous guidelines were to merge them into one concise guideline, to refocus the sensitivity and specificity of the in vitro assays (e.g., via restriction of upper concentrations to be tested), to include the option to replace these in vitro assays by using mammalian cells, and to define a suitable in vivo test battery option according to improved scientific standards and guidelines from other sources (e.g., OECD guidelines). The revised guideline continues to support the goals of testing to deselect mutagenic/genotoxic drug candidates as early as possible in the development of new molecular therapeutic entities to contribute to the safety of humans. In terms of coverage of genetic endpoints, valid information on gene mutations, structure chromosome aberrations (clastogenicity), and numerical chromosome aberrations (aneugenicity) is required. In genetic toxicology, no single test is capable of detecting all relevant genotoxic agents; therefore, a battery of tests is considered appropriate. It traditionally starts with a test for gene mutations in bacteria (Ames). Ames published this test as early as 1973, and it remains in the screening battery also for its easiness of conduct and fast/reliable turnaround of data. It is considered to be robust and predictive for mutagenic carcinogens (Kirkland et al. 2006). As a second test, the ICH S2R guideline offers, besides the in vitro chromosomal aberration test and the mouse lymphoma tk assay, an in vitro micronucleus test (the latter test was not available with a standard protocol before). This assay is supported by many years of validation and considered to be an adequate alternative to the traditional chromosome test (Corvi et al. 2008). Regarding the dose and exposure levels having been used, awareness increased that this procedure did often substantially exaggerate clinical exposure conditions. Accordingly, concentrations of 1 mM (instead of 10 mM) were considered sufficient for nontoxic drug candidates in

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mammalian cells in vitro. And additionally, these perspectives were further strengthened by limiting the levels of cytotoxicity of not more than 50%. ICH generally pursues the important goal of a reduction in animal testing in all experimental strategies. While traditionally an area of importance of in vitro models, this principle has been followed also in S2 R. The assessment of genotoxicity should, where possible, be integrated into repeat dose toxicity studies, e.g., into 4-week rodent studies, where the bone marrow micronucleus test could help to optimize the usage of animals (Fig. 5). Modifications of the standard battery may be necessary for some classes, e.g., antibiotics which are toxic to bacteria or, e.g., for compounds like topoisomerase inhibitors which interfere with the mammalian cell replication system. A selection of additional assays is being proposed; further modifications may be acceptable via discussion in the ICH maintenance process. Alternative strategies may consider assays like the in vivo comet assay (single cell gel electrophoresis measuring DNA strand breaks) or gene mutation tests with transgenic animals or in vivo DNA adduct studies. Support for the interpretation of positive test results and considerations on conditions leading to false-positive data can be found in many excellent publications of this special field of science. It is sharing this special emphasis with the carcinogenicity testing guidelines in terms of the general notion that effects, once incurred, are not reversible.

Genotoxicity Standard Battery Test Systems Test battery is selected from the following systems: AMES-Test Mouse lymphoma tk+/- assay In vitro chromosome aberration assay In vitro micronucleus test In vivo micronucleus test In vivo Comet assay

Fig. 5 Selection of genotoxicity assays

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Toxicokinetics/Pharmacokinetics (ICH/S3A and S3B) ICH/S3A The objectives of toxicokinetics are primarily to describe the systemic exposure achieved in animals and its relationship to dose level and the time course of the toxicity study and, further, to relate exposure levels to toxicological findings, to assess the relevance of these findings to clinical safety, and to support the design as the choice of species and treatment regimen in nonclinical studies. If animals or humans are exposed to pharmaceutical compounds, they will either elicit a pharmacodynamic effect, e.g., show a suppression of blood pressure, or reveal in analytical blood samples exposure levels of the compound. This kinetic information of the parent compound and its metabolites is an important contribution for the extrapolation of safety data from animal studies to humans. Species differ considerably in regard to their kinetic conditions as Cmax, Tmax, area under the curve (AUC), t1/2, and ADME (absorption, distribution, metabolism, and excretion). Therefore, it is important to know what a drug does with the body (what pharmacology and toxicology is induced?); it is also crucial to know what the body does with the drug. The following toxicokinetic studies are usually distinguished: concomitant toxicokinetics, which are normally integrated in the toxicity studies and other supportive studies which mimic the conditions of the toxicity study. The focus before IND (Table 13 ) is on Tmax, Cmax, and AUCs, while the complexity of pharmacokinetic characterization (like oral bioavailability, plasma half-life, volume of distribution, mean residence time, absorption, solubility, and concentration) is built up during clinical trials and on the basis of comparable human data. Kinetic data should be considered in repeat dose toxicity studies, in genotoxicity studies when there are discrepancies between in vitro and in vivo assays and sufficient exposure can be characterized in the indicator tissue confirming that lack of exposure was not the reason for a negative reaction. Such data are useful for the evaluation of reproductive tests and in

1106 Table 13 Kinetic data and timing Kinetic information before first in man Area under the curve (AUC) Cmax and Tmax In vitro metabolism (P 450 cytochrome) Plasma protein binding data for animals and humans Kinetic information during clinical trials ADME (animal and humans) (absorption, distribution, metabolism, excretion) In vivo metabolism animal versus human Bioavailability Half-life Accumulation versus inhibition Safety margins

cancerogenicity studies where often high exposures cause the appearance of adverse effects. The compound can be bound to plasma proteins, erythrocytes, or other cells or tissues. Therefore, a distinction between “unbound drug” and “free fraction” is relevant. Distribution studies help to optimize the design of preclinical studies. Demonstration of accumulation can, for example, explain toxicity at the site of increased compound accumulation. The affinities of different organs to some drugs can vary considerably. The International Conferences deal with increasing knowledge; scientific data more and more dominate the regulatory area. Translateral data exchange increases. Adverse effects need to be interpretated in the light of dose levels or more precisely of exposure levels. When in 1995 ICH S3 was created, the rule was accepted that all safety data should be supported by kinetic analyses. But today this guideline is more than 20 years old; it needs urgently a revision. AUC and cmax alone are not sufficient any more at the start of development. In contrast to the guideline text, cancerogenicity studies need data also after longer periods than 6 months, e.g., after 12 and 24 months, sometimes including more metabolic refinement. Consider in that context that some drugs induce after longer-term exposure, e.g., hepatotoxicity or renal lesions, leading to increases of blood concentrations of parent compounds or metabolites.

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Therefore, already before FIM (before first human clinical trials), evaluation of in vitro metabolic data (cytochrome p450) is needed (see ICH M3 R). Plasma protein binding data for animals and humans are essential before exposing large numbers of humans (Phase III). Data for ADME (administration, distribution, metabolism, and excretion) must be compared in animals and humans. This facilitates in the end the decision, if the preclinical safety assessment had used the Most human-like animal species for optimal predictions for both the desirable and undesirable effects for patient conditions. These objectives are supported by new progresses in techniques; there are new sampling techniques, e.g., microsampling, blood spot analyses, tissue samples, or in vitro concentrations; there are more sophisticated and refined analytical procedures, and there is an increased interrelationship of kinetics and pharmacological/ toxicological qualifications, facilitated by an increasing number of identified biomarkers, e. g., genomics.

ICH/S3B Single-dose studies provide usually sufficient information about tissue distribution, but there may be cases where assessments after repeated dosing may provide better information. Such studies are necessary when: 1. Single-dose distribution studies suggest that the half-life of the test compound and/or metabolites in organs or tissues significantly exceeds the half-life of the elimination phase in plasma. 2. Steady-state levels of a compound/metabolite in the circulation, determined in repeated dose pharmacokinetic or toxicokinetic studies, are markedly higher than those predicted from single-dose kinetic studies. 3. When histopathological changes were observed that were not predicted from shortterm toxicity studies. This information is provided in the ICH guideline S3B: “Pharmacokinetics: Guidance for

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repeated dose distribution studies (CPMP/ICH/ 395/95).” Other relevant kinetic questions are the investigations of the potential of compounds to penetrate the barriers of placenta, blood-brain, or excretion into milk. Knowledge about metabolites and their activity is further an important criterion for the assessment of species-specific effects and differences, e.g., the search for the most human-like test model as the best predictor for human reactions focuses on such differences. Metabolism can lead to pharmacologically active metabolites; such knowledge is desirable early in development. In vitro metabolism studies normally precede in vivo preclinical safety assessments. For safety reasons, it is important to identify, and perhaps eliminate, drugs from further development if they are subject to polymorphic metabolism or extensive metabolism by key human enzymes. Knowledge about the cytochrome P450 (CYP450) superfamily of drug metabolizing enzymes is of particular interest.

ICH/S4A (20) This is one of the guidelines where harmonization continued to be difficult for a long time. This lack of harmonization to recommend clear advice for duration led to the decision for pharmaceutical companies to perform partially duplicative studies for both 6 and 12 months duration. As a consequence the regulatory authorities in the EU, Japan, and USA wondered whether a more unified duration for chronic toxicity testing could be identified. There was clear agreement that rodent studies were only necessary for a maximum duration of 6 months. When a 6-month study in rodents is conducted, then continuous application of pharmaceutical compounds to the indicated population can be assumed. Under such conditions, a long-term carcinogenicity study will usually be performed. This allows long-term exposure and lifelong observation and compensates any lack of long repeated observations.

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For non-rodents, lifelong exposure is not required, but which duration can be considered as sufficient? The EU proposed as a start a common maximum duration of 6-month studies for rodents and non-rodents. A unified analysis of data from 6- to 12-month dog studies was performed by the ICH members. In 16 cases there were findings observed by 12 months, but not by 6 months. It was concluded that these would or could have been detected in a study of 9 months duration. An agreement on the clinical relevance of these findings could not be reached. The guideline S4A recommends the following: For non-rodents, 12-month studies are usually not necessary, and in the EU 6-month studies are acceptable based on the Directive 75/318/EEC. Accordingly, as a compromise, the Expert Working Group agreed that 9-month studies could be recommended in general. In the US Federal Register, this guideline was published with an FDA Note on ICH/S4A as follows: 9-month studies are acceptable for most development plans; shorter ones like 6-month studies may be acceptable for some drugs, while longer durations, e.g., 12 months, may be more appropriate for others. This advice is characterized by the FDA as their current line of thinking. In practice, FDA agreed with the sponsors in 50% for 9-month studies and in 32% for 6-month studies, while 12-month studies were only requested for compounds with novel mechanisms, or when only sparse clinical data were available, as in indications like HIV. Aids patients should be provided early on with the new treatment, and the lack of complex clinical data is compensated by longer non-rodent studies. Sponsors are advised by the FDA to get in contact with the agency when the maximum duration needs to be determined for non-rodents. Table 14 summarizes the duration of repeat dose studies as agreed in ICH S4. S4 focuses only on dog studies. There is no recommendation on what to do with primate studies. S6 offers some help for tests with biotechnology-derived drugs; the final decision is often only

1108 Table 14 Duration of repeat dose toxicity studies Duration of repeat dose toxicity studies During clinical trials: Ratio 1:1 (4-week clinical trial requests 4-week toxicity studies) For marketing authorization: Ratio 1:2/3 (4-week treatment of patients requires 3-month toxicity data) Maximum duration of rodent repeat studies (6-month studies, followed by 2-year cancer test) Maximum duration of non-rodent repeat studies (9month tests in general)

possible by asking for a scientific advice from the agencies. Further, a new species gains relevance for repeat dose assays, which is the Goettingen minipig, a result of breeding without genetic manipulation by the Department of Animal Sciences, Georg-August University Goettingen, Germany. There are many situations where the minipig is excellently predictive for human conditions, as examples cardiovascular or gastrointestinal issues. In the Rethink project (Forster et al. 2010), an impact assessment on minipigs as models for the toxicity of new medicines and chemicals was performed. In this context, Bode et al. (2010) discuss intensively the utility of the minipig as an animal model in regulatory toxicology. Further details were edited in 2012 by McAnulty, Dayan, Ganderup, and Hastings (CRC Press). When new systems or methods are introduced into regulatory toxicological assessments, then a sequence of preclinical studies will be conducted to validate these new methods. The European Center for the Validation of Alternative Methods (ECVAM) has a long tradition to support the validation of such methods which reduce, refine, or replace the use of animals for safety testing and efficacy/potency testing of chemicals, biologicals, and vaccines. The introduction of new animal models is more difficult out of reasons of animal protection. Here the support from regulatory agencies by sharing their experience from often huge databases provides the stimulus. A. Jacobs and JW. van der Laan (2012) provide an overview on the regulatory acceptability of minipigs as a species of choice for the safety evaluation of human pharmaceuticals and medical devices and

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stress that this species can be a valuable alternative to dogs or nonhuman primates.

Reproductive Studies (ICH/S5 A+B) ICH/S5A+B (21 and 22) The special toxicology discipline “Reproductive and Developmental Toxicity” focuses on adverse effects on male and female fertility, birth defects (developmental toxicity, malformations, teratogenicity), and nonphysiological changes that appear shortly before, during, and after birth and during the weaning period. The relevant ICH guidelines describing the requirements for testing and detailing experimental studies are the ICH guideline S5A and S5B. These were among the first guidelines finalized in 1995, supporting the successful ICH process. What was missing initially was the integration into the combinatorial test programs, the issues addressing male fertility, namely, for reason of protecting especially young male volunteers in clinical Phase I, when humans are being exposed for the first time to developmental medicinal compounds. Finally, the decision was taken to incorporate the considerations of such male fertility testing into the main text of the guideline, which took place when the previous addendum, actually dated from 9 November, 2000, was incorporated into the common guideline S5 (R) in November 2005 by a successful revision process. In humans, malformations and changes in development are relatively rare (around 6%) and often caused by accidental genetic errors; some are induced by external factors, e.g., chemical drugs. If a compound is labeled as a developmental toxicant, then the occurrence of structural or functional abnormalities in offspring is significantly increased at a dose level which does not induce severe maternal toxicity. If malformations are developed, then exposure levels of the individual fetuses are important, but also at which time of the development exposure was high. The inclusion of kinetic data helps to support the evaluation. In general, there is a distinction between the following time periods (Figs. 6 and 7 ): Segment I: male and female fertility studies

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Fig. 6 Timing and design of reproductive toxicity studies

Reproduction Toxicology Model Diagram (Rat) Mating Testes investigation

m

Spermatogenesis 70d

f

Weaning

Pregnancy 21-23d

Postnatal Dev. 21-28d

Several Cycles 14-21 d d0

d6

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Fig. 7 Segment I, II, and III of reproductive cycles

Parturition (birth)

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d21 = C-section

Reproductive Cycle Sex maturation Lactation

Gamet production and release

Postnatal development Perinatal development

Fertilization

Seg III Seg I

Parturition

Fetogenesis

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Zygote transportation Blastogenesis Implantation

Organogenesis Embryogenesis

Segment II: the embryo-fetal development (embryo during major organ development, i.e., organogenesis, the fetus in the postembryonic period) Segment III: the prenatal and postnatal development (neonate or postnatal offspring) These segments can be tested separately or in a combined manner. All stages of development from conception to maturity and the detection of acute and delayed effects of exposure through one complete life cycle should be examined. The standard species are rodents; rats as the preferred rodent species for all study types. The rabbit is the second non-rodent species for the embryo-fetal toxicity studies. The introduction of the rabbit as a sensitive and predictive model is due to the thalidomide (Contergan) catastrophe in the

Placentation

1960s, when 10.000 of babies were born with a range of severe and debilitating malformations after their mothers had been exposed to this teratogen to treat their morning sickness (Vargesson 2015). In some rare cases, mice, monkeys, or Goettingen minipigs (Bode et al. 2010) are used too, if special conditions – usually kinetic data – justify such species. The guideline S5 (R) should be read in conjunction with the multidisciplinary ICH guideline M3, which was first introduced in 1997 and then revised several times until its current version from June 11, 2009. In this M3 (R), the different conditions are illustrated which have to be fulfilled before men, women of childbearing potential, pregnant women, or children are included into clinical trials.

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It is interesting that men and women not of childbearing potential can be included into clinical trials without any experimental fertility studies because it is considered that the histopathological assessments of the gonads recovered from repeat dose toxicity studies provide sufficient insight into any possible disturbances of, e.g., the spermatogenesis. Our ICH collaborating Japanese colleagues contributed to this conclusion convincingly, literature examples are Kishi et al. (1995), Takayama et al. (1995), or Sakai et al. (2000). In future, it is to expect that in vitro methods may facilitate early discovery of reproductive risks, whole body cultures, or stem cell investigations that help to validate these procedures. Another aspect will continue to expand scientific approaches but combined with political impact to give women of childbearing potential the same early access to new medicines.

ICH/S6: Preclinical Safety Evaluation of Biotechnology-Derived Products ICH guideline S6 outlines the development of products which have been created by using biotechnology techniques. These compounds have been synthesized in living systems (cell cultures, virus, bacteria, and transgenic animals). These drugs are special also in regard to their size; in general they are >30 kDa, up to 800 kDa, which limits their distribution within the body or penetration into cells and organelles. This guidance is applicable for compounds such as recombinant DNA proteins, vaccines, peptides, plasmaderived products, endogenous proteins extracted from human tissues, oligonucleotide drugs, etc., while heparin, vitamins, and cellular blood components, for example, are not covered. The production process of these compounds is complex; impurities may appear, e.g., host cellderived products, which are difficult to qualify. Degradation or proteolysis may complicate their purification, to guarantee their stability and avoid aggregation; these are challenges for the formulation. All these characteristics explain why “the process is the product.”

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These bio-products are generally administered parenterally (subcutaneous, intramuscular, or intravenous application) and then submitted to catabolism and degradation leading to small peptides and nontoxic amino acids. This explains why studies of metabolism and excretion are generally not required. An important characteristic is further their kinetics; their half-life is usually days to weeks with a limited distribution to plasma. When efficacy is long, then not surprisingly, there are long recovery periods to be expected. In general, for biotech products high flexibility for the developmental scheme is recommended and should be reconsidered on a case-by-case basis. Usually, as in other toxicity studies, two species should be used, but when the biological activity is well understood or when in short-term toxicity studies the effects were similar in both species, then longer-term studies could be run only with one species. In any case, a justification that tests are conducted with a relevant species (predictive for humans) is needed. This means using a species in which the test material is pharmacologically active due to the expression of the receptor or an epitope (in case of monoclonal antibodies). When no relevant species can be identified, one should consider the use of homologous proteins or transgenic animals. With regard to safety pharmacology, testing of the vital function such as cardiovascular, respiratory, and CNS functions is recommended, but S6 also mentions renal function, which, from today’s perspective, would not be necessary before first administration of a compound in humans if there is no specific concern. Some information on absorption, disposition, and clearance in the animal models are desirable before clinical trials; systemic exposure of the compound should be monitored as well as the appearance of antibodies and their ability to neutralize the intended effect. On the other hand, appearance of antibodies in animals when treated with human proteins is not predictive for clinical conditions. Nevertheless, immune responses could alter the pharmacokinetic or pharmacodynamic effects. Anaphylactic responses tested in the guinea pig, at any rate, are not predictive for humans and therefore not necessary. The same holds true for the

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standard testing batteries for immunotoxicity; these are not recommended. Studies with duration between 2 weeks and 3 months are often sufficient, and it is only with chronic use in humans that 6-month studies should be considered. Flexibility may allow further reduction of the traditional testing program for the reproductive endpoints. Such studies may not be necessary if a new compound, related to well-known compounds, shows similar effects. In addition, there are profound differences in maternofetal transfer of immunoglobulins between species with extensive gestational transfer of maternal immunoglobulins in primates (including humans) via the chorioallantoic placenta as well as in rabbits and guinea pigs via the inverted yolk sac splanchnopleure. In contrast, other neonatal rodents (rats and mice) receive passive immunity predominantly postnatally. This transfer is mediated principally via FcRn receptors. Therapeutic monoclonal antibodies (mAbs) are most commonly of the IgG1 subclass, which is transported most efficiently to the fetus. In all animal species used for testing developmental toxicity, fetal exposure to IgG is very low during organogenesis, but this increases during the latter half of gestation (when there is only growth of the organs) such that the neonate is born with an IgG1 concentration similar to the mother (but not rats and mice). Review of mAb developmental toxicity studies of licensed products reveals that cynomolgus monkey is the species used in the majority of the cases (10 out of 15). Pregnancy outcome data from women gestationally exposed to mAb is limited. In general, the findings are consistent with the expected low exposure during organogenesis. Guinea pigs and rabbits are potential candidates as “alternatives” to the use of nonhuman primates as the maternofetal transfer in the last part of gestation is at a level similar in humans. Based on the pattern of placental transfer of IgG in humans, study designs that allow detection of both the indirect effects in early gestation plus the effects of direct fetal exposure in mid and late gestation are recommended for developmental toxicity of mAbs (Pentsuk and van der Laan, 2009). The risk of genotoxicity also reveals an exception: The standard studies are not appropriate;

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DNA damage is not expected, but testing on a case-by-case basis of impurities or promoter studies might be helpful. Long-term carcinogenicity studies are usually not appropriate, but when there is cause for concern, studies with a single rodent species are sufficient. Cause for concern may have arisen in general toxicity studies or when a stimulation of the growth of normal as well as malignant cells can be assumed. For biotechnology-derived compounds, always a high flexibility has been asked for. The main principle was the case-by-case strategies, developers, and agencies contributing their knowledge to improve the predictive value for extrapolating to humans and patients. The guideline was first published in 1997, but the accumulation of collective observations and experience caused permanent discussions and justified revisions until the recent S6 R in June 2011. Within the biotechnology-derived compounds, the monoclonal drugs play a major role. Here clinical experience has shown that more care for patients is needed especially when the first dose is administered in humans, mostly volunteers. The use of the minimum anticipated biological effect level (MABEL) for selection of first human dose in clinical trials with monoclonal antibodies (Fig. 8) is offered. Muller et al. (2009) report the selection and strategies. The authors plead that the dose selection for first-in-human (FIH) clinical trials with monoclonal antibodies (mAbs) is based on specifically designed preclinical pharmacology and toxicology studies, mechanistic ex vivo/in vitro investigations with human and animal cells, and pharmacokinetic/pharmacodynamic (PK/PD) modeling approaches and requires a thorough understanding of the biology of the target and the relative binding and pharmacological activity of the mAb in animals and humans. The authors highlight the scientific and regulatory challenges and the intensive care for enrolled humans when estimating the minimal anticipated biological effect level (MABEL). The EMA supports these prudent strategies with their recommendations in the Guideline on strategies to identify and mitigate risks for first-in-

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Fig. 8 Illustration of Mabel versus minimum toxic dose

Selection of starting Dose NOAEL 100

MABEL

PD effect

% effect

Toxicity 50

inacceptable toxicity

Anticipated therapeutic range

10

toxic range

0 PAD 1

human and early clinical trials with investigational medicinal products (February 2018).

Safety Pharmacology (ICH/S7 A+B) S7A was implemented in 2001 and the S7B reached Step 4 in June 2005 and has been implemented in 2006. S7A informs in general about the requirements necessary for testing the vital functions usually in single-dose studies in safety pharmacology. S7A differentiates between three types of studies: core battery, follow-up, and supplemental studies. The core battery of tests/S7A consists of an investigation of the effects of a test substance on vital functions: central nervous system, cardiovascular system, respiratory system, and other systems as appropriate. The exclusion of a system or function should be justified. Safety pharmacology studies carried out as necessary are: • Follow-up studies for core battery (they provide a greater depth of understanding than, or additional knowledge to, that provided by the core battery [e.g., mechanistic studies]) • Supplemental studies: they evaluate effects of the test substance on systems not addressed by the core battery when there is cause for

dose or exposure

10

MTD

100

Table 15 Timing of safety pharmacology studies Before first administration to humans: Core battery tests Possibly follow-up/supplemental studies if concern During clinical development: Additional studies as required to clarify observed or suspected undesirable effects in animals or humans Before approval Effects on all organ systems, either covered by preclinical or clinical investigations GLP conditions (S7A) Core battery tests should be conducted according to GLP Follow-up + supplemental tests according to GLP as far as possible Primary and secondary pharmacodynamic studies need not be conducted according to GLP

concern not addressed elsewhere (e.g., in toxicology). S7A expresses very clearly when (Table 15) such studies should be available and what conditions should be considered in regard to good laboratory procedures. Special focus is given to the cardiovascular system. For the core battery of the cardiovascular system according to S7A blood pressure, heart rate, and electrocardiogram should be assessed,

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Fig. 9 Strategies of cardiovascular safety

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ICH/S7B :Nonclinical Testing Strategy InVitro Ikr assay

Followup Studies

In Vivo QT assay

Chemical/ Pharmacological Class

Integrated Risk Assessment

Relevant Nonclinical and Clinical Information

Evidence of Risk but also in vivo, in vitro, and/or ex vivo evaluations, including methods for repolarization and conductance abnormalities, should be considered. This text was finalized at a time when the details of S7B were not yet outlined. During recent years there has been an increase of regulatory concern. The awareness that non-cardioactive drugs (used for sometimes non-lifethreatening diseases) can cause QT prolongation, and serious dysrhythmias such as torsades de pointes (TdP) was intensified. A greater number of compounds became known to be associated with QT prolongation and the potential to cause torsades de pointes (severe arrhythmias). Accordingly, the ICH Expert Working Group for S7B was created to work on this specific concern.

ICH S7B: Nonclinical Studies for Assessing Risk of Repolarization: Associated Ventricular Tachyarrhythmia for Human Pharmaceuticals The background of S7B is summarized as follows: • The QT interval (time from the beginning of the QRS complex to the end of the T wave) of the electrocardiogram (ECG) is a measure of the duration of ventricular depolarization and repolarization. • QT interval prolongation can be congenital or acquired (e.g., pharmaceutical-induced).

• When the QT interval is prolonged, there is an increased risk of ventricular tachyarrhythmia, including torsade de pointes (TdP), particularly when combined with other risk factors (e.g., hypokalemia, structural heart disease, bradycardia), see Fig. 1. For this important issue, a complex strategy was set up in the ICH guideline S7B (Fig. 9). The basis for integrated risk assessment is in vitro and in vivo assays, supported by any knowledge about the chemical/pharmaceutical class. This first risk assessment may later be modified when results from follow-up studies or relevant nonclinical or clinical information becomes available. The evidence of risk summarizes the preclinical evaluation of the proarrhythmic potential as essential information for clinicians. Parallel to the development of S7B, a clinical guideline (ICH/E14) was drafted and reached Step 4, also in 2005. During the discussion between these two expert groups, the question was raised again and again, if toxicologists could exclude any risk for QT prolongation for humans in their testing strategies. The answer is: they cannot. Preclinical researchers and clinical developers identify hazards and assess the definitive risk (the probability that an adverse effect may be induced), but a prediction for the individual patient is not possible. There is extrapolation from animal to man, from volunteers to patients under defined conditions, and from these to patients under daily living and health conditions, but all these data do not have the power to exclude

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risks for future patient generations as can be seen by the fact that post-marketing authorization withdrawals can happen due to severe side effects. The Food and Drug Administration (FDA) has during the ICH process compared preclinical data with clinical results and identified few cases where QT prolongation was observed under clinical conditions, while the preclinical tests were negative. This discrepancy is the basis for the diplomatic text in regard to the need for availability of S7B QT studies: Timing of S7B Nonclinical Studies and Integrated Risk Assessment in Relation to Clinical Development/Step 4, June 2005, Brussels:

scientific and medical research communities has been initiated (Trepakova et al. 2009). The objectives are:

• Conduct of S7B nonclinical studies assessing the risk for delayed ventricular repolarization and QT interval prolongation prior to first administration in humans should be considered. • These results, as part of an integrated risk assessment, can support the planning and interpretation of subsequent clinical studies.

At present, the consortium is conducting a retrospective analysis of nonclinical and clinical data from both FDA and contributing companies’ databases and supplementing with a literature review. The overall objectives of these efforts are to establish a quantitative integrated risk assessment for each compound, to define criteria for concordance, and to apply them to the database in order to identify non-concordant compounds.

The term “should be considered” allows flexibility either to do the studies before first time in humans or at a later stage of development. In practice, these studies are most often available before IND, because one wants to cope with this issue in time and wants to provide best safety to volunteers and patients. But in conclusion it has to be stated that: S7B proposes a series of nonclinical tests which are believed to predict the likelihood that a compound will prolong cardiac repolarization in vivo, in animals and in humans. These data currently seem to have limited impact on the clinical development proposals contained in the draft E14 guideline now. The ICH E14 Expert Working Group was not convinced that the preclinical in vitro and in vivo study results would predict the clinical cardiovascular tolerance. Under the auspices of the International Life Science Institute (ILSI)-Health and Environmental Sciences Institute (HESI), a consortium involving representatives from pharmaceutical companies, regulatory agencies, and opinion leaders from the

1. To assess the concordance between signals in nonclinical repolarization assays and clinical QT interval prolongation 2. To investigate the mechanisms for any discrepancy identified between nonclinical and clinical results and to determine viable and successful alternative approaches to identify these compounds 3. To assess the proarrhythmic potential of such compounds

Immunotoxicology Studies (ICH/S8) Drug-induced immune dysfunction can induce increased susceptibility to infections, hypersensitivity reactions (immunological sensitization due to a drug and/or its metabolites), autoimmunity (immune reactions to self-antigens), or finally the development of tumors, but the present status of S8 is restricted to unintended immunosuppression and immunoenhancement, excluding allergenicity or drug-specific autoimmunity. The missing elements may be resolved in future revisions. The guideline applies to new pharmaceuticals intended for use in humans, as well as to marketed drug products proposed for different indications or other variations on the current product label. The guideline does not apply to biotechnology-derived pharmaceutical products covered by ICH S6. Immunosuppression or enhancement can be associated with two distinct groups:

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Table 16 Immunotoxicity testing assay Methods to evaluate immunotoxicity I. Standard repeat dose toxicity tests first (signals for immunosuppression or stimulation) If concern then II. Additional immunotoxicity studies T-cell-dependent antibody response (TDAR) Immunophenotyping (lymphocyte subsets) Natural killer cell activity assays Host resistance studies Macrophage/neutrophil function Assays to measure cell-mediated immunity

1. Drugs intended to modulate immune function for therapeutic purposes (e.g., to prevent organ transplant rejection), here adverse immunosuppression can be considered as exaggerated pharmacodynamics. 2. Drugs not intended to affect immune function but cause immunotoxicity due to, e.g., necrosis or apoptosis of immune cells or interaction with cellular receptors shared by both target tissues and nontarget immune system cells. Methods include (Table 16) standard toxicity studies (STS) and additional immunotoxicity studies conducted as appropriate. Whether additional immunotoxicity studies are necessary should be determined by a weight-of- evidence review of cause(s) for concern. Illustrations of the thinking behind the guidances of the European proposals first as a forerunner of the final ICH S8 are publications like van der Laan et al. (1997) or in 2000 Putman et al.: Assessment of immunotoxic potential of human pharmaceuticals; Drug Info J 36: 417–427. Table 16 illustrates the options and Fig. 10 the decision tree for the tests procedure. The assessment of immunotoxicity should include the following: • Statistical and biological significance of the changes • Severity of the effects dose/exposure relationship • Safety factor above the expected clinical dose • Treatment duration

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• Number of species and endpoints affected • Changes that may occur secondarily to other factors (e.g., stress) • Possible cellular targets and/or mechanism of action • Doses which produce these changes in relation to doses which produce other toxicities and reversibility of effect(s). Additional immunotoxicity testing should be considered (Table 17): • If the pharmacological properties of a test compound indicate it has the potential to affect immune function (e.g., anti-inflammatory drugs). • If the majority of the patient population for whom the drug is intended is immunecompromised by a disease state or concurrent therapy • If a compound is structurally similar to compounds with known immunosuppressive properties • If the compound and/or its metabolites are retained at high concentrations in cells of the immune system • If clinical findings suggestive of immunotoxicity in patients exposed to the drug occur

More in detail: If the weight-of-evidence review indicates that additional immunotoxicity studies are needed, there are a number of assays which can be used (Fig. 10). It is recommended that an immune function study be conducted, such as a T-cell-dependent antibody response (TDAR). If specific cell types are affected in STS not involving cells participating in a TDAR, assays that measure function of that specific cell type might be conducted. Immunophenotyping of leukocyte populations, a nonfunctional assay, may be conducted to identify the specific cell populations affected and may provide useful clinical biomarkers. Generally accepted are 28 consecutive daily doses in rodents. Adaptations of immunotoxicity assays have been described using non-rodent species. The species, strain, dose, duration, and route

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All human pharmaceuticals (non-biologicals)

(2.1) Identify factors to consider

(2.2) Weight of evidence (WoE) review

WoE review warrants additional immunotoxicity Testing ?

