Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery (Methods in Pharmacology and Toxicology) 1071612492, 9781071612491

Citation preview

Methods in Pharmacology and Toxicology

Gus R. Rosania Greg M. Thurber Editors

Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery

METHODS

IN

PHARMACOLOGY

TOXICOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7653

AND

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery Edited by

Gus R. Rosania College of Pharmacy, University of Michigan, Ann Arbor, MI, USA

Greg M. Thurber Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

Editors Gus R. Rosania College of Pharmacy University of Michigan Ann Arbor, MI, USA

Greg M. Thurber Department of Chemical Engineering University of Michigan Ann Arbor, MI, USA

ISSN 1557-2153 ISSN 1940-6053 (electronic) Methods in Pharmacology and Toxicology ISBN 978-1-0716-1249-1 ISBN 978-1-0716-1250-7 (eBook) https://doi.org/10.1007/978-1-0716-1250-7 © Springer Science+Business Media, LLC, part of Springer Nature 2021 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. Cover Illustration Caption: In this image, the red is a fluorescent biologic (cetuximab) and the green is a fluorescent small molecule (PARP inhibitor) in a breast cancer cell, highlighting the two main classes of drugs discussed in the book. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface With independent research groups aimed at understanding the transport properties of macromolecules (Greg) and small molecules (Gus) in terms of their fundamental interactions with cells and subcellular compartments, co-editing this book has been a tremendously valuable opportunity for both of us to coalesce our thoughts around the most important concepts and methods that we perceive will drive progress in this field. At the outset, we hesitated at the prospect of embarking on this task, as there are many other books that cover what seemed to be similar methods. Nevertheless, as we brainstormed about this subject, we realized that there were no comprehensive treatises that approached cellular pharmacokinetics from the different perspectives of pharmaceutical scientists, chemical biologists, and medicinal chemists dealing with very different chemical agents spanning a wide range of sizes, physicochemical properties, and targeting mechanisms. These topics are often handled by separate fields rather than as a set of interrelated concepts. Furthermore, there were often too many unwarranted assumptions, unmentioned discrepancies, and anomalous measurements affecting interpretation of cellular pharmacokinetics phenomena that were being left out of previous attempts to study molecular transport pathways responsible for drug targeting and disposition. As our thoughts began to focus on addressing the limitations and inconsistencies of current measurement methodologies, we were able to convince eighteen other research groups with expertise in cellular pharmacokinetics that contributing to this volume would be a worthwhile undertaking. Whenever possible, the cellular pharmacokinetic rates are expressed in terms of molecules per cell per second, providing a consistent set of terms to compare often disparate therapeutic agents—from small organic molecules to large proteins and nanoparticles. Thus, we are now able to bring to light this very thorough treatise that we hope will serve to both spur and guide the future development of this fascinating field of scientific research. In the first half of the book, the chapters are focused on small organic molecules with drug-like characteristics and highlight the methods used for studying the cellular transport, targeting, and disposition mechanisms. In general, these are chemical agents which are able to enter cells by crossing the plasma membrane, which make them distinctively different from larger macromolecules and nanoparticles, which do not readily cross membranes and are the subject of the second half of the book. Once inside the cell, small molecules can bind to macromolecular components such as proteins, lipids, or nucleic acids that are present in the cytoplasm or the nucleus of the cell. They can also accumulate inside organelles like mitochondria or lysosomes, where they become trapped through specific interactions with resident proteins or through physicochemical mechanisms driven by electrical potentials and pH gradients across organelle membranes. Furthermore, in certain cases these small molecule chemical agents can be substrates of enzymatic mechanisms that lead to their degradation, or to active transport mechanisms that are responsible for driving their efflux in a particular direction across the cell, or for promoting their sequestration inside specific organelles. In terms of the underlying conditions under which the transport and distribution of small molecule chemical agents have been measured within cells, the chapters in the first half of the book provide comparative methods for measuring transport across cells both in the presence of a transcellular concentration gradient and in the presence of a homogenous

v

vi

Preface

extracellular concentration. In particular, the methods take into consideration the effect of intracellular binding on the apparent permeability of the molecules, including the importance of accounting for the intracellular mass of drug accumulated by cells. This intracellular accumulated mass is likely to play an increasingly important role in terms of our ability to understand and quantitatively analyze intracellular phenomena impacting important pharmaceutically relevant problems. These include the prediction of drug-drug interactions mediated through interference with active transport or enzymatic degradation, as well as the destabilization of cellular membranes and physiological homeostasis. Furthermore, intracellular drug sequestration can lead to soluble-to-insoluble phase transitions, and such phase transitions can account for concentration-dependent cellular pharmacokinetic phenomena that are not necessarily mediated by saturable interactions with intracellular components but are rather due to the formation of insoluble localized, intracellular complexes or precipitates. Introducing the general concept behind the book, the first chapter by Min’s group discusses a variety of different transcellular transport assay systems that have been used to monitor the unidirectional movement of chemical agents across cell monolayers. Such assay systems are broadly used in academic and industrial research laboratories for the purpose of establishing the ability of compounds to cross cell barriers, which is important for establishing the absorption of drugs formulated for administration through various different routes (e.g., oral, inhaled, ocular, etc.). Compounds capable of readily crossing these barriers will generally be able to penetrate other body tissues to reach the target of interest. While covering the basic methodology that is routinely used, the chapter also looks at the intracellular accumulation of chemical agents as they are transported inside cells. In certain cases and under particular conditions, the extent of intracellular accumulation can be quite significant, leading to net effects on the apparent transcellular permeability measurements that are not necessarily being taken into consideration by the routine methods established in the pharmaceutical industry. Following this introductory chapter, Bentz discusses an alternative strategy for analyzing transcellular transport in the presence of a transcellular concentration gradient, which takes into account the mass balance issues introduced in the previous chapter. Although it is not widely used, this alternative methodology could be applied to the same types of experimental setup discussed in the previous chapter. Comparing the results obtained using Bentz’s analysis with those of Min’s would indicate intracellular sequestration phenomena that affect the net movement of molecules from one side of the cell to the other. Furthermore, such interactions could also impact the measurement and interpretation of drug-transporter and drug-enzyme interactions, which is pharmaceutically relevant to our understanding of drug-drug interactions as may occur when cells are exposed to combinations of drugs that are metabolized or transported through the same pathway. Following these considerations, Hidalgo and colleagues delve more deeply into the actual measurement of drug-drug interactions by exposing cells that are transfected with specific transporters to drug combinations that are substrates for those transporters. These studies allow the assessment of how a specific molecular transport pathway can be impacted by the presence of a competitive interaction between two different molecule substrates that are acted upon by the same transporter. By analyzing the measurements in terms of the impact of the competitive inhibition on the rate of transport given as a number of molecules per cell, the effect of the specific transport pathway being analyzed over the baseline nonspecific transport rates across the cell can be readily visualized.

Preface

vii

The two subsequent chapters by the Di and Brouwer research groups present methods for measuring unbound intracellular drug concentrations, which are key to the analysis of the kinetics of small molecule transport. It is generally accepted that it is the unbound intracellular fraction, rather than the total intracellular drug (that includes protein and membrane-bound molecules), that interacts with the drug target. In the Di chapter, the methodology is focused on detailed in vitro analysis protocols. Complementing the in vitro measurement methodology, the Brouwer chapter presents protocols focused on ex vivo determination of unbound intracellular concentrations in the isolated perfused liver. The liver is the primary organ involved in the metabolism of most small molecule pharmaceutical agents (and many biologics) currently on the market. Because of its role in the first pass metabolism of drugs absorbed via the gastrointestinal route, the liver is of major importance for determining the systemic bioavailability of oral drugs. Such measurements are key for predicting human bioavailability and assessing the propensity that significant drug-drug interactions may occur. Increased liver uptake, leading to increased liver drug concentrations, could potentially cause drug-induced liver injury upon exposure to drug combinations. Elaborating on the central role played by unbound drug concentrations in the liver to better understand drug transport, the two subsequent chapters by Rzeczycki and Murashov focus on direct measurement of the formation of insoluble drug precipitates that are prone to extensively accumulate in the liver following prolonged oral drug administration of certain small molecule chemical agents, as well as the characterization of specialized subpopulations of cells that may be involved in the disposition of insoluble drug aggregates or precipitates. Using mice as a model organism, Rzeczycki presents methods for the qualitative and quantitative analysis of insoluble drug aggregates and precipitates in the liver. Murashov focuses on the quantitative analysis of specific macrophage subpopulations that ultimately accumulate and stabilize drug precipitates in the liver, while maintaining normal liver function. While it specifically refers to the ultimate accumulation of such insoluble material within these immune cells of the liver, the general methodology presented by Rzeczycki and Murashov is relevant to the direct measurement of insoluble drug complexes anywhere in the organism and can be used to complement the methods elaborated by Brouwer and Li to account for any “missing” soluble molecules that impact the mass balance. The next chapter by Horobin focuses on the prediction of intracellular membrane binding mechanisms—a primary mechanism impacting intracellular unbound drug concentrations for hydrophobic drugs. Such hydrophobic, freely soluble molecules closely resemble many orally bioavailable drugs on the market and therefore impact how one thinks about drug accumulation inside cells. Horobin’s analysis of the distribution of small molecule chemical agents is summarized as a general, baseline rule or “decision tree” that establishes the expected partitioning of small molecules in association with different cellular organelles and their associated membrane compartments. This baseline analysis does not include specific drug-macromolecule interactions, self-aggregate formation, soluble-to-insoluble phase transitions, or effects of chemical agents on cell viability or membrane integrity. Therefore, it serves as a starting point when thinking about the mechanisms that could affect the distribution and sequestration of molecules inside cells. Concluding the first section of the book, the chapter by Su and Chang presents insights from the fields of chemical biology and combinatorial chemistry into the specific interactions between chemical agents and resident intracellular macromolecules that may occur within cells. These interactions can be partly responsible for driving the intracellular accumulation,

viii

Preface

localization, and activity of chemical agents within specific cell types. Furthermore, their chapter presents combinatorial chemistry and QSPR approaches aimed at “Taming” chemical agents. Taming refers to the design and identification of small molecules that minimize nonspecific interaction with intracellular components, leading to reduced intracellular retention and rapid efflux from cells after removal of the chemical agent from the extracellular medium. These properties can also be advantageous for molecular imaging agents, discussed in a later chapter. Applied to drug development, such approaches could help identify drug leads that may be less prone to nonspecific interactions leading to intracellular sequestration or precipitation, a major complicating factor in predicting cellular pharmacokinetics. The second half of the book is organized around the cellular pharmacokinetics of biologics and other macromolecules. Broadly speaking, biologics are typically characterized by (a) an inability to cross cell membranes, which sequesters them in extracellular spaces or in the lumen of the endophagolysosomal compartment of specialized cell populations, and (b) a more diffuse metabolism throughout the body (versus the more central role in liver metabolism for small molecules). However, exceptions to these general trends exist, particularly at the lower end of the molecular weight spectrum for biologics, where the distinction between “small molecule” and “macromolecule” becomes blurred. In this region, the properties of modified peptides and peptidomimetics may endow molecules with the ability to cross membranes and enter the cytosol—the so-called Beyond Rule of Five space between ~500 and 5,000 Da. The biologics section begins with a chapter by Hochman and colleagues, reviewing the development of peptide therapeutics. As the prototypical “small” biologic, this class of agents has been around since the advent of insulin therapy, long before recombinant technology was widely available. However, there has been renewed interest in this field, particularly around macrocycles capable of entering the cytosol and accessing intracellular targets. Due to their larger size than classic small molecule drugs (which are typically less than 500 Da), they have the ability to increase specificity and block large protein-protein interactions that are prevalent in cell signaling networks. Therefore, the most critical aspects of their success lie in the cellular pharmacokinetics—how quickly do they cross cell membranes? What is their intracellular stability? How much unbound drug is available to bind the target? This chapter reviews the current state of the art and how these cellular pharmacokinetics fit in the broader picture of ADME (drug absorption, distribution, metabolism, and excretion). The next chapter, by Nicole Lawrence and David Craik, describes methods for studying the cellular pharmacokinetics of cyclotides. This class of natural (and nature-inspired) compounds lies at the interface of peptides and globular proteins. For specific binding, drug molecules must retain a defined 3D structure to interact with high affinity against the desired target. Small molecule drugs often achieve this using rigid heterocycles, and the previous chapter on peptides describes the use of macrocyclization techniques to provide structure (e.g., the stapled peptide ATSP-0741, MW ¼ 1744 Da, uses ring-closing metathesis). Cyclotides and related structures are still too small to form the hydrophobic core that stabilizes the tertiary structure of globular protein drugs. Instead, cyclotides use a series of three disulfide bonds arranged in a “knot” to tie the polypeptide into a rigid structure, giving rise to extreme stability. The loops of these structures, such as kalata B1 and MCoTIII (with MW ¼ 2.8 and 3.4 kDa, respectively), have been engineered to bind both intracellular and extracellular targets.

Preface

ix

After the chapters on peptides and cyclotides—two macromolecules lying at the interface of small molecules and biologics—we move to one of the most ubiquitous protein drugs: antibodies. Since the development of monoclonal antibodies in 1975 by Kohler and Milstein, these agents, and their derivatives, have played a leading role in protein drug development. Importantly, these large (150 kDa), long-circulating, and highly specific proteins can readily be engineered to bind a wide array of extracellular targets. Their large size results in negligible membrane permeability, and therefore, their cellular pharmacokinetics are almost exclusively driven by the specific interactions with the target. With the advent of bispecific antibodies, which can bind two (or more) targets at the same time, the internalization and trafficking of the receptors and antibodies becomes increasingly complex. Because their binding is so specific and nonspecific partitioning into membranes is negligible, the binding itself can dictate the overall plasma clearance. Therefore, the cellular kinetics can play a dominant role in drug exposure and dosing. In their chapter, Rhoden and Wiethoff describe methods for quantifying the cellular kinetics of internalization and trafficking of antibodies, particularly bispecific antibodies, to determine the impact on overall pharmacokinetics and drug development. When scaling the cellular pharmacokinetics of antibodies to systemic PK, receptor expression plays a crucial role. As mentioned above, the lack of partitioning into membranes and highly specific receptor interactions results in target binding and cellular processing playing a major role in drug disposition. This is often referred to as “target-mediated drug disposition” or TMDD. While receptor levels can readily be measured in cell culture, for example using quantitative beads and fluorescently labeled antibodies on flow cytometry, measurement in animal models and the clinic is more challenging. However, this is the relevant expression level needed for drug development. Fortunately, molecular imaging provides a noninvasive manner to quantify receptor levels. These agents, such as monoclonal antibodies, smaller protein binding fragments, and/or affinity ligands, are labeled with a detectable probe (radioactive label, fluorophore, etc.) and bind specifically to the target. Because the probe signal can be quantified, this enables the quantification of the target. However, the approach in vivo is not that simple. As Tichauer and colleagues explain in their chapter, the signal from these probes is often convoluted with delivery to the region of interest. The probe signal may therefore be as much a function of delivery (based on local physiology) as target expression. This is particularly problematic for high molecular weight compounds like biologics that may have slower transport rates to and from the tissue. Therefore, these authors describe the use of “paired agent imaging,” where a second nonbinding probe is administered. By tracking the localization of both the binding and nonbinding probe, the specific receptor binding can be calculated using different data analysis and/or modeling techniques. Together, the true target expression can be measured in the relevant in vivo environment. Once the cellular trafficking and in vivo expression are known, the influence of the cellular pharmacokinetics on systemic distribution can be better predicted. The trafficking described in the Rhoden and Wiethoff chapter dealt primarily with internalization, followed by trafficking to recycling endosomes and back to the surface or to late endosomes and lysosomes for degradation. However, there can be intermediate cellular kinetics that play a critical role in drug efficacy. A prominent example of this is the linker cleavage of antibody drug conjugates (ADCs). These drugs connect potent small molecules (typically cytotoxic agents) to an antibody via a linker. Because antibodies cannot cross membranes, the payload must be released from inside vesicles to cross the membrane and access the payload target in the cytosol. Linkers may be cleavable (e.g., protease cleavage, hydrolysis, disulfide

x

Preface

reduction) or noncleavable, which typically requires complete degradation of the antibody to release the payload-linker metabolite. In either case, the kinetics and efficiency of cleavage are critical design steps in the development of ADCs. Too labile of a linker can release payload before the ADC reaches the target cell, causing toxicity, while too stable of a linker can cause slow or incomplete release, compromising efficacy. To measure these kinetics in the dynamic endosomal/lysosomal microenvironment, Alabi and colleagues developed imaging methods to quantify the cleavage rate in real-time inside cells. These FRET-based techniques can be multiplexed with some of the other imaging techniques described in this and other chapters. Together, these critical cellular kinetic rates can be measured during drug design. The cellular kinetics of biologics do not exist in a vacuum, and these rates have significant influence on the systemic pharmacokinetics, subcellular kinetics, and drug efficacy. In the chapter by Shah and colleagues, the authors use multiscale pharmacokinetic models to integrate the cellular kinetics of antibody drug conjugates with drug efficacy. This work even extends to running simulated clinical trials, connecting the cellular kinetics of ADCs to systemic clearance, cancer cell killing, and ultimately scaling to patient response. What makes this model particularly intriguing is the incorporation of bystander effects. Here, the small payloads released to form ADCs, as indicated in the Alabi chapter, can not only target that particular cell but they can also diffuse to adjacent “bystander” cells, exerting their effect on antigen negative cancer cells and/or immune cells. The cellular kinetics of these “bystander payloads” are guided by the same principles described in the first half of the book. However, the concentrations are often much lower and dictated by the release kinetics of the ADC rather than the systemic clearance of small molecule drugs. Overall, this provides an overview of how cellular kinetics for both small molecules and biologics can be incorporated into a single simulation for drug development. Up until this point, the focus on the cellular kinetics of biologics has been on targetmediated disposition—binding kinetics, receptor quantification, cellular trafficking, intracellular processing, and related simulations. However, off-target effects can be equally important in determining the cellular kinetics, including which cells impact the drug. In the case of biologics, these can be divided into two broad classes—specific off-target binding and nonspecific interactions. An example of the former may include the binding of a phospho-specific antibody to the un-phosphorylated version of the target (i.e., specific but off-target). An example of the latter would include CDR loops of an antibody containing cationic charges that interact with cell membranes, causing nonspecific internalization and rapid plasma clearance (nonspecific, off-target). Using ADCs as a case study, Lyon and colleagues describe how both these types of non-target-mediated cellular kinetics can negatively impact a drug development program and what strategies can be used to eliminate these problems. For off-target effects, the authors highlight how Fc-receptor binding of antibodies can drive uptake in tumor-associated macrophages (TAMs). This led to metabolism and payload release of ADCs even in antigen negative tumors. If desired, the Fc domain of the antibody can be engineered to eliminate this binding (e.g., with non-bystander payloads that would not reach cancer cells). Likewise, the authors describe nonspecific sticking of ADCs when labeled with lipophilic payloads, which can increase plasma clearance and reduce efficacy. However, the authors were able to eliminate this effect by designing hydrophilic linkers to help shield the antibody from rapid clearance, restoring efficacy. The final two chapters provide some background on the cellular kinetics of nanoparticles. These agents are much larger than biologics and are typically measured based on physical dimensions (e.g., 100 nm particles) versus molecular weight (e.g., 150 kDa

Preface

xi

antibody, which has ~7 nm hydrodynamic radius for comparison). The diversity of polymerbased formulations (e.g., PLGA, caprolactone, etc.), natural product adaptations (e.g., liposomes, exosomes, lipoproteins, etc.), and inorganic structures (gold nanoparticles, quantum dots, etc.), combined with the range of goals (local delivery versus increased systemic exposure, delivery of small molecules versus macromolecules/siRNA/gene therapy, targeting immune cells for vaccines versus cancer cells for therapy, etc.), requires a dedicated text to explore in any detail. However, we included two chapters to highlight some of the fundamental principles to be considered with nanoparticle cellular kinetics. The first chapter highlights techniques for the characterization of nanoparticle formulation and disassembly in cellular microenvironments. This is a critical facet of nanoparticle development. As drug size and complexity increase, so does the need for characterization. Small molecule drugs form a single molecular entity that can be well characterized through mass spectrometry and NMR. Biologics, like antibodies, are more complex with the potential for more variability; although recombinant technology can result in the same protein sequence, there exists the potential for differences in protein folding, posttranslational modification (e.g., glycosylation), and even differences in chemical structure (e.g., epimerization, isomerization, methionine oxidation, etc.). Nanoparticles are even more complex, often containing multiple domains to achieve different functions (targeting, packaging, payload delivery). The advantage of these domains, however, is that they can achieve multiple functions not possible by a single entity. For example, Merkel and colleagues describe the assembly and disassembly of siRNA nanocarriers utilizing quantum dots. Nucleic acids used for gene silencing (e.g., siRNA) or gene delivery are too large to cross membranes and require endosomal escape within cells. Polycationic polymers can both package the anionic nucleic acids in nanoparticle form and facilitate disruption of the endosome following internalization. PEG domains can be used to shield the charges during systemic circulation. In this example, the authors even included fluorophores and quantum dots to aid in dynamic characterization, and these can be used for image delivery as well. Many systemically delivered nanoparticles (such as intravenous injection) are cleared fairly rapidly by the reticuloendothelial system, such as Kupffer cells in the liver. This property can be a significant hurdle for certain applications of nanoparticles, such as molecular imaging. However, this property can also be exploited to improve drug exposure in certain tissues. Miller et al. highlight the use of this mechanism to increase drug exposure in tumors via small molecule drug delivery from nanoparticles targeted to tumor-associated macrophages. The phagocytic nature of these cells in tumors results in significant uptake of nanoparticles, which act as a depot, releasing chemotherapeutics locally over time. The utility of these strategies has been demonstrated with several clinical therapeutics, including Doxil and Onivyde. The authors use quantitative measurements of drug loading and nanoparticle uptake, in cell culture and with intravital microscopy, to measure the cellular kinetics both in vitro and in vivo. Interestingly, the authors paired this imaging with a fluorescent protein readout of the pharmacodynamic drug response. This type of multichannel in vivo imaging provides cellular resolution of pharmacokinetics and pharmacodynamics in vivo, enabling drug delivery and drug action to be imaged in real time. This final application highlights the importance of cellular kinetics. Ultimately, these rates influence both the systemic clearance and the molecular release of therapeutics, and together, these drive the drug response needed for effective therapies. To conclude this introduction, in the process of co-editing this book, we have defined and organized some of the most important methods and concepts affecting the quantitative analysis of the transport, targeting, and disposition of chemicals within cells, which in turn

xii

Preface

impact the macroscopic pharmacokinetics of chemical agents in the whole organism. We are thankful that so many expert scientists from both academia and the pharmaceutical industry have contributed to the book. These authors have highlighted some of the most important emerging concepts that impact our understanding of the multiscale distribution of therapeutic agents and bioimaging probes. By building on a long history of drug development and adding quantitative methods at the cellular scale, the study of cellular pharmacokinetics is on a much more solid footing than ever before. We hope this book will stimulate conversation and further research into the fundamental mechanisms that mediate how chemical agents move within and across cells and affect the ability to design and develop more targeted drug candidates. This research could also lead to new approaches to drug development that are better able to take advantage of phenomena such as soluble-toinsoluble phase transitions, which could ultimately be exploited for the development of drug depot formulations and nanotechnology-based controlled drug delivery systems. Finally, we hope this book will inspire young students and scientists at the start of their careers to embark on a journey that will take them through the roads less traveled, through a cellular and molecular world, leading to explorations, discoveries, and inventions well beyond what may be presently considered possible. Ann Arbor, MI, USA

Gus R. Rosania Greg M. Thurber

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

v xv

SMALL MOLECULES

1 Measurement of Transcellular Transport Rates and Intracellular Drug Sequestration in the Presence of an Extracellular Concentration Gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kyoung Ah Min and Gus R. Rosania 2 Kinetic Design for Establishing Long-Term Stationary Cytosol Concentrations During Drug Transport across P-gp Expressing Confluent Cell Monolayers to Facilitate Measuring Cytosol Concentration, Fitting Drug Molar Partition Coefficients into the Cytosolic Monolayer of the Plasma Membrane, and Kinetically Identifying Drug Uptake Transporters . . . . . . . . . . . . . . . . . . . . . . . Joe Bentz 3 In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug–Drug Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jibin Li, Qing Wang, and Ismael J. Hidalgo 4 Determination of Fraction Unbound and Unbound Partition Coefficient to Estimate Intracellular Free Drug Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . Sangwoo Ryu, Keith Riccardi, Samantha Jordan, Nathaniel Johnson, and Li Di 5 Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes. . . . . Chitra Saran, James J. Beaudoin, Nathan D. Pfeifer, and Kim L. R. Brouwer 6 Quantification of Intracellular Drug Aggregates and Precipitates. . . . . . . . . . . . . . Phillip Rzeczycki and Gus R. Rosania 7 Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mikhail D. Murashov 8 Using an Integrated QSAR Model to Check Whether Small-Molecule Xenobiotics Will Accumulate in Biomembranes, with Particular Reference to Fluorescent Imaging Probes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Richard W. Horobin and Juan C. Stockert 9 Diversity-Oriented Fluorescence Library Approach (DOFLA) for Discovery of Cell-Permeable Probes for Applications in Live Cell Imaging . . . . . . . . . . . . . . Dongdong Su and Young-Tae Chang

xiii

3

41

67

81

97

127

147

163

179

xiv

Contents

PART II 10

11 12 13

14 15

16

17 18

MACROMOLECULES, BIOLOGICS, AND NANOPARTICLES

Overcoming Cellular and Systemic Barriers to Design the Next Wave of Peptide Therapeutics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jerome Hochman, Tomi Sawyer, and Ruchia Duggal Intracellular Targeting of Cyclotides for Therapeutic Applications . . . . . . . . . . . . Nicole Lawrence and David J. Craik Cellular Trafficking of Monoclonal and Bispecific Antibodies. . . . . . . . . . . . . . . . . John J. Rhoden and Christopher M. Wiethoff Quantitative Drug Target Imaging Using Paired-Agent Principles . . . . . . . . . . . . Kenneth M. Tichauer, Negar Sadeghipour, Yu “ Winston” Wang, Summer L. Gibbs, Jonathan T. C. Liu, and Kimberley S. Samkoe Quantitative Determination of Intracellular Bond Cleavage . . . . . . . . . . . . . . . . . . Joshua A. Walker, Michelle R. Sorkin, and Christopher A. Alabi Development and Application of a Single Cell-Level PK-PD Model for ADCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shufang Liu and Dhaval K. Shah Contribution of Nontarget Cells to the Disposition, Antitumor Activity, and Antigen-Independent Toxicity of Antibody–Drug Conjugates . . . . . . . . . . . . David W. Meyer, Fu Li, and Robert P. Lyon Tracking siRNA–Nanocarrier Assembly and Disassembly Using FRET . . . . . . . . Lorenz Isert, Aditi Mehta, Friederike Adams, and Olivia M. Merkel Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs . . . . . . . . . . . . Ran Li, Ralph Weissleder, and Miles A. Miller

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

201 229 249 275

305

331

357 383 397 435

Contributors FRIEDERIKE ADAMS • Department of Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximilians-Universit€ at Mu¨nchen, Munich, Germany CHRISTOPHER A. ALABI • Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA JAMES J. BEAUDOIN • Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA JOE BENTZ • Department of Biology, Drexel University, Philadelphia, PA, USA KIM L. R. BROUWER • Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA YOUNG-TAE CHANG • Center for Self-assembly and Complexity, Institute for Basic Science (IBS), Pohang, Republic of Korea; Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea DAVID J. CRAIK • Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD, Australia LI DI • Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Groton, CT, USA RUCHIA DUGGAL • Department of PPDM Merck & Co., Inc., Boston, MA, USA SUMMER L. GIBBS • Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA ISMAEL J. HIDALGO • Absorption Systems, LLC, Exton, PA, USA JEROME HOCHMAN • Modalities ADME, Lansdale, PA, USA RICHARD W. HOROBIN • Chemical Biology & Precision Synthesis, School of Chemistry, Glasgow University, Glasgow, Scotland, UK LORENZ ISERT • Department of Pharmaceutical Technology and Biopharmaceutics, LudwigMaximilians-Universit€ at Mu¨nchen, Munich, Germany NATHANIEL JOHNSON • Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Groton, CT, USA SAMANTHA JORDAN • Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Groton, CT, USA NICOLE LAWRENCE • Institute for Molecular Bioscience, Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Queensland, Brisbane, QLD, Australia FU LI • Mersana Therapeutics Inc., Cambridge, MA, USA JIBIN LI • Absorption Systems, LLC, Exton, PA, USA RAN LI • Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA JONATHAN T. C. LIU • Department of Mechanical Engineering, University of Washington, Seattle, WA, USA SHUFANG LIU • Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, USA ROBERT P. LYON • Seagen Inc., Bothell, WA, USA

xv

xvi

Contributors

ADITI MEHTA • Department of Pharmaceutical Technology and Biopharmaceutics, LudwigMaximilians-Universit€ at Mu¨nchen, Munich, Germany OLIVIA M. MERKEL • Department of Pharmaceutical Technology and Biopharmaceutics, Ludwig-Maximilians-Universit€ at Mu¨nchen, Munich, Germany DAVID W. MEYER • Seagen Inc., Bothell, WA, USA MILES A. MILLER • Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA KYOUNG AH. MIN • College of Pharmacy and Inje Institute of Pharmaceutical Sciences and Research, Inje University, Gimhae, Gyeongnam, Republic of Korea MIKHAIL D. MURASHOV • Department of Pharmaceutical Sciences, University of Michigan, College of Pharmacy, Ann Arbor, MI, USA NATHAN D. PFEIFER • Theravance Biopharma, Inc, South San Francisco, CA, USA JOHN J. RHODEN • Fusion Pharmaceuticals, Boston, MA, USA KEITH RICCARDI • Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Groton, CT, USA GUS R. ROSANIA • Department of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI, USA SANGWOO RYU • Pharmacokinetics, Dynamics and Metabolism, Pfizer, Inc., Groton, CT, USA PHILLIP RZECZYCKI • Department of Pharmaceutical Sciences, University of Michigan College of Pharmacy, Ann Arbor, MI, USA NEGAR SADEGHIPOUR • Molecular Imaging Program, Stanford University, Stanford, CA, USA KIMBERLEY S. SAMKOE • Thayer School of Engineering, Dartmouth University, Hanover, NH, USA CHITRA SARAN • Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA TOMI SAWYER • Maestro Therapeutics, Southborough, MA, USA DHAVAL K. SHAH • Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, NY, USA MICHELLE R. SORKIN • Berkeley Lights Inc., Emeryville, CA, USA JUAN C. STOCKERT • Facultad de Ciencias Veterinarias, Instituto de Investigacion y Tecnologı´a en Reproduccion Animal, Universidad de Buenos Aires, Buenos Aires, Argentina; Instituto de Oncologı´a Angel H. Roffo, Area Investigacion, Universidad de Buenos Aires, Buenos Aires, Argentina DONGDONG SU • Department of Chemistry and Biology, Faculty of Environment and Life Science, Beijing University of Technology, Beijing, P. R. China KENNETH M. TICHAUER • Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA JOSHUA A. WALKER • Department of Chemistry, University of California, Berkeley, CA, USA QING WANG • Absorption Systems, LLC, Exton, PA, USA YU “WINSTON” WANG • Department of Mechanical Engineering, University of Washington, Seattle, WA, USA

Contributors

xvii

RALPH WEISSLEDER • Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Systems Biology, Harvard Medical School, Boston, MA, USA CHRISTOPHER M. WIETHOFF • Biologics Investigational ADME, Eli Lilly and Company, Indianapolis, IN, USA

Part I Small Molecules

Chapter 1 Measurement of Transcellular Transport Rates and Intracellular Drug Sequestration in the Presence of an Extracellular Concentration Gradient Kyoung Ah Min and Gus R. Rosania Abstract In order for a drug to be effective and ultimately successful, it must first gain access to its molecular target at a desired site of action. Conventionally, for the purpose of high-throughput screening of drug candidates during the earliest stages of drug development, transport assays are performed with commercially available, in vitro cell cultures as experimental models. Based on theoretical, physiologically based pharmacokinetics principles, the quantitative measurements obtained with these assays are used to predict drug absorption, distribution, and elimination within the organism. Transcellular drug permeability coefficients, transcellular transport rates, and intracellular drug accumulation can be simultaneously measured using adherent cells grown on porous membrane supports. These measurements are used to elucidate the molecular mechanisms that mediate the transport pathways across epithelial, endothelial, and other cell monolayers, which function to determine drug absorption and distribution within the living organism. In this chapter, we describe the most typical, routine procedures used for measuring the transcellular transport rates and intracellular accumulation of small molecular drugs, in the presence of a drug concentration gradient across a cell monolayer. We will highlight various in vitro cell models that are used to represent different cell barriers in the body. Finally, we will discuss the factors that can cause variations in these experimental measurements and their interpretation, along with the theoretical aspects related to the transcellular transport phenomena driven by extracellular concentration gradients. Therefore, this chapter provides a comprehensive introduction to the quantitative analysis of cellular drug transport, targeting, and disposition. It will serve as a guide to choose the most appropriate in vitro cell culture models, to assist with the interpretation of the data obtained through these experiments, and to outline knowledge gaps and areas of improvement that will be further discussed in the subsequent chapters. Key words Drug transport kinetics, Intracellular concentration, Uncharged drug species, Extracellular environment, pH, Acidic constant (pKa), Cell model, Polarization, Diffusion, Biomembrane, Internalization, Permeability, High-throughput drug screening, Epithelium, Endothelium, Weak base, Weak acid

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

3

4

1

Kyoung Ah Min and Gus R. Rosania

Introduction High-throughput screening methods have been widely used to identify the pharmacologically active compounds during the process of drug discovery and development [1]. However, a large number of new chemical entities (NCEs) showing promise as drug candidates in the earliest, preclinical stages of drug development often fail in clinical trials before one ultimately reaches the stage of drug approval [2]. As the attrition rates of drug candidates have escalated due to suboptimal pharmacokinetics in human body or due to unexpected toxicity issues, there is increasing interest in predicting the pharmacokinetics of drug candidates at the earliest possible stages of the pharmaceutical development process [3, 4]. Therefore, in addition to assaying pharmacological activity in vitro and in vivo, predicting the human ADME/Tox (Absorption, Distribution, Metabolism, Excretion/Toxicity) properties of a drug is now considered an essential component in drug candidate selection prior to clinical evaluation [5, 6]. Indeed, clinical efficacy of an NCE depends on its ADME properties, as much as it depends on pharmacological activity. For large collections of compounds such as those synthesized using combinatorial chemistry, high-throughput screening assays using well-established, in vitro cell culture models have proven to be useful for the selection of drug-like candidates with favorable pharmacokinetic properties [7]. Previously, pharmaceutical researchers developed drug transport assays using nonbiological models like the parallel artificial membrane permeability (PAMPA) assays [8]. However, PAMPA assays have many limitations. For example, artificial membranes do not account for the active transport and enzymatic catalysis mechanisms that facilitate or limit the transport of drugs in biological systems [9]. Such active drug transporters or drug metabolizing enzymes are ubiquitously present in cells or tissues that act as physiological barriers to determine the rates of drug transport into the different anatomical structures of the organism. Therefore, PAMPA assays provide a baseline measurement assessing the diffusion of a compound across phospholipid membranes, but more sophisticated methods are needed to fully describe relevant transport rates for effective drugs. Alternatively, researchers recur to very elaborate models, such as ex vivo intestinal segments and in vivo animal pharmacokinetic experiments, to complement PAMPA assays and quantify drug transport. In vivo animal models are considered to be more physiologically relevant systems for secondary screening of drug candidates. However, these more complex experimental systems make data interpretation difficult since there are many uncontrolled biological factors (i.e., blood flow rates, intestinal motility characteristics, molecular specificity of drug transporters and

Measurement of Drug Transport in a Concentration Gradient

5

metabolizing enzymes including species differences) that affect the transport of the molecules. In contrast to these approaches, in vitro cell culture models used for measuring drug permeability in the presence of an extracellular concentration gradient have become increasingly prevalent and are now a standard tool used by pharmaceutical researchers interested in identifying drug candidates with the most favorable ADME properties [10, 11]. 1.1 Cell Membranes as Functional Barriers Determining the Drug Transport Rates Between Body Compartments

The membranes of cells function as permeability barriers that hinder the free motion of drug molecules in the extracellular space (Fig. 1). Most drug molecules of less than 500 Daltons in molecular weight are dissolved in the aqueous environment surrounding the cell before they are able to cross cell barriers via two pathways: transcellular (by entering the cell through the lipid bilayer) or

Fig. 1 Anatomical and histological considerations related to the methods used to measure drug transport across cells in the presence of an extracellular gradient. (a) Histological features of airway or intestinal epithelial cells associated with the transport of drug molecules from the airway surface lining liquid, or from the intestinal lumen, into the blood circulation, (b) Cross-sectional diagram of a Transwell insert with a porous membrane supporting a monolayer of differentiated cells that form tight seals between each other. In vitro, the architecture of the differentiated cell monolayers on these porous transmembrane supports corresponds to the in vivo architecture of the cells lining the inner surface of the airways or intestine in the living organism. Thus, the in vitro Transwell insert system serves to model drug transport into and out the cells that form the barriers determining the absorption of drugs delivered via inhalation or oral routes

6

Kyoung Ah Min and Gus R. Rosania

paracellular (by passing through aqueous pores formed between cells) [12, 13]. Drug transport in the organism is primarily driven by concentration gradients of dissolved molecules. In the body, cells are often present as a ‘monolayer’ comprised of a single planar layer of cells attached to each other via junctional complexes [14]. These junctional complexes connect cells via specialized structures known as gap junctions, desmosomes, and tight junctions, all of which play an important role in forming the functional barrier of the cells by controlling diffusion of small molecules across and between neighboring cells. The membrane that forms the outer surface of cells is mostly made out of phospholipid molecules, which self-assemble to form a bilayer structure that acts as the primary determinant of the transcellular transport rate of drug molecules from one side of the cell to the other. Passive transport driven by the diffusion of molecules from high to low concentration is the primary transport mechanism for molecules moving across the epithelial or endothelial cell monolayers [15]. These monolayers act as physiological barriers that determine the distribution of drug molecules from the circulation to the various organs and tissues [16]. Nevertheless, specific proteins that span the phospholipid bilayer can act as active transporters or form aqueous pores on the surface of the cells. These proteins contribute towards enhancing or inhibiting the rate of transcellular transport of some drugs (i.e., those that are substrates of these specialized, protein ‘pumps’ or ‘channels’ present in association with cell membranes) [17, 18]. Cell monolayers often exist as polarized barriers with two distinctively different membrane surface areas: (1) a free-standing, luminal, or “apical” surface which may contain “villi” or fingerlike projections that extend to the surrounding media in order to maximize the absorptive surface area (e.g., the brush border membrane lining of the lumen of the intestine) and (2) a “basolateral” surface which attaches the cells to an underlying substratum (Fig. 1). The passive transport of compounds through a cell monolayer depends on the permeability properties of the apical and basolateral membrane surfaces, as well as on the presence of mucinous secretions that often coat the apical cell surface [19]. Because cell membranes are mostly composed of lipids, the lipophilicity of molecules acts as a key determinant of the transport rate. One of the most common measurements for quantifying drug lipophilicity is the octanol/water partition coefficient, where octanol serves as a mimic of the hydrophobic core of the phospholipid bilayer that forms the outer surface of cells. This value is often expressed as the logarithm of the partition coefficient, or logP. Note that this is the partition coefficient (concentration in octanol versus concentration in water) of the neutral drug species. Ionized drug forms vary according to the pKa of ionizable groups present in the molecule and are determined by the local pH condition of the

Measurement of Drug Transport in a Concentration Gradient

7

aqueous environment immediately surrounding each molecule (which can be calculated according to the Henderson-Hasselbalch equation) [20, 21]. Together, the relative fractions of neutral drug forms and ionized drug forms (which primarily partition into the polar aqueous phase) constitute another commonly used parameter, the logD. This is the octanol/water partition coefficient of all drug species at a given pH (e.g., logDpH 5.5 which commonly represents the partitioning at the pH in the intestinal tract). These logP or logD measurements provide insight into the ability of drugs to passively diffuse across cell membranes by virtue of their ability to diffuse into and across the hydrophobic core of the phospholipid bilayer. However, they suffer from similar limitations as the PAMPA assays, since they lack the biological mechanisms that facilitate or inhibit transport across cells. Of note, as molecules move across cells from a region of higher concentration to a lower concentration, accumulation of molecules inside cells can occur through the partitioning of drug molecules between the cytosol and subcellular organelles such as lysosomes, mitochondria, endoplasmic reticulum, golgi body, or the cell nucleus (Fig. 2) [23]. These organelles are also delimited by lipidrich membranes. Overall, a number of different models, many of them empirical, have been used to understand and predict the partitioning and accumulation of small drug-like molecules inside intracellular organelles [22, 23]. In addition to the partitioning of molecules into these organelles, there are additional factors that can promote the concentration and precipitation of drug molecules within specific intracellular microenvironments and which could influence the net accumulation of molecules inside cells as they make their way across cells [24]. These factors include the pH gradients (e.g., lysosomes) and electrical potential (e.g., mitochondria) across the membranes delimiting the cells and organelles, together with the presence of lipids within specific organelles, and other cellular components that can be bound by soluble drug molecules present in their immediate environment [25, 26]. Furthermore, interaction of soluble drug molecules with resident macromolecules like DNA in the nucleus or mitochondria, or with RNA or specific macromolecular ligands that may be preferentially present in the cytoplasm or nucleus, can lead to a local accumulation of the drug molecules in a bound form. Finally, the formation of drug aggregates that are unable to readily cross membranes, the binding to insoluble or membrane-impermeant cytoskeletal or organelle “matrix” components, or the thermodynamic phase transition of drug molecules from a soluble into an insoluble state can provide the driving force for potent targeting mechanisms determining the accumulation of small molecules within specific cells and subcellular compartments ( [24, 27]; also, please refer to chapters by Rzeczycki and Murashov).

8

Kyoung Ah Min and Gus R. Rosania

Fig. 2 Diagram representing the fundamental processes that affect intracellular drug sequestration and disposition, when cells are exposed to an extracellular, apical (luminal) to basolateral drug concentration gradient. In the luminal side, drug molecules can exist in a free or bound form. Once the molecules enter the cell through the apical membrane, free intracellular drug can (1) be transformed into other compounds through metabolism; (2) interact with macromolecules in the cytosol or in specific organelles; (3) adsorb on—or partition into—intracellular membranes; or (4) accumulate in organelles through the action of pH gradients, membrane potentials, self-aggregation, membrane-impermeant complex formation, or soluble-to-insoluble phase transitions, and then move out of the cell into the basolateral side, to reach the systemic circulation. In terms of modeling this process, many different parameters have to be understood in order to fully capture the manner in which the chemical biology of the cell will impact the ultimate transport of drug molecules across the cell. (The diagram was adopted from the figure (Min KA et al. 2014) with permission [22]. Min KA, Zhang X, Yu J, Rosania GR. (2014) Computational approaches to analyse and predict small molecule transport and distribution at cellular and subcellular levels. Biopharm Drug Dispos 35:15–32) 1.2 Transcellular Permeability as a Key Component Determining Transport and Distribution of Drugs in the Body

After the administration of a solid (particulate) or liquid (emulsion) dosage form of a drug, the particles or droplets containing the drug in the dosage form are designed to become dispersed in an aqueous medium such as gastric fluid, blood, or extracellular fluid. After drug molecules are released from their solid particulate carriers, they must dissolve in order to be able to pass through cell barriers, enter the blood circulation, and distribute into the intended target tissues and cells that form the different organs. Measuring the permeability of drug molecules in cell models that faithfully represent the transport properties of the absorptive epithelia or the endothelial cells that mediate the transport from the blood to the various tissues of the body has been used to obtain information about the ultimate fate of drug molecules when administered to patients. Based on the measured transcellular transport properties,

Measurement of Drug Transport in a Concentration Gradient

9

the biological factors affecting distribution of drug molecules into different organs can be elucidated and studied in molecular detail. For example, for oral delivered drugs, the drug permeability across intestinal epithelial cells determines absorptive clearance of drugs in gut lumen [28]. For orally delivered drugs, the drug permeability across intestinal epithelial cells determines absorptive clearance of drugs in the gut lumen. As shown in Fig. 1, once dissolved in the lumen of the intestine, the drug molecules can be transported across the cells that form the mucous membrane lining the wall of the gastrointestinal tract, driven by the concentration gradient present between intestinal lumen and blood. The rate of change in the mass of drug in the blood per time (dM/dt) can be mathematically related to the mass balance (i.e., “molecular accounting”) through the equation: dM ¼ Q ðC In  C Out Þ dt

ð1Þ

Where CIn and COut are drug concentration in inlet (extracellular tube entrance) and outlet (extracellular tube exit) and Q is flow through the tube, normally assumed as constant for the gut lumen. Equation 1 describes the loss of drug in the lumen, which must equal the increase in drug in the body assuming no accumulation in the gut (steady-state assumption). Using Fick’s law for passive diffusion with an average drug concentration in the lumen (CLumen) and blood sides (CBlood), the equation can be written as: dM ¼ CLabs  ðC Lumen  C Blood Þ  A  P eff ðC AP  C BL Þ dt

ð2Þ

CAP and CBL, drug concentration measured in the apical and basolateral sides of cells, correspond to the drug concentration in the lumen and blood sides, respectively. A is the surface area of the membrane and Peff is the effective cellular permeability. The absorptive clearance concept means a virtual volume of fluid containing drugs removed from the luminal side per unit time, with the units of mL/min. The clearance concept is difficult to use for comparing drug transport data in various experimental settings in different laboratories because it depends on the surface area of the segment studied, which can vary significantly because of the corrugated architecture of the intestinal mucosa. For the sake of simplicity, one can think of the drug concentration in the lumen of the intestine remaining constant. However, as the drug is absorbed into and across the intestinal wall, the concentration of the drug molecules will decrease along the length of the intestine. Nevertheless, the greatest fraction of drug absorption will occur at the highest dissolved drug concentrations which occurs in the proximal segments of the intestine following dispersion and dissolution of the swallowed pill or capsule in the stomach. For intestinal drug permeability determinations, the proximal, intestinal drug

10

Kyoung Ah Min and Gus R. Rosania

concentration is generally used in vitro when performing intestinal cell permeability assays. Accordingly, approximating the absorptive drug clearance per unit area, this effective cellular permeability measurement is often used as a surrogate measure of the mass transport properties of drug molecules across the cells that line the intestinal wall. The effective cellular permeability, Peff, includes the process of drug transport through the membrane (i.e., membrane permeability of the intestinal mucosa (Pmem)) and transport through the cytoplasm and interstitial space. This value is mostly dependent on the permeability of the apical (“brush border”) membrane of cells in direct contact with the fluids in the intestinal lumen and the extent of drug accumulation or metabolism inside the cells. At present, most pharmaceutical research in industry or academia is focused on enhancing drug absorption via transcellular transport through cell barriers and increasing bioavailability in target sites with less toxicity or side effects by reducing unnecessary accumulations of drug molecules in off-target sites. Various in vitro cellular models are used to capture the transport properties of absorptive epithelia in the intestine. In addition, other in vitro cellular models (Table 1) have been used to assay the permeability of airway epithelial cells (Calu-3 cells) [29], kidney tubules (MDCK cells) [30], as well as other physiological cell barriers [31, 32].

2 In Vitro Cell Models to Measure Transcellular Drug Transport in the Presence of an Extracellular Concentration Gradient Many different in vitro cell models can be differentiated into functional monolayers on semipermeable membrane supports for the purpose of screening the ADME/Tox properties of drug candidates (Table 1) [33, 34]. Permeability measurements together with intestinal drug absorption studies demonstrate that Caco-2 cells are a good cell model for predicting intestinal absorption [31, 35, 36]. Endothelial cells obtained from the blood–brain barrier (BBB) can be differentiated in culture and are useful to study the permeation of brain-targeting drugs for central nervous system (CNS) drug discovery/development [37]. Compared to complex animal experiments or isolated perfused organs [38–40], the use of cell culture models has greatly facilitated molecular mechanistic understanding of drug transport pathways. Transport experiments can be readily performed using polarized cell monolayers that are grown and differentiated on commercially available, sterile plastic ware incorporating the necessary membrane supports in standard multiwall assay formats (e.g., Transwell inserts). Not only are there many practical advantages in using these in vitro cell culture systems [41, 42], but the precise structure and function of cells growing on

Complete differentiation into columnar epithelium Monolayer cells Tight junctions Polarized

Strain1: 4000 Ωcm2 Strain2: 200–300 Ωcm2

Highly differentiated Monolayer Epithelial-like Expression of brush border enzymes and transporters Tight junctions Polarized

300 Ωcm2

TEER

Human intestinal Canine kidney epithelial cell cell line; from line human colon adenocarcinoma

MDCK [30]

Morphology

Origin

Caco-2 [18]

50–100 Ωcm2

>30 Ωcm2

Confluent polarized Roughly monolayers hexagonal in Tight junctions shape forming adherent, elongated, and fusiform shape with pigmentation Contains several multinuclear cells Polarized

Primary, immortalized Well-formed stratified corneal epithelium Tight junctions Polarized

200–800 Ωcm2

Adult human retinal pigment epithelium

Human placenta, from human choriocarcinoma

BeWo [85]

Human corneal epithelium

HCE [61, 83]

ARPE-19 [61, 84]

Calu-3 [53]

>200 Ωcm2

Tight junctions Micropinocytic vesicles Polarized

(continued)

>300 Ωcm2

Well-differentiated monolayer Polarized Tight junctions Resemble native airway epithelia

Human lung Normal brain cancer cell line endothelium cocultured with astrocytes and pericytes

BEC+ Astrocytes + Pericytes co-culture [86]

Table 1 Characteristics of representative in vitro cell culture models used to study or predict small molecule transport routes across various physiological barriers controlling the ADME properties of drug candidates

Measurement of Drug Transport in a Concentration Gradient 11

Functions/ properties

Polarized and tight Gene-expression Expression of of junction efflux, HCE-specific formation microvillar proteins Low inherent transporters transporters Secretions of enzymes such as Low level of P-gp uptake hydrolase, transporters membrane peptidase, disaccharidases and phase II conjugation enzymes

Mannitol: A, as indicated on

Joe Bentz

adjacent cells held together by tight junctions, depicted as “brickwall” like structures, which connect the adjacent cells, separate the outer plasma monolayers of the apical and basolateral sides, while not functionally separating the cytosol monolayer [10]. The drug concentrations are denoted as CA in the apical aqueous chamber, CC in the cytosol, and CB in the basolateral aqueous chamber. Transport in the Transwell system can be either from the donor apical aqueous chamber to the receiver basolateral chamber, denoted A > B, or from donor basolateral chamber to receiver apical chamber, denoted B > A, as indicated on the upper and lower left side the figure. Transport through the cell involves several distinct mass action reactions. Passive permeability kinetics through the confluent cell monolayer are defined in Fig. 1A by the passive permeability coefficient between the apical aqueous chamber and the cytosol, PAC, and between the basolateral aqueous chamber and the cytosol, PBC. However, these “elementary” passive permeability coefficients within confluent cell monolayers cannot yet be measured since the drug concentration in the cytosol has not yet been measured reliably. As explained in Tran et al. [5, 6], we estimated their values by measuring the transcellular passive permeability coefficients A > B, denoted PAB, and from B > A, denoted PBA, and defining PAC
PAB and PBC
PBA. Obviously, it would be better to be able to directly fit the elementary permeability coefficients PAC and PBC using cytosol drug concentrations within confluent cell monolayers during transport. Figure 1A also shows P-gp in the apical membrane by the dark gray square with the substrate binding site indicated by the white ä

44

Fig. 1 (continued) the upper and lower left side the figure. P-gp is shown in the apical membrane by the gray box with the association rate constant k1 for the drug to the P-gp binding site, shown by the white circle within the cytosolic monolayer, the dissociation rate constant kr for the drug back into the acyl chains of the cytosolic monolayer and the efflux rate constant for the drug into the apical aqueous chamber. The apical membrane also shows the apical uptake transporter, AT, and the passive permeability coefficient PAC through the apical membrane. The basolateral membrane shows the basolateral uptake transporter, BT, and the passive permeability coefficient PBC through the basolateral membrane. Figure B shows a blow up of Fig. A, showing the rapid equilibrium between the drug concentration in the apical aqueous chamber, CA, and the lipids in the apical outer monolayer, AO, which have a concentration CAO defined by the molar partition coefficient KAO ¼ [CAO]/[CA]. The other membrane concentrations are defined as shown in Eq. 1 in the text. The phospholipids in each monolayer are different, as depicted by the different headgroups, which defines much of the individuality for the plasma membrane monolayers [11]

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

45

dot within apical inner monolayer portion of P-gp, as determined from the structural x-ray diffraction studies for human P-gp expressed in Pichia yeast [12, 13] and for the C. elegans P-gp [14]. So, the drug substrate binding to P-gp resides within the cytosolic monolayer of the plasma membrane or the cytosolic monolayer for brevity. The elementary rate constant for drug association to P-gp from the cytosolic monolayer is denoted as k1 for the red lines indicating the passage of drug throughout the cytosolic monolayer and entering P-gp at the arrows. To date, no kinetic evidence has suggested that the fitted value of k1 from the apical side, mostly from A > B transport, should be significantly different from the fitted value of k1 from the basolateral side, mostly from B > A transport [5–8, 15, 16]. To date, the fitted value of k1 is close to the lipid lateral diffusion limit for phospholipid bilayers [17–19], as discussed in Tran et al. [5]. Thus, the drug entrance into the P-gp binding site likely involves a relatively small number of collisions between the drug and P-gp prior to entering the binding site. P-gp bound substrate can be actively effluxed via the P-gp ATPase activity into the apical aqueous chamber according to the efflux rate constant k2 or (presumably) passively released back into the apical membrane according to the dissociation rate constant kr. For all drugs examined, dissociation is at least three orders of magnitude faster than efflux, kr> > k2, meaning simple release back into the apical membrane is the far more likely outcome for any bound drug [6, 15, 16]. This is illustrated by the thick arrow for kr exiting the binding pocket, as compared with the thin arrow for k2. A recent addition to transcellular transport for many P-gp substrates has been the kinetic requirement for basolateral and apical uptake transporters. The term “kinetically required” means that a particular drug shows more transport than can be predicted using just the fitted kinetic parameters for P-gp alone. This protocol was initiated in Acharya et al. [20], where the kinetic requirement for basolateral uptake transporters for digoxin and loperamide, as well as an apical uptake transporter for digoxin, in the MDCKII-hMDR1-NKI cells was first published. Lumen et al. [8] showed that vinblastine also kinetically required a basolateral uptake transporter in these cells, which need not be the same protein as the basolateral digoxin uptake transporter. Chaudhry et al. [21] fitted the IC50 data for B > A transport from five laboratories participating in the P-glycoprotein (P-gp) IC50 initiative [22–24], which included Caco-2, MDCKII-hMDR1, and LLC-PK1-hMDR1 cells. The A > B data was too variable [22]. All these B > A data kinetically required a basolateral uptake transporter for digoxin, which was also specifically inhibited by all 14 P-gp substrate/inhibitors used in that study. Thus, all of the

46

Joe Bentz

IC50 values for B > A transport in ref. (22–24) must be assumed to be the product of the convolution of the inhibition of both P-gp and BT, which are likely to have different expression levels for each cell line and laboratory, until proven otherwise. They need not even be identical transporters. The B > A IC50 data for inhibition of digoxin transport through monolayers of primary human proximal tubule cells (HPTCs) also showed the kinetic requirement for a basolateral digoxin uptake transporter that was also inhibited by 9 P-gp substrate/inhibitors [21]. Thus, these uptake transporters have been found in the common P-gp expressing cell lines used in the pharmaceutical industry and are inhibited by all P-gp substrates tested. The identity of these transporters is currently unknown, so they are simply denoted as AT and BT in Fig. 1A, and kinetically characterized by the respective clearances, kA(s1) and kB(s1) and shown just beside the elementary passive permeability arrows for PAC and PBC. It has been shown that BT in MDCKII-hMDR1-NKI cells was better fitted as being facilitated and bidirectional, rather than as an active importer [15]. The data for AT was inadequate to establish its directionality. BT and AT are currently modeled being facilitated and bidirectional, until more specific data can be obtained. Once these uptake transporters can be isolated, their true transporter characteristics, e.g., surface density in the plasma membrane, binding constants to substrates, and specific mechanisms of transport, can be elucidated. The coexistence of basolateral and apical uptake transporters for P-gp substrates in native P-gp expressing cells, e.g., essentially all epithelial cells, may have an interesting evolutionary explanation. Most P-gp substrates generically resemble positively charged soaps, including a large fraction of the modern drugs that P-gp transports. Positively charged soaps are intrinsically detrimental to the integrity of biological membranes, especially to the negatively charged eukaryotic cells, since the membrane’s negative surface charges attract the positively charge soaps and soaps disrupt membrane bilayers. A basolateral uptake transporter facilitates their reaching P-gp and being effluxed before they accumulate adequately in tissue or epithelial cells to disrupt membrane integrity, causing cell death. An apical uptake transporter for these P-gp substrates appears to be a futile cycle: transporting positively charged soaps into the cell, so that P-gp can efflux back into apical aqueous chamber. Perhaps it is, but the extra load on P-gp is compensated by the function of other substrates of this apical transporter. On the other hand, perhaps the apical uptake transporter evolved prior to P-gp and was the original efflux transporter of these positively charged soaps out of epithelial cells.

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

3

47

Drug Partitioning into Membrane Monolayers Our kinetic model explicitly assumes that an amphipathic drug in the aqueous compartment rapidly partitions between the aqueous phase and the lipid monolayer facing that compartment. This is a reasonable assumption for this system, where transport occurs on an hourly timescale, since Abreu et al. [25], for example, have shown that the fluorophore Rhodamine Green (TM)-tetradecylamide associates with lipid bilayers of liposomes within a few seconds. The drug in the plasma cytosolic monolayer is the substrate for binding to P-gp and we predict the drug’s concentration in the cytosolic monolayer. This is the primary reason for the inclusion of the word “structural” in our model’s name and for the significant difference between our model and the other P-gp kinetic models used to analyze confluent cell monolayer transport [1–4]. Figure 1B is a blow up of the right hand portion of Fig. 1A, showing the plasma cytosolic monolayer (PC, darker gray), the apical outer monolayer (AO, lighter gray), and the basolateral outer monolayer (BO, black dots on white), with the tight junction separating AO from BO, but leaving the cytosol monolayer PC essentially continuous. The “phospholipid molecules” are there to signify and represent the actual lipid constituents of the monolayers, omitting cholesterol for visual simplicity. The phospholipid headgroups are distinct for each monolayer, which defines much of the relevant lipid physical chemistry relevant to each monolayer [11, 26]. The apical outer monolayer, AO, shows rapid equilibrium partitioning of drugs between it and the apical aqueous chamber, CAO$CA, whose magnitude is defined below by the molar partition coefficient, KAO. The plasma cytosolic monolayer, PC, shows rapid equilibrium partitioning of drugs between it and the cytosol, CPC$CC, whose magnitude is defined below by the molar partition coefficient, KPC. The basolateral outer monolayer, BO, shows rapid equilibrium partitioning of drugs between it and the basolateral aqueous chamber, CBO$CB, whose magnitude is defined below by the molar partition coefficient, KBO. This rapid partitioning between the lipid monolayers and the aqueous phase they face implies that it is essentially kinetically irrelevant whether uptake transporters, AT and/or BT, bind to and release substrate from the aqueous phase, or from the plasma membrane lipid monolayers, or even some mixture of these possibilities. Equilibrium dialysis binding of drugs to intact cells cannot readily estimate these individual cell membrane monolayer molar partition coefficients, especially for the cytosolic monolayer, because P-gp substrates are all permeable enough to bind throughout the cell within an hour [7]. Given that these cell monolayers have complex lipid compositions and that our goal was to model P-gp transport kinetics as rigorously as possible, we chose to

48

Joe Bentz

estimate the molar partition coefficients using 0.1 μm extruded large unilamellar liposomes, LUV, with compositions that roughly mimic the major lipid components of the relevant monolayers of the plasma membrane [11], while not exceeding ternary mixtures [5, 6, 8]. Thus, the AO monolayer mimic was a (1:1:1) molar mixture of phosphatidylcholine/sphingomyelin/cholesterol, denoted PC/SM/chol. The BO monolayer mimic was a (2:1) molar mixture of phosphatidylcholine/cholesterol, denoted PC/chol. The PC monolayer mimic was a (1:1:1) molar mixture of phosphatidylserine/phosphatidylethanolamine/cholesterol, denoted PS/PE/ chol. The molar partition coefficients were calculated as, ½C AO  ½C A  ½C  K BO ðPC=chol, 2 : 1Þ ¼ BO ½C B  ½C  K PC ðPE=PS=chol, 1 : 1 : 1Þ ¼ PC ½C C 

K AO ðPC=SM=chol, 1 : 1 : 1Þ ¼

ð1Þ

The square brackets, [], denote equilibrium molar concentrations and the molar partition coefficients were fitted by equilibrium dialysis of drugs and liposomes [5, 6, 8]. The molar partition coefficients show that with association time constants of seconds, the drug dissociation constants from the liposomes are on the order of minutes, validating our assumption of the partition equilibrium over the hourly time courses of transport.

4

Passive Permeability with Drug Loss We next examined the passive permeability of drug across the membranes of confluent cell monolayers. It is important to note that while the equations for passive permeability rates are usually written as gradients between the drug concentrations in the aqueous chambers, our assumption of rapid equilibrium between lipid monolayers facing the aqueous chamber, with the data shown below, aligns with the model used by molecular dynamics simulations of bilayer permeability, where the barrier is maximal in the center of the bilayer [27, 28]. The classic approximate solution for the initial passive permeability coefficient for a single barrier is [29]: P apx ¼

V R C R ðt i Þ At i C D ð0Þ

ð2Þ

where VR is the volume of the receiver chamber, CR(ti) is drug concentration in the receiver chamber at the measurement time ti, A is the surface area of the permeability barrier, and CD(0) is the

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

49

initial drug concentration in the donor chamber. Equation 2 is based upon the predicted initial rate of permeation and is accurate for an early time point, roughly defined by all these conditions being met: 1. Linear drug transport with time, which typically accounts for less than 10% of total drug transport. 2. Negligible backflow. 3. No significant mass balance problems. This approximate equation depends only on the total mass transported to the receiver chamber relative to initial mass in the donor chamber, making the equation insensitive to mass balance problems, e.g., drug stability/hydrolysis or irreversible binding within cells and/or apparatus. This is especially problematic when the donor side concentration is predicted from the pipetted aliquot, rather than measured directly, since drugs can bind to the donor chamber. We initiate our transport measurements with a measurement 6 min after the initial pipetting, to allow some equilibration in the system [5, 6, 8]. The objective of Tran et al. [5] was to derive an exact mathematical solution for the passive permeability coefficient for a single barrier over the entire transport curve, which accounts for mass balance problems. Equation 3 was the result of that derivation.   C R ðt Þ V RV D P ¼ ð3Þ ln 1  ðV R þ V D ÞAt hC ðt Þi where VD and VR are the donor and receiver chamber volumes (cm3), A is the area of the permeability barrier (cm2), and t is the time of measurement (s). For the confluent cell monolayer on a Transwell insert, the area of the permeability barrier A was chosen to be twice the area of the Transwell plastic insert, i.e., 2  1.13 cm2 ¼ 2.26 cm2 [5, 6]. This roughly accounts for the efflux active surface area of the apical membrane and the basolateral plasma membrane attached to the plastic insert [9]. As time t!0, Eq. 3 becomes identical to Eq. 2, as required since they are derived from the same general mass action equations. This equation for computing the permeability coefficient was novel, rigorous, and mechanistically simpler, because it depends the measurable average system concentration of drug within the donor and receiver chambers defined by: hC ðt Þi ¼

V D C D ðt Þ þ V R C R ðt Þ VD þVR

ð4Þ

The content of drug in the cell monolayer and the cytosol is negligible compared to the aqueous chambers. However, the average drug concentration in the cytosol can be greater than that given by Eq. 4.

50

Joe Bentz

The data needed to use Eqs. 3 and 4 are the receiver and donor concentrations at each time point. To eliminate the activity of P-gp and any uptake transporters for P-gp substrates for these passive permeability experiments, 2.0 μM GF120918 was added to the system, which completely inhibits all these transporters, i.e., P-gp, BT, and AT [5–8, 16, 20]. While GF120918 was originally designated as a P-gp inhibitor [30–32], its efficacy at inhibition of these unidentified P-gp substrate uptake transporters, AT, and BT, suggests an interesting evolutionary history between P-gp and these uptake transporters [4]. Equation 3 has replaced the typical concentration gradient between the chambers, CD(t)-CR(t), as the thermodynamic force term for permeation, with the mathematically equivalent gradient between the receiver side concentration and the system average concentration, , which is calculated from the same data used to calculate the permeability coefficient. This makes the equation more compact and allows mass balance problems to be incorporated directly into the calculated permeability coefficient. Equilibrium is reached when receiver side and donor side concentrations equal the system average concentration. When there are no mass balance issues, this exact equation is similar in form to an approximation derived by Ho et al. [33]. is a direct measure of loss of drug and is obtained from the experimental data at each time point, as was shown in Tran et al. [5]. This term can accommodate the drug loss into intracellular organelles and/or drugs exceeding their molar solubility limit with intracellular organelles, as described by Min et al. [34], as well as other chapters in this volume. If there is no drug loss, then is essentially constant in time, which was seen for amprenavir and quinidine [5]. If drug is lost from solution, for whatever reason, then will decrease in time. If that loss is first order, which was the case for loperamide [5], then can be written as:   C D ð0ÞV D hC ðt Þi ¼ exp fkv t g ð5Þ VR þVD as shown by Eq. (A.4) in Tran et al. [5], where kv is just the first order loss rate constant. If drug loss is not simply first order, due to more complex mechanisms of drug loss, then one can calculate the time dependence of to obtain the appropriate expression for the passive permeability coefficient, as was shown in Eq. (B.6) in Tran et al. [5]. In this way, loss of drug does not affect the calculation of the passive permeability coefficient.

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

5

51

Stationary Cytosolic Concentrations During Active Transport by P-gp In our model, the cytoplasm of the cell is simply an aqueous compartment, without nucleus, mitochondria, or all the other common cytoplasmic organelles [35]. If it were shown that drug sequestration within cytosolic organelles significantly altered cytosolic concentrations, this could be accommodated the same way as drug loss during transcellular transport was accommodated [5–8]. The simulated cytosolic drug concentrations are the values required to fit the experimentally measured drug concentrations in the donor and receiver aqueous chambers over time, given the fitted values of molar partition coefficients, elementary passive permeability coefficients, and the P-gp kinetic parameters [5–8, 15, 16, 20, 21]. Within these fittings for transcellular transport, the adsorption of drug into the cytosolic organelles and the initial instability of passive permeabilities appear to be largely controlled by accounting for drug loss over time and starting the fitting process 6 min after drug addition to the Transwell cells using the measured values of drug in both aqueous compartments [5, 6, 8]. In fact, using Eq. 3 with apical chamber transport into just the cytosol, without allowing further transport to the basolateral chamber, so that VD ¼ 0.5 mL, VC ~ 1 μL, and P ¼ 40 nm/s for digoxin, the cytosol would reach 90% of its steady-state concentration within 10 min [7]. While our kinetic model has fit the time dependence of the donor and receiver drug concentrations over time well enough for many P-gp substrates, it remains essential to discover how accurately the cytosolic concentrations are predicted. If the cytosolic concentrations turn out to be adequately predicted, then the model adequately represents the significant elements of the drug transport process through confluent cell monolayers and should be used for in vitro–in vivo extrapolations. If the cytosolic concentrations turn out to be poorly predicted, then the model must be revised to better represent what happens to the drugs within the cytosol, e.g., what are the correct values for the molar partition coefficients of the drugs to the relevant membrane monolayers and to what extent do the cytosolic organelles affect transport beyond simple adsorption of drug. This knowledge is essential to developing robust predictions for in vitro–in vivo extrapolations.