NO

Additional nonclinical immunotoxicity testing not needed

YES (3.0) Conduct additional immunotoxicity studies

(3.4) Significant changes observed?

NO

(3.4 Pt 1) Further nonclinical immunotoxicity testing not needed

YES

(3.4 Pt 3) Further nonclinical immunotoxicity testing not needed

YES

(3.4) Sufficient data for risk assessment / risk management?

NO (3.4 Pt 2) Consider further immunotoxicity testing Fig. 10 Decision tree for immunotoxicological evaluation

of administration used in immune function assays should be consistent with the nonclinical toxicology study in which an adverse immune effect was observed. Usually both sexes should be used in these studies, excluding nonhuman primates. The high dose should be above the no observed adverse effect level (NOAEL) but below a level inducing changes secondary to stress. Multiple dose levels are recommended in order to determine dose-

response relationships and the dose at which no immunotoxicity is observed. If no risk of immunotoxicity can be detected, then no further testing is needed. If a risk of immunotoxicity is identified, additional testing may offer precision for the risk-benefit decision. Should the risk of immunotoxicity be considered as acceptable and/or can be addressed in a risk management plan (see ICH E2E), then no further testing in animals might be called for.

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Table 17 Concern for immunotoxicological reactions Causes for immunotoxicological concern Findings from standard toxicity studies (STS) Pharmacological properties for the intended patient population Structural similarities to known immunomodulators Disposition of the drug Hematological changes, e.g., leukocytosis, granulocytopenia, or lymphopenia Alterations in immune system organ weights and/or histology (e.g., changes in the thymus, spleen, lymph nodes, and/or bone marrow) Changes in serum globulins (without plausible explanation) Increased incidence of infections Increased occurrence of tumors (possibly a sign of immunosuppression in absence of other plausible causes such as genotoxicity, hormonal effects, or liver enzyme induction

Any additional immunotoxicity studies should be completed before exposure of a large population of patients, usually Phase III. If the target patient population is immunocompromised, immunotoxicity testing can be initiated at an earlier time point in the development of the drug. The TDAR is administered to rats a week before the end of a 4-week repeat dose study recognized T-cell-dependent antigens (e.g., sheep red blood cells, SRBC or keyhole limpet hemocyanin, KLH), leading to a robust antibody response. Use antigens for immunization without adjuvants, (Alum acceptable for nonhuman primate studies). Antibody measurement is done via ELISA or other immunoassay methods (samples can be collected serially during the study). Immunophenotyping (lymphocyte subsets) differentiates T- and B-lymphocytes and subtypes. Their maturation and functions are in bone marrow for B-lymphocytes and NK cells and in thymus for T-lymphocytes with subsequent differentiation in cytotoxic (Tc) or T-helper (Th) cells. The cytotoxic T cells are cytotoxic to virusinfected cells; they form CD 8 positive cells as memory cells. The T-helper cells fortify defense mechanisms, secrete IL-2, IFN, and TNF, and form CD4 pos. cells. Natural killer cells mature as lymphoid precursors in bone marrow. They show fast reactions

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during defense mechanisms and are therefore the first line of defense, e.g., for virus or tumors. They are cytotoxic when glycoproteins are recognized on cell membranes, but with identification of MHC-class 1, molecules on the surface of healthy cells show immediate activation of killing inhibitory receptors. All NK cell assays are ex vivo assays in which tissues (e.g., spleen) or blood are obtained from animals that have been treated with the test compound. Cell preparations are coincubated with target cells that have been labeled with 51Cr. New methods that involve nonradioactive labels can be used if adequately validated. Host resistance studies involve challenging groups of mice or rats treated with different doses of test compound with varying concentrations of a pathogen (bacteria, fungal, viral, parasitic) or tumor cells. Infectivity of the pathogens or tumor burden observed in vehicle versus test compound treated animals is used to determine if the test compound is able to alter host resistance. Host resistance assays involve innate immune mechanisms, for which specific immune function assays have not been developed. Careful observation of the direct or indirect (nonimmune mediated) effects of the test compound on the growth and pathogenicity of the organism or tumor cell is needed, e.g., compounds that inhibit the proliferation of certain tumor cells can increase host resistance. Macrophages living for several months have a strong affinity to inflammatory locations and are powerful in phagocytosis, cytotoxicity, and secretion of mediators. Localized macrophages are, e.g., alveolar macrophages, Kupffer cells in the liver, sessile macrophages in the spleen, and osteoclasts in bones. They phagocyte bacteria, fungus, parasites, damaged cells, old erythrocytes, immune complexes, they are cytotoxic to tumor cells, virus-infected cells, transplanted cells, and secrete enzymes, cytokines, prostaglandins, etc. Accordingly, their part in immunoregulation is the antigen production, cytokines, and unspecific immunosuppression. Assays to measure cell-mediated immunity have not been as well established as those used for

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the antibody response. They are in vivo assays where antigens are used for sensitization. The endpoint is the ability of drugs to modulate the response to challenge. Examples are delayed-type hypersensitivity (DTH) reactions with protein immunization and challenge reported for mice and rats. This new ICH guideline S8 replaces all guidances from the EU, USA, and Japan. It represents a very pragmatic approach and uses studies, e.g., standard toxicity studies, which are conducted anyhow. There is great confidence in the prediction of these assays for any potential of new compounds to induce immune suppression or immune stimulation. This guideline helps to reduce the number of animals and requires additional studies only in special cases for concern.

ICH S9: Nonclinical Evaluation of Anticancer Pharmaceuticals This guideline was endorsed by ICH Steering Committee in May 2007. It had been observed that separate regional oncology guidances were in development. All experts realized that malignant tumors are life-threatening, death rate from diseases is high, and existing therapies have limited effectiveness. This explains the need to provide new effective anticancer pharmaceuticals to patients more expeditiously. The question was if this group of indications may allow higher flexibility in the design and timing of nonclinical studies for anticancer pharmaceuticals. A working group was established delivering answers to questions like which nonclinical studies to support the development of anticancer pharmaceuticals in patients with advanced disease and limited therapeutic options are really needed? Does one sufficiently understand the toxicological profile of a pharmaceutical, e.g., identification of the target organs, exposure-response relationship, and reversibility in regard to the fact that with anticancer pharmaceuticals, the clinical dose level is often close to or at an adverse effect level? How could one nevertheless protect patients from unnecessary adverse effects and at the same

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time avoid unnecessary use of animals in accordance with the 3 R principles (reduce/refine/ replace)? As all indications, the discipline of pharmacology/pharmacodynamics needs to deliver the nonclinical proof of principle, what is the preliminary characterization, what are the underlying mechanism(s) of action, how does the drug exert its antitumor activity, which schedule dependence is needed, and, if necessary, which combinations can be justified? The discipline of safety pharmacology is requested to identify any acute risks of the vital organ functions. Are the cardiovascular, respiratory, and central nervous systems not disturbed? This answer must be given before the initiation of clinical studies and based either on stand-alone safety pharmacology studies or study part included into general toxicology studies. How could specific concerns be managed? In the absence of a specific risk, such studies will not be called for to support clinical trials or for marketing. Kinetics: limited pharmacokinetic parameters (e.g., Cmax in plasma/serum, AUC, and half-life) in the animal species used for nonclinical studies can facilitate dose selection, schedule, and escalation during Phase I studies. Further information (ADME) can be generated in parallel with clinical studies. General toxicity: the identification of a NOEL/NOAEL is not essential. Small molecules should be usually conducted in both rodent and non-rodent, with the exception for genotoxic drugs targeting rapidly dividing cells; here one rodent species might be considered sufficient. For biopharmaceuticals, see ICH S6. Important is the issue of reversibility: severe toxicity at approximate clinical exposure and recovery cannot be predicted by scientific assessment, but the demonstration of complete recovery is not considered essential. Reproductive toxicity: Embryo-fetal toxicity studies of anticancer pharmaceuticals should be available for marketing with the exception if they target rapidly dividing cells in general toxicity studies or belong to a class which has been well characterized in causing developmental toxicity.

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If a pharmaceutical is positive for embryo-fetal lethality or is teratogenic, a confirmatory study in a second species is usually not warranted. For biopharmaceuticals, the assessment might be done by evaluating the toxicity during the period of organogenesis or study designs as described by ICH S6. Alternative approaches might be considered appropriate if scientifically justified. The alternative approaches might include a literature assessment, assessment of placental transfer, the direct or indirect effects of the biopharmaceutical, or other factors. A fertility study is generally not warranted to support the treatment of patients with advanced cancer. Information available from general toxicology studies on reproductive organs should be incorporated into the assessment of reproductive toxicology. A peri- and postnatal toxicology study is generally not warranted to support the treatment of patients with advance cancer. Genotoxicity: Studies should be performed to support marketing. The principles outlined in ICH S6 should be followed for biopharmaceuticals. If the in vitro assays are positive, an in vivo assay might not be warranted. Carcinogenicity: Studies are usually not warranted based on the underlying disease. Immunotoxicology: General toxicology studies are considered sufficient to evaluate the immunotoxicological potential. Photosafety studies: Initial assessment of phototoxic potential should be conducted prior to Phase I. Photochemical properties of the drug and information on other members in the class should be assessed. If assessment of these data indicates a potential risk, appropriate protective measures should be taken during outpatient trials. If the photosafety risk cannot be adequately evaluated based on nonclinical data or clinical experience, a photosafety assessment consistent with the principles described in ICH M3 should be provided prior to marketing. Setting the starting dose: Pharmacologically active dose should be reasonably safe to use. Start dose should be scientifically justified using all available nonclinical data (e.g.,

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pharmacokinetics, pharmacodynamics, toxicity). Interspecies scaling usually based on normalization to body surface area. Interspecies scaling based on body weight, AUC, or other exposure parameters might be appropriate. The FDA is offering good advice and help for calculations in their Guidance for Industry: Estimating the maximum safe starting dose in initial clinical trials for Therapeutics in adult healthy volunteers (CDER July 2005). Here conversions on animal doses to human equivalent doses based on body surface area are tabled. For biopharmaceuticals with agonistic properties, minimally anticipated biologic effect level (MABEL) should be considered During clinical trials: Highest clinical dose is not limited by nonclinical data. In Phase I, treatment dose depends on the patient’s response. No new toxicology study is called for to support continued treatment beyond duration of the toxicology studies. Examples of duration and schedule of toxicology studies to support initial clinical trials are provided. In cases where the available toxicology information does not support a change in clinical schedules, an additional toxicology study in a single species is usually sufficient. The highest clinical dose is not limited by nonclinical data. Beyond Phase I, the pharmaceutical development could continue with the delivery of results from repeat dose studies of 3 months prior the initiation of phase III studies. This maximal duration would for most pharmaceuticals be considered sufficient to support marketing. A special option for this indication is offered by a Phase 0 clinical trial: This is a first-in-human clinical trial conducted under an exploratory IND that has no therapeutic or diagnostic intent and involves very limited human exposure. The results of such a Phase 0 trial can provide essential pharmacodynamic, pharmacokinetic, and/or imaging data at the initial stage of the clinical trials process to inform and expedite the subsequent development of promising new agents.

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The results of the first “Phase 0” clinical trial in oncology of a therapeutic agent under the Exploratory Investigational New Drug Guidance of the US FDA have recently been reported and considered to be a successful and expeditious new paradigm for early therapeutics development in oncology, but according to Kummar et al. (2009), several additional phase 0 trials will need to be completed under the Exploratory IND Guidance, before phase 0 trials will be considered to have an established role in the anticancer drug development process. Nevertheless, the US Food and Drug Administration’s new regulatory policy has provided an important and timely opportunity to expeditiously conduct and complete novel, proof-of-principle clinical trials of molecularly targeted therapeutic and imaging agents. The potential for a major impact of phase 0 trials and the exploratory IND on developing new anticancer drugs provides a strong stimulus for the broader uptake and enhanced application of carefully conceived, pharmacodynamically driven early-phase clinical trials in oncology. In Summary (Table 18), one can conclude that this new guideline ICH S9 facilitates and speeds up the development of new oncology compounds, since there is no need for 6-/9-month studies, no need for fertility and peri- and postnatal studies, only one embryo-fetal study if a positive reaction is observed, no in vivo micronucleus test if in vitro genotoxicity assays are positive, safety

Table 18 Facilitation of development of oncologics Development of oncologic compounds 1. Pharmacology Primary PD studies (in vivo and/ or in vitro) 2. Safety Safety pharmacology core pharmacology battery studies 3. Kinetics Kinetics: AUC, Cmax, ADME 4. Human Nonclinical test of human metabolites metabolite(s) 5. Toxicity Repeat dose toxicity studies 4 weeks 4 weeks 6. Toxicity Repeat dose toxicity studies 3 months 3 months 7. Genotoxicity Genotoxicity 8. Teratogenicity Embryo-fetal studies 9. Phototoxicity Phototoxicity

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pharmacology assessments that could be conducted within the general toxicology studies, no need for non-rodent studies for initiation of clinical trials with cytotoxic drugs, and no or limited studies to be conducted in late-stage development. All restrictions help to conserve resources and reduce animal use.

ICH Guideline S10 The ICH S10 guideline on photosafety evaluation reached Step 4 of the ICH Process in November 2013 and has entered the implementation period (Step 5) between 2014 and 2016 in the different areas. Forerunners of this global guideline were the EMA guideline (European Agency for the Evaluation of Medicinal Products), note for guidance on photosafety testing. Committee for Proprietary Medicinal Products from June 27, 2002. CPMP /SWP/ 398 /01 and the Guidance on photosafety testing, from May 2003. US Department of Health and Human Services Food and Drug Administration (CDER) (Fig. 11). The multidisciplinary guideline ICH M3(R2) recommends that an initial assessment of phototoxicity potential be conducted before exposure of large numbers of subjects (Phase 3). This safety evaluation is especially important for compounds with known phototoxic risk, like antibiotics or oncology products. However, neither ICH M3(R2) nor ICH S9 provide specific information regarding testing strategies. Here ICH S10 guideline outlines when photosafety testing is warranted and which possible assessment strategies can be recommended. This guideline should hereby reduce the likelihood that substantial differences in recommendations for photosafety assessment will exist among regions. S10 generally applies to new active pharmaceutical ingredients (APIs), new excipients, clinical formulations for dermal application (including dermal patches), and photodynamic therapy products but excludes peptides, proteins, antibody drug conjugates, or oligonucleotides. The photosafety assessment of a drug considers for the evaluation the photochemical

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Initial Assessment of Phototoxic Potential

UV-vis spectrum in methanol (290-700 nm)

MEC < 1000 L mol-1 cm-1 MEC > 1000 L mol-1 cm-1

Chemical photoreactivity assay

optional

“negative”

no further phototoxicity testing

otherwise*

no light protective measures in clinical trials

Experimental Evaluation of Phototoxicity Options for collecting additional data in biological systems in vitro phototoxicity test

or

Distribution to lightexposed tissues §

or

in vivo preclinical phototoxicity test

or

Clinical evaluation $ “negative”

Clinical > in vivo > in vitro #

otherwise*

Determine adequate risk minimization measures to prevent adverse events in humans

Fig. 11 Assessment of phototoxic evaluation

characteristics, results from nonclinical studies, and human safety information. Four different effects have been discussed in connection with photosafety testing: phototoxicity, photoallergy, photogenotoxicity, and photocarcinogenicity. But testing for photogenotoxicity (Note 2) and photocarcinogenicity (Note 6 of ICH M3 (R2)) is not currently considered useful for human pharmaceuticals. Accordingly, the focus is on phototoxicity and photoallergy effects as defined below: • Phototoxicity (photoirritation): An acute lightinduced tissue response to a photoreactive chemical • Photoallergy: An immunologically mediated reaction to a chemical, initiated by the formation of photoproducts (e.g., protein adducts) following a photochemical reaction

Photosensitization is a general term occasionally used to describe all light-induced tissue reactions. However, in order to clearly distinguish between photoallergy and phototoxicity, the term photosensitization is not used in this guideline. For a chemical to demonstrate phototoxicity and/or photoallergy, the following characteristics are critical: • Absorbs light within the range of natural sunlight (290–700 nm). • Generates a reactive species following absorption of UV-visible light. • Distributes sufficiently to light-exposed tissues (e.g., skin, eye). If one or more of these conditions is not met, a compound will usually not present a concern for direct phototoxicity.

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Photochemical properties: The first question when assessing the photoreactive potential is whether a compound absorbs photons at any wavelength between 290 and 700 nm. A compound that does not have a molar extinction coefficient (MEC) greater than 1000 L mol-1 cm-1 at any wavelength between 290 and 700 nm (Bauer et al. 2014) is not considered to be sufficiently photoreactive and therefore able to induce direct phototoxicity. Excitation of molecules by light can lead to generate reactive oxygen species (ROS), including superoxide anion and singlet oxygen via energy transfer mechanisms. Thus, ROS generation following irradiation with UVvisible light can be an indicator of phototoxicity potential. Photostability testing (EMA/CHMP/ICH/ 536328/2013 2016a) can also suggest the potential for photoreactivity, but photostability testing alone should not be used to determine whether further photosafety evaluation is warranted. Tissue distribution: The concentration of a photoreactive chemical in tissue at the time of light exposure is a very important pharmacokinetic parameter in determining whether a phototoxic reaction will occur. This concentration depends on a variety of factors, such as plasma concentration, perfusion of the tissue, partitioning from vascular to interstitial and cellular compartments, and binding, retention, and accumulation of the chemical in the tissue. The duration of exposure depends upon clearance rates as reflected by half lives in plasma and tissue. Further, the longer the concentration of a compound is maintained at a level above that critical for a photochemical reaction, the longer a person is at risk for phototoxicity. Although a tissue concentration threshold below which the risk for phototoxic reactions would be negligible is scientifically plausible, there are currently no data to delineate such generic thresholds for all compounds. Risks are considered low when a drug shows an overall very low exposure or has a very short plasma half-life or tissue residence. On the other hand, compound binding to tissue components (e.g., melanin, keratin) can explain tissue retention and/or accumulation, but such binding alone does not present a photosafety

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concern. A single-dose tissue distribution study, with animals assessed at multiple time points after dosing, will generally provide an adequate assessment of relative tissue to plasma concentration ratios, tissue residence time, and the potential for retention and accumulation. Nonclinical photosafety tests: Generally, it is most important that nonclinical photosafety assays show high sensitivity resulting in a low frequency of false negatives (i.e., a high negative predictive value). This is because negative assay results usually do not warrant further photosafety evaluation. Natural sunlight represents the broadest range of light exposure that humans might be exposed to regularly. However, sunlight per se is not well defined. Suitability of a sunlight simulator light source should be well defined. In nonclinical phototoxicity assays, however, the amount of UVB should not limit the overall irradiation and might be attenuated (partially filtered) so that relevant UVA doses can be tested without reducing assay sensitivity. Penetration of UVB light into human skin is mainly limited to the epidermis, while UVA can reach capillary blood. Therefore, clinical relevance of photochemical activation by UVB is considered less important than activation by UVA for systemic drugs. However, UVB irradiation is relevant for topical formulations applied to light-exposed tissues. S10 discusses pros and cons of a number of different test approaches: – Photoreactivity tests using chemical assays High sensitivity for predicting direct in vivo phototoxicants, but low specificity, generating a high percentage of false-positive result. – Phototoxicity tests using in vitro assays The most widely used in vitro assay for photo toxicity is the three T3 neutral red uptake phototoxicity test (3 T3 NRU-PT). – Photosafety tests using in vivo assays and systemic administration Phototoxicity testing for systemically administered compounds has been conducted in a variety of species, including guinea pig, mouse, and rat. No standardized study design has been established.

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For 3T3 NRU-PT, the Organisation for Economic Co-operation and Development (OECD) guideline (EMA/CHMP/ICH/536328/ 2013 2016b) is available. This is currently considered the most appropriate in vitro screen for soluble compounds. The sensitivity of the 3T3 NRU-PT is high, and if a compound is negative in this assay, it would have a very low probability of being phototoxic in humans. However, a positive result in the 3T3 NRU-PT should not be regarded as indicative of a likely clinical phototoxic risk, but rather a flag for follow-up assessment. There are no in vitro models that specifically assess ocular phototoxicity, regardless of the route of administration. While negative results in the 3T3 NRU-PT or a reconstructed human skin assay might suggest a low risk, the predictive value of these assays for ocular phototoxicity is unknown. The most sensitive early signs of compoundinduced phototoxicity are usually erythema followed by edema at a normally suberythemogenic irradiation dose. The type of response might vary with the compound. Any identified phototoxicity reaction should be evaluated regarding dose and time dependency, and, if possible, the no-observed-adverse-effect level (NOAEL) should be established. The hazard identification might be further supported by additional endpoints (e.g., early inflammatory markers in skin or lymph node reactions indicative of acute irritation). If a phototoxicity study is conducted in animals for a systemic drug that absorbs light above 400 nm, phototoxicity of the retina should be assessed using a detailed histopathological evaluation. For compounds that only absorb light below 400 nm, retinal assessment is usually not warranted because such wavelengths do not reach the retina of the adult human eye due to limited penetration of the cornea, lens, and vitreous body. Testing for photoallergy is not recommended for compounds that are administered systemically. Photoallergy reactions in humans following systemic administration are rare, and there are no established nonclinical photoallergy assays for systemically administered compounds.

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Photosafety Tests Using In Vivo Assays and Dermal Administration For dermal drug products in general, the clinical formulation should be tested. The intended clinical conditions of administration should be used to the extent possible. Irradiation of the exposed area should take place at a specified time after application, and the interval between application and irradiation should be justified based on the specific properties of the formulation to be tested. For dermal drug products, contact photoallergy has often been assessed in a nonclinical study along with acute phototoxicity (photoirritation). However, no formal validation of such assays has been performed. While the acute photoirritation observed in these studies is considered relevant to humans, the predictivity of these studies for human photoallergy is unknown. For regulatory purposes, such nonclinical photoallergy testing is generally not recommended. Assessment strategies: The choice of the photosafety assessment strategy is up to the drug developer. ICH M3(R2) suggests that an initial assessment of the phototoxicity potential based on photosafety evaluation of pharmaceuticals, photochemical properties, and pharmacological/ chemical class be undertaken before outpatient studies. Characterization of the UV-visible absorption spectrum is recommended as the initial assessment because it can obviate any further photosafety evaluation. In addition, the distribution to the skin and eye can be evaluated to inform further on the human risk and the recommendations for further testing. Then, if appropriate, an experimental evaluation of phototoxicity potential (in vitro or in vivo, or clinical) should be undertaken before exposure of large numbers of subjects (Phase 3). Figure 11 provides an outline of possible phototoxicity assessment strategies. The figure is based on the strategies outlined in this section of this document. The strategies are flexible. Depending on the particular situation, some portions of the assessment are optional and might not be conducted.

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Recommendations for Pharmaceuticals Given via Systemic Routes

Recommendations for Pharmaceuticals Given via Dermal Routes

Assessment of Phototoxicity Potential If the substance does not have a MEC greater than 1000 L mol-1 cm-1 (between 290 and 700 nm), no photosafety testing is recommended and no direct phototoxicity is anticipated in humans. For compounds with MEC values of 1000 L mol-1 cm-1 or higher, if the drug developer chooses to conduct a test for photoreactivity, a negative result could support a decision that no further photosafety assessment is warranted.

Assessment of Phototoxicity Potential If the active substance and excipients do not have MEC values greater than 1000 L mol-1 cm-1 (between 290 and 700 nm), no further photosafety testing is recommended and no phototoxicity is anticipated in humans. Molar extinction coefficient (MEC) (also called molar absorptivity) reflects the efficiency with which a molecule can absorb a photon at a particular wavelength (typically expressed as L mol-1 cm-1) and is influenced by several factors, such as solvent. For compounds with MEC values of 1000 L mol-1 cm-1 or higher, negative photoreactivity test results (e.g., a ROS assay) (reactive oxygen species, including superoxide anion and singlet oxygen) can support a decision that no further photosafety assessment is warranted.

Experimental Evaluation of Phototoxicity The in vitro approach with the 3T3 NRU-PT is currently the most widely used assay and in many cases could be considered as an initial test for phototoxicity. The high sensitivity of the 3T3 NRU-PT results in good negative predictivity, and negative results are generally accepted as sufficient evidence that a substance is not phototoxic. In such cases no further testing is recommended and no direct phototoxicity is anticipated in humans. If an in vitro phototoxicity assay gives a positive result, a phototoxicity study in animals could be conducted to assess whether the potential phototoxicity identified in vitro correlates with a response in vivo. Alternatively, drug distribution data could, on a case-by-case basis, support a position that the risk of phototoxicity in vivo is very low and that no further photosafety assessment is warranted. As another option, the photosafety risk could be assessed in the clinical setting or managed by the use of light-protective measures. A negative result in an appropriately conducted phototoxicity study either in animals or humans supersedes a positive in vitro result. In such cases no further testing is recommended and no direct phototoxicity is anticipated in humans. In all cases a robust clinical phototoxicity assessment indicating no concern supersedes any positive nonclinical results.

Experimental Evaluation of Phototoxicity and Photoallergy The 3T3 NRU-PT can be used to assess individually the phototoxicity potential of the API and any new excipient(s), provided that appropriate testing conditions can be achieved (e.g., test concentrations not limited by poor solubility, relevant UVB dose can be applied). In cases where no phototoxic component has been identified in vitro, the overall phototoxicity potential of the clinical formulation can be regarded as low. Reconstructed human skin models can be used to assess the phototoxicity potential of clinical formulations. Under adequate test conditions, a negative result in a reconstructed human skin assay indicates that the direct phototoxicity potential of the formulation can be regarded as low. In this case, generally no further phototoxicity testing is recommended. As the predictivity of nonclinical photoallergy tests is unknown, this would typically be a clinical assessment using the to-be-marketed formulation and conducted during Phase 3.

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Photosafety evaluation of the clinical formulation delivered via dermal patches can follow the above-described principles for clinical dermal formulations. For transdermal patches, the principles for both dermal and systemic drugs should be applied. In addition, the intended clinical use (e.g., skin area recommended for use, duration of application) and the properties of the patch matrix (e.g., being opaque to UV and visible light) should be considered for the overall risk assessment. Testing for photogenotoxicity is not recommended as a part of the standard photosafety testing program. These tests are substantially oversensitive and even incidences of pseudo-photoclastogenicity have been reported (Lynch et al. 2008). Furthermore, the interpretation of photogenotoxicity data regarding its meaning for clinically relevant enhancement of UV-mediated skin cancer is unclear. A survey of pharmaceutical companies indicated that the 3T3 NRU-PT, as described in Organisation for Economic Co-operation and Development, Test Guideline (OECD TG) 432, generates a high percentage of positive results (approximately 50%), the majority of which do not correlate with phototoxicity responses in animals or humans (Lynch and Wilcox 2011). In the USA, for products applied dermally, a dedicated clinical trial for phototoxicity (photoirritation) on the to-be-marketed formulation (API plus all excipients) can be warranted in support of product approval.

S11 Nonclinical Pediatric Safety S11 Nonclinical Safety Testing in Support of Development of Pediatric Medicines This topic was endorsed by the ICH Steering Committee in November 2014 and is in 2018 still in ICH Step 1 (the consensus building process of ICH). The S11 guideline will recommend standards for the conditions under which nonclinical

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juvenile animal testing is considered informative and necessary to support pediatric clinical trials. Clarification is necessary in regard to the need, study design, and/or timing of juvenile animal studies to support pediatric indications, since considerable differences exist within in the present regulatory guidelines from the ICH, EMA, FDA, and MHLW. The regional guidelines recommend a case-by-case approach for determining the need for a juvenile animal study after consideration of the available data (FDA 2006; EMA 2008; MHLW, 2012). The guideline may propose a modification of a repeat dose general toxicity study or a pre- and postnatal developmental toxicity study. The ICH M3(R2) guideline, which focuses on the need and, when warranted, the timing of juvenile animal studies, states, “The conduct of any juvenile animal toxicity studies should be considered only when previous animal data and human safety data, including effects from other drugs of the pharmacological class, are judged to be insufficient to support pediatric studies.” If a study is warranted, one relevant species, preferably rodent, is generally considered adequate. The expectation is that the inconsistencies in interpretation and application of the present different guidelines can be harmonized.

Multidisciplinary Guidelines These ICH recommendations or guidelines are the crosscutting topics; they do not fit uniquely into one of the quality, safety, and efficacy categories but usually request cooperations between several scientific disciplines. M3 as an example represents the contribution of safety and efficacy. Or M4 refers to all three categories. Additionally multidisciplinary guidelines includes the ICH medical terminology (MedDRA) and the development of Electronic Standards for the Transfer of Regulatory Information (ESTRI). The following list summons up all types of these guidelines; in detail M3 and M4 are handled.

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M1 (Medical Dictionary for Regulatory Activities) (Concept Paper Available, Step 1) The development of a Medical Dictionary for Regulatory Activities was approved by the ICH Steering Committee in 1997 and the terminology launched in 1999. Further development of this topic followed over the years, all information about MedDRA and the points to consider documents developed for every MedDRA version are available on the MedDRA page under the work products. The concept paper was approved in 2016. M2: Electronic Standards for the Transfer of Regulatory Information (ESTRI) (Concept Paper Available, Step 1) This Expert Working Group (EWG) was established by the ICH Steering Committee in 1994. The objective was to facilitate international electronic communication by evaluating and recommending, open and nonproprietary – to the extent possible – Electronic Standards for the Transfer of Regulatory Information (ESTRI) that will meet the requirements of the pharmaceutical companies and regulatory authorities. The ICH Steering Committee modified in 2010 the mandate of the M2 EWG. Important changes included agreement that work related to the Electronic Common Technical Document (eCTD) be undertaken by a newly established M8 EWG and that the M2 EWG would no longer be directly involved in the development of technical solutions in relation to topics such as E2B(R3) and M5 but would instead provide the framework for the efficient and effective development of the solutions by groups dedicated to these topics. Under its new mandate, the M2 EWG continues to be responsible for the evaluation and recommendation of standards. M5 Data Elements and Standards for Drug Dictionaries (See E2 B) In 2003, the ICH Steering Committee endorsed a concept paper for topic M5, and EWG develops since the requirements for the standardization of medicinal product identifiers and related terminology. In particular, a need was identified to harmonize product information that would facilitate the

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electronic exchange of Individual Case Safety Reports (ICSRs) within and across ICH regions using the ICH E2B format in post-marketing pharmacovigilance. In 2005, an M5 consensus draft guideline was published for public consultation at Step 2 of the ICH process. To support the electronic exchange of the M5 data elements proposed by the M5 EWG, technical messaging specifications were needed. Electronic messaging development for utilization by the ICH Parties had been the domain of the ICH M2 EWG, but in June 2006 the ICH Steering Committee took a key decision to develop electronic specifications in collaboration with Standards Development Organizations (SDOs). This would enable wider interoperability across regulatory and healthcare communities.

M6 Virus and Gene Therapy Vector Shedding and Transmission (Step 1) The ICH Steering Committee endorsed this topic in 2009. Work by the Gene Therapy Discussion Group (GTDG) followed, a document “General Principles to Address Virus and Vector Shedding” became available. It was recognized that more extensive information to improve harmonization among the ICH regions was needed. In 2011 this topic was ceased following Steering Committee discussion that concluded due to the current state of science and related resource allocation would not allow this to be supported as a topic for harmonization. M7(R1)Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk (Step 5) This guideline was finalized as a step 5 document in May 2017; it offers guidance on analysis of structure activity relationships (SAR) for genotoxicity. Furthermore, it is intended to resolve questions such as whether impurities with similar alerts that potentially have similar mechanism of action should not be combined in calculating a Threshold of Toxicological Concern (TTC) and whether the TTC may differ based on differences in the approved duration of use.

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To complement this ICH M7 guideline, an addendum was finalized in 2017 to summarize known mutagenic impurities commonly found or used in drug synthesis. The intent of this addendum is to provide useful information regarding the acceptable limits of known mutagenic impurities/ carcinogenic and supporting monographs. In a maintenance process, work followed to define acceptable limits (acceptable intakes (AIs) or permitted daily exposures (PDEs)) for new DNA reactive (mutagenic) impurities and revising acceptable limits for impurities already listed in the addendum as new data becomes available. Data and/or proposals pertaining to the revision of the ICH M7(R1) guideline with supporting information can be submitted directly to the ICH Secretariat from either an ICH Member or Observer or other interested ICH stakeholders.