6

Kinetic Design to achieve Stationary Cytosol Concentrations Given the obvious difficulties of experimentally measuring a cytosolic concentration, it seems useful to find experimental scenarios where the predicted cytosolic drug concentration would be essentially stationary over the extended time intervals during transport

52

Joe Bentz

through confluent cell monolayers. An optimal case would predict a transitory initial phase that stabilized as an essentially stationary cytosolic concentration for several hours, where the drug concentration of that stationary phase would depend in a predictable way on the initial drug concentration in the donor chamber. These properties would allow a robust statistical analysis to guide revisions of the structural model. According to our kinetic model simulations shown below, such cases do exist. 6.1 Case 1: Ketoconazole

We start with ketoconazole, a drug with cytosolic targets [36]. Lumen et al. [8] fitted the kinetic parameters of ketoconazole for P-gp-mediated transcellular transport across confluent MDCKII-hMDR1-NKI cells. The passive permeability coefficients for ketoconazole, PAC ¼ 730 nm/s and PBC ¼ 680 nm/s, are at the upper range for all the drugs we have tested. Figure 2A shows simulations of ketoconazole cytosol concentrations for A > B and B > A transport over 6 h. The solid lines show A > B transport and the dashed lines show B > A transport. Initial donor chamber concentrations of ketoconazole, CD(0), of 1–1000 nM, are noted on the right side of the figure. There is a rapid phase of drug reaching the receiver within 0.5 h, which was not plotted, then there continues a slow change until the true steady state is achieved after about 12 h. This extended duration may only happen in a simulation, since we have shown that a 12-h exposure of these cells in a Transwell system to amprenavir is toxic to the cells [6]. The cytosolic concentrations with A > B transport show the slow decrease toward its true steady state, while the B > A transport shows the slow increase toward its true steady state. This allows adequate time to experimentally measure the cytosolic concentration at several different initial drug concentrations to test whether the model was predicting the correct concentrations or not. Nevertheless, it would be better to have a more stable stationary phase predicted, with essentially no change, to allow for more control of other currently unanticipated transients within the cytosolic compartment that may occur. A stationary state for the cytosolic concentration requires a strict temporal balance between passive transport processes and active P-gp efflux. Figure 2B shows the cytosolic concentrations predicted when ketoconazole’s passive permeability coefficients are simply “reset” to be ten-fold smaller, PAC ¼ 73 nm/s and PBC ¼ 68 nm/s, labeled as “Slow Ketoconazole.” Clearly, the predicted cytosolic concentrations are essentially flat over time, and the predicted cytosolic concentrations for A > B and B > A transport are essentially identical. This occurs because the passive permeability kinetics work on the same timescale as the P-gp efflux kinetics. This system would be excellent to test whether these predicted cytosolic concentrations are accurate or not and whether this symmetry for transport direction is experimentally validated or not.

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

53

Fig. 2 Simulated cytosolic concentration of ketoconazole. Simulations using MDCKII-hMDR1-NKI cells over 6 h for B > A transport of the ketoconazole cytosol concentrations are shown by the dashed lines, with initial concentrations in the donor basolateral chamber ranging from 1 to 1000 nM. Matched simulations with A > B transport are shown by the solid lines. Figure A shows the experimentally fitted passive permeability coefficients for ketoconazole are used, PAC ¼ 730 nm/s and PBC ¼ 680 nm/s. Figure B shows the passive permeability coefficients for ketoconazole are reduced tenfold to PAC ¼ 73 nm/s and PBC ¼ 68 nm/s, to illustrate the need for slower passive permeability to create a stationary state in the cytosol concentration balanced by P-gp efflux kinetics for these cells

54

Joe Bentz

However, we have discovered that drugs that are P-gp substrates and have passive permeability coefficients less than about 320 nm/ s all show the “kinetic requirement” for uptake transporters to enter the commonly used cell lines in the pharmaceutical industry, all of which have been derived from animal tissues [4, 8, 16, 20, 21]. The quotation marks signify that while all P-gp substrates might be transported by these uptake transporters, the effect of that transport on the drug concentration in the receiver aqueous chamber is experimentally measurable only if the drug’s passive permeability coefficient is less than about 320 nm/s [8]. Lumen et al. [8] showed that verapamil and ketoconazole, which have high passive permeabilities and do not kinetically require any uptake transporters for their transcellular transport, specifically inhibited digoxin transport through the basolateral uptake transporter in MDCKII-hMDR1-NKI cells, i.e., in addition to their inhibition of digoxin transport by P-gp. This study proved that verapamil and ketoconazole bind to the basolateral digoxin uptake transporter, so as to inhibit digoxin’s transport, e.g., they did not bind directly to digoxin to make transport impossible. Until proven otherwise, our current hypothesis is that many, if not all, of the P-gp substrates we have tested use these basolateral and/or apical uptake transporters, whether or not they kinetically require them [4, 16, 21, 37]. For the purposes of this kinetic design, we turn to a wellstudied P-gp substrate that has small passive permeability and is already known to kinetically require the uptake transporters to study intracellular concentrations, e.g., digoxin [4, 20, 32, 37]. 6.2

Case 2: Digoxin

In MDCKII-hMDR1-NKI cells, the passive permeability coefficients of digoxin are PAC ¼ 40  10 nm/s and PBC ¼ 50  10 nm/s, e.g., more than tenfold smaller than those for ketoconazole and naturally in the passive permeability range in Fig. 2B. Digoxin’s uptake transporter clearances are kA ¼ 40  20 s1 for the apical uptake transporter, AT, and kB ¼ 40  3 s1 for the basolateral uptake transporter, BT [15]. Since clearance is a function of the transporter surface density and its binding and efflux kinetic parameters, it is clear why its value could vary substantially between cell lines and depend upon cell culture conditions [16, 21], as will become significant below. Once these transporters have been isolated and identified, these clearances can be replaced with concentrations of the transporters in the membranes, the binding constants of the drug to the transporters, and the rate constant for transit through that basolateral or apical uptake transporter. This would make the modeling simpler and more quantitative. Figure 3 uses the kinetic and structural parameters values for the MDCKII-hMDR1-NKI cell line [6], except as noted for these simulations. Figure 3A shows the predicted cytosolic concentration

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

55

Fig. 3 Simulated cytosolic concentration of digoxin transport with an initial concentration of 100 nM. Simulations using MDCKII-hMDR1-NKI cells over 6 h for digoxin cytosol concentrations for as a function of uptake transporter clearances. Figure A for B > A transport with kA ¼ 40 s1 and kB ranging from 0 to 40 s1. Figure B for B > A transport with kB ¼ 40 s1 and kA ranging from 0 to 40 s1. Figure C for A > B transport with kB ¼ 40 s1 and kA ranging from 0 to 40 s1. Figure D for A > B transport with kB ranging from 0 to 40 s1. The X bundle shows kA ¼ 40 s1 for all kB values. The Y bundle shows kA ¼ 20s1 for all kB values. The Z bundle shows kA ¼ 0 s1 for all kB values

of digoxin over time for B > A transport, as a function of the value of the clearance for the basolateral uptake transporter, BT, from kB ¼ 0 to 40 s1, while the apical uptake transporter, AT, is fixed at kA ¼ 40 s1. With kB ¼ 0, the cytosolic concentration increases over time, since the influx due to passive permeability exceeds the P-gp efflux. With larger kB values, the cytosolic concentration at t ¼ 0.5 h is greater and increases even faster over time, due to the increased BT mediated influx. In all cases, the AT mediated transport from the cytosol to the apical compartment simply augments the P-gp efflux, since the concentration of digoxin in the apical compartment never exceeds that of the cytosol. So, these simulations do not show stationary cytosol concentrations.

56

Joe Bentz

Figure 3B shows the predicted cytosolic concentration of digoxin over time for B > A transport, same transport system as a function of the value of the clearance for the apical uptake transporter, AT, from kA ¼ 0 to 40 s1, while the basolateral uptake transporter, BT, is fixed at kB ¼ 40 s1. Since the digoxin influx from the basolateral compartment is constant, from the combined passive permeability and BT transport, all the curves on the plot are essentially at the same concentration at 0.5 h. Clearly the initial phase of transport does not depend significantly on AT transport. After that, for kA > 5 s1 the cytosol concentration curves increase in time, which means that the digoxin concentration in the apical compartment is greater than that in the cytosol, so that net AT transport is back into the cytosol. Thus, B > A transport cannot be stationary, except when kA just happens to be very small, so we turn to A > B transport. For A > B transport, Fig. 3C shows the predicted cytosolic concentration of digoxin over time, as a function of the value of the clearance for the apical uptake transporter, AT, from kA ¼ 0 to 40 s1, while the basolateral uptake transporter, BT, is fixed at kB ¼ 40 s1. These simulated curves show the desired property of being essentially stationary over time. Given that P-gp actively effluxes drug C > A, this system sets up a stationary state as soon as enough drug reaches the cytosol to allow P-gp to create adequate efflux back into the apical chamber after 0.5 h. Obviously, the clearance value, kA, for AT will be important for establishing the cytosol concentration at the stationary state. As a bonus for this kinetic design, this type of A > C data for digoxin, or any drug, allows the elementary passive permeability coefficients PAC and PBC to be fitted using the +GF120918 A > B data. This solves the problem of having to define their values using the overall PAB and PBA values. PAC can be fitted directly from the data and PBC could then be fitted from the PAB and PAC values [5, 29]. The next question is to what extent the value of kB affects this stationary state, since we have shown that kB for digoxin varies significantly with cell line [21]. Figure 3D shows the predicted cytosolic concentration of digoxin over time for A > B transport with 100 nM digoxin initially in the apical chamber, as a function of the value of the clearance for the basolateral uptake transporter, BT, over all of the kB ¼ 0 to 40 s1 range, while the apical uptake transporter, AT, is fixed at the few different values, as indicated on the figure. For the curve bundle labeled “X,” with kA ¼ 40 s1, all of the predicted values for the cytosolic drug concentration, for kB ¼ 0 to 40 s1, are clustered together around [CC] ffi 5 nM. For the curve bundles labeled “Y” and “Z,” with kA ¼ 20 s1 or kA ¼ 0 s1, respectively, all of the predicted values for the cytosolic drug concentration are clustered together around [CC] ffi 3.3 and 1.5 nM, respectively. The different colors used for the kB clearances

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

57

are the same as used for the kB clearances in Fig. 3A, e.g., blue for kB ¼ 0 s1 to black for kB ¼ 40 s1. The “rainbow” is slightly visible for kA ¼ 40 s1, but is wholly obscured for kA ¼ 0 s1, where all the curves overlay one another. These predicted curves show the desired property of being essentially stationary over time, from t ¼ 0.5 h onward, while the specific value of the BT clearance is essentially irrelevant. The time independent amplitude of the cytosol concentration can fit the value of the AT clearance kA for different cell lines, which would be very useful in itself, as acquiring the data needed to fit this value from the apical aqueous chamber for B > A transport was very time-consuming and arduous [20]. For the MDCKII-hMDR1NKI cells, the value of kA is known, so this would clearly show whether the predicted cytosolic concentration matches the experimentally measured value. As a second bonus for this kinetic design, this type of A > C data for digoxin, or any drug, shows whether this confluent monolayer of P-gp expressing cells also expresses the kinetic requirement for an apical digoxin uptake transporter or not. If the data fitted the simulations for kA ¼ 0, it has no kinetic requirement for the apical uptake transporter, far more readily than what was used originally for digoxin and the MDCKII-hMDR1-NKI cells [20]. Since Fig. 3D was with 100 nM digoxin as the initial drug concentration in the donor apical chamber, CD(0), the next question was how the value of CD(0) affects these results. Figure 4 shows fC, the predicted cytosolic concentration, CC, divided by the initial digoxin concentration in the apical chamber, CD(0), ranging from 1 to 10,000 nM, over time for kA values ranging from 40 to 0 s1. The value of kB was not relevant to the test, since Fig. 3D showed that the value of kB made no significant difference to these plots. The Z’ bundle shown in Fig. 4 shows the fC value predicted for kA ¼ 0 s1, i.e., no AT, is essentially constant at fC  0.015 over time for all initial digoxin concentrations, i.e., 1 nM to 10,000 nM. The Y0 bundle shows the same results when kA ¼ 20 s1, where the bundle has fC  0.033, except for the highest CD(0) ¼ 10,000 nM which was fC  0.037. The X’ bundle shows the same results, where the bundle has fC  0.051, except for the highest CD(0) ¼ 10,000 nM which was fC  0.06. Thus, except for digoxin concentrations exceeding 1 μM, the digoxin concentration used to fit the kA was essentially irrelevant and can be tuned by assay requirements. Figure 4 shows the kinetic design needed to determine whether our predicted intracellular concentrations are essentially correct or not. Using the MDCKII-hMDR1-NKI cells, where kA ¼ 40  20 s1, the prediction would be that fC ¼ 0.05  0.02 over the time span of 0.5–6 h for any initial digoxin concentrations in the range of 1–1000 nM. If fC were significantly different from 0.05, then the most likely structural parameter in error would be the molar

58

Joe Bentz

partition coefficient, KPC in Eq. 1, for digoxin between the cytosol and the cytosolic plasma membrane. It is worth noting here that these simulated “quasi-linear” behaviors make sense, because at the low nM drug concentrations, the nonlinear mass action kinetic equations that govern P-gp transport kinetics are exhibiting mostly linear responses, so doubling the initial drug concentration in the donor chamber just roughly doubles the drug concentrations in the cytosol and the receiver chambers. The nonlinear behavior of the kinetic equations show up when the initial digoxin concentration exceeds 1000 nM and kA > 0 s1, which provides P-gp with enough bound drug to exhibit nonlinear efflux kinetics observed in the blue lines, CD(0) ¼ 104 nM, on Fig. 4. Figure 5 shows the predicted values of fC when the digoxin KPC ranges from 50 to 200, where the fC value is essentially stationary from 0.5 to 6 hrs. The value of fC ¼ 0.090 for KPC ¼ 50 decreases to fC ¼ 0.026 for KPC ¼ 200. Thus, if the experimentally measured fC for the MDCKII-hMDR1-NKI cells fell into the range of 0.02–0.1, then the simplest hypothesis would be that the cytosolic monolayer KPC would be given by fitting the experimental value of fC to Fig. 5. Our liposome fitted value of KPC ¼ 100 [8] yields an

Fig. 4 The plot that can fit the value of kA for any P-gp expressing confluent cell monolayer. Shown here for the MDCKII-hMDR1-NKI cells. Simulated ratio fC ¼ cytosolic concentration of digoxin/Initial concentration of digoxin due to transport with an initial concentration, CD(0), ranging from 1 to 10,000 nM over 6 h. The X’ bundle shows kA ¼ 40 s1 for all CD(0). Figure 3D showed that the value of kB was irrelevant to these simulations and was just set to kB ¼ 20 s1 for this figure. The Y0 bundle shows kA ¼ 20s1 for all CD(0). The Z’ bundle shows kA ¼ 0 s1 for all CD(0). Only the largest CD(0) ¼ 10,000 nM, the blue line, was significantly different for the lower initial digoxin concentrations and only when the apical uptake transporter clearance was significant

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

59

Fig. 5 Simulated ratio fC ¼ cytosolic concentration of digoxin/initial concentration of digoxin for A>B transport, as a function of the molar partition coefficient for digoxin between the cytosol and the cytosolic monolayer for the MDCKII-hMDR1-NKI cells with the digoxin kA ¼ 40 s1. This curve is accurate for 1–1000 nM initial digoxin concentration. The blue arrows show the experimental value of KPC ¼ 100 for the liposomes and the predicted fC ¼ 0.05. The outer monolayer drug partition coefficients, KAO and KBO, have no experimentally significant effect on the cytosolic drug concentration, provided that they are within a factor of 5 of the measured liposomal values

fC ¼ 0.05, as shown by the arrows leading from experimental fC to fitted KPC value. If the experimentally measured fC exceeds this fivefold range around fc ¼ 0.05, which likely covers the physiologically relevant range, then more complex corrections to the structural mass action kinetic model would likely be required, assuming that the experimental method for measuring the intracellular drug concentration had been proven reasonably sound. The outer monolayer drug partition coefficients, KAO and KBO, are predicted to have no significant effect on the cytosolic drug concentration when their values were altered by a factor of 5 from those published [7], simulations not shown. These same experiments are appropriate for other commonly used cell lines, like Caco-2 and LLC-PK1-hMDR1-NKI cells. Chaudhry et al. [21] has shown using B > A IC50 data from labs participating in the IC50 initiative [22–24] that both of these cell lines have a basolateral digoxin uptake transporter, with different kB clearances than those measured for the MDCKII-hMDR1-NKI cells. These B > A IC50 data could not show whether or not these cells have an apical digoxin uptake transporter, but the experiment described in Fig. 4 can. Once the kA is fitted, then the experiment shown in Fig. 3A could fit the kB for the basolateral uptake transporter, with some simple refinements.

60

7

Joe Bentz

Simulation to Experiment Extrapolation We will conclude with a real-world view of transport through confluent monolayers of P-gp expressing cells and how we model it in order to capture all the transport KP effects on drug transport. Tran et al. [5] found that the drugs amprenavir and quinidine showed no significant mass balance problems, i.e., where some of the drug entering the cells were not exiting on the timescale of the experiments. However, their passive permeability coefficients did not become essentially drug concentration independent until after about 2 h. For loperamide, with a BT uptake transporter, there was a significant mass balance problem, but the passive permeability coefficients were drug concentration independent at all times. Time-dependent passive permeabilities and mass balance were two different problems for fitting transport data that required different computational controls. Clearly, there were substantial cellular processes occurring upon introduction of some drugs to the confluent cell monolayers that strongly affected passive permeability of these drugs. While these instabilities can be due to more than one cause, one hypothesis was clearly supported by these data. Tran et al. [6] hypothesized that only P-gp roughly at the tips of microvilli would significantly contribute to total P-gp efflux activity, i.e., those P-gp whose effluxed drug actually reaches the apical aqueous compartment, as opposed to being readsorbed back into the same or an adjacent microvillus prior to reaching the apical aqueous chamber. An amphipathic compound effluxed at the base of a microvillus would randomly diffuse in the aqueous space between the microvilli and most likely be adsorbed back into the microvillus membrane for another round of efflux. In this scenario, the morphology of microvilli would alter the number of efflux active P-gp compared to total functional P-gp: the denser the microvilli “forest,” the smaller the fraction of efflux active P-gp. Meng et al. [16] compared the efflux active P-gp number to the total P-gp level, using liquid chromatography–tandem mass spectrometry, and showed for Caco-2 cells that the total P-gp was about ten-fold greater than efflux active P-gp; whereas for MDCKII- hMDR1-NKI cells these values were two-fold different. That is, a P-gp in the Caco-2 cells was five-fold less likely to successfully efflux drug into the apical chamber than a P-gp in the MDCKII-hMDR1-NKI cells. To understand the basis for this disparity, Meng et al. [38] visualized the microvilli in MDCKIIhMDR1-NKI and Caco-2 cells using three-dimensional structured illumination super-resolution microscopy, SIM, and found that the microvilli in Caco-2 cells are taller and more densely packed than those in MDCK-hMDR1-NKI cells [38]. Assuming that the P-gp expressed in these cells was equally active, one can conclude that

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

61

those P-gp that are not at or very near the tips of the microvilli are involved in a futile cycle of efflux of amphipathic drugs from the microvillus membrane, due to their reabsorption into the same or nearby microvillus membranes. This was quantitatively modeled in the same work [38]. Passive permeability would be similarly affected by microvilli morphology, so addition of drugs that affect microvilli morphology would transiently changing passive permeability. Then, there is the basolateral membrane. The micrographs in Butor and Davoust [9] show cross sections of confluent cell monolayers grown in Transwell cells without drugs. Substantial basolateral membrane processes have grown within the Transwell pores, whose movements due to drugs, could also transiently affect passive permeability. Fitting these passive permeability instabilities was a standard part of the data analysis for all of our studies and the best reason for not using the Eq. 2 to predict passive permeability coefficients. Figure 6 shows passive permeability data with 2 μM GF120918, through confluent monolayers of Caco-2 cells for 0.1–10 μM digoxin initially in the donor chamber from Meng et al. [16]. Figure 6A shows the B > A passive permeability coefficients over 0.1–4 h for 0.1(O), 0.3(Δ), 1(□), 3(X), and 10(+) μM initial digoxin concentrations in the donor basolateral aqueous chamber. Each data point was the average of triplicate wells, but the error bars were omitted for clarity. Basically, up to 1 h the calculated passive permeability coefficients were not significantly different over the digoxin concentration range. Thus, for B > A transport, the passive permeability coefficient increases over time, until about 2 h, after which it becomes reasonably constant. The thick black line traces the “average” passive permeability trajectory over time. The microvilli hypothesis mentioned above would suggest that the microvilli morphology is changing, which appears to expose more of its surface directly to the apical aqueous chamber. There may also be changes to the basolateral membrane morphology, as discussed above [9]. As described in Tran et al. [5], we have always integrated the time dependence of passive permeability changes into the fitting protocol of all our data to account for these kinetic behaviors. When those morphological changes stabilize, then the efflux active P-gp concentration and the passive permeability coefficients level off. For A > B transport, the story appears more complicated. Figure 6B shows the passive permeability coefficients for A > B transport from 0.1 to 4 h for the same initial digoxin concentrations, with the same symbols as Fig. 6A. Each data point was the average of triplicate wells and the error bars are omitted for clarity. The digoxin concentration in the apical microvilli is rapidly fixed by the initial digoxin concentration in the apical aqueous chamber, Fig. 6, as opposed to the slower process with B > A transport, and

62

Joe Bentz

Fig. 6 The experimentally measured passive permeability coefficients over time for transport of digoxin through Caco-2 cells, with 2 μM GF120918, unpublished data from Meng et al. [16]. Figure A shows passive permeability coefficients for B > A transport for 0.1(O), 0.3(Δ), 1(□), 3(X), and 10(+) μM initial digoxin concentrations in the donor chamber, i.e., shapes at the lower drug concentrations and crossed sticks at the higher drug concentrations. Each data point was the average of triplicate wells and those error bars were omitted for clarity of this figure. The thick black line suggests the basic passive permeability trajectory over time. Figure B shows passive permeability coefficients for A > B transport using the same symbols

the passive permeability coefficients for 10 μM digoxin (+) reach a roughly maximal value within 0.5 h. The data for the smaller initial digoxin concentrations required about 2 h to stabilize, with a small overshoot at 3 h. The thick black line suggests their “average” trajectory over time, not including the 10 μM digoxin data or the slight overshoot. The conclusion here is that the A > B data using initial digoxin concentrations of 1 μM or smaller yield essentially the same passive permeability coefficients after 2 h. Nevertheless, the time dependence of these passive permeability kinetic behaviors need to be part of the fitting of any transport data in order to obtain physiologically relevant fits of all the kinetic parameters.

Kinetic Design for Establishing Long-Term Stationary Cytosol. . .

63

The simulations shown in Figs. 2–5 do not include this microvilli reorganization, i.e., the passive permeabilities were fixed at their average experimental values throughout. Why not also assume that the apical clearance kA responded to the microvilli instabilities? Because so little is known about this apical digoxin uptake transporter that assuming it behaves like P-gp during these microvilli remodeling is no more reasonable than assuming that its clearance remains indifferent to this remodeling. Perhaps AT is anchored at the tips of the microvilli. Therefore, for now, the plot for fitting the molar partition coefficient for the cytosolic monolayer according to the cytosolic concentrations shown in Fig. 5 is the place to discover whether the structural mass action kinetic model for transport through confluent monolayers of P-gp expressing cells predicts the cytosol concentration well enough by changing KPC somewhat or needs to be revised to accommodate drug-induced microvilli reorganization with all transporters. The mechanism for these drug-induced intracellular passive permeability perturbations is quite likely to affect quantitative in vitro–in vivo extrapolations, IVIVE. References 1. Heikkinen AT, Korjamo T, Mo¨nkko¨nen J (2009) Modelling of drug disposition kinetics in in vitro intestinal absorption cell models. J Basic Clin Pharmacology & Toxicology 106:180–188 2. Zamek-Gliszczynski MJ, Lee CA, Poirier A, Bentz J, Chu X, Ellens H, Ishikawa T, Jamei M, Kalvass JC, Nagar S, Pang KS, Korzekwa K, Swaan PW, Taub ME, Zhao P, Galetin A; International Transporter Consortium (2013) ITC recommendations for transporter kinetic parameter estimation and translational modeling of transport-mediated PK and DDIs in humans. Clin Pharmacol Ther 94:64–79 3. Nagar S, Argikar UA, Tweedie DJ (2014) Enzyme kinetics in drug metabolism: fundamentals and applications. Methods Mol Biol 1113:1–6. https://doi.org/10.1007/978-162703-758-7_1 4. Ellens H, Meng Z, Le Marchand SJ, Bentz J (2018) Mechanistic kinetic modeling generates system-independent P-glycoprotein mediated transport elementary rate constants for inhibition and, in combination with 3D SIM microscopy, elucidates the importance of microvilli morphology on P-glycoprotein mediated efflux activity. Expert Opin Drug Metab Toxicol 14:571–584. https://doi.org/10.1080/ 17425255.2018.1480720

5. Tran TT, Mittal A, Gales T, Maleeff B, Aldinger T, Polli JW, Ayrton A, Ellens H, Bentz J (2004) Exact kinetic analysis of passive transport across a polarized confluent MDCK cell monolayer modeled as a single barrier. J Pharm Sci 93(8):2108–2123 6. Tran TT, Mittal A, Aldinger T, Polli JW, Ayrton A, Ellens H, Bentz J (2005) The elementary mass action rate constants of P-gp transport for a confluent monolayer of MDCKII-hMDR1 cells. Biophys J 88:715–738 7. Lumen AA, Acharya P, Polli JW, Ayrton A, Ellens H, Bentz J (2010) If the KI is defined by the free energy of binding to P-glycoprotein, which kinetic parameters define the IC50 for the Madin-Darby canine kidney II cell line overexpressing human multidrug resistance 1 confluent cell monolayer? Drug Metab Dispos 38:260–269 8. Lumen AA, Li L, Li J, Ahmed Z, Meng Z, Owen A, Ellens H, Hidalgo IJ, Bentz J (2013) Transport inhibition of digoxin using several common P-gp expressing cell lines is not necessarily reporting only on inhibitor binding to P-gp. PLoS One 8:e69394 9. Butor C, Davoust J (1992) Apical to basolateral surface area ratio and polarity of MDCK cells grown on different supports. Exp Cell Res 203:115–127

64

Joe Bentz

10. van Meer G, Simons K (1986) The function of tight junctions in maintaining differences in lipid composition between the apical and the basolateral cell surface domains of MDCK cells. EMBO J 5(7):1455–1464 11. van Meer G, Voelker DR, Feigenson GW (2008) Membrane lipids: where they are and how they behave. Nat Rev Mol Cell Biol 9:112–124 12. Aller SG, Yu J, Ward A, Weng Y, Chittaboina S, Zhuo R, Harrell PM, Trinh YT, Zhang Q, Urbatsch IL, Chang G (2009) Structure of P-glycoprotein reveals a molecular basis for poly-specific drug binding. Science 323:1719–1722 13. Li J, Jaimes KF, Aller SG (2014) Refined structures of mouse P-glycoprotein. Protein Sci 23 (1):34–46. https://doi.org/10.1002/pro. 2387. Epub 2013 Nov 15 14. Jin MS, Oldham ML, Zhang Q, Chen J (2012) Crystal structure of the multidrug transporter P-glycoprotein from Caenorhabditis elegans. Nature 490(7421):566–569. https://doi. org/10.1038/nature11448. Epub 2012 Sep 23 15. Agnani D, Acharya P, Martinez E, Tran TT, Abraham F, Tobin F, Bentz J (2011) Fitting the elementary rate constants of the P-gp transporter network in the hMDR1-MDCK confluent cell monolayer using a particle swarm algorithm. PLoS One 6(10):e25086 16. Meng Z, Ellens H, Bentz J (2017) Extrapolation of elementary rate constants of P-glycoprotein mediated transport from MDCKII-hMDR1-NKI to Caco-2 cells. Drug Metab Dispos 45:190–197. https://doi.org/ 10.1124/dmd.116.072140 17. Keizer J (1987) Nonequilibrium chemical potentials and free energies for enzymecatalyzed reactions. Cell Biophys 11:331–344 18. Molski A, Berling S, Keizer J (1996) Rapid chemical reactions in two dimensions: spatially nonlocal effects. J Phys Chem 100:19049–19054 19. Hinterdorfer P, Gruber HJ, Striessnig J, Glossmann H, Schindler H (1997) Analysis of membrane protein self-association in lipid systems by fluorescence particle counting: application to the dihydropyr- idine receptor. Biochemistry 36:4497–4504 20. Acharya P, O’Connor MP, Polli JW, Ayrton A, Ellens H, Bentz J (2008) Kinetic identification of membrane transporters that assist Pglycoprotein-mediated transport of digoxin and loperamide through a confluent monolayer of MDCKII-hMDR1 cells. Drug Metab Dispos 36:452–460

21. Chaudhry A, Chung G, Lynn A, Yalvigi A, Brown C, Ellens H, O’Connor M, Lee C, Bentz J (2018) Derivation of a systemindependent Ki for Pgp-mediated digoxin transport from system-dependent IC50 data. Drug Metab Dispos 46:279–290 22. Bentz J, O’Connor MP, Bednarczyk D, Coleman J, Lee C, Palm J, Pak YA, Perloff ES, Reyner E, Balimane P, Br€annstro¨m M, Chu X, Funk C, Guo A, Hanna I, Here´diSzabo´ K, Hillgren K, Li L, Hollnack-Pusch E, Jamei M, Lin X, Mason AK, Neuhoff S, Patel A, Podila L, Plise E, Rajaraman GG, Salphati L, Sands E, Taub ME, Taur J, Weitz D, Wortelboer HM, Xia CQ, Xiao G, Yabut J, Yamagata T, Zhang L, Ellens H (2013) Variability in P-glycoprotein inhibitory potency (IC50) using various in vitro experimental systems: implications for universal digoxin DDI risk assessment decision criteria. Drug Metab Dispos 41:1347–1366 23. Ellens H, Deng S, Coleman J, Bentz J, Taub ME, Ragueneau-Majlessi I, Chung SP, Here´diSzabo´ K, Neuhoff S, Palm J, Balimane P, Zhang L, Jamei M, Hanna I, O’Connor M, Bednarczyk D, Forsgard M, Chu X, Funk C, Guo A, Hillgren KM, Li L, Pak AY, Perloff ES, Rajaraman G, Salphati L, Taur JS, Weitz D, Wortelboer HM, Xia CQ, Xiao G, Yamagata T, Lee CA (2013) Application of receiver operating characteristic analysis to refine the prediction of potential digoxin drug interactions. Drug Metab Dispos 41:1367–1374. https://doi.org/10.1124/ dmd.112.050542 24. O’Connor MP, Lee C, Ellens H, Bentz J (2014) A novel application of t-statistics to objectively assess the quality of IC50 fits for P-glycoprotein and other transporters. Pharma Res Per 2(5):e00078. https://doi.org/10. 1002/prp2.78 25. Abreu MS, Estronca LM, Moreno MJ, Vaz WL (2003) Binding of a fluorescent lipid amphiphile to albumin and its transfer to lipid bilayer membranes. Biophys J 84:386–399 26. Hill WG, Zeidel ML (2000) Reconstituting the barrier properties of a water-tight epithelial membrane by design of leaflet-specific liposomes. J Biol Chem 275:30176–30185 27. Marrink SJ, J€ahnig F, Berendsen HJ (1996) Proton transport across transient single-file water pores in a lipid membrane studied by molecular dynamics simulations. Biophys J 71 (2):632–647 28. Marrink SJ, Corradi V, Souza PCT, Ingo´lfsson HI, Tieleman DP, Sansom MSP (2019) Computational modeling of realistic cell membranes. Chem Rev 119(9):6184–6226.

Kinetic Design for Establishing Long-Term Stationary Cytosol. . . https://doi.org/10.1021/acs.chemrev. 8b00460. Epub 2019 Jan 9 29. Stein WD (1986) Transport and diffusion across cell membranes. Academic Press, Orlando, FL 30. Hyafil F, Vergely C, DuVignaud P, GrandPerret T (1993) In vitro and in vivo reversal of multidrug resistance by GF120918, an acridonecarboxamide derivative. Cancer Res 53:4595–4602 31. Evers R, Kool M, Smith AJ, van Deemter L, de Haas M, Borst P (2000) Inhibitory effect of the reversal agents V-104, GF120918 and Pluronic L61 on MDR1 P-gp-, MRP1- and MRP2mediated transport. Br J Cancer 83:366–374 32. Polli JW, Wring SA, Humphreys JE, Huang L, Morgan JB, Webster LO, Serabjit-Singh CS (2001) Rational use of in vitro P-glycoprotein assays in drug discovery. J Pharmacol Exp Ther 299:620–628 33. Ho NFH, Raub TJ, Burton PS, Bausuhn CL, Adson A, Audus KL, Borchardt R (2000) Quantitative approaches to delineate passive transport mechanisms in cell culture monolayers. In: Amidon GL, Lee PI (eds) Transport processes in pharmaceutical systems. Marcel Dekker, New York, pp 219–316 34. Min KA, Zhang X, Yu JY, Rosania GR (2014) Computational approaches to analyse and predict small molecule transport and distribution

65

at cellular and subcellular levels. Biopharm Drug Dispos 35(1):15–32. https://doi.org/ 10.1002/bdd.1879. Epub 2013 Dec 10 35. Valm AM, Cohen S, Legant WR, Melunis J, Hershberg U, Wait E, Cohen AR, Davidson MW, Betzig E, Lippincott-Schwartz J (2017) Applying systems-level spectral imaging and analysis to reveal the organelle interactome. Nature 546(7656):162–167. https://doi. org/10.1038/nature22369. Epub 2017 May 24 36. Smith DA, Di L, Kerns EH (2010) The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov 9(12):929–939. https://doi. org/10.1038/nrd3287 37. Bentz J, Ellens H (2021) Status of the structural mass action kinetic model of P-gp mediated transport through confluent cell monolayers. Enzyme Kinetics in Drug Metabolism: Fundamentals and Applications. Methods Mol Biol, in press 38. Meng Z, Le Marchand SJ, Agnani D, Szapacs ME, Ellens H, Bentz J (2017) Microvilli morphology can affect efflux active P-glycoprotein in confluent MDCKII-hMDR1-NKI and Caco-2 cell monolayers. Drug Metab Dispos 45:145–151. https://doi.org/10.1124/dmd. 116.072157

Chapter 3 In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug–Drug Interactions Jibin Li, Qing Wang, and Ismael J. Hidalgo Abstract The importance of membrane transporters and drug-metabolizing enzymes on drug absorption and disposition is widely known. Our current understanding of transporters and enzymes has been achieved largely through the development and utilization of in vitro techniques to study drug transport and metabolism processes. As a result of the increasing sophistication and reliability of these techniques, the FDA has issued guidance documents outlining the type of in vitro tests that can be used to assess whether drug candidates are substrates and/or inhibitors of transporters and/or enzymes. These studies are conducted under the premise that if a drug is a substrate or inhibitor of a transporter, it may interact with other drugs that interact with the same transporter(s). Ultimately, for in vitro drug–drug interactions to be predictive of potential interactions in vivo, in vitro experiments must take into account the drug concentrations expected in vivo. As in vitro tests can be used to waive the conduct of clinical drug–drug interaction studies, they could have a major impact in drug development times and costs and spare healthy individuals from exposure to the unavoidable risks associated with clinical drug–drug interaction studies. Further, since the type and quality of data obtained from in vitro experiments is dependent on the study design used, it is very important for pharmaceutical scientists working in this field to rely on sound and robust experimental protocols to generate reliable and reproducible data. However, the various conditions used for the conduct of these assays in different laboratories have resulted in large interlaboratory variabilities that create much uncertainty regarding published data and the best experimental approaches. In fact, interlaboratory variability may not only reflect the quantitative differences inherent to the repeated performance of biological assays but also differences in the experimental design or strategies used by different laboratories. For example, in running a test to assess whether a compound is a P-gp substrate, some laboratories may use Caco-2 cells while others may use MDR1-MDCK cells. In addition, while two laboratories may use the same cell line (e.g., MDR1-MDCK) to run this test, one laboratory may make the assessment based on the efflux ratio of the substrate drug while another may utilize unidirectional (B-to-A) transport with and without a known inhibitor of P-gp. This chapter will present protocols that have been extensively used for the conduct of in vitro studies; some of which have enabled decision-making on drug–drug interaction studies and thus impacted drug development programs. Key words Drug–Drug Interactions, C2BBe1, Drug transport methodology, In vitro transport, Cellular transporter models, Interlaboratory variability

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

67

68

Jibin Li et al.

Abbreviations AP AP-to-BL BCRP BL BL-to-AP DDI DPBS EMA FDA FTC HEK HPLC IC50 LC-MS/MS MATE MPP+ N OAT OATP OCT PAH P-gp VC

1

Apical Apical-to-basolateral Breast cancer resistance protein Basolateral Basolateral to apical Drug–drug interaction Dulbecco’s phosphate-buffered saline European Medicines Agency US Food and Drug Administration Fumitremorgin C Human embryonic kidney 293 cells High performance liquid chromatography Inhibitor concentration at 50% inhibition Liquid chromatography–tandem mass spectrometry Multidrug and toxin extrusion protein 1-methyl-4-phenylpyridinium Cell number Organic anion transporter Organic anion transporting polypeptide Organic cation transporter Para-aminohippurate P-glycoprotein Vector control

Introduction The last two decades have witnessed a dramatic transformation of drug discovery research. Such transformation can be attributed to, at least in part, a rapid evolution in the development and implementation of in vitro model systems to study drug interactions with membrane transporters and/or drug-metabolizing enzymes. Among in vitro systems used to study drug transport and metabolism, cellular models have played a crucial role owing to their versatility and physiological relevance. Earlier drug–transporter and drug–enzyme interaction studies relied on primary cells isolated from the liver, intestinal mucosal, or brain capillary endothelium cells as well as on native (wild-type) cell lines such as Caco-2 or MDCK. More recently, the convergence between a need for more detailed studies and advances in cell/ molecular biology has made possible the production of cell lines expressing transgenic transporters and cells in which the expression of endogenous transporters is silenced either partially (i.e., knocked down) or completely (i.e., knocked out) [1, 2]. These systems are valuable because they help identify the role of individual

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

69

transporters in drug absorption and/or disposition. In addition, the translational value of in vitro model systems could be greatly enhanced if they express not only drug transporters but also relevant enzymes, as the integration of transport and metabolism processes would permit to examine transporter-mediated and enzyme-mediated drug interactions under conditions that take into account the interplay between transporters and enzymes and the complementary interaction of apical and basolateral transporters during transcellular transport. In contrast to the widespread development of mammalian cell lines expressing human and animal transporters of interest, success in the expression of relevant enzymes in cellular systems has been very limited [3, 4]. The continuous increase in the use of cellular systems to study transporter- or enzyme-mediated drug interactions has shown the existence of large interlaboratory variabilities that makes it difficult to compare and/or pool data from different laboratories. For example, a recent examination of the literature found that the relative expression factors for intestinal P-gp and BCRP in Caco2 cells ranged from 0.4 to 5.l and from 1.1 to 90, respectively [5]. In addition, a collaborative study among 23 entities (representing pharmaceutical, contract research, and academic laboratories), which undertook the comparison of IC50 values obtained using four in vitro systems (Caco-2, MDCKII-MDR1, LLC-PK1MDR1, and membrane vesicles with human MDR1), six transport activity equations, and 16 model P-gp inhibitors, found that the minimum variability in IC50 values was 20-fold (with sertraline) and the maximum was 796-fold (with verapamil) [6]. This observation, which is hardly surprising in light of the large number of variables that can impact the results from these experiments, indicates the needs of attempts to decrease inter-laboratory variability through experimental condition standardization or superior data analysis techniques such as comprehensive mechanistic analysis developed by Chaudhry et al. [7]. As with membrane transporter assays, results from drug metabolism assays also exhibit a great deal of interlaboratory variability [8]. Metabolism assays, which most commonly use either subcellular preparations of liver or intestine from different animal species or cellular systems, also exhibit large variability and are susceptible to the same factors that affect transporter assays. Cell-based metabolism assays generally consist of primary hepatic cells from humans or animals. The large variability associated with primary cells may reflect interindividual differences among donor animals, the impact of the isolation and/or preservation (e.g., freeze–thaw) processes on cellular function, and assay procedures. Thus, if results are influenced by the biological system and experimental design used, it makes sense to strive to minimize differences among protocols used (e.g., probe substrate, substrate concentration, with or without preincubation) to run metabolism experiments and to reconcile

70

Jibin Li et al.

determined parameters (e.g., IC50 vs Ki, %uptake vs pmol/min/mg protein). Because, even if ultimately the conclusions are similar, a similarity in the experimental conditions used and determined parameters should be of great help to enhance the comparability of data from different laboratories. The utilization of cell-based assay systems in drug discovery quickly expanded to drug development. In particular, Caco-2 permeability measurements were introduced in the context of the biopharmaceutics classification system (BCS) [9], to undertake drug classification, which in some cases would allow drug manufacturers to use in vitro drug dissolution profiles as surrogates of bioequivalence studies. The introduction of the BCS guidelines in the year 2000 provided much impetus for the continued development of cellular systems that led to the issuance of separate guidelines for the conduct of studies to assess transporter- and enzyme-mediated drug–drug interaction processes [10, 11]. This acceptance of in vitro (mostly cell-based) tests that permit regulatory agencies to decide on the need to conduct in vivo enzyme- or transporter-mediated drug interaction studies demonstrates the increasing acceptance of data derived from cell-based models by regulatory authorities. Owing to the impact of decisions made based on in vitro data, it is essential to pay attention to the reliability of data generated in these systems, and since experimental results are susceptible to the conditions used to culture cells and to conduct permeability experiments, a straightforward approach to reduce laboratory variability is the utilization of sufficiently harmonized procedures for cell culture and experimental measurements. This chapter presents experimental approaches we have extensively used to conduct assays with the most common transporters. 1.1 Drug Transporters

The vast majority of membrane transporters belong to one of two superfamilies: solute-link carrier (SLC) or ATP-binding cassette (ABC) transporters. Generally, SLC transporters mediate drug uptake and ABC transporters mediate drug efflux, and both types of transporters can influence drug absorption and disposition that may give rise to transporter-mediated drug interactions. The preferred in vitro methods to assess the potential of new molecular entities as substrates or inhibitors of efflux transporters, such as P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP), are cell-based bidirectional permeability assays. Cell lines recognized by the European Medicine Agency (EMA) and US Food and Drug Administration (FDA) for studying these transporters include the human colon adenocarcinoma cell line Caco-2 as well as other transporter-overexpressing cell lines [10, 11]. The cell lines most commonly used to study efflux transporters (as described in this chapter) are C2BBe1 (a clone of Caco-2) and MDCK cells overexpressing BCRP or P-gp, and protocols routinely used for the

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

71

performance of transporter and metabolism assays are presented in some detail. The probe substrates we use for P-gp and BCRP are digoxin and cladribine, respectively. In general, for studies involving uptake transporters, including organic anion (OATs and OATPs) and organic cation (OCTs) transporters, the most commonly used system consists of cell lines overexpressing the transporter(s) of interest [11]. Assay systems described in the current chapter utilized human kidney epithelial 293 (HEK) transfected with various transporters. In all cases, transporters-selective substrates and inhibitors were used to demonstrate the functionality of each transporter.

2

Transporter Substrate

2.1 Efflux Transporters (P-gp and BCRP) 2.1.1 Experimental Procedures

1. The cells are cultured on 12-well Transwell® plates and used for permeability assessment after they reach maturity (i.e., 100% confluence, suitable barrier properties and full transporter expression). 2. The cell culture medium is aspirated and the cell monolayers are rinsed once with Hanks’ buffer (pH 7.4). 3. For apical-to-basolateral (AP-to-BL) transport, 0.5 mL of dosing solution is added to the apical compartment and 1.5 mL of Hanks’ buffer (pH 7.4) is added to the basolateral compartment. 4. For basolateral-to-apical (BL-to-AP) transport, 1.5 mL of dosing solution is added to the basolateral compartment, and 0.5 mL of Hanks’ buffer (pH 7.4) is added to the apical compartment. 5. The assay plate is incubated at 37
C, typically for 2 h; samples are collected from both apical and basolateral compartments at the end of the incubation period. 6. Drug concentrations are determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS).

2.1.2 Data Analysis

Transport rate ðTR Þ ¼ Efflux ratio ¼ Net flux ratio ¼

C R final  V R  A=120 N

ð1Þ

TR ðBLtoAPÞ TR ðAPtoBLÞ

ð2Þ

Efflux ratioðtransfected MDCK Þ Efflux ratioðMDCK Þ

ð3Þ

where VR is the volume of the receiver compartment (mL); A, Avogadro’s number (6.02  1023 molecules/mole); N, cell number on Transwell insert; CRfinal, cumulative receiver concentration

72

Jibin Li et al.

at the end of the incubation (μM). The unit of TR is molecules/ min/cell. 2.1.3 Typical Results for Efflux Transporter Probe Substrates

In the absence of a P-gp inhibitor, the efflux ratios (ER) of digoxin were 21.4, 380, and 8.80 in C2BBe1, MDR1-MDCK, and MDCK cells, respectively (Table 1). The net flux ratio (ERMDR1-MDCK/ ERMDCK) was 43.2. These results indicate normal function of P-gp in both test systems. In the absence of a BCRP inhibitor, the efflux ratios of cladribine were 15.0, 55.1, and 1.00 in C2BBe1, BCRP-MDCK, and MDCK cells, respectively (Table 2). The net flux ratio (ERBCRPMDCK/ERMDCK) was 54.9. These results indicate normal function of BCRP in both test systems.

2.1.4 Results Interpretation

Currently, one approach for determining the P-gp (or BCRP) substrate potential of test drugs using C2BBe1 cells is as follows: when the efflux ratio (determined with Eq. 2) is greater than or equal to 2, the test drug is considered a likely substrate of the transporter. Subsequently, the bidirectional permeability of the test drug is determined in the presence of more than one transporter-specific inhibitor, and if the inhibition is greater than 50%, the test drug is classified as a substrate of the transporter [12]. An alternative approach is to conduct the bidirectional permeability assay using

Table 1 Transport rate and efflux ratio of digoxin TR(106molecules/min/cell)a Cell line

Treatment

AP-to-BL

BL-to-AP

Efflux ratio

C2BBe1

10μM digoxin

0.203  0.0229

4.35  0.502

21.4

MDR1-MDCK

10μM digoxin

0.0111  0.00174

4.23  0.464

380

MDCK

10μM digoxin

0.0676  0.0549

0.595  0.0754

8.80

Mean  SD (n ¼ 3)

a

Table 2 Transport rate and efflux ratio of cladribine TR(106molecules/min/cell)a Cell line

Treatment

AP-to-BL

BL-to-AP

Efflux ratio

C2BBe1

10μM Cladribine

0.332  0.0439

4.98  0.105

15.0

BCRP-MDCK

10μM Cladribine

0.107  0.0124

5.91  0.710

55.1

MDCK

10μM Cladribine

0.0292  0.00625

0.0293  0.00187

1.00

Mean  SD (n ¼ 3)

a

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

73

transporter-transfected MDCK cell systems, as follows: if the net flux ratio (determined with Eq. 3) is greater than or equal to 2, the test drug is likely a substrate of the transporter. Also, the bidirectional permeability of the test drug is further determined in the presence of one transporter-specific inhibitor in transportertransfected MDCK cells, and if the inhibition is greater than 50%, the test drug is classified as a substrate of the transporter [12]. Since both, P-gp and BCRP are endogenously expressed in C2BBe1 cells, it is necessary to use more than one transporter inhibitor to determine which transporter is responsible for the observed [12]. If the efflux ratio (or net flux ratio) indicates that the test drug is not a substrate of P-gp or BCRP, the conduct of clinical drug– drug interaction studies is not necessary. However, if the test drug is a P-gp or BCRP substrate in vitro, several factors need to be considered before a clinical drug–drug interaction study is conducted. For example, the drug’s safety margin, therapeutic index, and likely concomitant medications [12]. It is worth mentioning that the bioavailability of highly permeable and highly soluble (e.g., BCS Class 1) drugs is unlikely to be affected by interaction with P-gp or BCRP. 2.2 Uptake Transporters (OATP1B1, OATP1B3, OAT1, OAT3, OCT1, OCT2, MATE1, MATE2K) 2.2.1 Experimental Procedures

2.2.2 Data Analysis

1. The cells are cultured in 24-well plates and used in drug uptake experiments after reaching confluency. 2. The culture medium is gently aspirated and the cells incubated with dosing solution for different periods of time. 3. At the end of the incubation period, the solution is gently aspirated; the cells are rinsed twice (1000μL per rinse) with ice-cold Hanks’ buffer (pH 7.40). 4. The cells are lysed in 400μL acetonitrile:water (3:1, v/v) solution containing internal standard (IS) and the lysates (300μL) collected for determination of probe substrate concentrations by LC-MS/MS. Cs  V s  A N T IR TF Influx rate ratio ¼ IR VC

Influx rate ðIR Þ ¼

ð4Þ ð5Þ

where IR is average influx rate (molecules/cell/min); Cs, drug concentration in cell lysate; Vs, volume of cell lysate; A, Avogadro’s number (6.02  1023 molecules/mole); N, cell number; T, incubation duration; IRVC, influx rate in vector control-transfected cells; IRTF, influx rate in transporter-transfected cells.

74

Jibin Li et al.

Table 3 Influx rate and influx rate ratios of the probe substrate Cell line

Probe substrate

IR (105 molecules/min/cell)a

Influx rate ratio

VC OATP1B1 OATP1B3

Atorvastatin

0.280  0.00725 6.06  0.350 3.58  0.141

21.7 12.8

VC OAT1

PAH

3.18  0.433 40.3  3.80

12.7

VC OAT3

Furosemide

2.19  0.505 46.2  1.76

21.1

VC OCT1 VC OCT2

MPP+

VC MATE1 MATE2K

Metformin

12.4  1.01 615  19.3 14.0  1.57 535  18.4 13.6  1.13 988  58.2 88.2  4.59

49.6 38.1 72.7 6.49

Mean  SD (n ¼ 3)

a

2.2.3 Typical Results of Uptake Transporter Probe Substrates

In the absence of inhibitor, the influx rate ratios (Eq. 5) of the probe substrates ranged from 6.49 to 49.6 (Table 3).

2.2.4 Results Interpretation

Currently, the influx rate ratio (Eq. 5) is used to assess the substrate potential of a test drug as per the EMA and FDA guidelines: if the influx rate ratio is greater than 2 and is reduced more than 50% by a transporter-specific inhibitor, the test drug in question is a substrate of the uptake transporter [11, 12]. In contrast to the wide tissue expression of P-gp or BCRP, the expression of many uptake transporters is tissue-specific. Therefore, it becomes necessary to assess the substrate potential of the investigational drug only when it has shown significant clearance in certain organs. Per FDA guidance, if an investigational drug’s hepatic uptake or elimination is 25% of the total drug’s clearance, or the drug’s uptake into the liver is clinically important (e.g., for biotransformation or to exert a pharmacological effect), the in vitro determination of interaction with hepatic uptake transporter is needed [12]. Similarly, if a test drug’s renal secretion is 25% of the systemic clearance, in vitro assessment of interaction with renal uptake transporter is needed [12]. If the test drug is a substrate of hepatic or renal uptake transporters in vitro, additional factors, such as drug’s safety margin, therapeutic index, and likely concomitant medications, need to be considered first before clinical drug–drug interactions studies are conducted [12].

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

3

75

Transporter Inhibition

3.1 Efflux Transporters (P-gp and BCRP)

1. The cells are cultured on 12-well Transwell® plates and used for the directional permeability assessment upon reaching maturity.

3.1.1 Experimental Procedures

2. The cell culture medium is aspirated and cell monolayers rinsed once with Hanks’ buffer (pH 7.4). 3. A 30-min preincubation with the known inhibitor solution is performed to preload the cells; the preincubation solution is placed on both sides (AP and BL) of the cell monolayers. The inhibitors used are valspodar (P-gp) and Ko143 (BCRP). After 30 min, the preincubation solution is aspirated, and the permeability assay is initiated as follows. 4. For apical-to-basolateral (AP-to-BL) transport, 0.5 mL of dosing solution with compounds is added to the apical compartment and 1.5 mL of Hanks’ buffer (pH 7.4) is added to the basolateral compartment. For basolateral-to-apical (BL-to-AP) transport, 1.5 mL of dosing solution is added to the basolateral compartment, and 0.5 mL of Hanks’ buffer (pH 7.4) is added to the apical compartment. Inhibitors are present in the donor and receiver solutions. 5. The assay plate is incubated at 37
C, typically for 2 h; samples are collected from both apical and basolateral compartments at the end of the incubation. 6. The concentrations of compounds are determined by LC-MS/ MS. 

3.1.2 Data Analysis

Percent inhibition ¼

Efflux ratioinhibitor 1 Efflux ratioNo inhibitor

  100

ð6Þ

The calculation of transport rate (Eq. 1) and efflux ratio (Eq. 2) are described in Subheading 2.1.2. 3.1.3 Typical Results of Efflux Transporter Inhibitors P-gp Inhibitors

In C2BBe1 cells, in the absence of a P-gp inhibitor, the efflux ratio (ER) of digoxin was 26.7, and the addition of valspodar or CsA decreased the ER to 1.32 or 1.73, respectively, which corresponds to 98.7% and 97.2% inhibition (Table 4), indicating normal P-gp function in C2BBe1 cells. In MDR1-MDCK cells, in the absence of valspodar, the ER of digoxin was 267 and the addition of valspodar decreased the ER to 1.29, equivalent to 99.9% inhibition of P-gp (Table 4), demonstrating normal function of P-gp in these cells.

76

Jibin Li et al.

Table 4 Transport rate, efflux ratio, and percent inhibition of digoxin TR (106molecules/min/cell)a

Inhibition (%)

Cell line

Treatment

AP-to-BL

BL-to-AP

ER

C2BBe1

10μM digoxin 10μM digoxin with valspodar 10μM digoxin with CsA

0.129  0.0271 0.726  0.126

3.44  0.385 0.960  0.129

26.7 1.32 98.7

0.833  0.0930

1.44  0.161

1.73 97.2

MDR1MDCK

10μM digoxin 10μM digoxin with valspodar

0.0127  0.00110 3.38  0.325 267 0.0773  0.00345 0.100  0.00801 1.29 99.9

Mean  SD (n ¼ 3)

a

Table 5 Transport rate, efflux ratio, and percent inhibition of cladribine TR(106molecules/min/cell)a AP-to-BL

Cell line

Treatment

C2BBe1

10μM Cladribine 0.233  0.00576 10μM Cladribine with 0.431  0.0384 Ko143 10μM Cladribine with FTC 0.294  0.0414

BCRPMDCK

10μM Cladribine 10μM Cladribine with Ko143

Inhibition (%)

BL-to-AP

ER

4.71  0.540 0.513  0.0745

20.2 1.19 99.0

0.428  0.0608

1.46 97.6

0.0685  0.00566 4.33  0.474 63.2 0.0752  0.0155 0.111  0.00799 1.47 99.2

Mean  SD (n ¼ 3)

a

BCRP Inhibitors

In C2BBe1 cells, the efflux ratio (ER) of cladribine was 20.2 in the absence of inhibitor, but this ratio was decreased to 1.19 and 1.46, equivalent to 99.0% and 97.6% inhibition, in the presence, respectively, of the BCRP inhibitors Ko143 and FTC (Table 5), values consistent with normal function of BCRP in C2BBe1 cells. In BCRP-MDCK cells, in the absence of BCRP inhibition, the ER of cladribine was 63.2 and the addition of the BCRP inhibitor Ko143 reduced the ER to 1.47, a 99.2% inhibition (Table 5), which demonstrates normal function of BCRP in these cells.

3.1.4 Results Interpretation

Currently, for orally administered drugs, the EMA and the FDA use the following ratio to assess in vivo P-gp/BCRP inhibition potential: I2 /IC50 (I2 ¼ dose of inhibitor/250 mL). If [I]2/IC50 < 10, the test drug is unlikely to inhibit P-gp or BCRP in vivo (12, 13). However, if the test drug is administered by a parenteral route, the EMA and FDA have different criteria: EMA uses unbound I1/IC50 (I1 ¼ Cmax of the test drug), whereas FDA uses I1/IC50.

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

77

Correspondingly, if unbound I1/IC50 is less than 0.02 (per EMA), or I1/IC50 is less than 0.1 (per FDA), the test drug does not have the potential to inhibit P-gp or BCRP in vivo [12]. If in vitro studies indicate that (based on these ratios) the test drug has the potential to inhibit P-gp or BCRP in vivo, a clinical drug–drug interaction study with known P-gp (or BCRP) substrate drugs may be conducted, always taking into consideration the likelihood of coadministration with such P-gp/BCRP substrate drug(s) [12]. 3.2 Uptake Transporters (OATP1B1, OATP1B3, OAT1, OAT3, OCT1, OCT2, MATE1, MATE2K) (See Table 6)

1. The cells used in these experiments are seeded on 24-well plates and cultured until they achieved suitable density.

3.2.1 Experimental Procedures

3. At the end of the incubation period, the solution is gently aspirated and the cells rinsed twice with ice-cold Hanks’ buffer.

2. The culture medium is gently aspirated and the cells are incubated with dosing solution in the absence and presence of inhibitor for different periods of time (depending on the individual transporter system).

4. The cells are lysed in 400μL acetonitrile:water (3:1, v/v) and the lysates are collected for analysis of the probe substrate concentration with LC-MS/MS. 3.2.2 Data Analysis

Net influx rate ðNIR Þ ¼ IR TF  IR VC    NIR inhibitor  100 Percentage inhibition ¼ 1  NIR No inhibitor

ð7Þ ð8Þ

The calculation of influx rate (Eq. 4) and the influx rate ratio (Eq. 5) are described in Subheading 2.2.2. Table 6 Uptake transporter inhibitor assessment conditions Transporters

Probe substrate (Conc.)

Inhibitor (Conc.)

OATP1B1 and OATP1B3a

Atorvastatin (0.15μM)b

Rifamycin SV (10μM)

OAT1 OAT3

Furosemide (5μM)

OCT1

MPP+ (5μM)b

OCT2 MATE1 MATE2K a

PAH (10μM)

b

+

MPP (5μM)

Probenecid (100μM) b

Probenecid (100μM) Repaglinide (300μM)

b

Imipramine (300μM)

Metformin (50μM)

c

Cimetidine (100μM)

Metformin (50μM)

c

Ondansetron (100μM)

The cells are pre-incubated with plain HBSSg7.4 or rifamycin SV for 30 min prior to the assay The dosing solutions are prepared with HBSSg7.4 c The dosing solutions are prepared with HBSSg8.5 b

78

Jibin Li et al.

Table 7 Influx rate and influx rate ratios of probe substrates Influx rate (105 molecules/min/cell)a Cell line

Probe substrate

No inhibitor

With inhibitor

Inhibition (%)

VC OATP1B1 OATP1B3

Atorvastatin

0.372  0.0534 6.68  0.480 2.79  0.129

0.422  0.0406 0.753  0.121 0.527  0.0585

94.8 95.7

VC OAT1

PAH

3.58  0.488 45.4  4.28

3.81  0.461 6.57  0.763

93.4

VC OAT3

Furosemide

2.58  0.246 46.5  2.52

2.98  0.313 5.32  0.411

94.7

VC OCT1

+

14.8  2.03 614  23.7

8.45  0.129 85.3  2.07

87.2

11.6  0.601 615  2.42

8.24  0.897 17.8  1.02

98.4

20.7  6.74 824  49.2

17.9  2.29 47.6  2.40

96.3

5.67  0.35 63.5  0.993

5.42  0.270 9.88  3.25

92.3

MPP

+

VC OCT2

MPP

VC MATE1

Metformin

VC MATE2K

Metformin

Mean  SD (n ¼ 3)

a

3.2.3 Typical Results of Uptake Transporter Inhibition

The presence of inhibitor caused a significant decrease (90% or higher) in the influx rate of the probe substrate in transportertransfected cells (Table 7).

3.2.4 Results Interpretation

The in vitro inhibition criteria can be roughly categorized into two types: hepatic uptake transporters (including OATP1B1, OATP1B3, and OCT1) and renal uptake transporters (OAT1, OAT3, OCT2, MATE1, MATE2K). EMA and FDA differ in their criteria for the classification of drugs as inhibitors of these two types of transporter are described below. For hepatic uptake transporters, EMA uses Iu,inlet, max (maximal unbound inhibitor concentration in portal venous blood) for orally administered drug and unbound I1 for parenterally administered drugs. If the ratio Iu,inlet, max/IC50 is less than 0.04, or unbound I1/IC50 is less than 0.02, it is concluded that the test drug does not have the potential to inhibit the transporter in vivo. On the other hand, the FDA lists only one parameter, unbound Iin,max (maximal unbound inhibitor concentration at the inlet to the liver), to assess the inhibition potential in vivo. If the ratio unbound Iin,max/IC50 is less than 0.1, it is concluded that the investigational drug does not have the potential to inhibit the transporter in vivo.

In Vitro Methodologies to Assess Potential for Transporter-Mediated Drug-. . .

79

For renal uptake transporters, both EMA and FDA use the ratio of unbound I1/IC50, but the cutoff values are different. While for the EMA, if the ratio unbound I1/IC50 is less than 0.02, the test drug does not have the potential to inhibit the transporter in vivo, for the FDA, the test drug does not have the potential to inhibit the transporter in vivo, if the ratio unbound I1/ IC50 is less than 0.1. When in vitro studies indicate (based on these ratios) that the test drug has the potential to inhibit the uptake transporter in vivo, appropriate in vivo drug–drug interaction studies with known transporter substrate drugs may be conducted, as long as there is a real possibility that these two agents may be concurrently administered in real clinical situations [12].

4

Conclusions The increasing sophistication of using data from cellular models to predict potential drug–drug interactions in humans has led to the acceptance of certain in vitro test systems to decide the necessity of in vivo studies. Given the importance of the decisions made based on these data, it is imperative to ensure that the quality of in vitro data is suitable to avoid unwarranted decisions and enhance the translatability on in vitro data. Proper focus requires paying due attention to the experimental protocols being used to generate the in vitro data.

References 1. Zhang W, Li J, Allen SM, Weiskircher EA, Huang Y, George RA, Fong RG, Owen A, Hidalgo IJ (2009) Silencing the breast cancer resistance protein expression and function in Caco-2 cells using lentiviral vector-based short hairpin RNA. Drug Metab Dispos 37:737–744 2. Sampson KE, Brinker A, Pratt J, Venkatraman N, Xiao Y, Blasberg J, Steiner T, Bourner M, Thompson DC (2014) Zinc finger nuclease-mediated gene knockout results in loss of transport activity for P-glycoprotein, BCRP and MRP2 in Caco-2 cells. Drug Metab Dispos 43:199–207 3. Ahlin G, Hilgendorf C, Karlsson J, Szigyarto CA, Uhle´n M, Artursson P (2009) Endogenous gene and protein expression of drugtransporting proteins in cell lines routinely used in drug discovery programs. Drug Metab Dispos 37:2275–2283 4. Cummins CL, Jacobsen W, Christians U, Benet LZ (2004) CYP3A4-transfected Caco-

2 cells as a tool for understanding biochemical absorption barriers: studies with sirolimus and midazolam. J Pharmacol Exp Ther 308:143–155 5. Harwood MD, Achour B, Neuhoff S, Russell MR, Carlson G, Warhurst G, RostamiHodjegan A (2016) In vitro-in vivo extrapolation scaling factors for intestinal p-glycoprotein and breast cancer resistance protein: part II. The impact of cross-laboratory variations on intestinal transporter relative expression factors on predicted drug disposition. Drug Metab Dispos 44:476–480 6. Bentz J, O’Connor MP, Bednarczyk D, Coleman J, Lee C, Palm J, Pak YA, Perloff ES, Reyner E, Balimane P, Br€annstro¨m M, Chu X, Funk C, Guo A, Hanna I, Here´diSzabo´ K, Hillgren K, Li L, Hollnack-Pusch E, Jamei M, Lin X, Mason AK, Neuhoff S, Patel A, Podila L, Plise E, Rajaraman G, Salphati L, Sands E, Taub ME, Taur J-S, Weitz D, Wortelboer HM, Xia CQ, Xiao G,

80

Jibin Li et al.

Yabut J, Yamagata T, Zhang L, Ellens H (2013) Variability in p-glycoprotein inhibitory potency (IC50) using various in vitro experimental systems: implications for universal digoxin drug-drug interaction risk assessment decision criteria. Drug Metab Dispos 41:1347–1366 7. Chaudhry A, Chung G, Lynn A, Yalvigi A, Brown C, Ellens H, O’Connor M, Lee C, Bentz J (2018) Derivation of a systemindependent Ki for P-glycoprotein mediated digoxin transport from system-dependent IC50 data. Drug Metab Dispos 46:279–290 8. Bowman CM, Benet LZ (2019) Interlaboratory variability in human hepatocyte intrinsic clearance values and trends with physicochemical properties. Pharm Res 36:113 9. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) (2000) Guidance for industry. Waiver of

in vivo bioavailability and bioequivalence studies for immediate-release solid oral dosage forms based on a biopharmaceutics classification system. Rockville, MD 10. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) (2006) Draft guidance/guidance for industry. Drug-drug interaction studies-study design, data analysis, and implications for dosing and labeling recommendations. Rockville, MD 11. European Medicines Agency (2012) Guideline on the investigation of drug interactions 12. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) (2020) In vitro drug interaction studies — cytochrome P450 enzyme- and transportermediated drug interactions guidance for industry. Rockville, MD

Chapter 4 Determination of Fraction Unbound and Unbound Partition Coefficient to Estimate Intracellular Free Drug Concentration Sangwoo Ryu, Keith Riccardi, Samantha Jordan, Nathaniel Johnson, and Li Di Abstract Intracellular free drug concentrations are critical to develop pharmacokinetic and pharmacodynamic relationships, estimate therapeutic indices, and predict drug–drug interaction potentials. Free drug concentration at the site of action is most relevant to understand efficacy and toxicity. Because free drug concentrations are difficult to measure directly, indirect methods are often applied. One of the most commonly used indirect methods in drug discovery is to measure total drug concentration and fraction unbound ( fu). The free drug concentration is then calculated by multiplying the total drug concentration with fu. Many methods have been developed to measure fu of cells and tissues, such as using cell or tissue homogenate with equilibrium dialysis, or partition coefficient (Kp) of cells at 4
C ( fu ¼ 1/Kp). The method using equilibrium dialysis with tissue or cell homogenate tends to be higher throughput, more reproducible, and cost less. In addition, many tissues are readily available. The method of using a cell Kp at 4
C is useful in special cases when binding to the specific components in the cell occurs. Determining the unbound partition coefficient (Kpuu) involves measuring the total concentration of both cells/tissues and media/plasma, as well as binding in all the matrices. Intracellular free drug concentration can then be calculated by multiplying Kpuu with free media/plasma concentration. These methodologies are widely applied in drug discovery and development to estimate intracellular free drug concentration and to enable a more accurate prediction of safety and efficacy outcomes in the clinic. Key words Intracellular free drug concentration, Fraction unbound, fu, Partition coefficient, Kp, Unbound partition coefficient, Kpuu, Equilibrium dialysis, Pre-saturation method

1

Introduction According to the free drug hypothesis, intracellular free drug concentration plays an integral role in understanding the efficacy and toxicity of intracellular targets [1]. Therefore, determination of intracellular free drug concentration is essential to develop PK/PD (pharmacokinetic/pharmacodynamic) relationships, estimate therapeutic indices (TI), define toxicity, and predict drug–

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

81

82

Sangwoo Ryu et al.

Lysosome pH 4.7 +19 mV

Passive Diffusion

Metabolism

Influx Efflux

Cytosol pH 7.1 - 38 mV

Mitochondria pH 8, -167 mV

Media/Blood pH 7.4

Fig. 1 Processes impact intracellular free drug concentration

drug interaction (DDI) potentials. Intracellular free drug concentration is often assumed to be the same as free medium concentration in vitro or free plasma concentration in vivo at the steady state. However, certain processes can lead to a higher or lower intracellular free drug concentration compared to a free medium or plasma concentration [2–5]. These mechanisms include influx and efflux transport, metabolism, pH gradients across cell membranes, and membrane potentials (Fig. 1) [2]. Methods have been developed to determine intracellular free drug concentrations, although direct measurements have been shown to be challenging. As a result, several indirect methods are commonly applied to estimate intracellular free drug concentration. These include measurement of total cell/tissue concentration and fraction unbound ( fu), or unbound partition coefficient (Kpuu) and free medium/plasma concentration (Fig. 2). From these parameters, intracellular free drug concentration can be derived (Fig. 2). It is important to point out that the intracellular free drug concentration obtained by these approaches represents the average free drug concentration from all the subcellular organelles such as cytosol, lysosome, and mitochondria. It encompasses all the active (influx/efflux transporters) and passive (pH-gradient, membrane potential) processes. If a specific subcellular organelle (e.g., lysosome) is of interest for target engagement, the free drug concentration in that organelle will need to be determined. In these cases, other approaches can be used, such as imaging [6–8], fractionation by differential centrifugation (see Prof. Kim Brouwer’s chapter) [9], or modeling and simulations [10]. This chapter will discuss how to determine the average intracellular free drug concentration of cells in vitro and tissues in vivo.

Determination of Fraction Unbound and Unbound Partition Coefficient

In Vivo

In Vitro Cu,cell= Ct,cell x fu,cell

Cu,tissue= Ct,tissue x fu,tissue

Cu,cell = Kpuu x Cu,media

Cu,tissue = Kpuu x Cu,plasma

Cell-Based

83

Blood

Liver

Fig. 2 Approaches to obtain in vitro and in vivo intracellular free drug concentration

2

Measurement of Total Drug Concentration For measuring total drug concentrations in vitro, please see Subheading 4 on Kp determination at 37
C. For total drug concentration of tissues in vivo, it can be measured by taking tissue samples (e.g., terminal studies for preclinical species and biopsy in humans) and quantifying using LC-MS/MS, or using PET (positron emission tomography) imaging [11]. For steady-state parameter measurements (i.e., Kpuu), it is important to use intravenous (IV) infusion to steady state [2] or AUC (area under the curve) from IV bolus administration. Oral dosing tends to overestimate tissue concentration for certain organs (e.g., liver) due to first-pass effects.