M7(R2) Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk (Step 1) The M7(R2) EWG is currently undertaking a maintenance of the guideline, which will result in the future ICH M7(R2) version. The work is now in Step 1. M8 Electronic Common Technical Document (eCTD) (Step 4) ICH M8 EWG was formed in 2010 to take over the development and revision of eCTD v 4.0 Implementation Guide and related documents from the ICH M2 eCTD Subgroup. Discussions and revisions took place and updated versions are expected. The M8 EWG also provides technical review and impact assessment of issues arising from the use of the ICH M4 CTD guidelines within the context of the eCTD. M9 Biopharmaceutics Classification System-Based Biowaivers (Step 1) This topic was endorsed by the ICH Management Committee in October 2016. This new multidisciplinary guideline is proposed to address Biopharmaceutics Classification System (BCS)-based biowaivers. BCS-based

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biowaivers may be applicable to BCS Class I and III drugs; however, 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. This guideline will provide recommendations to support the biopharmaceutics classification of medicinal products and will provide recommendations to support the waiver of bioequivalence studies. This will result in the harmonization of current regional guidelines/guidance and support streamlined global drug development.

M10 Bioanalytical Method Validation (Step 1) This topic was endorsed by the ICH Management Committee in October 2016. This new multidisciplinary guideline will apply to the validation of bioanalytical methods and study sample analyses in nonclinical and clinical studies. Reliable data derived through validated bioanalytical methods are key for the review of marketing authorization application. This guideline will provide recommendations on the scientific regulatory requirements for bioanalysis conducted during the development of drugs of both chemical and biological origins. It will also address issues on method validation by considering the characteristics of the analytical methods used in bioanalysis, e.g., chromatographic assay and ligand binding assay. A harmonized bioanalytical method validation guideline will promote the prompt, rational, and effective nonclinical and clinical studies, thereby advancing the mission of the ICH.

ICH Multidisciplinary Guidelines M3 (Timing) This guideline was in Step 5 in 1997 and has been revised now several times (Table 19). The newest version is called:

Guidance on Nonclinical Safety Studies for the Conduct of Human Clinical Trials and Marketing Authorization for Pharmaceuticals M3(R2) Current Step 4 version dated June 11, 2009.

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This guideline focuses on duration and timing of preclinical studies but also very importantly under which conditions different patient populations (men, women, children) can be included into clinical investigations. The guideline stresses that drug development is a stepwise process. It starts with short-term studies, and during the following 10 years and more, the longer time tests follow (Fig. 12). ICH/M3 provides information about which studies – and of what duration – are needed before the different clinical phases of development can be started. Special attention is realized during the transition from preclinical animal testing and the first administration of a new drug to humans, mostly male volunteers. Table 19 General principles of ICH guideline M3 (timing) M3 R: general principles Development of a pharmaceutical is a stepwise process Evaluation of animal and human efficacy and safety needed The goals of the nonclinical safety evaluation include: Characterization of toxic effects Identification of target organs Clarification of dose dependency Relationship of toxicities to exposure Potential reversibility Possibly: mechanism of toxicity

Fig. 12 Schematic design of preclinical development

Table 20 summarizes the different endpoints needed to be available and acceptable before this FIM. And Table 21 pays attention to the important data analysis of metabolites. In addition to this timing-schedule of preclinical studies in dependence of clinical development plans, there are recommendations under which conditions different populations can be included into clinical trials, populations such as men, women of childbearing potential, or pregnant women and finally pediatric populations. The objectives of M3 are to reduce differences between regions, to facilitate timely conduct of clinical trials, to reduce unnecessary use of animals and other resources, and to promote early availability of new drugs. The background of this guidance was that regulatory recommendations differed among regions of Europe, the USA, and Japan. Would it be possible to develop a mutually acceptable guidance? The guideline provides general guidance for drug development. A guideline is not a legal requirement. Approaches should be scientifically and ethically appropriate. The different endpoints are addressed in details in this note for Guidance. Table 20 summarizes all study requirements before the first starting dose in humans. The core battery of safety pharmacology is recommended to be conducted prior to first administration in humans. Any follow-up or

• Nonclinical Development : Pharmacology, Toxicology, Kinetics, •

Only short-term studies are conducted before start of clinical Phase I, longterm studies run between Phase I and Market Authorization.

Cells, receptors “Screening“ Lead compound selecon

SP. 4 w Tox, Genotox, Kin. Non-clinical development Animal & cell culture studies

Repro, longterm Tox, in vivo Genotox, Kin., etc. Clinical development Phase I

Phase II

Phase III

rodent 2-year bioassay, reproducve studies

Market Authorization

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Table 20 Preclinical studies needed before first dose in man Preclinical studies before FIM 1. Primary PD studies (in vivo and/or in vitro) 2. Safety pharmacology core battery studies (CV, CNS, respiration) 3. Kinetics: In vitro metabolism, plasma protein binding data for animals and humans, systemic exposure data from Tox species (AUC, Cmax) 4. Repeat dose toxicity studies (minimum duration: 2 weeks in 2 species) 5. Genotoxicity (only in vitro: gene + chromosomal aberration) 6. No carcinogenicity 7. No single-dose studies or data on lethality needed 8. No reproductive studies 9. Other studies if concern, e.g., phototoxicity, etc.

Table 21 Investigations of metabolites Human metabolite(s) Nonclinical characterization of human metabolite(s), when exposures greater than 10% of total drug-related exposure Such nonclinical data support phase III clinical trials Some metabolites not of toxicological concern: (e.g., most glutathione conjugates). No need for testing Cause for concern may be a unique human metabolite. Consider testing such metabolite in animals

supplemental studies as appropriate. During clinical development, a clarification of observed or suspected adverse effects in animals or during clinical trials may be needed. Before NDA, an assessment of effects on all systems should be provided. For kinetics, an information on exposure data (AUC) in animals prior human clinical trials is needed, while ADME data are needed at completion of Phase I (human pharmacology) studies. The duration for repeat dose toxicity studies is related to the duration of clinical trials and their therapeutic indication. In principle, the duration of animal studies is equal to or exceeds the duration of the human clinical trials. In general, there is a relationship of 1:1 ratio for studies in two mammalian species (one nonrodent). The details can be found in Table 22. This table was updated by thorough additional Japanese studies in 2000 during the 5th ICH

1129 Table 22 Duration of repeated dose toxicity studies to support phase I and II trials in EU and phase I, II, and III trials in the USA and Japan

Maximum duration of clinical trial Up to 2 weeks Between 2 weeks and 6 months > 6 months

Recommended minimum duration of repeated-dose toxicity studies to support clinical trials Rodents Non-rodents 2 weeks 2 weeks Same as Same as clinical trial clinical trial 6 months 9 months

Conference in San Diego, USA. The Japanese scientists compared the utility of routine 4-week toxicity studies with 2-week studies and concluded that in regard to the prediction of toxicities to the male reproductive organs, 2-week studies were as valid as 4-week studies; therefore, the former regional Japanese requirement to ask for a minimum duration of 4-week studies before starting trials in men was dropped for a global consensus that the minimum duration of nonclinical studies is 2 weeks in rodents and 2 weeks in non-rodents. On the other hand, regional differences continue in regard to single-dose animal studies supporting single-dose studies in humans. In the USA, single-dose toxicity studies with extended examinations can support single-dose human trials. This concept encouraged the EU to offer comparable options with the microdosing concept. This principle may be especially valuable for gaining early data for go/no go decisions, when several candidates are being developed in parallel. Table 23 summarizes the different options and their conditions before applying drugs to humans. Results from two in vitro genotoxicity studies are recommended to be available prior to first administration to humans, while the standard battery should be completed prior to initiation of Phase II studies. Carcinogenicity studies do not need to be completed in advance of the conduct of clinical trials unless there is cause for concern (ICH: S1A). For pharmaceuticals to treat certain serious diseases, carcinogenicity testing, if needed, may be concluded post-approval.

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Table 23 Options for exploratory assays Exploratory clinical trials Study type PDy General Tox Microdose PD in Single dose, 1 vitro species, + in 1 route vivo Repeat microdose

Single dose

Up to 14 days

Up to 14 days

PD in vitro + in vivo PD in vitro + in vivo + SP PD in vitro + in vivo + SP PD in vitro + in vivo + SP

7 day repeat, PK, Hem. Chem, path

Genotox No studies, SAR recommended No studies, SAR recommended

Extended single dose Day repeat, PK, Hem. Chem, path 2 week study, 2 species PK, Hem. Chem, path

Ames

2 week study, 2 species, non-rodent as confirmatory, PK, Hem., Chem, path

Ames + Chrom, abb test in vitro or in vivo

Ames + Chro, abb test

The inclusion of different patient populations reveals regional differences, especially for women with child bearing potential. There is a high level of concern for unintentional exposure of an embryo/fetus. The currently regional differences in the timing of reproduction toxicity studies to support the inclusion of women with childbearing potential are: 1. Japan: assessment of female fertility and embryo-fetal development should be completed prior to the inclusion of women of childbearing potential using birth control in any type of clinical trial. 2. EU: assessment of embryo-fetal development should be completed prior inclusion of Women of Childbearing Potential, prior Phase III trials female fertility studies are needed. 3. US: women of childbearing potential may be included in early, carefully monitored studies without reproduction toxicity studies provided appropriate precautions are taken to minimize risk.

4. US: assessment of female fertility and embryofetal development should be completed before Phase III trials. The inclusion of children into clinical trials has gained tremendous interest. The following data are requested: • Safety data from previous adult human exposure: – Most relevant information – Necessary before pediatric clinical trials • Prior pediatric trials: – Appropriate repeated dose toxicity studies – All reproduction toxicity studies – Standard battery of genotoxicity tests • Juvenile animal studies should be considered • Carcinogenicity testing: – Prior to long-term exposure of children, if cause for concern With the revision of this guideline, a number of important aspects have been incorporated into the overall strategies. It is stressed that drug development for the preclinical area is always a stepwise process where a number of goals is pursued. Short-term tests come first; investigations of life-threatening conditions dictate their timing. Long-term studies like carcinogenicity or reproductive toxicity studies accompany the clinical Phases II and III. The guideline provides answers which metabolites need to be characterized. M3R takes up an old idea of the FDA, to facilitate the kinetic access to humans. FDA had offered already in the late 1990s to use the concept of single-dose toxicity study to allow a single dose in humans. With M3R additional options are given. Very important are the recommendations of M3R for the inclusion of children into clinical trials (Tables 24, 25, 26, and 27). One needs to realize that children are not small adults; they can be more vulnerable or sometimes more resistant than adults. Their organs need years to mature. The past did not consider the pediatric population to test their tolerance, clinical or toxicological

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Table 24 Safety requirements for pediatric population Clinical trials in pediatric populations (1) Requirements: Safety data from previous adult human experience, as most relevant information Review quality and extent of adult human data Exception: Extensive adult experience might not be available before pediatric exposures (e.g., for pediatricspecific indications)

Table 25 Studies needed for pediatric populations Clinical trials in pediatric populations Results from repeat dose toxicity studies of appropriate duration in adult animals Core safety pharmacology package Standard battery of genotoxicity tests Reproduction toxicity studies relevant to the age and gender of the pediatric patient populations with information on direct toxic or developmental risks (e.g., fertility and pre-postnatal developmental studies) Embryo-fetal developmental studies are not critical to support clinical studies for males or prepubescent females

Table 26 Use of juvenile animals Pediatric populations + juvenile animals studies The conduct of any juvenile animal toxicity studies should be considered only when previous animal data and human safety data, including effects from other drugs of the pharmacological class, are judged to be insufficient to support pediatric studies One relevant species, preferably rodent, is adequate A non-rodent species appropriate when scientifically justified Juvenile animal toxicity studies are not considered important for short-term PK studies (e.g., 1–3 doses) in pediatric populations

aspects. Most of the drugs were in former times never tested for children. This has lead now to a paradigm change: We try not to protect children against clinical trials but to protect the pediatric population by conducting specifically designed trials. And the agencies expect now that all activities in regard to the pediatric population should be planned in advance by submitting to the agencies a pediatric investigation plan (PIP) as soon as adult PK data available, which means, i.e., end of Phase 1.

1131 Table 27 Pediatric population: need or no need of preclinical studies Clinical trials in pediatric populations A chronic repeat dose study: Initiated in the appropriate age and species to address this developmental concern (e.g., 12-month duration dog or 6 month in rodent) A 12-month study covers full development period in the dog Carcinogenicity studies not generally recommended to support the conduct of pediatric clinical trials Consider carcinogenicity testing before long-term exposure in pediatric clinical trials, when there is: Evidence of genotoxicity in multiple tests Concern for pro-carcinogenic risk based on mechanistic considerations Findings from general toxicity studies

An important recommendation of M3 guidance refers to the question how to test drugs which are often administered in combination. The strategies are dictated by the possibility of an increase of concern when combined. Testing is needed when little is known for the different compounds, e.g., when compounds are still in early development, but very little is done when the pharmacological and toxicological characteristics are well investigated. Tables 28 and 29 give examples of these two extremes.

Common Technical Document (ICH/M4) M4 is another very important multidisciplinary guideline, which combines information for the three disciplines within the ICH Process: for quality, safety, and efficacy. The following section focuses predominantly on the preclinical safety issues (Fig. 13). The total document is divided into five modules: Module 1 contains regional specific aspects, it provides for the European Union, e.g., the European Community specific data. This module therefore is not harmonized but region-specific. Module 2 provides the summaries for quality, for safety, and efficacy. The quality part uses as a headline “Quality Overall Summary,” for safety and efficacy the terms “Non-Clinical or Clinical Overview,” The different names signal that the quality part is a clear summary, while the nonclinical and

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Table 28 Activities required for combination of two latestage compounds Combination of late-stage drugs Two late-stage entities with adequate clinical experience, combination toxicity studies not recommended for support clinical studies or marketing, unless significant toxicological concern (e.g., similar target organ toxicity) No concern when: Margins of safety high Monitoring of adverse effects in humans easy If concern, complete preclinical combination study before start of clinical trials

Table 29 Activities required for combination of two early-stage compounds Combination of two early-stage entities: Nonclinical combination toxicity studies recommended Duration of clinical trials and nonclinical comparable: Ratio 1.1 A 90-day combination toxicity study would support clinical trials and marketing

clinical part should be critical evaluations. Module 3 provides chemical, pharmaceutical, and biological Quality information. Module 4 contains the nonclinical reports and Module 5 provides clinical study reports. The objectives of M4 are to assist authors in the preparation of nonclinical pharmacology, pharmacokinetics, and toxicology written summaries in an acceptable format. The CTD is not intended to indicate what studies are required but provides an appropriate format for the nonclinical data. The Common Technical Document is nothing other than a placeholder for the different parts of a documentation for the market authorization process. No guideline can cover all eventualities; common sense and a clear focus on needs of regulatory authority assessor are best guides to constructing an acceptable document. Therefore, modify the format if needed with the aim to provide best possible presentation and facilitate the understanding for the evaluation of the results. The CTD-S is organized as follows: F. Nonclinical summary

Module

Not part of CTD

1

Regional Administrative Information 1.0 CTD Table of Contents 2.1 CTD Introduction 2.2

Module 2 Quality Overall Summary 2.3

3

Module Quality 3.0

Nonclinical Overview 2.4 Nonclinical Summary 2.6

4

Module Nonclinical Study Reports 4.0

Fig. 13 Structure of common technical document (CTD)

Clinical Overview 2.5

CTD

Clinical Summary 2.7

5

Module Clinical Study Reports 5.0

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1. Pharmacology (a) Written summary (b) Tabulated summary 2. Pharmacokinetics (a) Written summary (b) Tabulated summary 3. Toxicology (a) Written summary (b) Tabulated summary This organization is kept up in all parts of the dossier; it is repeated for the overview, the summaries, and the reports. The detailed organization for pharmacology, kinetics, and toxicology are as follows: 1. Pharmacology Written Summary • Brief Summary • Primary Pharmacodynamics • Secondary Pharmacodynamics • Safety Pharmacology • Pharmacodynamic Drug Interactions • Discussion and Conclusions • Tables and Figures (either here, or included in text) 2. Pharmacokinetics Written Summary • Brief Summary • Methods of Analysis • Absorption • Distribution • Metabolism • Excretion • Pharmacokinetic Drug Interactions (Nonhuman) • Other Pharmacokinetic Studies • Discussion and Conclusions 3. Toxicology Written Summary • Brief Summary • Single-Dose Toxicity • Repeat Dose Toxicity • Genotoxicity • Carcinogenicity • Reproduction Toxicity Local Tolerance • Other Toxicity Studies • Discussion and Conclusions Examples of detailed advice for sections on discussion and conclusion of pharmacokinetics:

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Information should be integrated across studies and across species; exposure in the test animals should be related to exposure in humans given the maximum intended doses. Similar examples for toxicology: in vitro studies should precede in vivo studies. Where multiple studies of the same type need to be summarized within the pharmacokinetics and toxicology sections, studies should be ordered by species, by route, and then by duration (shortest duration first). The species should be ordered as follows: (1) mouse, (2) rat, (3) hamster, (4) other rodent, (5) rabbit, (6) dog, (7) nonhuman primate, (8) other non-rodent mammal, and (9) non-mammals (see also Table 17.) It is also recommended to limit the information in the summaries and overview. The overview should contain the essential and critical results on approximately 30 pages. The length of the Nonclinical Written Summaries should in general not exceed 100–150 pages (Table 30). The brief summaries for pharmacology should be written on 2–3 pages, for pharmacokinetics the same length and for toxicology approximately 6 pages. The tabulated summaries (examples given in Figs. 14 and 15 and Table 31) help to get a quick insight into the detailed data. A first thorough review is therefore based on reading the nonclinical overview in connection with some of the detailed tabulated summaries. This CTD has been tested in practice for several years. Although not perfect for every case, it has proven its usefulness. Researchers/Industry know where to place specific information and data and regulators know where to find them. This has facilitated the review process tremendously; only one dossier is necessary for international registration; a lot of resources can be diverted to more important issues. Table 30 Summary of pages number for CTD Number of pages recommended in CTD 1. Nonclinical overview 30 pages, use: Pharmacology brief summary 3 pages Pharmacokinetics brief summary 3 pages Toxicology brief summary 6 pages 2. Written summaries 150 pages: 3. Module 4 = reports as long as needed

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Fig. 14 Example of tabulated summary (Pharmacology)

1 Pharmacology

Overview

Type of Study

Test System

Test Article Method of Study Administration Number

Location Volume, Page

1.1 Primary Pharmacodynamics 1.2 Secondary Pharmacodynamics 1.3 Safety Pharmacology 1.4 Pharmacodynamic Intervention

1.3 Safety Pharmacology Organ Species/ Systems Evaluated Strain

Method of Admin.

Test Article: Gender and No. Doses (mg/kg) per Group Noteworthy Findings

GLP Compliance

Study Number

Fig. 15 Example of tabulated summary for safety pharmacology studies Table 31 Example of parts of the toxicology program Tabulated summaries: toxicology Toxicology overview Overview of toxicokinetics studies Overview of toxicokinetics data Batches (and their impurities) used in toxicology studies Single-dose toxicity Repeat dose toxicity (non-pivotal and pivotal)

But the CTD is a living document; its weakness is apparent. In order to improve its quality and practicability, suggestions and proposals for improvements are invited from the public. These modifications are being dealt with within the process of “Questions and Answers,” the ICH experts publish their conclusions regularly.

Outlook and future of ICH ICH guidelines are living and dynamic documents which are submitted constantly to revisions and modifications. This is happening to the Common Technical Dossier also. The FDA is proposing a new method which is supported by Japan but also followed with great interest by the European Medicines Agency. Experts work on a Standards for Exchange of Nonclinical Data (SEND). This

SEND is an implementation of the CDISC Standard Data Tabulation Model (SDTM) for nonclinical studies, related to animal testing conducted during drug development. Send will facilitate the transfer of raw data of toxicology animal studies. When such experimental studies started after December 18, 2016, to support submission of new drugs to the US Food and Drug Administration, then such data should be submitted to the agency using SEND. Recommendations for implementing SEND, including how to model various nonclinical endpoints, rules to doing so, and examples with sample data are available on the CDISC SEND website. The Pharmaceuticals and Medical Devices Agency in Japan will enforce its use in the future also, most probably in 2020. More details are offered by, e.g., Caroline Hroncich (2016) (Steven Denham, MPI Research), the impact of SEND on the pharmaceutical industry: SEND is a nonclinical version of the Study Data Tabulation Model (SDTM), the standard format for electronic submission of clinical data to FDA. SEND and SDTM are expected to speed up the review process for drug applications by “developing electronic tools to analyze and visualize these submissions, and building data warehouses to rapid query data across drugs, companies, and clinical and nonclinical disciplines.”

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SEND will become a mandatory part of submissions for relevant investigational new drugs (INDs), new drug applications (NDAs), biologics license applications (BLAs), and abbreviated new drug applications (ANDAs) and will start by December 17, 2017. From now on companies have to provide electronic standards for capturing individual data from nonclinical studies, including both metadata about how the study is conducted and the actual data itself. The FDA is trying to move away from paper that is creating a physical storage problem. It should improve the review process; not only for review time but for the safety aspects in that there would be a data warehouse at the agency that can be searched across studies and across sponsors. SEND recommends how one stores truckloads of data and be able to access it in a finite review time and compare it with data from a similar active ingredient. Those studies could well have been submitted by different sponsors. The end result is that they can recommend a more focused approach to safety observations in clinical trials. For a new submission submitted to FDA/ CDER after March 15, 2020, studies need to adhere to the new standard. The other studies in the submission which started before March 15, 2020, are not required to be submitted according to the new standard, although it is encouraged/ preferred. Visit the SEND CT page to get the most recent CT and contact [email protected] for additional advice about such a submission. The FDA will use the files for the review process, via the Nonclinical Information Management System (NIMS) suite. This suite will facilitate the review of submissions more efficiently than with only PDF or printed submissions that contain the individual animal data. SEND will only be a requirement in the USA for certain FDA submissions. However, it has operational use, such as transfer between organizations, sponsor warehousing, etc. Accordingly, it is preferable to produce SEND datasets, even if not technically required for submission. However, it is a longer-term goal for the SEND datasets to eventually replace the individual tabulated datasets. A SEND package consists of a number of dataset files (in XPT

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format, a.k.a., SAS v5 transport format) and a define.xml file (which provides information about what is in the datasets). The publicly available sample SEND datasets can be seen in, e.g., PhUSE github, https://github. com/phuse-org/phuse-scripts/tree/master/data/send or contact [email protected] offering a subject line of “Send me SEND” to get an FDA validated SEND dataset for an example 28 day toxicity study. Finally go to SENDDataSet.org to download an FDA validated multi-organizational SEND dataset. SEND will offer data mining opportunities, which will be used by the FDA/industry. The benefits can only be realized with time when significant sufficient database of historical data or studies are converted and loaded into repository systems to facilitate such queries. Developers very certainly should follow FDA recommendation to avoid any delays for the marketing authorization process.

Outlook on ICH ICH can look back to 25 years of achievements. As the most successful international harmonization initiative in the world, its achievements include an understanding of innovation, a common regulatory platform based on more than 70 guidelines, improvement of scientific guidelines, facilitating communication between different regulatory agencies and between industry and the regulatory authorities, effective use of research and development (R&D) resources, and greater mutual acceptance of R&D data realized. In addition, the drafting process of guidelines has identified gaps in science; new studies analyzing problems have been conducted to validate the assays being used to confirm safe use of drug in humans, like validity of 2-week studies to assess male fertility or the validity of transgenic mouse models for assessing the carcinogenic potential within the ILSI/HESI evaluation process. Further guidance documents are in preparation or released for consultation. Among them gets the guideline on environmental risk assessments for pharmaceuticals more importance. Genotoxic impurities are

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handled with great care. Aspects like druginduced hepatotoxicity, gene transfer, genomics, etc. are waiting to be assessed for regulatory advice. It is a great success that guidelines in general today are science driven and based on valid assessment in practice. The open-minded acceptance of new technologies, e.g., in vitro systems, molecular databases, in silico data collections, computer sciences, imaging, and critical use of omics as animal and human biomarkers will continue to deliver high-quality products. At the same time, there is awareness that with the increase of high-quality results, the attrition rate does not increase. The improved cooperation of regulatory agencies and industry has created an atmosphere of mutual confidence and trust. The open dialogue attracts scientific strategic skillfulness and reduces rigidity. The road of success is the cooperation on a level below legislation while retaining national sovereignty. Many issues have been resolved; several problems wait for a harmonized solution. Enthusiasm continues; dynamic contributions support the further process of improving and facilitating pharmaceutical development under the leadership of ICH with the continuous aim to bring safe drugs faster to the patient.

References and Further Reading Ames BN et al (1973) Carcinogens are mutagens: a simple test system combining liver homogenates for activation and bacteria for detection. PNAS 70:2281–2285 Bauer D, Averett LA, De Smedt A, Kleinman MH, Muster W, Pettersen BA, Robles C (2014) Standardized UVvis spectra as the foundation for a threshold-based, integrated photosafety evaluation. Regul Toxicol Pharmacol 68(1):70–75 Berwald Y, Sachs L (1965) In vitro transformation of normal cells to tumor cells by carcinogenic hydrocarbons. J Natl Cancer Inst 35:641–661 Bode G, Clausing P, Gervais F, Loegstedt J, Luft J, Nogues V, Sims J (2010) The utility of the minipig as an animal model in regulatory toxicology. Journal Pharm Tox Methods 62:196–226 Contrera JF1, Jacobs AC, DeGeorge JJ (1997) Carcinogenicity testing and the evaluation of regulatory requirements for pharmaceuticals. Regul Toxicol Pharmacol 25(2):130–145

G. Bode Cohen SM, Robinson D, MacDonald J, Health and Environmental Sciences Institute (2001) Alternative models for carcinogenicity testing. Toxicol Sci 64(1):14–19. ilsi.org/publication/alternative-models-for-carcinogenic ity-testing/ Contrera JF, Aub B, Barbehenn E, Belair E, Chen C, Evoniuk G, Mainigei K, Mielach F, Sancilio L (1993) A retrospective comparison of the results of 6 and 12 months non-rodent toxicity studies. Adverse Drug React Toxicol Rev 12(1):63–76 Corvi A, Albertinein S, Hartung T, Hoffman S, Maurici D, Pfuhler S, van Bentham J, Vanparys P (2008) ECVASM retrospective validation of in vitro micronucleus test (MNT). Mutagenesis 23(4):271–283 EMA/CHMP/ICH/536328/2013 Rev (2016a) 1 Committee for human medicinal products ICH guideline S1 Regulatory notice on changes to core guideline on rodent carcinogenicity testing of pharmaceuticals, Mar 2016. www. ema.europa.eu/docs/...guideline/.../WC500136405.pdf EMA/CHMP/ICH/536328/2013 Rev (2016b) 1 CHMP (Committee for human medicinal products) ICH guideline S1 Regulatory notice on changes to core guideline on rodent carcinogenicity testing of pharmaceuticals, status 29 Feb 2016 Estimating the maximum safe starting dose in initial clinical trials for Therapeutics in adult healthy volunteers (CDER July 2005) Forster R, Bode G, Ellegaard, van der Laan W (2010) The rethink project. Minipigs as models for the toxicity testing of new medicines and chemicals : an impact assessment. Journal Pharm Tox Methods 62:158–159 and 236–242 Friedrich A, Olejniczak K (2011) Evaluation of carcinogenicity studies of medicinal product for human use authorized via the European centralized procedure (1995–2009). Regul Toxicol Pharmacol 60:225–248 From http://www.emea.eu.int/htms/human/ich/safety/ichfin.htm Guidance for Industry Estimating the Maximum Safe Starting Dose in Initial Clinical Trials for Therapeutics in Adult Healthy Volunteers U.S. Department of Health and Human Services Food and Drug Administration Center for Drug Evaluation and Research (CDER) (2005) Pharmacology and toxicology, July 2005 Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use S2(R1) (2011) Step 4 version dated 9 Nov 2011 Hroncich C (2016) The impact of SEND on the pharmaceutical industry. Pharm Technol 2016(1):28–30 (Discussions with Denham S, MCI) ICH Guideline (2009) M3(R2) on non-clinical safety studies for the conduct of human clinical trials and marketing authorisation for pharmaceuticals Step 5 from 2009 ICH M 4 R Organisation of the Common Technical Document for the Registration of Pharmaceuticals for Human Use M4 (2016) Current Step 4 version dated 15 June 2016 ICH S 11 (2014) Non-clinical Safety testing in support of development of paediatric Medicines, Concept paper, step 1

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Jacobs A, von der Laan JW (2012) Chapter 38. In: McAnulty P, Dayan A, Ganderup NC, Hastings KL (eds) The minipig in biomedical research. CRC Press, Boca Raton, pp 565–571. ISBN 978-1-43981118-4 Kirkland D, Aardema M, Mueller L, Hayashi M (2006) Evaluation of the ability of a battery of 3 in vitro genotoxicity tests to discriminate rodent carcinogens and non-carcinogens. II. Further analysis of mammalian cell results, relative predictivity and tumor profiles. Mutat Res 608:29–42 Kishi K1, Kanamori S, Maruyama T, Sasaki K, Hara K, Kawai M, Ikeuchi K (1995) Potential parameters of male reproductive toxicity: reproductive performance, histopathology and sperm evaluation in SD rats given nitrazepam. J Toxicol Sci 20(3):329–339 Kummar et al (2009) Phase 0 clinical trial of the poly (ADP-Ribose) polymerase inhibitor ABT-888 in patients with advanced malignancies. J Clin Oncol 27 (16):2705–2711 LeBoeuf RA, Kerckaert GA (1986) The induction of transformed-like morphology and enhanced growth in Syrian hamster embryo cells grown at acid pH. Carcinogenesis 7:1431–1440 LeBoeuf et al (1996) Mutat Res 356:85–127 Lynch AM, Wilcox P (2011) Review of the performance of the 3T3 NRU in vitro phototoxicity assay in the pharmaceutical industry. Exp Toxicol Pathol 63(3):209–214 Lynch AM, Robinson SA, Wilcox P, Smith MD, Kleinman M, Jiang K et al (2008) Cycloheximide and disulfoton are positive in the photoclastogenicity assay but do not absorb UV irradiation: another example of pseudophotoclastogenicity? Mutagenesis 23(2):111–118 Lumley C, van Cauteren H (1997) Harmonisation of international toxicity testing guidelines for contribution in refinement and reduction in animal use. EBRA Bull 1997; pp 4–9 Mauthe RJ1, Gibson DP, Bunch RT, Custer L (2001, 2001) The Syrian hamster embryo (SHE) cell transformation assay: review of the methods and results. Toxicol Pathol (29 Suppl):138–146 McAnulty P, Dayan A, Ganderup NC, Hastings KL (2012) The minipig in biomedical research. CRC Press, Boca Raton. ISBN 978-1-4398-1118-4 Muller PY, Milton M, Lloyd P, Sims J, Brennan FR (2009) The minimum anticipated biological effect level (MABEL) for selection of first human dose in clinical trials with monoclonal antibodies. Curr Opin Biotechnol 20(6):722–729. https://doi.org/10.1016/j. copbio.2009.10.013 Epub 5 Nov 2009 Nonclinical Evaluation for Anticancer Pharmaceuticals S9 (2009) Current Step 4 version dated 29 Oct 2009 OECD Guideline for Testing of Chemicals in vitro 3T3 NR U phototoxicity test (2004) Pentsuk N1, van der Laan JW (2009) An interspecies comparison of placental antibody transfer: new insights into developmental toxicity testing of monoclonal antibodies. Birth Defects Res B Dev Reprod Toxicol 86(4):328–344

1137 Photosafety Evaluation of Pharmaceuticals S10 (2013) Current Step 4 version dated 13 Nov 2013 Putman E, van Loveren H, Bode G, Dean J, Hastings K, Nakamura K, Verdier F, van der Laan JW (2000) Assessment of immunotoxic potential of human pharmaceuticals. Drug Info J 36:417–427 Sakai T, Takahashi M, Mitsumori K, Yasuhara K, Kawashima K, Mayahara H, Ohno Y (2000) Collaborative work to evaluate toxicity on male reproductive organs by 2- week repeated dose toxicity studies in rats. Overview of the studies. J Toxicol Sci 25:1–21 Sistare FD1, Morton D, Alden C, Christensen J, Keller D, Jonghe SD et al. (2011) An analysis of pharmaceutical experience with decades of rat carcinogenicity testing: support for a proposal to modify current regulatory guidelines. Toxicol Pathol 39(4):716–44. Takayama S, Akaike M, Kawashima K, Takahashi M, Kurokawa Y (1995) A collaborative study in Japan on optimal treatment period and parameters for detection of male fertility disorders in rats induced by medical drugs. J Amer Coll Toxicol 14:266–292 The Clinical Evaluation of QT/QTC Interval Prolongation and Proarrhythmic Potential for Nonantiarrhythmic Drugs E14 (2005) Current Step 4 version dated 12 May 2005 Topic S1C Note for Guidance on Dose Selection for Carcinogenicity Studies of Pharmaceutical Topic S1C(R) Note for Guidance on Dose Selection for Carcinogenicity Studies of Pharmaceuticals: Addition of a Limited Dose and related Notes Topic S2A Note for Guidance on Genotoxicity: Guidance on Specific Aspects of Regulatory Genotoxicity Tests for Pharmaceuticals Topic S2B Note for Guidance on Genotoxicity: A Standard Battery for Genotoxicity Testing of Pharmaceuticals Topic S3A Note for Guidance on Toxicokinetics: A Guidance for Assessing Systemic Exposure in Toxicology Studies Topic S3B Note for Guidance on PharmacoKinetics: Guidance for Repeated Dose Tissue Distribution Studies Topic S4A Note for guidance on Duration of Chronic Toxicity Testing in Animals (Rodent and non Rodent Toxicity Testing) Topic S5A Note for Guidance on Reproductive Toxicology: Detection of Toxicity to Reproduction for Medicinal Products Topic S5B Note for Guidance on Reproductive Toxicology: Toxicity on Male Fertility Topic S6 Note for Preclinical Safety Evaluation of Biotechnology-Derived Products Topic S7A Note for Guidance on Safety Pharmacology Studies for Human Pharmaceuticals Topic S7B Note for Guidance on Non-Clinical Evaluation of the potential for Delayed Ventricular Repolarization (QT Interval Prolongation) by Human Pharmaceuticals Topic S8 Immunotoxicity Studies for Human Pharmaceutical Trepakova ES et al (2009) A HESI consortium approach to assess the human predictive value of non-clinical

1138 repolarization assays. J Pharmacol Toxicol Methods 60(1):45–50. https://doi.org/10.1016/j.vascn.2009.05.002 Epub 9 May 2009 Van Cauteren H, Bentley P, Bode G, Cordier A, Coussment W, Heining P, Sims J (2000) The industry view on longterm toxicology testing in drug development of human pharmaceuticals. Pharmacol Toxicol 86(Suppl I):1–5 van der Laan, van Loveren H, Vos JG, Dean JH, Hastings K (1997) Immunotoxicity of pharmaceuticals: current knowledge, testing strategies, risk evaluation & consequences for human health. Drug Info J 31:1301–1306

G. Bode Vargesson N (2015) Thalidomide induced teratogenesis: history and mechanisms. Birth Defects Res C Embryo Today 105(2):140–156 www.ema.europa.eu/docs/...guideline/.../WC500136405. pdf. Revision of ICH guideline S 1 A www.ema.europa.eu/docs/en...guideline/.../WC500002699. pdf. Topic S1A note for guidance on the need for carcinogenicity studies of pharmaceuticals www.ema.europa.eu/docs/en...guideline/.../WC500003258. pdf. Topic S1B mote for guidance on carcinogenicity: testing for carcinogenicity of pharmaceuticals

Clinical Quality Management System

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Beat Widler

Contents Quality Is More than Compliance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1139 Elements of a Pharma/Health-Care Quality Management System (QMS) . . . . . . 1140 Quality by Design (QbD) Builds on Robust, Smart, and Well-Documented Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1141 Quality by Design and Quality Risk Management (QRM) . . . . . . . . . . . . . . . . . . . . . . . . 1141 Quality by Design in Other Domains and Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 Quality Means Standardization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1142 Misconceptions Around the Building of a QMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 Conclusive Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1143 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1144

Abstract

The ICH (R2) update finally introduces to the world of clinical trials quality and compliance management concepts such as quality by design and quality risk management that have been successfully applied in other industries such as the automotive and airline industries. This chapter reviews how these concepts as well as a high degree of standardization of processes and tools (e.g., trial protocols) can not only drive compliance and quality but also efficiency and cost-

B. Widler (*) Widler & Schiemann AG, Zug, Switzerland e-mail: [email protected]

effectiveness when engaging in a clinical development program. A lean quality management system needs to support such an approach by focusing on lean processes whereas quality is measured and not “estimated.” A proactive mindset with a clear risk management approach is a way to a successful clinical trial conduct.