3

Measurement of fu Many methods have been developed and implemented to measure fu [12]. This includes equilibrium dialysis, ultracentrifugation, ultrafiltration, Transil®, equilibrium gel filtration, and methods for measuring on–off rates (e.g., Biacore, charcoal method) [12]. Equilibrium dialysis is one of the most commonly applied methods for fu determination, as nonspecific binding to the device has minimal effect on the data accuracy once equilibrium is achieved. For challenging highly bound compounds, modified equilibrium dialysis methods (e.g., dilution method [13], pre-saturation method [13, 14], flux dialysis method [15], low temperature method [16], and the ultracentrifugation method [17]) can be used to generate reliable data [12]. This chapter will discuss the standard equilibrium dialysis method (Fig. 3) and the pre-saturation method for fu measurement. For other binding methods, readers can refer to the references discussed above.

84

Sangwoo Ryu et al. Membrane Plasma + Drug Buffer

Standard 6h

(1)

Dilution

Diluted Plasma + Drug 6h

(2)

Plasma + Drug

Pre-Saturation

Diluted Drug 6h

(3) Fig. 3 Comparison of three equilibrium dialysis methods: (1) standard method; (2) dilution method; (3) pre-saturation method

3.1 Equilibrium Dialysis Method 3.1.1 Preparation of Tissue and Cell Homogenate

Tissue Homogenate Preparation

For intracellular free drug concentration determination, fu is needed to convert total drug concentration (Ct) to free concentration (Cu ¼ Ct  fu). As it has been demonstrated previously, tissues have similar fu as cells (e.g., liver fu and hepatocyte fu is equivalent), and fu values across various cells are also comparable once the difference in cell diameters are corrected through dilution factor (e.g., fu of hepatocytes is similar to that of HEK-293 and Huh7) [18]. It has also been shown that tissue or cell binding is largely species independent [18–20]. Therefore, a single tissue (e.g., rat liver) can be used as a surrogate for binding of other tissues/cells of other species with certain scaling factors [18, 20]. Herein both tissue and cell homogenate preparations are discussed. For cell homogenate preparation, it is important to use high cell density (e.g., 50 million cells/mL). Otherwise, the measured fu value (i.e., diluted fu or fud) will not be able to be converted back to the undiluted fu correctly [18]. Using low cell density (e.g., 0.5 million cells/mL) gives binding information under the in vitro incubation conditions (i.e., fu,inc), but not fu,cell. 1. Take a small piece of tissue (~ 1 g liver, for example, large piece is more difficult to homogenize) and place it in a weight boat. Rinse the tissue three times with 20 mL of phosphate-buffered saline (PBS) each time at room temperature (RT) to remove residual blood in the tissue. Excess residual blood might lead to inaccurate fu measurements.

Determination of Fraction Unbound and Unbound Partition Coefficient

85

2. Dry tissue with paper towel, weigh tissue, and add tissue to a 50 mL conical tube. Cut tissue into small pieces with a large pair of scissors. This will make the homogenization process easier. 3. Add 4 times PBS based on the weight of the tissue into the conical tube to generate 5 times tissue dilution (e.g., 1 g tissue +4 mL PBS ¼ 5 times dilution, i.e., dilution factor D ¼ 5). If the tissue is particularly difficult to homogenize (e.g., adipose), a 10-time dilution by weight is often created. 4. Use an Omni TH tissue homogenizer (Omni International, Kennesaw, GA) to homogenize the tissue in the 50 mL conical tube for 1–2 min at RT. 5. Transfer the tissue homogenate from step 4 to the container for the Dounce homogenizer (Wheaton, Millville, NJ) and further homogenize the tissue at RT for about 5 times or until homogenate becomes smooth. 6. Repeat steps 1–5 for several pieces of tissues and pour them into a collection tube. At the end of homogenization, aliquot about 10 mL of the tissue homogenate into individual tubes and store at 80
C for future use. In future experiments, frozen tissue homogenate is put into refrigerator the night before the experiment to thaw. On the day of experiment, warm tissue homogenate in a 37
C water bath and repeat step 4 to generate smooth tissue homogenate before binding experiments. Cell Homogenate Preparation

1. Prepare cells at a cell density of 50 million cells/mL in a 50 mL conical tube. Calculate dilution factor based on the cell diameter and assume spherical shape (e.g., for a 50 million cells/mL with a cell diameter of 20 μm, the dilution factor D ¼ 1012/ [4/3  3.14  (20/2)3  50  106] ¼ 4.8). 2. Follow steps 4–6 above as in tissue homogenate preparation.

3.1.2 Preparation of Equilibrium Dialysis Device

There are several equilibrium dialysis devices that are commercially available, including the HTD96 from HTDialysis [21], the RED (rapid equilibrium dialysis device) from ThermoFisher [22], and the equilibrium dialyzers from Harvard Apparatus [23]. Herein the HTD96 device is used as an example to illustrate the assay setup. Other devices will require different preparation, based on manufacturer’s instructions. The advantages of the HTD96 device are: (1) use a Teflon plate in 96-well format, which has low surface area to minimize the impact of nonspecific binding; (2) the device is reusable and the consumable cost is relatively low. 1. Immerse cellulose membranes (MWCO 12–14 K) in deionized water and soak for at least 15 min to rehydrate the membrane at RT.

86

Sangwoo Ryu et al.

2. Transfer membranes, immerse them in 30% ethanol/deionized water, and soak for at least 15 min to solubilize the membranes to make them porous. 3. Transfer membranes and immerse them in Dulbecco’s PBS buffer (DPBS without calcium or magnesium) and separate the membrane pairs. Soak for at least 15 min. 4. The membranes can be stored at 4
C in DPBS up to 5 days for various experiments. 5. Place the first Teflon bar (labeled “A”) flat and insert the two stainless-steel rods onto the corresponding slots of the Teflon bar. 6. Take a DPBS soaked membrane out from buffer and place it over the wells of the Teflon bar. The membrane should be 2 mm below the top edge of the Teflon bar and should be not exposed outside of the apparatus. If the membrane is sticking outwards, the PBS or the matrix tends to leak outwards during the incubation process. 7. Layer the remaining Teflon bars with the membranes accordingly. Each row of a Teflon bar will be able to run three separate compounds when run in quadruplicate. You will only need one membrane per three compounds (i.e., if you have four compounds to run, you will need two membranes). A maximum of 96 incubation conditions can be measured in the assay per plate. 8. Place the assembled Teflon block into the open base of the equilibrium dialysis device with the stainless-steel plate at the top or bottom of the Teflon block. 9. Tighten the fully assembled equilibrium dialysis device by using the levers on both sides. 3.1.3 Equilibrium Dialysis Experiment Standard Method

A standard equilibrium dialysis method can be applied to most compounds. It is the first experiment run without any prior knowledge about the binding or stability of the compounds. If there is existing information regarding the binding or stability of the compound, indicating the compound may be challenging and fu might be low (e.g., C in OATP1B1 has been shown to decrease the hepatic uptake of multiple statins, resulting in increased plasma concentrations and risk of systemic adverse events (myopathies) [14–17]. 4. Troglitazone, a thiazolidinedione used for the treatment of type 2 non–insulin-dependent diabetes mellitus, was withdrawn from the market due to reports of liver toxicity and cholestasis. Troglitazone and troglitazone sulfate, the main generated metabolite, competitively inhibited rat Bsep leading to high intracellular accumulation of troglitazone sulfate within hepatocytes, providing a possible explanation for troglitazoneinduced intrahepatic cholestasis and liver toxicity in humans [18, 19]. Kpuu,liver is defined as the steady-state liver-to-sinusoidal blood partition coefficient for the unbound drug, assuming a homogenous well-stirred liver model [2]. For compounds with passive diffusion and no active processes involved, Kpuu,liver is approximately equal to 1. However, for compounds with active uptake or active clearance from the liver, the Kpuu,liver is either greater than 1 or less than 1, respectively [20, 21]. Kpuu,liver is a function of CLdiff, CLact,uptake, CLact,efflux, CLbile, and CLmet and is important in human translational studies to estimate dose and to assess safety margins and DDI risk for drugs with transporter-mediated uptake into the liver [22] (see Chapter 4, Ryu et al. in this book for more details). In addition to the factors above, intracellular binding, partitioning, and sequestration into subcellular organelles such as lysosomes, mitochondria, and cytosol may affect unbound intracellular drug concentrations. Lysosomal sequestration occurs when molecules with weakly basic properties, which are uncharged in the cytosol, acquire a charge upon entering lysosomes owing to the lower pH compared to the cytosol. This pH-driven trapping of molecules can result in higher partitioning of such drugs in the lysosomal compartment compared to the cytosolic compartment [23]. Similarly, the negative transmembrane electrical potential of the inner mitochondrial membrane has the ability to trap or

100

Chitra Saran et al.

sequester positively charged drug molecules [11, 24–26]. Such sequestration of drugs can lead to pharmacological or toxicological responses that are unexpected based on systemic drug concentrations. General Assumptions and Challenges: Although the liver is often assumed to be a well-stirred homogeneous compartment, hepatic vasculature and oxygenation vary among different regions of the liver owing to the mixing of portal and venous blood [27]. In a similar manner, hepatic intracellular drug concentrations within the liver might be variable, but they are frequently considered uniform for practical purposes. It is also important to note that intracellular concentration generally refers to the concentration of drug in the cytosol. However, this may not be an accurate reflection of the subcellular distribution into the mitochondria or lysosomes. Additionally, if the drug is bound extensively to the plasma membrane, then the measured “intracellular concentration” will be an overestimation of the true cytosolic concentration. Methods and tools to measure hepatic unbound intracellular concentration are limited and subject to the above assumptions. As discussed further, the isolated perfused liver and sandwich-cultured hepatocytes are methods that have been used widely to understand metabolic regulation, hepatobiliary drug disposition, and toxicity. 1.2 Introduction to the Isolated Perfused Liver (IPL) 1.2.1 Background

The isolated perfused liver (IPL; Fig. 1) is a well-established model to study hepatobiliary disposition of compounds (e.g., endogenous molecules, drugs, metabolites). Data generated during perfused liver studies can be used to estimate [28] and simulate [29] hepatocellular concentrations, and processes related to metabolism and vectorial hepatocellular transport [30]. In addition, the IPL model is used to study liver physiology and pathophysiology, and hepatic

Fig. 1 Schematic representation of the isolated perfused liver, perfused with Krebs–Henseleit bicarbonate buffer (inflow perfusate) and sample collection from bile and outflow perfusate. (Adapted from Beaudoin et al. (2019) Drug Metab Dispos)

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

101

preservation/reperfusion injury [31]. Advantages of the IPL over in vitro systems include the maintenance of hepatic architecture, bile production, and zonation of drug transporters and metabolizing enzymes, and the ability to incorporate the impact of perfusion rate [28, 30]. Although various species have been used in IPL experiments, including the mouse, guinea pig, and rhesus monkey, the rat is used most commonly [32]. The size of the rat’s liver is convenient for surgical manipulations, and the historical role of the rat in drug metabolism studies and the drug development process have made the rat a widespread choice for IPL studies. Most of the background information and methods described in the current chapter is directly related to the rat, but the majority of concepts discussed are translatable to other species. 1.2.2 Technical Considerations

Blood flow through the liver is determined largely by the portal vein (accounting for ~75% of hepatic blood flow) and the substantially smaller hepatic artery (accounting for the remainder). Thus, in IPL studies, the isolated liver is almost exclusively cannulated at the portal vein to introduce a compound of interest at the desired inflow concentration. In most IPL studies, a bicarbonate-buffered saline solution is used, in which a compound of interest is dissolved. It is crucial that during the perfusion, the liver tissue is provided with an energy source (e.g., glucose) and enough oxygen (e.g., using hemoglobin free- or red blood cell-containing perfusate), among other components. Ambient temperature and humidity should be optimized to retain hepatic functions such as the formation and flow of bile. Typically, the bile duct is cannulated for the collection of bile over predefined time intervals; bile flow serves as an important viability measure, and bile samples are a unique and meaningful endpoint of the model for measuring biliary excretion of the compound(s) of interest. There are numerous variations of the IPL model, including in situ and ex situ setups. While in situ IPLs may encounter problems such as loss of perfusate to other organs, difficulty maintaining a physiological temperature of the liver and contamination of samples from abdominal bacteria [32], ex situ IPLs require additional surgical procedures in order to transfer the isolated liver onto a platform in a perfusion chamber. Outflow perfusate from the in situ IPL can be collected by cannulating the thoracic vena cava and ligating the abdominal inferior vena cava [33], while outflow perfusate from the ex situ IPL can be collected from the stream of perfusate flowing freely from the inferior vena cava into a waste container. For a comprehensive overview of the perfusion apparatus (open versus closed systems, and system maintenance), perfusate composition and flow rate, surgical procedures, viability assessments, types of perfusions (single-pass, recirculating and retrograde), among other topics, please review Brouwer and Thurman [32], since these topics are out of scope for this book chapter. The focus of the current chapter

102

Chitra Saran et al.

is on measuring hepatic concentrations of compounds introduced to the isolated liver, as well as on estimating and simulating hepatic and hepatocellular concentrations. 1.2.3 Applications

Various research articles have published methods detailing the measurement of post-perfusion terminal hepatic concentrations of a compound after liver homogenization utilizing an appropriate analytical technique (e.g., mass spectroscopy, liquid scintillation counting). For instance, Mottino et al. (2003) investigated the effect of 60-min recirculating perfusions of the cholestatic estradiol metabolite estradiol-17β-D-glucuronide (E217G) and the non-cholestatic isomer estradiol-3-D-glucuronide (E23G) on hepatic and biliary glutathione concentrations in isolated rat livers [34]. To measure hepatic concentrations of these estradiol metabolites, which were radiolabeled with tritium (i.e., [3H] E217G and [3H] E23G) (see Note 1), the median lobe of the liver was removed, homogenized in four volumes of saline, and an aliquot was measured by liquid scintillation counting. In another example, Hobbs et al. (2012) perfused isolated livers from Wistar rats (with and without a deficiency in the hepatic efflux transporter Mrp2) in a recirculating fashion to understand the interplay of drug transporters potentially involved in the disposition of rosuvastatin, specifically rat Oatp1a1, Oatp1a4, Bcrp, and Mrp2 [35]. Livers were homogenized in water at the end of the experiment, and by employing validated high-performance liquid chromatography– tandem mass spectrometry (HPLC-MS/MS) methods, rosuvastatin concentrations were determined in these samples. Apart from directly measuring hepatic concentrations by liver homogenization, a large amount of useful data can be generated by measuring outflow perfusate concentrations and the amount excreted in bile of compounds at specific time points or intervals. Using these data, a pharmacokinetic model can be developed to estimate uptake, basolateral efflux, and biliary excretion rate constants or clearance values for the compound(s) of interest. These parameter estimates are informative when evaluating the impact of disease (e.g., a rodent model of polycystic liver disease), a genetic defect (e.g., a rat model with a defect in the gene encoding for the canalicular efflux transporter Mrp2), or another compound (e.g., a potent CYP3A4 inhibitor) on hepatobiliary disposition of a perfused drug. Apart from analyzing hepatobiliary uptake and efflux parameters, an appropriate pharmacokinetic model can be used to quantitatively simulate hepatocellular concentrations of a drug (and generated metabolites). Furthermore, studies involving MRI of a contrast agent or gamma counting of a radionuclide-labeled compound can be performed [28, 33, 36] to estimate hepatic and hepatocellular concentrations in the IPL, which can obviate both

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

103

the need to homogenize the liver to determine hepatic concentrations and to perform pharmacokinetic modeling to obtain estimated hepatocellular concentrations over time. 1.3 Introduction to Sandwich-Cultured Hepatocytes (SCH) 1.3.1 Background

1.3.2 Technical Considerations

Hepatocyte cultures are well-established in vitro tools to study drug metabolism and transport. Primary hepatocytes in standard culture conditions have limited viability and rapidly lose cell polarity and bile canalicular architecture [37]. Hepatocytes cultured between two layers of gelled collagen or extracellular matrix such as Matrigel™ in a sandwich configuration retain polarity, form bile canalicular structures with functional gap junctions, and retain expression of transporters and drug metabolizing enzymes [38, 39]. Sandwich-cultured hepatocytes (SCH) offer many advantages over other in vitro methods to measure or estimate intracellular concentrations. Importantly, the sandwich-cultured configuration allows the hepatocytes to form bile canalicular structures and restores normal polarity, including localization and measurable function of multiple transport proteins that can vectorially transport xenobiotics. SCH also retain metabolic activity, regulatory machinery, and subcellular organelles that are required for subcellular sequestration and trafficking of transport proteins, that enable directional excretion of drugs and derived metabolites [40]. One limitation of SCH as an in vitro tool is that this system does not reflect the microvasculature, oxygenation, and zonal differences arising from the mixing of portal and venous blood within the liver. Another limitation of human SCH is that since they are derived from a single donor, variability observed between donors may be large and can arise from a multitude of factors such as age, gender, race, prior medication history, genetics, environmental exposure, and disease states. A number of studies describe the impact of several technical factors such as culture media, media supplements, chemical modulators, and extracellular matrices on rat and human SCH [41]. Basal media employed for rat SCH include Waymouth’s MB-752/1, Ham’s F12, RPMI 1640, Dulbecco’s modified Eagle’s medium (DMEM), Williams’ medium E, Leibovitz’ L15, and modified Chee’s medium. It is recommended to use enriched media that contains 5–10x higher amino acid levels that improve cell survival by arresting lysosomal and autophagic protein degradation and thereby stabilize liver-specific enzymes. Serum also has favorable effects on SCH by improving cell attachment, survival, and morphology. Extracellular matrices such as collagen I, IV, and Matrigel™ (an acid-urea extract prepared from Engelbreth-HolmSwarm tumor tissue excised from lathyritic mice) have been demonstrated to aid in differentiation of hepatocytes, formation of functional canalicular structures, distribution of cytoskeletal proteins, and liver-specific gene expression. Additionally, supplements such as glucocorticoids (e.g., dexamethasone [DEX]) and

104

Chitra Saran et al.

hormones such as insulin also are recommended. DEX induces cytochrome P450 (CYP) 3A activity, attenuates a decrease in protein synthesis over the first 24 hours of culture, promotes cytoskeletal rearrangement, enhances gap junction expression and function, and improves formation of bile canalicular networks. Long-term survival of SCH requires insulin to improve amino acid transport, protein synthesis, glycogenesis, and lipogenesis and inhibits protein degradation [40, 41]. LeCluyse et al. showed that in rat SCH, bile canaliculi form a uniform and homogenous network through days 3–7 [38], while Liu et al. demonstrated that biliary excretion of taurocholic acid (TCA) reached a maximum value 4 days after overlay of rat SCH [42]. Notably, the process of architectural and functional maturation of canalicular networks in SCH differs across species: 6–10 days after overlay for human SCH and on day 3 after overlay for mouse SCH (Table 1) [40]. In the absence of freshly isolated hepatocytes, cryopreserved hepatocytes can be used; however, cell viability, seeding density, and confluency of plating are critical factors that must be adjusted to ensure proper cell contact, attachment, and differentiation [41]. Hepatocytes vary widely in quality, phenotype, and function. Transporter Certified™ Human Hepatocytes and AccuLiver™ Culture Kits are commercially available from BioIVT. When cultured under the specified conditions, these hepatocytes provide in vivo-relevant transporter expression, localization, and function. Because culture conditions influence metabolic and transporter expression and function, laboratories using in-house protocols for hepatocyte isolation and SCH studies need to validate transporter levels and function using appropriate quality controls.

Table 1 Sandwich-cultured hepatocyte recommendations for multiple species (adapted from Swift et al.) [40]

a

Seeding density

Culture day for experiments

Species

Plate formata

Extracellular matrix

Human (freshly isolated)

6-well BioCoat 24-well BioCoat

Matrigel™ Matrigel™

1.75
106 0.35
106

7 7

Human (cryopreserved)

24-well BioCoat

Matrigel™

0.35
106

5

Rat

6-well BioCoat 24-well BioCoat

Matrigel™ Matrigel™

1.75
10 0.35
106

4 4

Mouse (C57BL/6)

6-well BioCoat

Matrigel™

8 weeks) and sex, selected based on maturity considerations, disease progression, sex-dependent effects, etc.

l

Heating pad.

l

Anesthesia (e.g., ketamine/xylazine) and syringe with ~25gauge needle for intraperitoneal injection of anesthesia.

l

At least one straight forceps, and two microdissection forceps.

l

Scissors.

l

Saline solution.

l

Gauze.

l

Iris scissors.

l

Krebs–Henseleit buffer (or another type of perfusate, such as one containing red blood cells) supplemented in double distilled water with calcium chloride dihydrate (0.373 g/L final concentration) and sodium bicarbonate (2.1 g/L final concentration); for the maintenance of bile flow, also add taurocholate (15 μM final concentration). Adjust the pH to 7.4 with HCl or NaOH each time before use, and store at 4  C up to one week.

l

Water bath at 37  C.

l

~16–20-gauge catheter for portal vein cannulation.

l

PE-10 tubing for bile duct cannulation.

l

Surgical silk (3-0 for the portal vein cannulation, and 5-0 for the bile duct cannulation).

The following materials are based on a published protocol for measurement of unbound intracellular concentrations and subcellular distribution of ritonavir, rosuvastatin, and furamidine in rat SCH [46]. l

Full DMEM Media: DMEM without phenol red supplemented with 2 mM L-glutamine, 1% (v/v) MEM nonessential amino acids, 100 units penicillin G sodium, 100 μg streptomycin sulfate, 1 μM dexamethasone, 5% (v/v) FBS, and 10 μM insulin.

l

Matrigel™.

l

Multiwell BioCoat Plates.

l

Biological safety cabinet.

l

Tissue culture incubator.

l

HBSS buffer (with and without Ca2+), pH to 7.4 prior to use.

108

3

Chitra Saran et al. l

Fractionation buffer: 250 mM sucrose, 10 mM HEPES, 10 mM KCl, 1 mM EDTA, 1.5 mM MgCl2, 1 mM dithiothreitol, cOmplete Protease Inhibitor Cocktail™.

l

Ultracentrifuge.

l

BCA Protein Assay Kit.

l

Phosphate buffer saline solution (PBS), pH to 7.4 before use.

l

Equilibrium Dialysis apparatus (96-well format is preferred for high-throughput analysis and low working volume of 30–150 μL).

l

Equilibrium Dialysis Cellulose Membrane (8 K molecular weight cutoff is ideal for small molecule–protein binding studies).

l

Bio-Safe II counting cocktail.

l

Liquid scintillation counter.

l

HPLC-grade methanol, acetonitrile, and water.

l

LC-MS/MS instrument.

Methods

3.1 Perfusion of the Isolated Liver, Collection of Samples, and Liver Tissue Homogenization

1. Isolate the rat liver, and maintain the liver in a humidified perfusion chamber, per the detailed methods described in Brouwer and Thurman [32]. 2. After an approximately 15-min equilibration phase in which the liver is perfused (via the portal vein) with blank Krebs–Henseleit bicarbonate buffer, perfuse the liver with compoundcontaining buffer for 30–60 min, followed by a washout phase for another 20–30 min (see Note 2). 3. During the perfusion, collect perfusate samples either from the stream of outflow perfusate flowing from the liver’s inferior vena cava into the waste reservoir at specific time points [29, 53] or from separate collection reservoirs during time intervals [54] (see Note 3). It is recommended to collect perfusate samples at least every ~2.5–5 min. Simultaneously, it is recommended to collect bile samples in separate tubes over consecutive ~5-min intervals. 4. After collecting a sample, store the corresponding tube appropriately (e.g., regular ice or dry ice followed by long-term storage at 80  C) depending on the analytical time frame (e.g., it is recommended to analyze LDH release into the perfusate to determine % cytotoxicity as soon as possible after each perfusion). 5. If the goal is to measure hepatic concentrations of the compound of interest at a single time point (without relying on pharmacokinetic modeling using perfusate and biliary data to

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

109

simulate hepatic concentrations), the liver needs to be handled and stored appropriately. Blot the liver (or optionally flush the liver with ~50 mL blank perfusate before removing the IPL from the perfusion chamber; see Note 4) briefly at the end of the experiment (after the washout phase) to remove excess buffer that could affect measurement of hepatic concentrations. 6. Remove all extrahepatic tissue. 7. Weigh the liver. 8. After blotting, handle the liver (or a specific liver section, if desired) per the selected/ideal time frame for compound analysis (e.g., snap freeze the tissue on dry ice for subsequent longterm storage at 80  C, if the compound(s) are expected to be stable at those conditions; if not, directly continue with step 9 [see Note 5]). 9. In preparation for analysis of hepatic drug concentrations, mix the (thawed) liver tissue in a volume of cold saline (1:2–1:4, w/v) and homogenize on ice with a cell disruption device (e.g., a Shake Master). (a) Optional: after this step of the protocol, the liver homogenate can be further fractionated as described in Subheading 3.3. It is possible to continue with step 10 in that section (Fig. 2). 10. Use an appropriate extraction method and analytical technique (e.g., LC-MS/MS, scintillation counting) to measure the concentration of the compound(s) of interest in the perfusate, bile and liver samples. 11. After measuring the hepatic concentration of a compound, the intracellular concentration in hepatocytes can be inferred based on simple hepatic intra- and extracellular volume assumptions, which is discussed in more detail in the following sections (Subheading 3.2 and 3.4). 3.2 Use of Pharmacokinetic Modeling to Simulate Hepatocellular Concentrations from IPL Data

In the next section of this chapter, we will discuss a pharmacokinetic approach that can be utilized to simulate intracellular concentrations at any time for a compound introduced to the ex situ isolated liver in a single-pass manner, although the method can be modified to virtually all IPL setups. The basis of this approach has been discussed in three publications from our research group in which we studied the hepatobiliary disposition of (1) rosuvastatin and a pentanoic acid derivative in Mrp2-deficient rats [54], (2) 5(6)carboxy-20 ,70 -dichlorofluorescein (CDF) in polycystic kidney (PCK) rats [53], and (3) tolvaptan and two generated metabolites in PCK rats [29]. In the latter study, the pharmacokinetic model was used to predict compound concentrations at various time points during and after the perfusion.

110

Chitra Saran et al.

The next step is to develop a pharmacokinetic model to describe compound disposition in the IPL. In the three studies from the Brouwer lab, a compartmental model assuming linear kinetics and/or nonlinear kinetics (when a saturable process following Michaelis-Menten kinetics was suspected to play a role in the hepatobiliary disposition of the compound) was developed to describe rate-versus-time data using the pharmacokinetic modeling and simulation software Phoenix WinNonlin (St. Louis, MO). Alternative software packages such as ADAPT (Los Angeles, CA) also can be used, if the selected software package provides the flexibility to add differential equations, since structural models for the IPL are typically not included by default. In all three studies, an extracellular (sinusoidal) space and an intracellular (hepatocellular) space were included to represent the liver, in addition to a bile compartment. See Notes 6–14 for some suggestions when performing pharmacokinetic modeling of the IPL data. The differential equations corresponding to the simplest model with a single extracellular compartment, a single intracellular compartment, and a bile compartment, assuming first-order kinetics, no noteworthy role for metabolism, and an initial amount of zero in all compartments, are as follows: Outflow perfusate concentration data expressed as an appeardX ance rate : dt dX out ¼ Q  C EC dt

ð1Þ

Extracellular compartment: dX EC ¼ Q  C in þ CLBL  C IC  ðCLUP þ Q Þ  C EC dt

ð2Þ

Intracellular compartment: dX IC ¼ CLUP  C EC  ðCLBL þ CLBile Þ  C IC dt Biliary concentration data expressed as an excretion rate dX Bile ¼ CLBile  C IC dt

ð3Þ dX : dt ð4Þ

where dX dt represents the rate of change with respect to time of the compound of interest in a particular compartment, and Xout, XEC, XIC, and XBile denote the mass of the compound in the outflow perfusate, extracellular space, intracellular space, and bile, respectively. CEC or XEC/VEC represents the concentration of compound in the extracellular compartment, while CIC or XIC/VIC represents the concentration of compound in the intracellular compartment. After blotting a rat liver at the end of each perfusion, the mass of each liver is recorded and converted to total liver volume (VL) using the average density of rat liver (1.084 g/mL) [55]. The extracellular volume of the liver, VEC, is estimated as 20% of VL [35, 56],

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

111

while the intracellular volume of the liver, VIC, is estimated as the remaining 80% of VL. Although only a reasonable assumption, this construction allows one to predict intracellular concentrations of a compound in the hepatocyte based on pharmacokinetic modeling while only using outflow perfusate and biliary data of a compound. Perfusate flow rate multiplied by the concentration in the extracellular compartment (Q  CEC) is fit to the observed outflow perfusate appearance rate data, while the biliary excretion rate (dXBile/dt) is fit to the observed biliary excretion rate data. Cin, CLBL, CLUP, CLBile denote the inflow concentration, basolateral efflux clearance from the intracellular space to the extracellular space, uptake clearance from the extracellular space to the intracellular space, and biliary clearance from the intracellular space to bile, respectively. Using this type of model, pharmacokinetic modeling and simulation software (including WinNonlin and ADAPT) allow the user to simulate the amount or concentrations of a compound over time in one or more compartments. It is straightforward to obtain simulated amounts or concentrations of a compound in the intracellular compartment (i.e., XIC or CIC, respectively) of the previously described IPL structural model. Typically, an output function needs to be specified (e.g., Y ¼ XIC or Y ¼ XIC/VIC) in the software in order for the software to produce graphs and/or data points for simulated amounts or concentrations. Finally, while this section focused on the measurement and estimation of total concentrations and clearances, the unbound fraction of the compound in the various matrices (e.g., perfusate, bile, cytosol) can be used to estimate the unbound concentrations and clearances, which may be more appropriate in certain scenarios. 3.3 Measuring Hepatic Unbound Intracellular Concentrations in SCH

The following method describes the protocol for studying the accumulation of 1 μM ritonavir or [3H]-rosuvastatin or 10 μM pafuramidine (prodrug of furamidine) in rat SCH [46]. This protocol must be adapted as needed for incubation time and concentration of the compound of interest based on pilot studies or available literature showing established steady-state total concentrations. Subsequently, hepatocyte fractionation and protein binding of the isolated fractions was performed to determine unbound concentration of these three drugs in hepatic subcellular fractions by LC-MS/MS or scintillation counting and measurement of Kpuu, liver. For other detailed protocols on SCH, please refer to more thorough descriptions [40, 57, 58]. 1. Briefly, seed 1.75
106 rat hepatocytes in 6-well BioCoat plates in Full DMEM media (day 0 of culture). 2. Allow hepatocytes to attach for 2–6 h in a humidified incubator (95% O2, 5% CO2) at 37  C; replace media after 2–6 h. (See Table 1 to adapt the cell culture for plate format and for human, mouse, dog, or monkey hepatocytes).

112

Chitra Saran et al.

3. After an overnight incubation (16–24 h), overlay the hepatocytes with ice-cold Matrigel basement membrane matrix at a concentration of 0.25 mg/mL (1.5 mL working volume) in Full DMEM media to initiate the sandwich-cultured configuration (day 1 of culture) (see Note 15); replace media every 24 h. 4. On day 3, treat the SCH with 10 μM pafuramidine for 24 h to allow for formation of furamidine. 5. On day 4, preincubate the SCH with Ca2+-free HBSS for 10 min (5 min for pafuramidine-treated cells) in a humidified incubator (95% O2, 5% CO2) at 37  C to disrupt the tight junctions and prevent biliary excretion into the canalicular space (see Notes 16–19). 6. After preincubation, treat cells with 1 μM ritonavir or [3H]rosuvastatin (100 nCi/ml) in standard HBSS for 10 min at 37  C. Collect the incubation medium after 10 min and wash the hepatocytes three times with ice-cold standard HBSS. 7. After drug treatment and washing, harvest and pool cell lysate from all six wells by scraping each well into 1 mL fractionation buffer (see Note 20). 8. Homogenize hepatocyte lysate in buffer by passing through a 27-gauge needle 10 times. 9. After a 10-min rest on ice, disrupt the cell membranes by an additional 10 passes. 10. Reserve approximately 200 μL of the crude lysate for measurement of total and unbound drug concentrations, protein content, and enzyme activity assays; use the remaining 800 μL of crude lysate for stepwise differential centrifugation to separate cellular fractions. 11. Firstly, centrifuge the crude lysate at 600
g at 4  C for 10 min to obtain nuclei and cellular debris as pellet (Fig. 3). 12. Transfer the supernatant to a fresh 1.5 mL ultracentrifuge tube and resuspend the pellet in 0.3 mL fractionation buffer. Centrifuge the supernatant at 10,000
g at 4  C for 10 min to obtain the mitochondrial fraction as pellet. 13. Again, transfer the supernatant to a fresh 1.5 mL ultracentrifuge tube and resuspend the pellet in 0.15 mL fractionation buffer. Centrifuge the supernatant at 35,000
g at 4  C for 10 min to obtain lysosomes and other membrane-bound organelles. 14. Collect the supernatant and resuspend the lysosomal pellet in 0.15 mL fractionation buffer. Centrifuge the supernatant at 100,000
g at 4  C for 60 min to obtain microsomes and the membrane fraction as pellet.

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

600 xg

10K – 35K xg

113

100K xg

(supernatant) (pellet)

Organelle

Nuclei

Resident Histones Marker Protein Compounds Known to Accumulate

Doxorubicin DAPI

(pellet)

(pellet)

(pellet)

Mitochondria

Lysosomes

Microsomes

Cytosol

VDAC

LAMP

Calreticulin

HSP90

Rhodamine 123

Lysotracker Red

CDF

Fig. 3 Schematic representation of differential centrifugation to isolate subcellular fractions with resident marker proteins and compounds known to accumulate in these fractions. DAPI, 40 ,6-diamidino-2-phenylindole; LAMP, Lysosome-Associated Membrane Protein; VDAC, Voltage-Dependent Anion Channel; CDF, 5(6)carboxy-2’,7’-dichlorofluorescein

15. Dissolve this pellet in 0.2 mL fractionation buffer and collect the supernatant as the cytosolic fraction (see Note 21). 16. Determine the total protein content of each fraction using the BCA Protein assay (see Notes 22–23). 17. Determine the unbound fraction in hepatocytes by performing equilibrium dialysis on the crude lysate and the cytosolic fraction. 18. Load samples into a 96-well equilibrium dialysis apparatus, and perform dialysis against PBS for 6 h at 37  C with constant shaking (see Notes 24–26) (see Chapter 4, Ryu et al. in this book for a detailed protocol). 19. Collect 25–50 μL sample after incubation and transfer to an LC-MS/MS compatible vial or plate and matrix match crude lysate and cytosolic fraction with blank PBS and PBS samples with blank crude lysate or cytosolic fraction to ensure samples are extracted from identical matrices for LC-MS/MS. 20. Extract samples using acetonitrile or methanol by protein precipitation, liquid-liquid extraction, or solid-phase extraction. 21. For liquid scintillation counting, transfer 25–50 μL sample after incubation to a scintillation vial with Bio-Safe II counting cocktail.

114

Chitra Saran et al.

3.3.1 Sample and Data Analysis

The total amount of ritonavir, furamidine, and [3H]-rosuvastatin can be measured in the incubation media and hepatic cell lysate, and subcellular distribution can be measured in the isolated fractions using LC-MS/MS or liquid scintillation counting. The unbound fraction ( fu) can be measured from the equilibrium dialysis and calculated using the following equations to account for dilutions during fractionation [59]. f u,measured ¼½concentration in phosphate buffer =½concentration in lysate or cytosolic fraction h

  i Undiluted f u ¼ ½1=D = 1= f u,measured  1 þ 1=D where D represents the fold dilution of the tissue or lysate. Total hepatic intracellular concentrations in rat SCH can be calculated by dividing the total amount of drug in whole cell lysate by the estimated hepatocellular volume (7.4 μL/mg protein) determined by [3H]3-O-methyl-D-glucose [60]. Based on previously published results, drugs such as ritonavir, rosuvastatin, and furamidine could be utilized as reference standards or controls to compare the total and unbound intracellular concentration in hepatocytes or liver tissue and to compare subcellular distribution (Table 2). Kpuu,liver also can be calculated as the ratio of intracellular unbound concentration (cytosolic) and extracellular unbound concentration (incubation media). Unbound and total concentration in the incubation media is assumed to be equal if no protein is added to the HBSS incubation media.

3.4 Use of Imaging Methods to Estimate Hepatic Concentrations in the IPL

MRI of contrast agents (e.g., gadolinium complexes) and positron emission tomography (PET) or gamma scintigraphy (e.g., the bile acid tracer [N-methyl-11C]cholylsarcosine [61], 153Gd) are other methods that have been used, or are under investigation, to estimate liver or hepatocellular concentrations of compounds. To enhance the understanding of these modalities for the quantitative analysis of liver drug concentrations, the rat IPL has been used by the Pastor research group for MRI-based signal intensity analysis of the commercialized hepatobiliary contrast agent gadobenate dimeglumine (Gd-BOPTA) or analysis of the 153Gd-labeled compound by gamma scintigraphy, both of which can be used to measure concentrations in liver compartments [28]. Like other IPL studies, Gd-BOPTA can be dissolved in bicarbonate buffer at a desired concentration in order to study the hepatobiliary disposition of the molecule and calculate the hepatic concentration. The measurement of 153Gd-BOPTA using gamma scintigraphy in IPLs has the relevant feature of being measurable, albeit indirectly, in hepatocytes. The Pastor group typically studies Gd-BOPTA in conjunction with gadopentetate dimeglumine (Gd-DTPA) using in situ rat livers perfused in a single pass manner. In vivo, Gd-DTPA is

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

115

Table 2 Total and unbound intracellular concentrations, unbound fraction and subcellular distribution of ritonavir, rosuvastatin and furamidine in rat isolated perfused livers, and sandwich-cultured hepatocytes (adapted from Pfeifer et al.) [46, 62] Ritonavir Drug

IPL

Rosuvastatin SCH

Furamidine

IPL

SCH

IPL

SCH

Ctotal, tissue (μM) 56

15  2

18

17  3

24

1500  500

Cu, lysate (μM)

0.56

0.46  0.16

4.1

>6.1

0.073

14.0  6.0

fu, lysate (%)

1.0  0.1

3.0  1.0

23.0  1.0

>36  3.0b

0.3  0.1a

0.9  0.2

Kpu,u (lysate)

1.1

1.1

7.9

>6.1

23

53

Cu, cytosol (μM)

0.62

0.76  0.31

5.9

>5.0

fu, cytosol (%)

3.3  0.2

11.0  1.0

46.0  8.0

>34  3.0

fcytosol (%)

36

43  8

72

Kpu,u (cytosol)

1.3

1.7

11

Notes

Distributes primarily into cytosol

a

4.3  1.6

0.3 6.2  1.2

11.0  4.0

88  1

20

31

>5.0

93

16

b

Distributes primarily into cytosol

a

Distributes into mitochondria (43%)

Whole livers (IPL) were perfused in a single-pass manner with 1 μM ritonavir or 1 μM rosuvastatin for 30 min after a 15-min equilibration period. Rat SCH were incubated with 1 μM ritonavir or 1 μM [3H]-rosuvastatin (100 nCi/mL) for 10 min at 37  C. Pafuramidine (10 μM) was added to rat SCH on day 3 for 24 h to allow for formation of furamidine (Methods Subheading 3.3) Ctotal, tissue represents the total cellular concentration in IPL or SCH, calculated as the product of Ctotal, lysate and volume of lysate or tissue. For IPL studies, the liver volume was determined gravimetrically prior to homogenization (Methods Subheading 3.1). For SCH studies, the volume was calculated as 7.4 μL/mg protein (Methods Subheading 3.3.1) Cu, lysate and Cu, cytosol represent the unbound drug concentration in the lysate and cytosol and were calculated as the product of ( fu, lysate
Clysate) and ( fu, cytosol
Ccytosol) fcytosol represents the fraction of total drug mass measured in the tissue (IPL or SCH) residing in the cytosol, calculated as (Ccytosol
volume of cytosol)/(Clysate
volume of lysate) Kpu,u was calculated as the unbound tissue concentration divided by the unbound concentration in perfusate or buffer ( fu,buffer assumed as 1 in the absence of protein in both systems) a from Yan et al. 2011. Isolated livers were perfused in a recirculating manner with pafuramidine (10 μM) for up to 2 h b fu, measured was >80% at lowest dilution. Therefore, due to loss of precise measurement, fu represented as “>” undiluted fu

excreted into urine by glomerular filtration, while Gd-BOPTA undergoes both renal and biliary elimination [36]. Upon liver perfusion, Gd-DTPA diffuses exclusively into the extracellular space of the liver with negligible hepatocellular uptake and biliary excretion [36], while Gd-BOPTA distributes in the extracellular space and is taken up by hepatocytes and undergoes biliary excretion, but is metabolically stable. When using Gd-BOPTA and Gd-DTPA in an IPL study, the same liver is typically exposed to both compounds separately after a short washout between exposures. Gd-DTPA is perfused first, since it only distributes in the extracellular space, and thus washes out quickly. Following a washout phase of 10–35 min, Gd-BOPTA is perfused through the IPL.

116

Chitra Saran et al.

MRI of hepatic Gd-BOPTA and Gd-DTPA can be performed using a 1.5 T MR system with a fast gradient-echo T1-weighted MRI sequence, following a 90 saturation pulse [36]. Alternatively, a gamma scintillation probe placed 1 cm above the liver can be used to quantify hepatic 153Gd-DTPA and 153Gd-BOPTA [28, 33]. MR signal intensities can be correlated to hepatic contrast agent concentrations by separately perfusing livers with contrast agent, sacrificing the rats at various time points, and directly measuring the contrast agent concentrations using alternative methods (e.g., mass spectrometry) [36]. Radioactivity counts can be converted into the amount of contrast agent by measuring the radioactivity in the entire liver at the end of each experiment using a probe, which is subsequently related to the last count detected [28, 33, 63]. Although it is ideal to measure MR signal intensities (and gamma counts) in the same anatomical region across experiments, sometimes the region varies between experiments, but at least the region should remain constant during an individual experiment, and should exclude large vessels such as the portal and hepatic veins [36, 64]. In addition to MR- or gamma-based analysis of the whole liver, outflow perfusate and biliary samples should be collected to allow for additional types of analyses (e.g., pharmacokinetic modeling), as described in a previous section (see Subheading 3.2). Furthermore, to estimate intracellular concentrations in hepatocytes, at least the biliary samples will need to be collected, as described in the next paragraph. Hepatic gamma counts (and MR-based signal intensities) originating from the region of interest near the scintillation probe can be attributed to one of four compartments: sinusoids and interstitium (together constituting the extracellular space), bile canaliculi, and, most importantly for this chapter, hepatocytes [28]. In order to estimate 153Gd-BOPTA concentrations in bile canaliculi, the biliary concentrations of 153Gd-BOPTA (from the bile samples) are multiplied by the relative volume of bile canaliculi (i.e., 0.43%) in the liver [65]. Extracellular concentrations of 153Gd-BOPTA can be estimated by concentrations of 153Gd-DTPA, which only distribute extracellularly. Subtracting these two values from total hepatic 153Gd-BOPTA concentrations yields hepatocellular 153 Gd-BOPTA concentrations in the region of interest. Since a hepatic volume of 78% gives rise to these concentrations [65], a correction to 100% needs to be made to obtain the true intracellular 153 Gd-BOPTA concentrations in hepatocytes [28]. The imaging method by the Pastor group elegantly allows one to deduce hepatocellular concentrations of 153Gd-BOPTA (without the need for liver homogenization) by using certain assumptions and subtracting concentrations measured in other hepatic compartments. However, hepatocellular concentrations of the compound are not directly measured using this method. Other mass spectrometry-based imaging methods could achieve the

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

117

desired spatial resolution [66], if further improved, and could become a useful modality to measure hepatocellular concentrations of a large variety of compounds.

4

Notes 1. If a compound is metabolized, radiochemical detection will be needed to separate radioactivity associated with the radiolabeled parent compound used in an IPL experiment from the generated metabolite(s). It is important to demonstrate that the metabolism of the compound is on a molecular site that does not affect the radiochemical label. 2. Although perfusion times (e.g., duration of calibration, loading, washout) are relatively arbitrary, it is not recommended to use a rat liver for perfusion experiments for more than 2 h postisolation, due to viability concerns. 3. When using data generated from samples collected over time intervals for pharmacokinetic modeling, it is important to use the midpoint of the time interval when the goal is to link the measured concentration with excretion over a particular time interval. 4. Compounds can still efflux from the hepatocyte at the end of the experiment, so the liver should be flushed quickly if blotting is considered insufficient. 5. If the compound of interest undergoes hepatic metabolism, it is essential to snap-freeze the liver quickly, and/or continue with the analysis of the compound immediately to avoid undesired post-experiment metabolism of the compound of interest. 6. The user has flexibility to make variations in the model, such as using more than one liver sub-compartment from inflow to outflow perfusate to mimic the “dispersion” model [56]. 7. In addition, if a delay is observed in a certain process, such as in the detection of a specific compound, transit compartments can be included to account for the delay. 8. Metabolism-related parameters can be included if metabolism plays a significant role in the hepatobiliary disposition of the compound. 9. As mentioned, instead of assuming linear kinetics, the pharmacokinetic model generated from the IPL data can be extended to Michaelis-Menten kinetics (e.g., instead of a single clearance term for a first-order process, both Vmax and Km would need to be included for a saturable process). However, the increase in estimated parameters penalizes the model and must be supported by the data.

118

Chitra Saran et al.

10. Depending on experimental, instrumentation, and unexplained measurement error, an appropriate residual error model needs to be selected (e.g., additive, proportional, mixed, power), along with an estimation method (e.g., stiff, weighted least squares, maximum likelihood). 11. When more than one rat is studied, as is typically the case, the user should decide whether to perform a naı¨ve averaged, naı¨ve pooled, two-stage, or one-stage population analysis, the choice for which depends on the need to include uncertainty at the level of each observation and/or each individual liver, and whether to explore covariates. 12. If both outflow perfusate appearance rate and biliary excretion rate datasets are available to the researcher, it is recommended to first optimize the model fit for the outflow perfusate appearance rate (either on the averaged or pooled data, individual animal data, or data from all animals simultaneously) and to subsequently simultaneously fit both the outflow perfusate appearance rate and biliary excretion rate datasets. 13. When modeling both parent and metabolite compounds, it is convenient to express the mass of each compound in moles (and concentrations in moles/volume). Alternatively additional mathematical expressions are needed to consolidate the differences in mass between compounds. 14. As outlined in Brouwer and Thurman [32], there are many other practical guidelines for IPL studies (e.g., flow rate to ensure a sufficient supply of oxygen to the liver, energy source, addition of protein/albumin to the perfusate to mimic physiologically relevant unbound drug concentrations) [32]. 15. Matrigel™ is extremely sensitive to temperature and forms a gel rapidly when warmed. It is critical to keep the Matrigel™ and media ice-cold. To prevent warming, tubes and pipette tips can be cooled beforehand. 16. Make sure to add 2.5 mM EGTA (ethylene glycol-bis (β-aminoethyl ether)-N,N,N0 ,N0 -tetra-acetic acid), which acts as a calcium chelator. 17. Prior to preincubation, wash the SCH briefly with warm standard HBSS buffer twice. 18. In some studies, such as Guo et al. [58], protein or 4% bovine serum albumin is added to the incubation media to mimic the protein binding of the drug being investigated in vivo. 19. It is recommended to measure nonspecific binding of the substrate of interest in a separate cell-free well. This is a necessary correction for particularly “sticky” compounds that may bind nonspecifically to plastic or glass. 20. Process and store samples on ice for fractionation.

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

119

21. Repeat the final centrifugation step to ensure removal of all membrane bound organelles. 22. Subcellular fractions should be assessed by measuring the enzyme activity of subcellular markers such as lactate dehydrogenase (LDH) as a cytosolic marker, acid phosphatase as a lysosomal marker, succinate dehydrogenase as a mitochondrial marker, and glucose-6-phosphatase as a microsomal marker. Recovery can be calculated as a percentage of organelle-specific enzyme activity in each subcellular fraction compared to whole cell lysate. 23. At this stage, it is possible to freeze the cell or tissue homogenate fractions at 80  C. These fractions can be thawed prior to equilibrium dialysis as described in Chapter 4, Ryu et al. 24. Protein binding studies can be performed using threefold dilutions of each sample in at least three experimental replicates (n ¼ 3). 25. Drug binding to proteins is pH and temperature dependent. It is critical to maintain temperature at 37  C and pH of the buffers at 7.4. 26. It is recommended to use appropriate controls (high binding and low binding drugs) with known protein binding. Please refer to Table 2 for examples.

5

Emerging Tools and Technologies 1. HepatoPac®: This technology is a micropatterned coculture in vitro model that enables long-term metabolism, toxicity, and efficacy studies. These cultures consist of hepatocytes surrounded by stromal cells that provide a specialized architecture to replicate the microenvironment of the liver. With a growing appreciation for drug metabolizing enzyme and transporter interplay, these cocultures were developed with the goal of maintaining viability, functional transporter expression, and metabolic activity for up to 4 weeks [67]. Additionally, HepatoPac® models show better in vitro–in vivo correlations than suspended hepatocytes [68, 69] and can be cultured for rat, dog, monkey, and human species in a high-throughput format. 2. Mass spectrometry imaging (MSI): Imaging platforms offer a unique advantage to visualize and quantify differential spatial distribution of drugs within a tissue or a cell. This is particularly advantageous since this spatial distribution of a drug may be lost during cell or tissue isolation and homogenization. MSI techniques have been used to quantify antiretroviral spatial distribution in tissues [66, 70, 71]. The MSI technique called IR-MALDESI involves maintaining 10-μm tissue sections at

120

Chitra Saran et al.

10  C in a source chamber. The tissue sections are then ablated at a distance of 100 μm by two pulses of an IR laser that causes complete desorption and further ionization of molecules for MS analysis. Recent advances in technology and improvements in MS resolution and sensitivity have gradually enabled using MSI platforms to measure cellular spatial distribution of cholesterol by reducing sampling distance to 10 μm [72]. Another similar technique called liquid extraction surface analysis mass spectrometry (LESA-MS) or surface sampling micro liquid chromatography tandem mass spectrometry (SSμLC-MS/MS) is being used to quantitatively measure tissue distribution of drugs [73, 74]. These techniques utilize liquid extraction from the tissue section surface followed by electrospray ionization, thereby, eliminating MALDIassociated interferences. Currently, such platforms are utilized only for tissue distribution. However, in the future, MSI techniques may prove powerful for measuring intracellular content of drugs, metabolites as well as endogenous substances. In conclusion, measuring intracellular concentrations of drugs or endogenous substances in hepatocytes can be challenging when spatial distribution is considered. However, with a growing interest in measuring subcellular sequestration of drugs as well as endogenous substances, in vitro and bioanalytical methods are being developed to enable such quantitative analysis. Additionally, modeling and simulation approaches also enable estimation of hepatic intracellular concentrations. As discussed in this chapter, numerous methods can be used to evaluate hepatic unbound intracellular concentrations of drugs and new chemical entities. This information may be critical in understanding the pharmacokinetics, pharmacodynamics, efficacy, and/or toxicity of compounds, and accurately predicting DDI potential during drug discovery and development. Editor’s Note: The liver is a complex organ with many different cell types of varying sizes and morphologies. A typical hepatocyte cytosolic volume is 1–10 pL (2.7 pL according to Li Di’s chapter). For a cell with a 3 pL volume of cytosol (3000 μm3), a 1 μM cytosolic concentration would contain 1.8 million molecules.

Acknowledgments This work was supported, in part, by the National Institutes of Health under award number R35 GM122576 from the National Institute of General Medical Sciences (K.L.R.B.) and F31 DK120196 from the National Institute of Diabetes and Digestive and Kidney Diseases (J.J.B.). Any opinions, findings, conclusions,

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes

121

or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the National Institutes of Health. Conflict of Interest: K.L.R.B. is a coinventor of the sandwichcultured hepatocyte technology for quantification of biliary excretion (B-CLEAR®) and related technologies, which have been licensed exclusively to Qualyst Transporter Solutions, recently acquired by BioIVT. References 1. Smith DA, Di L, Kerns EH (2010) The effect of plasma protein binding on in vivo efficacy: misconceptions in drug discovery. Nat Rev Drug Discov 9(12):929–939. https://doi. org/10.1038/nrd3287 2. Chu X, Korzekwa K, Elsby R, Fenner K, Galetin A, Lai Y, Matsson P, Moss A, Nagar S, Rosania GR, Bai JP, Polli JW, Sugiyama Y, Brouwer KLR, Consortium IT (2013) Intracellular drug concentrations and transporters: measurement, modeling, and implications for the liver. Clin Pharmacol Ther 94(1):126–141. https://doi.org/10.1038/clpt.2013.78 3. Giacomini KM, Huang SM, Tweedie DJ, Benet LZ, Brouwer KLR, Chu X, Dahlin A, Evers R, Fischer V, Hillgren KM, Hoffmaster KA, Ishikawa T, Keppler D, Kim RB, Lee CA, Niemi M, Polli JW, Sugiyama Y, Swaan PW, Ware JA, Wright SH, Yee SW, ZamekGliszczynski MJ, Zhang L, Consortium IT (2010) Membrane transporters in drug development. Nat Rev Drug Discov 9(3):215–236. https://doi.org/10.1038/nrd3028 4. Mosedale M, Watkins PB (2017) Druginduced liver injury: advances in mechanistic understanding that will inform risk management. Clin Pharmacol Ther 101(4):469–480. https://doi.org/10.1002/cpt.564 5. Morgan RE, Trauner M, van Staden CJ, Lee PH, Ramachandran B, Eschenberg M, Afshari CA, Qualls CW, Lightfoot-Dunn R, Hamadeh HK (2010) Interference with bile salt export pump function is a susceptibility factor for human liver injury in drug development. Toxicol Sci 118(2):485–500. https://doi.org/10. 1093/toxsci/kfq269 6. Kusuhara H, Sugiyama Y (2010) Pharmacokinetic modeling of the hepatobiliary transport mediated by cooperation of uptake and efflux transporters. Drug Metab Rev 42(3):539–550. https://doi.org/10.3109/ 03602530903491824 7. Zhou F, Zhang J, Li P, Niu F, Wu X, Wang G, Roberts MS (2011) Toward a new age of cellular pharmacokinetics in drug discovery. Drug

Metab Rev 43(3):335–345. https://doi.org/ 10.3109/03602532.2011.560607 8. Leonhardt M, Keiser M, Oswald S, Ku¨hn J, Jia J, Grube M, Kroemer HK, Siegmund W, Weitschies W (2010) Hepatic uptake of the magnetic resonance imaging contrast agent Gd-EOB-DTPA: role of human organic anion transporters. Drug Metab Dispos 38 (7):1024–1028. https://doi.org/10.1124/ dmd.110.032862 9. Nakamura Y, Hirokawa Y, Kitamura S, Yamasaki W, Arihiro K, Tatsugami F, Iida M, Kakizawa H, Date S, Awai K (2013) Effect of lapatinib on hepatic parenchymal enhancement on gadoxetate disodium (EOB)-enhanced MRI scans of the rat liver. Jpn J Radiol 31 (6):386–392. https://doi.org/10.1007/ s11604-013-0208-6 10. McKenzie R, Fried MW, Sallie R, Conjeevaram H, Di Bisceglie AM, Park Y, Savarese B, Kleiner D, Tsokos M, Luciano C (1995) Hepatic failure and lactic acidosis due to fialuridine (FIAU), an investigational nucleoside analogue for chronic hepatitis B. N Engl J Med 333(17):1099–1105. https://doi.org/ 10.1056/NEJM199510263331702 11. Lai Y, Tse CM, Unadkat JD (2004) Mitochondrial expression of the human equilibrative nucleoside transporter 1 (hENT1) results in enhanced mitochondrial toxicity of antiviral drugs. J Biol Chem 279(6):4490–4497. https://doi.org/10.1074/jbc.M307938200 12. Hendriks DFG, Hurrell T, Riede J, van der Horst M, Tuovinen S, Ingelman-Sundberg M (2019) Mechanisms of chronic fialuridine hepatotoxicity as revealed in primary human hepatocyte spheroids. Toxicol Sci 171(2):385–395. Epub 2019/08/27. PubMed PMID: 31505000. https://doi.org/10.1093/toxsci/ kfz195 13. Romaine SP, Bailey KM, Hall AS, Balmforth AJ (2010) The influence of SLCO1B1 (OATP1B1) gene polymorphisms on response to statin therapy. Pharmacogenomics J 10

122

Chitra Saran et al.

(1):1–11. https://doi.org/10.1038/tpj. 2009.54 14. Pasanen MK, Fredrikson H, Neuvonen PJ, Niemi M (2007) Different effects of SLCO1B1 polymorphism on the pharmacokinetics of atorvastatin and rosuvastatin. Clin Pharmacol Ther 82(6):726–733. https://doi. org/10.1038/sj.clpt.6100220 15. Feng Q, Wilke RA, Baye TM (2012) Individualized risk for statin-induced myopathy: current knowledge, emerging challenges and potential solutions. Pharmacogenomics 13 (5):579–594. https://doi.org/10.2217/pgs. 12.11 16. Ho RH, Choi L, Lee W, Mayo G, Schwarz UI, Tirona RG, Bailey DG, Stein CM, Kim RB (2007) Effect of drug transporter genotypes on pravastatin disposition in European- and African-American participants. Pharmacogenet Genomics 17(8):647–656. https://doi.org/ 10.1097/FPC.0b013e3280ef698f 17. Canestaro WJ, Austin MA, Thummel KE (2014) Genetic factors affecting statin concentrations and subsequent myopathy: a HuGENet systematic review. Genet Med 16 (11):810–819. https://doi.org/10.1038/ gim.2014.41 18. Funk C, Ponelle C, Scheuermann G, Pantze M (2001) Cholestatic potential of troglitazone as a possible factor contributing to troglitazoneinduced hepatotoxicity: in vivo and in vitro interaction at the canalicular bile salt export pump (Bsep) in the rat. Mol Pharmacol 59 (3):627–635 19. Funk C, Pantze M, Jehle L, Ponelle C, Scheuermann G, Lazendic M, Gasser R (2001) Troglitazone-induced intrahepatic cholestasis by an interference with the hepatobiliary export of bile acids in male and female rats. Correlation with the gender difference in troglitazone sulfate formation and the inhibition of the canalicular bile salt export pump (Bsep) by troglitazone and troglitazone sulfate. Toxicology 167(1):83–98 20. Shitara Y, Maeda K, Ikejiri K, Yoshida K, Horie T, Sugiyama Y (2013) Clinical significance of organic anion transporting polypeptides (OATPs) in drug disposition: their roles in hepatic clearance and intestinal absorption. Biopharm Drug Dispos 34(1):45–78. https:// doi.org/10.1002/bdd.1823 21. Yabe Y, Galetin A, Houston JB (2011) Kinetic characterization of rat hepatic uptake of 16 actively transported drugs. Drug Metab Dispos 39(10):1808–1814. https://doi.org/ 10.1124/dmd.111.040477 22. Riccardi K, Li Z, Brown JA, Gorgoglione MF, Niosi M, Gosset J, Huard K, Erion DM, Di L

(2016) Determination of unbound partition coefficient and in vitro-in vivo extrapolation for SLC13A transporter-mediated uptake. Drug Metab Dispos 44(10):1633–1642. https://doi.org/10.1124/dmd.116.071837 23. Hallifax D, Houston JB (2007) Saturable uptake of lipophilic amine drugs into isolated hepatocytes: mechanisms and consequences for quantitative clearance prediction. Drug Metab Dispos 35(8):1325–1332. https://doi.org/ 10.1124/dmd.107.015131 24. Mateus A, Matsson P, Artursson P (2013) Rapid measurement of intracellular unbound drug concentrations. Mol Pharm 10 (6):2467–2478. https://doi.org/10.1021/ mp4000822 25. Chunta JL, Vistisen KS, Yazdi Z, Braun RD (2012) Uptake rate of cationic mitochondrial inhibitor MKT-077 determines cellular oxygen consumption change in carcinoma cells. PLoS One 7(5):e37471. https://doi.org/10.1371/ journal.pone.0037471 26. Zheng N, Tsai HN, Zhang X, Shedden K, Rosania GR (2011) The subcellular distribution of small molecules: a meta-analysis. Mol Pharm 8(5):1611–1618. https://doi.org/10. 1021/mp200093z 27. Pang KS, Rowland M (1977) Hepatic clearance of drugs. I. Theoretical considerations of a "well-stirred" model and a "parallel tube" model. Influence of hepatic blood flow, plasma and blood cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance. J Pharmacokinet Biopharm 5(6):625–653 28. Pastor CM (2018) Isolated perfused rat livers to quantify the pharmacokinetics and concentrations of Gd-BOPTA. Contrast Media Mol Imaging 2018:3839108. https://doi.org/10. 1155/2018/3839108 29. Beaudoin JJ, Bezenc¸on J, Cao Y, Mizuno K, Roth SE, Brock WJ, Brouwer KLR (2019) Altered hepatobiliary disposition of tolvaptan and selected tolvaptan metabolites in a rodent model of polycystic kidney disease. Drug Metab Dispos 47(2):155–163. https://doi. org/10.1124/dmd.118.083907 30. Godoy P, Hewitt NJ, Albrecht U, Andersen ME, Ansari N, Bhattacharya S, Bode JG, Bolleyn J, Borner C, Bottger J, Braeuning A, Budinsky RA, Burkhardt B, Cameron NR, Camussi G, Cho CS, Choi YJ, Craig Rowlands J, Dahmen U, Damm G, Dirsch O, Donato MT, Dong J, Dooley S, Drasdo D, Eakins R, Ferreira KS, Fonsato V, Fraczek J, Gebhardt R, Gibson A, Glanemann M, Goldring CE, Gomez-Lechon MJ, Groothuis GM, Gustavsson L, Guyot C, Hallifax D, Hammad S, Hayward A, Haussinger D,

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes Hellerbrand C, Hewitt P, Hoehme S, Holzhutter HG, Houston JB, Hrach J, Ito K, Jaeschke H, Keitel V, Kelm JM, Kevin Park B, Kordes C, Kullak-Ublick GA, LeCluyse EL, Lu P, Luebke-Wheeler J, Lutz A, Maltman DJ, Matz-Soja M, McMullen P, Merfort I, Messner S, Meyer C, Mwinyi J, Naisbitt DJ, Nussler AK, Olinga P, Pampaloni F, Pi J, Pluta L, Przyborski SA, Ramachandran A, Rogiers V, Rowe C, Schelcher C, Schmich K, Schwarz M, Singh B, Stelzer EH, Stieger B, Stober R, Sugiyama Y, Tetta C, Thasler WE, Vanhaecke T, Vinken M, Weiss TS, Widera A, Woods CG, Xu JJ, Yarborough KM, Hengstler JG (2013) Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch Toxicol 87 (8):1315–1530. https://doi.org/10.1007/ s00204-013-1078-5 31. Ferrigno A, Richelmi P, Vairetti M (2013) Troubleshooting and improving the mouse and rat isolated perfused liver preparation. J Pharmacol Toxicol Methods 67(2):107–114. https://doi.org/10.1016/j.vascn.2012.10. 001 32. Brouwer KLR, Thurman RG (1996) Isolated perfused liver. Pharm Biotechnol 8:161–192 33. Daali Y, Millet P, Dayer P, Pastor CM (2013) Evidence of drug-drug interactions through uptake and efflux transport systems in rat hepatocytes: implications for cellular concentrations of competing drugs. Drug Metab Dispos 41 (8):1548–1556. https://doi.org/10.1124/ dmd.113.051870 34. Mottino AD, Veggi LM, Wood M, Roman JM, Vore M (2003) Biliary secretion of glutathione in estradiol 17beta-D-glucuronide-induced cholestasis. J Pharmacol Exp Ther 307 (1):306–313. https://doi.org/10.1124/jpet. 103.054544 35. Hobbs M, Parker C, Birch H, Kenworthy K (2012) Understanding the interplay of drug transporters involved in the disposition of rosuvastatin in the isolated perfused rat liver using a physiologically-based pharmacokinetic model. Xenobiotica 42(4):327–338. https://doi.org/ 10.3109/00498254.2011.625452 36. Pastor CM, Planchamp C, Pochon S, Lorusso V, Montet X, Mayer J, Terrier F, Vallee JP (2003) Kinetics of gadobenate dimeglumine in isolated perfused rat liver: MR imaging evaluation. Radiology 229(1):119–125. https:// doi.org/10.1148/radiol.2291020726 37. Treyer A, Mu¨sch A (2013) Hepatocyte polarity. Compr Physiol 3(1):243–287. https://doi. org/10.1002/cphy.c120009

123

38. LeCluyse EL, Audus KL, Hochman JH (1994) Formation of extensive canalicular networks by rat hepatocytes cultured in collagen-sandwich configuration. Am J Phys 266(6 Pt 1): C1764–C1774. https://doi.org/10.1152/ ajpcell.1994.266.6.C1764 39. Liu X, Brouwer KLR, Gan LS, Brouwer KR, Stieger B, Meier PJ, Audus KL, LeCluyse EL (1998) Partial maintenance of taurocholate uptake by adult rat hepatocytes cultured in a collagen sandwich configuration. Pharm Res 15(10):1533–1539. https://doi.org/10. 1023/a:1011994831139 40. Swift B, Pfeifer ND, Brouwer KLR (2010) Sandwich-cultured hepatocytes: an in vitro model to evaluate hepatobiliary transporterbased drug interactions and hepatotoxicity. Drug Metab Rev 42(3):446–471. https://doi. org/10.3109/03602530903491881 41. LeCluyse EL, Bullock PL, Parkinson A, Hochman JH (1996) Cultured rat hepatocytes. Pharm Biotechnol 8:121–159 42. Liu X, LeCluyse EL, Brouwer KR, Gan LS, Lemasters JJ, Stieger B, Meier PJ, Brouwer KLR (1999) Biliary excretion in primary rat hepatocytes cultured in a collagen-sandwich configuration. Am J Phys 277(1 Pt 1): G12–G21 43. Liu X, LeCluyse EL, Brouwer KR, Lightfoot RM, Lee JI, Brouwer KLR (1999) Use of Ca2+ modulation to evaluate biliary excretion in sandwich-cultured rat hepatocytes. J Pharmacol Exp Ther 289(3):1592–1599 44. Liu X, Chism JP, LeCluyse EL, Brouwer KR, Brouwer KLR (1999) Correlation of biliary excretion in sandwich-cultured rat hepatocytes and in vivo in rats. Drug Metab Dispos 27 (6):637–644 45. Ghibellini G, Vasist LS, Leslie EM, Heizer WD, Kowalsky RJ, Calvo BF, Brouwer KLR (2007) In vitro-in vivo correlation of hepatobiliary drug clearance in humans. Clin Pharmacol Ther 81(3):406–413. https://doi.org/10. 1038/sj.clpt.6100059 46. Pfeifer ND, Harris KB, Yan GZ, Brouwer KLR (2013) Determination of intracellular unbound concentrations and subcellular localization of drugs in rat sandwich-cultured hepatocytes compared with liver tissue. Drug Metab Dispos 41(11):1949–1956. https://doi.org/ 10.1124/dmd.113.052134 47. Marion TL, Perry CH, St Claire RL, Brouwer KLR (2012) Endogenous bile acid disposition in rat and human sandwich-cultured hepatocytes. Toxicol Appl Pharmacol 261(1):1–9. https://doi.org/10.1016/j.taap.2012.02.002 48. Chatterjee S, Bijsmans IT, van Mil SW, Augustijns P, Annaert P (2014) Toxicity and

124

Chitra Saran et al.

intracellular accumulation of bile acids in sandwich-cultured rat hepatocytes: role of glycine conjugates. Toxicol In Vitro 28 (2):218–230. https://doi.org/10.1016/j.tiv. 2013.10.020 49. Kanda K, Takahashi R, Yoshikado T, Sugiyama Y (2018) Total hepatocellular disposition profiling of rosuvastatin and pitavastatin in sandwich-cultured human hepatocytes. Drug Metab Pharmacokinet 33(3):164–172. https://doi.org/10.1016/j.dmpk.2018.04. 001 50. Kawase A, Hashimoto R, Shibata M, Shimada H, Iwaki M (2017) Involvement of reactive metabolites of diclofenac in cytotoxicity in sandwich-cultured rat hepatocytes. Int J Toxicol 36(3):260–267. https://doi.org/10. 1177/1091581817700584 51. Tetsuka K, Gerst N, Tamura K, Masters JN (2014) Glucuronidation and subsequent biliary excretion of mycophenolic acid in rat sandwich-cultured hepatocytes. Drug Metab Pharmacokinet 29(2):129–134 52. Matsunaga N, Wada S, Nakanishi T, Ikenaga M, Ogawa M, Tamai I (2014) Mathematical modeling of the in vitro hepatic disposition of mycophenolic acid and its glucuronide in sandwich-cultured human hepatocytes. Mol Pharm 11(2):568–579. https://doi.org/10. 1021/mp400513k 53. Bezenc¸on J, Beaudoin JJ, Ito K, Fu D, Roth SE, Brock WJ, Brouwer KLR (2019) Altered expression and function of hepatic transporters in a rodent model of polycystic kidney disease. Drug Metab Dispos 47(8):899–906. https:// doi.org/10.1124/dmd.119.086785 54. Pfeifer ND, Bridges AS, Ferslew BC, Hardwick RN, Brouwer KLR (2013) Hepatic basolateral efflux contributes significantly to rosuvastatin disposition II: characterization of hepatic elimination by basolateral, biliary, and metabolic clearance pathways in rat isolated perfused liver. J Pharmacol Exp Ther 347(3):737–745. https://doi.org/10.1124/jpet.113.208314 55. Pretlow TG II, Pretlow TP (1987) Cell separation: methods and selected applications. Harcourt Brace Jovanovich, Orlando, FL, pp 1–20 56. Watanabe T, Kusuhara H, Maeda K, Shitara Y, Sugiyama Y (2009) Physiologically based pharmacokinetic modeling to predict transportermediated clearance and distribution of pravastatin in humans. J Pharmacol Exp Ther 328 (2):652–662. https://doi.org/10.1124/jpet. 108.146647 57. Fu D, Lippincott-Schwartz J (2018) Monitoring the effects of pharmacological reagents on mitochondrial morphology. Curr Protoc Cell

Biol 79(1):e45. https://doi.org/10.1002/ cpcb.45 58. Guo C, LaCerte C, Edwards JE, Brouwer KR, Brouwer KLR (2018) Farnesoid X receptor agonists obeticholic acid and chenodeoxycholic acid increase bile acid efflux in sandwich-cultured human hepatocytes: functional evidence and mechanisms. J Pharmacol Exp Ther 365(2):413–421. https://doi.org/ 10.1124/jpet.117.246033 59. Kalvass JC, Maurer TS, Pollack GM (2007) Use of plasma and brain unbound fractions to assess the extent of brain distribution of 34 drugs: comparison of unbound concentration ratios to in vivo p-glycoprotein efflux ratios. Drug Metab Dispos 35(4):660–666. https://doi.org/10.1124/dmd.106.012294 60. Lee JK, Brouwer KR (2010) Determination of intracellular volume of rat and human sandwich-cultured hepatocytes. Toxicol Sci 114(S):339 61. Orntoft NW, Munk OL, Frisch K, Ott P, Keiding S, Sorensen M (2017) Hepatobiliary transport kinetics of the conjugated bile acid tracer 11C-CSar quantified in healthy humans and patients by positron emission tomography. J Hepatol 67(2):321–327. https://doi.org/ 10.1016/j.jhep.2017.02.023 62. Yan GZ, Brouwer KLR, Pollack GM, Wang MZ, Tidwell RR, Hall JE, Paine MF (2011) Mechanisms underlying differences in systemic exposure of structurally similar active metabolites: comparison of two preclinical hepatic models. J Pharmacol Exp Ther 337 (2):503–512. https://doi.org/10.1124/jpet. 110.177220 63. Daire JL, Leporq B, Vilgrain V, Van Beers BE, Schmidt S, Pastor CM (2017) Liver perfusion modifies Gd-DTPA and Gd-BOPTA hepatocyte concentrations through transfer clearances across sinusoidal membranes. Eur J Drug Metab Pharmacokinet 42(4):657–667. https://doi.org/10.1007/s13318-016-0382x 64. Planchamp C, Montet X, Frossard JL, Quadri R, Stieger B, Meier PJ, Ivancevic MK, Vallee JP, Terrier F, Pastor CM (2005) Magnetic resonance imaging with hepatospecific contrast agents in cirrhotic rat livers. Investig Radiol 40(4):187–194. https://doi.org/10. 1097/01.rli.0000154587.00638.77 65. Blouin A, Bolender RP, Weibel ER (1977) Distribution of organelles and membranes between hepatocytes and nonhepatocytes in the rat liver parenchyma. A stereological study. J Cell Biol 72(2):441–455. https://doi. org/10.1083/jcb.72.2.441

Quantitative Analysis of Intracellular Drug Concentrations in Hepatocytes 66. Rosen EP, Thompson CG, Bokhart MT, Prince HM, Sykes C, Muddiman DC, Kashuba AD (2016) Analysis of antiretrovirals in single hair strands for evaluation of drug adherence with infrared-matrix-assisted laser desorption electrospray ionization mass spectrometry imaging. Anal Chem 88(2):1336–1344. https:// doi.org/10.1021/acs.analchem.5b03794 67. Ramsden D, Tweedie DJ, Chan TS, Taub ME, Li Y (2014) Bridging in vitro and in vivo metabolism and transport of faldaprevir in human using a novel cocultured human hepatocyte system. HepatoPac Drug Metab Dispos 42(3):394–406. https://doi.org/10.1124/ dmd.113.055897 68. Chan TS, Yu H, Moore A, Khetani SR, Tweedie D (2019) Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized by cytochrome P450 using a novel hepatocyte model. HepatoPac Drug Metab Dispos 47(1):58–66. https://doi.org/ 10.1124/dmd.113. 053397fullarticlecorrection 69. Ramsden D, Tweedie DJ, St George R, Chen LZ, Li Y (2014) Generating an in vitro-in vivo correlation for metabolism and liver enrichment of a hepatitis C virus drug, faldaprevir, using a rat hepatocyte model (HepatoPac). Drug Metab Dispos 42(3):407–414. https:// doi.org/10.1124/dmd.113.055947 70. Thompson CG, Bokhart MT, Sykes C, Adamson L, Fedoriw Y, Luciw PA, Muddiman DC, Kashuba AD, Rosen EP (2015) Mass spectrometry imaging reveals heterogeneous efavirenz distribution within putative HIV reservoirs. Antimicrob Agents Chemother 59

125

(5):2944–2948. https://doi.org/10.1128/ AAC.04952-14 71. Bokhart MT, Rosen E, Thompson C, Sykes C, Kashuba AD, Muddiman DC (2015) Quantitative mass spectrometry imaging of emtricitabine in cervical tissue model using infrared matrix-assisted laser desorption electrospray ionization. Anal Bioanal Chem 407 (8):2073–2084. https://doi.org/10.1007/ s00216-014-8220-y 72. Nazari M, Muddiman DC (2015) Cellularlevel mass spectrometry imaging using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) by oversampling. Anal Bioanal Chem 407(8):2265–2271. https://doi.org/10.1007/s00216-014-83765 73. Eikel D, Vavrek M, Smith S, Bason C, Yeh S, Korfmacher WA, Henion JD (2011) Liquid extraction surface analysis mass spectrometry (LESA-MS) as a novel profiling tool for drug distribution and metabolism analysis: the terfenadine example. Rapid Commun Mass Spectrom 25(23):3587–3596. https://doi.org/ 10.1002/rcm.5274 74. Chen X, Hatsis P, Judge J, Argikar UA, Ren X, Sarber J, Mansfield K, Liang G, Amaral A, Catoire A, Bentley A, Ramos L, Moench P, Hintermann S, Carcache D, Glick J, Flarakos J (2016) Compound property optimization in drug discovery using quantitative surface sampling micro liquid chromatography with tandem mass spectrometry. Anal Chem 88 (23):11813–11820. https://doi.org/10. 1021/acs.analchem.6b03449

Chapter 6 Quantification of Intracellular Drug Aggregates and Precipitates Phillip Rzeczycki and Gus R. Rosania Abstract The ability of a drug to concentrate at its primary site of action is what leads to its clinical efficacy, while the drug becoming to concentrated at off-target sites can result in unwanted side effects and potential toxic events. For many drugs, the primary site of action is localized within a certain cell type, and ensuring that the drug is able to accumulate and reach the therapeutic concentration is often a barrier in developing successful small molecule drug products. However, in drug development and testing, the ultimate in vivo cellular fate of drugs tends to not be studied, with the majority of pharmacokinetic analysis being done on a macroscopic level. Here, we describe a microscopic cellular pharmacokinetic technique that can be applied to studying the cellular localization of drug following prolonged oral administration, allowing researchers to determine the extent of cellular accumulation and targeting that a certain therapeutic may have. The use of cellular isolation techniques and microscopic imaging analysis allows for measurements of drug content down to a cellular level, giving researchers a powerful technique to determine the cellular distribution of a compound and how different cell populations may be impacted by therapy. Ke ywords Pharmacokinetics, Aggregate, Macrophage, Bioaccumulation, Clofazimine

1

Introduction In order for a therapeutic agent to demonstrate efficacy, it must reach a sufficient concentration at the site of action. Oral therapeutics must have favorable pharmacokinetic parameters in order for this to occur. A key factor in determining these parameters is bioavailability. The bioavailability of a drug is determined by measuring the percentage of the dose that enters systemic circulation in a form that is usable by the body. For an intravenous treatment, bioavailability is 100% and decreases with other dosage forms due to incomplete absorption of the drug, or metabolism and elimination in urine or feces [1, 2]. Hurdles such as this limit the ability of the drug to reach therapeutic concentrations. The most frequent cause of poor oral bioavailability is poor solubility of the parent compound [3]. As drugs with poor solubility are often candidates

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

127

128

Phillip Rzeczycki and Gus R. Rosania

for development, there are a variety of strategies used to improve solubility. The addition of an ionizable group on a drug moiety is one of the most commonly used approaches to improve solubility [4]. Adding a weakly basic group to a molecule, like an amine, can greatly improve solubility and dissolution of a compound [5]. However, the addition of a weakly basic group can be a double-edged sword; it can result in trapping and accumulation of protonated drug within acidic compartments of cells [6]. Aside from impacting the pharmacokinetics of the drug, accumulation can lead to toxicity. Transporters in the liver remove exogenous substrates from the body, and their inhibition can result in damage to hepatocytes. Drugs such as cyclosporine A can inhibit the canalicular transport protein multidrug resistance-associated protein 2 (MRP2), leading to hepatotoxicity [7]. In vivo drug precipitation can occur due to change in pH, such as when exiting the stomach and entering the small intestine [8]. The antibiotic amoxicillin [9] and antiviral indinavir [10] can form insoluble aggregates in urine and the kidney, demonstrating that in vivo drug precipitation must be considered when designing a new drug product. For the purposes of studying intracellular drug accumulation, and for measuring the cellular pharmacokinetics of an accumulating drug that undergoes ion trapping in vivo, the antibiotic clofazimine (CFZ) will be used and its properties are discussed in detail. The physicochemical properties of CFZ imbue it was unique pharmacokinetics. Clofazimine is weakly basic, with two ionizable amine groups, and is extremely lipophilic, causing extensive accumulation in fat-rich tissues [11] which causes an extremely long half-life and large volume of distribution. Prolonged CFZ leads to drug accumulation in macrophages in the form of highly ordered crystal-like drug inclusions (CLDIs) [12]. Their formation is likely dependent on ion-trapping within the lysosome [13]; following entry into the acidic lysosome, the majority of the drug will exist in a protonated form [14, 15], preventing it from diffusing across the membrane back into the cytosol. The drug then becomes supersaturated and precipitates within the lysosome. These CLDIs can be isolated from tissues through the use of a simple sucrose gradient; soluble CFZ has a density of 1.3 g/mL, while CLDIs have a slightly higher density of 1.36 g/mL [16]. Protonated and free base CFZ display distinct optical properties that allow their presence to be detected via both fluorescence and polarization microscopy. Free base CFZ is highly fluorescent in the FITC (495 nm excitation/519 nm emission) channel, allowing intracellular accumulation to be tracked [17]. Protonation of CFZ results in a loss of signal in the green channel and development of signal in the far red, Cy5 (650 nm excitation/670 nm emission) channel [18]. Isolated CLDIs also polarize light as a single domain and are homogenously birefringent. Pure soluble CFZ crystals, on

Quantification of Intracellular Drug Aggregates and Precipitates

129

the other hand, give a mixed birefringent signal [12]. In order to detect CFZ and determine the cellular accumulation, microscopic methods in conjunction with biochemical analysis methods are employed to determine per-cell level accumulation rather than just a general cellular type accumulation, since not every cell will have drug. Because of the unique chemical change which clofazimine undergoes during accumulation, by transforming into an insoluble aggregate, multiparameter polarization and fluorescence microscopy is employed to study cellular populations. Polarized light microscopy (PLM) is a highly sensitive analytical methodology used to study the underlying molecular order or chemical composition [19], making PLM a powerful tool for the study of the molecular arrangement of biological structures [20]. One way PLM can be used is to study optical anisotropy. Optical anisotropy arises when a material interacts with light in a nonuniform manner, allowing certain orientations of light to pass to a greater extent than other orientations [21]. A commonly seen optically anisotropic biological material is the cell membrane; the arrangement of the various biological polymers that comprise the membrane differentially transmit light depending on its placement [20–22]. This property allows for linear diattenuation to be used to study molecular order within anisotropic materials. Linear diattenuation is a property of a material wherein the intensity of linearly polarized light transmitted through a sample is dependent on the polarization state of the light used to probe the sample [20, 23, 24] and has numerous applications in cellular biology, ranging from studying changes in molecular organization in cancers [25, 26] to characterizing articular cartilage [27]. Thus, this intrinsic property allows for the detection and study of the formation of insoluble ordered molecular aggregates within cells and tissues. For the purposes of this chapter, we will be describing a specialized microscopic imaging platform that has been developed in our laboratory: the LC-PolScope [28] is a microscope imaging system that utilizes a computer controlled liquid crystal (LC) compensator capable of generating linearly polarized light of any orientation [29]. This microscope setup allows linear diattenuation, fluorescence, and brightfield images to be captured from the same field of view, allowing these optical properties to be correlated with one another during various stages of treatment and washout of the drug. By combining fluorescence, transmitted light, and polarized light microscopy, one is able to study changes in the protonation state (fluorescence), accumulation (transmitted light/absorbance), and physical state (diattenuation) of the drug within live cells or tissue sections without the need for dyes or other processing techniques. This technique, and more will be applied to studying the specific cellular accumulation of a small molecule drug, which can potentially be applied to other drugs to study their intracellular pharmacokinetics. Methods follow.

130

2

Phillip Rzeczycki and Gus R. Rosania

Drug Administration to Animals For the purposes of studying intracellular drug accumulation in vivo, clofazimine will be utilized as a model compound due to its ability to be detected with the previously described imaging methodologies. 1. Acclimatize mice to specific pathogen-free animal housing facility within your university. 2. Dissolve clofazimine (CFZ) (C8895; Sigma, St. Louis, MO) in sesame oil (Shirakiku, Japan) to achieve a concentration of 3 mg/ml, and mix with Powdered Lab Diet 5001 (PMI International, Inc., St. Louis, MO) to produce a 0.03% drug to powdered feed mix and orally administered ad libitum. 3. Total drug loads fed to the animals are estimated from the estimated total mouse food consumption of 3 g per day, resulting in an average of 0.25 mg of drug ingested daily. This amount of drug is equivalent to the human dosing of CFZ during therapy for multidrug resistant tuberculosis of 10 mg/ kg/day. 4. Mice are treated with CFZ for 2, 3, 4, and 8 weeks, yielding an estimated whole-body drug load of 3.5 mg, 5.25 mg, 7 mg, and 14 mg, respectively. 5. Mix a corresponding amount of sesame oil with chow for vehicle treatment (control). 6. For washout experiments, treat mice with vehicle-containing diet for 8 weeks after an 8-week loading period with the CFZ-containing diet. 7. At the end of experimentation, euthanize mice via carbon dioxide asphyxiation followed by exsanguination, after which cells and tissues can be collected for further analysis.

3

Cellular Isolation Techniques Immediately following euthanasia of the mouse, it is crucial to isolate each cell type and place them into culture as quickly as possible to limit cell death and loss of sample. For our purposes, we will be isolating four unique macrophage and monocyte cell populations: peritoneal and alveolar macrophages, Kupffer cells of the liver, and bone marrow monocytes, and the isolation and culture of each will be described below.

Quantification of Intracellular Drug Aggregates and Precipitates

3.1 Peritoneal Macrophage Isolation and Culture

131

1. Euthanize mouse and secure the mouse on a dissecting station and douse the abdomen of the mouse in 70% ethanol, and dry. 2. Using a small pair of scissors or a scalpel, make a small, shallow incision on the lower central quadrant of the abdomen, careful to not cut any internal organs. Once a small incision has been made, cut the flap of skin down to the base of the tail and up to the diaphragm. 3. Pull the two sides of skin to the side and secure with a pin, and carefully add DPBS with 5% FBS to the cavity in 2–3 mL aliquots, up to 10 mL total. 4. Using a sterile spatula, gently mix the exudate, and aspirate out into a sterile tube, and keep on ice. Repeat until 5 mL of exudate is removed from the cavity. 5. Centrifuge at 100
g, 4 ˚C for 10 min, and resuspend in 1 mL DMEM with 5% FBS. 6. Count cells using hemocytometer, note total live and dead cells, and plate approximately 300,000 cells in single chamber of an 8-well Nunc treated chamber slide, and allow cells to adhere overnight. 7. Keep remaining cell solution on ice for analysis of drug content.

3.2 Alveolar Macrophage Isolation and Culture

1. Following the peritoneal lavage, the alveolar macrophages are removed via a bronco-alveolar lavage (BAL). 2. Expose the trachea by continuing the incision made for the peritoneal lavage up the thoracic cavity. Place a small piece of sterile thread under the trachea and pull so that a knot can be made, and cannulate the trachea by making a small incision and placing an 18G needle in, and tie thread around the base of the needle. 3. Lavage the lung with 6 1-mL passes of DPBS with 0.5 mM EDTA, collecting the exudate following each pass. 4. Centrifuge the cells for 10 min at 400
g at 4 ˚C. Resuspend cells in 1 mL RPMI 1640 media with 5% FBS. 5. Count cells using hemocytometer, note total live and dead cells, and plate approximately 300,000 cells in single chamber of an 8-well Nunc treated chamber slide, and allow cells to adhere overnight. 6. Keep remaining cell solution on ice for analysis of drug content.

3.3 Kupffer Cell Isolation and Culture

1. Inject the portal vein of the liver with 10 mL of 1 mg/mL Collagenase D in DMEM-low glucose with 15 mM HEPES.

132

Phillip Rzeczycki and Gus R. Rosania

2. Remove the tissue, place in a sterile petri dish, and mince into small (2–4 mm) pieces using a sterile scalpel blade. Add 15 mL of collagenase solution, and incubate the tissue for 40 min at 37  C with occasional pipetting to dissociate tissue. 3. Filter the suspension through a 100 μm cell strainer, and centrifuge at 200
g, 4 ˚C for 5 min. Discard the supernatant and resuspend cells in 15 mL DMEM-low glucose with 15 mM HEPES, and repeat centrifugation. Perform this wash two additional times. After the final wash, suspend the cells in DMEM:F/12 (1:1) with 10% FBS. 4. Count cells using hemocytometer, note total live and dead cells, and plate approximately 300,000 cells in single chamber of an 8-well Nunc treated chamber slide, and allow cells to adhere overnight. 5. Keep remaining cell solution on ice for analysis of drug content. 3.4 Bone Marrow Monocyte Isolation and Culture

1. Remove the skin surrounding the legs of the mice, and dislocate the femur from the hip. Store in PBS on ice until cells can be harvested. 2. Using sterile technique, remove the muscle from the femur and tibia using a scalpel blade or scissors. Separate the femur and tibia at the knee joint. Using two pairs of hemostats, gently remove the growth plates from the ends of the femur and the tibia. 3. Place the femur and tibia in a 0.6 mL microcentrifuge tube with a small hole poked through the bottom using a 25G needle. Place this microcentrifuge tube inside a 1.5 mL centrifuge tube that has had the top removed. Centrifuge for 5 min at 3000
g at 4 ˚C. 4. Resuspend cells in 1 mL RPMI 1640 media with 5% FBS. 5. Count cells using hemocytometer, note total live and dead cells, and plate approximately 300,000 cells in single chamber of an 8-well Nunc treated chamber slide, and allow cells to adhere overnight. 6. Keep remaining cell solution on ice for analysis of drug content.

4

Cellular Drug Quantification Due to the optical properties of clofazimine, we are able to detect the drug with a simple absorbance reading using a spectrophotometer at the absorbance maximum of protonated clofazimine (530 nm). Using other analytical techniques, such as HPLC or

Quantification of Intracellular Drug Aggregates and Precipitates

133

UPLC, single-cell measurements of drug can be measured as well, and methods will need to be adapted depending on the most efficient way of extracting drug from the intracellular environment. 1. Following isolation of macrophages from the animal, centrifuge each cell suspension at 1000
g for 10 min, remove the media, and resuspend in 1 mL of DI water. 2. Add 1 mL of xylene, and vortex to solubilize drug and remove from the cellular environment. Remove the upper organic layer and transfer to new tube, and repeat extraction two more times. 3. Add 1 mL of 9 M sulfuric acid to the xylene solution, vortex and remove the aqueous layer, which will contain solubilized, protonated clofazimine. Repeat two times to remove all drug from the xylene. 4. Using a standard plate reader, read the absorbance of each sample at 530 nm and determine concentration using a standard curve of CFZ dissolved in sulfuric acid. 5. The total recovered drug mass can be determined from the final volume of sulfuric acid that was used to extract from the xylene, representing the total mass of drug sequestered by macrophages. 6. Tables 3, 4, and 5 show the number of recovered cells and recovered molecules of clofazimine hydrochloride in the Kupffer cell, and alveolar and peritoneal macrophage populations at 2, 4, and 8 weeks of clofazimine administration. Cellular yields can be highly variable depending on the skill of the user and the time that cells are allowed to sit out on ice prior to being counted, so it is important to count the cells as close to isolation as possible to ensure that there is minimal cell death. Following measurement of the total cellular accumulation of drug, a more precise measurement can be determined. By using multiparameter imaging and analysis, the percentage of drugsequestering cells can be determined, and using this value, the total drug in xenobiotic-sequestering cells to be measured.

5 Multiparameter Imaging and Determination of Sequestering Vs. Non-sequestering Cell Populations 1. Remove media and replace with fresh media to remove unattached or dead cells. 2. For each cell type from each animal, you will want to image a minimum of 150 cells at high (20
or greater) magnification.

134

Phillip Rzeczycki and Gus R. Rosania

3. For the purposes of this chapter, we will describe the imaging setup that has been previously published [30–33] and utilized which combines fluorescence, polarization, and absorbance readings into one imaging modality. This simple setup allows one to compare changes in the protonation state, molecular order, and cellular accumulation of clofazimine via changes in far-red fluorescence, linear diattenuation, and optical density, respectively. 4. Due to the optical properties of clofazimine. Once protonated within an acidic cellular compartment, the drug undergoes a fluorescence shift, becoming fluorescent in the far-red regions, and this shift can be detected via a standard Cy5 filter cube [34]. As the drug accumulates and induces changes to the cell itself by formation of new membranes, or through the formation of large crystalline aggregates, linear diattenuation can be used to detect these minute changes. Finally, because the drug has a deep red color, using the absorbance of light in the red region (623 nm), we can quantify the loading of drug within the cell with changes to this optical density value. 5. Prior to imaging, and before each individual imaging session, the instrument must be calibrated to ensure consistent measurements across samples. 5.1 Calibration of LC-PolScope and Imaging

1. The LC-PolScope is calibrated through the use of a specialized microscope slide which contains four sheets of perfectly polarized glass, polarized to light of 0 , 45 , 90 , and 135 . 2. To calibrate the polarizer, the glass that is polarized orthogonally to that angle setting of the polarizer is chosen. Calibrate the 0 polarized light, by generating an ROI square on the glass oriented 90 . Repeat with each polarizer setting to calibrate the 45 , 90 , and 135 polarizer settings. 3. Verify the calibration by visualizing a blank region of the slide and bringing the image out of focus, and taking a background image. The background image will account for any intrinsic absorbance and dichroic properties of the imaging vessel. Then, return to the calibration slide and generate a sample image data set. The calibration is verified by ensuring that the mean transmittance for each polarization square is at or near 0.5, the diattenuation for each square is 1.0, and the angle of maximal transmittance is equal to the orientation of the square. 4. The instrument must be calibrated for each individual filter that is applied to the imaging session.

Quantification of Intracellular Drug Aggregates and Precipitates

135

Fig. 1 Multiparameter imaging setup utilized to analyze mixed populations of drug-exposed macrophages. This system combines polarization, fluorescence, and brightfield imaging to acquire multidimensional images of cells or tissue sections 5.2 Imaging of XenobioticSequestering Cells

1. Following calibration, place the chamber slide on the imaging platform. Take a background image at each magnification that will be used for imaging in a similar manner to how the background was taken when calibrating. 2. Following this, capture a polarization imaging stack with each filter that will be utilized for the course of the imaging experiment. 3. After capturing the polarization images, switch to fluorescence imaging, and set the exposure to minimize background autofluorescence, noting the exposure settings to be used throughout the course of the experiment for each cell type. Capture images using a standard Cy5 (650 excitation, 670 emission) filter cube or another light source. For untreated control cells, there will be little to no far-red fluorescence present. Figure 1 displays the multiparameter imaging setup described in this methodology. 4. Repeat the process until the desired number of cells per animal have been imaged, and repeat with each cell type and for each animal. A background image must be taken for each new chamber slide to correct for the minute differences in the optical properties of each individual chamber.

136

Phillip Rzeczycki and Gus R. Rosania

With the images that have been taken, the total percentage of xenobiotic-sequestering cells for each animal at each individual time point can be determined. This percentage can then be applied back to the original cell counts obtained in Subheading 3 to determine the total number of xenobiotic-sequestering cells, and the cellular accumulation of drug can then be measured with the total recovered drug measured in Subheading 4. 5.3 Image Analysis and Quantification of XenobioticSequestering Cell Populations

1. Images generated are 8-bit, yielding intensity values from 0 to 255. The values for diattenuation, mean transmittance, and fluorescence are obtained by measuring the pixel value at each image map at the exact locations across each image. 2. To obtain the pixel values at a whole-cell level, an automated image analysis strategy is employed and is summarized in Fig. 2. 3. Open the 0 polarization state image (Fig. 2a) and adjust the brightness and contrast manually to allow for cells to stand out from the well-plate background (Fig. 2b).

Fig. 2 Generation of masks for measurement of intracellular aggregate properties. Representative schematic of mask generation for data collection. The 0-degree polarization state image is selected (Panel A) and has the brightness and contrast adjusted (Panel B). Following this, the image undergoes a manual threshold in ImageJ, delineating between objects and background (Panel C). The image then undergoes the “Fill Holes” function, generating the mask for data analysis (Panel D)

Quantification of Intracellular Drug Aggregates and Precipitates

137

Table 1 Optical density of control and drug-treated macrophages at 2, 4, and 8 weeks determined using multiparameter imaging and analysis Cell type Alveolar macrophage Peritoneal macrophage

Untreated

2 Week

0.0336  0.024 0.1152  0.0516

4 Week

8 Week

0.1008  0.072

0.1584  0.096

0.0192  0.0288 0.0384  0.0432 0.0528  0.0288

0.384  0.1248

Bone marrow monocyte 0.0336  0.0336 0.0384  0.0288 0.0096  0.0096 0.0672  0.0432 Kupffer cell

0.0192  0.0288

0.024  0.0384 0.0816  0.0672 0.3936  0.1152

Table 2 Linear diattenuation of control and drug-treated macrophages at 2, 4, and 8 weeks determined using multiparameter imaging and analysis Cell type

Untreated

2 Week

4 Week

8 Week

Alveolar macrophage

0.0576  0.0336

0.072  0.0288 0.1056  0.0384 0.1248  0.0528

Peritoneal macrophage

0.0528  0.0144

0.0432  0.024 0.0384  0.0432

0.264  0.1248

Bone marrow monocyte 0.0624  0.0384 0.0624  0.0336 0.1008  0.0864 0.0864  0.0912 Kupffer cell

0.0432  0.0288 0.0432  0.0336 0.1392  0.0816 0.3216  0.2112

4. Manually threshold the image to generate a binary mask (Fig. 2c). Use the “Fill Holes” function in ImageJ to ensure that the total cell is enclosed by the mask (Fig. 2d). 5. Open the corresponding diattenuation, mean transmittance, and fluorescence image maps for the mask that was just generated. Analyze the cells in the image using the “Analyze Particles” function within ImageJ with the previously generated binary image serving as the mask. Set an appropriate size limit for particle analysis to avoid analyzing cellular debris. 6. Repeat this with each image that has been taken to collect the optical properties of the isolated cells for each animal and each time point. Tables 1 and 2 display the mean optical density and linear diattenuation at 623 nm for each cell type in vehicle 2, 4, and 8 weeks treated groups. 7. Import all data into a statistical analysis software package and perform a K-clustering analysis, with two sets of cells to be clustered. The clustering should be performed by grouping all time points of one cell type together for classification. 8. Following importation of the data, perform a negative log base 10 transformation of the data, and perform a K-means clustering with two clusters, which will be xenobiotic sequestering and non-xenobiotic sequestering.

138

Phillip Rzeczycki and Gus R. Rosania

Table 3 CFZ accumulation in Kupffer cells collected at 2, 4, and 8 weeks of CFZ therapy. Cells were collected, imaged, and analyzed using the methods described in this chapter. Results represent average of three animals. Note: At t ¼ 2 weeks, no insoluble drug was present, and all cells were sorted as negative. Results are reported with assumption that recovered drug was present in soluble form within cell

Time (weeks) Cells recovered

Molecules CFZ recovered

Percentage xenobiotic sequestering (%)

Molecules CFZ/cell

2

2.9
106  1.9
106 2.3
1015  1.3
1015 0

1.2
109  9.0
108

4

8.1
106  2.3
106 9.6
1016  7.2
1015 33

3.8
1010  9.8
109

8

3.8
106  6.7
105 2.1
1017  1.1
108

8.4
1010  1.9
1010

63

Table 4 CFZ accumulation in peritoneal macrophages collected at 2, 4, and 8 weeks of CFZ therapy. Cells were collected, imaged, and analyzed using the methods described in this chapter. Results represent average of three animals. Note: At t ¼ 2 weeks, no insoluble drug was present, and all cells were sorted as negative. Results are reported with assumption that recovered drug was present in soluble form within cell

Time (weeks) Cells recovered

Molecules CFZ recovered

Percentage xenobiotic sequestering (%)

Molecules CFZ/cell

2

1.4
106  3.9
105 1.7
1015  5.3
1014 0

1.4
109  8.4
108

4

9.3
105  1.7
105 9.6
1015  2.0
1015 15

2.1
1010  1.2
1010

8

8.2
105  1.7
105 1.6
1016  1.5
1015 23

7.5
1010  2.8
1010

9. Using the total percentage of xenobiotic-sequestering cells that were determined for each time point, multiply that percentage by the corresponding number of macrophages that were isolated from that animal during cell isolation to determine the total number of xenobiotic-sequestering cells. Then, divide the total recovered drug mass by the total number of xenobiotic-sequestering cells to obtain the mass of drug per xenobiotic-sequestering cell. 10. Tables 3, 4, and 5 show the percentage of cells that were determined to be xenobiotic sequestering at 2, 4, and 8 weeks of clofazimine administration using K-means clustering, and the final total number of clofazimine molecules within each xenobiotic-sequestering cell.

Quantification of Intracellular Drug Aggregates and Precipitates

139

Table 5 CFZ accumulation in alveolar macrophages collected at 2, 4, and 8 weeks of CFZ therapy. Cells were collected, imaged, and analyzed using the methods described in this chapter. Results represent average of three animals. Note: Alveolar macrophages did accumulate the far-red fluorescent insoluble form of CFZ-HCl at t ¼ 2 weeks, and thus were sorted accordingly

Time (weeks) Cells recovered

Molecules CFZ recovered

Percentage xenobiotic sequestering (%)

Molecules CFZ/cell

2

2.3
105  9.0
104 3.7
1015  4.3
1014 74

1.8
1010  7.5
109

4

5.3
105  6.3
104 1.1
1015  5.4
1015 99

2.1
1010  1.2
1010

8

2.7
105  1.4
105 1.1
1016  2.8
1015 84

5.8
1010  2.7
1010

Using these techniques, the cellular accumulation of various therapeutics can be determined in live cells. However, there may be instances where isolation of populations of cells may be difficult, or the isolation causes the release of drug, making measurements of intracellular drug difficult. For these cases, a technique to measure cellular drug accumulation using tissue cryosections can be employed.

6

Cellular Drug Measurements from Tissue Cryosections To determine intracellular drug within a tissue cryosection, three steps must be performed: isolation and measurement of soluble or insoluble drug that you wish to measure, determination of total macrophage or other cell type within tissue, and quantification of percentage of cells which are loaded with drug. Each step will be described and how it can be applied to measurement of intracellular clofazimine in the lung, liver, and spleen.

6.1 Isolation of Insoluble CFZ and Quantification

1. Following euthanasia, remove the lung, liver, and spleen from the animal and weigh each organ. Remove portion of organ for drug measurement, weigh, and mince each organ on a sterile petri dish using a scalpel blade and a syringe plunger, with DPBS used to help suspend the resulting homogenate. Process the remainder of the organ for cryopreservation for histology. 2. Filter through a 40 μm cell strainer into a centrifuge tube and pellet the insoluble fraction by centrifuging at 300
g for 10 min. 3. Remove the supernatant and resuspend in 10% sucrose in DPBS. Insoluble drug can be further purified from tissue by placing on top of a 50%, 30%, and 10% sucrose gradient and centrifuging at 3200
g for 60 min.

140

Phillip Rzeczycki and Gus R. Rosania

4. Remove the supernatant, and dissolve resulting pellet in 9 M sulfuric acid, and determine the concentration using the absorbance at 450 nm, with a standard curve. This methodology assumes that all drug that is recovered is insoluble, and that all soluble drug present in the organ was removed during the isolation procedure. 5. With the recovered concentration, the mass of drug per milligram of tissue can be determined, which can then be used to determine the total amount of drug within the organ. Once the total mass of drug within the organ has been determined, tissue cryosections from the organ can be generated that will be used to quantify any expansion in cell populations that may arise following drug treatment and to determine the percentage of xenobiotic-sequestering cells within the organ. There have been studies performed in the past that have calculated the total number of macrophages present within each organ and will be used as the baseline macrophage population found in the organ, and for our purposes, the measurements performed by Gordon et al. using the pan-macrophage marker F4/80 [35] will serve as the baseline macrophage population. Due to treatment with clofazimine, and this may be the case with other therapeutic agents or in different disease states, there is an expansion of the macrophage population. To determine how the drug-sequestering cell population of interest is altered, immunohistochemical methods are employed. For the purposes of clofazimine, we utilized the pan-macrophage marker F4/80 as our marker for quantifying the increased infiltration of macrophages to the organs of interest. 6.2 Total Macrophage or Other Drug-Sequestering Cell Population Determination and Measurement of Percentage of DrugSequestering Cells

1. Perform immunohistochemical staining for F4/80 or other marker of interest for cells that may be implicated in xenobiotic sequestration. It is vital to choose a secondary antibody that does not have spectral overlap with the fluorescence of your drug of interest if it is fluorescent. 2. Image sections stained for cell marker in both vehicle and drugtreated animals. Acquire images of both the cell marker and corresponding Cy5 image for each treated section. 3. Determine the relative macrophage population expansion for each organ by comparing the average F4/80 signal per image field in the drug-treated sections to the average F4/80 signal per image field in the vehicle treated section. Apply this percentage change to the literature-determined macrophage counts. This new population is referred to as the “expanded macrophage population.”

Quantification of Intracellular Drug Aggregates and Precipitates

141

4. Generate image masks using the F4/80 signal through manual thresholding to generate accurate masks of the F4/80 (+) cell populations within the organ. Using this mask, measure the F4/80 associated Cy5 signal, which will represent the macrophage associated drug accumulation. 5. Similar to the analysis performed using single-cell data, perform a  Log10 transformation on the fluorescence signal, and perform a K-means clustering with the clusters set to 2. The total number of cells that are classified as “high fluorescence” are the xenobiotic-sequestering population. This percentage of xenobiotic-sequestering cells is then applied to the expanded macrophage population, yielding the total number of xenobiotic-sequestering macrophages within the organ. 6. The individual drug loading per macrophage is then measured by dividing the total measured insoluble drug as measured in Subheading 6.1 by the total expanded drug-sequestering macrophage population determined using immunofluorescence. This methodology assumes that there is negligible soluble drug present within these cells, and that all drug that was recovered in Subheading 6.1 comprises the entirety of the insoluble drug within the organ of interest. Table 6 contains the total recovered clofazimine from each organ, the percentage of xenobiotic-sequestering cells as determined via K-means clustering, the expanded macrophage populations, and total number of molecules of CFZ per macrophage in the liver, lung, and spleens following 8 weeks of prolonged clofazimine administration. Using this methodology, individual macrophage drug loading values were found to be on the same order of magnitude as those determined using isolated cell populations, pointing to the validity and reproducibility of the technique. One potential advantage of the histological technique over the isolated cell technique would be the ability to use multiple cellular markers on a single tissue section to determine the individual drug loading in multiple cell populations such as specific subtypes of immune cells.

7

Conclusions and Future Applications Using the techniques described here, the cellular pharmacokinetics of clofazimine within four different macrophage subsets were characterized, revealing an uneven accumulation pattern in the early stages of treatment, followed by approximately even levels of accumulation within the three mature macrophage populations that were studied. By combining quantitative, multiparameter

Percent xenobiotic sequestering (%) 88.5  3.3 83.9  12.5 81.1  3.2

Recovered CFZ (molecules)

5.81
1018  9.92
1017

4.11
1018  3.43
1017

4.07
1017  7.63
1016

Macrophage population

Liver

Spleen

Lung

3.7
106  1.7
106

2.2
107  5.9
106

8.0
107  1.9
107

Total xenobiotic sequestering population

1.1
1011  5.4
1010

1.9
1011  5.2
1010

7.3
1010  2.1
1010

Molecules of CFZ/xenobiotic sequestering cell

Table 6 CFZ accumulation in liver, spleen, and lung macrophages following 8 weeks of treatment determined via IHC analysis and whole-organ insoluble drug isolation described in chapter

142 Phillip Rzeczycki and Gus R. Rosania

Quantification of Intracellular Drug Aggregates and Precipitates

143

microscopy with biochemical analyses of tissues and cells, the ultimate cellular fate and accumulation of a therapeutic agent can be measured within cells that accumulate the compound. Determining the intracellular fate of drugs in early stages of development may allow for detection of therapeutics that may cause unwanted off-target effects. In the case of clofazimine, the macrophagetargeting may actually be beneficial for the host, through the formation of anti-inflammatory macrophage-loaded granulomas [36] which help to sequester the relatively cytotoxic soluble form of the drug away from the body in the form of insoluble aggregates. However, this may not always be the case. Certain therapeutics may be cytotoxic if they accumulate to an extensive degree in certain cell types, leading to immune responses or other toxic events that may be detrimental to the host [37–39]. Other hydrophobic drugs, such as hydroxychloroquine, can induce retinopathy following long-term treatment, and this is due in part to its high lipophilicity and extensive half-life [40, 41]. Applying these techniques early on in in vivo animal testing may allow for researchers to determine if there is extensive off-target accumulation that may be responsible for unwanted side effects, improving drug design. Additionally, this technique can potentially be applied to any other drug out on the market, even with those that are not intrinsically fluorescent. The lack of fluorescence in the majority of therapeutics can be overcome through the use of a fluorescent tag [42] being added to the drug, or by combining the biochemical analyses with hyperspectral imaging moieties such as Raman microscopy [43, 44], which can allow researchers to determine the specific cells that may extensively accumulate the therapeutic. Finally, the analysis reported here uses automated clustering to remove bias from determining positive versus negative cells, and the image analysis methodology is highly automated, allowing very high throughput analyses of numerous cell types and therapeutics to be performed in a rapid manner. By applying quantitative, intracellular drug pharmacokinetic measurements, safer and more effective therapeutics can ultimately be designed and implemented. References 1. Veber D et al (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615–2623 2. Urso R, Blardi P, Giorgi G (2002) A short introduction to pharmacokinetics. Eur Rev Med Pharmacol Sci 6(2–3):33–44 3. Savjani K, Gajjar A, Savjani J (2012) Drug solubility: importance and enhancement techniques. ISRN Pharmaceutics 2012:10 4. Manallack DT et al (2013) The significance of acid/base properties in drug discovery. Chem Soc Rev 42(2):485–496

5. Williams H et al (2013) Strategies to address low drug solubility in discovery and development. Pharmacol Rev 65(1):315–499 6. Kaufmann A, Krise J (2006) Lysosomal sequestration of amine-containing drugs: analysis and therapeutic implications. J Pharm Sci 96 (4):729–746 7. Padda MS et al (2011) Drug induced cholestasis. Hepatology 53(4):1377–1387 8. Kostewicz E et al (2004) Predicting the precipitation of poorly soluble weak bases upon entry

144

Phillip Rzeczycki and Gus R. Rosania

in the small intestine. J Pharm Pharmacol 56:43–51 9. Fogazzi G et al (2003) Amoxycillin, a rare but possible cause of crystalluria. Images in Nephrology 18:212–214 10. Koop J et al (1997) Crystalluria and urinary tract abnormalities associated with Indinavir. Ann Intern Med 127(2):119–125 11. Cholo M et al (2012) Clofazimine: current status and future prospects. J Antimicrob Chemother 67(2):290–298 12. Baik J, Rosania GR (2012) Macrophages sequester Clofazimine in an intracellular liquid crystal-like Supramolecular organization. PLoS One 7(10):e47494 13. Baik J et al (2013) Multiscale distribution and bioaccumulation analysis of Clofazimine reveals a massive immune system-mediated xenobiotic sequestration response. Antimicrob Agents Chemother 57(3):1218–1230 14. Information, N.C.f.B. PubChem Compound Database; CID¼2794. [cited 2015 June 1]. http://pubchem.ncbi.nlm.nih.gov/com pound/2794 15. Woldemichael T et al (2018) Reverse engineering the intracellular self-assembly of a functional Mechanopharmaceutical device. Sci Rep 8(1):2934 16. Keswani R et al (2015) Chemical analysis of drug biocrystals: a role for counterion transport pathways in intracellular drug disposition. Mol Pharm 12(7):2528–2536 17. Baik J, Rosania GR (2011) Molecular imaging of intracellular drug-membrane aggregate formation. Mol Pharm 8(5):1742–1749 18. Keswani R et al (2015) A far-red fluorescent probe for flow cytometric xenobioticsequestering cell functional studies. Cytom A 87(9):855–867 19. Sparenga SB (2008) The importance of polarized light microscopy in the analytical setting. Microsc Microanal 14(Suppl 2):1032–1033 20. Mehta SB, Shribak M, Oldenbourg R (2013) Polarized light imaging of birefringence and diattenuation at high resolution and high sensitivity. J Opt 15(9):094007 21. Inoue´ S (2002) Polarization microscopy. In: Current protocols in cell biology. Wiley, Hoboken, NJ, p 27 22. Massoumian F et al (2003) Quantitative polarized light microscopy. J Microsc 209(1):13–22 23. Chen P-C et al (2009) Measurement of linear birefringence and diattenuation properties of optical samples using polarimeter and stokes parameters. Opt Express 17 (18):15860–15884

24. Oldenbourg R. Diattenuation. [Internet] 2013 May 8 2015]. openpolscope.org/ pages/Diattenuation.htm 25. Okoro C et al (2018) Second-harmonic patterned polarization-analyzed reflection confocal microscopy of stromal collagen in benign and malignant breast tissues. Sci Rep 8 (1):16243 26. Ahmad I et al (2015) Ex vivo characterization of normal and adenocarcinoma colon samples by Mueller matrix polarimetry. J Biomed Opt 20(5):56012 27. Chang CM et al (2018) Optical characterization of porcine articular cartilage using a polarimetry technique with differential Mueller matrix formulism. Appl Opt 57(9):2121–2127 28. Harris G, Verma A, Oldenbourg A (2014) LC-PolScope 29. Oldenbourg R. Polarization state generated by universal polarizer. Tutorials 2013 [cited 2015 8 May]. openpolscope.org/pages/ PolarizationEllipseUniversalPolarizer.htm 30. Rzeczycki P et al (2017) Detecting ordered small molecule drug aggregates in live macrophages: a multi-parameter microscope image data acquisition and analysis strategy. Biomed Opt Express 8(2):860–872 31. Horstman EM et al (2017) Elasticity in macrophage-synthesized biocrystals. Angew Chem Int Ed Engl 56(7):1815–1819 32. Rzeczycki P et al (2018) An expandable mechanopharmaceutical device (1): measuring the cargo capacity of macrophages in a living organism. Pharm Res 36(1):12 33. Min KA et al (2015) Massive bioaccumulation and self-assembly of phenazine compounds in live cells. Adv Sci 2(8):1500025 34. Keswani RK et al (2015) A far-red fluorescent probe for flow cytometry and image-based functional studies of xenobiotic sequestering macrophages. Cytometry A 87(9):855–867 35. Lee SH, Starkey PM, Gordon S (1985) Quantitative analysis of total macrophage content in adult mouse tissues. Immunochemical studies with monoclonal antibody F4/80. J Exp Med 161(3):475–489 36. Rzeczycki P et al (2018) An expandable mechanopharmaceutical device (2): drug induced granulomas maximize the cargo sequestering capacity of macrophages in the liver. Pharm Res 36(1):3 37. Busse P et al (2019) Cytotoxicity of drugs injected into joints in orthopaedics. Bone Joint Res 8(2):41–48 38. Naughton CA (2008) Drug-induced nephrotoxicity. Am Fam Physician 78(6):743–750

Quantification of Intracellular Drug Aggregates and Precipitates 39. Cullen JM et al (2012) Effects of Kupffer cell depletion on acute alphanaphthylisothiocyanate-induced liver toxicity in male mice. Toxicol Pathol 41(1):7–17 40. Ducharme J, Farinotti R (1996) Clinical pharmacokinetics and metabolism of chloroquine. Clin Pharmacokinet 31(4):257–274 41. Ndolo RA, Forrest ML, Krise JP (2010) The role of lysosomes in limiting drug toxicity in mice. J Pharmacol Exp Ther 333(1):120–128 42. Chan J, Dodani SC, Chang CJ (2012) Reaction-based small-molecule fluorescent

145

probes for chemoselective bioimaging. Nat Chem 4(12):973–984 43. LaLone V et al (2019) Inkjet-printed microcalibration standards for ultraquantitative Raman spectral cytometry. Analyst 144 (12):3790–3799 44. LaLone V et al (2018) An expandable mechanopharmaceutical device (3): a versatile Raman spectral cytometry approach to study the drug cargo capacity of individual macrophages. Pharm Res 36(1):2

Chapter 7 Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations Mikhail D. Murashov Abstract Macrophages reside in every tissue of the body and are the first-line of defense of the immune system. Their function is to entrap apoptotic cells, pathogens, and other particles and produce immune effector cytokines. It has also been shown that macrophages sequester xenobiotics, particularly lysosomotropic agents. One of the well-known and best studied lysosomotropic agents is the weakly basic antibiotic clofazimine. Interestingly, not all macrophages within a population, such as those present in a particular tissue or organ, accumulate xenobiotics to the same extent. Within every general population of macrophages, there is a distinct subpopulation of macrophages that express the xenobiotic sequestering phenotype. Thus, in this chapter, we explain how one can utilize clofazimine as a probe to explore the physical and biological markers that can ultimately distinguish this xenobiotic sequestering subpopulation of macrophages, specifically among the alveolar macrophages isolated from the lungs of mice treated with oral clofazimine for a period of several weeks. The experimental approach and the rationale behind choosing each marker is also explained. As a result, out of various biological markers (i.e., TLR2, TLR4, CLC7, TFEB, p65, V-ATPase) and physical markers (i.e., cell area and size), the physical markers were most closely associated with the xenobiotic sequestering subpopulation of macrophages. Other investigated molecular markers showed no statistically significant association with the xenobiotic sequestering phenotype. Some markers exhibited differences, but the variability in the phenotype rendered them a less reliable marker as compared to the associated increase in cell area and size. Key words Xenobiotic sequestration, Macrophages, Biomarker, Size, Area, TLR2, TLR4, CLC7, TFEB, p65, V-ATPase

1

Introduction Macrophages (and their precursors, monocytes) reside in every tissue of the body, although in different guises depending on the anatomic location (e.g., microglia, Kupffer cells, and osteoclasts) [1]. They are the “big eaters” and the first-line of defense of the immune system [1]. Through their ability to clear pathogens and instruct other immune cells in terms of recognizing self vs nonself, these cells have a central role in protecting the host against foreign

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

147

148

Mikhail D. Murashov

agents [1]. Upon tissue damage or infection, monocytes are rapidly recruited to the tissue, where they differentiate into tissue macrophages that engulf apoptotic cells, pathogens, and other xenobiotics and produce immune effector cytokines [1]. Accordingly, they also play a central role in wound healing and regeneration by debriding dead or defective cells and tissues while stimulating scar formation or stem cell differentiation to facilitate the replacement or recovery of any tissue structure or function that is lost due to injury or disease [1]. There have been many different investigations indicating that macrophages are also involved in the accumulation of weakly basic drugs [2]. Many of these weakly basic drugs are substrates for pH-dependent ion trapping in the acidic endo-lysosomal system [2]. Chemicals with a propensity to accumulate in the endolysosomal system are commonly referred to as lysosomotropic agents. The trapping and accumulation of a drug within the lysosome, which is the subcellular organelle in macrophages that digests unwanted materials delivered from the cytoplasm and extracellular environment, has a variety of consequences. Several studies show that the drug exposure can activate the cell, which leads to several responses, such as proton and chloride channel upregulation, expanded lysosomal volume, and nuclear localization of the transcription factor EB (TFEB) [2–7]. TFEB is a master regulator of lysosomal biogenesis, and its activation leads to the upregulation of genes that encode for proteins with lysosome-specific structural and functional roles [8]. Importantly, the trapping and accumulation of a drug within the lysosome can have significant biological consequences for macrophages due to the fact that these cells are also involved in the phagocytosis and lysosomal degradation of foreign pathogens and the fact that macrophage lysosomes play a key role in coordinating the self–nonself immune recognition pathways and orchestrate many immune signaling functions involved in inflammation, innate immunity, and long-term immunological memory. Furthermore, lysosomal drug sequestration in macrophages can exert a major impact in the pharmacokinetics and pharmacodynamics of an accumulating drug to the point in which the drugs with the largest volumes of distribution in humans are the ones that most avidly accumulate in macrophage lysosomes. In fact, one of the most interesting and best studied examples of lysosomotropic weakly basic drugs whose pharmacokinetics and pharmacodynamics is influenced by extensive macrophage sequestration is clofazimine (CFZ). Clofazimine is a weakly basic, very lipophilic, phenazine red dye and FDA-approved antibiotic that is used against leprosy and multidrug resistant tuberculosis [9, 10]. It has been used effectively against leprosy for over 40 years, curing over 14 million people worldwide, which is the main reason it has been included in the World Health Organization’s list of essential medications for many

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

149

decades [11]. Exhibiting many interesting pharmacokinetics features, orally administered CFZ free base has a half-life of up to 70 days in both animals and humans and is associated with extensive intracellular bioaccumulation of the solid drug, primarily in tissue macrophages [12–14], where it can account for its extremely large volume of distribution (see Chapter 6 by Rzeczycki and Rosania). Within these cells, the drug is present in the form of crystal-like drug inclusions (CLDIs) that possess molecular features that resemble precipitated, solid CFZ hydrochloride salt (CFZ-HCl) crystals [12–15]. Both CLDIs and CFZ-HCl salt crystals are deep red in color (visible with the naked eye) and have a strong near-infrared fluorescence, which can be clearly detected with the Cy5 fluorescence channel of a commercial flow cytometer or an epifluorescence microscope set up (650 nm excitation/ 670 nm emission) [14, 16, 17]. The neutral, free base form of the drug has a yellowish-orange color and exhibits fluorescence in the blue/green region of the spectrum that is detectable in the FITC channel (490 nm excitation/510 nm emission) but not in the Cy5 channel [14, 16, 17]. The protonation of the drug to the hydrochloride salt form occurs within the macrophage lysosomes, and its accompanying precipitation in complex with chloride counterions is associated with a pronounced red shift in fluorescence excitation and emission, which is readily detectable using conventional laboratory instrumentation. Even though prolonged CFZ treatment is associated with extensive CLDI accumulation within resident tissue macrophages, there are no obvious toxicological manifestations from these biocrystals [18]. Instead, CLDIs have been shown to be biocompatible, stable, long-lived, relatively nontoxic and have anti-inflammatory properties which may underlie many of the clinical benefits of the drug in treating leprosy, multidrug resistant tuberculosis, and other pro-inflammatory bacterial infections [18]. While xenobiotic sequestration and supramolecular aggregate formation within live cells is a poorly studied area of pharmaceutical or toxicological research, this chapter investigates the existence of a subpopulation of macrophages within the general, parent population of differentiated, resident tissue macrophages, which preferentially expresses a xenobiotic sequestering phenotype. In order to perform a quantitative analysis of this specific subpopulation of macrophages, the optical fluorescence properties of CLDIs were used as a visual probe to identify the xenobiotic sequestering cells, whereas physical markers (i.e., cell area) and biological markers (i.e., TLR2, TLR4, CLC7, TFEB, p65, V-ATPase) were used to characterize the biological characteristics that are specific to this subpopulation of cells by comparing macrophages obtained from clofazimine-treated mice (CLDI(
) and CLDI(+) populations) to the general macrophage population obtained from control, vehicletreated mice. Characterizing this subpopulation of drug sequestering macrophages is a small but important step towards elucidating

150

Mikhail D. Murashov

the biological underpinnings of the drug sequestering cellular phenotype and studying its impact on drug distribution and accumulation within the body from a more fundamental biological perspective.

2

Materials and Methods

2.1 Mice Clofazimine Treatment (8 Weeks)

Clofazimine (CFZ; Sigma-Aldrich, catalog no. C8895) was prepared in sesame oil (Shirakiku, Japan, or Roland, China) with Powdered Lab Diet 5001 (PMI International, Inc., St. Louis, MO) and orally administered to wild-type (WT) C57BL/6 mice (4–5 weeks old; Jackson Laboratory, Bar Harbor, ME) for up to 8 weeks ad libitum as previously described [13, 14]. Control mice were fed with the same diet without CFZ. Animal care was provided by the University of Michigan’s Unit for Laboratory Animal Medicine (ULAM), and the experimental protocol was submitted to and approved by the University of Michigan’s Institutional Committee on Use and Care of Animals.

2.2 Alveolar Macrophage Isolation

Alveolar macrophage isolation was performed using the same methods as in the “Quantification of Intracellular Drug Aggregates and Precipitates” Chapter 6, written by Phillip Rzeczycki and Gus R. Rosania. Briefly, alveolar macrophages are removed via a broncoalveolar lavage (BAL) with six 1 mL passes of DPBS with 0.5 mM EDTA, collecting the exudates following each pass. Then, the cells are centrifuged for 10 min at 400  g at 4  C and resuspended in 1 mL RPMI 1640 media with 5% FBS, after which they are allowed to settle and adhere onto glass slides for additional preparation steps and analysis (vide infra).

2.3 Immunocytochemistry Analysis

Immunocytochemistry (ICC) analysis involves using biochemically or optically tagged antibodies against a specific intracellular or cell surface antigen to allow detecting and visualizing a particular protein biomarker within a single cell [19]. To illustrate this method, isolated alveolar macrophages obtained from the mice were subjected to ICC utilizing fluorescent antibodies for the cell biomarkers of interest: TLR2, TLR4, CLC7, TFEB, p65, and V-ATPase. For ICC, fluorescent antibodies are either commercially available (a fluorescent dye moiety is chemically attached via a linker) or fluorescence staining can be performed by using unlabeled “primary” antibodies that recognize the biomarker of interest, followed by incubation with the fluorescently tagged “secondary” antibody that binds to the primary antibody to reveal its presence and location. The detailed protocol for ICC strictly depends on the specificity of each individual antibody used and the availability of antibodies for any desired protein marker of interest; however, in general, the protocol for ICC elaborated herein involves six general

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

151

steps: slide preparation, fixation, permeabilization, blocking and immunostaining, counter staining, and mounting [19]. At the start, after the cells of interest (i.e., alveolar macrophages) are isolated and allowed to attach on the microscope slides, the cells are fixed by using 1–4% paraformaldehyde dissolved in PBS (pH 7.4) for 10 min at room temperature [19]. This solution is best when made fresh and filtered to remove any undissolved particles or aggregates. The exact concentration of paraformaldehyde can be adjusted to optimize labeling, based on the instructions of the manufacturer or by trial and error experiments aimed to identify the best labeling conditions for each particular antibody. After fixing, the cells should be washed with ice-cold PBS three times to remove any fixative [19]. Next, if the target protein or biomarker of interest is inside the cell, the cells need to be permeabilized (for assessing cell surface markers, which are typically detected and quantified by flow cytometric analysis, this membrane permeabilization step can be avoided). While other fixation techniques, such as methanol extraction, do not require the permeabilization step [19], any fixation technique involving organic solvents that remove the membrane lipids will also lead to the extraction of any drug molecules present within cells and should be avoided if the goal is to retain the drug molecules within the cells for subsequent analysis. Instead, when the cells are fixed with paraformaldehyde or other chemical crosslinkers, they can be made permeable to the antibodies by utilizing a variety of membrane perturbing detergents that do not lead to extraction of intracellular membranes, leaving any drug precipitates intact within the cells. The permeabilization agents used for this purpose are Triton X-100 and Tween-20 (more gentle) [19]. After a 10-minute incubation of the cells of interest with an appropriate permeabilization reagent (empirically selected depending on the cell type, target protein, and the intended antibody), the cells are washed three times with PBS wash buffer for 5 min [19]. After this permeabilization step, to prevent nonspecific antibody binding, the cells of interest have to be blocked using a protein that binds and coats nonspecific antibody binding sites [19]. The type of blocking agent and incubation times that need to be used depend on the cell type and antibody used in the experiment [19]. Bovine serum albumin (BSA) can be used as blocking agent as a 2% solution in PBS [19]. Alternatively, serum can be used as the blocking component of the blocking buffer. The serum is chosen to match the species of the secondary antibody to avoid immunological cross-reactivity (i.e., goat or donkey serum would be used with a goat or donkey secondary antibody, respectively) [19]. Typically, the data sheet provided with each antibody reagent made against specific intracellular or cell surface markers includes a recommendation for the appropriate blocking buffer to use [19]. After blocking, the cells are washed three times with PBS

152

Mikhail D. Murashov

wash buffer for 5 min to prepare them for incubation with primary antibody overnight in the dark at 4  C in a humidified chamber [19]. The primary antibody is specifically chosen to bind to the phenotypic, intracellular protein, or cell surface marker of interest. The dilution of the primary antibody solution (usually in 1% or 2% BSA) strictly depends on the antibody used and the recommendations from the antibody’s manufacturer, meaning that it needs to be optimized empirically for each phenotypic marker [19]. The following day, after incubation with the primary antibody, the cells of interest are washed three times with the PBS for 5 min to prepare them for incubation with the secondary antibody [19]. In this case, the secondary antibody is tagged with the fluorescent dye and specifically recognizes the primary antibody. Secondary antibodies are often specifically elicited to recognize primary antibodies obtained from a particular animal species; thus, the species specificity of the secondary antibody needs to match the species from which the primary antibody was obtained [19]. There are many commercial suppliers of secondary antibodies with different kinds of species specificity and that are conjugated to a wide variety of fluorescent labels. In order to use a fluorescent label that is orthogonal to the fluorescence signal of the CLDIs, commercially available secondary antibodies that fluoresce in the FITC channel were used in our specific experiments that will be described in the subsequent sections of this chapter. Secondary antibody incubation can be performed for 1 h at room temperature in the dark and is usually done in a blocking buffer [19]. After incubation with the secondary antibody, the cells of interest are washed again three times with the PBS wash buffer for 5 min in the dark to prepare them for counter staining with the nuclear stain [19]. For nuclear staining, the cells of interest are typically incubated with 0.1–1 μg/mL Hoechst 33258 solution for 1 min and then rinsed with PBS to prepare them for mounting with appropriate mounting medium and adding a cover slip [19]. 2.4 Fluorescence Microscopy

In our experiments, microscopy was performed using a Nikon Eclipse Ti inverted microscope (Nikon Instruments, Melville, NY, USA) as previously described [16, 20, 21]. Briefly, fluorescence imaging in FITC channel (490/510 nm, green), Cy5 channel (640/670 nm, far-red), and DAPI channel (350/405 nm, blue) was performed with the Photometrics CoolSNAP MYO camera system (Photometrics, Tuscon, AZ, USA) under the control of Nikon NIS-Elements AR software (Nikon Instruments, Melville, NY, USA). Illumination for fluorescence imaging was provided by the X-Cite 120Q Widefield Fluorescence Microscope Excitation Light Source (Excelitas Technology, Waltham, MA, USA).

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

153

2.5 Image Analysis and Statistics

Following acquisition of monochrome images using the specific fluorescence channels available in conventional epifluorescence microscopic imaging systems, such as the one described above, the images can be quantitatively analyzed, following the subtraction of the background pixel intensity values obtained from an empty region of the image. This image preprocessing and subsequent analysis can be done using the basic functionality of the ImageJ software package [22] or the basic quantitative imaging tools provided with the microscope’s software package (i.e., Nikon NIS-Elements software, Nikon Instruments, Melville, NY, USA). For our experiments, statistical analyses of the measured cell populations were performed to compare the fluorescence signal intensities captured in the images of different cell subpopulations from the drug-treated mice in relation to the general macrophage population of the untreated, negative control mice, using Minitab Statistical Software (Version 17; Minitab, Inc., State College, PA, USA). Data is expressed as the mean  SD. A one-way analysis of variance (ANOVA) single factor followed by either a Tukey’s honest significant difference or Games-Howell post hoc test were used to assess whether there were significant differences in the biomarker among the different cell subpopulations, as applicable. For the purpose of observing the staining, the three separate monochrome images acquired with the FITC, Cy5, and DAPI filter set channels were digitally combined into a single multichannel color image to simultaneously visualize the pixel intensities for each of the three acquired images from each sample—using green color to represent the FITC channel image intensities; red color to represent the Cy5 channel image intensities; and blue color to represent the DAPI channel image intensities.

2.6 Physical and Biological Markers of Macrophage Differentiation into Xenobiotic Sequestering Cells

First, we evaluated the cell area and size as a potential marker to identify the population of xenobiotic sequestering macrophages. In order to do that, we utilized Nikon NIS-Elements AR software (Nikon Instruments) and manually selected the cells from three different groups that were obtained from mice that were treated with clofazimine for 8 weeks and the cells that were treated with vehicle for 8 weeks. From the drug-treated mice, two cell populations can be distinguished: CFZ(+)CLDI(+), the cells from the drug-treated mice that have a strong Cy5 signal; and CFZ(+) CLDI(
), the cells from the drug-treated mice that lack a Cy5 signal. In addition, the untreated macrophage population obtained from mice treated with vehicle alone for 8 weeks (Control) was used to compare the area of the drug-treated cells to the untreated cell populations. It has been shown that many lysosomotropic and cationic amphiphilic drugs induce lysosomal size and volume expansion, which leads to pronounced enlargement of the organelles, the concomitant expression of lysosomal markers, and the expansion of the overall cell volume [5, 23, 24]. Thus, we

2.6.1 Cell Area/Size

154

Mikhail D. Murashov

Fig. 1 Cell area/size measurements of CFZ(+)CLDI(+); CFZ(+)CDLI(
); and Control alveolar macrophage cell groups normalized to the Control

hypothesized that the xenobiotic sequestering macrophages would be significantly larger and specifically overexpress lysosomal proteins compared to the control groups. Following quantitative microscopic image analysis of the total cell area using ImageJ software package [22], it was observed that CFZ(+)CLDI(+) cells had significantly larger area than both CFZ(+)CLDI(
) and Control groups (Fig. 1). Furthermore, CFZ(+)CLDI(
) cells were not significantly different in size compared to the untreated Control cells (Fig. 1). Thus, cell area and size is a biomarker that can be used to readily identify the xenobiotic sequestering population of macrophages following treatment with an avidly sequestered, lysosomotropic drug, such as clofazimine. Potentially, this increase in cell area and size could also serve as a biomarker to assess the differentiation of macrophages into xenobiotic sequestering cells after treating mice with other drugs. 2.6.2 TLR2 and TLR4

Toll-like receptors (TLRs) belong to the family of pattern recognition receptor proteins which have a primary function in pathogen and xenobiotic recognition and activation of innate immunity [25]. These receptors recognize pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) associated with the presence of a pathogen or xenobiotics, which results in cytokine production to elicit an effective immune response [25–27]. The TLR family is made up of 10 functional receptors in humans (TLR1-10), and each of these receptors has the ability to recognize distinct molecular patterns, which allows an appropriate immune response to be initiated [25]. Human TLRs are localized in two regions of the cell: plasma membrane (TLR2, 4, 5, 6, and 10) and intracellular compartments

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

155

(TLR3, 7, 8, and 9) [25, 26, 28]. For this chapter, we decided to examine only two TLRs that are localized on the plasma membrane, TLR2 and TLR4, due to their high expression levels in macrophages [25] and the possibility that they may serve as negative internal controls for assessing lysosomal-specific protein overexpression. To identify the subpopulation of xenobiotic sequestering macrophages, we performed ICC analysis on isolated alveolar macrophages (from 8-week CFZ-fed mice and from 8-week vehicle-fed mice) to evaluate their TLR2 and TLR4 expression levels. Specifically, we compared TLR2 and TLR4 expression levels of alveolar macrophages from CFZ-treated mice (CFZ(+) CLDI(+) cells and CFZ(+) CLDI(
) cells) to alveolar macrophages from the untreated animals (Control). In theory, if there are significantly elevated levels of TLR2 and TLR4 in CFZ(+) CLDI(+) cells compared to CFZ(+) CLDI(
) and Control cells, then TLR2 and TLR4 can be used as potential biomarkers for xenobiotic sequestering subpopulation of alveolar macrophages. To quantify the TLR2 and TLR4 expression levels, we utilized Nikon NIS-Elements AR software (Nikon Instruments) and manually selected the cells to calculate the mean intensity in FITC channel alone, which reflects the number of bound secondary antibodies and corresponds to the expression level of TLR2 or TLR4. Based on this analysis, there was no significant difference in terms of the expression level of either TLR2 or TLR4 (Fig. 2). Thus, TLR2 and TLR4 cannot be used as biomarkers for positive identification of the xenobiotic sequestering subpopulation of macrophages in this case, but they could provide a negative internal control biomarker for assessing the relative overexpression of other gene products, such as the lysosome-specific membrane proteins that facilitate CLDI formation. 2.6.3 TFEB

Transcription factor EB (TFEB) is another candidate biological marker that could be useful to identify the subpopulation of xenobiotic sequestering macrophages. TFEB (a member of the microphthalmia-associated transcription factor subfamily of transcription factors [8, 29]) is considered to be a master regulator of a “lysosomal gene network” and a central regulator of autophagic, intracellular protein degradation pathways [8, 30]. Specifically, upon activation, TFEB translocates from the cytoplasm to the nucleus, where it binds to specific promoter sites in the DNA associated with the genes encoding for resident proteins of the endophagolysosomal compartment [8, 30]. TFEB activation upregulates expression of proteins involved in lysosomal biogenesis, autophagy, exocytosis, endocytosis, and additional lysosomeassociated processes, such as phagocytosis, the immune response, and lipid catabolism [8]. Numerous studies have demonstrated that

156

Mikhail D. Murashov

Fig. 2 TLR2 and TLR4 expression levels in CFZ(+)CLDI(+) alveolar macrophages compared to CFZ(+)CLDI(
) and Control alveolar macrophages. Scale Bar ¼ 2 μm

bioaccumulation of lysosomotropic agents results in nuclear localization of the TFEB [2–7]. As a weakly basic lysosomotropic drug, CFZ is prone to pH-dependent ion trapping in lysosomes and could therefore be an activator of TFEB [6]. To test this hypothesis, we performed ICC analysis on isolated alveolar macrophages (from 8-week CFZ-fed mice and the corresponding vehicle-treated control animals) to evaluate the nuclear translocation of TFEB. Specifically, we compared TFEB activation levels of alveolar macrophages from CFZ-treated mice (CFZ(+)CLDI(+) cells and CFZ(+)CLDI(
) cells) to alveolar macrophages from the untreated animals (Control). We sought to detect significantly elevated levels of TFEB activation in CFZ(+) CLDI(+) cells compared to CFZ(+)CLDI(
) and Control cells. This is important, as the status of TFEB activation could potentially be utilized as a biomarker for the xenobiotic sequestering subpopulation of alveolar macrophages. To quantify and compare the TFEB activation levels, we utilized Nikon NIS-Elements AR software (Nikon Instruments) and ImageJ software [22] to manually determine the mean TFEB signal intensity from nucleus and cytoplasm of each cell in each group. This data was used to calculate the nuclear to cytoplasmic (N:C) TFEB ratio that reflects the TFEB nuclear translocation or activation for each cell in each group. As measured in this experiment, the CFZ(+)CLDI(+) group showed elevated N:C TFEB ratio levels compared to CFZ(+)CLDI(
) and Control groups (Fig. 3). However, the difference was not statistically significant as that between CFZ(+)CLDI(
) and Control groups based on the statistical test

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

157

Fig. 3 TFEB activation levels in CFZ(+)CLDI(+) alveolar macrophages compared to CFZ(+)CLDI(
) and Control alveolar macrophages. Scale Bar ¼ 2 μm

we used, although the extent of nuclear translocation was much greater in the CFZ(+)CLDI(+) cells. Therefore, based on these results, TFEB would not be a reliable biomarker for the identification of subpopulation of xenobiotic sequestering macrophages; although, there are important differences in the extent of TFEB activation that parallel the extent of drug exposure and the extent to which macrophages differentiate into CLDI-containing, xenobiotic sequestering cells. 2.6.4 NF-kB (p65)

Nuclear factor kB (NF-kB), originally discovered as a kappa immunoglobulin enhancer DNA-binding activity that correlated with k gene transcription [31–33], has been shown to act as a second messenger that activates transcription of a number of genes in multiple tissues, including macrophages [33]. NF-kB exists in the cytoplasm of most cells in an inactive form, complexed to an inhibitor, termed IkB [33–35]. Stimulation by a number of agents, such as LPS or TNF-a, results in the dissociation of the IkB-NF-kB complex with the subsequent translocation of NF-kB to the nucleus, which regulates the transcription activity of nearby genes that are involved in all types of cellular processes, including cellular metabolism, chemotaxis, and immune response [32–34]. NF-kB is a complex of two proteins of 50 (p50) and 65 (p65) kDa [33]. The encoding gene for p65 has been cloned and verified to belong to the REL family of genes [33]; hence, p65 is also known as RELA or REL-associated protein. For the purposes of identifying xenobiotic sequestering macrophage subpopulations, we hypothesized that this p65 protein may be associated with this particular phenotype. Previously, it had been reported that CFZ(+)CLDI(+) containing cells did not exhibit increased nuclear translocation of p65, but instead showed the reduction of p65 nuclear translocation levels compared to control cells in vitro [18]. Thus, we sought to explore if this reduction of p65 nuclear translocation in CFZ(+) CLDI(+) cells could be lower than in CFZ(+)CLDI(
) relative to Control alveolar macrophages.