Quality Is More than Compliance Even 20 years after the issuing of the ICH GCP Guidelines (ICH E6), the principles of GCP are still sound, and little can be criticized about them. The updates of the Guidelines and especially its

© Springer Nature Switzerland AG 2020 F. J. Hock, M. R. Gralinski (eds.), Drug Discovery and Evaluation: Methods in Clinical Pharmacology, https://doi.org/10.1007/978-3-319-68864-0_42

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Addendum [ICH E6(R2)] that became effective in Europe in 2017 have not fundamentally changed the content of this Guideline but introduced technology and approaches that were unknown when the GCP Guideline got issued for the first time. Twenty years ago clinical development was largely a paper-based process, and the Internet and even more so the Cloud were technology and tools only known to a few techies. The value of ICH E6 is also evidenced by the fact that ICH GCP successfully made its inroads even into new regions where public welfare priorities and medical practice differ from what is the norm in the original ICH regions: Western Europe, USA, and Japan. When these principles are so robust and still so sound, why is there then an ongoing debate about the inefficiencies of the clinical trial process, the growing disinterest of physicians in clinical trial activities, and an apparently dwindling quality of results generated through clinical trials which results in distrust by patients and the public at large into the clinical trials enterprise? What is causing this disenchantment? Could it be that we – sponsors, health authorities, and also public health services – burdened the sound principles of GCP with bureaucratic requirements that at first seemed to be “good ideas” but as a matter of fact do not contribute to what are the two fundamental dimensions of GCP and quality: protecting patients’ safety, right, and integrity as well as ensuring data integrity? Is the ICH GCP revision and especially its Addendum, which became effective this year, going to improve the way in which GCP gets implemented? In fact, this Addendum stresses the importance of the sponsor’s oversight on all stakeholders in a clinical trial, the personal responsibilities of the principal investigator, and the roles and responsibilities of a CRO. The Addendum also highlights the importance of a robust quality management system and calls for a structured risk assessment of protocols and processes applied to execute a trial protocol that are enablers of risk-based monitoring or what is a more accurate rendition of the concept: evidence-based trial quality management. When implemented correctly evidence-based trial quality management allows: 1. Ensuring the protection of patients’ safety, integrity, and rights.

B. Widler

2. As the flip side of the above, ensuring data integrity what is nothing else than the longterm dimension of the first requirement: avoid any false positive or negative conclusions because the data collected in a trial is faulty. When this principle is recognized and correctly implemented – remember decisions should be data/evidence driven and not be based on opinions, even if decisions are reached by team consensus, as stated by Edwards Deming’s quality without data, is just another opinion – then a liberating epiphany emerges: errors “that are understood” or “factored into the process,” on the basis of the sound principles of a Quality by Design (QbD) methodology and approach, do not matter! In other terms, on this basis an isolated GCP non-compliance, such as an isolated transcription error, omission of a “minor” adverse event can be accepted as “forgivable sins.” Conversely, system failures that do result or may result in a risk to the two fundamentals of GCP must be identified, proactively dealt with, and if they have materialized be corrected or at least mitigated swiftly. Methodologies of Quality Risk Management (QRM) and Quality by Design are instrumental for a systematic quality management approach that allows focusing on the essentials of GCP.

Elements of a Pharma/Health-Care Quality Management System (QMS) Any QMS should include the following elements: A. People, i.e., those individuals who have a role and responsibility in a B. Process related to the development of a new medicinal product/health-care activity. Processes are typically described in SOPs (standard operating procedures) and systems’ (validation) documentation and are owned by the business units involved in these activities. A quality manual is a useful tool to summarize the quality principles and high-level processes of an organization. C. Controls, i.e., those activities implemented by the business or process owner under the oversight of the independent quality assurance

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(QA) unit to identify and prevent process deviations and defective products. “Classical” control elements are review and approvals of essential documents, monitoring and co-monitoring of process and its deliverables, auditing, and inspections to verify and confirm compliance. Controls must include evidence (documentation) reviews and approvals that have been executed in a timely manner. In a Quality Risk Management, environment control needs to include KPIs (key performance indicator) and KRIs (key risk indicator) to allow for a continuous monitoring of the performance of systems and products or outputs meeting predetermined specifications. D. Documentation, i.e., a transparent description of the systems and processes used allowing to reconstruct at any time the sequence of events as well as the body of evidence of compliance with stipulated checks and controls.

Quality by Design (QbD) Builds on Robust, Smart, and WellDocumented Processes

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•

•

• •

•

process chart, then within the supplier’s organization, a named individual needs to be identified. Input, the (sometimes, semifinished) product or service – can also be a decision such as an approval – that serves as the building element for the next “production” step. Process, predefined, documented, and agreed sequence of activities that each meets predefined, documented, and agreed specifications. Critical process steps are linked to control steps. Output, the deliverable – can be a product, service, or decision – of the above process. Customer, a named individual (exceptionally a functional entity) who receives the above output. The customer can be a third party or internal client. Controls, this is the C2 in SIPOC and refers to all quality verification and governance activities as well as any corrective and preventive actions.

The purpose of the SIPOC2 approach is to break down a complex (generally a multistep) process into discrete elements to drive transparency and accountabilities. Robust controls are defined and implemented at each supplier – customer interface and also specifications for input and output. The SIPOC2 concept can also be applied to the design of a protocol and the planning and implementation of a clinical trial. For instance, the protocol “designer” should not only define clear roles and responsibilities for all protocol tasks but as part of the process and control description also anticipate what could go wrong, build up-front contingency plans, define controls to identify early deviations from the design specifications, and include in the design of a new trial learnings from past, good, or bad experience.

The classical approach to a QMS has its shortcomings as more and more the costs (for instance, for monitoring clinical trial centers) and resources needed for the management and controls of a clinical development process are not matched by a commensurate process efficiency and quality of the “product.” From a QbD perspective, quality is best defined as a product, service or process that meets customers’ needs, whereas the customer can be an independent third party such as a buyer, regulator, prescriber, etc. or an “internal” client, e.g., the next in the value chain. To define the process leading to a quality product or service, a concept known from the 6-Sigma methodology – SIPOC – has proven to be very effective. We prefer to refer to SIPOC2:

Quality by Design and Quality Risk Management (QRM)

• Supplier, a named individual who delivers a product or service – can also be an instruction – that enables the next individual in the value chain to execute a predefined task. It is good practice not to designate a team as the supplier; if a function is designated as the supplier in a

The QbD approach and QRM are intimately related. The smart implementation of a QRM strategy leverages operational or transactional data generated as a by-product of the clinical or pharmacovigilance processes to return inferences about process robustness and compliance with set

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specifications. Typically, QRM uses an array of so-called key risk indicators: a KRI is a measurable entity and is always associated with a threshold of acceptance/rejection. A KRI is comparable to a KPI but focuses on the quality rather than the efficiency aspect. For instance, the audit trail generated for each change to a database entry (i.e., the GCP mandated tracking of the date of a change and the originator of a change) can be trended across all sites of a trial to determine whether an unexpected pattern in these changes emerges. The number of data entry changes is an example of a KRI. For example, investigators in a given country or clinical trial center may “produce” an above average number of changes. QRM requires that the root cause and reason for this “aberration” are investigated and understood in order to take either corrective and preventive action (CAPA) or to accept the fact that there is such an “aberration” when this has a logical and justified reason. As shown in this example, QRM typically uses meta-data as drivers and input for KRIs and thus takes QC and QA by sampling to a seamless oversight of mission critical activities, processes, and deliverables. 6-Sigma, FMEA (failure mode and effect analysis), and Kaizen are established methods or models supporting a QbD and QRM approach.

Quality by Design in Other Domains and Industries ICH had started the discussion about QbD (i.e., ICH Q8, Q9, and Q10 guidelines) in the GMP area, and the revision of ICH GCP and especially its Addendum has extended these concepts also to clinical trials. Nevertheless, compared to other industries, the health care and pharmaceutical sector are lagging behind in applying QbD and QRM approaches. Therefore, learning from other industries on how to successfully introduce QbD and QRM processes should become a priority. The airplane engine manufacturer Pratt & Whitney is a good example for the successful shift from a trial and error approach in product

B. Widler

development to a disciplined and process driven engineering standard work (ESW). In ESW process steps are documented and described in extensive but targeted Workflow Maps with a focus on the interdependencies between the successive process steps. This approach establishes design criteria with clear deliverables for each ESW step and demands for clear and unequivocal ownership of each ESW element. ESW also introduced the practitioner proficiency assessments to capture coaching needs as well as coaching capabilities of individuals involved in product development. ESW is a prime example for the application of Edwards Deming’s principles of the PDCA cycle (Plan–Do–Check–Act). Back in the early 1950s, the US Navy introduced under the leadership of Hyman Rickover a QbD methodology and mind-set that allowed to operate nuclear reactors on their ships without any radiation incident for more than 60 years. Rickover was a stern advocate of a process driven approach. One of his fundamental contributions was reinventing the methodology of testing. In a traditional approach, testing is seen as an enabler for discerning good from bad. Rickover redefined the purpose of testing as an enabler for discerning an understood from a not understood process or process step. This coupled with his non-tolerance of a work-around approach – a work-around being seen as evidence of a poorly understood or designed process – a data driven and disciplined (also in terms of detail) problem-solving approach, as well as a low threshold for what is counted as an incident resulted in an unprecedented reliability of a highly complex system such as a nuclear reactor on a submarine.

Quality Means Standardization The pharmaceutical industry has failed to date to develop and implement common standards for its key activities. As a result of this deficiency and inefficiency, investigators are frustrated, quality of clinical trials is negatively impacted, and inefficiencies in the clinical trial process are the norm.

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This “state” is compounded by a silo mentality of this industry by which learnings of pre- or postcompetitive nature around clinical trial activities are not shared or leveraged. The consequence is that the same errors are repeated again and again. From this follows that the successful implementation of QbD and QRM approaches will require that standards for routine processes get developed within the company and across companies. As demonstrated in other industries (e.g., airline industry), shared standards drive quality and eventually also efficiency as the need for retraining to different formats for an identical process becomes obsolete. Although TransCelerate – a cross-industry initiative – was established to streamline drug development processes, some of their achievements such as the publication of a template for a clinical protocol, a tool to conduct a risk assessment, etc., fall short of a true standardization: What would truly streamline the clinical trial process was developing and implementing for each “common” indication a shared protocol, which would simplify the review by ethics committees and health authorities, the implementation of a trial by the investigators and their teams, training of all involved stakeholders such as monitors, and also the setting up of the database, the eCRF, by the sponsor.

Misconceptions Around the Building of a QMS A common error in establishing a QMS is to build it as an afterthought rather than a strategic priority. Especially, start-up companies fall into this trap by focusing on their “science” and consider SOPs and other elements of the QMS as a nuisance rather than an asset. From experience a robust QMS should be in place no later than 6 months prior to starting entry into men trials. On one hand also inspectorates have moved to a risk-based approach and may inspect a trial as soon as phase 1 which is a significant change to past practices when inspections were triggered by a submission of a marketing authorization dossier. Moreover, thinking early about the scope and content of the QMS allows streamlining the procurement policy and aligning

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processes across the various functions of a company. Lack of processes and procedures inevitably lead to waste of time (rework because of errors caused by miscommunication) and money (bad contractual terms or even switching of service providers because of unsatisfactory performance). As a result unhappy or overworked staff members are a sad consequence which often exacerbates the compliance challenge.

Conclusive Remarks The successful implementation of a modern QMS based on QbD and QRM approaches does not depend so much on the choice of the right methodology or tool kit but primarily requires a change in mind-set that must be initiated by the top management of the involved organizations. QbD needs a long-term commitment and is not a short-term measure to realize cost savings. It will eventually result in operational excellence if the process is applied consistently and in a disciplined manner. In this context Deming’s profound observations are of significance: “in the 1970s, Dr. Deming’s philosophy was summarized by some of his Japanese proponents with the following ‘a’-versus‘b’ comparison: (a) When people and organizations focus primarily on quality, quality tends to increase and costs fall over time. (b) However, when people and organizations focus primarily on costs, costs tend to rise and quality declines over time.”1 Moreover, it should also be emphasized that the QMS must be owned by senior management and the process owner and that the QMS must be designed and implemented as an in-process and not an epi-process activity. To ensure a disciplined approach, senior management must ensure the true independence of the involved QA and QC functions.

1

Quote from Wikipedia

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References and Further Reading Deming EW (1966) Some theory of sampling. Dover Publications, Mineola NY, USA Imai M (1986) Kaizen: the key to Japanese competitive success. McGraw Hill, New York, USA

B. Widler Lunau S et al (2006) Six sigma + lean toolset. Springer Verlag, Heidelberg, Germany Spear SJ (2009) The high velocity edge. McGraw Hill, New York, USA

Future of Regulatory Safety Assessments

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Gerd Bode and Petra Starck-Lantova

Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146 Review of History, Methods, Regulatory, and Industrial Environment . . . . . . . . . . 1146 Animal Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1149 The Controversy of the Animal Use in Pharmaceutical R&D . . . . . . . . . . . . . . . . . . . . 1152 Suggestions for Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Consider Entering the Clinic Without Animal Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reduce the Need of Animal Studies by Gaining Information in Exploratory Clinical Trials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Use Alternative Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Validation of Alternative Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Minimalize Bias in Experimental Data and Mind Good Research Practices . . . . . . . . .

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Conclusions and Outlooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1162 References and Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163

Abstract

Drug development contributes to improve health, duration, and quality of life. Lethal diseases have turned into chronic tolerable conditions, but medical need for many pathological processes continues. Concerns appear that in spite of extensive workload, success of G. Bode (*) Institute for Pharmacology and Toxicology, University Medical Center, University of Goettingen, Goettingen, Germany e-mail: [email protected] P. Starck-Lantova The University of Bonn/German Society for Regulatory Affairs, Bonn, Germany

pharmaceutical activity, and included facilitated access to novel drugs, may slow down. The preclinical testing via in vitro and animal experimentation reveals limitations to select the right promising candidates, most likely to be effective in humans and predict undesirable side effects early on. Therefore, constant efforts are necessary to improve the strategies. Courage needs to be stimulated to leave traditional paths and find new and better ways. This “rethinking” process needs directions to focus on additional options: use of more in silico data, deeper insight via cell cultures or receptor studies, new methods to explore more intensively relevant mechanisms of diseases and pharmacodynamics,

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more comparative data from different animal models, which species really deliver signals relevant for patients; for this objective, disease models or implementation of human conditions into transgenic animals may be supportive. More rigorous randomized designs of preclinical studies and their blinded assessment may improve reproducible and therefore validated results. In times of “big data” regulatory agencies, academic and industry researchers (possibly under political pressure) should feel obliged to stop selective publications (only positive effects) but create access also to options to learn from failures. The use of available knowledge (literature, experience, scientific advice) may limit the risks of reducing attrition rates and help to shorten timelines. Discussions with agencies have already facilitated a number of strategies. Examples are ICH guidelines M3 (allowing early access to new compounds for women of childbearing potential) or S 9 (reducing the preclinical development package for patients suffering from tumors). The purpose of this chapter is to prompt openness and imagination to use new methods, more science, experience, and communication among researchers to the benefit of patients.

Introduction The dualism of desirable effects and undesired reactions by chemical and pharmaceutical compounds on biological systems continues to be a fascinating and difficult phenomenon. Research, preclinical and clinical developments carry on to identify the main characteristics. Pharmacology detects new mechanisms for therapeutic purposes and thereby improves quality of life and survival, while toxicology and safety pharmacology submit their results to rigorous evaluations when extrapolation to humans takes place. The success is based on refinement of analytical methods, on paradigm change from morphology to inclusion of physiological functions, on better understanding of diseases and options for correcting such dysfunctions. In silico, in vitro, and in vivo studies

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support this process, but high-throughput screening does not seem to lead to higher success rates, just the opposite, the attrition rates slow down. This chapter tries to recommend some options how regulatory safety assessments could try to counterbalance this growing weakness.

Review of History, Methods, Regulatory, and Industrial Environment Patent expirations, fast-rising competition of developing countries, and an increasingly complicated regulatory environment has reduced the attrition rates. Further, growing cost-constraining healthcare systems reduce the potential of many new drug discoveries to generate revenues sufficient to cover the costs of development, which raised for an individual substance up to US $ 2.6 billion (Kaitin and DiMasi 2011). The continuous pressure, fueled by public interest for effective and safe medicines, tightens regulatory requirements on the safety assurance before launch. On the other hand, payers enforce drug pricing limits and proofs of therapeutic or economic advantages over existing products. Biotechnological products can serve as an example for this trend. Due to their novelty and their substantial impact on disease progression, biotech products were originally well received in health care systems. Their acceptance, however, quickly became unsustainable when during the last few years, more than ten new products treating cancer were priced at over $100,000 per year per patient, along with a continuing stream of many new orphan drugs costing from $150,000 to $500,000 per year, hepatitis drugs at $80,000 per year, and others. This resulted in the public pressure to advocate for heavy price discounts and formal restrictions (Evens 2016). As a counterbalance, pharmaceutical companies try to reduce their R&D costs by building strategic alliances with academic institutions, Contract Research Organizations (CROs), patient groups and other developers. Promising drug candidates are often acquired through mergers and acquisitions. This increases complexity,

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fragmentation, obstructed communication and coordination among globally active partners and can result in insular solutions and addressing only part of a larger systemic problem. Mergers keep many pharmaceutical companies in a continuous state of reorganization; this does not favor stable research departments allowing to pursuing longterm scientific goals, like drug development, needing mostly more than 10 years of perseverance (Schueler and Buckley 2014; DiMasi et al. 2014). No doubt, the tragedies of the past (Drug, Food and Cosmetics Act – USA, 1938 – due to mortalities of more than 100 patients after using sulfanilamide elixir with diethylene glycol as a solvent or thalidomide (Contergan) intake leading to phocomelia and other deformities) caused rigid legislations and requirements for animal studies to confirm safety before human treatment with new drugs. Accordingly, the Kefauver Harris Amendment to the US Drug, Food and Cosmetics Act in 1962 defined the need to prove safety and efficacy of new pharmaceutics (mostly through animal testing) before their exposure to humans and later approval; the predecessor of the EU, the European Economic Community, reacted similarly three years later by introducing the Directive 65/65/ EEC. Since then, public attention and expectations on medicines safety is rising and pharmaceutical manufacturers are required to perform safety tests on their new drugs and submit the data to supervisory organs before being allowed to market their products. The characterization of new products starts today often with high-throughput screening (leading to a ten-fold reduction of the cost of testing compound libraries), with combinatory chemistry (increasing by 800-fold the number of new molecular entities to be potentially synthesized), or new generations of DNA sequencing (Scannel et al. 2012). However, although the turnover of tested substances had significantly increased, this enormous effort did not improve the final goal. Out of every 10,000 new molecular entities discovered, only one receives regulatory approval to be marketed, and the percentage of drugs entering clinical trials resulting in an approved medicine

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is estimated to less than 11.3% compared to 16.4% success rate ten years ago (tufts CSDD 2015; PhRMA 2015). This failure rate can partially be explained by the fact that many companies investigate new molecules before they clarify the pathological mechanisms of the diseases they are trying to treat. A more successful strategy would, therefore, attempt to understand the pathophysiology and epidemiology of the disease as early as possible before embarking expensive development programs (pwc 2012). Some discrepancies can also be stated when looking at the relationship of preclinical and clinical results. The US-American FDA published already 2004 a white paper that critically evaluated preclinical animal models, recognizing them as one of the reasons for the disconnection between increased expenditure in R&D and attrition rate in drug discovery (FDA 2004). It was suggested that the high attrition rates of clinical trials indicate a discrepancy between the promising studies in animal models documenting the efficacy of a drug, and the real effects of the drug in human trial subjects. The quality and translational value of nonclinical research, particularly animal studies, has been therefore questioned. Two directives referring to animal studies stress different sides of this issue. In the European Union (EU), the rationale and requirements for animal testing in the development of medicinal products for human use are defined in Directive 2001/83/EC Annex I, which states that: “An integrated and critical assessment of the non-clinical evaluation of the medicinal product in animals/in vitro shall be required. . .” (Annex I, Part I, Art. 2.4), “Clinical trials must always be preceded by adequate pharmacological and toxicological tests, carried out on animals . . .” (Annex I, Part I, Art. 5.2b) with the option that “Studies in animals can be substituted by validated in vitro tests provided that the test results are of comparable quality and usefulness for the purpose of safety evaluation” (Annex I, Part I, Art. 4.2.3f). Animal studies have therefore become a standard component of pharmaceutical R&D.

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This fact is in contrast to Directive 2010/63/ EU on the protection of animals used for scientific purposes. This Directive has taken full effect on 1 January 2013 and refers directly to the principles of Three Rs (Refinement, Reduction, and Replacement). According to this Directive “the use of animals for scientific or educational purposes should only be considered where a nonanimal alternative is unavailable” (preamble 12) and “Member States shall ensure that, wherever possible, a scientifically satisfactory method or testing strategy, not entailing the use of live animals, shall be used instead” (Art. 4.1). Over the past three decades, the preclinical safety evaluation paradigm has developed in two parallel branches. For new chemical entities, the general approach has provided common ground for evaluation across different product classes; for new biological entities, where classical toxicology was acknowledged to be less relevant, a more product specific approach evolved. This resulted in two guidelines, ICH M3(R2) and ICH S6(R1), both published fist 1997 in the Proceedings of the International Congresses on Harmonization (ICH) and then later revised, which is discernible by the letter R in the title for the recent guidelines. The guideline ICH M3(R2) delivers practical recommendations for timing (when to conduct which safety studies) and conditions to include different patient population from male adults, over women without or with child bearing potential to pregnant women and children. The guideline ICH S6(R1), on the other hand, stimulates to reconsider the traditional strategies in preclinical development. As one example, in case of recombinant proteins no studies on carcinogenicity and metabolism are required (because proteins are not metabolized to reactive species) and off-target effects are not expected (because of a high specificity of the recombinant proteins to the target). Another example is the assessment of potential effects on the cardiovascular, respiratory and nervous system (safety pharmacology): instead of stand-alone studies, these functions are recommended to incorporate into the pivotal chronic toxicity studies.

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ICH S6(R1)emphasizes especially the selection of a relevant animal species, which are able to predict human reactions. The selection should be usually accomplished by an in-vitro comparison of binding affinity or functional activity of the product in human and animal cells, followed by in-vivo confirmation of the pharmacological activity or cross reactivity in that test species. In case of monoclonal antibodies, relevant species for testing are those that express the desired epitope and demonstrate a similar tissue cross-reactivity profile as for human tissues. A number of flexible options should be considered: e.g. one relevant species may suffice, e.g., when only one relevant species can be identified, or where the biological activity of the biopharmaceutical is well understood. Or when no relevant species exists, the use of relevant transgenic or gene knock-out animals expressing the human receptor or homologous proteins could be chosen, no doubt, this option will prolong the evaluation process. In humanized mice, the comparability of pharmacodynamics in the animal model and humans is an important conclusion to consider the mouse as a suitable relevant model (van Meer et al. 2015). The S 6(R1) guideline recognizes also animal models of disease as a relevant option. These models were originally used mainly to better understand the pharmacological action of the product, the pharmacokinetics and dosimetry. In all cases, the use of animal models of disease to support safety should be scientifically justified (ICH S6(R1). Nevertheless, the guideline caused criticism by some researchers: e.g. the guideline missed the chance to catch up with scientific progress (critical review by Kooijman et al. 2012). Indeed, for example the option to use in-vitro alternative approaches is mentioned only briefly, even in the guideline’s revised version S6(R1) (approved 2011): “Although not discussed in this guidance, consideration should be given to the use of appropriate in-vitro alternative methods for safety evaluation. These methods, if accepted by all ICH regulatory authorities, can be used to replace current standard methods.” (Part II, Chap. 1.1).

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It was therefore recommended (e.g., by Kooijman et al. (2012)) to make safety evaluations on a case-by-case basis, driven by product specific aspects such as the cause, mechanisms and reversibility of adverse effects. However, not much of experience and scientific expertise with these products could be gained in the meantime. In addition, the flexible case-by-case approach may lead to diverging interpretations and inconsistency of opinions between regulatory agencies. The Tegenero case (monoclonal antibody) from 2006, leading unexpectedly to a cytokine storm inducing severe shock symptoms in volunteers, stimulated Agencies to reconsider their recommendations for the First in Man use of new compounds. The EMA guidance for first-in-man studies, “Guideline on strategies to identify and mitigate risks for first-in-human clinical trials with investigational medicinal products” (EMA 2017a) came in February 2018 into effect. Requested are a better integration of pharmacokinetic, pharmacodynamic data and toxicological findings into the overall risk assessment; nonclinical data should help to define the estimated therapeutic dose, the maximum dose, and dose steps and intervals. The plead includes stronger stress on using alternative methods and encourages to use more in vitro studies whenever scientifically relevant and sufficiently validated.” The “weight of evidence” should include a comparison with humans in regard to target expression, distribution and primary structure, pharmacodynamics, metabolism and other PK aspects, and onand off-target binding affinities and receptor/ ligand occupancy and kinetics. Nevertheless, the guideline warns that even a high degree of homology between the selected animal model and human, or a similar response in human and animal cells in vitro, does not necessarily imply comparable effects in vivo. “For example, there might be differences in affinity of the new candidate for molecular targets, physiology differences in tissue distribution of the molecular target, cellular consequences of target binding, cellular regulatory mechanisms, metabolic pathways, or compensatory responses to an initial physiological perturbation.” In such situation understanding of the

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relevance of the animal models and their translational differences may be improved by using in vitro human cell systems or human-derived materials.

Animal Models Animal models certainly play an important role for the overall assessments. The request is for new pharmaceuticals to include relevant models, relevant for the prediction of reactions in humans. No animal model can fully reproduce all features of human diseases. And no human models (volunteers or Phase II patients) can predict all reactions possibly seen later under broad exposure of thousands of patients under differing life styles and co-morbidities. But animal models allow to gain early on important signals for any severe effects. But selecting the optimal model is not a trivial task. Despite the S7A ICH Guideline recommends that “consideration should be given to the selection of relevant animal models or other test systems so that scientifically valid information can be derived”, the selection of animal species follows rather long-established practices and less scientifically justified deliberations. A short reflexion about animal models may be helpful. Traditionally, over 90% of animals used in drug discovery are mice and rats. In drug development, the two species-testing is the rule. Rodent experiments should be completed with non-rodent species like dogs, non-human primates or minipigs. Non-human primates (NHPs) should only be used when the purpose of the study cannot be achieved by any other species (Article 8.1(b) Directive 2010/63/EU). Mouse: Easily available; low cost; ease of handling; fast reproduction rate, important for reproductive toxicity studies. Many transgenic models. Many well-established disease models created in mice, allowing both pharmacology and toxicology investigations. The limited blood volume can be overcome today by new sampling techniques and refined analytical methods, allowing microdosing studies.

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Rats: Enormous historical background; important animal model for research in psychology and biomedical science, especially cancer research. Their advantage: good availability; larger body size; easy handling. Gene knockout and embryonic stem cell techniques are relatively more difficult in rats. The role of genes is easy to study: many inbred strains, all members are closely genetically identical. Syrian hamsters: Less used today; few times for assessing the potential for cancinogenesis, metabolic diseases, non-cancer respiratory diseases, cardiovascular or infectious diseases; less easy to handle because of fighting of males. Rabbits: Particularly useful for assessing ocular and dermal irritation; primary non-rodent species for embryo-fetal developmental toxicity studies ever since the tragedy of thalidomide. Dogs: Most frequently used non-rodent species in preclinical drug development; genetically diverse; a convenient model for many human diseases, e.g. the bone cancer osteosarcoma (Rowell et al. 2012). Dogs naturally develop beta-amyloid plaques (the protein’s amino acid sequence in dogs is identical to humans), they show cognitive decline when growing older (Davis and Head 2014). Good model for complex neurocognitive disorders such as Alzheimer’s disease. Dogs are expensive; need more sophisticated housing conditions and higher doses of an experimental drug. In addition, there is elevated public scrutiny and reluctance for the public and many evaluators. Nonhuman primates (NHP): Use allowed when scientifically justified for safety testing. Most frequently used model to study potential adverse effects of monoclonal antibodies (mAbs). High public scrutiny. Target expression and function comparable to humans. Relatively large body size (allows repeated blood sampling), good availability of reagents, assays and methods (often adapted from humans), and generally good availability of animals are advantages of NHPs. Potential limitations: high costs, limited group size, and often heterogeneous population with occasional background infections (like humans?). Testing often requires adult animals (4–5 years). NHPs inadequate for carcinogenicity testing and

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inconvenient for reproductive toxicity studies with their low fertility rate, high spontaneous abortion rate and long pre- and postnatal development times (Baumann et al. 2014). When testing mAbs, results may be confounded by anti-drug antibodies formation, leading to a neutralization of pharmacologic effect or a clearance of the mAbs from the circulation (Bussiere et al. 2009). Chapman et al. (2012) demonstrated that in some therapeutic areas rodents can support biologic development and provide relevant data and could therefore reduce the use of NHP. In controversial discussion is the duration of repeat-dose studies: six months provide sufficient data and nine months or longer did not bring any additional benefit (Clarke et al. 2008). There is also a plead to use only two dose groups instead of standard three, leading equally to relevant data (Chapman et al. 2010). Parallel to public demands, criticism of unnecessary or even uninformative use of NHPs can be heard from professional circles too. Van Meer et al. (2013) evaluated safety studies in NHPs for mAbs registered in the EU and concluded that NHPs have been used even when there were other pharmacologically-responsive species available and the testing was in some cases not informative. The authors could also show that pharmacology-mediated adverse effects of mAbs are highly predictive from in vitro studies. Minipigs: Increasingly used for toxicity testing of pharmaceuticals, experience in Europe (Ganderup et al. 2012), also in USA and Japan. Increasing acceptability by regulatory agencies, e.g. FDA. Up today: mostly testing on small molecule-based therapeutics and dermal administration (Ganderup et al. 2012), but increasingly also for repeat-dose administration of biologics (reviewed in Baumann et al. 2014). Zheng et al. (2012) demonstrated on several human mAbs that they show low clearance, long half-life and low volume of distribution in minipigs and therefore good translation to humans. Also, according to Baumann et al. (2014), studies on tissue cross reactivity of biopharmaceuticals as well as safety pharmacology and fertility endpoints in repeat-dose studies can be carried out in minipigs.

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Advantage of the minipigs: immune system with structures and functions largely analogous to the human immune system (Bode et al. 2010). Additionally, minipigs show less undue effects than dogs (Weaver et al. 2016). Also the complete genome (of a size as well as the number of genes comparable to humans) is known due to intensive data information based on research of house swines. Minipigs are not genetically transformed but the result of chronic breeding selection. Housing and handling easy with training. Disadvantage: lack of placental transfer of macromolecules (Bode et al. 2010), which may limit their role in developmental toxicity testing of mAbs. In addition, rapid body weight gain, requiring more flexible testing strategies (shorter studies and use of younger lighter animals) and lack of published experience may be considered disadvantageous (Baumann et al. 2014). Animal models of human disease: Primarily utilized to gain insight into the potential efficacy and mode of action of novel pharmaceuticals. Their value in understanding safety risks of compounds begins to be recognized (Morgan et al. 2013). Their use as part of a preclinical safety submission/dossier has been driven by the need to test a specific hypothesis and combine efficacy and safety evaluations; these models are even recommended by regulatory authorities (Cavagnaro and Lima 2015). Examples of the use of disease models include: infected animals to test the efficacy of a vaccine, mice inoculated with xenogenic (human) tumors expressing the target antigen, or genetically modified animals that develop spontaneous disease (Bussiere et al. 2009; Te Koppele and Witkamp 2008). In general, animal models of human disease reflect rather simple mechanistic pathways while human diseases are mostly complicated by multifactorial pathological processes, often poorly understood. From a pathology perspective, the evaluation of animal disease models is challenging as the induced disease results in effects confounding safety assessment (Morgan et al. 2013). Historical data on spontaneous background finding are usually missing. Therefore, discerning whether the clinical and anatomic pathology findings are attributable to incidental

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age-related or background changes, tested agent, or disease manifestations require additional experience and background data need to be accumulated. Multigenerational studies or increased numbers of control animals may be necessary (Cavagnaro and Lima 2015; Morgan et al. 2013; Bussiere et al. 2009). First observations signal that lifespan of disease models may be limited, therefore, adequacy of such animals in regard to chronic experiments may be an issue (Cavagnaro and Lima 2015). Further, animal models of disease have intrinsic variability and immutable genetic and species differences. These factors can complicate the interpretation of the data. Investigators should therefore carefully evaluate the results and keep in mind that over- or under-estimating of adverse side effects may be possible. Also, analyze the target behavior in the animals, for low molecular weight chemicals the metabolite profile, for recombinant proteins the pharmacological effect (e.g., activity, clearance, target expression, immune phenotype, and immunogenicity) (van Meer et al. 2015). Transgenic animals: The most common models are gene-targeted or knock-out (KO) animals; they lack an endogenous gene and therefore fail to express the related protein(s). This property offers the chance to assess drug specificity, investigate mechanisms of toxicity, screen for mutagenic and carcinogenic activities of therapeutic candidates, or study target blockade by novel therapeutic candidates (Bussiere et al. 2009). KO-mice have been valuable to study obesity, heart disease, diabetes, arthritis, substance abuse, anxiety, aging and Parkinson disease (NIH 2009). The transfer of new genetic information can overexpress a target protein. The “humanized” knock-in animals with a human gene can evaluate the efficacy and toxicity of human biopharmaceuticals that are not pharmacologically active in normal rodents. Transgenic mice generated to carry cloned oncogenes and knockout mice lacking tumor suppressing genes have provided good models for studying risk of human cancer; but in spite of their recommendation by ICH S1A, their use is still limited for the assessing

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the carcinogenic potential of new drugs (Friedrich and Olejniczak 2011) . For testing recombinant proteins and cell therapy products compensatory mechanisms may take on the function of the absent protein(s) or target (e. g., induction of other calcium transporter genes in calbindin-D9k gene KO-mice described by Lee et al. 2007). Additionally, physiological effects of genetic mutations underlying diseases may differ in humans and in mice (Hirano et al. 2007).

The Controversy of the Animal Use in Pharmaceutical R&D The selection of animal models should be based on their relevance for humans. In practice, the selection of species beyond those cited above is limited. There is a striking paucity of quantitative comparative data for animal models (Schein et al. 1970; Heywood 1981; Greaves et al. 2004; Matthews 2008). This makes any request using validated models and methods difficult. Moreover, literature offers only informative data, where animal models were positively contributing for Market Authorization. Data on failures and lack of success are not selected by researchers and editors for publication: reports about unacceptable adverse effects in animals are unattractive for journals; such data do not raise public interest; there could be also reasons for commercial confidentiality (Matthews 2008). Such data are thus stored in internal databases of pharmaceutical companies or research organizations and forlorn for public research. This dilemma was addressed by Olson et al. in 2000: they compiled a survey of 150 compounds which revealed to be toxic under clinical test conditions in humans. There was a true positive human toxicity concordance rate of 71% for rodent and non-rodent species, with non-rodents alone being predictive for 63% of human toxicity and rodents alone for 43%. The authors appraised safety testing on (healthy) animals as significantly beneficial. But Matthews (2008) criticizes Olson’s analysis for being inconclusive or even misleading because the authors did not attempt to estimate the corresponding specificity (true negative rate) and sensitivity (true positive rate) without which it

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is impossible to assess the evidential weight provided by the animal models. Van Meer (2013) contributes to this discouraging interpretation and predicts that this poor outcome increases when looking at highly complex and species specific protein drugs, which are usually immunogenic in animals. Perrin (2014) fortifies this issue by reporting that more than 80% of animal studies on safety and efficiency of potential therapeutics fail to predict the desired success rate in patients. Similar data by Hay et al. (2014) on success rates of 835 drug developers show that the proportion of therapies advancing from Phase 1 to regulatory approval is only around 10%. Bailey et al. (2014) analyzed datasets of 2366 drugs with both animal (rat, mouse and rabbit as preclinical species) and human data. The authors concluded that the absence of toxicity in the animals provided little or virtually no evidential signals for the lack of adverse drug reactions in humans.; a (re-)analysis of data specific for dogs from the same original dataset reinforced recent criticism that dogs are used mainly for historical instead of scientific reasons (Bailey et al. 2013): No evidence appeared, that canine data would predict efficacy and toxicology of medical compounds in clinical trials; they suggested that alternative methods are urgently required (Bailey et al. 2013). Finally, van Meer et al. (2012), when focusing on post-marketing data, confirmed that animal data were not predictive for detecting serious adverse drug reactions in patients. Because 63% of adverse drug reactions had no counterparts in animals and less than 20% of serious reactions had an actual positive corollary in animal studies the authors conclude that animal safety studies in their current form should not be included in prospective pharmacovigilance studies. Is there any chance to overcome this dilemma? Kooijman (2013) explains the persistent use of animal studies in drug development by inertia of the system with animal studies embedded in a set of institutions (i.e., regulations, norms and values) that are taken for granted, normatively endorsed, and backed up by regulatory authorities. This is the motivation why the industry stays reluctant to move away from established conservative models. Although the standard animal models

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are as the result of guidelines by definition not binding, and it is possible to provide preclinical data from own scientifically justified experimental programs adapted to a specific product, but this option is in practice not frequently used. This reluctance is the result of fears about possible delays or even failures of the marketing authorization process. Despite of some official support for progressive trend-setting new trends, skepticism prevails towards new strategies and conservatism dominates in industry. Admittedly the situation of regulators is challenging. On one hand they try to promote innovations and recognize rapid growth in knowledge and technologies (Cavagnaro and Lima 2015), on the other hand they have to protect patients from risks. Van Meer (2013) attacks both the industry and the regulators by saying: The adoption of the precautionary principle by the regulatory authorities and the relative ease with which this burden of proof is accepted by the pharmaceutical industry – without attempts to improve the current paradigm – has created a stalemate in which animal studies, predictive or not, continue to exist with little room for innovation.

Therefore, all stakeholders should critically rethink their developmental strategies and should be encouraged to implement new technologies that predict the safety and efficacy of therapeutics better than current animal studies do.

Suggestions for Improvement To break successfully with long-term traditions is only possible when academic and industrial researchers and developers cooperate openly with regulators and transmit their innovative thoughts into new guidelines and/or good practices. Some options will be illustrated in the following sections.

Consider Entering the Clinic Without Animal Studies Biosimilars are taken as an example. The option to circumvent in-vivo preclinical studies has been

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recently recommended in revised guidelines on biosimilar medicinal products (EMA 2012, 2014). In contrast to generic medicinal products, nonclinical data, including animal studies, have been traditionally requested for marketing authorization of biosimilars. Nonetheless, this praxis has been recently abandoned by European regulators, because animal models turned out not to be sensitive enough to provide sufficient information on pharmaceutical similarity of these products (EMA 2012, 2014). The guidelines on biosimilars acknowledge that “in-vitro assays may often be more specific and sensitive to detect differences between the biosimilar and the reference product than studies in animal” and, therefore, “these assays can be considered as paramount for the non-clinical biosimilar comparability exercise.” Therefore, in-vivo testing should no longer be performed by default and its necessity should be considered on a case-by-case basis in a stepwise approach where the extent and nature of the development program depends on the level of evidence obtained in the previous step(s). This regulatory decision is regarded as revolutionary, it opens new ways for pharmaceutical developments with no new animal testing at all, and it implies that regulators may even discourage developers from performing such studies (van Aerts et al. 2014). This applies especially to highly specific mAbs, where only NHPs are pharmacologically responsive. Therefore, as toxicological studies in NHPs have notably small group sizes, their conduct has been explicitly not recommended for biosimilars. In situation, when no relevant invivo animal model is available, the guidance leaves the option to proceed directly to human studies while applying principles to mitigate any potential risk (EMA 2012, 2014). The recommended step-wise approach should proceed as follows: after physicochemical and biological characterization of the product, pharmacodynamic comparability should be evaluated in in-vitro assays. Assays using human cells or human receptors can be used to assess binding to the target and the subsequent functional effects. Pharmacokinetic comparability can then be best evaluated directly in clinical studies. When close similarity of the biological and its reference product can be demonstrated, it is highly unlikely that

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new safety issues different from the reference product, with the exception of immunogenicity issues, would arise. For immunogenicity, animal studies have no predictive value anyway. Only after performing this biosimilarity exercise, it should be determined whether additional in-vivo non-clinical work is deemed necessary (EMA 2012, 2014; van Aerts et al. 2014).

Reduce the Need of Animal Studies by Gaining Information in Exploratory Clinical Trials Exploratory clinical trials are an approach described in ICH M3(R) guideline, which recognizes that in some cases early access to human data can provide valuable information on human physiology/pharmacology, on drug candidate behavior, and on therapeutic target relevance to disease. Such data can reduce the need of information gained in animal studies. Central to this approach is the concept that “the best model for man is man.” Exploratory clinical trials are conducted in early Phase I (sometimes called Phase 0), have no therapeutic intent, are not intended to explore clinical tolerability, and can be conducted on patients or healthy individuals (ICH M3R). Their advantage is that they may give information on exposure and allow early comparison of kinetic/metabolic data between animal models and humans. They certainly help very early on to prioritize compounds when several candidates are available; these aspects again help to reduce animal usage compared to traditional development. ICH M3(R) recommends also several approaches based on applying micro- or subtherapeutic doses. Microdosing (most often: single microdose of 100 μg) is a method assessing the basic behavior of drugs by applying small doses directly to human volunteers. The doses are well below those expected to produce whole-body effects but high enough to allow the cellular response to be studied. A candidate drug is labeled by radiocarbon isotopes and extremely sensitive analytical methods (mostly positron emission tomography (PET) and accelerator mass

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spectrometry (AMS)) are used for its biochemical quantitation. AMS is used for determining PK data by taking blood samples over time, processing the samples in the laboratory, and then analyzing their drug content. PET provides primarily PD data through real-time imaging and some limited PK data. The method provides important information about pharmacokinetics and pharmacodynamics, but it does not reveal information about toxicity or toxicology. Those endpoints will be addressed by supportive rigidly reduced conventional study designs. Eliminating less promising molecules saves costs, resources, animals, and time. It avoids unnecessary exposure of the participants in clinical trials. Because the trials mostly involve a single dose administration (usually 1  100 μg, the alternative is 5  100 μg), the method poses very little risk of human toxic side effects (low dose and short duration of exposure). Very limited number of subjects is usually involved. Further, preclinical safety package required by authorities can be smaller as compared to the traditional Phase I studies, less animals are needed, and also only small quantity of the test drug is required. Other valuable advantages of microdosing studies are that they help to establish a likely pharmacological dose and select the first dose for the subsequent Phase I studies. A limitation of the method is shortage of data that exemplify whether the body’s reaction to a particular compound is similar when applied as microdose or in its pharmacologically active dose (Tiwari 2014).

Use Alternative Approaches Alternative models should be more efficient and provide additional information to supplement the results from traditional animal models. Although animal models are still often considered to be a “gold standard,” they have never undergone validation to the same extent as non-animal technologies. The need for improvement is recognized by Agencies. There is a new Regulatory guidance on alternative/3Rs testing approaches in discussion: “Guideline on the principles of regulatory

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acceptance of 3Rs (replacement, reduction, refinement) testing approaches” (EMA 2016a) and related reflection papers (still in the form of drafts – EMA 2016b, c). The guideline provides information on the scientific and technical criteria for regulatory acceptance of alternative/3Rs testing approaches and encourages stakeholders and authorities to initiate, support, and accept development and use of such approaches. The reflection papers summarize the main animal tests required for the regulatory testing of medicinal products and presents opportunities for limiting the use of animals. The guideline recommends the following criteria: availability of test methodology, test protocols with clearly defined and scientifically sound endpoints; relevance of the test for a particular purpose and accuracy/extent to which the test correctly measures the biological effect of interest; robustness of the test (i.e., reproducibility of the test results); a comparison with existing methods; and a description of circumstances under which the 3Rs testing approach is/is not applicable. The reflection paper on opportunities for implementation of the 3Rs during regulatory testing of medicinal products for human use provides an overview of options to limit or completely skip the use of animal studies in nonclinical evaluation of drug substances. The paper also clearly indicates that the 3R approach is in the state of dynamic development and there will be more options coming. It is, however, already clear that, for example, toxicity evaluation will change. Traditionally, repeat dose toxicology studies follow a standard design and in rodents and nonrodents yield information on general characteristics of the toxicity, the target organs of toxicity, the dose–response (curve) for each toxicity endpoint, responses to toxic metabolites formed in the organism, delayed responses, cumulative effects, the margin between toxic and nontoxic dose, information on reversibility/irreversibility of the effect, and NOAEL (no observed adverse effect level), NOEL (no observed effect level) for toxicity (EMA 2008, 2010; ICH M(R2)). In contrast to this standard approach, the reflection paper (EMA 2016b) concedes the option to perform the

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tests on one species only (“on a case by case approach, and if clearly justified”) and to possibly omit a study on reversibility of compound-related effects. Also changes for safety evaluation paradigms are recommended; for instance, in vivo genotoxicity can be assessed by integrating this endpoint into repeated dose toxicity studies, usually of 4 weeks duration. The reflection paper recommends a standard test battery (in-vitro tests plus in vivo genotoxicity integrated in repeated dose toxicity study) without the isolated single invivo study. Likewise, carcinogenicity and reproductive toxicity test requirements are currently under revision with the aim to induce new testing paradigms based on a more comprehensive weight-of-evidence approach and potential to replace in-vivo studies or not doing them at all (Bode and Van der Laan 2016). “Core battery” tests for safety pharmacology could also be integrated in repeated dose toxicity studies. And a variety of tests aiming at manufacture, characterization, and control of the drug substance should be primarily performed in-vitro unless thoroughly justified. Other more specific examples of the recommended 3Rs approaches involve avoiding physiological distribution test of radiopharmaceutical preparations as required by the Ph.Eur., using duck cells rather than live animals when testing plasma derived hepatitis B vaccine, or discouraging from using animals for potency testing of investigational, or biological medicinal products. Alternatives to animal testing (called 3Rs testing approaches in EMA’s guidelines) are being developed for – besides ethical reasons – their time efficiency, less man power required, and cost effectiveness. Two most important approaches involve in-vitro cell culture techniques and in-silico computer simulations. These two approaches are then combined in another method known under the name “organs on a chip.” Also microdosing described above can be considered as an alternative method. All these approaches do not replace animals completely; however, they help to significantly reduce animal numbers needed. There is knowledge that alternative methods also have their specific advantages and

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drawbacks. For example, cell cultures are criticized for not providing enough information about the complex interactions of living systems, computer simulations for using data from prior animal experiments, and microdosing for not revealing information about toxicity or toxicology. Thorough knowledge of the strengths and limitations of one’s model is therefore crucial for its appropriate use and interpretation of results. For cells and tissue cultures, in vitro tests are recommended in OECD guidelines. Examples are: TG 428 TG 430 TG 431 TG 432 TG 437 TG 438 TG 439

Skin absorption: in vitro method In vitro skin corrosion: transcutaneous electrical resistance (TER) In vitro skin corrosion: human skin model test In vitro 3 T3 NRU phototoxicity test Bovine corneal opacity and permeability test method for identifying ocular corrosives and severe irritants Isolated chicken eye test method for identifying ocular corrosives and severe irritants In vitro skin irritation: reconstructed human epidermis (RhE) test method

Various types of cultures like cell culture, callus culture, tissue culture, organ culture, or separated cellular components are used for various purposes. For instance, for safety testing, bovine corneal organ culture can replace rabbits eye irritancy test, or models of human skin derived from cultured human skin (Corrositex ®, EPISKIN™, EpiDerm™) can replace animalbased skin irritative and corrosive studies. Test systems based on the activation of human monocytes or monocytoid cell lines have been developed that take advantage of the role of these cells in the fever response and can replace rabbit pyrogen test. Similarly, mouse fibroblast (3 T3) and normal human keratinocyte (NHK) cells can be used in basal cytotoxicity test (e.g., phototoxicity) and support to determine the starting dose for the acute oral systemic toxicity test method and

thereby reducing overall animal use requirements (NTP 2017). Cell cultures are further used to measure the rate of chemical absorption by the skin or phototoxic reactions and cultured cells have been developed to create monoclonal antibodies (Hester et al. 2006; Doke and Dhawale 2015). Another example is represented by tissue models. For example, in-vitro metabolism studies have traditionally involved cells cultured into monolayers. However, because the interactions of cells with their surrounding environment can greatly affect shape, cell function and gene expression, two-dimensional or threedimensional models have been developed. These models are supposed to better mimic mechanisms such as cell-to-cell adhesion and resistance to drug-induced apoptosis. Among the 3D-tissue reconstruction models are models of epidermis, full-thickness skin models, respiratory epithelia, keratinocyte eye cornea, vaginal epithelia, oral epithelia, and even models of the blood–brain barrier or three-dimensional models such as placenta, lymph node, and liver (Liebsch et al. 2011).

Organs on a Chip Organ on a chip is a multichannel 3D microfluidic cell culture chip that simulates to some extent the activities, mechanics, and physiological response of entire organs. The chip is formed by small chambers containing a sample of tissue from a particular organ. When nutrients, air, blood and test compounds, such as experimental drugs, are pumped through the chambers, the cells replicate some of the key functions of that organ, just as they do in the body. By recapitulating the multicellular architectures, tissue-tissue interfaces, physicochemical microenvironments, and vascular perfusion of the body, these devices produce levels of tissue and organ functionality not possible with conventional 2D or 3D-culture systems. Biochemical, genetic, and metabolic activities of the cells are then measured by sensors and transferred for computer analysis. In the context of drug discovery and development, this technology is valuable for the study of molecular mechanisms of action, prioritization of lead candidates, toxicity testing,

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and biomarker identification (Bhatia and Ingberg 2014; Prot and Leclerc 2012).

Validation of Alternative Methods Alternative approaches may produce relevant and reliable results, but all new methods must be confirmed as suitable for its scientific and regulatory purpose. These methods are used routinely and repeatedly; they should be acceptable across countries; formal validation is a necessity. Therefore, it is recommended to involve regulators already in the process of definition of performance standards. Such cooperation will facilitate regulatory acceptance and help to implement new test methods (Liebsch et al. 2011). The need of new alternative and validated methods is expressed in the EU legislation. Directive 2010/63/EU describes the coordination of formal validation studies at EU level to facilitate rapid uptake of new methods and approaches to replace reliance on animal testing as one of its key tasks. For this purpose, the European Union Reference Laboratory for alternatives to animal testing, EURL ECVAM, was established by this Directive. Through its network of laboratories (EU-NETVAL, European Union Network of Laboratories for the Validation of Alternative Methods), EURL ECVAM focuses on the validation of 3Rs methods for safety testing and efficacy/potency testing of chemicals, biologicals, and vaccines. It offers to research laboratories to scientifically validate alternative methods to animal testing. Through a dialogue with the stakeholder community and provision of information systems (DataBase service on Alternative Methods, DB-ALM, QSAR Model database and TSAR tracking system on alternative methods), EURL ECVAM further promotes the use and acceptance of new alternative methods in industry, academia, and by regulators. Examples are non-animal approaches for skin sensitization (allergy) testing, or co-developing two new (VICH) guidelines for the reduction of animal tests for the quality control of veterinary vaccines (EURL ECVAM 2017).

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Method validation relates mainly to: 1. The repeatability and reproducibility of results obtained 2. The test’s relevance for measuring or predicting relevant biological effects Validity assessment can include general knowledge of the method, the scientific principles on which it is based, historical data from using the method, and the use of pilot studies (when using in vivo methods) with smaller numbers of animals before embarking on a full scale study (European Commission 2016). The formal validation process involves multiple phases including preparatory method refinement, small-scale transfer studies, and finally large-scale international collaborative studies with manufacturers and national control laboratories (EMA 2016a). Alternatively, testing approaches that have sufficiently demonstrated their scientific validity according to the criteria described but have not been assessed in a formal validation process can also be evaluated on a caseby-case basis by the competent authorities (EMA 2016a).

Minimalize Bias in Experimental Data and Mind Good Research Practices Animal studies can elucidate normal biology and improve the understanding for the pathogenesis of a disease, a deficiency often appearing when developing therapeutic interventions. However, animal studies produce insights only if tests are carefully designed, critically interpreted, and thoroughly reported. These quality features amplify good laboratory practices (GLP), compliant to which many animal studies (e.g., safety studies) should be performed. GLP ensures traceability and uniform, reproducible quality, but it does not guarantee the quality of the animal model or scientific valuable interpretation of the outcome for human purposes. The lack of methodological rigor in preclinical studies acts as a barrier to translation of research findings and represents a major source of reduced attrition rates in drug development (Glasziou

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2014; Green 2015). The Lancet published 2009 a review on the production and reporting of biomedical research in which it was calculated that 85% of basic and clinical research is wasted because of inadequate or inappropriate design, nonpublication, and poor reporting (Chalmers and Glasziou 2009). This represents an estimated annual loss of over $100 billion research funding. Clinical trials erroneously based on poorly conducted preclinical safety studies may lead to unnecessary exposure of trial participants to potentially harmful agents or to prevent them from participating in other trials with possibly effective products (Landis et al. 2012). Particularly widespread are deficiencies in reporting key methodological parameters and poor experimental designs, both correlating with overstated findings (Landis et al. 2012; Gulin et al. 2015). Scientists from hematology and oncology department at the biotechnology firm Amgen (Begley and Ellis 2012) tried to confirm published findings related to their work and despite efforts to avoid technical differences they could confirm scientific findings in only 11% of cases. Reproducible studies were mainly those, in which authors had paid close attention to controls, reagents, investigator bias and describing complete data set. In the other cases, results could not be reproduced, the data were not routinely analyzed by investigators blinded to the experimental versus control groups and/or only selected experimental results supporting an underlying hypothesis were presented (Begley and Ellis 2012). Corresponding results were reported by Bayer HealthCare who could validate only about 25% of published preclinical studies (Prinz et al. 2011). The recognition grows, that the use of techniques that assess the impact of publication and study-quality biases on estimates of efficacy in animal experiments is necessary (Sena et al. 2007). An adoption of newly and better defined quality standards would lead to improved effectiveness and efficiency in the selection of promising candidate drugs. There are a number of sources of experimental bias which reduce the quality of the research.

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Bias from Poor Reporting Reporting details of a study including methods of statistical analyses used, sample sizes, inclusion/ exclusion criteria, methods of randomization, blinding, gender, strain, species selection, and age of animals is essential to avoid publication bias, assist replication, and justify the research. Meanwhile, several guidelines have been issued to improve poor reporting, among them the ARRIVE guidelines (“Animal Research: Reporting of In-Vivo Experiments,” 2010), the GSPC (“Gold Standard Publication Checklist,” 2011), or the checklist of the Nature Journal (2013). Although the guidelines list suggestions for improved reporting, lack of pressure to apply these suggestions and report comprehensively and uniformly leads to noticeably inconsistency, obstructing correct assessment of reported results (Green 2015). Here, journal editors and regulators/assessors of clinical trial applications can support improvements considerably. Bias from Nonpublication Selective reporting is another reason for the lack of translation from basic research to the clinical situation. An increasing number of studies demonstrate publication bias that only about 50% of animal research results are published. The main motivation seems to be the lack of statistical significance as there is relatively little incentive for journals to publish negative, non-novel, or repeated findings (Korevaar et al. 2011; Sena et al. 2010; Ter Riet et al. 2012; Tsilidis et al. 2013). Nonpublication causes unnecessary duplication of research and poses a serious problem for performing valid literature syntheses. There should be a plead to publish all results regardless of whether the outcomes are positive or negative; all studies (equivalent to existing registers of clinical trials) should be registered in professional circles (Kimmelman and Anderson 2012). Registration of animal trials would impede retrospective changes of endpoints and study protocols and not publishing negative or unfavorable results. And today such publications of failures should be easy since safety studies are always done under GLP conditions and collecting data and archiving is performed on local computers.