158

Mikhail D. Murashov

Fig. 4 NF-kB activation levels/p65 nuclear translocation in CFZ(+)CLDI(+) alveolar macrophages compared to CFZ(+)CLDI(
) and Control alveolar macrophages. Scale Bar ¼ 2 μm

To test this hypothesis, we utilized the same methodology as with the TFEB biomarker (see TFEB subsection above) to calculate the nuclear to cytoplasmic (N:C) p65 ratio, reflecting the p65 nuclear translocation or NF-kB activation phenotype, for each cell in each group. Based on the image analysis measurements, CFZ(+) CLDI(+) and CFZ(+)CLDI(
) cell groups showed significant reduction in NF-kB activation compared to the Control cells; however, they did not show significant difference between each other (Fig. 4). Thus, the reduction in NF-kB activation can be considered an anti-inflammatory phenotypic feature associated with exposure to CFZ but is not a specific marker of the CLDIforming, xenobiotic sequestering macrophage subpopulation. Furthermore, these measurements also reveal that the extent of nuclear accumulation of TFEB that was observed in the previous experiment is not a nonspecific result of some staining artifact that would indiscriminately induce an increased nuclear signal of every transcription factor in the cytoplasm. Accordingly, p65 protein localization is not a distinguishing, functional biomarker of xenobiotic sequestering macrophages; although, it can serve as a negative control to show the specificity of the TFEB activation response. 2.6.5 V-ATPase and CLC7

The acidification of the lysosome is a very important process involved in lysosomal function and is the result of interplay of many ion channels, transporters, and pores, each playing different roles. Vacuolar-type H+-ATPase (V-ATPase) is the primary membrane channel that is involved in the acidification of the endolysosomal system [36]. This channel works by hydrolyzing ATP, which provides the free energy needed for protons to be pumped against their concentration gradient into the lysosome [36]. In fact, for each hydrolyzed ATP molecule, there are two protons that cross into the lysosome, lowering the pH [36]. It was shown that inhibition of V-ATPase in the endolysosomal system leads to significantly slower cycling of receptors, proving that acidification is necessary for the normal physiological function of the endolysosomal vesicle trafficking pathway [37].

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

159

The V-ATPase is an electrogenic transmembrane proton pump, which results in an increase of the membrane potential of the lysosomal compartment [38]. The resting membrane potential (
20 to
40 mV) increases towards more a neutral membrane potential as the lysosome lumen becomes acidified through the addition of protons [39]. Therefore, to effectively acidify the lysosome, it is necessary to dissipate the high membrane potential that forms upon V-ATPase-mediated proton translocation. For this reason, counter-ion exchange channels are also a very important part of the mechanism of lysosomal acidification. These channels can dissipate the membrane potential by facilitating the movement of a cation back into the cytosol or of an anion into the lysosome, counteracting the electrogenicity of the V-ATPase [38, 39]. The family of voltage-dependent chloride channels (CLCs) regulates cellular trafficking of chloride ions, a critical component of all living cells [40]. In general, CLCs regulate excitability in muscle and nerve cells, aid in organic solute transport, and maintain cellular volume [40]. In this case, specifically CLC-7 plays a key role in the maintaining physiological membrane potential during acidification of the lysosome via V-ATPase by pumping two chloride ions into the lysosome and removing one proton out of it [41]. Due to the fact that macrophages express high levels of V-ATPase and chloride channels (e.g., CLC-7) on their lysosomal membranes [42–44], in vitro and in silico studies have been performed to determine the extent to which the expression levels of the V-ATPase and chloride channels are necessary and sufficient to explain the cell-type dependent formation and stabilization of weakly basic drug precipitates within lysosomes, including CFZ-HCl crystals, which are only found to be present in CLDIs that form within macrophage lysosomes [23, 45]. Hence, we hypothesized that V-ATPase and CLC-7 could potentially be utilized as biomarkers to identify the xenobiotic sequestering subpopulation of macrophages. In theory, the xenobiotic sequestering subpopulation of macrophages should have higher V-ATPase and CLC-7 expression levels compared to the control groups. To test this hypothesis, ICC analysis on isolated alveolar macrophages was performed to evaluate their V-ATPase and CLC-7 expression levels. Specifically, we compared V-ATPase and CLC-7 expression levels of alveolar macrophages from 8-week CFZ-treated mice (CFZ(+) CLDI(+) cells and CFZ(+) CLDI(
) cells) to untreated alveolar macrophage populations obtained from 8-week vehicle-treated animals (Control). Using image analysis, we did not detect any significant difference among the testing groups, suggesting that V-ATPase and CLC-7 protein biomarkers cannot be used for identification of population of xenobiotic sequestering macrophages in this case (Fig. 5). We infer that these results are due to the fact that the expansion of the lysosomal volume may not

160

Mikhail D. Murashov

Fig. 5 V-ATPase and CLC-7 expression levels in CFZ(+)CLDI(+) alveolar macrophages compared to CFZ(+) CLDI(
) and Control alveolar macrophages. Scale Bar ¼ 2 μm

necessarily have to be accompanied by an increase in the lysosomal surface area or the overexpression of lysosomal proteins. The interpretation of this experiment is complicated because the drug exposure may also be reducing the expression of protein markers in the lysosomes independently from any increases in the transcription of lysosomal protein genes (possibly through an autophagic induction mechanism that leads to increased lysosomal protein turnover). The mechanistic details underpinning these phenotypic characteristics are still a matter of speculation and need to be elucidated with additional experiments that are beyond the scope of this chapter.

3

Conclusion This chapter described methods that were used to investigate whether physical characteristics (i.e., cell area and size) and/or molecular features (i.e., TLR2, TLR4, CLC7, TFEB, p65, and V-ATPase) could be used as potential biomarkers to identify the subpopulation of macrophages that express the xenobiotic sequestering phenotype, and not general biomarkers associated with the drug exposure. Interestingly, the physical markers (i.e., cell area and size) showed greatest potential in terms of identifying the xenobiotic sequestering subpopulation of macrophages. None of the biological markers were as clearly associated with the CFZ(+) CLDI(+) vs CFZ(+)CLDI(
) and Control groups, even though TFEB did show greater activation in the CFZ(+)CLDI(+) group. Xenobiotic sequestration and supramolecular aggregate formation

Quantitative Phenotypic Analysis of Drug Sequestering Macrophage Subpopulations

161

within live cells is a poorly studied phenomenon. Characterization of the specific subpopulation of xenobiotic sequestering macrophages can lead to an improved molecular mechanistic understanding of drug distribution and accumulation within the body down to the transcriptional regulatory pathways that govern the expression of specific functions of differentiated xenobiotic sequestering cell subpopulations that may be responsible for the large volumes of distribution of many different drugs. References 1. Nature reviews: immunology. Monocytes and macrophages. 2011 [cited 2016 Aug 19]. http://www.nature.com/nri/focus/ macrophages/index.html?WT.ec_id¼SLBU_ COMMS 2. Kaufmann AM, Krise JP (2007) Lysosomal sequestration of amine-containing drugs: analysis and therapeutic implications. J Pharm Sci 96(4):729–746 3. Funk RS, Krise JP (2012) Cationic amphiphilic drugs cause a marked expansion of apparent lysosomal volume: implications for an intracellular distribution-based drug interaction. Mol Pharm 9(5):1384–1395 4. Logan R et al (2014) Amine-containing molecules and the induction of an expanded lysosomal volume phenotype: a structure-activity relationship study. J Pharm Sci 103 (5):1572–1580 5. Logan R, Kong AC, Krise JP (2014) Timedependent effects of hydrophobic aminecontaining drugs on lysosome structure and biogenesis in cultured human fibroblasts. J Pharm Sci 103(10):3287–3296 6. Sardiello M et al (2009) A gene network regulating lysosomal biogenesis and function. Science 325(5939):473–477 7. Visvikis O et al (2014) Innate host defense requires TFEB-mediated transcription of cytoprotective and antimicrobial genes. Immunity 40(6):896–909 8. Settembre C et al (2013) Signals from the lysosome: a control centre for cellular clearance and energy metabolism. Nat Rev Mol Cell Biol 14 (5):283–296 9. Arbiser JL, Moschella SL (1995) Clofazimine: a review of its medical uses and mechanisms of action. J Am Acad Dermatol 32(2 Pt 1):241–247 10. Dey T et al (2013) Outcomes of clofazimine for the treatment of drug-resistant tuberculosis: a systematic review and meta-analysis. J Antimicrob Chemother 68(2):284–293

11. World Health Organization. Leprosy report. 2014 [cited 2016 Aug 16]. http://www.who. int/mediacentre/factsheets/fs101/en 12. Baik J, Rosania GR (2012) Macrophages sequester clofazimine in an intracellular liquid crystal-like supramolecular organization. PLoS One 7(10):e47494 13. Baik J, Rosania GR (2011) Molecular imaging of intracellular drug-membrane aggregate formation. Mol Pharm 8(5):1742–1749 14. Baik J et al (2013) Multiscale distribution and bioaccumulation analysis of clofazimine reveals a massive immune system-mediated xenobiotic sequestration response. Antimicrob Agents Chemother 57(3):1218–1230 15. Keswani RK et al (2015) Chemical analysis of drug biocrystals: a role for Counterion transport pathways in intracellular drug disposition. Mol Pharm 12(7):2528–2536 16. Rzeczycki P et al (2017) Detecting ordered small molecule drug aggregates in live macrophages: a multi-parameter microscope image data acquisition and analysis strategy. Biomed Opt Express 8(2):860–872 17. Keswani RK et al (2015) A far-red fluorescent probe for flow cytometry and image-based functional studies of xenobiotic sequestering macrophages. Cytometry A 87(9):855–867 18. Yoon GS et al (2015) Phagocytosed clofazimine biocrystals can modulate innate immune signaling by inhibiting TNFalpha and boosting IL-1RA secretion. Mol Pharm 12 (7):2517–2527 19. Abcam (2020) Immunocytochemistry and immunofluorescence protocol. https://www. abcam.com/protocols/immunocytochemis try-immunofluorescence-protocol 20. Murashov MD et al (2018) The physicochemical basis of Clofazimine-induced skin pigmentation. J Invest Dermatol 138(3):697–703 21. Murashov MD et al (2018) Synthesis and characterization of a biomimetic formulation of

162

Mikhail D. Murashov

clofazimine hydrochloride microcrystals for parenteral administration. Pharmaceutics 10 (4) 22. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH image to ImageJ: 25 years of image analysis. Nat Methods 9(7):671–675 23. Woldemichael T, Rosania GR (2017) The physiological determinants of drug-induced lysosomal stress resistance. PLoS One 12(11): e0187627 24. Morissette G, Lodge R, Marceau F (2008) Intense pseudotransport of a cationic drug mediated by vacuolar ATPase: procainamideinduced autophagic cell vacuolization. Toxicol Appl Pharmacol 228(3):364–377 25. Ayala-Cuellar AP, Cho J, Choi KC (2019) Tolllike receptors: a pathway alluding to cancer control. J Cell Physiol 234(12):21707–21715 26. Kawasaki T, Kawai T (2014) Toll-like receptor signaling pathways. Front Immunol 5:461 27. Majewska M, Szczepanik M (2006) The role of Toll-like receptors (TLR) in innate and adaptive immune responses and their function in immune response regulation. Postepy Hig Med Dosw (Online) 60:52–63 28. Hopkins PA, Sriskandan S (2005) Mammalian toll-like receptors: to immunity and beyond. Clin Exp Immunol 140(3):395–407 29. Rehli M et al (1999) Cloning and characterization of the murine genes for bHLH-ZIP transcription factors TFEC and TFEB reveal a common gene organization for all MiT subfamily members. Genomics 56(1):111–120 30. Palmieri M et al (2011) Characterization of the CLEAR network reveals an integrated control of cellular clearance pathways. Hum Mol Genet 20(19):3852–3866 31. Schlissel MS, Baltimore D (1989) Activation of immunoglobulin kappa gene rearrangement correlates with induction of germline kappa gene transcription. Cell 58(5):1001–1007 32. Sen R, Baltimore D (1986) Multiple nuclear factors interact with the immunoglobulin enhancer sequences. Cell 46(5):705–716 33. Nolan GP et al (1991) DNA binding and I kappa B inhibition of the cloned p65 subunit

of NF-kappa B, a rel-related polypeptide. Cell 64(5):961–969 34. Baeuerle PA, Baltimore D (1988) I kappa B: a specific inhibitor of the NF-kappa B transcription factor. Science 242(4878):540–546 35. Baeuerle PA, Baltimore D (1988) Activation of DNA-binding activity in an apparently cytoplasmic precursor of the NF-kappa B transcription factor. Cell 53(2):211–217 36. Mindell JA (2012) Lysosomal acidification mechanisms. Annu Rev Physiol 74:69–86 37. Finbow ME, Harrison MA (1997) The vacuolar H+-ATPase: a universal proton pump of eukaryotes. Biochem J 324(Pt 3):697–712 38. Dietz KJ et al (2001) Significance of the V-type ATPase for the adaptation to stressful growth conditions and its regulation on the molecular and biochemical level. J Exp Bot 52 (363):1969–1980 39. Xu H, Ren D (2015) Lysosomal physiology. Annu Rev Physiol 77:57–80 40. Santa Cruz Biotechnology. CLC-7 Antibody (C-15): sc-16444. 2016 [cited 2016 Aug 22]. http://www.scbt.com/datasheet-16444-clc7-c-15-antibody.html 41. Leisle L et al (2011) ClC-7 is a slowly voltagegated 2Cl(
)/1H(+)-exchanger and requires Ostm1 for transport activity. EMBO J 30 (11):2140–2152 42. Wang SP et al (2002) Regulation of enhanced vacuolar H+-ATPase expression in macrophages. J Biol Chem 277(11):8827–8834 43. Hong L et al (2011) Alteration of volumeregulated chloride channel during macrophage-derived foam cell formation in atherosclerosis. Atherosclerosis 216(1):59–66 44. Jiang L et al (2012) Intracellular chloride channel protein CLIC1 regulates macrophage function through modulation of phagosomal acidification. J Cell Sci 125(Pt 22):5479–5488 45. Woldemichael T et al (2018) Reverse engineering the intracellular self-assembly of a functional mechanopharmaceutical device. Sci Rep 8(1):2934

Chapter 8 Using an Integrated QSAR Model to Check Whether SmallMolecule Xenobiotics Will Accumulate in Biomembranes, with Particular Reference to Fluorescent Imaging Probes Richard W. Horobin and Juan C. Stockert Abstract The use of a simple quantitative structure–activity relations (QSAR) model to predict membrane uptake of xenobiotics, and so aid their design and application, is described. The model can also be used to check whether unwanted membrane binding is likely to occur. Xenobiotics considered are fluorescent imaging probes, but the models can also be applied to substances used as biocides or pharmaceuticals. In addition to predicting the targeting of generic biomembranes and the plasma membrane, the model predicts selective uptake into the membranes of the endoplasmic reticulum, endosomes/lysosomes, Golgi complex, and mitochondria. Physicochemical accumulation mechanisms considered include partitioning of lipophiles, insertion of amphiphiles, and nonspecific protein binding. However, uptake involving ligand–receptor mechanisms is outside the purview of the approach. The algorithm predicting membrane accumulation is an integration of a variety of published simplistic localization-QSAR models. The integrated model is provided as a flowchart, which can be used to check putative membrane binding properties of routine, novel, or indeed proposed, molecular structures. The empirical validity of the integrated model is shown by its ability to predict membrane localization of a set of fluorescent probes not previously considered. The application of this fast and simple tool to diverse case examples is demonstrated. Key words Amphiphilicity, Fluorescent probe, Guided synthesis, Lipophilicity, Membrane targeting, Partitioning, Protein binding, QSAR

1

What Types of Membranes Are Considered? The integrated model considers several distinct types of membrane. The first of these is the generic biomembrane, namely phospholipid bilayers possessing hydrophobic cores plus a surface of hydrophilic headgroups. Such membranes are assumed to possess embedded proteins. In addition, when considering xenobiotics applied externally to intact, living eukaryotic cells, the membranes of several organelles can be discussed. These are the plasma membrane; the membranes of the endoplasmic reticulum and the nucleus; of endosomes/lysosomes; of the Golgi complex; and of the

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

163

164

Richard W. Horobin and Juan C. Stockert

Fig. 1 Micrographs showing fluorescent labeling of membranes of organelles from cultured HeLa (a, c) and 3 T3 (b) cells, induced by selective imaging probes. (a) Plasma and nuclear membranes (PM, NM) labeled by merocyanine 540 (20μM for 5 min, double blue-green excitation: 460–490 + 510–550 nm; some cytoplasmic vesicles and the Golgi region are also labeled). (b) Mitochondria (M) labeled by the carbocyanine dye DiOC1 [2] (15μg/mL for 1 min, λexc: 450–490 nm). (c) Golgi complex (G) labeled by the photosensitizing dye zinc(II)phthalocyanine (0.5μM in dipalmitoyl-phosphatidylcholine liposomes for 3 h, λexc: 365 nm; the blue signal corresponds to mitochondrial autofluorescence from the endogenous coenzyme NAD(P)H, see [3]. In all cases, labeling was followed by a washing step using probe-free medium for 10–30 min. Nuclei (N) are not labeled

mitochondrion. Prediction of uptake into the membranes of prokaryotic cells [1] and into biomembrane mimics, such as liposomes or supported bilayers, is also possible. A gallery of micrographs illustrating the fluorescent labeling of different organelle membranes from cultured cells, induced by treatment with routine imaging probes, is shown in Fig. 1.

2

What Types of Xenobiotic Are Considered? The integrated model deals only with small molecule xenobiotics. The core QSAR models used to construct the integrated model (see Table 1) were originally derived from observations of the interactions of fluorescent imaging probes with living eukaryotic cells. Such molecules are typically under 1000 Daltons. However the models have been found to be applicable to some other types of xenobiotic, such as pharmaceuticals [2] including porphyrin PDT drugs. Indeed a wide variety of structural types of xenobiotic may be dealt with successfully, including polyionic species and both rigid and flexible molecules. While the integrated model is general, and is expected to apply to other classes of compounds, such as spinlabeled or radiolabeled imaging probes, validation of this supposition awaits exploration of suitable datasets. Some types of xenobiotic are, however, not readily addressed. Although a wide variety of aliphatic and aromatic organic species can be considered, many inorganic compounds—and some metal

Checking if Small-Molecule Xenobiotics Accumulate in Biomembranes

165

Table 1 Summary of decision-rule QSAR core models for predicting uptake of xenobiotics into various membranes in living cells, when applied from an external solution. Except for generic biomembranes and the plasma membrane, the models assume conditions for membrane permeability are met (i.e., CBN < 40, HGH > -4, HGS < 400). Structure parameter abbreviations are defined in the text. For more extended accounts of these models see [4] Target membrane

Is uptake specific?

Summary of QSAR model

Mechanism of uptake process(s)

Endo/ lysosomes

Only if pulse labeling and extended incubation are used

As with plasma membrane Any plasma models, but occurs only membrane uptake if labeled plasma model, but with membrane is extended internalized incubation times

Generic Not applicable biomembrane

8 > logP >5 or Membrane permeable, 8 > AI >3.5, and partitioning of lipophils
4 < HGH <
1 or insertion of amphiphils

Comments

Golgi complex membranes

8 > logP >0, and Only if pulse 8 > AI>3.5, and labeling and Z¼0 postlabeling solvent extraction are used

Mitochondrion

Yes

5 > logP >0, and Membrane permeable pKa > 12, and AI lipophilic cation 0 accumulates, in part, due to membrane potential

Plasma membrane, both leaflets

Yes, if endocytosis does not occur

logP >8

Partitioning and trapping Temperature of superlipophils dependent

Plasma membrane, outer leaflet

Yes, if flip-flop does not occur

AI>8, and [HGH <
1 or HGS > 400) or CBN > 40, and
15 < logP logP >0, and 6 > AI>3.5, and
4 < HGH <
1

Membrane permeable, insertion into more fluid membranes

If Z > 0, then initial uptake is into mitochondrial membrane

See comments Rough endoplasmic reticulum and nuclear membrane

Complex Membrane permeable, protocols involves membrane necessary trafficking and extraction of xenobiotic from non-Golgi sites

coordination complexes with organic ligands—cannot be handled, both due to difficulties in assessing their hydrophilicity/lipophilicity and to their often-dynamic solvation behavior. Polymers—and in particular polymers with secondary or tertiary structure—are

166

Richard W. Horobin and Juan C. Stockert

also excluded from consideration as, again, key structure parameters cannot be estimated. For the same reason nanoparticles are not addressed; see [5] for an account of the problems arising when attempting to apply existing QSAR localization models to such materials. Xenobiotics exhibiting strong ligand–receptor binding constitute a more complex case. Localization of such compounds will be simultaneously influenced by the receptor binding and by the coarser grained physicochemical effects underlying the QSAR models. If these processes are in competition, observation of localization patterns not predicted by the QSAR models can indicate the occurrence of such receptor binding. The selective uptake of sulforhodamine 101 into astrocytes is a case in point, as this may be mediated by a transporter in the plasma membrane [4]. The same limitation applies to metabolites such as membrane-binding peptides.

3

What Types of Physicochemical Processes Are Involved? Three phenomena—partitioning, insertion of amphiphiles into membranes, and protein-binding—will be discussed separately. This is for convenience, as actual membrane uptake can involve multiple processes. Partitioning of lipophilic compounds into the hydrophobic cores of membrane bilayers occurs. This depends on the overall lipophilicity of the xenobiotic. If the compound is superlipophilic it will become trapped in the first membrane encountered, the identity of this membrane depending on the protocol of the experiment. Somewhat less lipophilic compounds, which accumulate in all cell membranes nonselectively but are not trapped, are membrane permeable. Uptake of weakly or moderately lipophilic permeant xenobiotics may occur selectively, in the membranes of particular organelles, due to additional physicochemical properties of the compound or to protocol factors, see below. Variations in lipophilicity can be modelled numerically, using the logarithm of the octanol–water partition coefficient (logP) as a structure parameter, as mentioned below. Partial insertion of a xenobiotic into membrane bilayers also occurs. This arises when a compound has a sufficiently lipophilic domain to enter the hydrophobic core, while leaving a hydrophilic domain (the “headgroup”) at the membrane surface, or in the aqueous phase outside the membrane. Overall, such an amphiphilic molecule may either be lipophilic or hydrophilic. Paralleling the case of partitioning, superamphiphilicity arises if the lipophilic domain is extremely lipophilic. Such a xenobiotic will be trapped in the first membrane encountered. Accumulation of xenobiotics in all cellular membranes, or selective accumulation in the membranes

Checking if Small-Molecule Xenobiotics Accumulate in Biomembranes

167

of certain organelles, can occur with compounds possessing less strongly lipophilic domains. In such processes both the hydrophilicity and lipophilicity of xenobiotic domains can be modelled using the headgroup hydrophilicity (HGH) and logP parameters respectively, see below. The third type of uptake process involves membrane proteins, not the lipid bilayers. Nonspecific protein binding can occur with xenobiotics which have large aromatic (conjugated) systems. Such binding, however, occurs only with molecules falling within a restricted range of hydrophilicity/lipophilicity. Extremely hydrophilic compounds, unless amphiphilic, will remain in aqueous solution even if possessing large aromatic systems. On the other hand the behavior of lipophilic compounds tends to be dominated by partitioning and amphiphilicity. The extent of the aromatic system of a compound can be modelled by a structure parameter such as the conjugated bond number (CBN), as considered below. There is sometimes a lack of clarity in the literature concerning amphiphilicity and lipophilicity, and occasionally these the two distinct processes are confused. Lipophilicity receives the most attention, perhaps because of the ease of modelling this property using the logP parameter. The role of nonspecific protein binding in membrane uptake is another under discussed topic.

4

Additional Factors Influencing Membrane Uptake and Accumulation The simplistic QSAR models are surprisingly robust. Although they are based on observations made on eukaryotic cells, they can also be applied to prokaryotic cells [1]. However there are complications. These include probe factors, discussed later; both structural and functional cell factors; and protocol factors. Examples of the influence of such factors on the development and application of the QSAR models will now be sketched. As structural cell factors have been reviewed [6], only problems of particular relevance to membrane uptake of xenobiotics will be mentioned here. First is the membrane-within-membrane compartmentalization of eukaryotic cells [7]. Superlipophilic or superamphiphilic xenobiotics will accumulate in the membrane they initially contact. Consequently external application of such a compound to a eukaryotic cell results in initial uptake into the plasma membrane. However, if the same compound is microinjected into the cytosol, uptake will then occur into the membranes of most organelles. Next consider the effect of membrane-membrane proximity. Some tissues contain close membrane-membrane configurations, examples being the plasma membranes of a sheet of epithelial cells, or cultured cells at confluence; or the plasma membranes and myelin sheaths of neurons. In such cases uptake of a superlipophilic xenobiotic at a single location can result, following diffusion, in the

168

Richard W. Horobin and Juan C. Stockert

presence of the compound throughout a sheet or chain of cells or processes. QSAR models can be constructed to predict uptake patterns into various membrane configurations and following various modes of application of xenobiotic to cell. However the integrated QSAR model described in this chapter assumes nonattached cell monolayers, to which xenobiotics are applied from an external solution. Of functional cell factors discussed in [6] three are of particular significance for accumulation of xenobiotics in membranes. The first is endocytosis. If plasma membrane is internalized, any accumulated xenobiotic will enter the cell. This will initially be trafficked to endosomes, then on to lysosomes, with the possibility of further distribution if incubation is prolonged. A second factor is variation in membrane fluidity, since uptake is usually favored by high membrane fluidity. Fluid plasma membranes arise during various processes (e.g., apoptosis, capacitation of spermatozoa, and differentiation of erythroblasts into red cells). Fluidity also varies with stage of cell cycle [8]. A third significant factor is the possible biomodification of a xenobiotic localized in a membrane, to yield a product with some different localization properties; this is mentioned in Cases 2 and 3 in Subheading 7, below. Since there are innumerable protocols in which membranes are exposed to xenobiotics, the range of protocol factors—in which aspects of experimental procedure may have unexpected effects on xenobiotic uptake into membranes—is open ended. However, several protocol factors relating to phenomena already alluded to in this chapter may be noted. Thus, time of exposure of cell to xenobiotic is important if membrane trafficking is occurring. The temperature of the system is also significant for internalization of plasma membrane, as this is negligible below 10  C. The nature of the solution must also be considered. If the xenobiotic of interest is a weak acid or weak base, any free acid or free base species present will be more lipophilic than the corresponding salts. Consequently, membrane uptake of acids will be favored by use of low pH solutions, with such effects depending on the pKa of the compound. Note also that the amphiphilicity of the salts are often greater than that of the free acid or base. Yet another important protocol factor is the mode of application of the xenobiotic to the cell. Possible differences in outcome after applying a xenobiotic from an external solution or by microinjection have already been mentioned. Another application issue is whether a cell is exposed to a xenobiotic continuously or discontinuously by pulse labeling. If membrane trafficking is occurring, the former will result in all membranes in a trafficking sequence accumulating xenobiotic. Pulse labeling may result in a single membrane type containing xenobiotic, with the length of the period of incubation controlling which membrane this is. Note in passing that use of liposomal delivery of xenobiotics may not be a problem for prediction of membrane localization, as indicated by a study

Checking if Small-Molecule Xenobiotics Accumulate in Biomembranes

169

involving uptake of the lipophilic phthalocyanine ZnPc into live cells. This was achieved both by using ZnPc solubilized in liposomes and by ZnPc being first dissolved in dimethylformamide and then in culture medium. Although these two procedures involved different cell uptake mechanisms, respectively based on clathrin- or caveolindependent endocytosis, the final site of localization was the same in both cases, namely, the membranes of the Golgi apparatus [9].

5

QSAR Modelling of Uptake in Various Membranes Decision-rule QSAR models predicting membrane uptake are available for a variety of separate cell membranes, see Table 1. It may be noted in passing that while decision-rule models are not widely used, QSAR is a general strategy not restricted to any single mathematical formalism. It can be seen from Table 1 that use of these core models to predict uptake of a xenobiotic requires one or more of the following numerical structure parameters: amphiphilicity index (AI), conjugated bond number (CBN), headgroup hydrophilicity (HGH), headgroup size (HGS), the logarithm of the octanol–water partition coefficient (logP), pKa, and electric charge (Z). Note that AI and HGH are nonexperimental factors, estimated in a similar way to logP. For step by step accounts of how these parameter values are obtained, either from experimental measurements or estimation, see [6, 10, 11]. Nevertheless, since lipophilicity and amphiphilicity are so important for membrane uptake, some comments on how relevant parameters are obtained are provided here. Experimentally derived logP values from the literature may sometimes be used, but more usually this parameter is estimated. Unfortunately the various estimation procedures can give divergent results [12]. The reader should therefore note that the models used in this chapter were derived using the original Hansch and Leo procedure [13, 14] since this provides logP values for ammonium salts. However, if some other estimation procedure is adopted for obtaining logP values, then on occasion the boundaries in the parameter spaces defined by the models may be shifted, and will need recalibrating. The same problems arise with HGH and AI, as these parameters also require logP estimates. Moreover, as the core models do not describe the kinetics of uptake, and do not address mass transfer quantitatively, derived uptake predictions must be regarded as advisory not prescriptive.

6

The Integrated Model: Comments and Extensions The flowchart shown in Fig. 2 represents an integration of the core QSAR membrane uptake models. Although simplistic, this flowchart permits a rapid evaluation of membrane targeting by

170

Richard W. Horobin and Juan C. Stockert Step 2 start

Step 1 start Record all predicted uptake sites [color coded green].

Specify the chemical structure of the xenobiotic of interest

Is logP > 8 ?

Y

Is AI > 8 & HGH < -1 ?

Y

Is incubation time long?

Is AI > 3.5 & HGH < -4 ?

For each ionic species obtain relevant structure parameters

Endosome/ lysosome

Y

Plasma membrane Outer leaflet

Y

N Is 0>LogP>-14 ?

Y

N

N

For all compounds specify chemical structures of all plausible ionic species.

Plasma membrane Both leaflets

Is incubation time long?

N

Specify the chemical structures of any plausible metabolites or reaction products which may arise from the xenobiotic under experimental conditions.

N

N

Y

Is CBN>40 ?

Membrane impermeant

Y

N Is incubation time long?

Y

N Is Z=0& 8 >AI> 3.5 & -1 >HGH> -4 ?

Assess incubation time

Y

Golgi apparatus

Y

Nuclear membrane & RER

Y

All (generic) membranes

Y

Mitochondria

N

For each ionic species, go to Step 2 and proceed through the chart.

Is 5 >AI> 0 & -4 >HGH> -1 & logP 5 ?

N

Cytosol

N

Is Z>0?

Fig. 2 An integrated QSAR model, expressed as a decision-tree flowchart, which allows assessment of possible membrane binding of small molecule xenobiotics

Checking if Small-Molecule Xenobiotics Accumulate in Biomembranes

171

xenobiotics. However, several implicit features of this integrated uptake model require comment. (a) Prediction of xenobiotic uptake into any membrane other than the plasma membrane (or its internalized derivatives) implies that the xenobiotic molecule concerned is membrane permeant; a feature accounted for by the model. While membrane permeant molecules will often be relatively small, large molecules can also enter the cell if bound to internalized plasma membrane. (b) A xenobiotic may accumulate in more than one target structure. (c) The flowchart ignores nonmembrane uptake of xenobiotics, although uptake of xenobiotics into lipid droplets, and into other organelles, may also take place. (d) Even restricting our view to membrane uptake, the degree of selectivity a xenobiotic may have for the membrane of a particular organelle can vary. (e) The flowchart also ignores the protocol factor “temperature,” and implicitly considers this to be >10  C. Consequently membrane internalization is always assumed possible. For a more detailed account of these, and other, complications see [10, 11]. The flowchart only considers xenobiotics which are superamphiphilic (i.e., AI > 8 and HGH <
1), or are less amphiphilic but have very hydrophilic headgroups (i.e., 8 > AI >3.5 and HGH <
4), with both types being trapped in the plasma membrane. Extensions to the model can also be noted. While all amphiphilic xenobiotics will initially enter the outer leaflet of the plasma membrane, those with HGH >
4 may subsequently undergo flipflop, and move to the inner leaflet, from where they may be lost into the cytosol. Those xenobiotics with more strongly lipophilic domains (i.e., AI > 5) can then accumulate in all accessible membranes. Xenobiotics with large headgroups (i.e., with HGS > 400) behave similarly to the more strongly hydrophilic compounds, and also fail to easily cross membranes. It is also of interest that some xenobiotics which selectively accumulate in more fluid plasma membranes, such as those in apoptotic cells, form a subset of such amphiphiles, of lower amphiphilicity and lipophilicity, namely, 3.5 < AI 18 h) at 37
C, 5% CO2. 2. Prior to microscopic examination, premix 90 μL of media (see Note 9) without serum and 10 μL of 10 concentrated stock of the labeled peptide in a separate tube. 3. Gently remove the media from one well of the chamber slide and replace with the 100 μL of premixed media with labeled peptide (record the time of addition for later time-stamping of events, see Notes 10 and 11). 4. Identify regions on the chamber slide that show individual cells and set up 3D confocal Z-stacks that cover the full height of the cells. Multiple positions can be included to create a larger data set for quantification studies (see Notes 12 and 13). 5. Capture full Z-stacks every 5 min for a duration of 120 min (or suitable time frame according to peptide concentration and relative toxicity to the cells). 6. Fluorescence emission intensity of the selected regions (individual cells) can be visualized using image analysis software (e.g., FIJI) [25].

Characterizing Cyclotide Entry into Cells

4

245

Notes Labeling 1. We preferentially use Alexa Fluor 488™ SDP ester for labeling Lys. This dye is photostable and the SDP ester provides greater stability of the protein–dye conjugate in aqueous solutions. Also, the fluorescence quantum yield of Alexa Fluor 488 (A488) is insensitive to the environment [26], thus the mean fluorescence emission intensity of cells incubated with A488-labeled peptides is directly proportional to the amount of peptide associated with cells. 2. It is important to perform positional scanning studies to select residues that to not contribute the peptide’s ability to enter cells. These residues can be targeted for labeling. 3. Naturally occurring Lys residues have historically been targeted for amine bond formation with dye molecules, but modification may be required to ensure incorporation of a single dye molecule. For example, we recommend single Lys analogs [K6R,K9R,K10R,D34K]MCoTI-II [18] and [T20K]kalata B1 [16]. 4. If modifications have been made to the peptide that increase the hydrophobicity, some insoluble precipitate may result following the labeling reaction. In this case, the soluble supernatant should be collected, and any insoluble labeled peptide dissolved in DMSO. This can be diluted to ~20% DMSO in Buffer A for loading onto HPLC. 5. Site specific labeling can also be achieved through incorporating azido amino acids in the peptide. Copper catalyzed cycloaddition can be used for adding alkyne Alexa Fluor 488 onto an azide peptide. In our hands, a twofold molar excess of dye for each of these reactions yields >80% labeled peptide. Internalization, characterizing endocytosis 6. Lower temperatures have been widely used to indicate energydependent internalization, or endocytosis. However, cell membranes are more rigid at 4
C compared to 37
C, which can affect the ability of molecules to cross the plasma membrane. A more reliable way of determining whether peptides enter cells via endocytic pathways is to include specific endocytosis inhibitors and perform internalization assays at the physiologically relevant temperature of 37
C. For example, see ref. [16]. Comparing peptide internalization 7. Relative amounts of internalized peptide can only be compared when identical gain and threshold settings have been applied as these settings affect FI signal amplification.

246

Nicole Lawrence and David J. Craik

Confocal microscopy 8. Cells need to be plated at low enough density to allow visualization of single cells the following day. 9. We have previously compared image quality using specialized media (e.g., FluoroBrite™ DMEM), but did not observe improved signal–noise at the tested concentrations of peptide (and A488 fluorophore). 10. Labeled peptide can be added to chambered slides while on the microscope stage, but it is easier to manage adding the peptide away from the microscope, taking a note of the time, then moving the chamber slide onto the stage as quickly as possible (i.e., less than 5 min). 11. In our hands, we have found it useful to prestain the cells with a locational marker that does not affect cell viability (e.g., MitoTracker Red, Molecular Probes). This can be achieved by diluting the marker in medium without serum, then removing the medium from the cells and adding the diluted marker for 5 min incubation on the cells prior, with removal prior to treating the cells with labeled peptide. These markers tend to stain very rapidly and provide a means of selecting fields of view for recording images. The medium with labeled peptide can be added directly after removing the marker and immediately prior to moving the chamber slide moved onto the microscope stage. 12. Labeled peptide in the media will stain as a negative image of the cell, which necessitates the use of confocal microscopy for visualizing peptide entry into cells. High speed Z-capture (spinning disc) allows distinction between individual intracellular structures, including endosomes. 13. In the FIJI software package [25], processing steps can be automated to distinguish cell boundaries and to quantify the relative amount of labeled peptide (area or FI) inside cells and/or associated with endosomes [27].

Acknowledgments Work in our laboratory on cyclic peptides was supported by the National Health and Medical Research Council (APP1084965) and the Australian Research Council (Laureate Fellowship to DJC FL150100146), and by access to the facilities of the Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science (CE200100012).

Characterizing Cyclotide Entry into Cells

247

References 1. Craik DJ (2015) Advances in botanical research: plant Cyclotides, vol 76. Academic Press, London 2. Craik DJ, Daly NL, Bond T et al (1999) Plant cyclotides: a unique family of cyclic and knotted proteins that defines the cyclic cystine knot structural motif. J Mol Biol 294:1327–1336 3. de Veer SJ, Kan M-W, Craik DJ (2019) Cyclotides: from structure to function. Chem Rev 119:12375–12421 4. Colgrave ML, Craik DJ (2004) Thermal, chemical, and enzymatic stability of the cyclotide kalata B1: the importance of the cyclic cystine knot. Biochemistry 43:5965–5975 5. Gran L (1973) On the effect of a polypeptide isolated from “Kalata-Kalata” (Oldenlandia affinis DC) on the oestrogen dominated uterus. Acta Pharmacol Toxicol (Copenh) 33:400–408 6. Craik DJ, Du J (2017) Cyclotides as drug design scaffolds. Curr Opin Chem Biol 38:8–16 7. Chaudhuri D, Aboye T, Camarero JA (2019) Using backbone-cyclized Cys-rich polypeptides as molecular scaffolds to target protein–protein interactions. Biochem J 476:67–83 8. Gao X, Stanger K, Kaluarachchi H et al (2016) Cellular uptake of a cystine-knot peptide and modulation of its intracellular trafficking. Sci Rep 6:35179 9. Handley TNG, Harvey PJ, Wang CK et al (2020) Cyclotide structures revealed by NMR, with a little help from X-ray crystallography. Chembiochem 21:34633475 10. Cheneval O, Schroeder CI, Durek T et al (2014) Fmoc-based synthesis of disulfide-rich cyclic peptides. J Org Chem 79:5538–5544 11. Greenwood KP, Daly NL, Brown DL et al (2007) The cyclic cystine knot miniprotein MCoTI-II is internalized into cells by macropinocytosis. Int J Biochem Cell Biol 39:2252–2264 12. Contreras J, Elnagar AYO, Hamm-Alvarez SF et al (2011) Cellular uptake of cyclotide MCoTI-I follows multiple endocytic pathways. J Control Release 155:134–143 13. Cascales L, Henriques ST, Kerr MC et al (2011) Identification and characterization of a new family of cell-penetrating peptides: cyclic cell-penetrating peptides. J Biol Chem 286:36932–36943 14. Yin H, Huang Y-H, Deprey K et al (2020) Cellular uptake and cytosolic delivery of a cyclic

cystine knot scaffold. ACS Chem Biol 15:1650–1661 15. Sawyer TK, Partridge AW, Kaan HYK et al (2018) Macrocyclic α helical peptide therapeutic modality: a perspective of learnings and challenges. Bioorg Med Chem 26:2807–2815 16. Henriques So´nia T, Huang Y-H, Chaousis S et al (2015) The prototypic cyclotide kalata B1 has a unique mechanism of entering cells. Chem Biol 22:1087–1097 17. Richard JP, Melikov K, Vives E et al (2003) Cell-penetrating peptides: a reevaluation of the mechanism of cellular uptake. J Biol Chem 278:585–590 18. D’Souza C, Henriques ST, Wang CK et al (2014) Structural parameters modulating the cellular uptake of disulfide-rich cyclic cellpenetrating peptides: MCoTI-II and SFTI-1. Eur J Med Chem 88:10–18 19. Huang Y-H, Chaousis S, Cheneval O et al (2015) Optimization of the cyclotide framework to improve cell penetration properties. Front Pharmacol 6:17 20. Huang Y-H, Henriques ST, Wang CK et al (2015) Design of substrate-based BCR-ABL kinase inhibitors using the cyclotide scaffold. Sci Rep 5:12974–12974 21. Ji Y, Majumder S, Millard M et al (2013) In vivo activation of the p53 tumor suppressor pathway by an engineered cyclotide. J Am Chem Soc 135:11623–11633 22. Deprey K, Becker L, Kritzer J et al (2019) Trapped! A critical evaluation of methods for measuring total cellular uptake versus cytosolic localization. Bioconjug Chem 30:1006–1027 23. Peraro L, Deprey KL, Moser MK et al (2018) Cell penetration profiling using the chloroalkane penetration assay. J Am Chem Soc 140:11360–11369 24. Hong V, Presolski SI, Ma C et al (2009) Analysis and optimization of copper-catalyzed azide–alkyne cycloaddition for bioconjugation. Angew Chem Int Ed Engl 48:9879–9883 25. Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682 26. Gadd JC, Fujimoto BS, Bajjalieh SM et al (2012) Single-molecule fluorescence quantification with a photobleached internal standard. Anal Chem 84:10522–10525 27. Condon ND, Wall AA, Yeo JC et al (2017) Image-based analysis of phagocytosis: measuring engulfment and internalization. In:

248

Nicole Lawrence and David J. Craik

Botelho R (ed) Phagocytosis and phagosomes: methods and protocols. Springer New York, New York, NY, pp 201–214 28. Henriques ST, Huang Y-H, Rosengren KJ et al (2011) Decoding the membrane activity of the cyclotide kalata B1: the importance of phosphatidylethanolamine phospholipids and lipid organization on hemolytic and anti-HIV activities. J Biol Chem 286:24231–24241 29. Yeshak MY, Go¨ransson U, Burman R et al (2012) Genotoxicity and cellular uptake of cyclotides: evidence for multiple modes of action. Mutat Res Genet Toxicol Environ Mutagen 747:176–181

30. Pazgier M, Liu M, Zou G et al (2009) Structural basis for high-affinity peptide inhibition of p53 interactions with MDM2 and MDMX. Proc Natl Acad Sci U S A 106:4665 31. Kim D, Jeon C, Kim J-H et al (2006) Cytoplasmic transduction peptide (CTP): new approach for the delivery of biomolecules into cytoplasm in vitro and in vivo. Exp Cell Res 312:1277–1288 32. Songyang Z, Carraway KL, Eck MJ et al (1995) Catalytic specificity of protein-tyrosine kinases is critical for selective signalling. Nature 373:536–539

Chapter 12 Cellular Trafficking of Monoclonal and Bispecific Antibodies John J. Rhoden and Christopher M. Wiethoff Abstract Monoclonal antibodies, including bispecific antibodies, represent versatile platforms for protein drug development. They have been successfully utilized for broad applications including agonizing or antagonizing cell surface receptors, bridging immune effector cells with cancer cells, and facilitating cell specific uptake of antibody–drug or antibody–oligonucleotide conjugates. Understanding the fate of antibodies and bispecific antibodies after binding their cell surface target(s) is of critical importance to drug development and pharmacology. Numerous publications have reported methods to assess antibody cell binding, internalization, intracellular trafficking, and the fate of the molecules. These methods can provide qualitative assessments for screening drug candidates, or quantitative assessments which can be used to inform mathematical models of cellular kinetics and drug disposition. Here, we focus on assays which offer quantitative assessments of the kinetics of antibody internalization, intracellular trafficking, and degradation or recycling. Experimental design, practical considerations for conducting experiments, and interpretation of results are considered. Key words Drug transport, Internalization rate, Trafficking, Degradation, Quantitative microscopy, Flow cytometry, Recycling, Antibody, Bispecific antibody

1

Introduction Antibodies form the most widely used format for biotherapeutics and will be the focus of this chapter. However, a broad array of macromolecules beyond antibodies have been studied as potential biotherapeutics and described in the literature. Many of the principles and assays described in this chapter apply or can easily be adapted to other macromolecules against cellular targets. Following binding of an antibody to its cellular target antigen (often a cell surface receptor), the antibody is often subject to the basal or pharmacologically altered trafficking of the antigen as an antibody–antigen complex. This includes intracellular internalization through invagination of the cell surface by a variety of biologically driven processes (clathrin coated pits, caveolae, micropinocytosis, etc.) and shuttling to different intracellular

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021

249

250

John J. Rhoden and Christopher M. Wiethoff

compartments, physically isolated from the cytosol by vesicle membranes, via a complex array of trafficking proteins. In certain pharmacological contexts, a quantitative understanding of antibody and/or antigen trafficking and fate can be important. For example, some antibodies are thought to exert effects in part by driving internalization, thereby removing receptors from the cell surface, (receptor downregulation), such as downregulating growth factor receptors in the context of oncology therapies [1–4]. Depending on the antibody affinity and binding kinetics, the location on the target antigen where the antibody binds (the epitope), and the relative ability of an antibody to crosslink or cluster bound receptors, the rate and extent of receptor downregulation may vary and affect the efficacy of the antibody [5–8]. In other contexts, the trafficking and degradation of a monoclonal or bispecific antibody may be a mechanism of drug clearance and may impact the plasma half-life/pharmacokinetics of the drug in a manner that can limit the ability of the antibody to maintain target antigen engagement and efficacy over time. Antibodies that target high density antigens or antigens that are rapidly turned over and degraded may be especially affected. Unlike small molecules, where are metabolized mostly by the liver, antibodies are degraded more broadly throughout the body including within the target tissue. This phenomenon has been implicated in diverse biological contexts as a potential concern, for example in failure to maintain sufficient exposures for some antibodies or an inability to fully penetrate solid tumor tissues in cancer patients [9–13]. These concerns can potentially be mitigated by screening for and identifying antibodies and target antigens that have favorable binding and trafficking properties to improve the chances of achieving the desired pharmacology. Another class of antibody therapeutics for which cellular trafficking and intracellular disposition is often critical is antibody– drug conjugates (ADCs). ADCs consist of potent small-molecule drugs connected to an antibody via a linker. As a therapeutic class, ADCs are particularly reliant upon antigen binding and subsequent internalization and intracellular trafficking. The pharmacological effect of the small-molecule payload is dependent upon intracellular release of the payload. Antibody trafficking, whereby the antibody is shuttled between the cell surface and different intracellular compartments, typically excludes antibodies and other large-molecule biotherapeutics from the cytosol where the site of action of the cytotoxic small-molecule drug target is located. Therefore, the small molecule must be released to access the cytosol, and so factors such as antigen internalization kinetics and trafficking pathways can strongly influence the potency and toxicity of ADCs [14– 16]. Many efforts to discover and develop ADCs have incorporated screening for rapidly internalized antibodies in target cells as a factor in selecting and engineering potent ADCs [5, 17, 18].

Cellular Trafficking of Monoclonal and Bispecific Antibodies

251

Herein, we describe distinct methods for assessing the binding, internalization, and intracellular trafficking of monoclonal and bispecific antibodies. The first approach described utilizes flow cytometry and confocal microscopy to provide quantitative estimates of the number of antibodies bound, internalized, and trafficked to lysosomes or recycling endosomes in cells of interest. This method provides quantitative kinetic and equilibrium parameters which can be used to inform pharmacokinetic models of target mediated drug disposition. The second method employs a high content imaging approach allowing relative comparisons of cell binding and internalization between bispecific antibodies. This method is well suited to screen antibodies for desired binding and internalization properties in cells of interest. The choice of method and the level of rigor, throughput, and degree of quantitation of each method can be aligned with the needs of the analysis or project. 1.1 Factors Influencing Antibody Binding, Trafficking, and Disposition

The disposition and fate of monoclonal and bispecific antibodies directed against cellular target antigens is dictated by multiple factors related to the antibody–antigen binding kinetics, the cellular trafficking of the target antigen, and the trafficking dynamics of the antibody–antigen complex. Following binding to a cell surface antigen, bound antibodies may dissociate from the antigen, bind multivalently to a neighboring antigen with the other arm(s) of the monoclonal and bispecific antibody, or be subject to internalization and subsequent intracellular trafficking steps that may involve endosomal sorting, recycling back to the cell surface, or lysosomal delivery and eventual degradation to constituent amino acids (Fig. 1). Each of these steps and potential fates occurs at a rate which is governed by factors such as the native function of the target antigen, the epitope to which the antibody binds, and any cellular signaling that may be modulated upon binding to the antigen. These rates can vary by orders of magnitude; for example, the transferrin receptor is internalized and recycled with a half-life on the order of 15 min, while some intercellular junction proteins are internalized with a half-life greater than 1 day [19, 20]. Bispecific antibodies have the additional complexity of the potential of simultaneous binding to one or more different antigens, each with its own trafficking kinetics and potentially differing in relative abundance on the targeted cell(s) as described in Fig. 1. There are diverse approaches to quantitatively assess the various aspects of cell binding and monoclonal and bispecific antibody trafficking. Here, we focus on fluorescent approaches to detecting and quantifying antibodies because these approaches are flexible, quantitative with high resolution, and can be implemented in a straightforward fashion for nearly all monoclonal and bispecific antibodies without the need for specialized equipment or experience. Fluorescently labeled antibodies can be detected by instruments such as flow cytometers and light microscopes and the signal

252

John J. Rhoden and Christopher M. Wiethoff

Fig. 1 Depiction of antibody or bispecific antibody binding, crosslinking, and trafficking on the cell surface and intracellularly. Once bound, antibodies and bispecific antibodies can dissociate, bind another antigen if bi- or multivalent (crosslink antigens), and be subject to internalization within the cell into the endosomal compartment. Once localized in an endosome, antibodies and cognate antigens can be recycled back to the cell surface or interstitial space or can be routed to degradation in a lysosome

from these instruments is amenable to downstream processing and quantitation. These approaches can be used in diverse assays to characterize the extent and rates of internalization, cellular trafficking, and degradation of biomolecules interacting with live cells. In this review, we outline three methods which can be utilized to quantify the cellular trafficking and fate of fluorescently labeled antibodies and bispecific antibodies. The first is a flow cytometric method to quantify the internalization rate of antibodies on adherent or non-adherent cell types of interest, including freshly isolated primary cells. This method can measure a wide dynamic range of antibody–antigen internalization rates among antibodies to a common target and as a result is a useful tool for screening for rapidly or slowly internalization antibodies against a target. The second method is a quantitative microscopy method to assess trafficking and intracellular localization of biomolecules to determine their subcellular transport and degradation. The third is a high content quantitative microscopy method which can be utilized to determine the binding and internalization of biomolecules and can quantitively differentiate internalization rates of antibodies and bispecific antibodies to different cellular targets and even to antibodies or bispecific antibodies with differing affinities to those targets.

Cellular Trafficking of Monoclonal and Bispecific Antibodies

1.2 Considerations for Fluorescent Labeling of Proteins

253

The methods in this chapter focus on detection and quantitation of biomolecules using fluorophore labeling of the molecule(s) of interest. Fluorescent detection is widely available on diverse scientific instruments and can bring highly specific and sensitive detection to biological systems both in vitro and in vivo. Fluorescent detection can be multiplexed to some degree by choosing appropriate fluorophores and optical filters on instruments. Moreover, fluorescent probes and antibody labeling methods are widely available with a variety of different fluorophores and can generally be applied to most proteins of interest. However, there are some practical guidelines that should be considered when choosing a fluorophore and fluorescently labeling an antibody. Fluorescent labeling kits are available for purchase from many vendors and can be obtained for a range of fluorophores differing in molecular size, excitation and emission wavelength, quantum yield, and conjugation chemistry. The needs of the assay should be considered when choosing a fluorophore (e.g., is a very strong fluorescent signal needed, or a certain excitation or emission wavelength desirable?) and balanced with the potential effects of fluorophore labeling on the overall biochemical status of the labeled biomolecule. The desire is to label an antibody such that it can be efficiently detected without appreciably altering the key properties of the molecule itself. Otherwise, the labeled protein may not behave in a manner representative of the unlabeled antibody. For example, it has been observed that labeling with different fluorophores or at different degrees of labeling (fluorophores per labeled protein) can affect the binding of an antibody to its target and can also have a significant negative impact on the pharmacokinetics in vivo relative to unlabeled antibodies [21]. For in vitro experiments, it can be helpful to test the binding after labeling of monoclonal or bispecific antibodies to confirm that the function is not appreciably altered. Maintaining a relatively low degree of labeling, in general as low as feasible to maintain an appropriate signal in the assay, can help to minimize the chances of unfavorable changes to the molecule. If loss of binding, aggregation, or other undesired changes are observed following fluorescent labeling of a molecule, a lower degree of labeling can be tried, a different fluorophore conjugated, or a different conjugation chemistry employed. Common methods include reacting primary amines (typically lysine residues) with NHS ester dyes or reacting cysteines with maleimide dyes (by partially reducing antibody disulfide bonds or introducing cysteines through protein engineering). Dyes with both these chemistries are commonly available from many commercial sources (see Note 1). Size exclusion chromatography can also be a useful technique to assess the quality and homogeneity of fluorescently labeled antibodies.

254

John J. Rhoden and Christopher M. Wiethoff

1.3 Quantitative Assessment of Cellular Internalization

2

Unlike lipophilic small-molecule drugs, where non-specific partitioning into tissues based on lipophilicity dominates drug pharmacokinetics, the specific binding of antibodies to their target can substantially alter drug distribution and clearance. Antibodybased therapeutics which recognize cell surface antigens are frequently subjected to target mediated drug disposition which can significantly impact the clearance of the drug [22, 23]. Understanding the kinetics of this process can help predict exposures [24] and understand the durability of the resulting pharmacodynamics [25]. There are numerous methods to characterize antibody internalization, the choice of which is dependent upon the intent of the study. Assessing antibody internalization may be useful to screen for molecules with faster or slower internalization kinetics depending upon the desired effect or mechanism of action [9, 26]. Alternatively, more quantitative measurement of internalization kinetics may be necessary to inform pharmacokinetic or pharmacodynamic models [27]. Assays involving radioiodination or chromatographic methods have been reported for biochemically tracking antibody internalization [28, 29], but due to the advantages described previously, fluorescence based assays are the predominant assays in use [30– 32]. The format of these fluorescence-based assays is determined based on the precision of the information necessary, sensitivity requirements or the throughput needed. They may include the use of confocal or deconvolution microscopy to assess subcellular localization of antibody in cells or flow cytometry to assess internalization. Commonly, fluorescent labels which undergo spectral changes upon encountering environmental stimuli (e.g., low pH or proteases in endosomes, or accessibility to fluorophore-specific quenching antibodies) are used to assess internalization by flow cytometry, microscopy, or via fluorescent plate reader [33]. Methods involving the measurement of fluorescence signals to track antibody internalization and intracellular trafficking are described herein.

Quantitative Assessment of Antibody Internalization and Intracellular Trafficking An accurate description of the pharmacokinetics is critical to the development of antibodies as drugs and to project doses when designing clinical trials. Target mediated drug disposition (TMDD) presents a significant challenge when attempting to use preclinical data to project human pharmacokinetics [22, 23, 34]. In contrast to small-molecules pharmacokinetics, which can often be described by the lipophilicity and liver metabolism, the high level of specific binding of antibodies makes these predictions more challenging [35]. Modeling the impact of TMDD on antibody pharmacokinetics can be greatly improved if the model is informed with

Cellular Trafficking of Monoclonal and Bispecific Antibodies

255

more accurate estimates of the internalization rates of receptorantibody complexes. Pharmacokinetic models which capture this TMDD can be simple Michealis-Menten models [36, 37] or mechanistic models that account not only for antibody internalization, but also degradation, recycling, and overall receptor turnover rates [38, 39]. In either case, an estimate of the internalization rate is necessary. Below is one strategy to estimate internalization rate constants for antibodies undergoing receptor mediated endocytosis. This assay relies on the ability to specifically quench the fluorescence emitted by a fluorophore–antibody conjugate with an antifluorophore antibody. The key principle behind this assay is the fact that the fluorescence signal can only be quenched when the antibody–fluorophore conjugate is present on the surface of the cell due to the inability of the anti-fluorophore antibody to diffuse across the cell membranes (Fig. 2). Thus, the fluorescence signal in the presence of quenching antibody decreases if fluorescently labeled antibodies remain on the cell surface, but fluorescence signal from internalized antibodies will not be affected in the presence of extracellular quenching antibodies. Several reports exist which describe this approach to measure internalization of a receptor-ligand pair [40, 41].

Fig. 2 Detection of internalized fluorescence. AF488-labeled antibody is allowed to bind and internalize into cells. The cell suspension is split in half and one half is treated with an AF488-quenching antibody. Flow cytometry is used to quantify the fluorescence from the total cell-associated or internalized AF488-labeled antibody

256

John J. Rhoden and Christopher M. Wiethoff

Several commercially available fluorophore-specific antibodies which quench fluorescence emission upon binding are available (see Note 2). In choosing a fluorophore/anti-fluorophore antibody pair, there are several qualities to consider. First, the fluorophore should display pH-independent extinction coefficient and fluorescence quantum yield in the pH range of 7.4 to 3. For these assays, the fluorescence signal is assumed to directly correlate with antibody concentration. If the fluorophore possesses pH-dependent emission, then the same intensity of fluorescence signal from a labeled antibody in acidic lysosomes would represent a different concentration than that of a labeled antibody at the cell surface where the pH is neutral. For example, there exist commercially available antibodies to both Alexa Fluor 488 and Fluorescein (see Note 2). However, unlike Alexa Fluor 488, Fluorescein conjugates (e.g., FITC) have reduced fluorescence signal when the pH is lowered below pH 6 [42]. Alternatively, endosomal pH can be normalized prior to fluorescence detection by the use of nigericin and monensin ionophores to reduce the impact of lowered pH on observed fluorescence signal [42]. Second, the efficiency of fluorescence quenching can influence the interpretation of the resulting signal. While a method to account for the contribution of unquenched cell surface fluorescence signal to the total quenched fluorescence signal is described below, inefficient quenching significantly reduces the signal window available for quantification purposes. To best describe how this approach is implemented, an example of the determination of internalization kinetics of 2 antibodies is presented. While this method describes details that are somewhat specific, this approach is easily generalizable to other antibodies or biomolecules and to varying cell types. In this example, one antibody, Ab1, is a conventional mAb which binds a receptor present on, among other cell types, liver sinusoidal endothelial cells (LSECs) and is capable of binding bivalently to LSECs. The other antibody, Ab2, is a bispecific antibody (IgG-like structure) containing one binding arm (a single Fv) identical to Ab1 and a non-binding isotype control Fv for the second arm and is therefore capable of monovalent binding only. Studies examining the pharmacokinetics of these mAbs in CD-1 mice indicate that valency greatly impacts antibody clearance due to greater TMDD (data not shown). The goal of these studies is to determine the impact of valency for receptor binding on the Michaelis-Menten (MM) kinetics of internalization. While derivation of these equations can be found elsewhere [36, 37], the equations that will be used to determine the MM parameters of Km and Vmax are below: V ¼

V max ½mAb K m þ ½mAb

ð1Þ

Cellular Trafficking of Monoclonal and Bispecific Antibodies

257

In short, Km represents an equilibrium constant corresponding to a concentration at which the velocity of internalization is halfmaximal. Vmax represents the maximal velocity of internalization. [mAb] represents the molar concentration of the antibody or other biomolecule under study. Once these parameters are known, the cellular kinetics of internalization can be incorporated into models of plasma clearance to understand the pharmacokinetics and predict the timing and doses needed for efficacy. To estimate Km and Vmax, we must (1) measure the velocity of internalization at multiple concentrations of antibody [mAb] and (2) fit the velocity vs. concentration data to obtain a Km and Vmax. The ideal units for these parameters are molarity for Km (e.g., nM) and moles/unit time (e.g., nmol/h) for Vmax. To design experiments which will estimate these parameters, we must make the following assumptions: 1. Internalization is saturable. 2. We are able to explore an antibody concentration range  and  than the estimated Km. Additionally, an ability to convert fluorescence intensity to molar concentrations is necessary to obtain values which can be used to inform pharmacokinetic models. To this end, we employed flow cytometry to quantify the internalization of antibodies in molar and molecules per cell units. The geometric mean of fluorescence intensity measured on the fluorescent bead standards can be converted to molecules/cell by generating a standard curve using Quantum™ Simply Cellular® beads as a calibrant using the manufacturer’s instructions [43]. To design these experiments and convert the data from molar units into a receptors/cell format, an assessment of the total number of cell surface receptors per cell is required. This can be achieved by staining unfixed cells with the Alexa Fluor 488–labeled antibody of interest while on ice and quantifying receptor numbers using the protocol which comes with the Simply Cellular® beads (see Note 3). Briefly, cells should be preincubated on ice for 30 min to ensure that endocytosis has been halted. Cells should then be stained with saturating amounts of antibody to ensure an accurate assessment of the total number of cell surface receptors. A concentration of ten times or more the Kd is a good rule-of-thumb for saturation. Ideally, performing this experiment at 3 concentrations of antibody will help ensure that cell binding has been saturated. This information will be used to confirm the concentration of the receptor (e.g., # receptors/cell * # cells/volume of cell suspension), [R]Total. Additionally, the fluorescence quenching efficiency needs to be determined. This quenching efficiency is typically greater than 90% and can be used to adjust the values of intracellular fluorescence (Fig. 2) to account for cell surface antibody that has not been

258

John J. Rhoden and Christopher M. Wiethoff

quenched. A good practice is to screen several lots of anti-fluorophore quenching reagent for quenching efficiency since this parameter can vary from lot to lot. This is particularly important for situations where receptor density is low and the fraction of drug internalized is minimal. In most cases, quenching efficiency will be less than 100%, and so correcting the signal to account for the unquenched fluorescence of antibody remaining on the cell surface is necessary to obtain accurate estimates of internalized antibody concentrations. Determining the concentration of anti-fluorophore antibody necessary to achieve maximal quenching efficiency can minimize the impact of incomplete quenching. 2.1 Protocol for Determining Fluorescence Quenching Efficiency

1. A key step in this method for measuring internalization of fluorescently labeled antibodies is determining the quenching efficiency of the anti-fluorophore antibody chosen for this assay. In the example below, anti-Alexa Fluor 488 (antiAF488) was chosen for reasons outlined above and in Note 2. To determine the quenching efficiency, the AF488 labeled antibody is bound to cells in ice cold binding buffer at 4  C to minimize internalization (see Note 4). 2. Cells are washed in ice cold binding buffer three times. The cell suspension is then stained with anti-AF488 antibody in binding buffer for 30 min on ice (see Note 5), while the other is left untreated. 3. Cells are then washed three times in binding buffer, then subjected to analysis by flow cytometry following the manufacturer’s instructions. As seen in Fig. 3, the anti-AF488 antibody can quench >90% of the AF488-labeled mAb fluorescence signal. Quenching efficiency can be a function of the anti-fluorophore antibody lot, the degree of AF488 labeling on the antibody of interest and the amount of AF488-labeled antibody associated with cells. Using a fluorophore degree of labeling of less than four fluorophores per antibody yields the best quenching efficiencies in our hands. With quenching efficiency confirmed, the determination of Michaelis-Menten kinetic parameters for Ab1 and Ab2 in LSECs can be performed. The internalization rate of cell surface receptors can vary widely with half-lives of internalization varying from minutes to hours. Thus, it might be useful to perform pilot studies using an antibody concentration which is expected to saturate the receptor and explore an intermediate time range (e.g., 4–6 h), with timepoints for data collection every 0.5–1 h. In our case, prior data suggested that this internalization is rapid, with a half-life of 5–10 min. We therefore chose to measure internalization at 1, 2, 4, 6, 8, 10, 15, 30, 45, and 60 min.

259

geoMFI

Cellular Trafficking of Monoclonal and Bispecific Antibodies

Fig. 3 Determination of concentration-dependent quenching of AF488-labeled mAb. AF488-labeled mAb is incubated with mouse LSECs on ice. Unbound mAb is washed away and the quenching antibody is titrated onto the cells prior to analysis by flow cytometry as described in the text 2.2 Protocol for Assessing Cellular Internalization of Antibodies

1. In this example, antibody internalization is assessed in primary mouse liver sinusoidal endothelial cells (LSECs). To perform the experiment, LSECs are isolated from C57BL/6 mice as previously reported [44]. Note, however, that other cells or cell lines can be used that express the target of interest. 2. Freshly isolated cells are resuspended in prewarmed RPMI with 0.5% BSA (500,000 cells in 100 μl) and incubated at 37  C for 15 min in low protein binding 1.5 ml microcentrifuge tubes. 3. Ab1 or Ab2 are preincubated in RPMI + 0.5% BSA at 37  C for 15 min at 0.1, 1 and 10 μg/ml in low protein binding 1.5 ml microcentrifuge tubes. 4. One hundred microliters of antibody solution is then added to each tube containing cells and the suspension pipetted up and down three times to mix. 5. At appropriate times*, 1.4 ml of ice cold 4% paraformaldehyde in PBS is added to each tube, mixed by inverting and centrifuged at 200  g for 5 min at 4  C. Cells are then resuspended in ice cold PBS and washed three times in ice cold PBS, centrifuging and resuspending each time as before. (a) *Note: Appropriate times are determined by the characteristic rate of internalization of the antibody–antigen complex. In general, time points chose should cover 2–3 half-lives of internalization and include enough time points for robust curve fitting (typically 4–6 time points or more). If internalization kinetics are unknown, a pilot study may be warranted to determine appropriate time points for a more definitive quantitative study.