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Bias from Using Inappropriate Animal Model Prestige, economy, convenience, and poor awareness of the translation of basic research into medical practice influences decisions on animal studies more than scientific rigor and patient need (Green 2015). For instance, laboratory mice are disproportionately more often used than any other animal species (JAXmice ® alone stocks tens of thousands of types of strains of mouse models to choose from), and the common practice of using inbred rodent strains completely ignores genetic variation of target populations. Bennani (2012) points out that for some conditions (e.g., influenza, bacterial, and fungal infections, measuring CVD and LDL and simple blood chemistry) animal models are more reliable predictors, whereas for other diseases (oncology, immunology, psychiatry, HIV, etc.), animal models are to large extent nonpredictive of clinical outcome. The importance of selecting the best possible animal model should be therefore not underestimated. Bias from the Regulation of Animal Research Regulatory agencies require sometimes preclinical investigations that use animal models known to have no predictive value. Among such problematic disease areas are oncology, immunology, or diseases of the central nervous system (Bennani 2012). In addition, compliance with the 3Rs and animal welfare are in many countries controlled by veterinary inspectors and ethic committees. The assessment process is however neither open nor transparent and relies on individual opinions of the experts (Green 2015). Pressure to apply the 3Rs principles may be overstretched; it may reduce the statistical power of experiments under meaningful values. Scott et al. (2008) demonstrated that the failure of murine amyotrophic lateral sclerosis treatments to translate to the clinic was due to small group size numbers and underpowered experiments. Many good research principles are actually long known, it seems that they sometimes get forgotten in the complex process as R&D of new medicines. Awareness of quality guidelines for

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biomedical research should therefore reanimated, examples are, for example, good research practice system of the World Health Organization (2006), guidelines published by the Research Quality Association in the UK (2008), or the Quality Assurance Toolkit developed at the University of Minnesota, USA (Michelson Prize and Grants 2014). Few details should be stressed. Planning an Experimental Protocol The methodological quality of an animal study starts with preparing a detailed experimental protocol. Checklist of factors listed, for example, in the ARRIVE guidelines, can be meaningful. Variations in the experiments must be considered and outlined in the protocol. Results from control animals need to be known and interpretation should benefit from these historical data. Study directors should seek consultancy from interdisciplinary interactions of the primary investigative team with experts in ancillary disciplines (statistics, laboratory animal science, pathology, etc.) and include the data generation and collection process (Everitt 2015). The experimental hypothesis to be tested must be well explained and defined as well as the experimental aims, design, and endpoints. Recognizing Sources of Variation Sources of variation can include inherent factors of the animal (e.g., stock/strain/substrain, source, sex, age, weight, source, pathogen status, etc.) as well as the animal facility environment (diet, bedding, housing, water delivery, lighting, noise, vibration, temperature, humidity, etc.). For this reason, harmonization of international standards for animal care would already reduce one important source of internal variation. Other factors are the methods used, dose form and timing of dose administration, types and preparation of excipients and vehicles, blood and tissue sampling sites and methods, handling of subjects, etc. (Everitt 2015). Example is the significant difference in the serum hepatic enzyme, alanine transaminase, which can occur if mice are handled by the body instead of the tail (Swaim et al. 1985). Similarly, significant differences have been reported in research endpoints, such as cytokine

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concentrations, depending on method/site of blood removal (Mella et al. 2014). Collecting Data Although usually inbred rodent strains with minimal genetic differences are used, data show how important randomization is. For instance, in a systematic review of hypothermia in experimental stroke, nonrandomized studies overstated the reduction in infarct volume by 27% and studies without blinded outcome assessment overstated efficacy by 19% when they were compared to randomized and blinded studies, respectively (van der Worp et al. 2005). To minimize bias resulting from internal variation in the data, following steps should be always taken: • Randomization: Animals should be assigned randomly to the various experimental groups and the method of randomization reported. Information on the allocation, treatment and handling of animals across study groups, the selection and source of control animals, including whether they are true littermates of the test groups should be provided. Data should be collected and processed randomly or appropriately blocked. • Blinding: The investigator should be unaware of the group to which the next animal taken from a cage will be allocated (allocation concealment). Animal caretakers and investigators conducting the experiments should be blinded to the allocation sequence (blinded conduct of the experiment). Investigators assessing, measuring, or quantifying experimental outcomes should be blinded to the intervention (blinded assessment of outcome). This may hold true for all instances of the experiment, including also post-mortal investigations like macroscopical and pathohistological inspections and assessments. • Sample-size estimation: Underpowered experiments with low predictive value may either falsely conclude that interventions are without efficacy or provide falsely positive results leading to needless subsequent studies building upon the incorrect results. Too large studies will be unnecessarily costly. Both cases

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mean wasted resources in terms of time, money, and animals. An appropriate sample size should be therefore computed when the study is being designed and the statistical method of computation reported, which would also provide some assurance that sample size has not been increased incrementally in the light of ongoing analyses. Statistical methods that take into account multiple evaluations of the data should be used when an interim evaluation is carried out (Sena et al. 2007; Landis et al. 2012). • Data handling: Rules for stopping data collection should be defined in advance. Also criteria for inclusion and exclusion of data should be established prospectively. How outliers will be defined and handled should be decided when the experiment is being designed, and any data removed before analysis should be reported. The primary endpoint should be prospectively selected. If multiple endpoints are to be assessed, then appropriate statistical corrections should be applied. Pseudoreplicate issues need to be considered before determining study design and analysis. For example, when analyzing effects of pollutants on reproductive health, multiple sampling from a litter, regardless of how many littermates are quantified, provides data from only a single biologic replicate. Investigators should also report how often a particular experiment was performed and whether results were substantiated by repetition under a range of conditions. Additionally, it should not be forgotten that a significant result does not provide information on the magnitude of the effect and thus does not necessarily mean that the effect is robust and highly reproducible (Landis et al. 2012). • Fighting experimental noise sound: Irrelevant animals like those that die for reasons unrelated to disease (such as mishandling) should not be counted in results. Reasons for exclusion should be well documented. Whenever possible, numbers of males and females should be balanced because they can show sexdependent differences in symptoms that

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obscure modest drug effects. Littermates should be splitted among experimental groups (Perrin 2014). • Retrospective primary end-point selection: Selection of a primary end-point only after data have been analyzed inflates the type-I error (false-positive results). This can be avoided by specifying a primary end point before the study is undertaken, the time(s) at which the end point will be assessed, and the method(s) of analysis. Significant findings for secondary end-points can and should be reported but should be delineated as exploratory in nature. • Reporting of individual data: Nonrodent data are usually reported and interpreted on the basis of individual observations, reactions, and results. With rodent data using considerable higher numbers of animals, the statistical results often prevail and rare individual reactions get lost. The rule should be that preclinical investigations are handled like clinical results: Individual by individual, and not as a group mean. Hereby, possibly human relevant, but rare reactions do not get lost. • Avoid publication bias. Register all experiments. Use systematic reviews Systematic review (SR) (Sanderscock and Roberts 2002) is a simple technique developed to provide summary information by combining results from different sources and to make judgments on possible translation into clinical trials. In contrast to a narrative review which has no standardized methodology, the SR is a type of review that is structured, thorough, and transparent. Performing such appraisal can save resources and improve safety for participants in clinical trials achieved (van Lujik et al. 2014; Ritskes-Hoitinga et al. 2014; Vesterinen et al. 2014). Examples of the use of SR include, for instance, the study of Horn et al. (2001), who found no evidence to justify the start of clinical trials of nimodipine for focal cerebral ischemia in humans. The study emerged, however, only after 7665 patients participated in clinical trials. Comparably, Pound et al. (2004) demonstrated that drug side effects (in this case, excess risk of intracranial hemorrhage after thrombolysis treatment for acute stroke)

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found during a clinical trial could have been identified beforehand if a SR of preclinical animal studies had been performed. When performing a SR, it is important to evaluate the quality of data collected by other researches. Its relevance can be illustrated on Alzheimer’s disease, a condition which is despite of decades of experimental research known for a lack of effective disease modifying interventions. Egan et al. (2016) performed a SR and a metaanalysis of interventions tested in transgenic mouse model of the disease and after analyzing 427 publications describing 357 interventions in 55 transgenic models, involving 11,118 animals in 838 experiments, the authors found that the quality of these experiments was relatively poor – less than one in four publications reported blinded assessment of outcome or random allocation to group and no study reported a sample size calculation. Additionally, “trim and fill” analyses suggested that one in seven pathological and neurobehavioral experiments remained unpublished. Likewise, Tsilidis et al. (2013) evaluated 4445 animal studies or 160 candidate treatments of neurological disorders and observed that 1719 of them had a “positive” result, whereas only 919 studies would a priori be expected to have such a result. From these 160 treatments, only 8 should have been subsequently tested in humans. These examples illustrate not only historical methodological weaknesses in preclinical animal testing but also insufficient critical appraisal of existing animal data before starting clinical research. Considering ethical issues and enormous financial costs related to clinical trials this is a rather alarming finding. The bias resulting from not publishing could be significantly reduced by registering all experiments in a system similar to the one established for clinical trials. In this way, negative data would be published, unnecessary duplication of experiments would be prevented, investigators would receive credit for their work done and those seeking to summarize what is known would have access to all relevant data. The registration to the system could be flexible, with information

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embargoed for a time to protect intellectual property (Macleod 2011). To facilitate assessment of data collected, and to point out critical factors several study-quality checklists have been proposed. Among these are the CAMARADES checklist (Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Stroke), Macleod et al. 2004), Stroke Therapy Academic Industry Roundtable (STAIR 1999), Amsterdam criteria (Horn et al. 2001), Utrecht criteria (van der Worp et al. 2005), ARRIVE Guidelines (Animal Research: Reporting of In Vivo Experiments), Kilkenny et al. 2010), and the “Guidance for the Description of Animal Research in Scientific Publications” (National Research Council [US] Institute for Laboratory Animal Research 2011). Factors itemized on the checklists are, e.g., publication in peer-reviewed journal, assessment of functional and histological outcome, replication in two laboratories, testing both males and females, behavioral outcome measured for at least 1 month, assessment made in acute and chronic phase, randomization of treatment or control, blinded assessment of outcome, sample-size calculation before start of an experiment, and others.

Conclusions and Outlooks Preclinical development and especially here animal studies have been identified as possible factors, responsible for the insufficient efficiency of nonclinical pharmaceutical R&D. Quantitative analyses of publicly available animal toxicity studies revealed that their results were inconsistent predictors of undesirable or toxic responses in humans. There is a lack of powerful data and sometimes only a poor basis available for deciding whether a compound should proceed to clinical testing (e.g., Bailey et al. 2014). The selection and justification of the studies is frequently based on regulatory principles 50 years old. The way how they are designed and performed is in many cases decided rather on habit and tradition than on modern, scientifically sound justifications. Yet, although new approaches and technologies are

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being developed in a fast pace, their integration into drug development is rather slow. But progress and optimization is required and can be achieved by opening and accepting new pathways: better animal models are needed, and better predictive non-animal models required. Provided that fit-for-purpose animal models are used and the design and execution of the testing is implemented according to stringent quality criteria in vitro and in vivo experiments can provide valuable information for the clinical performance of the drug. Unfortunately, besides a few exceptions, like development of humanized experimental animals, investment in development of more predictive animal models has been during the last decades considerably lower than in development of new technologies in areas such as molecular biology or clinical trial biomarkers (Denayer et al. 2014). Better predictive state-of-the-art in vitro assays and in silico data, applied during early stages of drug discovery, can facilitate the long-term process of drug development. Replacing current acute and selected chronic in vivo regulatory toxicology studies by validated in vitro replacements would result in reduced animal use in pharmaceutical development of individual compounds. Following such strategy can be already observed in, e.g., some OECD test guidelines. The guideline no. 404 (Acute Dermal Irritation/Corrosion) recommends the conduct of in vitro assays (EOECD TG 430, 431) to limit the severity of toxicity for compounds that progress to in vivo evaluation. The concept of the 3Rs exists since almost 60 years but its value has been mostly perceived only as a European regulatory issue (Chapman et al. 2013). Recognition is growing during recent years that there are benefits for improving the quality of research and reducing costs. The quality, reliability, and predictive value of many well established methods have not sufficiently been validated, but to abandon them is associated with insecurity and reduced trust in the view of remaining data. Lack of confidence in novel approaches is rooted in limited experience among researchers and Agencies and additionally in the lack of historical data. There is fear to change long established pathways, which have been successful

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in the past. Joined effort of all parties involved is therefore needed to achieve progress and better acceptance of new approaches. Sharing knowledge (positive as well as negative experience) among stake-holders would facilitate the selection of the most promising methods. Such cooperation would fasten the transition towards novel approaches and reveal gaps for future research. To achieve the objective of such a paradigm change is only successful with full governmental support: the common goal should be to develop, optimize, and validate new translational tools, to revize some of the older guidelines and harmonize their acceptance on a global level. On the regulatory side, first attempt to catch up with progress is already happening: example the publication of the new guidelines on the evaluation of biosimilars on the European level. The International Conferences on Harmonization continue their awareness of scientific advances and their Expert Working Groups modify and improve the important recommendations of their global guidelines. The objective of this chapter is to propose several approaches that can contribute to improving efficiency and translational value of nonclinical testing. The suggestions target at: 1. Enlarge in silico data bases and improve their accessibility. 2. For researchers and editors: Publish all data, knowledge and experience. Include all data, positive and negative results. 3. Expand options for in vitro methods and elucidate their advantages and limitations. 4. Improve the selection of validated approaches and document the real values of animal models and applicability of methods. 5. Improve the design and execution of experiments and use fully randomization, blinding, optimal statistical interpretation etc. 6. Reflect clinical conditions in preclinical studies: Standard programs, biomarkers, specified endpoints. Understand the mechanisms of disease. 7. Identify weaknesses of methods: Which are the most relevant and predictive models and methods for human conditions?.

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8. Introduce state-of-the-art methods into daily practice: GLP, statistics, combine functions with morphology, use non-invasive methods, provide support by kinetic data, etc. 9. Improve quality of reporting: analyze and assess all results, from all studies, focus on mean and individual effects. 10. Conscientious review of literature: built up “weight of evidence” approach, use information from Quality, Safety and Efficacy. 11. Encourage an open dialogue among researchers from all disciplines in industry and agencies. 12. Use scientific advice offered by Agencies to facilitate the decisions for best strategy during all phases of development. 13. Gain meaningful information of animal usage for human conditions at every step of development, get human data early as possible, use expedited explorations. 14. Better prediction of drug reactions in humans based on modern intelligent complex approaches will fasten access to efficient and safe drugs. For all these objectives, courage should be stimulated to swing from preconceived concepts to new methods and pathways. Only frank imaginative discussions will open the doors to an optimum way forward.

References and Further Reading Akhtar A (2015) The flaws and human harms of animal experimentation. Camb Q Healthc Ethics 24(4):407–419 Arlington S (2012) From vision to decision: pharma 2020. Price Waterhouse Coopers (PwC), London. Available at https://www.pwc.com/gx/en/pharma-life-sciences/ pharma2020/assets/pwc-pharma-success-strategies. pdf. Accessed on 3 July 2017 ARRIVE – Animal Research: Reporting of In Vivo Experiments. Available at https://www.nc3rs.org.uk/arriveanimal-research-reporting-vivo-experiments. Accessed on 3 July 2017 Arrowsmith J (2011) Trial watch: phase III and submission failures: 2007–2010. Nat Rev Drug Discov 10:87 Bailey J, Thew M, Balls M (2013) An analysis of the use of dogs in predicting human toxicology and drug safety. Altern Lab Anim 41(5):335–350 Bailey J, Thew M, Balls M (2014) An analysis of the use of animal models in predicting human toxicity and drug safety. Altern Lab Anim 42:181–199

1164 Baumann A, Flagella K, Forster R, de Haan L, Kronenberg S, Locher M et al (2014) New challenches and opportunities in nonclinical safety testing of biologics. Regul Toxicol Pharmacol 69(2):226–233 Begley C, Ellis L (2012) Drug development: raise standards for preclinical cancer research. Nature 483:531–533 Bennani YL (2012) Drug discovery in the next decade: innovation needed ASAP. Drug Discov Today 16(17–18):779–792 Bhatia SN, Ingberg DE (2014) Microfluidic organs-onchips. Nat Biotechnol 32:760–772 Bluemel J (2012) Considerations for the use of nonhuman primates in nonclinical safety assessment. In: Weinbauer GF, Vogel F (eds) Challenges in nonhuman primate research in the 21st century, Waxman (2012). Charles River Publication Series, pp 59–70. ISBN 9783-8309-2839-3 Bode G, Van der Laan JW (2016) Paradigm change in cancerogenicity, Presentation: German Pharm-Tox Summit, Berlin, 29-02 to 03-03-2016, Berlin, ACS Publications, pubs.acs.org./crt, Symposium 19 Bode G, Clausing P, Gervais F, Loegsted J, Luft J, Nogues V, Sims J et al (2010) The utility of the minipig as an animal model in regulatory toxicology. J Pharmacol Toxicol Methods 62(3):196–220 Bussiere JL, Martin P, Horner M, Couch J, Flaherty M, Andrews L, Beyer J, Horvath C (2009) Alternative strategies for toxicity testing of species-specific biopharmaceuticals. Int J Toxicol 28(3):230–253 CAMARADES: Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies. Available at http://www.dcn.ed.ac.uk/ camarades/. Accessed 3 July 2017 Casty F, Wieman M (2013) Drug development in the 21st century. The synergy of public, private, and international collaboration. Ther Innov Regul Sci 47(3):375–383 Cavagnaro J, Lima BS (2015) Regulatory acceptance of animal models of disease to support clinical trials of medicines and advanced therapy medicinal products. Eur J Pharmacol 759:51–62 Chalmers I, Glasziou P (2009) Avoidable waste in the production and reporting of research evidence. Lancet 374:86–89 Chapman KL, Pullen N, Andrews L, Ragan I (2010) The future of non-human primate use in mAb development. Drug Discov Today 15:235–242 Chapman K, Andrews L, Bajramovic JJ, Baldrick P et al (2012) The design of chronic toxicology studies of monoclonal antibodies: implications for the reduction in use of non-human primate. Regul Toxicol Pharmacol 62:347–354 Chapman KL, Holzgrefe H, Black LE, Brown M, Chellman G, Copeman C, Couch J et al (2013) Pharmaceutical toxicology: designing studies to reduce animal use, while maximizing human translation. Regul Toxicol Pharmacol 66:88–103

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G. Bode and P. Starck-Lantova Lee GS, Lee KY, Choi KC, Ryu YH, Paik SG, Oh GT, Jeung EB (2007) Phenotype of a calbindin-D9k gene knockout is compensated for by the induction of other calcium transporter genes in a mouse model. J Bone Miner Res 22(12):1968–1978 Liebsch M, Grune B, Seiler A, Butzke D, Oelgeschläger M et al (2011) Alternatives to animal testing: current status and future perspectives. Arch Toxicol 85(8):841–858 Macleod M (2011) Why animal research need to improve. Nature 477:511 Macleod MR, O’Collins T, Howells DW, Donnan GA (2004) Pooling of animal experimental data reveals influence of study design and publication bias. Stroke 35:1203–1208 Matthews R (2008) Medical progress depends on animal models – doesn’t it? J R Soc Med 101(2):95–98 Mella JR, Chiswick EL, King E, Remick DG (2014) Location, location, location: cytokine concentrations are dependent on blood sampling site. Shock 42:337–342 Michelson Prize & Grants (2014) The quality assurance toolkit. Available at http://www.michelsonprizeandgr ants.org/resources/qa-toolkit. Accessed 29 Aug 2017 Morgan SJ, Elangbam CS, Berens S, Janovitz E, Vitsky A, Zabka T, Conour L (2013) Use of animal models of human disease for nonclinical safety assessment of novel pharmaceuticals. Toxicol Pathol 41(3):508–518 National Research Council [US] Institute for Laboratory Animal Research 2011: Guidance for the Description of Animal Research in Scientific Publications NIH, National Human Genome Research Institute (2009) Knockout mice. Available at: https://www.genome. gov/12514551/. Accessed 2 Aug 2017 NTP (2017) https://ntp.niehs.nih.gov/pubhealth/evalatm/ test-method-evaluations/acute-systemic-tox/in-vitrovalidation/index.html Nuffield Council on Bioethics (2005) The ethics of research involving animals. Available at: https:// nuffieldbioethics.org/wp-content/uploads/The-ethicsof-research-involving-animals-full-report.pdf. Accessed 12 Sept 2017 OECD (2015) Guidance document on revisions to OECD genetic toxicology test guidelines. Available at https:// www.oecd.org/chemicalsafety/testing/Genetic%20Tox icology%20Guidance%20Document%20Aug% 2031%202015.pdf Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J et al (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56–67 Paul S, Mytelka D, Dunwiddie C, Persinger C, Munos B, Lindborg S, Schacht A (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9:203–214 Perrin S (2014) Preclinical research: make mouse studies work. Nature 507:7493 PhRMA (Pharmaceutical Research and Manufacturers of America) 2015: 2015 biopharmaceutical research industry profile.

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Index

A Aβ-fibers, 102, 103, 110 Absorption, 725–727 Accelerator mass spectrometry (AMS), 792, 1154 Accessory pathway (AP), 25, 26 Acidic drug, 719 Acitretin, 301 Action potentials, 53 Activated partial thromboplastin time (aPTT), 736 Active counting (ACMTM), 789 Activities of daily living (ADL), 420 Acupressure, 461 Acupuncture, 462 Acute kidney injury (AKI), 656 Adaptive proof of concept, 600 Addiction, see Drug addiction Aδ-fibers, 104, 106, 108, 110–112, 116 Adeno-associated vectors, 368–369 Adenotonsillectomy, 240 Adenoviral vectors, 368 Adherence, 556 co-morbidity effect on therapy, 556 disease-dependent medication, 556 improvement in patients, 558 and medication side-effects, 557 Administration, distribution, metabolism and excretion (ADME), 1106 Adolescents and adults, pharmacokinetics in, 1011 Adrenals, 292–293 Adult multipotent stem cells HSCs, 642 MSCs, 643 NSCs, 646 Advanced therapy medicinal product (ATMP), 639 Adverse drug reactions (ADRs), 418, 419, 422, 548, 1074, 1075 Adverse event (AE), 622, 1074 categorization for decision making, 6–8 clinical monitoring, 8–9 collection of, 1075, 1077 decision to stop dose escalation, 8 definition, 5 Adverse events of special interest (AESI), 1076 Agar diffusion methods, 327

Ageism, 423 Age-related macular degeneration (AMD), 192–199, 657 Albumin, 718 Alcohol, 152–155, 720, 781, 782 Alcohol dependence syndrome, 152 Alcohol withdrawal syndrome, 152 Alkalinizers, 719 Allergic conjunctivitis assays and animals models, 201 PAC, 200 SAC, 200 Allodynia, 102, 104, 109, 114–116 Allogeneic transplantation, 639 Allometric scaling approaches, 672–675 critical assessment of method, 677–679 evaluation, 675–677 α1-acid glycoprotein (AAG), 766, 770 Alpha-glucosidase inhibitors (AGIs), 286 Alpha-synuclein, 91 Alzheimer’s disease (AD), 89, 421, 658 Ambulatory blood pressure monitoring (ABPM), 10, 23 Ambulatory devices, 33 Ambulatory ECG (AECG), 34 American Academy of Ophthalmology, 164 American Standard AAMI EC11, 77 Amiloride, 939 Aminoglycosides, 743 Amiodarone, 58, 63, 735 Amphetamines and derivatives, 139–144 Amylin analogues, 287 Amyotrophic lateral sclerosis (ALS), 88, 92, 658 Analog–digital conversion, 26 Anderson disease, 394 Androgen agonist steroids (AASs), 294 Anterior chamber (ANC), 166, 175, 182, 183, 187 Anti-aliasing, 67, 68 Antiandrogens, 295 Antiarrhythmic drugs, 735–736 Antiasthmatics, 502, 504 Antibacterial herbs, 475 Anticholinergic, 422 Anticoagulants/antiplatelets, 736–738

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1169

1170 Anti-dAMD/anti-GA drug animal models, 197 assay systems, 195–197 Antidiuretic hormone (ADH), 290 Anti-drug antibodies (ATIs), 271 Antifungal herbs, 475 Antifungals, 744–745 Antihistaminergic, 422 Antihypertensive drugs, 509–511, 734–735 Antimicrobial resistance, 474 Antimicrobials, 741–744 Antinausea therapy, 525 Antiparasitic herbs, 475 Anti-programmed cell death 1, 312 Antipsychotic drugs, 447 Antiulcer drugs, 504 Antiviral herbs, 475 Anti-wAMD/anti-CNV drugs animal models, 199 cell-based assays, 197–199 APAP machines, 229 Apheresis, 648 Apnea-hypopnea index (AHI), 237, 238 Approximate entropy (ApEn) analysis, 37 Apremilast, 302 Aqueous humor (AQH), 166, 175, 181, 183, 187, 188 Area under the curve (AUC), 696, 698, 700, 701, 705, 710, 713 Arousal index (ArI), 236 Arousals, 228 ArrayExpress, 940 Arterial blood pressure (ABP), 22 Arterial spin labelling (ASL), 441, 448 advantages for pharmacological imaging, 442 CASL, 442 pASL, 442 Artificial neural networks, 26, 39 Asfotase alfa, 395 Atopic dermatitis, 302, 307 antibacterial and antiviral treatment, 306 clinical features, 303–304 diagnostics, 304 education and use of emollients, 304–305 environmental irritation, 303 genetics, 303 immune dysregulation, 303 systemic medication, 306 therapeutic management, 306 topical corticosteroids and calcineurin inhibitors, 305–306 ultraviolet phototherapy, 306 ATP-binding cassette (ABC) transporters, 958, 967 BCRP, 994–995 BSEP, 993–994 MDR1, 991–993 MRP2, 994 Atrial antiarrhythmic drugs, 64–65 Atrial arrhythmias, 61

Index Atrial fibrillation, 34, 36, 40, 54 remodeling evolution, 61–62 subtypes, 62–63 vagal, 63 Atrial hypertrophy, 29 Atrial tachycardia, 61 Atrial velocity, 48 Atrioventricular conduction, 25, 27, 34 Attrition rate, 87 Auditory signals, 212 Augmented renal clearance (ARC), 742 Autoimmune diseases (AD), 649 Autologous transplantation, 639 Autonomic nervous system (ANS), 30, 59 Autoradiography, 175 Autoregressive linear analysis, 40 Autoregressive modeling (AR), 31 B Bacterial infection/ocular inflammation, 202–204 Bacterial submodel, 331 Barbiturates, 147, 149 Baroreflex sensitivity (BRS), 39 Barostat, 105 Basal cell carcinoma (BCC) clinical features, 307–308 diagnostics, 309 pathophysiology, 307 therapeutic management, 310 treatment, 309 Basal cell nevus syndrome, 307 Base-level variation, 26 Baseline wandering, 77 Basic drug, 719 Bath salts, see Synthetic cathinones Bayesian analysis, 26 Beat-to-beat techniques, 34 Benzoate, 392 Benzodiazepines (BZDs), 147–149 Benzoic acid, 775 Berlin questionnaire, 231 Best Pharmaceuticals for Children Act (BPCA), 403, 404 β-lactam antibiotics, 741, 1064 β-lactamase inhibitor, 1064 Big data analytics (BDA), 590 Bihormonal insulin pumps, 251, 252 Bile salt export pump (BSEP), 993 Bilevel-PAP (BPAP), 229 Biliary efflux clearance, 972 Biliary excretion index (BEI), 972 Bioavailability, 858 absolute, 880 characterization, 889–891 compartmental analysis, 884 definition, 888 evaluation, 881 factors affecting, 882

Index non-compartmental analysis, 883 pharmacokinetic and pharmacodynamic analysis, 883 purpose, 880 rationale, 880 relative, 881, 882 Biocompatibility, 618–619 Bioequivalence assessment, 891–892, 897 bioanalysis, 895–896 challenges and specificities in assessment, 898–899 definition, 888 drug-food interactions, 894 immediate release vs. modified release oral products, 896–897 multiple dose studies, 893–894 single dose studies, 892–893 BIO-international conferences, 901 Biologic agents, 302 Biological clock, 500–501 Biological marker, 599 Biology, mathematical models in, 1048 Biomarkers, 87, 409, 420, 906 in Alzheimer’s disease, 89–91 in amyotrophic lateral sclerosis, 92 cerebral-spinal fluid, 88 clinical, 89 definition, 87 electrophysiology, 89 genomics, 89 in Huntington’s disease, 92 MRI with gadolinium, 88 in multiple sclerosis, 91 in Parkinson’s disease, 91 peripheral, 89 PET, 88 SPECT/DaTScan, 89 structural and volumetric MRI, 88 Biomarkers Definitions Working Group, 87 Biomeasure, 906 Biopharmaceutics Classification System (BCS)-based biowaiver, 899–900, 1127 Biopharmaceutics Drug Disposition Classification System (BDDCS), 961 Biophase, 911 Biotinidase deficiency, 391 Biphasic kinetics, 850 Bipolar Einthoven leads (I, II, III), 25 Bird’s disease, 938 Bladder function, 422 Block, 25 Blood-brain barrier (BBB), 82, 83, 85, 87, 88 Blood disease, 374–376 Blood-oxygen-level dependent (BOLD) imaging, 440, 442–444 Body’s repair mechanisms, 526 Bowen’s disease, see Squamous cell carcinoma (SCC) BP variability, 23 BRAF kinase inhibitors, 313 Breast cancer resistance protein (BCRP), 994

1171 British Hypertension Society, 24 Brugada syndrome, 20, 26, 55–56 Bruton’s tyrosine kinase (BTK) inhibitors, 351 C Caco-2 cells, 965 Caffeine, 782 Calcineurin inhibitors, 305 Cancer, 376–380 research, 476–477 therapeutics, 553 Cancer Therapy Evaluation Program (CTEP), 1009 Cannabinoid receptors, 265–266 Cannabinoids, 144, 145 Cannabis, 144–147 Carcinogenicity Assessment Documents (CADs), 1099 Carcinogenicity studies, 1097, 1104 dose selection for, 1102–1104 general regulatory background, 1097–1098 need for, 1098–1100 testing for, 1100–1102 Cardiac arrhythmias, 53 Cardiac conduction defect, 57 Cardiac fibrosis, 54 Cardiac loop recorders, 33 Cardiomyocytes, 652 Cardiosphere-derived cells (CDC), 653 Cardiovascular diseases, 376 Cardiovascular methodologies, pharmacodynamics assessment ABPM, 23–25 circadian blood pressure, 22–23 clinical trial legal regulations and good clinical practice, 22 diastolic performance, 47–48 electrocardiography (see Electrocardiography (ECG)) empirical quality criteria, 21 fuzzy logic, 38–40 HRV, 30 measurements validity, 21–22 MPI, 45 myocardial mechanical dispersion, 41–43 non-linear indexes of cardiovascular variability, 36–38 phase 0, 21 phase I, 21 phase II, 21 phase III, 21 phase IV, 21 stroke volume and cardiac output, non-invasive estimates of, 45–47 symbolic dynamic analysis, 36 systolic function, 43 systolic time intervals, 43 turbulence onset, 35 vectorcardiography, 28–29 Cardiovascular system, obesity antiarrhythmic drugs, 735–736 anticoagulants/antiplatelets, 736–738

1172 Cardiovascular system (cont.) antihypertensive drugs, 734–735 Carebastine, 763 Caregivers, 419, 422, 423 Case report form (CRF), 1075 Catecholaminergic polymorphic ventricular tachycardia (CPVT), 56–57 Cationic polymer vectors, 366 Cefazolin, 726 Celivarone, 59 Cell-based assays, 190–192 Cell-mediated immunity, 1117 Cell-to-cell coupling, 54 Center for iPS Cell Research and Application (CiRA), 650 Central disorders of hypersomnolence, 225, 226 Central nervous system (CNS), 646 brain penetration and PK, 83 depression, 720 in vivo dialysis methods, 85 neurodegenerative diseases, 658–659 positron emission tomography, 84 retinal disorders, 657–658 spinal cord injury, 659 target drug exposure, 82–83 target exposure and target engagement, 83 Central sensitization, 104, 109, 112–116 ® Cerdelga , 390 Cerebral-spinal fluid (CSF), 88 C-fibers, 104–106, 110–112, 116, 119 Channelopathies, 20, 52 Chemical muscle stimulation, 116 Chemical skin stimulation capsaicin, 115 menthol, 116 mustard oil, 116 nerve growth factor injection, 115 Chemical visceral stimulation, 117 Chemotherapy, 314 Cheyne-Stokes respiration, 240 Child-Pugh score, 760 Chimeric antigen receptor (CAR) T-cell therapy, 427, 649 Chinese herbal medicine, 463–467 adverse reactions, 473 antibacterial herbs, 475 antifungal herbs, 475 antiparasitic herbs, 475 antiviral herbs, 475 characteristics of herbal medicine, 467 herbal formulations, 469 herbal processing, 471–473 herb/herb interactions, 469–470 herbs and drug interactions, 471 mortality, 473–474 quality assurance and standardization, 471–473 TCM herbs, contemporary applications of, 467–469 toxicity, 473–474 Chlorzoxazone, 729 Choroditis/chorioretinitis, 203 Chromatographic analysis, 784–785

Index Chronic kidney disease (CKD), 656 Chronic obstructive pulmonary disease (COPD), 29 Chronic plaque psoriasis, 301 Chronobiological fluctuations, 24 Chronopharmacology, drug development, 501 of antiasthmatic drugs, 501–503 AT1-receptor blockers, 509–510 calcium-channel blockers, 506–507 of cardiovascular active drugs, 504–506 converting enzyme inhibitors, 507–509 of CSE inhibitors, 512 diuretics and antihypertensive drugs, 510 peptic ulcer disease, H2-blockers in, 503 Ciclosporin, 301, 306 Ciliary muscle (CM), 182, 183, 187 Ciprofloxacin, 744 Circadian rhythm sleep-wake disorder, 225, 226 14 C-labelled compounds dilution, 816 purification, 816 stability of, 815–816 synthetic considerations, 810 technical considerations, 810 Classical test theory, 580 Clinical and Laboratory Standards Institute (CLSI), 742 Clinical evaluation report (CER), 619 Clinical insulin therapy, 246–248 Clinical outcome assessments (COAs), 572 Clinical pharmacology, 904 absorption and distribution of drugs, 596 ADME/AMES studies, 598 basal analog insulins, 248 bihormonal insulin pumps, 251 biosimilar insulins, 248 clinical protocol essential elements, 599 definition, 904 development, 247 dosing regimen, 602 dulaglutide, 254–255 early insulin therapy in type I diabetes, 246 early proof of concept and adaptive study design, 600 elimination, 597 exenatide, 253 GLP1 agonists and insulins, 255 glucagon, 251–252 hypoglycemia, 247 injectable products, 247 injection therapy, 244 insulin pens, 244 liraglutide, 253 lixisenatide, 254 metabolism, 597 metabolite profiling approaches, 598 patient acceptance, 248 pharmacodynamics, 599 pharmacokinetics (see Pharmacokinetics (PK)) selection of patients, 247 semaglutide, 254 sensor-augmented pumps, 246

Index simple insulin pumps (see Insulin pumps) stopping rules, 602 Clinical trial, 548 in gene therapy, 370–380 herbal medicine, 486, 487 Clopidogrel, 610, 738 Cluster analysis, 26 Clustered regularly interspaced short palindromic repeats (CRISPR), 365 CMAP, see Connectivity map (CMAP) CNS, see Central nervous system (CNS) Cobalamin, 391 Cocaine, 136–139 Cockcroft-Gault equation, 751, 752 Cognitive functions, 420 Cold pressor test, 106 Cold stimulation cold pressor, 106 cooling thermode, 107 skin freezing, 107 thermal grill, 107 Colistin, 335 Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Stroke (CAMARADES) checklist, 1162 Combinatorial Drug Assembler (CDA), 938 Common Technical Document (CTD), 1132 Comorbidities, 418, 422 Compartmental models, 1052 Complex drug interactions, 840 enzyme/transporter interplay, 840–841 multiple CYP inhibitors, 840 organ impairment, effect of, 841 pediatrics and geriatrics, 841 Complex systems, 38 Concurrent validity, 587 Conditional weighted residuals (CWRES), 916 Conditioned pain modulation (CPM), 102 Congenital heart disease, 26, 29 Connectivity map (CMAP), 936 colorectal cancer, 939 Spearman’s coefficient, 937 Constitutive androstane receptor (CAR), 854 Construct validity, 586 Content validity, 587 Continuous ECG, 32, 34 Continuously labelled ASL (CASL), 442, 443 Continuous monitors, 32 Continuous positive air pressure (CPAP), 229, 239 Continuous subcutaneous glucose monitoring (CSGM), 249 Contract research organization (CRO), 21, 1146 Contrast enhanced MRI (CE-MRI), 275–276 Contrast-to-noise ratio (CNR), 442 Convergent validity, 586 Coronary flow reserve (CFR), 20–48 Coronary heart disease (CHD), 652 Cor pulmonale, 29 Corrective and preventive action (CAPA), 1142

1173 Cox regression model, 1054 Crigler-Najjar syndrome, 655 CRISPR/cas9, 365, 649 Criterion validity, 587 Crohn’s disease, 645 Crossover study design, 704–707 dose–exposure relationship, assessment of, 704–705 inclusion criteria, 704 methods, 704 Cryosurgery, 310 14 C-syntheses, 810–815 Cumulative fraction of response (CFR), 1060 Cupping, 461 Cutoff frequency, 67 Cysteamine bitartrate, 393 Cystic fibrosis, 433–434 Cystinosis, 393–394 Cytochrome P450 (CYP), 837, 840 CYP1A2, 1020, 1021 CYP2A6, 1032 CYP2B6, 1033 CYP2C8, 1033 CYP2C9, 1023, 1024 CYP2C19, 1025, 1026 CYP2D6, 1027 CYP2E1, 1034 CYP3A, 1029 CYP3A4, 690, 719 D Daptomycin, 743 Data and Safety Monitoring Boards (DSMB), 1076 Database for annotation, visualization and integrated discovery (DAVID), 942 Deceleration time of E velocity (DT), 48 Deep vein thrombosis (DVT), 736 Delayed afterdepolarization, 52–53 Delayed onset muscle soreness (DOMS), 104 Dendrimers, 536 Deoxynojirimycin, 391 Depot medroxyprogesterone acetate, 739 Depression, 557 Detrended fluctuation (DFA), 37 Development Safety Update Report (DSUR), 1080 Dextroamphetamine, 525 Diabetes mellitus (DM), 285 β-cells, 654 type 1, 654 type 2, 654 Diabetic macular edema (DME), 199–200 Diabetic retinopathy (DR), 175, 181 assay systems and animal models, 200 non-proliferative, 199 proliferative, 199 Diastolic blood pressure, 22, 23, 39 Diazepam, 728 Dietary and nutritional consultation, 461–462 Differential expression, 941

1174 Digital-and-decimation filter, 71–73 Digital signal processing chip, 26 Digoxin, 736 DIGREM, see Drug-Induced Genomic Response Model (DIGREM) Dimethylfumarate, 301 Dipole theory, 28 Directive 2002/46/EC, 626, 629 Direct response model, 1053 Direct-to-costumer businesses, 654 Discrete-event model, 38 Discrete-time model, 38 Discriminant validity, 586 Disease etiology, 933–934 companion diagnostics, 934 stratified medicine approach, 934 Disease-dependent medication adherence, 556 Dissociative anesthetics, 150–152 Dissolution, 900 Distensibility, 43 Distribution, 727–728 Disulfiram-like reactions, 720 Diurnal rhythm of BP, 23 Dominantly-inherited AD, 90 Dopamine transporter (DAT) imaging, 91 Dose-/concentration-response curve analysis critical assessment of method, 606 noncompetitive antagonist, 605 procedure, 605 purpose and rationale, 605 right shift, 605 Dose-limiting toxicities (DLT), 347 Dose linearity and proportionality, 696–697, 707–712 accumulation, 711, 712 clinical assessment of, 697 crossover study design, 704–707 descriptive analyses, 698 discrete model, 698 dose effect, 710 lack of, 697 plasma concentrations and pharmacokinetic parameters, 711 power model, 698–699 single dose study design, 699–703 steady state, 711, 712 Dose-response relationships, 83 Dose selection, 842, 906 model simulations, 923 for phase II studies, 923 Downregulation, 855 Down-sampling, 67 Doxorubicin (Dox) prodrug, 1007 DPP-4 inhibitors, 286 Dronedarone, 64 Drug absorption, 717–718 Drug action mechanism, 128 Drug addiction, 128, 130, 157 amphetamine intake, 140 barbiturates, 149

Index benzodiazepines, 148 cocaine, 137, 138 dissociative anesthetics, 152 nicotine, 155 opioid, 131, 134 Drug bioperformance, 958 DrugComboRanker, 940 Drug concentration, 82, 84 Drug concentration-time profile, 84 Drug dependence amphetamine intake, 140 barbiturates, 149 cocaine, 137 opioid, 131, 134 Drug-disease interactions, 418 Drug disposition and obesity absorption, 725–727 distribution, 727–728 excretion, 731–732 metabolism, 728–731 Drug-drug interactions (DDIs), 421, 604, 848 additive, 604 antagonistic, 604 cocktail approaches, 843 complex drug interactions (see Complex drug interactions) CYP-mediated interactions, 837–838 dose selection, 842 enzyme induction, 854 experimental considerations in vitro, 855–857 free drug hypothesis, 862 general considerations, 828–829 general strategies, 832–834 in vitro enzyme kinetics, 849–850 metabolism-based, 830 NBEs, 828–844 non-clinical assessment of, 829 pharmacogenetics, impact of, 863–864 pharmacogenomic considerations, 843–844 pharmacokinetic endpoints, 842 phase II and phase III trials, PopPK in, 844 PK principles, 858 plasma protein displacement, 863 quantitative prediction of, 858–859 and regulatory guidance, 859–862 reversible inhibition, 851–853 route of administration, 841 statistical considerations, clinical relevance and sample size, 842–843 study design, 835–836 study population, 836–837 synergistic, 604 TDI, 853 transporter-based, 831–832 transporter-mediated interactions, 838–840 Drug-food interactions, 894–895 Drug-induced channelopathies, 58 Drug-Induced Genomic Response Model (DIGREM), 938 Drug interactions

Index alcohol related types, 154–155 amphetamine, 142 benzodiazepines, 148 cocaine, 138 for specific opioids, 135 Drug metabolizing enzymes (DMEs), 849, 854, 857, 863, 1020 Drug-nutrient interactions, 715 Drug-protein interactions, 766 Drug repositioning, 931 class labels, 941 EGFR-targeting compound, 945 gene2drug, 938 gene list derivation, 940 GEO, 940 in silico analysis, 932 in vitro validation, 934 in vitro vs. in silico application, 932 in vivo model, 939 KRAS mutation, 934 LINCS/CLUE, 937 MANTRA, 939 mechanism network, 940 metformin, 931 molecular stratification of disease, 934 molecular subtyping, 941 neurodegenerative disorders, 938 observations during clinical trials, 932 resistant phenotypes, 941 side effects in clinical trials, 932 sildenafil, 931 topoisomerase II inhibitors, 939 Drug repurposing, see Drug repositioning Drug response, 933 Drug-set enrichment analysis (DSEA), 940 Drug-transporter interaction critical assessment, 975–976 hepatobiliary transport, 972 induction studies, 974 inhibition study, 972–974 interaction with metabolites, 974 in vitro/in vivo extrapolation (see In vitro/in vivo extrapolation) in vitro systems, 965–967 kinetic parameter estimation methods, 968 probe substrates, 967 substrates for P-gp and BCRP efflux transporters, 969–970 substrates for solute carrier transporters, 970–972 Drug transporters ABC transporters (see ATP-binding cassette (ABC) transporters) with clinical relevance, 990–991 SLC transporters (see Solute carrier (SLC) transporters) Dry AMD (dAMD), 192, 194, 195 Dry eye disease (DED) in vitro assays and animal models, 202 signs and symptoms of, 202

1175 DTS-201, 1007 Duchenne muscular dystrophy, 434 Dulaglutide, 254 Dupilumab, 306 Dynamic beat-to-beat QT interval analysis, 34 Dynamic time-kill study, 1058 Dysphagia, 422 dZ/dt-curve, 44 E Eadie-Hofstee visualization, 849, 850 Early afterdepolarizations, 53 Early diastolic velocity (E), 48 Early proof-of-concept studies, 87 Ebastine, 761, 763 ECG/EKG, see Electrocardiography (ECG) Economy, 21 Ectopic activity, 53 Ectopism, 25, 27, 34 Eczema Area and Severity Index (EASI), 306 EEG, see Electroencephalography (EEG) Effect compartment model, 911 Efficacy studies, 485, 487, 488 Eighth Joint National Committee (JNC 8), 22 Electrical conduction defects, 57 Electrical muscle stimulation, 114 Electrical skin stimulation electrical burst/temporal summation, 112 electrical single stimulation, 112 HFS, 113 Electrical stimulation of muscle tissue, 113 Electrical visceral stimulation, 114 Electrocardiography (ECG), 25 algorithms, 26 coronary flow reserve, evaluation of, 48 critical assessment, 26–28 evaluation, 25–26 Holter monitoring 24 hour ECG, 31–34 procedure, 25 restitution, 34 SAECG, 29 standard 12 lead ECG, 25 strain imaging, 40–41 Electroencephalography (EEG), 89, 100 Electromyographic (EMG) signals, 29 Electronic Common Technical Document (eCTD), 1126, 1127 Electronic Standards for the Transfer of Regulatory Information (ESTRI), 1126 Electrophysiological methods, 98, 99 Electrophysiologic study, 26 Eliglustat, 390 EMA Geriatric Expert Committee, 420 Emax model, 331 Embryonic stem cells (ESCs), 638 Emergency contraceptive pills (ECPs), 740 Emphysema, 29 Empirical Bayes estimates (EBEs), 915

1176 Empirical models, 1049 Encapsulation devices, 654 Endocrine disorders, pharmacodynamic evaluations ACTH, 288 adrenals, 292 AGIs, 286 amylin analogues, 287 antiandrogens, 295 antidiuretic hormone, 290 diabetes mellitus, 285 DPP-4 inhibitors, 286 epinephrine, 293 estrogens and progestogens, 295–296 GLP-1 agonists, 286 glucagon, 288 gonadal sex steroids, 294–295 growth hormone, 288–289 hypothyroidism, 291 insulins-types and forms, 287–288 melanocyte stimulating hormones, 290 metformin-biguanides, 285 methimazole, 291 oxytocin, 290 pituitary-hypothalamus, 288 prandial glucose regulators, 287 prolactin, 289 PTH, 291 PTU, 291 RAAS, 293 SGLT-2 inhibitors, 287 somatostatin, 289 sulfonylureas, 285 TZDs, 286 Endocrine system, obesity, 738–741 Endometriosis, 931 Enoxaparin, 736, 752, 754, 756 Enrichment technique, 90 Enterohepatic circulation model, 985 Entinostat, 945 Entropy, 37 Enzyme-linked immunosorbent assay (ELISA), 677 Enzyme replacement therapy (ERT) in lysosomal storage diseases, 395–396 phenylalanine ammonia lyase in phenylketonuria, 396–397 Epinephrine, 293 Epworth sleepiness scale (ESS), 230 ERMBT, 1030, 1031 Erythroplasia of Queyrat, 308 E-test, 327 Ethanol, 720 Ethical Committee, 21 Ethinyl estradiol (EE), 739 Etonogestrel (ENG), 739 EUFEPS Global Bioequivalence Harmonization Initiative, 901 EURL ECVAM, 1157 European Centre for the Validation of Alternative Methods (ECVAM), 1108

Index European Committee on Antimicrobial Susceptibility Testing (EUCAST), 741 European Economic Community, 1147 European Medicines Agency (EMA), 6, 562–563, 647 European Paediatric Regulation, 403 European Partnership for Alternative Approaches to Animal Testing, 950 Evidence based trial quality management, 1140 Evoked potentials (EP), 99 Exclusion criteria, 781 Excretion, 731–732 Exemplary biomarker, 87 Exenatide, 253 Exon-skipping therapy, 434 Expanded Disability Status Scale (EDSS), 89 Experimental bias blinding, 1160 data handling, 1160 experimental protocol, planning, 1159 inappropriate animal model, 1159 individual data, reporting of, 1161 non-publication, 1158 poor reporting, 1158 randomization, 1160 regulation of animal research, 1159 retrospective primary end-point selection, 1161 sample-size estimation, 1160 sources of variation, 1159–1160 systematic reviews, 1161 Expert Working Group (EWG), 1126 Exposure ratio, animal/human, 954 Exposure-response (E-R) relationship, 84 Extended clearance classification system (ECCS), 964 Extended clearance concept (ECC), 963 Extracellular fluid (ECF), 82, 85 Extreme dippers, 24 Ex vivo gene therapy, 362 Eye anatomy and basic physiology, 165–167

F Fabry disease, 394 Face validity, 587 Factorial design trials aspirin, 611 clopidogrel dose regimen, 611 critical assessment of method, 611 CURRENT-OASIS 7 study, 610 modifications of method, 611 procedure, 610 purpose and rationale, 609 22 factorial design, 610 Factor Xa, 737 Failure mode and effect analysis (FMEA), 1142 Falls, 419–422 Fanconi anemia, 375 Fast Fourier transformation (FFT), 31

Index Fear of injection, 245 Filaggrin, 303 Filter-processed noise, 30 First coformulation product, 248 First in human (FIH) study, 594 First-order conditional estimation (FOCE) method, 915 Fluconazole, 744 Fluorescein isothiocyanate (FITC), 277 Fluorouracil (FU), 310 fMRI, see Functional magnetic resonance imaging (fMRI) Food and Drug Administration (FDA), 560–562, 647, 1073 Food and Drug Administration Safety and Innovation Act (FDASIA), 404 Food-drug interactions, 715 Food Supplement Regulation, 626 Food supplements assessment guidelines, 634 clinical trials, 628 content, 626 definition, 626 food law provisions, 626–628 in Germany per year, 627 marketability of substances in, 629 vs. medicinal products, 627 novel food, 632 nutrition-specific, 630 purpose, 626 safety aspects, 632–634 special-law provisions, 628 Forced expiratory volume in 1 s (FEV1), 726 Formulation development HMR456 modified release formulations, 867–870 with SAR001, 869–870 Fourier analysis, 26 Fractional exhaled nitric oxide (FeNO), 726 Fragmented QRS (fQRS), 30, 34, 74 Frailty, 418–421 Frank lead system, 29 Free drug concentrations, 82 Free drug hypothesis, 862–863 Freestyle Libre I, 248 Freestyle Libre II, 245, 246, 248, 250 Frequency domain methods, 30 Fullerenes, 536 Fumarates, 301 Functional decline, 419, 420 Functional drug, 630 Functional magnetic resonance imaging (fMRI), 99, 446, 447, 449 and EEG, 449 resting state fMRI, 447 task-based fMRI, 440, 441, 443–444 Fuzzy logics, 38 algorithms, 26 complex systems, 38 discrete-event model, 38 discrete-time model, 38 input-output variables, 38

1177 G ® Galafold , 395 Gap-junction remodeling, 54 Gastroenterology CE-MRI, 275 local drug administration, 270 molecular endoscopy, 277–279 oral drug administration, 267–268 parenteral route of administration, 268–269 PET imaging, 275 transmucosal route of administration, 269 Gastrointestinal pH, 717 Gastrointestinal tract (GIT) cannabinoid receptors, 265 functions, 264 opioid receptors, 266–267 oral mucosal immune tolerance, suppression and silencing, 279 oral tolerogens, 281 oral vaccines, 280–281 serotonergic receptors, 265 TDM, 270 Gaucher disease, 390–391 Gaussian function, 38 Gene editing, 639 Gene list derivation functional enrichment, 942 HDAC inhibitors, 945 power, 941 sample size, 941 size, 945 Gene-set enrichment analysis (GSEA), 943 Gene therapy, 66, 542 bioproduction, 381 biosafety, 380 concept and strategies, 362–364 drug pricing, 381 genome editing systems, 364–365 legislation, 381 seminal trials, 370 synthetic vectors, 366–367 type of diseases, treatment, 373–380 viral vectors, 367–370 Gene Therapy Discussion Group (GTDG), 1126 Genetic engineering, 529, 649 Genome editing systems, 364–365 Genomics, 89 Genomics of Drug Sensitivity Cancer (GDSC), 936 Geographic atrophy (GA), 194 Geriatric population, clinical studies drug administration and formulation, 422 ethical considerations, recruitment and support, 422–423 functional decline, multimorbidity and frailty, 420–421 geriatric assessment, 421 pharmacokinetics, pharmacodynamics and drug interactions, 421–422 underrepresentation, 418 Geriatric syndromes, 419

1178 Glaucomatous optic neuropathy (GON), 166, 188–192 Glinides, see Prandial glucose regulators Gliptins, see DPP-4 inhibitors Global Cooperation Groups (GCG), 1089 Global longitudinal strain, 40 Globotriaosylceramide, 394 Glomerular filtration rate (GFR), 597, 731, 732, 744 GLP-1 agonists, 252–253, 255, 286–287 Glucagon, 251–252, 288 Glucose sensors, 249–251 Glutathione, 1040 Glutathione S-transferases (GSTs), 1040 Glycomacropeptides, 388 Goldberger leads (aVR, aVL, aVF), 25 Gonadotrophin releasing hormone (GnRH), 931 Good clinical practice (GCP), 22, 621 Gorlin syndrome, see Basal cell nevus syndrome G-protein coupled receptors (GPCRs), 265, 266 G-proteins, 168, 172 Gradient elution method, 785 Graft versus host disease (GvHD), 642, 644 Gram-negative bacterial infections, 334–336 Gram-positive bacterial infections, 333–334 Grapefruit juice, 718 Growth hormone (GH), 288 Guttmann scaling, 579 H Hallucinogens, 149–150 Handheld smartphone-enabled systems, 34 HDCT, see Hepatic dysfunction phase I clinical trials (HDCT) Heart rate turbulence (HRT), 34, 36 Heart rate variability (HRV), 30–31, 34 Heat burn model, 109 Heated saline, 110 Heat stimulation heat burn model, 109 heat thermode, 108 lasers, 110 UVB erythema, 109 Heat thermode, 108 Hematopoietic stem cells (HSCs), 642 Hemodialysis, 39 Hemodynamics, 22, 45 Hemoglobin disorders, 374 Hemophilia A and B, 375 Hepatic bioavailability, 767 Hepatic clearance classification system (HepCCS), 963 Hepatic dysfunction phase I clinical trials (HDCT), 1009 Hepatic impairment, 769–770, 1009 Hepatic PBPK models, 985 Hepatocytes cultures, 966 Hepatorenal tyrosinemia, 388 Hepatotoxic potential, 720 Herbal medicines chemistry manufacturing and control (CMC), 491–492 clinical trials, 487–491

Index ethical aspects in conducting human studies, 485–486 globalization of, 484–485 pharmacokinetic and bio-analytical Challenges, 492–495 regulatory challenges, 485 Hereditary tyrosinemia type-1, 388–390 Heterogeneity, 912–914 Hidden hearing loss, 210 High-fiber meals, 717 High-frequency electrical stimulation (HFS), 112–113 High frequency (HF), 30 Highly variable drugs, 899 Highpass filter, 71 High performance liquid chromatography (HPLC), 965 High-phytate foods, 717 HIRMAb, 396 3 H-labelling chemical methods for, 817–818 synthetic and technical considerations for, 816 HMG CoA reductase inhibitors, 719 HMR456 modified release formulations hypothetical in vivo dissolution, 876–877 inclusion criteria, 872 objectives, 871 pharmacokinetics, 872–876 study design, 872 treatments, 872 Holoclones, 650 Home sleep apnea testing (HSAT), 234, 235 homologous recombination (HR) process, 363 Host resistance studies, 1117 HPV-negative head and neck cancer, 943 Human absorption, distribution, metabolism and elimination (hADME) study ABC123 in plasma, 784 accelerator mass spectrometry, 792–793 clinical study design, 779–783 dosimetry, 777–779 inductively coupled plasma mass spectrometry, 792 mass balance, 785–787 metabolite quantification and identification, 787–791 NMR, 791–792 purpose and rationale, 775–777 radioactivity determination, 783–784 radiolabelled test compound, 779 Human albumin, 765 Human chorionic gonadotropin (hCG), 726 Human evoked pain models, see Pain Human induced pluripotent stem cells, 65 Humanized tumor mouse models, 319 Human leukocyte antigen (HLA) system, 642 Huntington’s disease (HD), 92, 658 Hyperalgesia, 98, 102–104, 106–110, 113–116 Hypertonic sodium chloride, 105 Hypoglycemia alert, 250 Hypothalamus-pituitary-adrenal (HPA) axis, 212

Index I Ibuprofen, 525 ICH GCP Guidelines (ICH E6), 1139, 1140 ICH Guidance on Toxicokinetics, 950 Ilet pump, 250 Illicit drug abuse, 130, 137, 149 Imidazole, 744 Imiquimod, 310 Immunohistochemistry, 175 Immunological safety assessment, 320–322 Immunophenotyping, 1117 Immunotherapy, 312–313 Immunotoxicology, 322 Impedance cardiogram (ZCG), 43, 44 Implantable cardioverter defibrillator (ICD), 43, 56 Inborn errors of metabolism (IEM) description, 385 enzyme activity by chaperones, 394–395 enzyme replacement therapy, 395–397 pathways for toxic compound elimination, 392–394 pharmacological substrate reduction, 388–391 substrate reduction by diet, 387–388 supplementation of missing cofactor/vitamin, 391–392 Inclusion criteria, 780 Indocyanine clearance, 763 Induced pluripotent stem cells (iPSCs), 639 Inductively coupled plasma mass spectrometry, 792 Infants and children, clinical pharmacological studies innovative trial designs, 408 label changes, 404 medicines and chemical entities, 402 pediatric medicines research, 413 pediatric off-label medicines, 402 pharmacodynamics, 405, 406 pharmacokinetics, 405, 406 stakeholders approach (see Stakeholders approach) Infectious diseases, 326, 328, 380 pharmacodynamics of antibodies for, 333 simulation aided PK/PD for, 333–336 treatments, 474–476 Inflammation, 108 Ingenol, 945 Inherited channelopathies, 54–55 Inhibitory turn-over model, 917 Innovations, 52 Inotropy, 43 Input-output variables, 38 In silico drug repositioning, 930 compound-phenotype match, 935 connectivity of map, 936 guilt by association hypothesis, 935 mechanism of action, 935 multi-drug resistant pathogen, 930 multi-omics, 930 novel drug development, 930 single-omics, 930 Insomnia, 225, 226 chronic disorder, 224 secondary, 225

1179 Insomnia Severity Index (ISI), 230 Instantaneous spatial vector, 28 Insulin degludec, 248 Insulin pumps bihormonal, 251 continuous subcutaneous glucose monitoring, 248 with glucose sensors, 250–251 patch pumps, 249 with sensor control, 250 Integrated medical model (IMM), 527 Inter-individual variability, 420 Intermittent long-term monitors, 33 Internal consistency, 586, 588 International Classification Of Sleep Disorders (ICSD-3) version 3, 224, 225 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), 20, 1073, 1086–1090, 1135–1136 anticancer pharmaceuticals, nonclinical evaluation of, 1118–1120 antihypertensive drugs, clinical evaluation of, 1095 BCS-based biowaivers, 1127 bioanalytical method validation, 1127 biotechnology-derived products, preclinical safety evaluation of, 1110–1112 carcinogenicity studies (see Carcinogenicity studies) clinical study reports, structure and content of, 1091 control group in clinical trials, 1094 CTD, 1131–1135 data elements and standards for drug dictionaries, 1126 DNA reactive impurities, in pharmaceuticals, 1126–1127 dose-response data, drug registration, 1092 eCTD, 1127 ESTRI, 1126 ethnic factors, foreign clinical data, 1092 general considerations for clinical trials, 1093 genotoxicity guidelines, 1104–1105 good clinical practice, guideline for, 1092–1093 immunotoxicology studies, 1114–1118 MedDRA, 1126 multi-regional clinical trials, planning and design of, 1096–1097 non-antiarrhythmic drugs, proarrhythmic potential for, 1095 nonclinical pediatric safety, 1125 nonclinical safety studies, 1127–1131 objectives of, 1090 pediatric population, medicinal products in, 1094–1095 photosafety evaluation, 1120–1125 population exposure, clinical safety for drugs, 1091 preparatory activities, for ICH conferences, 1090 QT/QTc interval prolongation, clinical evaluation of, 1095–1096 reproductive studies, 1108–1110 risk of repolarization, non-clinical studies, 1113–1114 safety data collection, optimization of, 1097 special populations, support of, 1093

1180 International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) (cont.) statistical principles for clinical trials, 1093–1094 toxicokinetics/pharmacokinetics, 1105–1108 virus and gene therapy vector shedding and transmission, 1126 International IEC 60601–2-51, 77 International Society for Holter and Noninvasive Electrocardiography (ISHNE), 35 Interobserver variability, 22 Inter-rater reliability, 589 Interspecies scaling, oral bioavaliability, 674 Intra-atrial conduction, 25 Intramuscular electrical stimulation paradigm, 113 Intramuscular injection, NGF, 116 Intraocular pressure (IOP), 178, 182–187 Intra-rater reliability, 589 Intrasubject repeatability, 21 Intrauterine systems (IUS), 739 Intraventricular conduction, 25 Investigator’s brochure (IB), 1074–1075, 1080 In vitro diagnostics (IVDs), 614, 615, 617 In vitro fertilization (IVF), 726 In vivo gene therapy, 362 In vitro human cellular test systems, 322 In vitro/in vivo extrapolation critical assessment of, 986 hepatobiliary transport, 978–980 inhibition studies, 981–982 oral absorption, 977–978 PBPK models, 982 renal clearance, 980–981 tissue models, 983–986 Ipilimumab, 313 Ischemic cardiomyopathy (ICM), 58–59 Isobolograms critical assessment of method, 607 dose-response curves, 607 ED50/EC50, 606 evaluation, 606 linear additive, 606 modifications of method, 608 procedure, 606 purpose and rationale, 606 Isovolumetric contraction phase, 43 Isovolumetric contraction time (ICT), 44 Isovolumetric relaxation time (IVRT), 48 Isovolumetric systolic relaxation, 43 Item, 576, 578, 579, 581, 584, 586, 587 analysis and construction, 584 parameter, 580, 581 Item characteristic curve (ICC), 580 Item response function (IRF), 581 Item response theory (IRT), 580–582 J Jagged stochastic appearance, 36 J point, 26 Junctional epidermolysis bullosa (JEB)-non-Herlitz, 650

Index K Kaizen model, 1142 Keratinocytes, 650 Keratinocytic dysplasia, 309 Kidney disease, 656–657 Kidney model, 985 Koebner phenomenon, 300 Kolmogorov–Smirnov-like statistic, 938 Kuvan ®, 394

L L1000, 936 LADME scheme, 129 Laser stimulation (LS), 110 Leading-edge analysis, 943 Learn and confirm approach, 1056 Left bundle branch block, 29 Lentiviral vectors, 370 Leuprorelin, 931 Levonorgestrel (LNG), 739, 740 Library of Integrated Network-Based Cellular Signatures (LINCS), 936 Life expectancy, 422 Ligand-gated ion-channels, 173 Light mechanical stimulation, 102 Lipid vectors, 367 Likert scaling, 578–579 Linear mixed effects model framework, 869 Line-frequency filter, 78 Lineweaver-Burk visualization, 851 Lipophilicity, 728 Lipoproteins, 766 Liposomes, 536 Liquid chromatography-mass spectrometry (LC-MS), 966 Liquid scintillation spectrometry, 176, 965 Liraglutide, 253 Lithium, 719 Liver blood flow (LBF) method, 673 Liver diseases cirrhosis, 656 fibrosis, 656 metabolic diseases, 655 urea cycle defects, 655 Lixisenatide, 253, 254 Logistic growth model, 331, 1064 Logistic regression, 1053 Log linear model, 330 Long and short QT syndrome, 55 Loss of response (LOR), 271 Low-amplitude signal (LAS) duration, 30 Low-density lipoprotein (LDL)-cholesterol, 923 Lower limit of quantification (LLOQ), 700, 704 Low frequency (LF), 30, 31 Low molecular weight heparins (LMWHs), 736 Low-pass filter (LPF), 67–69, 71 Lumbar punctures (LPs), 88 Lusitropic properties, 48 Lyapunov exponent, 39

Index M Macrophages, 1117 Madin-Darby canine kidney (MDCK) cells, 965, 966 Magnetic nanoparticles, 537 Magnetic resonance imaging (MRI) confounds and limitations, 448–449 drug effect, assessment of, 446 functional mechanism of action, drug, 446–447 with gadolinium, 88 MRS, 444 multimodal neuroimaging, 449 perfusion imaging, 441 phMRI, 445 pre-clinical, 440 principle methods, 440, 441 resting state BOLD imaging, 444 structural and volumetric, 88 Magnetic resonance spectroscopy (MRS), 441, 444– 446, 449 Malignant melanoma clinical features, 311–312 diagnosis, 311 follow up, 314 pathophysiology, 311 treatment, 312–314 MammaPrint, 432, 433 Mandibular advancement devices (MAD), 229, 239 Marijuana, 145 Mastermind approach, 85 Maximally tolerated dose (MTD), 1102, 1103 Maximum concentration (Cmax), 696, 698, 700, 701, 704, 705, 707, 710, 712 Maximum recommended human dose, 954 Maximum tolerated dose (MTD), 347, 956 Measurement, 572–574, 576, 577, 580–584, 590 Mechanical dispersion (MD), 41 Mechanical dyssynchrony, 41 Mechanical muscle stimulation hypertonic saline injection, 105 ischemic tourniquet, 104 post-exercise muscle soreness, 104 Mechanical skin stimulation impactometers/pinch interdigital web/joint, 103 touch and pinprick, 103 Mechanical visceral stimulation, 105 Mechanism and Drug Miner (MD-Miner), 940 Mechanism-based inactivation (MBI), 853 Mechanistic compartmental models, 968 Mechanistic static models (MSM), 859, 860, 862 Medical Device Directive (MDD), 614, 616, 618, 619, 622 Medical Device Regulation (MDR), 614–616, 618, 619, 622 Medical devices biocompatibility, 618–619 classification and inherent risk, 616, 617 clinical evaluation, 619–620 clinical trials with, 620–622 definition, 614–615 essential requirements, 615–616 European laws, 614 in vitro diagnostics as, 615

1181 risk management, 616–618 technical file, 616 usability, 618 Medical Dictionary for Regulatory Activities, 1126 Medications on space flights, 521–522 MEK-inhibitors, 314 Melanocyte stimulating hormones (MSH), 290 Membrane transporters classification, 958 drug disposition and clearance classification systems, 962 intrinsic property of, 959 Membrane vesicles assays, 976 Menthol, 116 Mercury-based auscultatory, 23 Mesenchymal stem cells (MSCs), 643, 644 Metabolism, 728–731 Metabolism-dependent inhibition (MDI), 853 Metabolite intermediate complex (MIC), 853 Metabolites, 955 Metabolites in safety testing (MIST), 776 Metachromatic leukodystrophy (MLD), 375 Metastatic melanoma, 312 Metformin, 285 Methimazole, 291 Methotrexate, 301, 306 Methylmalonic aciduria, 392 Methylphenidate, 448 Methyltransferases, 1039 Micelles and solid lipid nanoparticles, 536 Michaelis-Menten kinetics, 849 Microbivores, 538 Microdialysis, 726, 728, 1050 Microdosing, 1154 Microplate scintillation counting (MSC) plates, 790 Microsampling, 951 Microtracer concept, 808–810 Midazolam test, 1030 Migalastat, 395 Miglustat, 391 Minicells, 541 Minimal cognitive impairment (MCI), 90 Minimal erythema dose (MED), 108, 109 Minimum active biological effect level (MABEL), 6 Minimum anticipated biological effect level (MABEL), 1111 Minimum inhibitory concentration (MIC), 741 Mitogen-activated protein kinase (MAPK), 313 Mixed-effect modeling, 1056 Mobile cardiac outpatient telemetry, see Real-time cardiac telemetry systems Model building process, 917 Model-informed drug development (MIDD), 354 Model-informed precision dosing (MIPD), 1008 Modification of diet in renal disease (MDRD) study, 751 Mohs micrographic surgery, 310 Molecular endoscopy, 277 Monetary incentive delay (MID), 446 Monoamine oxidase inhibitors (MAOIs), 719 Monoclonal antibodies (mAbs), 1150, 1153 Mononuclear phagocytel system (MPS), 677