260

John J. Rhoden and Christopher M. Wiethoff

6. Cells are resuspended in FACs buffer (PBS pH 7.4 + 0.5% BSA) and split into two aliquots. One aliquot (quenched) is incubated with 100 μg/ml of anti-AF 488 antibody and the other aliquot in FACs buffer, both at room temperature. 7. After 30 min of incubation, cells are washed three times in 1.4 ml of FACS buffer, centrifuging and resuspending in fresh FACs buffer each time. Cell suspensions are then subjected to flow cytometry using a FACSCanto or other flow cytometer, employing manufacturer’s recommendations for setting detector voltages and flow rates, to determine the values of quenched and unquenched fluorescence staining. Using the Forward vs. Side Scatter plots, a live cell gate is determined based on manufacturer’s recommendations. 25,000 events are collected for each sample. Using the flow cytometry analysis software such as Flo Jo Software, geometric mean fluorescence intensity (geoMFI) for the gated cells is determined. 8. To convert the geoMFI to molecules of antibody per cell, a standard curve is generated using Simply Cellular® beads using the manufacturer’s instructions. Briefly, beads are incubated with an excess of each antibody for the prescribed time, washed as directed with FACS buffer and then subjected to flow cytometry. This assay should be conducted at the same time as the cell analysis to ensure that the instrument settings are identical. Using the spreadsheet available from the Bangs Labs website [43], the geoMFI data is converted to molecules/cell. With the number of antibodies detected for the quenched and unquenched portion of each sample, the value determined for the unquenched sample represents the total number of cell associated Ab1 or Ab2. To calculate the number of antibodies internalized, the values of molecules/cell for quenched (ABCquenched) and unquenched (ABCtotal) sample aliquots, and the quenching efficiency (QE) determined previously, are input into the equation below (Eq. (2)). The result of this calculation is the number of molecules internalized per cell. ABCinternalized ¼

ABCquenched  ð1  QEÞABCtotal QE

ð2Þ

Figure 4a shows plots of the internalized antibody molecules/ cell for each concentration of Ab1 or Ab2. Initial slopes for the linear portion of the curves are determined using a software package such as MS Excel, GraphPad Prism, etc. These zero-order slopes represent the velocity of internalization at each antibody concentration. Depending on the turnover rate of the receptor (ratio of the rate of internalization and cell surface arrival from intracellular pools), and the binding kinetics (binding and unbinding rates) of the antibody for the receptor, a plateau in these curves

Cellular Trafficking of Monoclonal and Bispecific Antibodies

261

10 µg/ml Ab1 1 µg/ml Ab1 0.1 µg/ml Ab1

velocity (molecules/cell/min)

B

A

10 µg/ml Ab2 1 µg/ml Ab2 0.1 µg/ml Ab2

Fig. 4 Internalization of Ab1 and Ab2 in LSECS. (a) Cells were incubated with varying concentrations of Ab1 or Ab2 and the number of molecules per cell internalized determined as described in the text. (b) The initial internalization rates determined from fitting data in (a) were plotted vs. the concentrations of Ab1 and Ab2. Data was fit to a MM-model in GraphPad Prism

at later times or a lag in the onset of internalization may be observed. Data from these plateaus should be excluded and the slope determined for data in the linear portion of the curve. To determine the Michaelis-Menten kinetic parameters, a fit to the velocity vs. antibody concentration profiles are obtained. Software such as GraphPad Prism or SigmaPlot possess functions to perform non-linear least squares fits to one binding site MichaelisMenten equation (Eq. (1)). Fits of the data generated for Ab 1 and 2 are shown in Fig. 4b. These data display the expected saturable kinetics implicit to receptor mediated endocytosis. From these fits, Km and Vmax values can be estimated. Estimates for Ab1 and Ab2 are shown in the table inset of Fig. 3b. These parameters could be used to inform a PK model incorporating Michaelis-Menten kinetics [45]. Additionally, based upon the known number of receptors per cell and the number of cells per volume of solution, the total receptor concentration, [R]T, can be calculated. To inform a mechanistic PK model, the intrinsic internalization rate constant, kint, would be used. Since Vmax ¼ [R]T kint, the receptor concentration (receptor#/cell x # cells/volume of solution) and Vmax could be used to generate this parameter. Note that the receptor expression and rate of internalization can vary by many orders of magnitude. For example, the receptor expression of some cancer cell targets varies from 1 million [46].

262

John J. Rhoden and Christopher M. Wiethoff

The data from these experiments can be used to inform the mechanism of internalization and trafficking. In the case of Ab1 and Ab2, several conclusions can be drawn from the data obtained from these studies. First, Since the estimates of Vmax differ, it seems possible that receptor crosslinking by bivalent binding of Ab1 may lead to faster internalization than monovalent binding of Ab2. Additionally, the Km values for Ab1 and Ab2 are in line with avidity-enhanced binding expected for the bivalent Ab1. Interestingly, when we compare clearance of Ab1 and Ab2 obtained from a low dose PK study in mice to the Michaelis-Menten parameters determined in vitro, there appears to be a good correlation between these observations (data not shown). Thus, this in vitro assay, capable of being completed in several days, might be a good surrogate for PK studies when assessing the impact of antibody affinity or valency on TMDD. 2.3 Quantitative Assessment of Intracellular Trafficking of Antibodies

2.4 Methods for Imaging the Intracellular Trafficking of mAbs

The fate of antibodies after internalization can be of critical importance to (1) predicting the pharmacokinetic properties of a mAb, (2) understanding the biodistribution of a systemically administered antibody to more privileged tissues such as the CNS or (3) in the case of antibody–drug conjugates, quantifying the delivery of payload to the appropriate subcellular target. To develop mechanistic PK models for antibodies in any of these scenarios, a quantitative assessment of intracellular trafficking and antibody fate is necessary. Numerous reports describing biochemical, microscopic or flow cytometric assessments of antibody intracellular trafficking have been reported [47–52]. Here, we provide an example employing fluorescence confocal microscopy to assess the intracellular trafficking kinetics of Ab1 and Ab2, described above, in mouse LSECs. This method is readily generalizable to other cells or antibodies. 1. Freshly isolated mouse LSECs (or appropriate cell type/cell line) are plated into 8 well coverglass slips (0.17 mm thickness, #1.5) at a cell density of 20,000 cells/well. For each timepoint to be assessed, a separate 8 well coverglass is used to plate 3 replicate wells. An additional 8 well coverglass is plated for use in staining controls (see below). Cells are cultured overnight in a 37  C CO2 incubator in Lonza EMEM + endothelial cell additives (Catalog #CC-3124) with 10% FBS (or cell-line specific conditions). 2. The following day, cells are washed 3 times in ice cold DMEM with 0.5% BSA and incubated in this media on ice for 30 min. After 30 min, cells are removed from ice, the media removed and prewarmed DMEM + 0.5% BSA containing 1 μg/ml of Ab1 or Ab2 is added to cells.

Cellular Trafficking of Monoclonal and Bispecific Antibodies

263

3. Cells are incubated at 37  C in a CO2 incubator for 5, 45, or 90 min. 4. For each timepoint, the appropriate 8 well coverglass is removed from the incubator, media is removed by blotting quickly on paper towel, the coverglass is placed on an ice cold metal block and 200 μl of ice cold 4% PFA in 200 mM PIPES buffer pH 6.8 was added to each well with cells. 5. After 15 min, wells are washed four times with 200 μl PBS and stored in the dark at room temp until the end of the study. 6. To assess intracellular trafficking of Ab1 and Ab2, cells are immunostained for lysosomes (LAMP-1), recycling endosomes (Rab11), and the antibody of interest (in this case human IgG4-derived Ab1 and Ab2). First, cells are simultaneously permeabilized and blocked for non-specific binding with 0.05% Saponin in PBS + 10% FBS for 15 min at room temperature to allow access for the immunostaining antibodies. 7. Cells are then stained with AF488-labeled LAMP-1 (Thermofisher clone H4A3, 1:200 dilution), AF568-labeled antihuman IgG (Thermofisher; Cat# A-21090) and AF647labeled Rab11 (Thermofisher Clone D4F5, 1:200 dilution) in 0.05% Saponin in PBS + 10% FBS for 1 h in the dark at room temperature. 8. Cells are then washed three times with 0.05% saponin in PBS + 10% FBS, incubated with 0.5 μg/ml Hoechst 33342 for 10 min, washed three times with PBS and then covered with Vectashield™ anti-fade mounting media (EMS, Cat# H-1000). For the control staining wells, each well is stained singly with LAMP-1, Rab11 or Ab1 followed by anti-hIgG. 9. Cells are imaged using a Zeiss LSM 800 confocal microscope using the Zen Blue software package. An oil immersion 63 NA 1.4 objective is used for imaging. Instrument settings are optimized to minimize the bleed through of each signal into the other two channels and to optimize the channel specific signal for each immunostain though changes in instrument settings as instructed by the microscope manufacturer by iteratively imaging each singly stained control sample. 10. Once the instrument settings are optimized, 3 independent images are collected for each well. Imaging locations are chosen based on LAMP-1 staining, to identify regions with good separation between cells to facilitate downstream image analysis, but including 3–5 cells per field. For each image, a z-series through the entire cell depth is collected in 0.25 μm increments. This degree of optical sectioning is important for quantifying antibody localization volumetrically in endosomal compartments which may be as small as 50–100 nm. Volumetric analyses of antibody localization is particularly important in

264

John J. Rhoden and Christopher M. Wiethoff

LSEC cells, which possess a somewhat polarized distribution of recycling endosomes and lysosomes (the latter present in greater numbers for sections obtained closer to the coverglass, with perinuclear concentration). Adjustments may need to be made for other cell types. 11. To quantify antibody localization with recycling endosomes and lysosomes, images are analyzed with the Imaris software package (Bitplane, Inc.) using the “Spots” module. This module identifies spheroid “surfaces” of punctate structures in a given channel [53, 54]. Within the voxels defined by these surface masks, the intensity of other channels can be determined. Thresholding of these other channels using cells stained only with AF568-hIgG allows for enumeration of the percentage of AF568-hIgG surface masks that are positive for the lysosomal or recycling endosome signal. Data derived from this analysis can be presented in an analogous manner as flow cytometry data. For example, as histograms for a given channel in a gate of organelles defined by different channel. Or for each image, a percentage of antibody voxels positive for lysosomes or recycling endosomes can be calculated. With 3 images per experimental replicate and 3 experimental replicates, and 3–5 cells per image, average values for 27–45 cells can be obtained. Results for image analysis of Ab1 and Ab2 internalization kinetics are shown in Fig. 5. Example images in Fig. 5a allow a visual check of colocalization between Ab1 or Ab2 and the lysosomal and recycling endosomal markers. The fraction of Ab1 or Ab2 colocalized with these markers as a function of time are shown in Fig. 5b. Recalling that Ab1 and Ab2 differ in valency for receptor binding, it is interesting to note the differences in trafficking. Qualitatively, these data would suggest that bivalent antibody trafficking results in greater lysosomal accumulation, with much less recycling occurring. On the other hand, monovalent binding results in a greater fraction of Ab2 recycling back to the cell surface. To provide a more quantitative assessment of the intracellular trafficking of Ab1 and Ab2, data on internalization can be combined with data describing intracellular kinetics to describe the number of molecules/cell trafficking to recycling endosomes or lysosomes. Fitting a first order reaction kinetic model to the data for recycling endosome and lysosomal trafficking vs. time in GraphPad Prism, allows for fractions of antibody in each endosomal compartment at any timepoint. These fractions can then be multiplied by the number of molecules internalized per cell obtained by flow cytometry to calculate the number of molecules in recycling endosomes and lysosomes as a function of times. The results of these calculations are shown in Fig. 6.

Cellular Trafficking of Monoclonal and Bispecific Antibodies

265

Fig. 5 Imaging antibody internalization and intracellular trafficking. (a) Images of LSECs fixed and immunostained 5 or 60 min after internalization of Ab1 or Ab2 at a concentration of 1 μg/ml. (b) Quantification of the fraction of cell associated Ab1 or Ab2 colocalized with LAMP-1 (lysosomes) or Rab11 (recycling endosomes/ REs) as a function of time as described in the text

Fig. 6 Quantification of intracellular trafficking of Ab1 and Ab2. The number of molecules of Ab1 or Ab2 internalized by LSECs was transformed into the number of molecules of each present in lysosomes or recycling endosomes/REs was performed as described in the text

3 Binding and Internalization of Antibodies and Bispecific Antibodies by Fluorescence Microscopy Antibodies and bispecific antibodies directed to cellular antigens must bind their targets to be pharmacologically active. Following antigen binding, the antibody–antigen complex can then be subject to internalization, recycling, and degradation (Fig. 1). The nature

266

John J. Rhoden and Christopher M. Wiethoff

of the binding interaction, the target antigen pharmacology, and the baseline turnover rate of the antigen all play a role in the kinetics of antigen internalization and intracellular fate. Additionally, in the case of bispecific molecules, the interplay of the kinetics and the relative abundance of both antigens can impact the internalization kinetics and intracellular fate of bound complexes. Here we describe a confocal microscopy method to measure over time the distribution of fluorescently labeled antibodies or bispecific antibodies between localization at the cell membrane and subsequent internalization and degradation. High content microscopy is combined with an unbiased rules-based image analysis technique to quantify the amount of labeled antibody on the cell membrane as well as intracellularly localized. This method can describe differing extents of binding depending on the cellular target antigen and its abundance (number of receptors per cell) and to show the differing kinetics of internalization caused by targeting to two different cell surface antigens. Finally, we show example data demonstrating how bispecific antibodies are affected by the internalization kinetics of the targets that they bind and are also strongly affected by the relative abundance of the two antigens targeted. This imaging method is differentiated from the method described in the previous section in several ways, most notably in that this method is significantly higher throughput and can rapidly generate kinetic data on cellular internalization and trafficking. In contrast to flow cytometric methods, this high content imaging method is applied to adherent cells and does not require suspending cells which can be advantageous for target antigen that may be perturbed by the process of suspending cells that are normally adherent. 3.1 High Content Microscopy Method

1. Antibodies and bispecific antibodies to be tested are labeled by common commercially available fluorophores, such as Alexa Fluor dyes. Care should be taken to label the antibodies at a relatively low degree of labeling and to test the ability of the labeled antibodies to bind to targeted receptor antigens in a manner comparable to the unlabeled antibodies. 2. Cells are labeled with a whole cell dye such as CellTrace™ dyes which are available in several wavelengths across the visible spectrum to delineate the cells and their borders for subsequent quantitative analysis. Follow manufacturer’s instructions as provided (see Note 6). Once labeled, plate cells on imaging plates such that cells are sufficiently abundant in a field of view but not confluent such that they can be distinguished from one another by the quantitation software.

Cellular Trafficking of Monoclonal and Bispecific Antibodies

267

3. Depending upon the experimental setup and desired data, labeled cells can be bound by labeled antibody/bispecific and then plated on imaging plates, or plated on imaging plates and then the labeled protein added to the plates during image acquisition. We used 96-well glass bottom plates (Perkin Elmer, Cat #6005530) for our experiments to enable high content imaging on multiple wells in one experiment. 4. Once labeled cells have settled on the imaging plate, prepare high content microscopy system for image acquisition. One or more fields of view containing labeled cells should be imaged and serial images acquired over a time course to capture the dynamics of antibody/bispecific binding and internalization, for example every 5 min for 1–2 h depending on the kinetics being observed. Some microscopy setups may allow for multiple fields or wells of a plate to be measured in one experiment which can dramatically improve the throughput of these studies. 3.2 Quantitative Image Analysis

1. Once images have been acquired, they can be exported for quantitative analysis by image analysis software. There are a variety of tools with which to perform these analyses available, and here we use the Columbus™ Image Data Storage and Analysis software package to analyze the images shown in Fig. 6. 2. Using the image analysis tools, use the whole cell stain imaging channel to identify cells and classify cellular structures (e.g., cell membrane and cytosol) in images based upon size, brightness, and the presence of the entire cell in the field of view. Subsequently define the border region of each cell and classify that region as the cell membrane. The region encircled by each cell membrane region is defined as the intracellular region. Appropriate rules must be implemented to reject artifacts such as incomplete cells at the border of images or noncellular artifacts that may appear and be erroneously classified as cells by automated computer analysis. These artifacts should be minimized as much as possible although it may not be possible to eliminate them entirely. Cell recognition and segmentation algorithms that work well for one cell type may not for another, for example very adherent cell types vs suspension cells, so the algorithm must be developed and validated for each cell type being analyzed. 3. Algorithmic rules are applied to quantify the labeled antibody/ bispecific fluorescent signal in both the cell membrane and intracellular regions. Multiple approaches may be taken to quantify the signal such as mean or median fluorescence intensity per unit area, counting fluorescent puncta, or other means.

268

John J. Rhoden and Christopher M. Wiethoff

Fig. 7 High content imaging and algorithmic analysis and quantitation of antibody and bispecific internalization. Cells were bound with control IgG or with antibodies or bispecific antibodies varying in affinity to targets A and B. Bispecific antibodies were bivalent bispecific (IgG-like) molecules with one arm targeting antigen A (high or low affinity) and the other targeting antigen B (high or low affinity). (a) Time course of antibody or bispecific antibody (red staining) on live cells showing binding to the cell surface and subsequence internalization of some of the antibody. In purple are nuclei (DAPI staining) and not shown are whole cell stains for identification of individual cells and subsequent algorithmic segmentation into cell surface and intracellular regions. (b) Representative output of whole cell segmentation algorithm identifying cell outlines prior to subsequent processing to identify whole cells and remove artifacts. (c) Quantitative output of internalization extent and rate comparing antibodies and bispecific antibodies varying in affinity to two cell surface receptors

An appropriate negative control, for example a labeled unbinding control antibody or bispecific, may be helpful in data analysis to understand the background signal in the assay. 4. Apply the image segmentation and subsequent quantitation algorithm to the series of images in the time course of one or more fields of images to determine the kinetics of antibody/ bispecific binding to the membrane and internalization/degradation. An example output of such analysis is shown in Fig. 7. As described, this high content microscopy method is useful in quantitating relative differences between molecules and between different molecular targets as shown in Fig. 7. In this example, we sought to evaluate the internalization extent and kinetics of antibodies and bispecific antibodies consisting of either high or low affinity binding arms to two different cell surface receptors. When the tested antibodies and bispecific antibodies were analyzed in the fashion outlined above, we found that the results clustered in 3 main groups. The most internalized group consisted of all the antibodies and bispecific antibodies that contained a high affinity binding arm to antigen A, regardless of whether the second arm was high or low affinity to antigen B. In the middle group, the low

Cellular Trafficking of Monoclonal and Bispecific Antibodies

269

affinity antibody to antigen A and the bispecific antibodies containing a low affinity arm to antigen A were clustered, again regardless of affinity to antigen B. Lastly, the least internalized group of molecules consisted of two monoclonal antibodies to antigen B which did not have the capacity to bind antigen A at any significant level. In this way, we were able to measure differences in antibody internalization extent as well as rate (by fitting the curve; data not shown) to determine the effects of antigen targeted and antibody– antigen affinity on internalization by antibodies and bispecific antibodies on cells of interest. In this example, the binding affinity and faster trafficking of target A appears to dominate the antibody and/or bispecific antibody internalization rate. To estimate kinetics of binding and internalization in terms of molecules per cell using this method, some assumptions must be made. If the total number of binding sites (antigens) per cell is measured as described previously or is known from the literature, one can add excess labeled antibody or bispecific during the experiment to saturate the available binding sites. In this way, images acquired of the bound cells at the beginning of an experiment can be assumed to be saturated such that all binding sites are occupied by labeled antibody. By normalizing to this initial image, subsequent measurements can be assumed to scale proportionally to the saturated cells image and the relative change in signal from the baseline initial image can be converted to an absolute signal in a molecules/cell basis for the experimental time course.

4

Conclusions Fluorescence-based assays allow for rapid, quantitative assessment of antibody internalization and intracellular trafficking. Depending upon the target antigen, these rates have the potential to have a major impact on the drug pharmacokinetics and pharmacodynamics. Slow internalization may improve pharmacokinetics and allow lower or less frequent dosing, while the mechanism of action of other antibodies may require faster internalization to downregulate/antagonize a receptor or efficiently deliver an antibody drug conjugate payload. Each of the methods described above could be used for screening purposes to identify lead drug candidates. Or, through a combination of methods, a quantitative description of cell binding and trafficking events can be achieved. Results from these experiments may allow for the generation of novel hypotheses to be explored in vivo. Alternatively, these results could be combined with in vivo preclinical data to inform drug disposition models which can provide valuable insights into the behavior of antibodies in preclinical studies and translationally to humans. These approaches offer considerable utility for the development of any antibody-based drug, especially those for which cellular interactions are pharmacologically important.

270

5

John J. Rhoden and Christopher M. Wiethoff

Notes 1. When labeling an antibody or bispecific, we typically start by trying to use an amine-based labeling kit from a commercial vendor and following the manufacturer’s instructions. We generally target a degree of labeling of 2 or less for most antibodies and have found that to be bright enough for most experiments while minimizing undesired changes to the biochemistry of the labeled proteins. If signal is not an issue for the experiment, we recommend targeting a degree of labeling even lower, often at 40 kDa, including plasma proteins. Association with plasma proteins greatly limits intracellular penetration of the reporter-vector. 10. Metabolites—There are two main considerations for the formation of metabolites of the reporter-vector: liver metabolism during excretion, and cellular metabolism after internalization. The most concerning from an experimental point of view is the metabolism by cellular internalization in which the reporter stays intact causing measured signal to arise from the intracellular compartment. If the control reporter-vector is not similarly internalized, there will be discrepancies in the kinetic modeling. 11. Nonspecific binding—Any off-target binding from the targeted reporter-vector should be similarly demonstrated in the control reporter-vector in order to remove it from the “bound” portion of the signal. Nonspecific, or off-target, binding can be assessed in tissues devoid of receptor. The binding measured by the paired-agents in control tissue should be zero, or close to zero, if the nonspecific binding of the targeted and control reporter-vectors are similar.

Quantitative Drug Target Imaging Using Paired-Agent Principles

283

2.2 Control ImagingAgent

Ideally, a targeted imaging agent should be paired with a control imaging-agent that is essentially identical to the targeted agent (in terms of the properties listed in Subheading 2.1.2), but that exhibits negligible binding affinity for the targeted biomolecule. The suitability of any targeted and control imaging-agent pair can and should be tested experimentally by ensuring that the dynamics of the two agents in any tissue devoid of targeted biomolecule are relatively equivalent (see Note 2). Subheading 3.5.4 describes how to correct for potential differences in the input functions of a targeted and a control imaging-agent, as long as extravasation, diffusion, and intracellularization properties are similar between the agents.

2.3

Though not strictly a “material,” mathematical models (such as the examples provided in Fig. 2) are fundamental tools in paired-agent imaging protocol optimization/testing and data analysis, and are therefore treated in this chapter as materials necessary to carry out the methods described in Subheading 3. Mathematical representation of imaging-agent distribution and binding is generally represented by compartmental and/or diffusion modeling. Compartmental models aim to simplify imagingagent distribution and binding by assuming that the imaging agent exist in only a distinct number of spatial compartments (e.g., unbound within the interstitial space or unbound within the intracellular space) or chemical compartments (e.g., bound to the specific biomolecule, bound to a nonspecific molecule). Each compartment is assumed to be “evenly mixed” (no spatial gradients) and transport between logical compartments is typically assumed to follow first-order kinetics (i.e., the rate of transport from one compartment to another is directly proportional to the concentration in the first compartment). More complex modeling may incorporate more compartments (e.g., nonspecific binding), second-order kinetics (e.g., binding site saturation), or diffusion within spatial compartments. Any attempt to mathematically model imaging agent distribution and binding in a biological system can only be an approximation. With simpler models yielding poorer approximation, it would seem obvious that more accurate, complex models should be preferable. However, there is value in developing both complex and simple models for any given application. Complex models can be used to accurately simulate the dynamics (kinetics) of targeted and control imaging agent (or drug) distribution and binding, in response to agent properties and/or physiological parameters (e.g., effects of agent/drug diffusion coefficient and binding affinity or effects of tissue blood flow and vascular permeability). The insight these models can provide in relating agent- or drug-distribution and binding to chemical and physiological parameters is invaluable to hypothesis testing in drug

Kinetic Modeling

Fig. 2 Illustrations of compartment- and diffusion-based mathematical models of systemically (i.e., to whole organism via an intravenous injection for example) and topically (onto the surface of tissue or cells) delivered paired-agent cocktails. (a) and (c) represent simplified distributions of the targeted imaging agent, while (b) and (d) identify distribution of both targeted and control agents. At the bottom of the illustrations are compartment model and diffusion model representations of the illustrations

284 Kenneth M. Tichauer et al.

Quantitative Drug Target Imaging Using Paired-Agent Principles

285

or imaging agent development/selection, and to the optimization of dosing and/or imaging and data processing protocols. However, it should be noted that attempts to fit complex models to noisy experimental data will generally yield unacceptable levels of parameter estimation error, with the mean squared error (see Note 3) dominated by variance (i.e., the precision of parameter estimation with complex models is poor, owing to high degrees of freedom in complex models, sensitivity to noise, and possible covariance between parameters). There is a trade-off between accuracy (bias) and precision (variance) with respect to complexity in a data fitting model (see Note 3). In general, simpler models yield larger bias errors but smaller variance errors, while more complex models can yield smaller bias errors but larger variance errors. The central challenge in most tracer kinetic-modeling projects is the identification of a simplified model that sufficiently reduces variance in parameter estimation without increasing bias error to unacceptable levels. Numerous simplifications can be made, and the exact nature of simplification needed will vary depending on the application (e.g., for small, rapidly diffusible agents/drugs, the homogeneous mixing of spatial compartments might be an acceptable assumption). The validity of any simplified model for parameter estimation can be tested using simulated data (with added noise) that is generated from a more-accurate complex model (see Subheading 3.7), but should ultimately be evaluated experimentally and assessed in terms of an effectiveness in performing the desired task (e.g., identifying which patients may benefit from a molecular targeted therapy and which will not). 2.3.1 Complex Model Example

To provide a framework for understanding the concepts of kinetic modeling and their importance in paired-agent molecular imaging protocols, two example models—one complex and one simple—of the same scenario are demonstrated. The scenario is the systemic administration (e.g., intravenous imaging-agent injection in a live organism) of a targeted and control imaging-agent pair, with the targeted agent designed to bind specifically to a biological molecule that is expressed on the outer surface of cell membranes (e.g., cellsurface signaling pathway receptor). In this scenario, the targeted imaging agent or drug in a region-of-interest (ROI) can exist in an unbound form in the blood plasma (Cp, T), unbound in the interstitial space (Cf, T—“f” typically for “freely associated”), or bound to the targeted biomolecule (Cb, T). The control agent can also be unbound in the blood plasma (Cp, C) or unbound in the interstitial space (Cf, C). In the example equations that follow, the capillary and the interstitial space are assumed to be described geometrically as two coaxial cylinders (with r representing the radial distance from the center of the capillary), where diffusion of the targeted and control imaging agent is defined by the effective diffusion

286

Kenneth M. Tichauer et al.

coefficients, Deff, T and Deff, C, respectively. The rate constants kon, T and koff, T represent the in vitro (100% free-volume) on- and off-rates of binding (likelihood of specific target binding and dissociation, respectively). Note, koff, T is generally equivalent in vivo and in vitro. The on-rate of binding in vitro, kon, T, should be scaled by the diffusible free space volume fraction (fraction of space in the ROI that the agent is free to diffuse in), vf, in order to convert it to the in vivo (or in situ tissue) on-rate of binding. The salient parameter is the concentration of available targeted biomolecule, represented by Bavail. Vascular permeability of targeted and control imaging agents is defined by PT and PC, respectively. In the tissue (for r > Rcapillary, the radius of the capillary, and r < Rcylinder, the user-defined distal boundary):

 ∂C f ,T ∂C f ,T 1 ∂ ¼ D eff ,T r r ∂r ∂t ∂r    vf kon,T B avail  C b,T C f ,T þ koff ,T C b,T , ð1Þ   dC b,T ¼ vf kon,T B avail  C b,T C f ,T  koff ,T C b,T , dt

 ∂C f ,C ∂C f ,C 1 ∂ r ¼ D eff ,T : r ∂r ∂t ∂r

ð2Þ ð3Þ

At r ¼ Rcapillary (boundary condition 1):   dC f ,T ¼ P T C p,T  vf C f ,T dr   dC f ,C ¼ P C C p,C  v f C f ,C , D eff ,C dr D eff ,T

ð4Þ ð5Þ

and at r ¼ Rcylinder (boundary condition 2): D eff ,T

dC f ,T ¼0 dr

ð6Þ

D eff ,C

dC f ,C ¼0 dr

ð7Þ

The above equations are effective for imaging agents that exhibit “slow” diffusion in tissue (i.e., spatial equilibrium in tissue takes longer than the time between imaging), resulting in image snapshots that average concentration gradients in the tissue within each voxel. However, this can be unnecessarily complicated for drugs with “faster” tissue penetration, where the concentration is relatively uniform and can be accurately represented by a simplified model. 2.3.2 Simplified Model Example

A simplified version of the system of equations presented in Eqs. 1– 7 could involve assuming relatively rapid diffusion or “spatial mixing” of the agents in each spatial compartment and a trace level of

Quantitative Drug Target Imaging Using Paired-Agent Principles

287

targeted agent compared to the concentration of targeted receptors. Under these conditions, Cf, T and Cf, C can be treated as homogeneously mixed compartments with first-order rate-constants K1 and k2 representing transport from blood-to-tissue and tissue-to-blood, respectively (see Note 4). Additionally, the binding rate-constant can be simplified as k3, T ¼ vfkon, TBavail (since Cb, T 50-to-1 concentration ratio of albumin and control imaging-agent. This provides two advantages for nonspecific protein binding agents (which are most in our experience): (a) The overwhelming abundance of albumin outcompetes the targeted agent in terms of control agent binding, thus minimizing targeted-to-control agent binding. (b) Since many imaging-agents will weakly bind to proteins, direct injection of “raw” imaging-agents can result in an early phase of equilibrium establishment, when the targeted and control agents, respectively, reach equilibrium binding conditions with proteins in the blood plasma (or interstitium, CSF, lymph, etc. depending on the route of administration). If the rates of equilibrium between the targeted and control imaging agents differ substantially and are not accounted for, these effects can alter the accuracy and precision of data analyses. 2. Allow this initial mixture to sit for at least 15 min. The time required may differ depending on the control agent used; however, most nonspecific binding reaches equilibrium relatively quickly and we have not observed any agents that require longer than 15 min to reach an equilibrium (currently unpublished)—Note 6. 3. Then mix in the solution of targeted agent. Ideally, at least 15 min should pass prior to administering this cocktail of agents (allowing the targeted agent also to reach an equilibrium with the albumin). 3.2 Imaging-Agent Dosing

For all paired-agent studies, it is not necessary for the concentration of the targeted and control agents to be equivalent. Rather, it is important that the following be ensured: 1. For the given imaging system and application, the signal from each agent must be of sufficient quality for analysis. Ultimately, the threshold on what is suitable should be established in simulations (see Subheading 3.7); however, experience has led to some rules of thumb (see Note 7):

290

Kenneth M. Tichauer et al.

2. For most paired-agent imaging scenarios, the concentration of the targeted agent in any ROI should be at “trace” levels; that is, 2 for both targeted and control agents at any post–agent-administration timepoints of interest for analysis. (b) If background is or cannot be measured, signal-to-background should be >10 for all post–agent-administration timepoints of interest. 8. Models that can account for binding site saturation [68] make it possible to ignore the “trace” level requirement, but care should be taken as the referenced method actually requires 100% saturation and is inaccurate without achieving this within the imaging time frame. More generalized methods have been developed recently [55]. 9. Not all motion correction needs to be carried out post–data collection. A number of respiratory or cardiac gating imaging protocols can also be used to mitigate motion artifacts. 10. This may be true for most nuclear medicine studies, as long as the subject or sample has not been administered a radioactive substance prior to paired-agent imaging-agent administration. 11. The suggested background subtraction protocol assumes that the background signal does not change over time. If the background signal is dependent on a dynamically changing parameter, then the only way to do background subtraction is to independently measure the background somehow at each paired-agent imaging timepoint. If the background has different spectral or fluorescence lifetime properties [69, 70], this may be conceivable. In fluorescence imaging, one should be aware of photobleaching of both the background and the imaging agents. Tests should be done to ensure that this is not an issue, or new solutions would need be developed and tested in order to account for these types of changes (similar to accounting for radioactive decay).

300

Kenneth M. Tichauer et al.

12. If the targeted and control agents have similar properties in terms of nonspecific binding and nonspecific binding is nonnegligible, and there is a possibility for saturation effects on the nonspecific binding sites, one may need to be careful about using targeted and control imaging agent concentration in the administration solution that differ by large amounts. However, nonspecific binding is usually quite ubiquitous and low affinity for good imaging agents, so the likelihood of running into nonspecific binding site saturation effects (particularly for the targeted agent) is small. 13. This simple normalization approach is generally available to nuclear medicine strategies. In optical imaging, tissue optical properties can vary greatly within an imaging field and are likely to affect the propagation of different wavelengths of light, differently. The salient exception to this in optics are applications using surface enhanced Raman scattering (SERS) nanoparticle-based imaging, which hold the advantage of allowing many different imaging agents to excited with a single light source, and all emitted signal propagating within the same wavelength window [30]. 14. This approach will only work if there is no dynamic and disparate binding of imaging agents in the biological fluids and the efficacy of its use can be bolstered by preloading imaging agent concentrations with albumin prior to administration (see Subheading 3.2). 15. Equation 12 is only appropriate if the staining and rinsing solutions used are orders-of-magnitude larger in volume than the tissue or cells being imaging, otherwise the kinetics in the tissue or cells will influence the “observed” concentration of imaging agent in the staining and rinsing solutions. 16. These factors may differ depending on the individual subject. For example, if a particular imaging-agent is filtered from the blood predominantly by the kidneys, a subject’s kidney health may significantly influence the agent’s input function. References 1. DiMasi JA, Reichert JM, Feldman L, Malins A (2013) Clinical approval success rates for investigational cancer drugs. Clin Pharmacol Ther 94(3):329–335. https://doi.org/10.1038/ clpt.2013.117 2. Longley DB, Johnston PG (2005) Molecular mechanisms of drug resistance. J Pathol 205 (2):275–292. https://doi.org/10.1002/path. 1706 3. Fang J, Nakamura H, Maeda H (2011) The EPR effect: unique features of tumor blood

vessels for drug delivery, factors involved, and limitations and augmentation of the effect. Adv Drug Deliv Rev 63(3):136–151. https://doi. org/10.1016/j.addr.2010.04.009 4. Rudin M, Weissleder R (2003) Molecular imaging in drug discovery and development. Nat Rev Drug Discov 2(2):123–131. https:// doi.org/10.1038/nrd1007 5. Gross S, Piwnica-Worms D (2006) Molecular imaging strategies for drug discovery and development. Curr Opin Chem Biol 10

Quantitative Drug Target Imaging Using Paired-Agent Principles (4):334–342. https://doi.org/10.1016/j. cbpa.2006.06.028 6. Hargreaves RJ (2008) The role of molecular imaging in drug discovery and development. Clin Pharmacol Ther 83(2):349–353. https://doi.org/10.1038/sj.clpt.6100467 7. Vinegoni C, Fumene Feruglio P, Brand C, Lee S, Nibbs AE, Stapleton S, Shah S, Gryczynski I, Reiner T, Mazitschek R, Weissleder R (2017) Measurement of drug-target engagement in live cells by two-photon fluorescence anisotropy imaging. Nat Protoc 12 (7):1472–1497. https://doi.org/10.1038/ nprot.2017.043 8. Dubach JM, Vinegoni C, Mazitschek R, Fumene Feruglio P, Cameron LA, Weissleder R (2014) In vivo imaging of specific drugtarget binding at subcellular resolution. Nat Commun 5:3946. https://doi.org/10.1038/ ncomms4946 9. Orcutt KD, Adams GP, Wu AM, Silva MD, Harwell C, Hoppin J, Matsumura M, Kotsuma M, Greenberg J, Scott AM, Beckman RA (2017) Molecular simulation of receptor occupancy and tumor penetration of an antibody and smaller scaffolds: application to molecular imaging. Mol Imaging Biol 19 (5):656–664. https://doi.org/10.1007/ s11307-016-1041-y 10. Herschman HR (2003) Molecular imaging: looking at problems, seeing solutions. Science 302(5645):605–608. https://doi.org/10. 1126/science.1090585 11. Matthews PM, Rabiner EA, Passchier J, Gunn RN (2012) Positron emission tomography molecular imaging for drug development. Br J Clin Pharmacol 73(2):175–186. https://doi. org/10.1111/j.1365-2125.2011.04085.x 12. Massoud TF, Gambhir SS (2003) Molecular imaging in living subjects: seeing fundamental biological processes in a new light. Genes Dev 17(5):545–580. https://doi.org/10.1101/ gad.1047403. 13. Stoeckli M, Chaurand P, Hallahan DE, Caprioli RM (2001) Imaging mass spectrometry: a new technology for the analysis of protein expression in mammalian tissues. Nat Med 7 (4):493–496. https://doi.org/10.1038/ 86573 14. Tichauer KM, Wang Y, Pogue BW, Liu JT (2015) Quantitative in vivo cell-surface receptor imaging in oncology: kinetic modeling and paired-agent principles from nuclear medicine and optical imaging. Phys Med Biol 60(14): R239–R269. https://doi.org/10.1088/ 0031-9155/60/14/R239

301

15. Xu X, Wang Y, Xiang J, Liu JTC, Tichauer KM (2017) Rinsing paired-agent model (RPAM) to quantify cell-surface receptor concentrations in topical staining applications of thick tissues. Phys Med Biol 62(12):5098–5113. https:// doi.org/10.1088/1361-6560/aa6cf1 16. Kang S, Wang YW, Xu X, Navarro E, Tichauer KM, Liu JTC (2018) Microscopic investigation of topically applied nanoparticles for molecular imaging of fresh tissue surfaces. J Biophotonics 11(4):e201700246. https://doi.org/10. 1002/jbio.201700246 17. Liu JT, Helms MW, Mandella MJ, Crawford JM, Kino GS, Contag CH (2009) Quantifying cell-surface biomarker expression in thick tissues with ratiometric three-dimensional microscopy. Biophys J 96(6):2405–2414. https://doi.org/10.1016/j.bpj.2008.12. 3908 18. Davis SC, Gibbs SL, Gunn JR, Pogue BW (2013) Topical dual-stain difference imaging for rapid intra-operative tumor identification in fresh specimens. Opt Lett 38 (23):5184–5187. https://doi.org/10.1364/ OL.38.005184 19. Korber J, Barth C, Gibbs S (2018) Nile red derivatives enable improved ratiometric imaging for nerve-specific contrast. J Biomed Opt 23(7):1–13. https://doi.org/10.1117/1. JBO.23.7.076002. 20. Zalutsky MR, Garg PK, Friedman HS, Bigner DD (1989) Labeling monoclonal antibodies and F(ab’)2 fragments with the alpha-particleemitting nuclide astatine-211: preservation of immunoreactivity and in vivo localizing capacity. Proc Natl Acad Sci U S A 86 (18):7149–7153. https://doi.org/10.1073/ pnas.86.18.7149 21. Baeten J, Haller J, Shih H, Ntziachristos V (2009) In vivo investigation of breast cancer progression by use of an internal control. Neoplasia 11(3):220–227. https://doi.org/10. 1593/neo.08648 22. Tichauer KM, Samkoe KS, Sexton KJ, Hextrum SK, Yang HH, Klubben WS, Gunn JR, Hasan T, Pogue BW (2012) In vivo quantification of tumor receptor binding potential with dual-reporter molecular imaging. Mol Imaging Biol 14(5):584–592. https://doi.org/10. 1007/s11307-011-0534-y 23. Zalutsky MR, Moseley RP, Coakham HB, Coleman RE, Bigner DD (1989) Pharmacokinetics and tumor localization of 131I-labeled anti-tenascin monoclonal antibody 81C6 in patients with gliomas and other intracranial malignancies. Cancer Res 49(10):2807–2813

302

Kenneth M. Tichauer et al.

24. Wu AM (2014) Engineered antibodies for molecular imaging of cancer. Methods 65 (1):139–147. https://doi.org/10.1016/j. ymeth.2013.09.015 25. Wu AM, Senter PD (2005) Arming antibodies: prospects and challenges for immunoconjugates. Nat Biotechnol 23(9):1137–1146. https://doi.org/10.1038/nbt1141 26. Lee S, Xie J, Chen X (2010) Peptide-based probes for targeted molecular imaging. Biochemistry 49(7):1364–1376. https://doi. org/10.1021/bi901135x 27. Weissleder R (2006) Molecular imaging in cancer. Science 312(5777):1168–1171. https:// doi.org/10.1126/science.1125949 28. Berman DS, Kiat H, Friedman JD, Wang FP, van Train K, Matzer L, Maddahi J, Germano G (1993) Separate acquisition rest thallium-201/ stress technetium-99m sestamibi dual-isotope myocardial perfusion single-photon emission computed tomography: a clinical validation study. J Am Coll Cardiol 22(5):1455–1464. https://doi.org/10.1016/0735-1097(93) 90557-h 29. Bourdon MA, Coleman RE, Blasberg RG, Groothuis DR, Bigner DD (1984) Monoclonal antibody localization in subcutaneous and intracranial human glioma xenografts: pairedlabel and imaging analysis. Anticancer Res 4 (3):133–140 30. Wang YW, Khan A, Som M, Wang D, Chen Y, Leigh SY, Meza D, McVeigh PZ, Wilson BC, Liu JT (2014) Rapid ratiometric biomarker detection with topically applied SERS nanoparticles. Technology (Singap World Sci) 2 (2):118–132. https://doi.org/10.1142/ S2339547814500125 31. Pogue BW, Samkoe KS, Hextrum S, O’Hara JA, Jermyn M, Srinivasan S, Hasan T (2010) Imaging targeted-agent binding in vivo with two probes. J Biomed Opt 15(3):030513. https://doi.org/10.1117/1.3449109 32. Pian Q, Yao R, Sinsuebphon N, Intes X (2017) Compressive hyperspectral time-resolved widefield fluorescence lifetime imaging. Nat Photonics 11:411–414. https://doi.org/10. 1038/NPHOTON.2017.82 33. Gilad AA, van Laarhoven HW, McMahon MT, Walczak P, Heerschap A, Neeman M, van Zijl PC, Bulte JW (2009) Feasibility of concurrent dual contrast enhancement using CEST contrast agents and superparamagnetic iron oxide particles. Magn Reson Med 61(4):970–974. https://doi.org/10.1002/mrm.21928 34. Andreyev A, Celler A (2011) Dual-isotope PET using positron-gamma emitters. Phys Med Biol 56(14):4539–4556. Epub 2011/ 07/01. https://doi.org/10.1088/00319155/56/14/020

35. Ntziachristos V, Razansky D (2010) Molecular imaging by means of multispectral optoacoustic tomography (MSOT). Chem Rev 110 (5):2783–2794. https://doi.org/10.1021/ cr9002566 36. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, Schu¨ffler PJ, Grolimund D, Buhmann JM, Brandt S, Varga Z, Wild PJ, Gu¨nther D, Bodenmiller B (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11(4):417–422. https://doi.org/10.1038/nmeth.2869 37. Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C, Borowsky AD, Levenson RM, Lowe JB, Liu SD, Zhao S, Natkunam Y, Nolan GP (2014) Multiplexed ion beam imaging of human breast tumors. Nat Med 20 (4):436–442. https://doi.org/10.1038/nm. 3488 38. Aime S, Battista Giovenzana G, Terreno E (2010) Chemistry of molecular imaging: an overview. In: Weissleder R, Ross BD, Rehemtulla A, Gambhir SS (eds) Molecular imaging: principles and practice. People’s Medical Publishing House-USA, Shelton, CT, pp 277–303 39. Choi HS, Nasr K, Alyabyev S, Feith D, Lee JH, Kim SH, Ashitate Y, Hyun H, Patonay G, Strekowski L, Henary M, Frangioni JV (2011) Synthesis and in vivo fate of zwitterionic near-infrared fluorophores. Angew Chem Int Ed Engl 50(28):6258–6263. https://doi.org/10.1002/anie.201102459 40. Thurber GM, Schmidt MM, Wittrup KD (2008) Factors determining antibody distribution in tumors. Trends Pharmacol Sci 29 (2):57–61. https://doi.org/10.1016/j.tips. 2007.11.004 41. Lammertsma AA, Bench CJ, Hume SP, Osman S, Gunn K, Brooks DJ, Frackowiak RS (1996) Comparison of methods for analysis of clinical [11C]raclopride studies. J Cereb Blood Flow Metab 16(1):42–52. https://doi.org/ 10.1097/00004647-199601000-00005 42. Lammertsma AA, Hume SP (1996) Simplified reference tissue model for PET receptor studies. NeuroImage 4(3 Pt 1):153–158. https:// doi.org/10.1006/nimg.1996.0066. 43. Hume SP, Myers R, Bloomfield PM, OpackaJuffry J, Cremer JE, Ahier RG, Luthra SK, Brooks DJ, Lammertsma AA (1992) Quantitation of carbon-11-labeled raclopride in rat striatum using positron emission tomography. Synapse 12(1):47–54. https://doi.org/10. 1002/syn.890120106 44. Hamzei N, Samkoe KS, Elliott JT, Holt RW, Gunn JR, Hasan T, Pogue BW, Tichauer KM (2014) Comparison of kinetic models for dual-

Quantitative Drug Target Imaging Using Paired-Agent Principles tracer receptor concentration imaging in tumors. Austin J Biomed Eng 1(1) 45. Innis RB, Cunningham VJ, Delforge J, Fujita M, Gjedde A, Gunn RN, Holden J, Houle S, Huang SC, Ichise M, Iida H, Ito H, Kimura Y, Koeppe RA, Knudsen GM, Knuuti J, Lammertsma AA, Laruelle M, Logan J, Maguire RP, Mintun MA, Morris ED, Parsey R, Price JC, Slifstein M, Sossi V, Suhara T, Votaw JR, Wong DF, Carson RE (2007) Consensus nomenclature for in vivo imaging of reversibly binding radioligands. J Cereb Blood Flow Metab 27(9):1533–1539. https://doi.org/10.1038/sj.jcbfm.9600493 46. Li C, Xu X, McMahon N, Alhaj Ibrahim O, Sattar HA, Tichauer KM (2019) Paired-agent fluorescence molecular imaging of sentinel lymph nodes using indocyanine green as a control agent for antibody-based targeted agents. Contrast Media Mol Imaging 2019:7561862. https://doi.org/10.1155/2019/7561862 47. Davis SC, Samkoe KS, Tichauer KM, Sexton KJ, Gunn JR, Deharvengt SJ, Hasan T, Pogue BW (2013) Dynamic dual-tracer MRI-guided fluorescence tomography to quantify receptor density in vivo. Proc Natl Acad Sci U S A 110 (22):9025–9030. https://doi.org/10.1073/ pnas.1213490110 48. Kanick SC, Tichauer KM, Gunn J, Samkoe KS, Pogue BW (2014) Pixel-based absorption correction for dual-tracer fluorescence imaging of receptor binding potential. Biomedical optics express 5(10):3280–3291. https://doi.org/ 10.1364/boe.5.003280 49. Kim A, Khurana M, Moriyama Y, Wilson BC (2010) Quantification of in vivo fluorescence decoupled from the effects of tissue optical properties using fiber-optic spectroscopy measurements. J Biomed Opt 15(6):067006. https://doi.org/10.1117/1.3523616 50. Sadeghipour N, Davis SC, Tichauer KM (2018) Correcting for targeted and control agent signal differences in paired-agent molecular imaging of cancer cell-surface receptors. J Biomed Opt 23(6):1–11. https://doi.org/10. 1117/1.JBO.23.6.066004 51. PRESSMAN D, DAY ED, BLAU M (1957) The use of paired labeling in the determination of tumor-localizing antibodies. Cancer Res 17 (9):845–850 52. Tichauer KM, Samkoe KS, Sexton KJ, Gunn JR, Hasan T, Pogue BW (2012) Improved tumor contrast achieved by single time point dual-reporter fluorescence imaging. J Biomed Opt 17(6):066001. https://doi.org/10. 1117/1.JBO.17.6.066001 53. Sinha L, Wang Y, Yang C, Khan A, Brankov JG, Liu JT, Tichauer KM (2015) Quantification of

303

the binding potential of cell-surface receptors in fresh excised specimens via dual-probe modeling of SERS nanoparticles. Sci Rep 5:8582. https://doi.org/10.1038/srep08582 54. Kang S, Xu X, Navarro E, Wang Y, Liu JTC, Tichauer KM (2019) Modeling the binding and diffusion of receptor-targeted nanoparticles topically applied on fresh tissue specimens. Phys Med Biol 64(4):045013. https://doi. org/10.1088/1361-6560/aaff81 55. Sadeghipour N, Davis SC, Tichauer KM (2017) Generalized paired-agent kinetic model for in vivo quantification of cancer cellsurface receptors under receptor saturation conditions. Phys Med Biol 62(2):394–414. https://doi.org/10.1088/1361-6560/62/ 2/394 56. Tichauer KM, Deharvengt SJ, Samkoe KS, Gunn JR, Bosenberg MW, Turk MJ, Hasan T, Stan RV, Pogue BW (2014) Tumor endothelial marker imaging in melanomas using dualtracer fluorescence molecular imaging. Mol Imaging Biol 16(3):372–382. https://doi. org/10.1007/s11307-013-0692-1 57. Tichauer KM, Diop M, Elliott JT, Samkoe KS, Hasan T, St Lawrence K, Pogue BW (2014) Accounting for pharmacokinetic differences in dual-tracer receptor density imaging. Phys Med Biol 59(10):2341–2351. https://doi. org/10.1088/0031-9155/59/10/2341 58. Gunn RN, Gunn SR, Cunningham VJ (2001) Positron emission tomography compartmental models. J Cereb Blood Flow Metab 21 (6):635–652. https://doi.org/10.1097/ 00004647-200106000-00002 59. Cilliers C, Guo H, Liao J, Christodolu N, Thurber GM (2016) Multiscale modeling of antibody-drug conjugates: connecting tissue and cellular distribution to whole animal pharmacokinetics and potential implications for efficacy. AAPS J 18(5):1117–1130. https:// doi.org/10.1208/s12248-016-9940-z 60. Bhatnagar S, Deschenes E, Liao J, Cilliers C, Thurber GM (2014) Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors. J Pharm Sci 103 (10):3276–3286. https://doi.org/10.1002/ jps.24086 61. Tzafriri AR, Lerner EI, Flashner-Barak M, Hinchcliffe M, Ratner E, Parnas H (2005) Mathematical modeling and optimization of drug delivery from intratumorally injected microspheres. Clin Cancer Res 11(2 Pt 1):826–834 62. B€ackman P, Arora S, Couet W, Forbes B, de Kruijf W, Paudel A (2018) Advances in experimental and mechanistic computational models to understand pulmonary exposure to inhaled

304

Kenneth M. Tichauer et al.

drugs. Eur J Pharm Sci 113:41–52. https:// doi.org/10.1016/j.ejps.2017.10.030 63. Holm R, Hoest J (2004) Successful in silico predicting of intestinal lymphatic transfer. Int J Pharm 272(1–2):189–193. https://doi.org/ 10.1016/j.ijpharm.2003.12.017 64. Winner KK, Steinkamp MP, Lee RJ, Swat M, Muller CY, Moses ME, Jiang Y, Wilson BS (2016) Spatial modeling of drug delivery routes for treatment of disseminated ovarian cancer. Cancer Res 76(6):1320–1334. https://doi.org/10.1158/0008-5472.CAN15-1620 65. Kuttler A, Dimke T, Kern S, Helmlinger G, Stanski D, Finelli LA (2010) Understanding pharmacokinetics using realistic computational models of fluid dynamics: biosimulation of drug distribution within the CSF space for intrathecal drugs. J Pharmacokinet Pharmacodyn 37(6):629–644. https://doi.org/10. 1007/s10928-010-9184-y 66. Slobbe P (2016) Radiolabeled tyrosine kinase inhibitors for drug development and cancer treatment: TKI-PET 67. Huang SC, Bahn MM, Barrio JR, Hoffman JM, Satyamurthy N, Hawkins RA, Mazziotta

JC, Phelps ME (1989) A double-injection technique for in vivo measurement of dopamine D2-receptor density in monkeys with 3-(20 -[18F]fluoroethyl)spiperone and dynamic positron emission tomography. J Cereb Blood Flow Metab 9(6):850–858. https://doi.org/ 10.1038/jcbfm.1989.119 68. Delforge J, Pappata S, Millet P, Samson Y, Bendriem B, Jobert A, Crouzel C, Syrota A (1995) Quantification of benzodiazepine receptors in human brain using PET, [11C] flumazenil, and a single-experiment protocol. J Cereb Blood Flow Metab 15(2):284–300. https://doi.org/10.1038/jcbfm.1995.34 69. Herman P, Maliwal BP, Lakowicz JR (2002) Real-time background suppression during frequency domain lifetime measurements. Anal Biochem 309(1):19–26. https://doi.org/10. 1016/s0003-2697(02)00213-0 70. Davis SC, Pogue BW, Springett R, Leussler C, Mazurkewitz P, Tuttle SB, Gibbs-Strauss SL, Jiang SS, Dehghani H, Paulsen KD (2008) Magnetic resonance-coupled fluorescence tomography scanner for molecular imaging of tissue. Rev Sci Instrum 79(6):064302. https:// doi.org/10.1063/1.2919131

Chapter 14 Quantitative Determination of Intracellular Bond Cleavage Joshua A. Walker, Michelle R. Sorkin, and Christopher A. Alabi Abstract Cleavable crosslinkers that respond to specific intracellular cues (pH, reducing environments, enzymes, etc.) are a critical component of drug delivery systems, which drives the subcellular localization and processing of therapeutic cargo. With numerous stimuli-responsive drug delivery systems in development, a quantitative measurement of their intracellular processing is of paramount importance. In this chapter, we discuss methods for determining the intracellular rate of bond cleavage and highlight a recent framework developed in our group for quantifying the rate of intracellular bond degradation in the endocytic pathway. This quantitative method involves the use of fluorescent FRET probes built on an oligothioetheramide (oligoTEA) trifunctional linker scaffold, that when attached to antibodies can report compartment specific cleavage events. This method involves the synthesis and site-specific bioconjugation of a reduction-sensitive FRET-based crosslinker to a variety of targeting ligands. We demonstrate this concept with trastuzumab, a humanized monoclonal antibody against the HER2 receptor. Furthermore, this chapter details a kinetic model based on mass-action kinetics to describe the intracellular processing of this conjugate. The kinetic model, developed in conjunction with live-cell experiments, can be used to extract the rate constant for intracellular bond degradation. We present an example of a trastuzumab FRET-probe conjugate bearing a reduction-sensitive disulfide bond. The framework outlined in this chapter is applicable to the quantification of drug delivery systems that employ alternative endocytosis pathways, bond types, and cell types. Key words Bond cleavage, Intracellular, Drug delivery, Drug release, HER2, Kinetic model, OligoTEA, FRET, Stimuli-responsive

1

Introduction: Components of Stimuli-Responsive Delivery Systems Stimuli-responsive delivery systems that act at the cellular level are comprised of multiple components: the drug carrier, the internalization pathway, and the stimuli-responsive chemistry. Both nanoparticle- and conjugate-based carriers are used to improve the pharmacokinetic properties of therapeutic cargo [1, 2]. At the target cell, these carriers facilitate internalization of their cargo via receptor-mediated [3, 4] or nonspecific (phagocytosis [5] or pinocytosis [6]) processes. Upon internalization, the carrier and cargo are encapsulated in an endosome, which may be targeted for recycling or lysosomal degradation [7]. Inside the endocytic

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021

305

306

Joshua A. Walker et al.

compartment, hydrogen ions, reducing agents, enzymes, or proteases act on the stimuli-responsive chemistry within the drug carrier to release the therapeutic cargo [8, 9]. Each component (carrier, pathway, and chemistry) is a fundamental piece of stimuliresponsive drug delivery systems. Together they dictate the function and efficacy of a given therapeutic strategy. A quantitative analysis of intracellular bond cleavage requires careful consideration of these individual functional components as well as existing methods for evaluating their intracellular processing. 1.1 Nanoparticleand Conjugate-Based Carriers

One key role of the carrier in stimuli-responsive drug delivery systems is to ensure that the cargo arrives at the tissue of interest. To do so, carriers rely on either passive accumulation or antigenspecific targeting [10, 11]. Beyond this function, carriers may be designed to have a variety of additional properties depending on the nature of the therapeutic cargo and the disease to be treated. With respect to small molecule therapeutics, carriers are used to improve water solubility [12, 13] as well as decrease nonspecific toxicity and enable controlled release [14, 15]. Meanwhile macromolecular therapeutics such as proteins and nucleic acids, rely on the carrier to provide protection from proteases and nucleases that may degrade the cargo [16, 17]. Generally, nanoparticle-based carriers are nontargeted and passively accumulate in the liver [18, 19] or tumors with leaky vasculature [20], though tissue-specific targeting has been explored through targeting ligands [21, 22] and high-throughput DNA barcoding studies [23, 24]. Nanoparticles can be either selfassembled or have a solid core structure. Often in self-assembled nanoparticles, a highly hydrophobic or charged cargo is used to drive the assembly of self-associating polymers or lipids [25, 26]. Solid core nanoparticles are typically metal- or siliconbased [27, 28]. Both self-assembled and solid core nanoparticles can be functionalized via decoration of the particle surface with drug cargo, hydrophilic polymer shields to prevent opsonization or targeting ligands [29]. Conjugate-based carriers can be distinguished from nanoparticle-based carriers in two ways. First, conjugates do not undergo self-assembly into higher order structures. Additionally, conjugates achieve tissue-specific targeting via the use of antigenspecific targeting ligands. Many conjugate-based systems have been developed for stimuli-responsive drug delivery. Polymeric conjugates place many copies of cargo molecules and/or targeting ligands along the backbone of a hydrophilic, biodegradable polymer. This concept has been developed for the delivery of small molecule and macromolecular therapeutics [30, 31]. Another approach, antibody–drug conjugates (ADCs), utilizes monoclonal antibodies to achieve tissue-specific targeting. With eight FDA-approved compounds ADCs are an established modality for

Quantitative Determination of Intracellular Bond Cleavage

307

the delivery of small molecule chemotherapeutics [32]. Chemotherapeutic cargos are conjugated to the antibody carrier via surface exposed lysine residues, interchain disulfide bonds, or engineered conjugation sites [33]. Finally, to a lesser degree, small molecule drugs have also been conjugated to aptamers and peptide-based targeting ligands [34–36]. 1.2 Receptor-Mediated and Nonspecific Internalization Pathways

Upon arriving at the target cell, the drug carrier interacts with the cell surface to facilitate internalization. The cell surface is made up of distinct membrane compartments which internalize molecules and surface proteins from the plasma membrane [37]. Endocytosis occurs via both receptor-mediated and nonspecific processes. Receptor internalization occurs through clathrin-mediated or caveolae-mediated endocytosis while nonspecific internalization occurs via macropinocytosis [6, 38–40]. Internalized molecules are then delivered to and sorted within early endosomes. Endosomes and their cargo are then recycled to the cell surface or targeted for lysosomal degradation [41, 42]. The rate of receptor internalization and the extent to which internalized cargo is recycled or degraded are controlled by a complex set of parameters such as receptor identity, receptor density, ligand binding site, and cell state. Here we focus on the endocytic pathway for human epidermal growth factor receptor 2 (HER2), which was used to validate our methodology for quantifying the rate constant for intracellular bond degradation. HER2 is a membrane tyrosine kinase which has been found to be overexpressed on 20–30% of breast cancer cells and is associated with poor prognosis [43–45]. HER2 overexpression in breast cancer has motivated researchers to develop a variety of HER2-targeted therapies. Trastuzumab, a humanized monoclonal antibody marketed as Herceptin, was the first HER2-targeted therapy approved by the FDA [46]. As a therapeutic, trastuzumab is able to illicit an anti-tumor response in multiple ways. Treatment with trastuzumab has been shown to decrease downstream HER2 signaling inhibiting HER2 homodimerization [47, 48]. Further, trastuzumab can induce apoptosis via antibody-dependent cellular cytotoxicity (ADCC) and the recruitment of natural killer cells [49]. Finally, trastuzumab has been shown to inhibit angiogenesis on its own or in combination with paclitaxel [50, 51]. Despite the multifaceted nature of trastuzumab, resistance to treatment is observed, which has led to the development of other therapeutic strategies. Trastuzumab emtansine is a HER2-targeted antibody–drug conjugate (ADC), which is marketed as Kadcyla. This ADC employs DM1, a tubulin-binding antimitotic drug derived from maytansine, as its therapeutic payload. The payload is conjugated to surface-exposed lysine residues via a noncleavable SMCC crosslinker, which requires lysosomal degradation to release the active payload [52]. Pertuzumab is a humanized monoclonal

308

Joshua A. Walker et al.

anti-HER2 antibody, which functions by blocking HER2-HER3 dimerization and inhibiting downstream signaling [53, 54]. Pertuzumab, which is marketed as Perjeta, has achieved FDA approval as a combination therapy with trastuzumab and docetaxel [55]. While HER2-targeted therapies have found the clinical success in antibody- and conjugate-based formats, nanoparticle-based strategies have been proposed and are under development [56–58]. The success of HER2-targeted therapies has motivated researchers to quantitatively study anti-HER2 antibody binding as well as HER2 receptor expression, internalization, and intracellular trafficking. Trastuzumab, the original anti-HER2 antibody, binds to its target receptor with a dissociation constant (KD) on the order of 0.1 nM depending on the measurement technique [59– 61]. Meanwhile, pertuzumab binds with affinity on the order of 1 nM [62]. Further, HER2-targeted affibody-, DARPin-, and peptide-based reagents have been developed with receptor affinities ranging from 0.1 to 100 nM [63–65]. Many HER2 positive cell lines have been used to study the HER2 receptor. As a point of reference, the cell lines AU565, BT474, MKN7, N87, SKBR3, and SKOV3 have been shown to express 0.5–3.5
106 HER2 receptors per cell [59, 66, 67]. The internalization rate of the HER2 receptor depends on the cell type, the nature of the ligand, and the measurement method with reported values ranging from 0.05 to 2.2 h1 [59, 68–71]. Following internalization, the HER2–ligand complex is sorted for either recycling to the cell surface or lysosomal degradation. Complexes selected for recycling are processed rapidly with a half-life of approximately 5 min while lysosomal processing is much slower with a half-life of approximately 16 h [59, 68]. 1.3 Types of Cleavable Bonds

The final step in stimuli-responsive delivery systems is intracellular release of the therapeutic cargo. For this purpose, chemistries have been designed to respond to three key intracellular stimuli: pH gradients, reducing environments, and enzymatic activity. These different stimuli enable control of drug carrier stability both intracellularly and in circulation. Hydrolytically sensitive chemistries are designed to display accelerated dissociation at acidic pH found within endosomes (pH 5.5–6.2) and lysosomes (pH 4.5–5.0). Nanoparticle carriers typically employ acetal, ester, and hydrazone linkages [72, 73]. However, ADC development has focused on carbonate and hydrazone bonds to achieve pH-responsive drug release [8]. These efforts have led to the FDA-approved ADCs sacituzumab govitecan, inotuzumab ozogamicin, and gemtuzumab ozogamicin [74–76]. The hydrolysis rate of acetal [77], ester [78], and hydrazone [79] bonds has been shown to be sensitive to their local chemical structure. While the hydrolysis rate of these bonds is accelerated at acidic pH, they are typically considered unstable at

Quantitative Determination of Intracellular Bond Cleavage

309

neutral pH as well. This feature complicates the quantitative analysis of hydrolysis rate within the cellular environment. Accordingly, few studies have sought to analyze the intracellular degradation rate of these bonds [80, 81]. While these studies detected bond degradation, neither study was able to provide estimation of bond degradation rate or half-life. The reduction sensitive disulfide bond is stable at physiological pH and in mildly oxidizing conditions such as those present in the bloodstream. Intracellular concentrations of the reducing agent glutathione are 2–3 orders of magnitude higher than in extracellular fluids [82]. Hence, disulfide bonds remain stable within circulation, but are rapidly reduced intracellularly to yield two sulfhydryl groups. The stability of disulfide bonds can be tuned further by the incorporation of sterically bulky groups near the reductionsensitive bond [83, 84]. Disulfide-linked nanoparticles have recently been synthesized for the delivery of the anticancer drug paclitaxel [85]. In another example, a nanoparticle conjugate was developed in which the antimitotic agent monomethyl auristatin E (MMAE) was conjugated through disulfide bonds [86]. Finally, two FDA-approved ADCs, inotuzumab ozogamicin and gemtuzumab ozogamicin, utilize a disulfide linker to release their active payload [75, 76]. The greater stability of disulfide bonds relative to acid-sensitive chemistries has enabled more robust evaluation of their intracellular degradation, albeit with mostly inconclusive and controversial results. Feener et al. used radiolabeled tyramine linked to poly(D-lysine) to evaluate intracellular disulfide bond reduction [87]. They observed that disulfide bond reduction occurred for at least 6 h inside of CHO cells. However, detection of radiolabel release requires cell lysis which limits the throughput of this assay and precludes determination of subcellular localization. Meanwhile, Austin et al. utilized intramolecular self-quenching of sulforhodamine b to study intracellular disulfide bond reduction on a trastuzumab carrier protein [88]. Using SKBR3 cells, this system was shown to detect disulfide bond reduction in lysosomally targeted vesicles over the course of 2.5 h. However, the use of intramolecular self-quenching does not enable quantification of the extent of conjugate processing, which would be required to quantitatively evaluate bond cleavage rate. Finally, Yang et al. used a FRET-based folate conjugate to evaluate endosomal disulfide bond reduction [89]. In their system, bond degradation occurred over the course of 12 h with a half-life of approximately 6 h. This methodology established a time scale for disulfide bond reduction but was unable to quantify the rate constant of disulfide bond degradation. Enzymatically and proteolytically cleavable bonds leverage the specificity of proteins to achieve high stability in circulation. ADCs have been developed containing a β-glucuronic acid-based linker, which respond to the lysosomal enzyme β-glucuronidase

310

Joshua A. Walker et al.

[90]. Further, the FDA-approved ADC trastuzumab deruxtecan employs a proteolytically sensitive tetrapeptide linker [91]. However, these chemistries have yet to be widely adopted and thus little is known regarding the kinetics of their intracellular processing. The most common protease-triggered linker is the self-immolative valine–citrulline–p-aminobenzyloxycarbonyl (PABC) linker, though others have been explored [92]. The valine–citrulline– PABC linker has traditionally been considered to a be cathepsin B sensitive [93]. However, in recent years it has become apparent that an array of cathepsin proteases act on the valine–citrulline–PABC linker. [94] Due to its greatly enhanced stability in circulation, the valine–citrulline–PABC linker is employed in three FDA-approved ADCs, brentuximab vedotin, polatuzumab vedotin, and enfortumab vedotin, as well as many ADCs currently under development [32, 95–97].The clinical success of the valine–citrulline–PABC linker has motivated researchers to study its intracellular degradation. Lee et al. utilized a FRET-based trastuzumab conjugate to detect intracellular cathepsin-mediated linker cleavage [98]. In this study, it was shown that the FRET-based bioconjugates were processed with a half-life of approximately 15 h. However, much like the work on disulfide bond degradation by Yang et al., this study was not able to quantify the rate constant for intracellular valine– citrulline–PABC linker degradation. Further, the constructs reported by Lee et al. did not contain the self-immolative PABC spacer that is commonly used in cathepsin-sensitive ADCs. The PABC spacer has been shown to influence the cell-free cleavage kinetics of this class of linker, which calls into question the accuracy of the results obtained [93]. 1.4 Phenomenological Models of Intracellular Processing

The major limitation of existing methods for evaluating intracellular bond degradation is that they do not allow for quantification of the fundamental rate constant for bond degradation. Existing methods that report fluorescence or radiolabel signal as a function of time are best understood as qualitative readouts of intracellular payload levels; not bond degradation rate. An illustrative example is the application of a FRET-based reagent to evaluate two different endocytic pathways within the same cell line. One pathway may yield a higher fluorescence signal and higher rate of fluorescence increase. Often, results of this nature are misconstrued as representing faster cleavage kinetics. However, the fluorescence signal in these systems is influenced by other factors such as ligand–receptor affinity, receptor density, receptor internalization rate, and intracellular receptor processing. The complexity of intracellular payload delivery obscures the effect of bond degradation rate on intracellular payload levels. Achieving a quantitative understanding of intracellular bond degradation requires a method capable of cutting through the complexity of intracellular drug processing. Phenomenological kinetic models are capable of

Quantitative Determination of Intracellular Bond Cleavage

311

decoupling the influence of individual processes on bulk readout such as fluorescence or radiolabel signal. Recently, Maass et al. developed a cellular-level model of ADC degradation, which describes payload release mediated by nonspecific proteolytic degradation within the lysosomal compartment [59]. This work is a useful theoretical framework from which to develop a method to quantitatively study the intracellular processing of stimuli-responsive drug carriers. Herein, we describe our recent efforts that combine chemical-crosslinker synthesis, antibody bioconjugate design, FRET-based readouts of bond degradation, and massaction kinetic modeling to quantify the fundamental rate constant of disulfide bond reduction within the HER2 pathway in SKBR3 cells. This framework is applicable to the quantification of drug delivery systems that employ alternative endocytosis pathways, bond types, and cell types.

2

Materials

2.1 Design of Antibody Conjugate to Probe Intracellular Bond Cleavage 2.1.1 Production and Purification of Recombinant Trastuzumab

1. FreeStyle™ 293-F cells (ThermoFisher). 2. Plasmid pVitro-Trastuzumab-IgG1/k (Addgene # 61883). 3. FreeStyle™ MAX transfection reagent (ThermoFisher). 4. FreeStyle™ 293 Expression Medium (ThermoFisher). 5. Hygromycin B (ThermoFisher). 6. Penicillin–streptomycin—10,000 U/mL (ThermoFisher). 7. Dimethyl sulfoxide. 8. Protein A/G Resin (ThermoFisher). 9. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4. 10. Elution buffer: 100 mM glycine, pH 2.5. 11. Neutralization buffer: 1 M Tris, pH 8.9. 12. Amicon Ultra-0.5 mL centrifugal filters 30 kDa MWCO (MilliporeSigma).