1182 Morphine, 730 Moxibustion, 461 MRI, see Magnetic resonance imaging (MRI) Multidimensional measures, 98 Multidimensional scaling ( MDS), 579 Multidrug and toxin extrusion 1 (MATE1), 998 Multidrug and toxin extrusion 2 (MATE2), 998–999 Multidrug resistance-associated protein 2 (MRP2), 994 Multidrug resistance protein 1 (MDR1), 991 Multimodal testing, 119 Multimorbidity, 418, 422 Multi-omics, 932, 934 Multiple ascending dose (MAD) study, 687 blood sampling, 688 CYP3A4 induction, assessment of, 690 dosing, impact of, 689 double-blinded study, 687 drug in urine, quantification of, 689 evaluation, 687 fed/fasted state, dosing in, 687–688 genotyping data, 688 inclusion criteria, 685 metabolites, characterization of, 690 objectives, 685 pharmacokinetic data, 686 pharmacokinetic parameters, oral dosing, 688 preliminary pharmacokinetic analysis, 687 study design, 685 treatments and doses, 685–686 Multiple sclerosis (MS), 87, 91–92 Multiple system atrophy (MSA), 89, 91 Multipotent stem cells, 638 Multiregional clinical trials (MRCTs), 1096 μ-opioid receptors (MOR), 267 Muscle artifact reduction, 66 Mustard oil, 116 Myocardial infarction (MI), 652 Myocardial Performance Index (MPI/Tei Index), 44–45 Myopia, 204 mySentryTM, 249 N N-acetylglutamatesynthase deficiency, 392 N-acetyltransferases type I (NAT1), 1035 Naïve pooled approach, 1055 Nano-bombs, 537 Nano-bubbles, 538 Nano-electro-mechanical sensors, 537 Nano-pores, 537 Nano-robots, 538 Nano-shells, 536 Nano-technology advantages and disadvantages of nano-materials, 545 description, 534–535 diagnostic approach, 539 nano-materials used in medicine, 536 nanoparticle application in medicine, 539 nanoparticle interaction with biological molecules, 538

Index pharmacokinetical and pharmacodynamical characteristics of nanoparticles, 542–544 safety and ethical issues, 544–545 treatment, 540–542 Nanotubes, 536 Nano-wires, 536 Narrow therapeutic index (NTI) drugs, 899 Narrow therapeutic range (NTR), 421, 830 National Eye Institute (NEI), 164 Natural killer cells, 1117 N-carbamylglutamate, 392 Nerve growth factor (NGF), 116 Nervus hypoglossus stimulating device, 239 Netherton syndrome (NS), 650 Network pharmacology, 478 Neural network, 39 Neural stem cells (NSCs), 646 Neurodegenerative diseases, 376, 658–659 Neurology biomarkers in, 87–92 pharmacokinetics and pharmacodynamics in, 82–86 Neuromuscular diseases, 373–374 Neuroplasticity, 130 New chemical entity (NCE), 684–689 Newcomb Wilson method, 335 New molecular entity (NME), 857, 862 Next-generation sequencing (NGS), 429 NFFinder, 938 NICE, 22 Nicotine, 155 Niemann-Pick type C (NPC), 391 Nifekalant, 59 Night blood pressure (NBP), 23, 24 Nitisinone, 389 Nivolumab, 313 NOAEL, see No observed adverse effect level (NOAEL) Nociceptive spinal flexion reflex (NFR), 100 Noise, 26 Nonalcoholic fatty liver disease (NAFLD), 733 Nonalcoholic steatohepatitis (NASH), 730 Non-auditory systems, 210–212 Non-compartmental analysis, 1052 Non-dipper, 24, 25 Non-interventional studies (NIS), 621, 622 Nonischemic cardiomyopathies, 60 Nonketotic hyperglycinemia (NKH), 392 Non-linear methods, 30, 37 Nonlinear mixed effect modeling (NONMEM), 914–916, 1056 Non melanoma skin cancer, 307–311 ® NONMEM , 914 Non-radioactive AMS technique, 793 NON-STEMI, 26 No observed adverse effect level (NOAEL), 954, 956 NoSAS score, 231 Notch ringing artifact, 78 Notified bodies (NB), 615 Numeric rating scale (NRS), 97, 98, 101, 115 Nyquist frequency, 67

Index O Obesity, 732–734 antiarrhythmic drugs, 735–736 anticoagulants/antiplatelets, 736–738 antifungals, 744–745 antihypertensive drugs, 734–735 antimicrobials, 741–744 definition, 724 and drug disposition, 725–732 estrogens/progestins/contraceptives, 738–741 Objectivity, 21 Obstructive sleep apnea (OSA) CPAP/APAP devices, 229 prevalence of, 228 therapy of choice for, 229 Obstructive sleep apnea syndrome (OSAS), 225 Ocular diseases, 374 Ocular pharmacology AMD, 192–199 DME and DR, 199–200 glaucomatous optic neuropathy, neuroprotective therapeutics for, 188, 189, 191 ocular drug discovery and development, pharmacodynamic principles in, 175–180 ocular surface diseases, 200–204 POAG/OHT, 182–188 receptors, ion-channels transporters and pharmacodynamics, 168–175 Off label medicines, 403 OHT/POAG, see Primary open-angle glaucoma/ocular hypertension (POAG/OHT) Øie-Tozer method, 678 OmniPod patch pump, 249 Oncotype DX, 431–433 One-parameter logistic (1PL) model, 581, 586 Onset Q/onset R-wave, 42 Opioid-induced bowel dysfunction (OBD), 267 Opioid receptors, 266 Opioids, 131–135 Optic coherence tomography (OCT), 92 Oral tolerogens, 281 Organ distention, 105 Organic anion transporter 1 (OAT1), 997–998 Organic anion transporter 3 (OAT3), 997–998 Organic anion transporting polypeptide 1B1 (OATP1B1), 999–1001 Organic anion transporting polypeptide 1B3 (OATP1B3), 999 Organic cation transporter 1 (OCT1), 995–996 Organic cation transporter 2 (OCT2), 996–997 Organisation for Economic Co-operation and Development (OECD) guideline, 1123, 1156 Organ on a chip, 1156–1157 Orphan diseases, 386 Osborn wave, 26 Oscillometric analysis, 24 Oscillometric devices, 23 Osteogenesis imperfecta, 645

1183 Oversampling, 67 Over-the-counter (OTC) drugs, 418 Oxytocin, 290 P Package opening, 422 Pain chemical stimulation, 117 CPM, 101 definition, 96 EEG and EP, 99 electrical stimulation, 114 fMRI, 99 mechanical stimulation, 102–105 multidimensional measures, 98 multi-model assessment, 118 nociceptive spinal flexion reflex, 101 thermal stimulation, 111 unidimensional measures, 98 Pain detection threshold (PDT), 97, 102, 103, 109 Pain tolerance threshold (PTT), 97, 102, 103, 106 Parasomnias, 225, 227 Parathyroid hormone (PTH), 291–292 Parkinson’s disease (PD), 87, 91, 421, 658 Passband, 67 Patient-oriented eczema measure (POEM), 306 Patient-reported outcome (PRO) measures, 574 Pediatric and young adult patient, pharmacokinetics in, 1010 Pediatric Epilepsy Academic Consortium on Extrapolation (PEACE), 86 Pediatric medicine development, 407 Pediatric medicines research, 413 Pediatric Research Equity Act (PREA), 86, 403, 404 Pegfilgrastim, 351 PEGylated immuno-liposomes (PILS), 367 Pembrolizumab, 354 Peptide vectors, 366 PEP/VET-ratio, 44 Perennial allergic conjunctivitis (PAC), 200 Perfusion imaging, 441–444, 450 Periodic leg movement syndrome (PLMS), 230 Peripheral sensitization, 113, 114 Personalized medicine for astronauts, 529 biomarkers, 435 clinical study design, 435–436 cystic fibrosis, 433 description, 426 Duchenne muscular dystrophy, 434–435 forms, 426 as individualized medicine, 426–427 as precision medicine, 427–429 prognostic and predictive testing in oncology, 431–433 targeted therapies and biomarkers in oncology, 430 Person parameter, 580, 581 Perturbagen, 936 PET response criteria in solid tumors (PERCIST), 274

1184 Pharmaceutical formulations, 422 Pharmaceutical Research and Manufacturers of America (PhRMA), 856, 857 Pharmaceuticals and Medical Devices Agency (PMDA), 1074 Pharmacodynamics (PD), 20, 129, 327, 418, 908–909, 1048 animal models, 1058 antibodies for infectious disease, 333 bacterial submodel, 331 combined treatment modalities, 351–353 concepts of, 1050–1052 continuous response variables, 1053 description, 128 early oncology trials, 347–349 Emax model, 331 evaluation, 1055 histone deacetylase inhibitor vorinostat, 350 illicit drugs, 131 indices for therapy, 336–338 interactions, 604, 716 interventional clinical oncology trials, 345–347 in vitro models, 329 in vivo models, 329–330 linear models, 330 logistic growth model, 331 log linear model, 330 minimum inhibitory concentration-based approach, 1057–1058 models of combination therapy, 1064–1065 model types, 1053 Monte Carlo simulation, 332, 1059 nonclinical development, 345 noncontinuous response variables, 1053–1055 obesity (see Obesity) optimization of dose regimen, 328 pain methodologies, pharmacodynamic evaluation (see Pain) pegfilgrastim, 351 probability of target attainment, 1059–1060 semi-mechanistic models, 1061–1064 sigmoidal Emax model, 331 special populations, 1065–1067 of substance, 128 time course-based approaches, 1058 Pharmacogenetics, 20, 558–560 Pharmacogenetic variability, 558 Pharmacogenomics, 350, 716 Pharmacokinetically-guided approach, 676 Pharmacokinetics/pharmacodynamics (PK/PD), 331 clinical development process, 353 CNS (see Central nervous system (CNS)) delayed effects models, 910–912 drug development program, 353 extrapolation, 85 goals, 904 immediate effects models, 910 LDL-cholesterol, 922 mastermind approach, 85

Index neurological disorders, drug effects in, 84 non-clinical model development, 353 pembrolizumab, 354 remimazolam, 86 translational approaches, 83–84 vigabatrin, 86 Pharmacokinetics (PK), 20, 327, 550, 858, 907, 1048 adolescents and adults, 1011–1013 animal models, 1058 biomarkers, 1008 biotherapeutic agents, 1008 concepts of, 1049–1050 description, 594 dose in humans, 595–596 drug absorption, 550 drug distribution, 550 drug metabolism, 550–552 evaluation, 1055 1999 FDA Guidance, 1006 hepatic impairment, 1009 herbal medicines, 492 interactions, 604, 716 interventional clinical oncology trials, 1007 minimum inhibitory concentration-based approach, 1057–1058 model-informed precision dosing, 1008 model types, 1052 Monte Carlo simulation, 332, 1059 multi-scale mechanistic models, 1013 obesity (see Obesity) optimization of dose regimen, 328 parameters, definitions and equations, 594 pediatric and young adult patient population, 1010–1011 pharmacodynamics components, 1007 physiologically based pharmacokinetic, 1008 probability of target attainment, 1059–1060 renal elimination pathways mechanisms, 552 renal impairment, 1010 semi-mechanistic models, 1061–1064 special populations, 1065–1067 systemic exposure levels, 1009 therapeutic drug monitoring, 1008 Pharmacological effect, 630 Pharmacological interventions, tinnitus, see Tinnitus Pharmacological MRI (phMRI), 441, 445, 450 Pharmacometrics, 353, 906, 1066 definition, 905 learn and confirm approach, 1056 modeling in, 1049 population approach, 1056 traditional approach, 1055 Pharmacopoeias, 318 Pharmacosensitivity, 21 Pharmacospecificity, 21 Pharmacovigilance, in clinical development actions and measures, 1080 adverse drug reaction, 1074 AE (see Adverse event (AE))

Index AESIs, 1076 DSMB, 1076 DSUR, 1080 Endpoint Adjudication Committee, 1077 EU, 1073–1074 IB, 1075 ICH, 1073 informed consent, 1075 Japan, 1074 outcome events/unblinding of data, 1075 patient’s safety, continuous monitoring of, 1079 SAE (see Serious adverse event (SAE)) standardized data collection, 1076 SUSAR, 1074 unexpected adverse drug reaction, 1074 USA, 1073 Phase II drug metabolism enzymes, 1035 Phenylketonuria, 394 Phonocardiogram (PCG), 43 Phorbol-12-myristate-13-acetate, 945 Photoallergy, 1121, 1123 Photochemical properties, 1122 Photodynamic therapy, 310 Photoreceptors (PR), 657 Photorefractive keratectomy (PRK), 203 Photostability testing, 1122 Physiologically-based pharmacokinetic (PBPK) modeling, 83, 85, 421, 678, 833, 841, 859, 861, 862, 864, 982–986, 1008, 1052 Pinprick, 102 Pittsburgh Sleep Quality Index (PSQI), 230 PK/PD modeling, see Pharmacokinetics/ pharmacodynamics (PK/PD) Placebo responses, 87 Plasma albumin concentrations, 769 Plasma protein binding, 82, 750, 1106 Plasma protein displacement, 863 Plausibility, 39 Pluripotent stem cells (PSCs), 638–640 Pmetrics package, 1057 Poincare plot, 37 Polycystic kidney disease (PKD), 656 Polygraphy, 234, 235 Polymorphic ventricular tachycardia, 53 Polypharmacy, 418, 422, 716 Polysomnography (PSG), 231, 233–235, 238 Population analysis, 916, 917 Population approach, 913, 1056 Population model, 907, 912 Population pharmacokinetic (PopPK), 760, 844, 904 Positron emission tomography (PET), 84, 88–90, 273–276, 1154 Post inflammatory hyperpigmentation (PIH), 109 Power spectral density (PSD), 31 PQ-interval, 25, 27 Prandial glucose regulators, 287 Preclinical trial, 548 Prediction-corrected visual predictive checks (pcVPC), 921, 923

1185 Predictive validity, 587 Preejection period (PEP), 44 Preferences and values, 423 Pregnane X receptor (PXR), 854 Primary cutaneous melanoma, 312 Primary human RPE (ph-RPE) cells, 197 Primary open-angle glaucoma/ocular hypertension (POAG/OHT), 182–183 animal models, 187–188 AQH/fluid extrusion, in ex-vivo systems, 187 receptor binding and functional assays, IOP-lowering agents, 183–187 Primary pharmacological effect, 318–320 Principal investigator, 21 Procainamide, 735 Prolactin (PRL), 289 Prophylactics, 538 Propofol, 728 Proportional hazards regression, 1054 Propranolol, 734 Propylthiouracil (PTU), 291 Prostaglandin FP-receptor agonist analogs, 183 Prostratin, 945 Protein binding, 954 Protein concentration hepatic impairment, 769–770 in oncology patients, 771 in pediatrics and elderly, 768–769 in pregnant women, 769 renal impairment, 770–771 Protocol, 21, 22, 24, 35 Psoriasis clinical features, 300–301 diagnostics, 300–301 feature treatments, 302 pathophysiology, 300 systemic medication, 301–302 therapeutic management, 302 topical corticosteroids and vitamin D analogs, 301 ultraviolet phototherapy, 301 Psychophysical methods, 98 Psychophysical scaling, 577 Pulsed ASL (pASL), 442 PVC tachogram sequence, 36 P wave, 25 Q QbD, see Quality by design (QbD) Qgut model, 978 Qigong, 462 QRM, see Quality risk management (QRM) QRS complex, 25, 29, 30, 34 QRSd, 30 QRS offset, 30 QRS onset, 30 QT/QTc-effect, 28 QT-dispersion, 34 QT-interval, 25, 27, 44

1186 QT prolongation, 28, 34 QT-related arrhythmogenic risk, 34 QT–TQ interval relationship, 34 Quality by design (QbD) in domains and industries, 1142 methodology, 1140 and QRM, 1141–1142 Quality management system (QMS) elements of, 1140 misconceptions, 1143 standardization, 1142, 1143 Quality risk management (QRM) methodology, 1140 QbD and, 1141–1142 Quantitative polymerase chain reaction (qPCR), 965 Quantitative systems pharmacology, 354, 604 Quantum dots, 536 R Radiation therapy, 310, 314 Radiocarbon dating technique, 792 Radio-chromatographic method, 790 Radiolabelled API to humans, 818–822 Radioligand binding assays, 178 Ranolazine, 63, 64 Rapid acetylators, 716 Rare diseases, 25 Rasch models, 586 Real-time cardiac telemetry systems, 33 Receptor-tyrosine kinase (RTK)-coupling, 175 Recessive dystrophic epidermolysis bullosa (RDEB), 650 Recommended phase 2 dose (RP2D), 347 Recruitment rates, 90 Re-entry, 53–54 Reference Safety Information (RSI), 1075, 1080 Refractive disorders/errors, 204 Regenerative medicine, 638 Regional cerebral blood flow (rCBF), 441 Regional lymph nodes, 310, 312 Regional mechanical dysfunction, 42 Regression Cox proportional hazards (PH), 943 univariate, 942 Regulatory safety assessments alternative approaches, 1154–1157 animal models, 1149–1153 bias in experimental data and good research practices, 1157–1162 biosimilar medicinal products, guidelines on, 1153 exploratory clinical trials, 1154 history, methods, regulatory and industrial environment, 1146–1149 preclinical safety evaluation paradigm, 1148 validation of alternative methods, 1157 Reliability, 587 inter-rater, 589 intra-rater, 589 and sensitivity, 21

Index Renal impairment, 1010 critical analysis, 752–753 dose adjustment, 756–757 evaluation, 752 glomerular filtration rate, 751 pharmacokinetic study, 753 population PK analysis, 756 protocol, 751–752 Renin-angiotensin-aldosterone system (RAAS), 293–294 Renin-angiotensin system, 555 Renormalized entropy (ReEn), 40 Repolarization phase, 25 Research Domain Criteria (RDoC), 573 Residual unexplained variability (RUV), 912 Respiratory sinus arrhythmia, 38 Respirocytes, 538 Response Evaluation Criteria in Solid Tumors (RECIST), 274 Resting state BOLD imaging, 444 Restless legs (RLS) syndrome, 230 Retinal degradation, 657–658 Retinal ganglion cells (RGCs), 166, 167, 182, 189, 190, 192, 198, 200 Retinal nerve fiber layer (RNFL), 182 Retinal pigment epithelium (RPE) cells, 192, 193, 657 Retinitis pigmentosa, 657 Retroviral vectors, 369–370 Reverse dippers, 24 Reversible inhibition, 833, 857, 859 atypical/two-site inhibition, 853 competitive inhibitors, 850–851 mixed inhibition, 850–853 noncompetitive inhibition, 852 uncompetitive inhibition, 852 Rhythm of genes, 500, 514 Risk stratification, 34, 40, 48 Rivaroxaban, 737 Root mean square (RMS) voltage, 30 Rule of exponents (ROE), 673 Ryanodine receptor type-2 (RyR2) channels, 53 S Safety assessment, drugs activated partial thromboplastin time, 14 adverse events (see Adverse event (AE)) alanine aminotransferase, 12 albumin in urine, 13 aspartate aminotransferase, 12 bilirubin, 12 blood pressure, 10 case study, 6 creatinine, 13 creatinphosphokinase, 13–14 ECG parameter, 10–11 glucose, 11 heart rate, 9 hemoglobin, 14

Index kidney injury molecule-1, 15 laboratory parameter, 11–14 management, 5–6 phosphatase, 12 platelets, 14 polymorphonuclear leucocytes, 14 potassium, 11 timing of monitoring, 9 visual analogue scale, 15–16 Safety ratio, 950, 954 Sapropterin dihydrochloride, 394 Sarcopenia, 420, 422 SAR001, formulation development inclusion criteria, 869 objectives, 869 pharmacokinetics, 869–870 relative bioavailability estimates and confidence intervals, 871 study design, 869 treatments, 869 Satellite animals, 951 Scale, 572 definition, 572 implementation of, 585 item, 576 observer-rated scales, 573, 575 patient-rated scales, 575 properties, 585 selection of, 583 unidimensional and multidimensional, 577 visual analog scale, 577 Scalogram analysis, 579 Schellong test, 10 Schild plots critical assessment of method, 608 evaluation, 608 modifications of method, 609 procedure, 608 purpose and rationale, 608 Schlemm’s canal (SC), 166, 182, 187 SCOPER system, 235 SCORing Atopic Dermatitis (SCORAD), 306 Screening tools, 34 Seasonal allergic conjunctivitis (SAC), 200 Sebelipase alfa, 395 Secondary hyperalgesia, 104, 109, 113, 115 Secondary pharmaceutical effect, 320 Sedative, 422 Semaglutide, 254 Semantic differential, 579 Senseonics, 250 Sensor-augmented pumps (SAP), 246 Serendipity, 932 Serious adverse drug reactions (SADR), 1075 Serious adverse event (SAE), 5, 1075, 1077–1079 Serious unexpected suspected adverse reactions (SUSAR), 1074, 1078 Serotonergic receptors, 265 Serum transfer models, 318

1187 Sex-gender differences adherence to therapy, 556–558 adverse effects, 554–555 drug interactions, 555–556 European Medicines Agency, 562–563 Food and Drug Administration, 560–562 ICH, 563 pharmacodynamics, 552 pharmacogenetics, 558–560 pharmacokinetics, 550–551 Sexual dimorphism biliary excretion, 552 pharmacodynamic responses, 554 phenotypes, 560 SGLT-2 inhibitors, 287 Sigma-delta modulator, 71, 73 Sigmoidal Emax model, 331 Signal-averaged ECG (SAECG), 29–30, 72 Simple factor structure, 586 Simple insulin pumps, 248–249 Simvastatin, 718 Sinc filter, 73 Single ascending dose (SAD) study, 598, 684, 685, 688, 690 dose finding, allometric scaling (see Allometric scaling) purpose, 672 Single dose study design, 699–703 dose effect, 701 dose proportionality, 700–701 Single equivalent dipole, 28 Single-omics, 933, 934 Sinus node disease, 57–58 Sinus rhythm, 25, 35, 36 SIPOC approach, 1141 6-SIGMA, 1142 Skin defects, 650 Holoclar, 651 holoclones, 651 keratinocyte, 650 limbal stem cells, 651 Skin diseases, 376 Skin freezing, 107 Sleep apnea, 227 fragmented sleep, 228 hypertension, 229 obstructive, 229 parameters and statistical evaluation, 235–237 pharmacological therapy, 240 polysomnography, 231, 233–234 positional therapy, 240 questionnaires, 230–232 and snoring, 228 therapy, 229 treatment, 229–230 ventilator devices, 229 Sleep disorders classification, 224–225 prevalence, 224

1188 Sleep related breathing disorders, 225, 226 obstructive sleep apnea, 228 treatment, 229–230 Sleep related movement disorders, 225, 227 Slow acetylators, 717 Sodium/calcium-exchanger, 53 Sodium/potassium-ATPase, 53 Solute carrier (SLC) transporters, 959, 970 MATE proteins, SLC47 family, 998–999 OATPs, SLCO family, 999–1001 OATs, SLC22 family, 997–998 OCTs, SLC22 family, 995–997 Somatosensoric tinnitus, 213 Somatostatin, 289–290 Sound of tinnitus, 212 Space adaptation syndrome (SAS), 524 Spaceflight-associated neuro-ocular syndrome (SANS), 526 Spaceflights effect on pharmacokinetics, 522–523 food system, 520 medications on, 521 Space motion sickness (SMS), 524 Space pharmacology medications for space mission formulary, 527–529 medications on space flights, 521 mission-related needs for medications, 523–526 Space shuttle missions, 521 Special populations, 418 Special situation reports (SSRs), 1075 Speckle-tracking echocardiography (STE), 40 SPECT/DaTScan, 89 Spectral entropy, 39 Spinal cord injury (SCI), 659–660 Spiral wave concept, 54 Squamous cell carcinoma (SCC) clinical features, 308–309 diagnostics, 309 pathophysiology, 307 therapeutic management, 311 treatment, 309–310 Stakeholders approach child related issues, 411 parent related issues, 412 parents and children, consent and assent, 410 pediatric drug therapy, 410 pediatric oncology, 413 recruitment challenges, 411 research capacity, 412 Standard error (SE), 582 Standard 12 lead ECG, 25 Standard toxicity studies (STS), 1115 Statistical assessment, 697–699 Statistically significant connections’ map (sscMAP), 937 Stem cell therapy cardiac diseases, 653 central nervous system, 657–660 diabetes, 653–655 hematopoietic system, 647–650

Index kidneys, 656–657 liver, 655–656 skin, 650–652 STEMI, 26 Stopband, 67 STOP-BANG questionnaire, 231 Stopping rules, in clinical protocol, 602 Strain imaging, 40–41 Structure-activity relationship (SAR), 178, 1126 Substrate inhibition kinetics, 850 Sudden cardiac arrest, 34 Sudden cardiac death, 41 Sulfatation conjugations, 1040 Sulfonylureas, 285–286 Sumatriptan, 726 Surgical excision, 309 Surgical metastasectomy, 314 Surrogate, 906 Survival analysis, 1054 Symbolic dynamics, 36 Sympathetic hyperinnervation, 59 Sympathetic nervous system (SNS), 734 Synthetic cathinones, 155–158 Synthetic vectors cationic polymer vectors, 366 lipid vectors, 367 peptide vectors, 366 Systematic review (SR), 1161–1162 Systemic corticosteroids, 306 Systems biology, 478 Systolic blood pressure, 22, 23, 39 Systolic time intervals (STI), 43–44 T Tachogram, 36 Taichi, 462 TALENs, 649 Target therapy, 313–314 Task-based fMRI, 440, 441, 443 Task Force SAECG report, 29 T-cell dependent antibody response (TDAR), 1115, 1117 Telomere length, 420 Temporal summation, 112 Test outputs, 31 Test–retest approach, 588 Test theory, 579–582 Test validity, 586 Thalidomide, 932 The Cancer Genome Atlas (TCGA), 941 Theranostics, 539 Therapeutic adherence, 556 Therapeutic drug monitoring (TDM), 270–272, 906 Therapeutic proteins (TPs), 828–845 Thermal grill, 107 Thermal muscle stimulation, 111 Thermal skin stimulation cold stimulation, 108 heat stimulation, 110

Index Thermal visceral stimulation, 111 Thiazolidinediones (TZDs), 286 Three-compartment tissue model, 984 Three-parameter logistic (3PL) model, 581 Threshold of Toxicological Concern (TTC), 1126 Thurstone scaling, 578 Time-dependent inhibition (TDI), 833, 835, 836, 853–854 Time domain analysis, 30 Time domain BRS methods, 40 Time-kill experiments, 1058 Time-series analysis, 30 Tinnitus auditory pathway, 213 characteristics, 210 clinical trials, countries in, 218 factors, 213 general design scheme, pharmacological trial, 218, 219 multidisciplinary aspects of, 212–213 on non-auditory systems, 210 primary outcome measures, 216, 217 sample selection, 215–216 study design, pharmacological trial, 218, 219 target selection, 215 tinnitus-related distress/comorbid disorders, 213 Tinnitus Functional Index, 218 Tinnitus-induced distress, 212 Tissue distribution studies, 777, 778 Tissue Doppler echocardiography (TDE), 41 Tissue models, 1156 Tofacitinib, 307 Tolerogenic nanoparticles, 541 Topical corticosteroids, 305 Topical treatment, 310 Topiramate, 939 Tourniquet model, 104 Toxicokinetics, 950, 951 analytical methods, 951 biotechnology-derived pharmaceuticals, 955 changes of exposure, 952–953 dose dependency, 952 duration of treatment, 954–955 evaluation, 951 ICH Guidance on Toxicokinetics, 950 metabolites, 955 protein binding, 954 safety factors, 956 satellite animals, 951 sex differences, 953–954 steady-state conditions, 954 systemic exposure, 955 Toxodynamics, see Pharmacodynamics (PD) T-peak to T-end, 34 TPMT, 1039 Trabecular meshwork (TM), 166, 182, 183, 187, 188 Traditional Chinese Medicine (TCM) Asian Anticancer Materia Database, 479 cancer research with, 476–477 Chem-TCM, 479 Chinese National Compound Library, 479

1189 Chinese Natural Products Database, 479 Comprehensive Herbal Medicine Information System for Cancer, 479 diagnostic methods, 458–461 Five Element Theory, 458, 460 herbs in clinical pharmacology, 467–469 historical TCM textbooks, 466–467 history of, 456 MAS3.0, 479–480 nobel prize honors, 480 perspective, 480 PharmMapper Server, 480 principles and practices, 457–458 pulse diagnosis, 460 tongue diagnosis, 460 treatments, 461–462 twelve meridian time flow chart, 466 Transcobalamin, 392 Transcription activator-like effector nucleases (TALENs), 365 Transhumanism, 535 Transient receptor potential (TRP), 173 Transient receptor potential vanilloid-1 (TRPV1) channel, 174 Transventricular conduction, 25 Trastuzumab, 429 Treatment of acute cathinone intoxication, 157 of acute intoxication, 138, 140 of BZDs addiction, 148 cocaine intoxication, 138 of cocaine withdrawal, 139 dissociative anesthetics, 152 of morphine addiction, 137 Tretinoin, 939 Triazole, 744 Tui Na massage, 461 Tumor node metastasis (TNM) staging, 311 Tumor xenograft models, 319 Turbulence onset, 34–36 Turbulence slope, 35 Turnover models, 912 T wave, 25 T-wave alternans, 54 Two-stage approach, 1055 Type II diabetes, 931 Tyramine, 719 Tyrosine kinase inhibitors (TKI), 350 Tyrphostin-AG-835, 945 U Ulipristal acetate (UPA), 740 Unbound brain/plasma ratio, 83 Unbound plasma concentration, 82 Underrepresentation of older patients, 418 Unexpected adverse drug reaction, 1074 Unfractionated heparin (UFH), 736 Unidimensional questionnaires, 97, 98

1190 Unified Code for Units of Measure (UCUM), 572 Unipolar precordial Wilson leads (V1–V6), 25 Upper-frequency cut-off, 68 Urea cycle defects (UCD), 392 Uridine diphosphate glucuronosyltransferases (UGTs), 1038 US Association for the Advancement of Medical Instrumentation, 24 US Drug, Food and Cosmetics Act, 1147 US Food and Drug Administration (FDA), 647 UVB model, 109 Uveitis, 203 Uvulopalatopharyngoplasty (UPPP), 240 V Vagal AF, 63 Validity, 21 Valvular heart diseases, 29 Vancomycin, 744 Vascular endothelial growth factor (VEGF), 175, 194 Vaughan-Williams classification system, 735 Vectorcardiogram (VCG), 28 Vector system synthetic vectors, 366–367 viral vectors, 367–370 Ventricular activation time (VAT), 30 Ventricular depolarization, 25 Ventricular hypertrophy, 29 Verapamil, 735 Verbal rating scale (VRS), 97, 98 Vernakalant, 64 Very low frequency (VLF), 31 Viral vectors adeno-associated vectors, 368–369 adenoviral vectors, 368 lentiviral vectors, 370 retroviral vectors, 369–370 Vismodegib, 310 Visual analogue scale (VAS), 15, 97, 101, 104, 112, 577, 578 Visual predictive checks (VPC), 921, 923

Index Vitamin B12 deficiency, 392 Vitamin D analog, 301 Vitamin K, 604 Vitreous humor (VH), 166 Volume of distribution (Vd), 727 Von Frey filaments, 102, 103, 107, 109 W Wajima method, 674, 678 Warfarin, 719, 737 Wavelet transform decomposition, 26 Weissler-Index, 44 Well-stirred renal model, 980 Wet AMD (wAMD), 192, 194 WinNonlin, 1056 Withdrawal symptoms, 129, 131 of alcohol, 152 amphetamine intake, 140 cocaine, 138, 139 dissociative anesthetics, 152 opioid dependence, 134 Withdrawal syndrome, 148 Wolff–Parkinson–White (WPW) syndrome, 26, 61 World Health Organization (WHO), 164 X Xanthine, 782 X-linked severe immunodeficiency syndrome, 365 XYZ leads, 29 Y Yin Yang clock, 465 Yin Yang correspondences, 457 Z Zaleplon, 523 ® Zavesca , 391 Zolpidem, 523