2.1.2 Site-Specific Modification of Trastuzumab with Dibenzocyclooctyne Functional Handles

1. DBCO-PEG4-amine (Broadpharm). 2. Transglutaminase (Meat Glue)—RM Formula by Moo Gloo (Amazon, Modernist Pantry). 3. PNGase F, Recombinant—500,000 U/mL (NEB). 4. NAb Protein A/G Spin Kit—0.2 mL (ThermoFisher). 5. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4 6. Moo Gloo Reaction Buffer: 1 M phosphate, 150 mM NaCl, pH 7.4. 7. Elution buffer: 100 mM glycine, pH 2.5. 8. Neutralization buffer: 1 M Tris, pH 8.9.

312

Joshua A. Walker et al.

2.1.3 Copper-Free “Click” Chemistry Attachment of Fluorescence Probe to Trastuzumab Carrier Protein

1. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4.

2.2 Confocal Microscopy-Based Visualization of Bond Cleavage

1. SKBR3 Cells (ATCC).

2.2.1 Confocal Analysis of Kinetic Bond Cleavage

2.2.2 Confocal Analysis of FRET Probe Colocalization

2. Dimethyl sulfoxide. 3. Amicon Ultra-0.5 mL centrifugal filters 30 kDa MWCO (MilliporeSigma).

2. McCoy’s 5a (Modified) Medium (ThermoFisher). 3. Fetal bovine serum, heat inactivated. 4. Penicillin–streptomycin—10,000 U/mL (ThermoFisher). 5. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4. 6. 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass (Cellvis). 1. SKBR3 Cells (ATCC). 2. McCoy’s 5a (Modified) Medium (ThermoFisher). 3. Fetal bovine serum, heat inactivated. 4. Penicillin–streptomycin—10,000 U/mL (ThermoFisher). 5. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4. 6. 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass (Cellvis). 7. Transferrin Alexa Fluor™ 647 conjugate (ThermoFisher). 8. LysoTracker Deep Red (ThermoFisher).

2.3 Flow Cytometry-Based Quantification of Bond Cleavage

1. SKBR3 Cells (ATCC). 2. McCoy’s 5a (Modified) Medium (ThermoFisher). 3. Fetal bovine serum, heat inactivated. 4. Penicillin–streptomycin—10,000 U/mL (ThermoFisher). 5. 1
PBS: 100 mM phosphate, 150 mM NaCl, pH 7.4. 6. 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass (Cellvis). 7. Trypsin–EDTA—0.25% (ThermoFisher).

3

Methods

3.1 Design of Fluorescent Probe to Detect Bond Cleavage

An intracellular probe for bond degradation must remain stable in the harsh environment of the endosome to avoid high levels of nonspecific probe degradation which would lead to a low signal-tonoise ratio. To achieve biostability, we used oligothioetheramides (oligoTEAs), a class of sequence-defined polymers developed by our lab 6 years ago (Fig. 1) [99]. OligoTEA synthesis has been used

Quantitative Determination of Intracellular Bond Cleavage

313

Fig. 1 Overview of oligothioetheramide (oligoTEA) synthesis

as a platform to discover synthetic antibacterial agents [100] and cell penetrating oligomers [101] as well as to synthesize cleavable, multifunctional chemical crosslinkers for bioconjugation [102] and model systems to study the solution phase structure of polymers [103]. OligoTEA synthesis is started by a photoinitiated thiol–ene “click” reaction of a dithiol monomer onto an allylamine-modified fluorous tag liquid support. Fluorous solid phase extraction (FSPE) is used to remove excess reagents and isolate the growing oligomer chain which is now terminated with a free thiol group. The oligomer is elongated by a phosphine-catalyzed thiol-Michael addition using an orthogonally reactive N-allylacrylamide monomer. FSPE is again used to remove excess reagents and isolate the growing oligomer chain which is now terminated with an allyl group. In this manner, iterative thiol–ene “click” reactions and thiol-Michael additions can be used to synthesize sequence-defined oligomers contained up to 16 monomer units [99]. Alternatively, oligoTEA synthesis may be terminated by capping the oligomer chain with a monofunctionalized thiol monomer or by performing thiol–disulfide exchange between 2,20 -dipyridyl disulfide and a terminal thiol group. The fluorous tag purification handle is removed postsynthesis by acid-catalyzed BOC deprotection to yield a sequencedefined oligomer with a terminal primary amine group. OligoTEA synthesis was adapted for the design of a FRETbased probe for intracellular bond degradation (Fig. 2). In the FRET-labeled crosslinker, the fluorescence emission of BODIPY (FRET donor, Ex/Em: 505/516 nm) is quenched by rhodamine (FRET acceptor, Ex/Em: 566/586 nm). When the disulfide bond within the crosslinker is cleaved, BODIPY regains its fluorescence emission. The resulting FRET crosslinker will henceforth be referred to as the cleavable crosslinker. A control crosslinker in which the disulfide bond is replaced with an amide bond is used

314

Joshua A. Walker et al.

Fig. 2 Structure and design of the cleavable (a) and control (b) crosslinkers

as a noncleavable control. Both the cleavable and noncleavable crosslinkers contain a pendant azide functional group which can be used for bioorthogonal “click” labeling of any carrier of interest. To realize this design, two oligoTEAs were synthesized [102]. Both oligoTEAs contained a terminal primary amine and a pendant azide functional group. One oligoTEA was capped with a thiol-reactive 2,20 -dipyridyl disulfide group while the other was capped with a carboxylic acid. To create cleavable crosslinker, the 2,20 -dipyridyl disulfide-capped oligoTEA was reacted with a thiolmodified derivative of sulforhodamine and then BODIPY-NHS. To create the noncleavable crosslinker, the carboxylic acid–capped oligoTEA was reacted with BODIPY-NHS and then an aminemodified derivative of sulforhodamine. Reverse-phase high-performance liquid chromatography (RP-HPLC) was used to purify the fluorophore-modified oligomers after each synthetic step. The fluorophore-modified crosslinkers were characterized via liquid chromatography–mass spectrometry (LC-MS). 3.2 Design of Antibody Conjugate to Probe Intracellular Bond Cleavage

To demonstrate our methodology for quantifying the rate constant for intracellular bond degradation, we chose the HER2 receptor due to its broad therapeutic relevance. To target the HER2 receptor we chose to use trastuzumab; one of the most well-studied ligands for the HER2 receptor. In recent years, site-specific modification has been identified as a powerful handle to enhance the properties of antibody-based bioconjugates for applications in both immunodetection [104, 105] and drug delivery [106– 109]. Therefore, to synthesize our antibody conjugate, we adapted a microbial transglutaminase (MTG)-based chemoenzymatic method for the site-specific antibody modification. MTG recognizes glutamine 295 (Q295) within the heavy chain of

Quantitative Determination of Intracellular Bond Cleavage

315

Fig. 3 Transglutaminase-mediated, site-specific modification of trastuzumab with dibenzocyclooctyne (DBCO) functional handles and subsequent functionalization with cleavable crosslinker

aglycosylated, human IgGs [110]. Cotreatment with Peptide:Nglycosidase F (PNGase F) removes the N-linked glycan at asparagine 297 (N297) and facilitates efficient bioconjugation [111]. By supplying nonnatural acyl acceptor substrates, we and others have co-opted this function for site-specific antibody modification [92, 112, 113]. Trastuzumab is first modified at Q295 with a dibenzocyclooctyne (DBCO) functional handle. Azide-modified FRET probes are then “clicked” onto trastuzumab (Fig. 3). This modular conjugation strategy can be used to create bioconjugates targeted against a variety of cell surface receptors and/or containing other stimuli-responsive bonds of interest. 3.2.1 Production and Purification of Recombinant Trastuzumab

FreeStyle™ 293-F cells and the plasmid pVITRO-TrastuzumabIgG1/k were used to produce a stably expressing cell line for trastuzumab. 1. Transfect FreeStyle™ 293-F cells with the plasmid pVITROTrastuzumab-IgG1/k using FreeStyle™ MAX transfection reagent. 2. Grow transfected cells in FreeStyle™ 293 Expression Medium with 50μg/mL hygromycin B for 2 weeks to establish a stably expressing cell line. Change media every 48 h and maintain cell density at 1
106 cells per mL. 3. After 2 weeks of selection, remaining cells are stably expressing. Prepare frozen stocks of stabling expressing cells at a density of 1
107 cells per mL in FreeStyle™ 293 Expression Medium containing 1% (v/v) Pen/Strep and 10% (v/v) dimethyl sulfoxide. Store cell stocks in liquid nitrogen. 4. For protein production, maintain stably expressing FreeStyle™ 293-F cells at a density of approximately 1
106 cells per mL in FreeStyle™ 293 Expression Medium with 1% (v/v) Pen/Strep. Change media every 48 h to maintain cell density. Collect conditioned media and stored at 4  C before purification. 5. Before purification, sterile filter trastuzumab containing conditioned media (see Note 1). Isolate trastuzumab from conditioned media by affinity purification using a gravity flow column charged with 1 mL of protein A/G resin. Before

316

Joshua A. Walker et al.

loading the gravity flow column, equilibrate the resin by passing 15 mL of 1
PBS over the column. Then, flow filtered, conditioned media over the column. Wash the bound protein with 15 mL of 1
PBS to remove nonspecifically bound contaminants. Remove bound protein from the resin using 5 mL elution buffer. Immediately neutralize eluted protein using 1 mL of neutralization buffer. 6. Concentrate and buffer exchange purified protein into 1
PBS buffer using Amicon Ultra-0.5 mL centrifugal filters with a 30 kDa molecular weight cut off according to the manufacturer’s instructions. 7. Quantify antibody yield using absorbance at 280 nm and an estimated molar extinction coefficient of 210,000 M1 cm1. Average antibody yield is 1–2 mg per liter of conditioned media. 3.2.2 Site-Specific Modification of Trastuzumab with Dibenzocyclooctyne Functional Handles

1. Add 600 units of PNGase F (1.2μL; 500,000 U/mL) to 1 mg of trastuzumab (1 equivalency; 33.3 uL; 30 mg/mL in 1
PBS). 2. Add 160 equivalencies of DBCO-PEG4-amine (6.7μL; 100 mM in Moo Gloo reaction buffer) to this mixture (see Note 2). 3. Add Moo Gloo (83.8μL; 500 mg/mL in Moo Gloo reaction buffer) to this mixture. The final trastuzumab concentration is 8 mg/mL. Carry out the conjugation reaction for 24 h at 37  C (see Note 3). 4. Remove excess DBCO-PEG4-amine, PNGase F, and Moo Gloo using NAb Protein A/G 0.2 mL spin columns. 5. Equilibrate the protein A/G spin column by mixing the resin with 400μL of 1
PBS and centrifuge the spin column at 5000
g for 1 min. Remove the flow through and repeat this step one time. 6. Load the Moo Gloo conjugation reaction onto the equilibrated resin and incubate at room temperature for 10 min. Centrifuge the spin column at 5000
g for 1 min. Pass the flow through over the spin column two additional times to fully bind the antibody. 7. Mix the resin with 400μL of 1
PBS and centrifuge the spin column at 5000
g for 1 min to remove nonspecifically bound contaminants. Repeat this step two times to fully remove contaminants. 8. Add 20μL of neutralization buffer to a collection tube. Add 90μL of elution buffer to the resin and centrifuge into collection tube at 5000
g for 1 min. Perform a second elution with 90μL of elution buffer.

Quantitative Determination of Intracellular Bond Cleavage

317

Fig. 4 Hydrophobic interaction chromatography (HIC) analysis of conjugation efficiency for site-specific incorporation of DBCO functional handles into trastuzumab. (Reproduced from [69] with permission)

9. Quantify antibody yield using absorbance at 280 nm using an estimated molar extinction coefficient of 210,000 M1 cm1. The typical recovery of DBCO-modified trastuzumab is 80% by moles. 10. Evaluate the purity of DBCO-modified trastuzumab using hydrophobic interaction chromatography (HIC). Perform HIC on an Agilent 1100 Series HPLC system equipped with a UV diode array detector and an 1100 Infinity fraction collector using a reversed-phase phenyl column (Tosoh Biosciences LLC, TSKgel Phenyl-5PW, 7.5
75 mm, 10μm). Use 25 mM phosphate, 1.5 M ammonium sulfate, pH 7.0 (solvent A) and 18.75 mM phosphate, 25% (v/v) isopropyl alcohol, pH 7.0 (solvent B) as the mobile phase for HIC. Elute conjugates at a flow rate of 1 mL/min using a linear solvent gradient from 0% to 100% solvent B over 60 min (Fig. 4) to evaluate conjugate purity. 3.2.3 Copper-Free “Click” Chemistry Attachment of Fluorescence Probe to Trastuzumab Carrier Protein

1. Add 5 equivalencies of cleavable or noncleavable crosslinker dissolved in dimethyl sulfoxide to 1 equivalency of DBCOmodified trastuzumab in 100 mM Tris, pH 7. 2. Add 1
PBS and dimethyl sulfoxide to bring the final concentration of DBCO-modified trastuzumab to 9.5μM and the solvent composition to 10% (v/v) dimethyl sulfoxide. 3. Carry out the reaction for 24 h at room temperature. Remove excess crosslinker and buffer exchange the fluorescent probes into 1
PBS buffer using Amicon Ultra-0.5 mL centrifugal filters with a 30 kDa molecular weight cut off according to the manufacturer’s instructions. 4. Quantify antibody yield using absorbance at 280 nm and an estimated molar extinction coefficient of 210,000 M1 cm1.

318

Joshua A. Walker et al.

3.3 Confocal Microscopy-Based Visualization of Bond Cleavage

An advantage of a FRET-based approach to detecting intracellular bond cleavage is the ability to simultaneously kinetically visualize bond cleavage and ascertain subcellular probe localization. We developed protocols to apply our FRET-based probe for both analyses (Fig. 5).

3.3.1 Confocal Analysis of Kinetic Bond Cleavage

1. One day prior to the experiment, plate SKBR3 cells into a 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass at 75,000 cells/chamber in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (500μL).

Fig. 5 Confocal laser scanning microscopy images of trastuzumab probes. All scale bars are 50 m. (a) Time course comparison of probes. BODIPY channel is shown in green. FRET channel is shown in red. (b) Colocalization of FRET channel from cleavable probe with Tf-AF647 after 3 h of incubation. (c) Colocalization of BODIPY channel from cleavable probe with Tf-AF647 after 3 h of incubation. (d) Colocalization of FRET channel from cleavable probe with LysoTracker Deep Red after 3 h of incubation. (e) Colocalization of BODIPY channel from cleavable probe with LysoTracker Deep Red after 3 h of incubation. (Reproduced from [69] with permission)

Quantitative Determination of Intracellular Bond Cleavage

319

2. The next day, remove the media, wash the cells with 1
PBS (500μL), and incubate the cells at 4  C with 10 nM of the probes in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (250μL) for 1 h. 3. After incubation, wash the cells twice with 1
PBS (500μL) and add fresh media (500μL). Incubate the cells at 37  C and 5% CO2 for varying amounts of time (0, 1.5, 3, and 5 h). 4. After the desired length of time, perform confocal laser scanning microscopy on a Zeiss LSM 800 Confocal Laser Scanning Microscope with a 20
objective. Set the BODIPY channel to excitation at 488 nm and emission in the range 400–545 nm with 1% laser power and a detector gain of 650 V. Set the rhodamine channel to excitation at 561 nm and emission in the range 565–700 nm with 1% laser power and a detector gain of 675 V. Set the FRET channel to excitation at 488 nm and emission in the range 565–700 nm with 1% laser power and a detector gain of 675 V. Collect phase images in the range 400–700 nm with a detector gain of 300 V. Perform postimage processing using Fiji software. 3.3.2 Confocal Analysis of FRET Probe Colocalization

1. One day prior to the experiment, plate SKBR3 cells into a 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass at 75,000 cells/chamber in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (500μL). 2. For colocalization with transferrin, the next day remove the media, wash the cells with 1
PBS pH 7.4, and incubate the cells at 4  C with 10 nM of the Cleavable Probe and 150 nM of Tf-AF647 in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (250μL) for 1 h. 3. After incubation, wash the cells twice with 1
PBS (500μL) and add 150 nM of Tf-AF647 in fresh media (500μL). Incubate cells at 37  C and 5% CO2 for 3 h. After the desired length of time, carry out confocal laser scanning microscopy on a Zeiss LSM 800 Confocal Laser Scanning Microscope with a 20
objective. 4. For colocalization with LysoTracker Deep Red, the next day remove the media, wash the cells with 1
PBS pH 7.4, and incubate the cells at 4  C with 10 nM of the Cleavable Probe in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (250μL) for 1 h. 5. After incubation, wash the cells twice with 1
PBS (500μL) and add fresh media (500μL). Incubate the cells at 37  C and 5% CO2 for 3 h. Add 150 nM of LysoTracker Deep Red during the last hour of incubation. After the desired length of time, carry out confocal laser scanning microscopy on a Zeiss LSM 800 Confocal Laser Scanning Microscope with a 20
objective.

320

Joshua A. Walker et al.

6. Set the BODIPY channel to excitation at 488 nm and emission in the range 400–545 nm with 1% laser power and a detector gain of 650 V. Set the rhodamine channel to excitation at 561 nm and emission in the range 565–700 nm with 1% laser power and a detector gain of 675 V. Set the FRET channel to excitation at 488 nm and emission in the range 565–700 nm with 1% laser power and a detector gain of 675 V. Collect phase images in the range 400–700 nm with a detector gain of 300 V. Perform post-image processing using Fiji software. Quantify Pearson’s correlation coefficients using the Coloc2 plugin in Fiji. 3.4 Phenomenological Model of Intracellular Bond Cleavage

To quantify the intracellular bond degradation rate, we expanded the kinetic model developed by Maass et al. [59] to describe the processing of our cleavable probe (Fig. 6). In our model, the trastuzumab cleavable probe binds to the HER2 receptor yielding species C, the probe–receptor complex. Association with a free HER2 receptor is defined by the rate constant kon, the concentration of probe in solution, and the density of free HER2 receptors on the cell surface. The probe–receptor complex (C) can dissociate to give species A (unbound probe) and R (free HER2 receptor). This process is defined by the rate constant koff and the density of probe–receptor complex on the cell surface. The probe–receptor complex (C) is internalized with an internalization rate constant kin to yield species I (internalized, intact probe). The overall rate for this process in defined by the rate constant kin and the density of probe–receptor complex (C) on the cell surface. The cleavable

Fig. 6 Overview of phenomenological model for the intracellular processing of stimuli-responsive antibodybased bioconjugates

Quantitative Determination of Intracellular Bond Cleavage

321

bond within the internalized, intact probe (I) is then degraded to yield species D (degraded probe). This degradation event is characterized by the rate constant kdeg. Cell growth dilutes intracellular contents and is accounted for by the rate constant kg. This model assumes bond degradation over a time period less than the timescale of proteolytic degradation of the antibody within the lysosomal compartment. It has been shown that the half-life for nonspecific degradation of trastuzumab in SKBR3 cells is 16 h [59, 68]. Confocal microscopy analysis of the fluorescent probes indicated that degradation of the disulfide bond occurs within approximately 5 h. By assuming that lysosomal degradation of the antibody is negligible, we can also assume that there is no exocytosis of the donor fluorophore within the timescale of our experiment. Complete degradation of the antibody would have to occur for the FRET probe to detach from the antibody and allow BODIPY to exit from the cell. The amount of each of species over time is described by a set of differential equations based on mass-action kinetics given below. dA ¼ koff C  kon ½A ðR
N  C Þ dt

ð1Þ

dC ¼ kon ½A ðR
N  C Þ  koff C  kin C  kg C dt

ð2Þ

dI ¼ kin C  kdeg I  kg I dt

ð3Þ

dD ¼ kdeg I  kg D dt

ð4Þ

dN ¼ kg N dt

ð5Þ

The differential equation corresponding to degraded ADCs over time corresponds to the increase in fluorescence observed when the cleavable probe is cleaved and BODIPY is no longer quenched by rhodamine. This is the cleavage event we observed via confocal microscopy. To determine kinetic parameters, this cleavage event must be quantified as a function of time. 3.5 Flow Cytometry-Based Quantification of Bond Cleavage

Flow cytometry was used to kinetically evaluate disulfide bond reduction within the HER2 pathway of SKBR3 cells. 1. One day prior to the experiment, plate SKBR3 cells into a 4-chamber 35 mm glass bottom dish with 20 mm microwell, #1.5 cover glass at 75,000 cells/well in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (500μL). 2. The next day, remove the media, wash the cells with 1
PBS (500μL), and incubate the cells at 4  C with 10 nM of the probes in McCoy’s 5A (Modified) Medium with 10% FBS and 1% Pen/Strep (250μL) for 1 h.

322

Joshua A. Walker et al.

3. After incubation, wash the cells twice with 1
PBS (250μL) and add fresh media (500μL). Incubate cells at 37  C and 5% CO2 for varying amounts of time (20 min intervals from 0 to 5 h). 4. Wash cells with 1
PBS (500μL), dissociate cells with trypsin, pellet trypsinized cells, and suspended cell pellet in 1
PBS (500μL) for flow cytometry analysis. 5. Measure green fluorescence on a BD FACSCalibur with the following instrument settings: FSC detector: E-1 Voltage, 3 Amp Gain, SSC detector: 400 Voltage, 1 Amp Gain, FL1 detector: 600 Voltage, 1 Amp Gain. Collect data for 3 biological replicates with 2 technical replicates per biological replicate. Perform data processing using FlowJo software. 3.6 Extraction of Bond Degradation Rate from Flow Cytometry Data

1. To solve the set of differential equations that describe intracellular probe processing, the initial conditions must be set. Given the “pulse-chase” nature of the experiment, at t ¼ 0 all HER2 receptors are saturated and no unbound FRET probe is present. Further, incubation at 4  C ensures that no FRET probe is internalized during the pulse. Therefore, both internalized and degraded probes are 0 at t ¼ 0. To prepare flow cytometry data for fitting, subtract nonspecific background fluorescence, as measured by the noncleavable control, from the cleavable crosslinker. Normalize the background-subtracted data from 0 to 1 by setting the initial and final fluorescence values as 0 and 1 respectively. Obtain the total number of receptors, R, and the rate constants kon, koff, and kg were obtained from Maass et al. as 2.36
105 nM cell1, 0.014 nM1 h1, 0.37 h1, and 0.011 h1, respectively. The rate constants kin and kdeg are considered model unknowns. Receptor internalization (kin) is left as a free parameter because HER2 internalization rate has been shown to depend on the cell type, ligand, and the measurement method [59, 68–71]. Degradation rate (kdeg) is considered an unknown because it is the parameter of interest. Fit the kinetic data to the mass-action kinetic model using a custom MATLAB code based on the built-in functions “lsqcurvefit” and “ode15s.” [69] Using this methodology, the HER2 internalization rate for this system is determined to be 2.2 h1. Further, the fundamental rate constant for intracellular disulfide bond reduction is found to be 0.45 h1 with a corresponding half-life of 92 min. We found that the degradation rate constant is sensitive to internalization rate when kin is slow ( Dilate. 5. To separate the masks that may be touching each other, select Binary- > Watershed.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

407

6. To generate cancer cell ROIs from the dilated masks, select Analyze- > Analyze Particles. “Add to Manager” option should be clicked. Each ROI outlines a single cancer cell. 7. Save the list of ROIs. Open the ROIs in the Pt(IV)-BODIPY image. This should generate ROIs that outline cell boundary in the drug payload images. 8. Measure the fluorescent intensity of Pt(IV)-BODIPY in each ROI by pressing More- > Multi Measure in the ROI manager. A list with fluorescent intensities of Pt(IV)-BODIPY is then generated. Transfer the list to GraphPad Prism or Excel for further analysis. 9. Compute the averaged intracellular Pt(IV)-BODIPY fluorescent intensity for each TNP concentration. 10. Graph a standard curve with intracellular Pt concentration, obtained via AAS, against averaged intracellular fluorescent intensity of BODIPY, obtained via fluorescent microscopy, for each TNP concentration. 11. Perform a linear fit of the scatter plot. Compute the equation for the standard curve from the fit. Evaluate the goodness of the fit. 12. This in vitro standard curve can be used to estimate intracellular Pt concentration by imaging in the future experiment, as long as the same exposure time and microscopy setting are used (see Notes 8–11). From the fluorescent images of Pt payload in the HT108053BP1-mApple cells (Fig. 1b), as well as the intracellular Pt content measurement from AAS, we can build a standard curve that enables the estimation of Pt concentration from the fluorescent intensity measurements of Pt(IV)-BODIPY. AAS was performed to measure total Pt content from a washed pellet of roughly two million cells. Therefore, in order to estimate molecules of Pt payload per cell, the amount of Pt (in moles) is first divided by total cell count (approximately two million). The resulting value (in moles of Pt per cell) is multiplied by Avogadro’s number (6.02  1023 molecules per mole) to obtain molecules of Pt per cell, which is reported in the left axis of Fig. 1c. Since the cells were incubated with the TNPs for 24 h, we can calculate the average amount of Pt payload internalized per cell per unit time by dividing the amount of Pt molecules per cell by 24 h. Therefore, the number of Pt molecules internalized per cell per unit time ranges from 1.25  105 molecules per h to 3.2  106 molecules per h. As an in vivo comparison, AAS measured the average Pt content throughout whole bulk tumor tissue 24 h after TNP treatment in the HT1080 xenograft model, revealing roughly 5 μM average Pt

408

Ran Li et al.

concentration [9]. Intracellular Pt concentration, in the unit of mol/L, can be converted to the unit of number of Pt molecules per cell, via the following formula: N i ¼ Ci  V c  A where Ni ¼ Intracellular molecular concentration (number of Pt molecules/cell). Ci ¼ Intracellular molar concentration (mol of Pt/L). Vc ¼ Volume of the cell (L/cell). A ¼ Avogadro’s number (6.02  1023 molecules/mol). A typical cell has volume of 4  1012 L. Using this conversion formula, 5 μM average Pt concentration equates to roughly 1.2  107 molecules per cell, or 5  105 Pt molecules per cell per hour. These calculations assume even and primarily intracellular TNP distribution and constant uptake rates, both of which are major simplifications (although by 24 h most TNP has cleared from circulation in mice), and likely underestimate local intracellular Pt accumulation (for instance as seen with the in vitro measurements above, where intracellular Pt concentration exceeds that of the supernatant). This approach makes additional assumptions. Fluorescence properties of a drug or nanoparticle can change dramatically depending on the physicochemical environment, binding, and chemical transformation. For example, Pt is known to quench fluorescence, and Pt(IV)-BODIPY increases in fluorescence by >fivefold upon intracellular reduction to Pt(II) and dissociation of BODIPY from Pt. Thus, inferring drug concentration from fluorescence requires the assumption that Pt reduction occurs in a relatively dose-independent manner, and that BODIPY localization correlates spatially with Pt, at least on a cell-by-cell level. Other fluorophore-drug conjugates can change brightness depending on physicochemical context. For instance, a fluorescent derivative of the microtubule targeting anticancer drug docetaxel becomes much brighter upon target engagement [32]. Shifts in fluorescence behavior of anthracycline compounds including doxorubicin are detectable but much less significant [33]. In general, these properties should be well understood for proper interpretation of imaging results, and in many cases can be leveraged to better understand drug processes, including target engagement or nanoparticle degradation. Care must also be taken in interpreting locally imaged concentrations of drugs and extrapolating to bulk tissue levels. For instance, plane of focus through the cell, bleed-through fluorescence signal from out of focus sources, and nonlinear relationships between concentration and the imaged fluorescence intensity

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

409

should all be considered. Appropriate standard curve analyses with careful attention to consistent microscope settings can address many of these issues. 3.2 Quantifying Subcellular Localization of TNP 3.2.1 Equipment, Experimental Setup, Protocol Imaging the Subcellular Localization of TNP and its Payloads

TNP are typically taken up by cells through phagocytosis or endocytosis. After entering the cells, TNP usually reside in endosomes or lysosomes until they are degraded. As TNP degrade, the payloads in TNP can be released into the cytosol. Therefore, characterizing the subcellular localization of TNP vehicle, and more importantly, its drug payload, is critical in understanding TNP drug action. Following the fluorescent labeling of cellular compartments (endosomes and lysosome) with BacMam 2.0 reagents as instructed by the manufacturer, the subcellular localization of TNP can be visualized using fluorescently labeled TNP vehicles and payloads. The fluorescent dyes labeling cellular compartments, vehicles, and drug payloads should be picked so that their emission and excitation spectra are completely unique with minimal overlap to prevent fluorescent signal bleed-through. The goal is to assess the degree of colocalization of fluorescent signals from subcellular compartments and from TNP components. Since subcellular localization of TNP vehicle and its payload can change over time, a time course experiment can also be performed. BODIPY630-labeled PLGAPEG TNP that carry Pt(IV)-BODIPY payloads are used as model TNP. The following steps outline the protocols for introducing TNP to HT1080 cells, and imaging the resulting subcellular localization (Fig. 2a): 1. Seed HT1080 cells that are labeled with BacMam 2.0 reagents in a glass bottom cell culture dish or plate. The glass bottom dish and plate are used to facilitate microscopy imaging at high magnification. We recommend MatTek glass bottom dishes (MatTek Corporation, cat#P35G-1.5-14-C) or ibidi 96 well μ-Plates (Ibidi, cat#89626) (see Note 12). 2. After overnight incubation at 37  C and 5% CO2, treat the cells with BODIPY630-labeled PLGA-PEG TNP that are loaded with Pt(IV)-BODIPY payload for a desirable amount of time, preferably at a concentration that is sublethal yet high enough to reliably image. 3. Following TNP treatment, counterstain the cells with Hoechst 33342 at a concentration of 1 μg/mL for an additional 30 min to visualize cell nuclei. 4. Gently wash the cells with 1X PBS for 5 times to remove freefloating TNP and Hoechst dyes. Be gentle with the aspiration and take care not to lift any attached cell (see Note 13).

410

Ran Li et al.

Fig. 2 Assessing subcellular distribution of TNP by colocalization analysis. (a) Experimental protocol for examining the accumulation of TNP components in subcellular compartments. In this example, adherent HT1080 cells are treated with a commercial baculovirus transgenic system (BacMam), which induces expression of fluorescent fusion proteins that localize to particular subcellular compartments such as the endoplasmic reticulum, early and late endosomes, mitochondria, and lysosomes. Cells are subsequently treated with TNP, counterstained with a nuclear dye (Hoechst 33342), and imaged by fluorescence microscopy with at least a 40–60 objective lens. (b) Representative image (scale bar ¼ 5 μm) of a cell expressing Rab7a-RFP, which localizes to late endosomes, and Pt(IV)-BODIPY delivered via 24 h treatment with TNP. White arrows mark intracellular bodies showing high levels of both Rab7a-RFP and Pt(IV)-BODIPY, suggesting late endosomal accumulation. (c) From within the region defined by the cell outline, pixel-by-pixel covariance was quantified between fluorescence from Rab7a-RFP and Pt(IV)BODIPY. A Pearson’s correlation coefficient of >0.9 generally indicates strong colocalization in such an experiment, and is highly suggestive of drug accumulation within Rab7a + compartments. (Adapted from [9] with permission from Nature Publishing Group)

5. Immediately transfer the sample to a fluorescent microscope equipped with humidified incubator operating at 37  C and 5% CO2 for imaging. 6. Image the cells at high-magnification to visualize the colocalization of nanoparticle vehicles, drug payloads, and cellular compartments. We recommend using 60 oil immersion objective to obtain detailed and high-resolution images.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs Single-Fluorophore Control Experiment to Evaluate the Degree of Fluorescence Bleed-through

411

Fluorescent bleed-through could complicate the analysis of fluorescent colocalization. This occurs when fluorescence emitted by a fluorophore from a neighboring fluorescent channel is visualized in the channel of interest. The fluorophore combination should be picked to minimize this bleed-through. Single-fluorophore control experiments can be used to evaluate the degree of fluorescent bleed-through. Briefly, cells are treated with one type of fluorophore at a time, and fluorescent signals are collected in all channels. If no bleed-through is observed, the fluorescent signals for a specific fluorophore should only be collected in its own channel, but not the other channels. For example, for GFP, the fluorescent signals should only be evident in the green fluorescent channel, but not red, blue, or far red fluorescent channel. The following protocol outlines the steps for singlefluorophore control experiment. For this example, the nuclei are labeled with Hoechst 33342 (blue fluorophore), the lysosomes are labeled with BacMam 2.0 RFP reagents (red fluorophore), PLGAPEG TNP vehicles are labeled with BODIPY630 (far red fluorophore), and Pt payloads are labeled with BODIPY (green fluorophore). 1. Synthesize the following two types of nanoparticles using nano-precipitation as described in the Material section: l

TNP-BODIPY630: PLGA-PEG polymer, BODIPY630conjugated PLGA, and C16-Pt(IV) are combined and nano-precipitated to form TNP with BODIPY630-labeled TNP vehicle and unlabeled C16-Pt(IV) payload.

l

TNP-BODIPY: PLGA-PEG polymer, unlabeled PLGA, and Pt(IV)-BODIPY are combined and nanoprecipitated to form TNP with unlabeled TNP vehicle and BODIPY-labeled Pt(IV) payload.

2. Seed four wells of 6-well plate with wild-type HT1080. Incubate overnight at 37  C and 5% CO2. 3. Treat the first well of HT1080 with BacMam 2.0 LysosomeRFP reagents. Incubate for at least 16 h at 37  C and 5% CO2. 4. Treat the second well of HT1080 with TNP-BODIPY630 for 24 h at 37  C and 5% CO2. 5. Treat the third well of HT1080 with TNP-BODIPY for 24 h at 37  C and 5% CO2. 6. Treat the fourth well of HT1080 with Hoechst 33342 at a concentration of 1 μg/mL for 30 min. 7. Wash the cell samples with 1 PBS for 5 times. 8. Transfer to microscope stage and image immediately. For each well, the fluorescent signals from all four channels (i.e., blue, red, green, and far red fluorescent channels) should be

412

Ran Li et al.

collected. If no fluorescent bleed-through, the fluorophore should only be visible in the fluorescent channel that correspond to its excitation and emission wavelength. For example, BODIPY should only be visible in green fluorescent channel (excitation filter at 490 nm/emission filter at 525 nm), while BODIPY630 should only be visible in far red channel (excitation filter at 649 nm/emission filter at 666 nm) (see Note 14). It is important to note that fluorescent bleed-through can also be influenced by the particular setup and setting of the microscope (i.e., filter combination or intensity of the light source). Therefore, in general, single-fluorophore control experiments should be performed with the same instrument and settings as those used for data collection. 3.2.2 Quantitative Data Analysis

To quantify the subcellular localization of the TNP vehicle and payload, the degree of colocalization between cellular compartments and TNP components is evaluated. This colocalization quantification, which can be performed by Coloc-2 plug-in in Fiji, is based on Pearson’s correlation analysis. It measures the relationship between the fluorescent intensities of TNP components and cellular compartments at each pixel. The following steps outline the protocol for quantifying the subcellular localization of TNP components. 1. Import the fluorescent images of TNP components (vehicle and payload) and cellular compartments (endosomes and lysosomes) into Fiji. 2. For cell segmentation, create a region of interest (ROI) for each cell using the fluorescent images of TNP components (i.e., TNP vehicle) and automatic thresholding function in Fiji. Automatic thresholding creates binary masks that cover the area with fluorescent signals. Adjust the thresholding manually as necessary to ensure accurate coverage of the mask (see Note 15). Alternatively, ROI for cells can also be generated from the images of nuclear staining as described in Subheading 3.1.2. 3. After desired thresholding is achieved, create ROI by pressing Edit- > Selection- > Create Selection. Launch the ROI manager, and add the ROI by pressing “Add.” 4. Use Coloc-2 to quantify the fluorescent colocalization between TNP components and cellular compartment. l

Launch Coloc-2 plug-in in Fiji.

l

Choose the TNP component image and cell compartment image in the “Channel 1” and “Channel 2” drop-down menus.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

413

l

For “ROI or mask” dropdown menu, choose “ROI Manager.”

l

Leave the rest of the setting as default.

l

Click “OK” to run the analysis.

5. The plug-in will output an x-y scatter plot of fluorescent intensity of TNP component vs fluorescent intensity of cellular compartment for each pixel in the ROI. Pearson’s R for each ROI will also be computed. 6. Pearson’s R can be used to determine the degree of colocalization. Higher the R value, the more co-localized the TNP components are to the cellular compartments of interest: l

l

l

l

l

0.6 < R  1: Signals strongly colocalized. Most TNP components reside in the labeled cellular compartment. 0.3 < R  0.6: Signals moderately co-localized. Some TNP components reside in the labeled cellular compartment. -0.3  R  0.3: Signals not co-localized. TNP components are freely diffused within the cells, with no apparent preference for labeled cellular compartment. R < 0.3: Signals anti-colocalized. TNP components do not reside in the labeled cellular compartment. For example, a Pearson’s R of 0.93 between the signals from TNP payload and late endosome (Rab7a-RFP) signifies that most TNP payloads are localized in the late endosomes (see Note 16) (Fig. 2b, c).

Besides performing colocalization analysis, the actual concentration of Pt payload in the organelle can be estimated from fluorescent Pt signals by using the in vitro standard curve described previously: 1. Import the fluorescent images of TNP payload and cellular compartments (endosomes and lysosomes) into Fiji. 2. For segmentation, create region of interests (ROIs) using the fluorescent images of cellular compartments (i.e., endosomes) and automatic thresholding function in Fiji to outline cell organelles. 3. After desired thresholding is achieved, create ROIs by pressing Edit- > Selection- > Create Selection. Launch the ROI manager, and add the ROIs by pressing “Add.” 4. Apply the ROIs that outline cell organelles to the TNP payload images. Measure the fluorescent intensity of Pt(IV)-BODIPY in the ROIs. 5. Convert the fluorescent intensity measured to actual Pt concentration measurement using standard curve described in the previous section (see Note 17).

414

4

Ran Li et al.

Methods: In Vivo Experiments

4.1 Building an In Vivo Standard Curve that Correlates TNP Fluorescent Intensity with Concentration

4.1.1 Equipment, Experimental Setup, and Protocol

A standard curve can be used to correlate the fluorescent intensity of TNP vehicle and payload with their actual concentrations. Since IVM is performed with live tissues, ideally the fluorescent readings to build this in vivo standard curve should be recorded in the tissue as well. However, an optical tissue phantom can be used to mimic the absorption and scattering characteristics of tissue but in a more cost-efficient manner. Commonly used optical tissue phantoms include gelatin, intralipid, hemoglobin, and indocyanine green [34]. In the protocol listed below, intralipid is chosen as the tissue phantom. It should be noted that intralipid has similar light scattering characteristics as tissues, especially for wavelengths of 460–690 nm. Yet intralipid is not as effective in approximating the absorption characteristics of the tissues [35, 36]. 1. Aliquot 1 mL of 1% intralipid (McKesson, cat#988248) in PBS into each well of the 6-well plate. 2. Make the standards for the TNP by dissolving various known concentrations of PLGA-PEG PLGA-BODIPY630 nanoparticles carrying Pt(IV)-BODIPY payloads into each well by serial dilution. Reduced Pt(IV)-BODIPY should also be imaged in the same manner [9], to monitor dequenched fluorescence through phantom tissue. 3. Leave one well without TNP as a blank control (see Note 18). 4. Use confocal microscopy to record the fluorescent signals of PLGA-PEG PLGA-BODIPY630 and Pt(IV)-BODIPY from each well, including the blank control well.

4.1.2 Quantitative Data Analysis

1. Import the images of PLGA-PEG PLGA-BODIPY630 and Pt (IV)-BODIPY into Fiji. 2. Quantify the fluorescent intensity of BODIPY630 and BODIPY in each well, as a function of the focal-plane depth into the solution (see Note 19). 3. Perform background normalization by subtracting the fluorescent intensity of each well by the intensity of blank control well. 4. Graph a standard curve with known concentration of Pt(IV)BODIPY payload (in the unit of mol/L) against the measured fluorescent intensity. Repeat for PLGA-PEG PLGABODIPY630. 5. Perform a fit for the standard curves to obtain the equations for the standard curves (see Notes 20–22).

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

415

6. These in vivo standard curves can be used to estimate the concentration of Pt(IV)-BODIPY and PLGA-PEG PLGABODIPY630 in the tissue samples via IVM, as long as the same exposure time and microscopy setting as those used for standard curve measurement are used. 7. Again, the concentration of the drug and nanoparticle vehicle in the standard curve can be converted to the unit of molecules per cell, via the following formula as described before: Ni ¼ C  Vc  A where Ni ¼ Intracellular molecular concentration (number of molecule/cell). C ¼ Molar concentration (mol/L). Vc ¼ Volume of the cell (L/cell) ¼ 4  1012 L/cell. A ¼ Avogadro’s number (6.02  1023 molecules/mol). This formula, when used in combination with in vivo standard curves described in step 6, can allow the user to estimate the intracellular molecular concentration of the drug and nanoparticle payload from the fluorescent intensities. 4.2 IVM Quantification of In Vivo PK of TNP Vehicle and Payload 4.2.1 Equipment, Experimental Setup, and Protocol Establishment of HT1080-53BP1-mApple Tumors in the Dorsal Window Chamber

Pharmacokinetics of fluorescently labeled TNP vehicle and drug payload can be assessed with IVM. The following section outlines the protocols for establishing HT1080-53BP1-mApple tumors in the dorsal skinfold window chamber, and the use of intravital timelapse microscopy to track TNP delivery in the tumor (Fig. 3a).

Dorsal skinfold window chambers facilitate the longitudinal imaging of tumors that are grown subcutaneously. The following procedures outline the steps for the surgical installation of dorsal skinfold window chamber and the establishment of HT1080 tumors in the chamber. All surgical procedures should be carried out under sterile conditions. 1. Anesthetize the mouse with 2% isoflurane supplied with 2 L/ min O2. Keep the mouse under anesthesia and on a heat pad for the duration of the surgery (see Note 23). Apply artificial tears ointment to the mouse’s eyes (see Note 24). 2. Disinfect skin forceps, micro forceps, scissors, micro scissors, and titanium window chamber with 70% ethanol and heat sterilizer and/or autoclave. 3. After the mouse becomes unresponsive, as verified by hind limb toe pinch, prepare the mouse for surgery by placing the animal in the prone position and marking the midline of the back with a black marker.

416

Ran Li et al.

Fig. 3 Intravital microscopy (IVM) quantifies joint kinetics of TNP vehicle and payload within a live mouse xenograft models. (a) Schematic of the IVM approach, whereby a subcutaneous xenograft tumor is imaged in live subjects through a surgically implanted dorsal skinfold chamber. TNP are injected via catheter through the tail vein, while the mouse is anesthetized and stabilized on a heated robotic microscope stage. (b–c) Representative images depict TNP fluorescence within tumor tissue and microvasculature over time. (b) Regions of interest, such as the vessel highlighted in gray, can be outlined in ImageJ to quantify fluorescence signals. Scale bar ¼ 100 μm (c) This ROI analysis is repeated across multiple tumors and vessels per tumor to yield pharmacokinetic measurements such as the vascular TNP concentration (Ct), shown here as a fraction of the extrapolated initial peak concentration C0. Shading in this plot denotes standard error of the mean (thick line). (Adapted from [9] with permission from Nature Publishing Group)

4. Lift the skin and decide the positioning of the titanium window chamber. The chamber should be positioned so that distal branches of the lateral thoracic artery and the deep circumflex iliac artery run through the center of the window. 5. Suture the dorsal skinfold window chamber on the back of the animal. A detailed step-by-step protocol of chamber implantation can be found in Nature Protocol Paper published by Palmer et al. [37].

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

417

6. Remove the top skin layer in the window and expose the fascia layer. Inject 20–50 μL of PBS containing 1  106 HT108053BP1-mApple cells under the fascia layer of the skin. 7. Apply small amount of sterile saline at the injection site, and close the window chamber with a sterile coverslip by securing a retaining ring (see Note 25). 8. Stop the supply of isoflurane, and let the animal recover from anesthesia on a heat pad. 9. Return the mouse to the cage after it has regained consciousness. 10. Supply the mouse with antibiotics in the drinking water for 72 h after window implantation to prevent infection. 11. Administer 0.05–0.15 mg/kg analgesic buprenorphine via intraperitoneal injection (i.p.) every 12 h for a total of 72 h after window implantation for pain management. 12. Allow the tumor to grow for 2 weeks prior to imaging experiment. Evaluating the Pharmacokinetics of TNP Via IVM

Following the maturation of the HT1080-53BP1-mApple tumors, TNP can be injected to observe its pharmacokinetics in live animal. In the protocol outlined below, the blood-half lives of PLGA-PEG PLGA-BODIPY630 TNP and Pt(IV)-BODIPY payload are quantified via IVM. 1. 30 mins prior to the IVM experiment, blood vessels in the tumors are labeled with intravenous (i.v.) injection of 150 μg Pacific Blue–labeled 500 kDa dextran (see Note 26). 2. Anesthetize the mouse with 2% isoflurane supplied with 2 L/ min O2. Keep the mouse under anesthetization for the duration of the imaging session. 3. Carefully place the tail-vein catheter onto the animal. Make sure the catheter is successfully placed by injecting a small amount of PBS through the catheter (see Note 27). 4. Immobilize the animal onto a heated microscope stage (see Note 28), take care not to disturb the tail-vein catheter. Position the window chamber at the center of the stage. 5. Apply artificial tears ointment on the animal’s eyes to prevent dryness. 6. Position the microscope objective over an imaging area with blood vessels, as visualized by Pacific Blue-labeled dextran, surrounded by 53BP1-mApple expressing HT1080 cells. Focus on these tumor blood vessels. 7. Dissolve PLGA-PEG PLGA-BODIPY630 TNP carrying Pt (IV)-BODIPY with 1X PBS to a final volume of 100 μL prior to injection.

418

Ran Li et al.

8. Slowly inject 100 μL of 1 mg/kg of TNP into the mouse via tail-vein catheter. 9. Record the time-lapse images of PLGA-PEG PLGA-BODIPY630 nanoparticle and Pt(IV)-BODIPY payload extravasating from the tumor blood vessels using the confocal imaging system for 180 mins, with 5 min-interval. Images should be recorded with a XLUMPLFLN 20x water immersion objective (NA ¼ 1). 10. For the intravital imaging, excite Pacific Blue, BODIPY, mApple, and BODIPY630 dyes sequentially with 405 nm, 473 nm, 559 nm, and 635 nm diode lasers, respectively, in combination with DM405/473/559/635 nm dichroic beam splitters. 11. Use SDM473/560/640 nm beam splitters to separate the emitted signals. Detect the Pacific Blue, BODIPY, mApple, and BODIPY630 signals with BA430–455 nm, BA490–540 nm, BA575–620 nm, and BA655–755 nm emission filters (Olympus), respectively. 12. Collect the images at 10 μm interval in z-direction to minimize acquisition time and photodamage to the tissue. 13. After the imaging session, remove the tail vein catheter, let the animal recover from anesthesia on a heat pad, and then return the animal to the cage. Make sure the animal successfully recovers from the anesthesia. 4.2.2 Quantitative Data Analysis

The fluorescent intensities of TNP vehicle (PLGA-PEG PLGABODIPY630) and payload (Pt(IV)-BODIPY) in the blood vessel can be monitored over time to build plasma concentration vs time curves. These curves measure the rate of drug removal from the blood vessels due either to drug extravasation to extravascular space or drug elimination through the clearance organ. From these curves, blood half-life values can be determined by fitting the kinetics to exponential decay models. The following protocol outlines the steps taken to build a plasma concentration vs time curve and to compute blood half-life (Fig. 3b). 1. Import the time-lapse images of dextran-Pacific Blue (to visualize blood vessel), PLGA-PEG PLGA-BODIPY630, and Pt(IV)-BODIPY into Fiji as image stacks. 2. Use dextran-Pacific Blue channel to create ROIs that define the blood vessel regions. This can be achieved by using the automatic thresholding function in Fiji to create binary masks that label dextran+ regions similar to protocols described in Subheading 3.2.2. 3. After the desired thresholding is achieved, create blood vessel ROIs by selecting Edit- > Selection- > Create Selection. Add the selection to the ROI manager.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

419

4. Apply the ROIs to the PLGA-PEG PLGA-BODIPY630 and Pt (IV)-BODIPY stacks. 5. Measure the intensities of PLGA-PEG PLGA-BODIPY630 and Pt(IV)-BODIPY in the blood vessel ROIs by selecting More- > Multi Measure. 6. Define a ROI far away from the blood vessel, measure the fluorescent intensity of PLGA-PEG PLGA-BODIPY630 and Pt(IV)-BODIPY to measure the background fluorescence in both of these channels (IBck). 7. Compute the relative concentration by dividing fluorescent intensity at any time-point (It) by the maximum fluorescent intensity at initial time point (I0). Normalize the fluorescent intensity measurements to background fluorescence (IBck): Relative Concentration ¼

I t  I Bck I 0  I Bck

This can be done for both PLGA-PEG PLGA-BODIPY630 and Pt(IV)-BODIPY. 8. Plot relative concentration of both vehicle and payload over time in GraphPad Prism or Excel. 9. Perform nonlinear fit to the plots using either one-phase exponential decay or two-phase exponential decay model (e.g., for time courses that span both distribution and terminal phase clearances) (see Note 29). 10. Evaluate the quality of the fit by computing R2. Blood half-life can be calculated from the decay equation resulting from the fit. 11. Instead of relative quantification, absolute quantification of vehicle and payload concentrations can be used in the plot of plasma concentration versus time. This absolute quantification is estimated from the in vivo standard curves that correlate fluorescent intensities to Pt or TNP vehicle concentration. The depth of focus for the confocal images should be matched to depth of images used to build the standard curve, as the fluorescent intensity of the dye decreases with increasing tissue depth. TNP vehicle and payload concentration in the tumor tissues can also be quantified over time using similar method as outlined above. For this quantification, ROIs should be defined around the extravascular space where HT1080-53BP1-mApple cancer cells are concentrated. When plotted over time, the plasma concentration of TNP vehicle and payload showed that TNP reached the tumor ~10 min after intravenous injection. Both the vehicle (PLGAPEG PLGA-BODIPY630) and payload (Pt(IV)-BODIPY) exhibited strong colocalization in the blood vessels, and both TNP

420

Ran Li et al.

components had similar initial blood half-lives. The half-life of TNP vehicle was 55  5 min, while the half-life of the payload was 61  6 min (Fig. 3c). 4.3 IVM Quantification of In Vivo Distribution of TNP Vehicles and Payloads 4.3.1 Equipment, Experimental Setup, and Protocol

In this section, we describe how to assess the biodistribution of TNP vehicle and its payload in the TME at a single-cell level. This experiment is performed 24 h after TNP injection, at which point most TNP has cleared the blood vessels and distributed into the tumor tissues. Specifically, we will use IVM to quantify the uptake of TNP vehicle and payload into different tumor compartments in the window chamber. 1. Implant the dorsal window chamber on a nu/nu mouse and establish the HT1080-53BP1-mApple tumor in the window as described in Subheading 4.2.1. 2. After tumor maturation and 24 h prior to imaging, intravenously inject 150 μg Pacific Blue–labeled 500-kDa dextran into the mouse via tail vein to fluorescently label phagocytes/TAM (see Notes 30 and 31). 3. Intravenously inject 1 mg/kg of PLGA-PEG PLGA-BODIPY630 nanoparticles that carry Pt(IV)-BODIPY via tail vein. Return the animal to the cage. 4. 24 h after TNP injection, anesthetize the mouse with 2% isoflurane supplied with 2 L/min O2. Keep the mouse under anesthetization for the duration of the imaging session. 5. Immobilize the animal onto a heated microscope stage. Position the window chamber at the center of the stage. Apply artificial tears ointment on the animal’s eyes to prevent dryness. 6. Position the microscope objective over an imaging area with tumor, as visualized by 53BP1-mApple signals. Focus on the tumor region with XLUMPLFLN 20 water immersion objective (NA ¼ 1). 7. Perform IVM imaging as described in Subheading 4.2.1. Tumor-associated phagocytes/TAM should be labeled with Pacific Blue-Dextran. Cancer cells are visualized by 53BP1mApple expression. 8. After the imaging session, bring the animal back from the anesthesia on a heat pad. Return the animal to the cage and monitor for successful anesthesia recovery. 9. Repeated IVM imaging sessions on subsequent days can be carried out to track TNP distribution longitudinally.

4.3.2 Quantitative Data Analysis

HT1080 cancer cells are labeled with 53BP1-mApple, while phagocytes/TAM are labeled with dextran-Pacific Blue. The fluorescent intensities of TNP vehicle and payload are quantified in ROIs that outline cancer cells and TAM (Fig. 4a).

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

421

Fig. 4 Confocal microscopy of tumor tissue quantifies heterogeneous TNP distribution and payload redistribution. (a) Representative images (scale bar ¼ 100 μm) depict variable accumulation of TNP vehicle and payload across cells at a microscopic scale within tumors. Corresponding to Fig. 3, images taken 24 h after administration of TNP show high levels of TNP uptake in tumor-associated phagocytes (shown through multiple complementary experiments to be largely TAM) in the HT1080 xenograft model. Although TNP payload (Pt(IV)-BODIPY) shows highest fluorescence in subcellular regions with high TNP vehicle (PLGABODIPY630), Pt(IV)-BODIPY also exhibits more diffusely elevated signal in cell and tissue regions immediately adjacent to areas with focally high TNP vehicle fluorescence. This is highlighted in the “MΦ rich region.” (b–c) One simple metric that can suggest drug redistribution is the relative heterogeneity in drug fluorescence from cell-to-cell in a given tissue. TNP vehicle exhibits high levels of heterogeneity in accumulation from cell-tocell, in part due to its high uptake in TAM and lack of uptake in cells such as lymphocytes. In contrast, a freely diffusing, solvent formulation of a cisplatin derivative (Pt(II)-BODIPY) distributes relatively evenly across cells as a soluble small molecule, and therefore exhibits low variability in uptake from cell to cell. Heterogeneity in TNP payload distribution falls in between these two examples, suggesting drug is partially released from its vehicle to more freely distribute. (Adapted from [9] with permission from Nature Publishing Group)

422

Ran Li et al.

To quantify TNP vehicle and payload uptake in cancer cells: 1. Import the fluorescent images of Pt(IV)-BODIPY, PLGAPEG PLGA-BODIPY630, and 53BP1 signals into Fiji. 2. Use 53BP1 signals and thresholding function in Fiji to obtain binary masks of the nuclei. Dilate the mask to include area that is within one cell radius (~10 μm) measured from the center of the nucleus. Generate ROIs based on the dilated mask. Detailed procedure is described in Subheading 3.1.2. These ROIs outline cancer cell boundary. More advanced segmentation software can also be used, including open-source CellProfiler (cellprofiler.org) and MATLAB-based methods that segment in 3D and leverage user-trained machine learning classifiers [9, 38], as well as commercial programs such as Imaris (Bitplane). 3. Save the list of ROIs. Open the ROIs in the Pt(IV)-BODIPY and PLGA-PEG PLGA-BODIPY630 images. 4. Measure the fluorescent intensity of Pt(IV)-BODIPY or PLGA-PEG PLGA-BODIPY630 in each ROI by selecting More- > Multi Measure in the ROI manager. A list with fluorescent intensities of TNP vehicle and payload in each ROI/cell is then generated. 5. Plot a histogram of fluorescent intensities of TNP vehicle and payload to visualize heterogeneity of TNP uptake in cancer cells. 6. Fluorescent intensities of TNP vehicle and payload can be translated to concentration measurements by using an in vivo standard curve as described before. To quantify TNP vehicle and payload uptake in phagocyte/ TAM: 1. Import the fluorescent images of Pt(IV)-BODIPY, PLGAPEG PLGA-BODIPY630, and Pacific Blue-dextran into Fiji. 2. Apply Gaussian blur to the Pacific Blue-dextran images by pressing Process- > Filter- > Gaussian Blur. This is done because dextran signals appear as discrete puncta in the phagocytes, since dextran tends to localize in endosomes and lysosomes. Gaussian blur flattens these discrete signals, making thresholding and ROI generation easier. 3. Use the Gaussian-blurred Pacific Blue-dextran images to generate ROIs that outline the dextran signals (TAM) by performing automatic or manual thresholding. Compare the results of thresholding with the original images to ensure accurate labeling of dextran+ cells. 4. When desirable thresholding is achieved, create masks of the phagocytes by pressing Binary- > Make Binary. Separate the

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

423

masks that may be touching each other by pressing Binary> Watershed. 5. Generate ROI from these masks by pressing Analyze- > Analyze Particles. Add the resulting ROIs to the ROI manager. These ROIs outline macrophage boundary. 6. Save the list of ROIs. Open ROIs in the Pt(IV)-BODIPY and PLGA-PEG PLGA-BODIPY630 images. 7. Measure the fluorescent intensity of Pt(IV)-BODIPY or PLGA-PEG PLGA-BODIPY630 in each ROIs by pressing More- > Multi Measure in the ROI manager. A list of fluorescent intensities of TNP vehicle and payload in each ROI/cell is then generated. 8. Plot a histogram of fluorescent intensities of TNP vehicle and payload to visualize the heterogeneity of TNP distribution in TAM/phagocytes. 9. Fluorescent intensities of TNP vehicle and payload can be translated to concentration measurements by using in vivo standard curves as described before. We quantified the single-cell distribution of TNP vehicle and payload uptake in the tumor. We compared this distribution to that of unencapsulated Pt-BODIPY conjugate injected directly into animal without nanoencapsulation. We found that compared to TNP vehicle and payload, there is a more homogeneous (diffusive) distribution of unencapsulated Pt as revealed by coefficient of variance quantification. TNP payload distributes in a more homogeneous manner compared to TNP vehicle, indicating that TNP payload could redistribute from TAM (Fig. 4b, c). 4.4 IVM Quantification of In Vivo Payload Redistribution 4.4.1 Equipment, Experimental Setup, and Protocol

4.4.2 Quantitative Data Analysis

From previous analysis, we know that TAM have enhanced ability to take up TNP compared to cancer cells [9]. We also observed evidence that TAM serve as drug depots that accumulate a significant amount of TNP from which Pt payload can gradually be released to neighboring cancer cells. This redistribution of TNP payload from TAM to cancer cells can be observed in vivo by tracking the localization of fluorescently labeled payload with IVM. The experimental protocol is identical to the protocol outlined in the Subheading 4.3.1. Again, PLGA-PEG PLGA-BODIPY630 and Pt(IV)-BODIPY are used as model TNP vehicle and payload, respectively, in this protocol. TNP Pt payload redistribution from TAM to cancer cells in vivo can be quantified with these metrics: 1. Gradient of Pt payload around TAM: Payload released by TAM can create a gradient of Pt around the TAM. This gradient can be quantified by measuring the spatial distribution of the fluorescent Pt(IV)-BODIPY around TAM (Fig. 5).

424

Ran Li et al.

Fig. 5 Quantifying spatial gradients of drug redistribution. (a) Immunofluorescence of excised tumor tissue suggests TNP payload concentration is elevated in cells that are in close proximity to TAM that have accumulated high levels of TNP vehicle (Scale bar ¼ 20 μm). Corresponding to Fig. 4, excised HT1080 xenograft tumor tissue was stained for tumor associated TAM using an F4/80 fluorescent antibody conjugate. (b) Using ImageJ, line profiles are drawn radially from TNP+ TAM, and Pt(IV)-BODIPY signal is quantified along each pixel of these profiles. Averaging over dozens of cells and multiple tumors, these profiles show a heterogeneous yet statistically significant pattern of increased Pt(IV)-BODIPY signal in immediately adjacent tissue (yellow line and shading denote mean +/ standard error). In contrast, no comparable pattern was observed for the TNP vehicle (blue line and shading), suggesting the TNP payload is able to redistribute. (Adapted from [9] with permission from Nature Publishing Group)

2. Gradient of DNA damage responses to Pt payload around TAM: Pt released by TAM can induce DNA damages in cancer cells adjacent to TAM. This can be measured by quantifying the spatial distribution of 53BP1-mApple puncta (Fig. 6). Following sections describe step-by-step protocols to perform these analyses. Evaluate the Gradient of Pt Payload and TNP Vehicle around TAM

1. Import the fluorescent images of Pt(IV)-BODIPY (payload), PLGA-PEG PLGA-BODIPY630 (vehicle), and Pacific Bluedextran, and 53BP1-mApple into Fiji (Fig. 5a). As above, more advanced methods can be used, including custom MATLAB analyses that compute spatial dependencies based on 3D segmentation and proximity [9, 38]. In general, Fiji requires less programming experience and less computing power.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

425

Fig. 6 Local payload redistribution correlates with single-cell reporters of elevated drug response. (a) The 53BP1-mApple fluorescent fusion protein forms punctate foci at points of nonhomologous end-joining repair in live cells, which can be imaged by confocal microscopy. Punta are highlighted in individual HT1080 nuclei by red arrows (scale bar ¼ 5 μm). (b) Quantification of 53BP1 puncta, on a per-cell basis across thousands of cells imaged in a tumor, reveal that cells exhibiting elevated DNA damage response have on average higher levels of TNP payload fluorescence. (c) Representative examples (scale bar ¼ 5 μm) of tumor cells showing few 53BP1 puncta, or the combination of high DNA damage response and close proximity to tumor associated phagocytes such as TAM, following 24 h treatment with TNP. In this example, phagocytes are labeled with a near-infrared fluorescent dextran nanoparticle. (d) The dependence of 53BP1-mediated DNA damage response on phagocyte proximity was calculated for tumors that are either untreated, treated with TNP, or treated with a high dose of the solvent-formulated, unencapsulated cisplatin. This experiment suggests that dependence on phagocyte proximity is especially prevalent in TNP, and is not due to differences in cellular behaviors that depend on TAM proximity (since cisplatin and TNP both share similar mechanisms of action through Pt-mediated DNA damage). (Adapted from [9] with permission from Nature Publishing Group)

2. Use Pacific Blue-dextran images to create ROIs that outlines TAM as described in Subheading 4.3.2. 3. Apply macrophage ROIs to Pt(IV)-BODIPY images. Use the “line” tool in Fiji to draw a line ~50 μm in length outward from the macrophage boundary. 4. Measure the fluorescent intensity of Pt(IV)-BODIPY along this line to create a line profile by pressing Analyze- > Plot Profile. Transfer the values by pressing “List” on the plot pop-up window and copy the results into Prism or Excel.

426

Ran Li et al.

Generate the plot of BODIPY-FL signals vs linear position on the line. This line profile represents the spatial distribution of Pt around TAM. 5. Apply macrophage ROIs to PLGA-PEG PLGA-BODIPY630 images. Use the “line” tool in Fiji to draw a line 50 μm in length outward from the macrophage boundary. 6. Measure the fluorescent intensity of PLGA-PEG PLGA-BODIPY630 along this line to create a line profile by pressing Analyze- > Plot Profile. Transfer the values by pressing “List” on the plot pop-up window and copy the results into Prism or Excel. Generate the plot of BODIPY630 signals vs linear position on the line. This line profile represents the spatial distribution of TNP vehicle around TAM. 7. For the ease of comparison, fluorescent intensity values in the profile should be normalized to maximum fluorescent intensity in the profile and background fluorescence. Measure background fluorescent intensity at the region where there is no fluorescent signal. Normalize fluorescent intensity value by using the following formula: Normalize Fluorescent Intensity ¼

I  I Bck I Max  I Bck

where IBck ¼ Background fluorescent intensity. IMax ¼ Maximum fluorescent intensity from the profile 8. Instead of plotting fluorescent intensity, absolute values of Pt and vehicle concentration can be plotted against the linear position. Convert the fluorescent intensity values to absolute values with the in vivo standard curve described in Subheading 4.1. 9. These line profiles can be used to assess if Pt or vehicle gradient exists around TAM. We quantified the fluorescent profile of TNP vehicle and payload around the TAM. We found that although there was no gradient of TNP vehicle around TAM, a fluorescent gradient of TNP payload was observed. This thus serves as an evidence that Pt payload can be released by TAM without the similar degree of TNP vehicle release (Fig. 5b). Evaluate the Gradient of DNA Damage around TAM

Cisplatin induces cancer cell death by directly binding to DNA, which damages DNA and interferes with its transcription and replication. DNA damage can be recognized by cells, resulting in DNA-damage responses that ultimately lead to apoptosis. Once DNA-damage responses are initiated, 53BP1 proteins can concentrate in the location of DNA damage and form nuclear puncta. Hence, the appearance of 53BP1 foci in the cancer cell nucleus is a sign of DNA damages, and it can be used as a reporter for cellular

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

427

responses to Pt treatment (Fig. 6a). As Pt payloads are released by TAM, the cancer cells that are adjacent to TAM will have more DNA damages than cancer cells that are far away from TAM. 1. Import the fluorescent images of Pt(IV)-BODIPY (payload), PLGA-PEG PLGA-BODIPY630 (vehicle), and Pacific Bluedextran, and 53BP1-mApple into Fiji. 2. Use Pacific Blue-dextran images to create ROIs that outlines TAM as described in the previous section. 3. Apply macrophage ROIs to 53BP1-mApple images. Use the “line” tool in Fiji to draw a line ~50 μm in length outward from the macrophage boundary. Any HT1080-53BP1-mApple cells within this radius is considered “adjacent” to TAM. 4. Quantify the number of 53BP1 puncta in each cancer cell that is adjacent to TAM. Also quantify this metrics for cancer cell that is outside of this 50 μm radius. 5. The number of puncta in the nucleus can be manually counted or evaluated with modified computer script published previously [9, 38] (see Notes 32 and 33). Using the fluorescent images of 53BP1-mApple and Pt(IV)BODIPY, we found that, on a per-cell level, the numbers of 53BP1 puncta and the cellular uptake of Pt(IV)-BODIPY were correlated (Fig. 6b). This indicates that the enhanced drug uptake leads to more damages to cellular DNA. Moreover, we found that cancer cells that are adjacent to TAM have more 53BP1-mApple puncta, and thus more DNA damage response, than cancer cells that are far away from TAM (Fig. 6c, d).

5

Notes Following lists outline the troubleshooting techniques and important consideration for materials and methods outline in the previous sections: 1. Use a pipette with gel loading tip to slowly drop the organic phase into water for optimal nanoprecipitation result. A gel loading tip facilitates the slow delivery of organic phase. 2. Drop the organic phase in the center of the vortex for optimal nanoprecipitation result. 3. If a large batch of nanoparticle is needed, instead of manually mixing the organic phase with water, the organic phase can be delivered with a syringe pump. 4. Nanoprecipitation usually produces TNP with size ranging from 50 nm to 150 nm. The size of the nanoparticle depends on various factors including polymer property, solvent property, and the duration of nanoprecipitation.

428

Ran Li et al.

5. Zeta potential (surface charge) measurement using PBS as diluent is more reflective of the in vivo condition, as the blood in animal is buffered. 6. In general, TNP with slight negative surface charge has more desirable pharmacokinetic characteristics than TNP with positive surface charge. 7. AAS measures the total amount of Pt in a sample. The amount of intracellular Pt per cell can be calculated by dividing the total Pt amount by the number of the cells in the sample. The concentration of Pt can be calculated by dividing the amount of intracellular Pt per cell by estimated volume of the cell, which is roughly 2000–4000 μm3. 8. Photoquenching of fluorescent dyes can make the standard curve inaccurate, for instance when local concentrations are high or via the inner filter effect. Such effects should be considered in data interpretation, and can be assessed by control studies including dose-response measurement, as well as comparing fluorescence in compounds that are either nanoencapsulated or unencapsulated. 9. When imaging the standards and the experimental samples, make sure the cells are in focus to ensure accurate fluorescent readings. Both bright-field microscope and confocal microscope can be used for imaging. 10. Fluorescent images captured by bright-field microscope are two-dimensional, while AAS measures volumetric concentration. Caveats apply in considering how representative a given plane of focus is in representing the average distribution of drug within a cell, as well as the contribution from out-ofplane fluorescent signals. When building the standard curve with bright-field microscopy and at a relatively low magnification of 10–20, we often assume out-of-plane fluorescent signals are minimal in cells, since they are relatively flat when cultured on tissue culture plates. Control imaging with fluorescent beads of a defined size, or dye that is attached to a plate surface, can help quantify fluorescence contribution from outof-plane signal if it is a concern. 11. When building the standard curve with confocal microscopy, collect images with a z-stack that spans the entire cell. This allows the quantification of volumetric fluorescent intensity, which should correlate well with volumetric concentration measurement from AAS. When using confocal microscope for imaging, make sure the experimental samples and standards are imaged with consistent depth and z-stack step-size. 12. Cell attachment to glass bottom dish or plates is relatively weak compared to plastic culture dishes. This could result in cell detachment during washing process. To enhance cell

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

429

attachment to the glass bottom dishes, these dishes can be coated with 100 μg/mL rat-tail collagen-I or 50 μg/mL poly D-lysine (PDL). l

To coat glass bottom dish with rat-tail collagen I: – Add 100 μg/mL rat-tail collagen-I (in 0.02 N acetic acid) to the glass bottom dish or plate, make sure the surface is completely covered. – Incubate the dish or plate in a humidified incubator at 37  C for 30 min. – Carefully wash the dish or plate with sterile 1 PBS 3 times. – Use the coated dish or plate immediately.

l

To coat glass bottom dish with PDL: – Add 50 μg/mL PDL solution (in tissue-culture water) to the glass bottom dish or plate, make sure the surface is entirely covered. – Incubate the dish or plate in a humidified incubator for 4 h. – Carefully wash the dish or plate with sterile water 3 times. – Use the coated dish or plate immediately.

13. During the cell washing process, we recommend using pipetaid to manually remove fluid rather than using vacuum, as manual pipetting results in the least amount of disturbance to cell attachment. 14. The following steps can be taken to minimize fluorescent bleed-through: l

Choose fluorophore with narrow emission spectra to avoid spectra overlap.

l

Choose fluorophore combination with minimal spectra overlap.

l

Decrease the concentration of the fluorophore that is producing bleed-through.

l

Software is freely available to estimate degree of bleed through across fluorescence channels, for instance with the BD Spectrum Viewer (https://www.bdbiosciences.com/ en-us/applications/research-applications/multicolor-flowcytometry/product-selection-tools/spectrum-viewer).

15. Use IsoData, MaxEntropy, or Moments method for automatic thresholding. IsoData tends to overestimate the threshold, while MaxEntropy tends to underestimate the threshold. Threshold can be manually adjusted for accuracy.

430

Ran Li et al.

16. When Pt(IV)-BODIPY is metabolized, the BODIPY dye can detach from the Pt(IV). Hence, it is possible that BODIPY dye may not be in the same location as Pt(IV) payload. 17. To apply standard curve to the fluorescent Pt measurement, images must be taken at same exposure time and microcopy setting as those images used to construct the standard curves. 18. When preparing the standards, make sure the content is mixed well, so the fluorescent signals are homogeneous during the imaging. For each tissue phantom standard, a z-stack image, from the surface of the phantom to 100 μm into the sample, should be taken with a confocal microscopy. 19. When quantifying the fluorescent intensity from confocal images, average the intensity in the z-direction to take into account of fluorescent signals that are not in the plane of focus. 20. Chemical reduction of Pt(IV)-BODIPY can enhance the fluorescent intensity of BODIPY. This property of BODIPY can create a disparity between the readings obtained from the standards and the actual samples. Since tissue phantom does not contain any cells, the chemical reduction of Pt payloads will not occur in the samples used to build the standard curve. However, cells in the real tissue may reduce Pt payload, increasing the fluorescent intensity of BODIPY. Therefore, this in vivo standard curve may be overestimating the actual concentration of Pt payload. 21. The change in fluorescent intensity of BODIPY as the result of chemical reduction could be used to monitor this reaction in vivo. 22. The change in fluorescent intensity of BODIPY as the result of chemical reduction is not a big issue for in vitro standard curve, as cell samples are used to build this standard curve and it is assumed that most drug has reduced intracellularly by 24 h of treatment. 23. Be sure to keep the animal on the heat pad for the entire duration of the surgery. Failure to do so may result in animal death as animals under anesthesia are susceptible to hypothermia. 24. Make sure to apply artificial tears ointment to the mouse’s eyes during surgery. This prevents the eyes from drying out and blinding the animal. 25. When closing the chamber with the coverslip, take care not to create any air bubble. Air bubbles may affect tumor growth and interfere with imaging. It is okay to allow excess saline to leak out of the chamber during the coverslip installation.

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs

431

26. Instead of 500 kDa dextran, blood vessels can be labeled with Pacific Blue–conjugated lectin. Fluorescent lectin binds to the carbohydrates expressed on the endothelial cell surface, thus efficiently labeling the blood vessels for imaging. To visualize blood vessels with fluorescently labeled lectin, inject the lectin 30 min before the imaging session. In our experience, lectin from Griffonia simplicifolia can efficiently label microvasculature and tumor vasculatures. Lectin from Lycopersicon esculentum can efficiently label arteries and veins. 27. When placing the tail vein catheter, a titanium Troutman needle holder (World Precision Instruments) can be used to manipulate and hold the needle. 28. Keep the animal on the heated stage during the imaging session, as the animal under anesthesia is susceptible to hypothermia. 29. Two-phase-exponential decay model produces two blood halflife values: initial blood half-life and terminal blood half-life. Initial blood half-life generally measures the half-life of drug extravasation and distribution into the extravascular space. Terminal blood half-life, on the other hand, measures how fast the drug is excreted from the body by clearance organ such as kidney, or for nanoparticles, the liver and mononuclear phagocyte system [39]. For TNP and materials above the renal excretion threshold in mice, initial half-life is usually on the order of 30–60 min, while terminal half-life can be longer [40]. 30. Dextran of 500 kDa molecular weight can be used to label both blood vessels and phagocytes in mouse, depending on the amount of time that has elapsed since dextran injection. 30 min–2 h after intravenous injection of dextran, 500 kDa dextran is generally concentrated within the blood vessels, and the dextran signal can be used to outline the blood vessels. However, 24 h after dextran injection, most dextran would have been taken up by phagocytes such as TAM. Hence, to visualize phagocytes/TAM, imaging session should be performed 24 h after the intravenous injection of 500 kDa dextran [40, 41]. 31. Tumor-associated TAM can also be visualized with Pacific Blue–labeled Macrin, a novel polyglucose nanoparticle that specifically targets TAM [13]. 32. Although manual counting is not an elegant method, it is the easiest way to process small amount of data. In our experience, it is difficult for the computer program to distinguish the fluorescent 53BP1 signals in the puncta versus signals diffused in the nucleus. A computer program, such as the one introduced in the prior publication, may aid in the analysis of a large

432

Ran Li et al.

data set. However, the program may need to be modified for images with different qualities and resolutions. For both manual and automated analysis procedures, blinding to sample identity should be applied wherever possible to mitigate risk for biased quantification. 33. In our experience, the number of 53BP1 puncta in cells can be extremely heterogeneous in a single sample. We recommend quantifying hundreds of cells per sample for an accurate quantification.

6

Future Directions In this chapter, we summarized in vitro and in vivo techniques to quantify (1) the uptake of TNP by cancer cells and TAM, (2) the ability of TAM to serve as reservoirs for TNP payloads, and (3) the degree to which such payloads redistribute from TAM to cancer cells. We believe that these techniques can also be useful in studying the redistribution of other types of TNP payloads, including paclitaxel, doxorubicin, irinotecan, and kinase inhibitors [4]. We also expect that these methods can be utilized to evaluate the ability of TNP to treat diseases in which TAM plays a major role, such as at sites of inflammation in response to infection and injury [42]. TNPs that are designed to treat these diseases may rely on drug depot effects for their efficacy. Moreover, with the advent of immunotherapies in treating solid cancers, TNP are increasingly considered as delivery vehicles for immunomodulatory agents. The ability of immunotherapeutic drugs to redistribute from TAM to their targets, such as dendritic cells and T cells, could thus be evaluated with techniques described in this chapter.

References 1. Cho K, Wang X, Nie S, Chen ZG, Shin DM (2008) Therapeutic nanoparticles for drug delivery in cancer. Clin Cancer Res 14:1310–1316 2. Fang J, Nakamura H, Maeda H (2011) The EPR effect: unique features of tumor blood vessels for drug delivery, factors involved, and limitations and augmentation of the effect. Adv Drug Deliv Rev 63:136–151 3. Ramanathan RK, Korn RL, Raghunand N, Sachdev JC, Newbold RG, Jameson G, Fetterly GJ, Prey J, Klinz SG, Kim J, Cain J, Hendriks BS, Drummond DC, Bayever E, Fitzgerald JB (2017) Correlation between Ferumoxytol uptake in tumor lesions by MRI and response to nanoliposomal irinotecan in patients with advanced solid tumors: a pilot study. Clin Cancer Res 23:3638–3648

4. Eifler AC, Thaxton CS (2011) Nanoparticle therapeutics: FDA approval, clinical trials, regulatory pathways, and case study. Methods Mol Biol 726:325–338 5. Choi YH, Han HK (2018) Nanomedicines: current status and future perspectives in aspect of drug delivery and pharmacokinetics. J Pharm Investig 48:43–60 6. Miller MA, Weissleder R (2017) Imaging of anticancer drug action in single cells. Nat Rev Cancer 17:399–414 7. Miller MA, Arlauckas S, Weissleder R (2017) Prediction of anti-cancer nanotherapy efficacy by imaging. Nano 1:296–312 8. Vinegoni C, Dubach JM, Thurber GM, Miller MA, Mazitschek R, Weissleder R (2015) Advances in measuring single-cell

Subcellular Drug Depots as Reservoirs for Small-Molecule Drugs pharmacology in vivo. Drug Discov Today 20:1087–1092 9. Miller MA, Zheng YR, Gadde S, Pfirschke C, Zope H, Engblom C, Kohler RH, Iwamoto Y, Yang KS, Askevold B, Kolishetti N, Pittet M, Lippard SJ, Farokhzad OC, Weissleder R (2015) Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt(IV) pro-drug. Nat Commun 6:8692 10. Mitchison TJ (2012) The proliferation rate paradox in antimitotic chemotherapy. Mol Biol Cell 23:1–6 11. Wang SJ, Li R, Ng TSC, Luthria G, Oudin MJ, Prytyskach M, Kohler RH, Weissleder R, Lauffenburger DA, Miller MA (2020) Efficient blockade of locally reciprocated tumormacrophage signaling using a TAM-avid nanotherapy. Sci Adv 6:eaaz8521 12. Kirpotin DB, Drummond DC, Shao Y, Shalaby MR, Hong K, Nielsen UB, Marks JD, Benz CC, Park JW (2006) Antibody targeting of long-circulating lipidic nanoparticles does not increase tumor localization but does increase internalization in animal models. Cancer Res 66:6732–6740 13. Kim HY, Li R, Ng TSC, Courties G, Rodell CB, Prytyskach M, Kohler RH, Pittet MJ, Nahrendorf M, Weissleder R, Miller MA (2018) Quantitative imaging of tumorassociated macrophages and their response to therapy using 64Cu-labeled Macrin. ACS Nano 12:12015–12029 14. Keswani RK, Baik J, Yeomans L, Hitzman C, Johnson AM, Pawate AS, Kenis PJ, RodriguezHornedo N, Stringer KA, Rosania GR (2015) Chemical analysis of drug biocrystals: a role for Counterion transport pathways in intracellular drug disposition. Mol Pharm 12:2528–2536 15. Miller MA, Chandra R, Cuccarese MF, Pfirschke C, Engblom C, Stapleton S, Adhikary U, Kohler RH, Mohan JF, Pittet MJ, Weissleder R (2017) Radiation therapy primes tumors for nanotherapeutic delivery via macrophage-mediated vascular bursts. Sci Transl Med 9 16. Yoon GS, Sud S, Keswani RK, Baik J, Standiford TJ, Stringer KA, Rosania GR (2015) Phagocytosed clofazimine biocrystals can modulate innate immune signaling by inhibiting TNFα and boosting IL-1RA secretion. Mol Pharm 12:2517–2527 17. Crotti S, Posocco B, Marangon E, Nitti D, Toffoli G, Agostini M (2017) Mass spectrometry in the pharmacokinetic studies of anticancer natural products. Mass Spectrom Rev 36:213–251

433

18. Cilliers C, Liao J, Atangcho L, Thurber GM (2015) Residualization rates of near-infrared dyes for the rational design of molecular imaging agents. Mol Imaging Biol 17:757–762 19. Bhatnagar S, Khera E, Liao J, Eniola V, Hu Y, Smith DE, Thurber GM (2019) Oral and subcutaneous administration of a near-infrared fluorescent molecular imaging agent detects inflammation in a mouse model of rheumatoid arthritis. Sci Rep 9:4661 20. Kim E, Yang KS, Kohler RH, Dubach JM, Mikula H, Weissleder R (2015) Optimized near-IR fluorescent agents for in vivo imaging of Btk expression. Bioconjug Chem 26:1513–1518 21. Laughney AM, Kim E, Sprachman MM, Miller MA, Kohler RH, Yang KS, Orth JD, Mitchison TJ, Weissleder R (2014) Single-cell pharmacokinetic imaging reveals a therapeutic strategy to overcome drug resistance to the microtubule inhibitor eribulin. Sci Transl Med 6:261ra152 22. Mikula H, Stapleton S, Kohler RH, Vinegoni C, Weissleder R (2017) Design and development of fluorescent vemurafenib analogs for in vivo imaging. Theranostics 7:1257–1265 23. Miller MA, Kim E, Cuccarese MF, Plotkin AL, Prytyskach M, Kohler RH, Pittet MJ, Weissleder R (2017) Near infrared imaging of Mer tyrosine kinase (MERTK) using MERi-SiR reveals tumor associated macrophage uptake in metastatic disease. Chem Commun (Camb) 54:42–45 24. Thurber GM, Yang KS, Reiner T, Kohler RH, Sorger P, Mitchison T, Weissleder R (2013) Single-cell and subcellular pharmacokinetic imaging allows insight into drug action in vivo. Nat Commun 4:1504 25. Thurber GM, Reiner T, Yang KS, Kohler RH, Weissleder R (2014) Effect of small-molecule modification on single-cell pharmacokinetics of PARP inhibitors. Mol Cancer Ther 13:986–995 26. Turetsky A, Kim E, Kohler RH, Miller MA, Weissleder R (2014) Single cell imaging of Bruton’s tyrosine kinase using an irreversible inhibitor. Sci Rep 4:4782 27. Dubach JM, Kim E, Yang K, Cuccarese M, Giedt RJ, Meimetis LG, Vinegoni C, Weissleder R (2017) Quantitating drug-target engagement in single cells in vitro and in vivo. Nat Chem Biol 13:168–173 28. Miller MA, Mikula H, Luthria G, Li R, Kronister S, Prytyskach M, Kohler RH, Mitchison T, Weissleder R (2018) Modular nanoparticulate prodrug design enables

434

Ran Li et al.

efficient treatment of solid tumors using bioorthogonal activation. ACS Nano 12:12814–12826 29. Dasari S, Tchounwou PB (2014) Cisplatin in cancer therapy: molecular mechanisms of action. Eur J Pharmacol 740:364–378 30. Villani G, Cazaux C, Pillaire MJ, Boehmer P (1993) Effects of a single intrastrand d(GpG) platinum adduct on the strand separating activity of the Escherichia coli proteins RecB and RecA. FEBS Lett 333:89–95 31. Yang KS, Kohler RH, Landon M, Giedt R, Weissleder R (2015) Single cell resolution in vivo imaging of DNA damage following PARP inhibition. Sci Rep 5:10129 32. Pineda JJ, Miller MA, Song Y, Kuhn H, Mikula H, Tallapragada N, Weissleder R, Mitchison TJ (2018) Site occupancy calibration of taxane pharmacology in live cells and tissues. Proc Natl Acad Sci U S A 115: E11406–E11414 33. Stuart CH, Horita DA, Thomas MJ, Salsbury FR, Lively MO, Gmeiner WH (2014) Sitespecific DNA-doxorubicin conjugates display enhanced cytotoxicity to breast cancer cells. Bioconjug Chem 25:406–413 34. Durkee MS, Nash LD, Nooshabadi F, Cirillo JD, Maitland DJ, Maitland KC (2018) Fabrication and characterization of optical tissue phantoms containing macrostructure. J Vis Exp (132):57031 35. Cheong WF, Prahl SA, Welch AJ (1990) A review of the optical properties of biological tissues. IEEE J Quantum Electron 26:2166–2185

36. Flock ST, Jacques SL, Wilson BC, Star WM, van Gemert MJ (1992) Optical properties of Intralipid: a phantom medium for light propagation studies. Lasers Surg Med 12:510–519 37. Palmer GM, Fontanella AN, Shan S, Hanna G, Zhang G, Fraser CL, Dewhirst MW (2011) In vivo optical molecular imaging and analysis in mice using dorsal window chamber models applied to hypoxia, vasculature and fluorescent reporters. Nat Protoc 6:1355–1366 38. Chittajallu DR, Florian S, Kohler RH, Iwamoto Y, Orth JD, Weissleder R, Danuser G, Mitchison TJ (2015) In vivo cellcycle profiling in xenograft tumors by quantitative intravital microscopy. Nat Methods 12:577–585 39. Fan J, de Lannoy IA (2014) Pharmacokinetics. Biochem Pharmacol 87:93–120 40. Li R, Attari A, Prytyskach M, Garlin MA, Weissleder R, Miller MA (2020) Single-cell Intravital microscopy of trastuzumab quantifies heterogeneous in vivo kinetics. Cytometry A. 97:528–539 41. Rodell CB, Arlauckas SP, Cuccarese MF, Garris CS, Li R, Ahmed MS, Kohler RH, Pittet MJ, Weissleder R (2018) TLR7/8-agonist-loaded nanoparticles promote the polarization of tumour-associated macrophages to enhance cancer immunotherapy. Nat Biomed Eng 2:578–588 42. Koh TJ, DiPietro LA (2011) Inflammation and wound healing: the role of the macrophage. Expert Rev Mol Med 13:e23

INDEX A A549 cells ........................................................................ 16 Absorption flip-flop kinetics phenomenon................................ 217 intramuscular injections .......................................... 217 jejunum and ileum .................................................. 215 leuprolide................................................................. 217 paracellular............................................................... 216 peptide drug ............................................................ 216 pharmacodynamic effect ......................................... 217 pharmacophore ....................................................... 216 QSAR predictions ................................................... 216 quantitative analysis ................................................ 217 route......................................................................... 217 SC injection ............................................................. 217 systemic exposures .................................................. 217 targets ...................................................................... 216 transcellular pathway............................................... 215 Absorption, Distribution, Metabolism, and Elimination (ADME)....................................................... 215 Absorption-limited elimination.................................... 217 Acclimatize mice ........................................................... 130 Accuracy/bias errors ..................................................... 298 Acid-catalyzed BOC deprotection ............................... 313 Acidic constant (pKa) ..................................................6, 21 Acidic endo-lysosomal system ...................................... 148 Acidic microenvironment ............................................... 27 Active drug transporters ................................................... 4 ADAPT ................................................................. 110, 111 ADC clearance ADC hydrophobicity vs. conjugation-induced clearance....................................................... 361 antigen interactions................................................. 359 biodistribution properties....................................... 359 conjugation-induced clearance...................... 360, 361 conjugation-induced nonspecific interactions ....... 364 drug conjugation..................................................... 360 FDA-approved agents ............................................. 359 hepatic enzyme elevation ........................................ 364 macropinocytosis..................................................... 361 nonspecific catabolism, IgG ................................... 360 PEG units ....................................................... 363, 364 PEGylated.............................................. 360, 362, 363 pharmacokinetics, antitumor activity ..................... 363

plasma clearance ............................................. 363, 364 prodrug.................................................................... 364 surface hydrophobicity hypothesis ......................... 360 systemic concentrations .......................................... 364 target-mediated disposition ........................... 359, 360 therapeutic antibodies............................................. 359 values........................................................................ 364 ADC degradation.......................................................... 311 ADME considerations .................................................. 222 ADME processes ........................................................... 215 ADME properties ......................................................... 223 ADME/Tox (Absorption, Distribution, Metabolism, Excretion/TOXicity) properties..................... 4 Administered imaging-agent ........................................ 298 AF488-labeled antibody ...................................... 255, 257 AF488-labeled LAMP-1 ............................................... 263 AF488-labeled mAb...................................................... 259 AF568-hIgG.................................................................. 264 AF568-labeled anti-human IgG................................... 263 AF647-labeled Rab11................................................... 263 Agent-to-agent binding ................................................ 289 Aggregates bioavailability........................................................... 127 cellular drug measurements (see Cellular drug measurements) cellular drug quantification ........................... 132, 133 CFZ (see Clofazimine (CFZ)) drug administration ................................................ 130 linear diattenuation ................................................. 129 macrophage (see Macrophages) measurement ........................................................... 136 optical anisotropy .................................................... 129 PLM ......................................................................... 129 sequestering vs. non-sequestering cell populations (see Sequestering vs. non-sequestering cell populations) Air–liquid interface systems (ALI) ................................. 16 Airway cell models .......................................................... 16 Airway epithelial cells........................................................ 9 Alexa Fluor 488 (A488) ............................. 245, 256, 270 Aliphatic and aromatic organic species ........................ 164 Alveolar macrophages .........................131, 139, 150, 151 Alzheimer’s disease (AD).............................................. 187 Amall-molecule ADME ................................................ 223 Amine............................................................................. 128

Gus R. Rosania and Greg M. Thurber (eds.), Quantitative Analysis of Cellular Drug Transport, Disposition, and Delivery, Methods in Pharmacology and Toxicology, https://doi.org/10.1007/978-1-0716-1250-7, © Springer Science+Business Media, LLC, part of Springer Nature 2021

435

QUANTITATIVE ANALYSIS

436 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Amphiphilic molecule ................................................... 166 Amphiphilicity ..................................... 167–169, 171–173 Amphiphilicity index (AI)............................................. 169 Amyloid β-peptide (Aβ) AD............................................................................ 187 BD-Oligo................................................................. 189 BD-Tau .................................................................... 190 brain related analytes...................................... 191, 192 CDr10b ................................................................... 191 CDr20...................................................................... 191 microglia ......................................................... 190, 191 NeuO selective labeling .......................................... 190 neurons .................................................................... 190 oligomers ................................................................. 189 tau proteins.............................................................. 190 Analyze Particles function ............................................ 137 Anaplastic large cell lymphoma (ALCL)...................... 369 Anionic peptide endothelin .......................................... 223 Anti-Alexa Fluor 488 (anti-AF488).................... 258, 270 Antibiotic amoxicillin.................................................... 128 Antibiotics ....................................................................... 20 Antibodies ADCs ....................................................................... 250 antibody–antigen binding kinetics ......................... 251 binding..................................................................... 251 biotherapeutics ........................................................ 249 cellular target antigen .................................... 249, 251 cellular trafficking.................................................... 250 internalization ......................................................... 250 intracellular disposition........................................... 250 intracellular internalization..................................... 249 pharmacological contexts ....................................... 250 target antigens ......................................................... 250 Antibody–antigen complex......................... 249, 259, 265 Antibody–antigen internalization rates........................ 252 Antibody-based therapeutics ........................................ 254 Antibody conjugate design, trastuzumab antibody-based bioconjugates ................................ 313 conjugation strategy................................................ 315 copper-free click chemistry ............................ 312, 317 HER2 receptor........................................................ 313 production and purification.................. 311, 315, 316 site-specific modification....................... 311, 316, 317 Antibody-dependent cellular cytotoxicity (ADCC) ....................................................... 307 Antibody–drug conjugate (ADC)......250, 260, 306, 307 amino acids .............................................................. 358 antibody ................................................................... 357 anticancer therapeutics ........................................... 331 antigen-independent mechanisms.......................... 358 anti-microtubule agents.......................................... 332 antitumor activity studies ....................................... 359 cellular-level PK-PD model (see Cellular-level PK-PD model)

AND

DELIVERY

cleavable linkers ....................................................... 332 cytotoxic agents....................................................... 332 cytotoxic drug molecules........................................ 332 cytotoxic molecules targeting DNA....................... 332 cytotoxic small-molecule drugs .............................. 331 drug payload............................................................ 358 Kupffer cells............................................................. 358 mAbs ........................................................................ 331 macrophages ............................................................ 358 noncleavable linkers ................................................ 333 oncology .................................................................. 357 payload molecules ................................................... 332 pharmacokinetic properties .................................... 358 pharmacology .......................................................... 333 PK profiles ............................................................... 340 plasma clearance ...................................................... 358 TAAs ........................................................................ 332 target antigens ......................................................... 332 targeted therapeutics............................................... 357 target-mediated disposition .................................... 358 therapeutic antibodies............................................. 358 toxicities................................................................... 358 Antibody internalization Ab1/Ab2 ........................................................ 260–262 Alexa Fluor 48................................................ 256, 257 antibody–antigen complex ..................................... 259 anti-fluorophore quenching reagent ...................... 258 cell suspensions ....................................................... 260 cellular kinetics ........................................................ 257 flow cytometry ........................................................ 260 fluorescence quenching efficiency ................. 256–258 fluorophore–antibody conjugate................... 255, 256 internalization rate .................................................. 255 LSECs ............................................................. 256, 259 MM ........................................................ 256, 261, 262 molar concentrations .............................................. 257 molecules/cell values .............................................. 260 pharmacokinetics............................................ 254, 255 Quantum™ Simply Cellular® ................................. 257 Simply Cellular® beads ........................................... 260 TMDD..................................................................... 254 velocity ..................................................................... 257 Anti-fluorophore antibody ........................................... 258 Anti-fluorophore quenching reagent ........................... 258 Antigen ADC uptake............................................................. 369 anti-CD22 and anti-CD79B ADCs ....................... 369 antitumor responses ................................................ 374 bystander effect .............................................. 374, 376 bystander killing ...................................................... 375 cancer cells ............................................................... 369 CD30 expression..................................................... 369 CD30- Karpas-35R cells ................................ 374, 375 CD30+ parental Karpas-299 cells........................... 374

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

CD71 and CD70 ADCs ......................................... 373 drug delivery ........................................................... 374 in vivo activity.......................................................... 374 in vivo tumor model ............................................... 374 intracellular payload delivery .................................. 369 MMAE ADCs................................................. 369, 373 Antigen internalization kinetics ................................... 250 Antigen-mediated drug delivery .................................. 374 Antigen-specific targeting............................................. 306 Anti-HER2 antibody binding ...................................... 308 Anti-inflammatory macrophage-loaded granulomas................................................... 143 Antitumor activity ....................................... 364, 370, 375 Apical membrane............................................................. 44 Apical outer (AO) monolayer......................................... 47 Apical uptake transporter (AT) ...................................... 56 Apical-to-basolateral (AP-to-BL) transport.............71, 75 Aqueous boundary layer ................................................. 28 Aqueous medium .............................................................. 8 Aromatic system ............................................................ 167 ARPE-19 cell line............................................................ 16 Artifacts.......................................................................... 173 Assay plate ....................................................................... 75 Assay systems ................................................................... 71 Atomic absorption spectroscopy (AAS)......................401, 403, 406, 407, 428 ATSP-7041.................................................................... 213 Aureobasidin A efflux.................................................... 223 Auristatin drug linkers ......................................... 366, 367

B Bafilomycin A .................................................................. 34 Basolateral aqueous chamber ......................................... 44 Basolateral membrane...............................................44, 61 Basolateral outer (BO) monolayer ................................. 47 Basolateral plasma membrane ........................................ 49 Basolateral surface ............................................................. 6 Basolateral uptake transporter (BT)............................... 56 BCRP inhibitor .........................................................72, 76 BCRP substrate ............................................................... 73 BCS guidelines ................................................................ 70 BD-105.......................................................................... 185 BD-Oligo....................................................................... 189 BD-Tau .......................................................................... 190 Bicyclic peptides ............................................................ 205 Big eaters ....................................................................... 147 Bile duct......................................................................... 101 Bile salt export pump (BSEP/ABCB11) ....................... 98 Binary mask ................................................................... 137 Binding kinetics............................................................. 260 Biochemical analysis methods ...................................... 129 Bioconjugate-based approach ...................................... 324 Biodegradable polymeric nanoparticles ....................... 400 Biological markers ......................................................... 149

AND

DELIVERY Index 437

Biomarkers cell area/size................................................... 153, 154 CLC7 .............................................................. 158–160 CLC-7...................................................................... 160 NF-kB ...................................................................... 158 NF-kB (p65)................................................... 156, 158 TFEB .............................................................. 155–157 TLR2 .............................................................. 154–156 TLR4 .............................................................. 154–156 V-ATPase ........................................................ 158–160 Biomembrane ................................................................ 163 Biomolecule-devoid ROI(s) ......................................... 290 Biopharmaceutics classification system (BCS)............... 70 Biotherapeutics.............................................................. 249 Bispecific antibodies antibody–antigen complex ..................................... 265 antibody internalization (see Antibody internalization) cellular internalization ............................................ 254 confocal microscopy method......................... 251, 266 flow cytometric methods ...................... 251, 252, 266 fluorescent approaches ................................... 251, 252 fluorescently labeled antibodies.............................. 253 high content microscopy ...................... 252, 266, 267 intracellular trafficking (see Intracellular trafficking) quantitative image analysis ............................ 267–269 quantitative microscopy method............................ 252 trafficking kinetics ................................................... 251 Blocking agent .............................................................. 151 Blood half-life values..................................................... 431 Blood plasma proteins .................................................. 289 Blood–brain barrier (BBB) ............................................... 9 BODIPY libraries ................................................. 184, 191 BODIPY probe.............................................................. 193 Bone marrow monocyte ............................................... 132 BO-Oligo....................................................................... 189 Borondipyrromethene (BODIPY) ............................... 181 Bovine serum albumin (BSA)................................ 21, 151 Breast cancer resistance protein (BCRP) .............. 70, 105 Bronchodilators............................................................... 16 Bronco-alveolar lavage (BAL) ...................................... 131 Bystander effect, ADCs antigen-independent mechanism ........................... 374 antigen-mediated drug delivery ............................. 376 antigen-positive cells ...................................... 374, 377 antitumor activity .................................................... 375 CD30- Karpas-35R cells ......................................... 375 cytotoxic effect ........................................................ 344 dual cell-level systems PK-PD model..................... 343 fitting coculture data............................................... 344 free drug molecules................................................. 340 GFP-MCF7 cells, tubulin occupancy function........................................................ 344 heterogeneous tumors ............................................ 343 in vivo model......................................... 344, 347, 348

QUANTITATIVE ANALYSIS

438 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Bystander effect (cont.) M&S strategy .......................................................... 344 quantitative characterization .................................. 343 tubulin occupancy .......................................... 343, 346 T-vc-MMAE ............................................................ 343 Bystander killing............................................................ 375

C Caco-2 ................................................................ 22, 60, 70 Cancer................................................................... 241, 242 Cancer stem cells (CSCs).............................................. 185 Cathepsin-sensitive ADCs ............................................ 310 Caveolin-dependent endocytosis.................................. 169 CD11b+F4/80+ myeloid cells...................................... 370 CDg4 ............................................................................. 184 CDr10b ......................................................................... 191 CDr20............................................................................ 191 CDy1 ............................................................................. 183 Cell area/size ....................................................... 153, 154 Cell barriers ....................................................................... 9 Cell-based assay systems ................................................. 70 Cell-based bidirectional permeability assays .................. 70 Cell-based functional assays.......................................... 220 Cell-based high-throughput screening Aβ .................................................................... 187–191 ESCs................................................................ 183–184 fluorescent probes ................................................... 183 pancreatic islets............................................... 184–186 taming probes................................................. 189–194 TICs ................................................................ 185–188 Cell culture medium ....................................................... 75 Cell culture system ........................................................ 222 Cell density ...................................................................... 23 Cell lines ............................................................. 59, 68, 70 Cell lysis ........................................................................... 25 Cell membranes............................................................. 5–8 Cell models........................................................... 8, 20, 29 Cell monolayers................................................................. 6 Cell penetrating peptide (CPP).................................... 231 accessible chemical space ........................................ 212 carboxylic acids........................................................ 211 cationic amino acids ................................................ 211 endosomal escape .................................................... 212 internalization ......................................................... 212 macrocyclic peptides ............................................... 212 membrane penetration............................................ 211 MMP-activatable charge shielded .......................... 212 protein delivery ....................................................... 211 sequence motifs ....................................................... 211 translocation ............................................................ 212 Cell-related species........................................................ 338 CellTrace™ dyes .................................................. 266, 271 Cellular compartments ................................................. 413 Cellular drug measurements

AND

DELIVERY

insoluble CFZ and quantification ................. 137, 140 macrophage and drug-sequestering cells...... 140, 141 Cellular drug quantification ................................ 132, 133 Cellular internalization ................................................. 254 Cellular pharmacokinetics............................................. 128 Cellular target antigens........................................ 251, 266 Cellular/tissue retention .............................................. 282 Cellular uptake ..................................................... 180, 404 Cellular-level PK-PD model ADCs .............................................................. 333, 352 applications .............................................................. 352 bystander effect (see Bystander effect, ADCs) cell killing process ................................................... 338 cell-related molecular species ................................. 334 cell-related species .......................................... 333, 338 cell-specific parameters............................................ 333 cell-surface antigen level ......................................... 338 cell-surface TAA ...................................................... 333 cellular disposition, ADCs ............................. 339–341 drug and system-related properties........................ 333 drug-specific parameters ......................................... 352 drug-specific properties .......................................... 333 free payload molecules ............................................ 334 GSA........................................................ 338, 339, 342 HER2....................................................................... 339 M&S strategy ................................................. 333, 349 model parameters .................................................... 336 PD portion .............................................................. 334 PK portion............................................................... 334 preclinical-to-clinical translation, ADCs347, 350, 351 sacituzumab govitecan ............................................ 353 TAA.......................................................................... 339 tubulin-bound drug molecules .............................. 338 variables ................................................................... 335 Central nervous system (CNS).................................9, 190 CFZ-HCl salt crystals ................................................... 149 Chameleon-like conformational adaptation ................ 210 Chameleon-like macrocycles ........................................ 213 Charge-neutralized masked polyarginine peptide ....... 223 Chemotherapeutic cargos ............................................. 307 Chenodeoxycholic acid (CDCA) ................................. 105 Chloride counterions .................................................... 149 Chloroalkane penetration assay (CAPA) ..................... 238 Chloroquine ................................................ 30, 31, 33, 34 Chromatographic methods .......................................... 254 Chromatography ........................................................... 253 Chronic obstructive pulmonary disease (COPD) ......... 16 Chymotrypsin................................................................ 214 Cladribine ........................................................... 71, 72, 76 Classic small molecule drugs ........................................ 222 Clathrin-dependent endocytosis .................................. 169 CLC7 .................................................................... 158–160 Cleavable crosslinker ..................................................... 313 Cleavable structure and design..................................... 314

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Clofazimine (CFZ) ....................................................... 150 antibiotic.................................................................. 128 cellular accumulation .............................................. 134 CLDIs ...................................................................... 128 drug accumulation .................................................. 128 FDA-approved antibiotic........................................ 148 hydrochloride .......................................................... 133 insoluble ......................................................... 137, 140 Kupffer cells............................................................. 138 macrophages alveolar ............................................................... 139 liver, spleen and lung ........................................ 142 peritoneal ........................................................... 138 macrophage sequestration ...................................... 148 multidrug resistant tuberculosis ............................. 130 optical properties............................................ 132, 134 phenazine red dye ................................................... 148 physicochemical properties..................................... 128 protonated and free base ........................................ 128 treatment ........................................................ 149, 150 Combinational chemistry ................................4, 181, 182 Compartment-/diffusion-based mathematical models .......................................................... 284 Complex models agent-/drug-distribution ....................................... 280 control agent ........................................................... 285 data fitting model.................................................... 285 diffusion................................................................... 286 experimental data .................................................... 285 in vivo and in vitro .................................................. 286 parameter ................................................................. 286 scenario .................................................................... 285 targeted biomolecule .............................................. 285 use ............................................................................ 280 Complexing agents ......................................................... 29 Complications ............................................................... 290 Computational analysis ................................................. 214 Conditioned media ....................................................... 324 Confluent cell monolayers adjacent cells.............................................................. 43 apical membrane .................................................44, 45 basolateral uptake...................................................... 45 biological membranes ............................................... 46 brick-wall like structures ........................................... 44 elementary passive permeability coefficients............ 44 free lateral diffusion .................................................. 42 HPTCs....................................................................... 46 identical transporters ................................................ 46 kinetic evidence ......................................................... 45 lipid lateral diffusion ................................................. 45 MDCK cell monolayer.............................................. 42 MDCKII-hMDR1-NKI cells ................................... 46 protocol ..................................................................... 45 transporters identity.................................................. 46

AND

DELIVERY Index 439

Transwell system ....................................................... 44 uptake transporters ................................................... 46 Confocal laser scanning microscopy ................... 318, 319 Confocal microscopy ........................ 237, 240, 243, 246, 266, 321, 428 Confocal microscopy-based visualization FRET probe colocalization .................. 312, 319, 320 kinetic bond cleavage ............................ 312, 318, 319 Conjugate-based carriers .............................................. 306 Conjugate-based drug delivery systems....................... 323 Conjugated bond number (CBN) ...................... 167, 169 Conjugation-induced clearance.......................... 360, 361, 366–368, 378 Content synthesis .......................................................... 206 Control agent delivery .................................................. 277 Control agent kinetics .................................................. 298 Control imaging-agents albumin .................................................................... 289 biological molecule ................................................. 285 cancerous/noncancerous conditions ..................... 297 data analyses ............................................................ 287 definition ................................................................. 285 detection channels.......................................... 292, 293 distribution and binding......................................... 280 equilibrium .............................................................. 289 extravasation ............................................................ 280 input functions ........................................................ 294 ROI .......................................................................... 295 selection ................................................................... 277 signals....................................................................... 293 targeted agent.......................................................... 280 targeted biomolecule .............................................. 295 Copper-free click chemistry........................ 194, 312, 317 Count cells..................................................................... 131 CRISPR-Cas9 knockout screening .............................. 191 Cryopreservation........................................................... 137 Crystal-like drug inclusions (CLDIs) ................ 128, 149, 152, 155, 159 Cultured cells ................................................................ 167 Cy5 fluorescence channel .................................... 149, 153 Cyclic chemotypes......................................................... 206 Cyclic peptides .............................................................. 205 Cyclodextrins (CDs) ....................................................... 29 Cyclosporine A (CsA) ................................. 128, 202, 210 Cyclotides biologic molecules................................................... 231 CAPA .............................................................. 238, 239 chemical synthesis ................................................... 230 confocal microscopy......................237, 240, 243, 246 cyclic peptides.......................................................... 229 drug discovery ......................................................... 230 EETI-II.................................................................... 230 endosomal trafficking.............................................. 238 globular proteins ..................................................... 230

QUANTITATIVE ANALYSIS

440 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Cyclotides (cont.) internalization studies .................................... 231, 232 internalized peptides ............................................... 231 internalized peptides, flow cytometry A488-labeled CPP ............................................ 244 cell culture ................................................ 240, 241 fluorescence emission intensity......................... 244 fluorescence intensity ........................................ 244 internalization assay .........................240–243, 245 sampling parameters.......................................... 243 intracellular localization ................................. 231, 238 kalata B1 ......................................................... 229, 231 labeling peptides.....................................239–241, 245 MCoTI-I.................................................................. 231 MCoTI-II ....................................................... 230, 231 molecular grafting ................................................... 230 multiple cell-entry mechanisms .............................. 237 prototypic cyclotide kalata B1 ....................... 230, 236 small-molecule drugs .............................................. 231 thermal unfolding ................................................... 229 Cytochrome P450 (CYP) 3A activity .......................... 104 Cytosolic drug concentrations digoxin (see Digoxin) experimental scenarios .............................................. 51 IVIVE ........................................................................ 51 ketoconazole........................................................ 52–54 P-gp substrates .......................................................... 51 statistical analysis ....................................................... 52 transcellular transport ............................................... 51 Transwell cells............................................................ 51 Cytosolic plasma membrane........................................... 58 Cytotoxic agents ........................................................... 332 Cytotoxic anticancer chemotherapies .......................... 397 Cytotoxic drug molecules............................................. 332 Cytotoxic molecules targeting DNA ........................... 332 Cytotoxic small-molecule drug target ................ 250, 331

D Damage-associated molecular patterns (DAMPs) ...... 154 DAPI.............................................................................. 153 Data collection methodology ....................................... 292 Data processing, paired-agent background subtraction.......................................... 293 input function correction .............................. 294, 296 motion correction .......................................... 292, 293 targeted and control agent concentrations .................................... 293, 294 DBCO-modified trastuzumab...................................... 317 DBCO-PEG4-amine..................................................... 324 Deoxycholic acid (DCA) .............................................. 105 Design–chemistry–biology testing cycles .................... 220 Desmosomes ..................................................................... 6 Determined transcellular transport rate (dM/dt).......... 18 Dextran .......................................................................... 431

AND

DELIVERY

Dibenzocyclooctyne (DBCO)...................................... 315 Diffusion......................................... 4, 6, 9, 15, 17, 21, 27 Digoxin apical chamber.....................................................56, 57 assay requirements .................................................... 57 AT mediated transport.............................................. 55 BT ........................................................................56, 57 cell lines ..................................................................... 59 cytosolic concentrations......................................55, 56 ER .............................................................................. 72 experimental value..................................................... 58 fC value ...................................................................... 58 kinetic design............................................................. 57 low nM drug concentrations .................................... 58 nonlinear behavior .................................................... 58 passive permeability...................................... 54, 56, 61 predicted cytosolic concentration ............................ 54 predicted values ......................................................... 56 predictions ................................................................. 57 stationary state .......................................................... 56 structural mass action kinetic model........................ 59 time independent amplitude .................................... 57 uptake transporter...............................................54, 59 Dipole–dipole interaction ............................................. 384 Dissociation constant (KD) .......................................... 308 Disulfide bonds ............................................................. 309 Disulfide-linked nanoparticles ...................................... 309 Diversity-oriented fluorescence library approach (DOFLA) compounds .............................................................. 181 fluorescent molecule library .......................... 180, 181 fluorescent probe..................................................... 180 high-throughput screening (see Cell-based high-throughput screening) in vivo experiments ................................................. 182 target recognition motif ......................................... 180 DMSO .......................................................................20, 21 DNA damage............................................... 403, 425, 427 Double-edged sword .................................................... 128 Downregulating growth factor receptors .................... 250 Drug absorption.............................................................. 15 Drug accumulation ....................................................... 128 Drug administration to animals................................................................. 130 Drug candidates ................................................................ 4 Drug classification........................................................... 70 Drug conjugation ......................................................... 360 Drug delivery ....................................................... 313, 363 Drug depots ......................................................... 398, 399 Drug–drug interactions (DDI) cellular models........................................................... 79 enzyme-/transporter-mediated................................ 70 in vivo ........................................................................ 79 P-gp/BCRP ........................................................73, 77

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Drug masses .................................................................... 26 Drug metabolism ........................................................4, 98 Drug molecules ...................................................... 5, 7, 21 Drug partitioning, membrane monolayers AO.............................................................................. 47 aqueous phase and lipid ............................................ 47 BO.............................................................................. 47 equilibrium dialysis binding ..................................... 47 equilibrium molar concentrations ............................ 48 molar mixture............................................................ 48 p-gp kinetic models................................................... 47 phospholipid molecules ............................................ 47 Drug passive permeability concentration gradient.............................................. 50 confluent cell monolayers ......................................... 48 digoxin ....................................................................... 62 drug concentration ................................................... 49 equation ..................................................................... 49 equilibrium ................................................................ 50 mathematical solution............................................... 49 uptake transporters ................................................... 50 Drug pharmacology ...................................................... 399 Drug related parameters ............................................... 340 Drug release .................................................................. 308 Drug solubility ................................................................ 21 Drug target imaging ..................................................... 279 Drug transport assays........................................................ 4 Drug transport kinetics ADME/Tox properties............................................... 4 cell membranes........................................................ 5–8 cellular penetration ................................................... 34 chemical characteristics ............................................. 34 experimental equipment ........................................... 22 high-throughput screenings ....................................... 4 in vitro cell models (see In vitro cell models) measurements ...................................................... 22–26 NCEs ........................................................................... 4 PAMPA assays.............................................................. 4 permeability ................................................8, 9, 11, 12 rates and intracellular accumulation................... 22–26 reagents................................................................20, 21 reproducibility and reliability ............................. 27–29 Drug transporters BCRP ......................................................................... 70 efflux .......................................................................... 70 HEK........................................................................... 71 SLC ............................................................................ 70 superfamilies .............................................................. 70 uptake ........................................................................ 71 Dual cell-level systems PK-PD model.........................343, 344, 347 Dulbecco’s modified Eagle’s medium (DMEM) .......... 20 Dynamic Light Scattering............................................. 391

AND

DELIVERY Index 441

E Efflux ratio (ER) ................................................ 72, 75, 76 Efflux transporter inhibitors BCRP ......................................................................... 76 P-gp inhibitor............................................................ 75 Electrospray ionization (ESI) ......................................... 94 Electrospray Ionization Mass Spectrometry (ESI-MS)...................................................... 239 Elementary passive permeability coefficients ................. 44 Embryonic stem cells (ESCs) CDb8 ....................................................................... 184 CDg4 ....................................................................... 184 CDy1 .............................................................. 183, 184 fluorescence molecules............................................ 183 MEF ......................................................................... 183 members rosamine library ...................................... 183 pluripotent stem cells.............................................. 183 selective probes............................................... 184, 185 selective staining...................................................... 183 subcellular localization............................................ 184 Endocytosis .........................................168, 243, 245, 307 Endolysosomal pathway................................................ 404 Endosomal escape ................................................ 210, 212 Endosomes .................................................................... 307 Endothelial cell monolayer .........................................6, 17 Endothelial cells ................................................................ 9 Engineered nucleic acids............................................... 279 Enhanced permeability and retention (EPR) ......................................... 384, 397, 398 Epidermal growth factor receptor (EGFR/ErbB-1).......................................... 338 Epithelial cell monolayer ............................................6, 17 Epithelial cells...........................9, 15, 17, 20–26, 28, 167 Epithelial-to-mesenchymal transition (EMT) ............. 188 Epithelium ....................................................................... 15 Equilibrium dialysis cell homogenate preparation .................................... 85 dilution method ........................................................ 84 HTD96 device ....................................................85, 86 pre-saturation method .............................................. 87 standard method .......................................... 84, 86, 87 tissue homogenate preparation ..........................84, 85 unstable compounds ...........................................87, 89 Estradiol metabolites .................................................... 102 Eukaryotic cells .................................................... 164, 167 membrane-within-membrane compartmentalization ................................. 167 European Medicine Agency (EMA) .............................. 70 Euthanize mice.............................................................. 130 Ex situ IPLs ................................................................... 101 Ex vivo intestinal segments............................................... 4 Exogenous molecular imaging-agent strategies .......... 276 Exposed polar surface area (EPSA)..................... 211, 216

QUANTITATIVE ANALYSIS

442 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Extracellular concentration....................... 5, 9–16, 19, 33 Extracellular drug concentration gradient..................... 17 Extracellular fluid .......................................................... 216 Extravasation ................................................................. 216

F FACs .............................................................................. 260 Far-red fluorescence ...................................................... 134 Far-red regions .............................................................. 134 Fcγ receptors ................................................................. 370 FcγR-expressing monocytes ......................................... 371 FcγR-mutated antibodies ............................................. 370 FDA-approved ADCs .......................................... 308–310 FDA-approved antibiotic.............................................. 148 FDA-approved compounds .......................................... 306 FDA-approved stimuli-responsive drug delivery systems ......................................................... 323 Fialuridine........................................................................ 98 Fick’s first law ..............................................................9, 17 Fiji software ................................................. 243, 246, 320 Flocculation ................................................................... 395 Flow cytometry ......................... 231, 236, 238–244, 251, 255, 258, 260, 266 Flow cytometry-based quantification......... 312, 321, 322 Fluorescein .................................................................... 256 Fluorescence ......................................................... 129, 136 emission intensity ........................................... 243, 244 imaging .................................................................... 299 intensity .......................................................... 388, 390 microscopy...................................................... 129, 151 quenching efficiency....................................... 256–258 Fluorescence-activated cell sorter (FACS)................... 183 Fluorescent antibodies .................................................. 150 Fluorescence-based assays............................................. 254 Fluorescence-based imaging......................................... 195 Fluorescent bleed-through ......................... 411, 412, 429 Fluorescent detection ................................................... 253 Fluorescent drug conjugates ............................... 399, 400 Fluorescent dyes ............................................................ 428 Fluorescence/Fo¨rster resonance energy transfer (FRET) capable system ......................................................... 385 dipole–dipole interaction ........................................ 384 donor and acceptor fluorophores........................... 384 EFRET ....................................................................... 384 Qdots ....................................................................... 385 quantitative and qualitative information................ 384 siRNA ...................................................................... 385 Fluorescent HeLa cells.................................................. 231 Fluorescent imaging probes eukaryotic cells ........................................................ 164 Fluorescent intensity ............................................ 426, 430 Fluorescent labeling ............................................. 179, 195 Fluorescent molecules................................................... 179 Fluorescent probe ...............................172, 174–176, 180

AND

DELIVERY

Fluorescent proteins...................................................... 404 Fluorescent sensors .............................................. 179, 181 Fluorescently labeled antibodies .................251–253, 258 Fluorescently labeled mAbs .......................................... 339 Fluorophore/anti-fluorophore antibody pair ............. 256 Fluorophore–antibody conjugate................................. 255 Fluorophore-drug conjugates ...................................... 408 Fluorophore Rhodamine Green (TM) .......................... 47 Fluorous solid phase extraction (FSPE) ...................... 313 Fluxes ............................................................................... 17 Fraction unbound (fu) indirect methods ....................................................... 82 measurement (see fu measurement) Free payload molecules ................................................. 334 Free-standing, luminal/apical surface.............................. 6 FreeStyle™ MAX transfection reagent ........................ 315 FRET-based approaches ............................................... 384 FRET-based bioconjugates........................................... 310 FRET-based crosslinker ................................................ 324 FRET-based folate conjugate ....................................... 309 FRET–based nanocarrier reporting system data analysis ............................................................. 395 fluorescence spectroscopy assessing polyplex stability ...............389, 391, 392 capability ....................................................388–390 heparin competition assay.............................. 392, 394 NMR (see NMR analysis) particle formation........................................... 386, 387 photobleaching........................................................ 386 polyplex assembly ........................................... 387, 388 quantitative analysis ....................................... 394, 395 FRET-based reagent ..................................................... 309 FRET-based stability assay............................................ 389 FRET-based trastuzumab conjugate............................ 310 FRET donor and acceptor molecule............................ 394 FRET efficiency (EFRET) ...................................... 384, 392 FRET-labeled crosslinker.............................................. 313 FRET signal intensity.................................................... 389 Functional cell factors ................................................... 168 Fungal metabolite mixture ........................................... 202

G Gadobenate dimeglumine (Gd-BOPTA) ........... 112, 115 Gadopentetate dimeglumine (Gd-DTPA)................... 112 Gadoxetate disodium ...................................................... 98 Games-Howell post hoc test ........................................ 153 Gamma scintigraphy ..................................................... 112 Gap junctions .................................................................... 6 Gastrointestinal tract......................................................... 9 Gd-DTPA ...................................................................... 112 GFP-MCF7 cells ........................................................... 345 Gibbs free energy .......................................................... 211 Global sensitivity analysis (GSA) ........................ 338, 339, 341, 342, 352

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Glucagon Yellow (GY).................................................. 185 Glucocorticoids ............................................................... 16 Glycine ........................................................................... 105 GraphPad Prism software ............................................. 395 Guided synthesis .................................................. 174, 176 Gut lumen ......................................................................... 9

H Hank’s balanced salt solution (HBSS).................. 20, 105 Headgroup hydrophilicity (HGH) ..................... 167, 169 Headgroup size (HGS)................................................. 169 Heat-inactivated fetal bovine serum (FBS) ................... 20 HeLa cells ............................................................. 236, 237 HELLMANEX buffer................................................... 391 Hemocytometer .............................................23, 131, 132 Heparin competition assay .................................. 392–394 Hepatic and extrahepatic metabolism.......................... 222 Hepatic gamma counts ................................................. 116 Hepatic intracellular drug concentrations drug-metabolizing enzymes ..................................... 98 fialuridine ................................................................... 98 free-drug hypothesis ................................................. 97 gadoxetate disodium................................................. 98 HepatoPac ............................................................... 119 intracellular pH ......................................................... 97 IPL (see Isolated perfused liver (IPL)) LESA-MS................................................................. 120 lysosomal sequestration ............................................ 99 membrane transport proteins ................................... 98 mitochondrial membrane proteins........................... 98 MSI ................................................................. 119, 120 plasma drug concentrations...................................... 97 processes .................................................................... 98 SCH (see Sandwich-cultured hepatocytes (SCH)) statins ......................................................................... 99 subcellular sequestration......................................... 120 transport proteins...................................................... 98 troglitazone ............................................................... 99 xenobiotics................................................................. 98 Hepatic metabolism ...................................................... 220 Hepatic uptake transporters ........................................... 78 HepatoPac ..................................................................... 119 HER2 homodimerization............................................. 307 HER2 positive cell lines................................................ 308 HER2-targeted therapies..................................... 307, 308 Hexamers....................................................................... 217 Hierarchical strategies, drug discovery bioavailability and distribution ............................... 223 cell culture system ................................................... 222 collective process ..................................................... 220 conventional ADME ............................................... 223 conventional therapeutics ....................................... 222 in vivo target engagement ............................. 220, 222 parallel peptide synthesis......................................... 222

AND

DELIVERY Index 443

peptide libraries ....................................................... 220 pharmacological activity.......................................... 222 scaffolds identification ............................................ 220 sequence optimization ............................................ 220 structure–activity relationships ............................... 222 High content microscopy ............................252, 266–268 High-performance liquid chromatography (HPLC)...................................... 132, 239, 314 High-performance liquid chromatography–tandem mass spectrometry (HPLC-MS/MS) ................. 102 High-quality fluorescent signal .................................... 189 High-throughput DNA barcoding studies.................. 306 High-throughput drug screening .................................... 4 High-throughput in vitro screening techniques ......... 181 High-throughput screening assays................................... 4 Histone deacetylase (HDAC)....................................... 184 HT1080 cancer cells ..................................................... 419 HT1080-53BP1-mApple tumors ...............415–417, 419 HTD96 device ..........................................................85–88 Human alveolar epithelial cells (hAEpC) ...................... 16 Human corneal epithelial (HCE-T) cell model ............ 16 Human epidermal growth factor receptor 2 (HER2) ............ 307, 338, 339, 347, 349, 350 Human kidney epithelial 293 (HEK) ............................ 71 Human MDR2.............................................................. 223 Human proximal tubule cells (HPTCs) ........................ 46 Hydrolytically sensitive chemistries.............................. 308 Hydrophilicity ............................................. 165, 167, 172 Hydrophobic drugs....................................................... 143 Hydrophobic interaction chromatography (HIC) .................................................. 317, 366 Hydrophobic prodrugs................................................. 400 Hydrophobic TNP components .................................. 400 Hydrophobicity ............................................................. 213

I Image analysis.............................................. 153, 267, 269 Image analysis program .................................................. 25 Image-based fluorescence quantification ..................... 184 Image-based reporters .................................................. 404 ImageJ................................................................... 137, 153 Imaging agents .............................................................. 276 administration ................................................ 290, 299 distribution .............................................................. 280 dosing ............................................................. 289, 299 preparation ..................................................... 287, 289 Imaging protocol .......................................................... 297 Imaging system detection sensitivity ........................... 297 Immunocytochemistry (ICC) analysis ................ 150–152 Immunohistochemical methods................................... 140 Immunohistochemistry (IHC)..................................... 377 In situ IPLs.................................................................... 101 In vitro cell culture models ........................................5, 27 In vitro cell models

QUANTITATIVE ANALYSIS

444 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

In vitro cell models (cont.) advantages.................................................................... 9 airway cell models ..................................................... 16 biological barrier ....................................................... 15 cell culture models ...................................................... 9 endothelial cells ........................................................... 9 intestinal cell models ...........................................15, 16 ocular cell models...................................................... 16 permeability ................................................................. 9 transporter proteins/drug metabolizing enzymes.......................................................... 15 In vitro cellular models ..................................9, 11, 13, 14 In vitro inhibition criteria ............................................... 78 In vitro internalization.................................................. 212 In vitro Kpuu.............................................................92, 94 In vitro model systems biological system and experimental design.............. 69 collaborative study .................................................... 69 complementary interaction....................................... 69 DDI (see Drug interactions (DDI)) drug–enzyme interaction.......................................... 68 drug–transporter ....................................................... 68 endogenous transporters .......................................... 68 membrane transporter assays.................................... 69 permeability experiments .......................................... 70 translational value...................................................... 69 transport activity equations ...................................... 69 transporter-/enzyme-mediated drug interactions .................................................... 69 In vitro respiratory epithelial cell models ...................... 16 In vitro, TNP fluorescent intensity, Pt(IV)-BODIPY................... 405 protocol .......................................................... 405, 406 Pt concentration...................................................... 405 quantification subcellular localization colocalization analysis ....................................... 410 drug payload...................................................... 407 fluorescently labeled TNP vehicles................... 407 payloads ............................................................. 407 phagocytosis/endocytosis................................. 407 protocols ................................................... 407, 410 quantitative data analysis ......................... 412, 413 single-fluorophore control experiments......................................... 411, 412 quantitative data analysis ............................... 406–409 In vitro–in vivo extrapolations (IVIVE) ........... 42, 51, 63 In vivo animal models ....................................................... 4 In vivo animal pharmacokinetic experiments .................. 4 In vivo systemic exposure absorption (see Absorption) ADME processes ..................................................... 215 drug distribution ............................................ 216–219 metabolism and elimination .......................... 219–221 passive permeability................................................. 215

AND

DELIVERY

peptide drug candidate ........................................... 215 pharmacodynamics.................................................. 215 proteolytic activity................................................... 215 In vivo, TNP fluorescent intensity and concentrations ............... 413 IVM quantification (see Intravital microscopy (IVM) quantification) optical tissue phantom ............................................ 413 protocol ................................................................... 413 quantitative data analysis ............................... 413, 415 Influx rate ratios ........................................................74, 78 Insoluble CFZ ...................................................... 137, 140 Insulin ................................................................... 202, 217 Internalization .............................. 25, 212, 241, 243, 245 kinetics ..................................................................... 254 rates................................................................. 252, 255 Internalized molecules .................................................. 307 Intestinal cell models ................................................15, 16 Intestinal mucosa .............................................................. 9 Intracellular accumulation ................................. 27, 33, 34 Intracellular bioanalytes ................................................ 195 Intracellular cleavage bonds antibody conjugate design............................. 311–317 bond degradation.................................................... 322 chemical structure ................................................... 308 confocal microscopy-based visualization ................................312, 318–320 degradation.............................................................. 309 disulfide.................................................................... 309 flow cytometry-based quantification............................. 312, 321, 322 fluorescent probe design................................ 312–314 intramolecular self-quenching ................................ 309 stimuli ...................................................................... 308 Intracellular drug accumulation ..................................... 29 Intracellular exposure.................................. 208, 209, 214 Intracellular free drug concentration free medium concentration in vitro ......................... 82 free plasma concentration in vivo............................. 82 fu measurement (see fu measurement) indirect methods .................................................82, 83 intracellular targets.................................................... 81 Kpuu .......................................................................... 93 LC-MS/MS method................................................. 94 processes .................................................................... 82 subcellular organelles ................................................ 82 total drug concentration measurement ................... 83 Intracellular location ..................................................... 240 Intracellular metabolism cell homogenates..................................................... 214 chymotrypsin ........................................................... 214 exposure................................................................... 214 inhibit/disrupt formation....................................... 214 peptidase genes........................................................ 214

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

peptide stability ....................................................... 214 protease inhibitors................................................... 214 proteolytic stability.................................................. 214 Intracellular oligosaccharide metabolism..................... 194 Intracellular pH............................................................... 97 Intracellular probe....................................... 312, 322, 323 Intracellular protein–protein interactions.................... 223 Intracellular stimuli ....................................................... 308 Intracellular trafficking Ab1 and Ab2 .................................................. 263–265 AF488-labeled LAMP-1 ......................................... 263 AF568-hIgG............................................................ 264 AF568-labeled anti-human IgG............................. 263 antibody localization...................................... 263, 264 antibody–drug conjugates ...................................... 260 cells........................................................................... 260 LAMP-1................................................................... 263 LSECs ...................................................................... 260 mechanistic PK models ........................................... 260 recycling endosomes ............................................... 264 Intracellularization ........................................................ 280 Intramolecular hydrogen bonds................................... 210 Intramolecular self-quenching ..................................... 309 Intravital microscopy (IVM) quantification biodistribution, TNP vehicle protocol ............................................................. 419 quantitative data analysis .................419, 421–423 pharmacokinetics fluorescently labeled TNP vehicle ........... 415, 416 HT1080-53BP1-mApple tumors ........... 415, 417 payload............................................................... 416 TNP .......................................................... 416, 418 quantitative data analysis ............................... 418, 419 TNP Pt payload redistribution DNA damage, TAM ........................424, 425, 427 TAM..................................................422, 424, 426 IOBA-NHC cell line....................................................... 16 Ionizable group ............................................................. 128 Ionized drug.................................................................. 6, 7 Ion-trapping .................................................................. 128 IR-MALDESI................................................................ 119 Irreversible models ........................................................ 295 IsoData .......................................................................... 429 Isolated liver ......................................................... 101, 102 Isolated perfused liver (IPL) advantages.................................................................. 98 ambient temperature and humidity ....................... 101 applications ..................................................... 102, 103 bicarbonate-buffered saline solution...................... 101 bile duct ................................................................... 101 blood flow ............................................................... 101 buffers ............................................................. 106, 107 differential centrifugation ....................................... 106 drug metabolism ....................................................... 98

AND

DELIVERY Index 445

equipment....................................................... 106, 107 ex situ....................................................................... 101 experiments ............................................................... 98 hepatic preservation/reperfusion injury .................. 98 hepatobiliary disposition, compounds ............ 98, 100 imaging methods....................................112, 115–117 in situ ....................................................................... 101 isolated liver............................................................. 101 liver physiology.......................................................... 98 liver tissue homogenization.................................... 109 Matrigel™ ................................................................ 118 media .............................................................. 106, 107 metabolism-related parameters .............................. 115 pathophysiology ........................................................ 98 perfusion .................................................................. 115 pharmacokinetic model..........................109–111, 115 radiochemical detection .......................................... 115 sample collection ............................................ 108, 109 technical considerations .......................................... 102 unbound intracellular concentrations data analysis ....................................................... 112 protocols ...........................................110, 112, 113 sample ................................................................ 112 subcellular fractions .......................................... 110 Iterative design–chemistry cycles ................................. 222

K K-clustering analysis...................................................... 137 Ketoconazole cytosol concentrations .............................................. 52 donor chamber concentrations ................................ 52 kinetic design............................................................. 54 MDCKII-hMDR1-NKI cells ................................... 54 passive permeability coefficients ............................... 52 uptake transporters ................................................... 54 Kinetic modeling central challenge...................................................... 285 complex (see Complex models) imaging-agent distribution..................................... 280 mathematical models .............................................. 280 simpler (see Simpler models) K-means clustering ....................................................... 138 Krogh Cylinder PBPK models ..................................... 297 Kupffer cells.............. 130–133, 138, 358, 361, 367, 368

L Labeling peptides .........................................239–241, 245 Lactate dehydrogenase (LDH) .................................... 119 LAMP-1......................................................................... 263 LCMS analysis ............................................................... 188 LC-MS/MS.........................................88, 90–92, 94, 113 LC-PolScope ........................................................ 129, 134 Lectin ............................................................................. 431

QUANTITATIVE ANALYSIS

446 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Leprosy .......................................................................... 149 Leuprolide and octreotide ............................................ 216 Library screening approaches .............................. 207, 223 Linear diattenuation............................................. 129, 134 Lipidation ...................................................................... 216 Lipid-rich membranes....................................................... 7 Lipophilic domain ......................................................... 166 Lipophilic drugs .............................................................. 27 Lipophilic partitioning Mdm2 and Mdmx................................................... 213 physical properties................................................... 213 subcellular fractionation ......................................... 213 uptake and intracellular distribution ...................... 213 Lipophilic reporter-vectors ........................................... 280 Lipophilic small-molecule drugs .................................. 254 Lipophilicity ......... 6, 165–167, 169, 171–173, 216, 280 Liquid chromatography–mass spectrometry (LC-MS)314 Liquid chromatography with tandem mass spectrometry (LC-MS/MS) ......................60, 71, 73, 75, 77 Liquid extraction surface analysis mass spectrometry (LESA-MS) .................................................. 120 Live/dead cells ................................................................ 25 Liver homogenization................................................... 102 Liver sinusoidal endothelial cells (LSECs).......... 256, 259 Liver tissue homogenization ........................................ 109 Livers.............................................................................. 102 LLC-PK1-hMDR1 cells ................................................. 45 logP parameter .............................................................. 167 logP/logD measurements ................................................ 7 Loperamide ...............................................................50, 60 Lysosomal degradation................................................. 321 Lysosomal drug sequestration...................................... 148 Lysosomal sequestration................................................. 99 Lysosomes ............................................................ 263, 264

M Macrocycles ................................................................... 216 Macrocyclic cell-penetrating peptides .......................... 206 Macrocyclic peptides ..................................................... 212 Macromolecular therapeutics ....................................... 306 Macrophages ........................................................ 358, 371 alveolar ................................................... 131, 139, 150 biomarkers ...................................................... 153–160 bone marrow monocyte.......................................... 132 characterizing .......................................................... 149 drug-exposed........................................................... 135 and drug-sequestering cells ........................... 140, 141 fluorescence microscopy ......................................... 151 ICC analysis .................................................... 150–152 Kupffer cells.................................................... 131, 132 linear diattenuation of control and drug-treated.......................................... 137 liver, spleen and lung .............................................. 142 lysosomal drug sequestration ................................. 148

AND

DELIVERY

optical density.......................................................... 137 peritoneal ........................................................ 131, 138 subpopulation.......................................................... 149 TFEB ....................................................................... 148 weakly basic drugs................................................... 148 Macropinocytosis .......................................................... 361 Magnetic resonance imaging (MRI).............................. 98 Maleimido-caproyl auristatin F (mc-MMAF) ............. 366 Mass action kinetic model ............................................ 322 Mass balance....................................................... 29–34, 60 Mass spectrometry imaging (MSI) .............................. 119 Mass transport ................................................................. 17 MATLAB code .............................................................. 322 Matrix metalloproteinases (MMP)............................... 212 MaxEntropy................................................................... 429 MDCK cells ...................................... 9, 15, 16, 30, 31, 33 MDCK II......................................................................... 23 MDCK II cells................................................................. 22 MDCKII-hMDR1-NKI cells basolateral uptake transporter .................................. 54 BT .............................................................................. 46 cytosolic monolayer .................................................. 58 digoxin .................................................................45, 57 disparity ..................................................................... 60 kA values .................................................................... 57 kB clearances ............................................................. 59 KP values ................................................................... 54 passive permeability coefficients ............................... 54 P-gp-mediated transcellular transport ..................... 52 predictions ................................................................. 57 simulations.................................................... 53, 55, 58 MDR1-MDCK cells........................................................ 75 Mean squared error (MSE) .......................................... 298 Mean transmittance ...................................................... 136 Mechanisms of ADC uptake, tumors antigen ....................................................369, 374–376 TAM........................................................373, 377–379 Membrane biomembrane .......................................................... 163 organelle ......................................................... 163, 164 plasma ...................................................................... 163 prokaryotic cells ...................................................... 164 uptake and accumulation ............................... 167–169 xenobiotics............................................................... 163 Membrane-binding molecules ..................................... 171 Membrane-binding peptides ........................................ 166 Membrane fluidity......................................................... 168 Membrane localization ................................................. 168 Membrane-membrane configurations ......................... 167 Membrane-membrane proximity ................................. 167 Membrane permeability................................................ 208 Membrane proteins....................................................... 167 Membrane targeting ............................................ 169, 172 Membrane trafficking.................................................... 168

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Membrane transport proteins ........................................ 98 Membrane transporter assays ......................................... 69 Metabolism............................................................. 69, 127 CYP mediated ................................................ 220, 221 cytochromes P450 mediated .................................. 219 hepatic...................................................................... 220 proteolytic................................................................ 219 systemic exposures .................................................. 221 systemic pathways.................................................... 219 Metabolites .................................................................... 282 MetaMorphTM software ................................................. 26 Methanol extraction...................................................... 151 Michaelis-Menten (MM) ............................ 255, 256, 261 Michaelis-Menten kinetic parameter.......... 258, 261, 262 Microbial transglutaminase (MTG)-based chemoenzymatic method ............................ 313 Microcentrifuge tubes..................................................... 24 Microfuge tubes .............................................................. 24 Microglia........................................................................ 190 Microvilli morphology amphipathic compound ............................................ 60 average trajectory ...................................................... 62 basolateral membrane ............................................... 61 Caco-2 cells ............................................................... 60 cytosol concentration................................................ 63 digoxin concentrations ............................................. 61 futile cycle .................................................................. 61 hypothesis .................................................................. 61 P-gp concentration ................................................... 61 P-gp efflux activity .................................................... 60 remodeling ................................................................ 63 reorganization ........................................................... 63 Minitab Statistical Software .......................................... 153 Mitochondrial membrane proteins ................................ 98 MMAE ADCs................................................................ 369 MMP-2/9 activated polyarginine CPP ....................... 223 Modeling and simulation (M&S) ................................ 333 Modified membrane permeability (PAMPA) .............. 213 Molar mixture ................................................................. 48 Molar partition coefficients ............................................ 48 Molecular accounting ....................................................... 9 Molecular grafting......................................................... 230 Molecular imaging ............................................... 275, 276 Monochrome images .................................................... 153 Monoclonal antibody (mAb) ....................................... 331 Monomerization ........................................................... 217 Monomethyl auristatin E (MMAE) ............................. 309 Mouse embryonic fibroblast (MEF) ............................ 183 Mouse ESC (mESC)..................................................... 183 MR- or gamma-based analysis...................................... 116 MR signal intensities..................................................... 116 Multidrug resistance-associated protein 2 (MRP2) ........................................................ 128 Multidrug resistant tuberculosis .................................. 149

AND

DELIVERY Index 447

Multiple reaction monitoring (MRM) .......................... 94 Murine xenografts......................................................... 366

N N87 cells ............................................................... 345, 348 Nanoparticle .................................................................. 427 Nanoparticle-based carriers ................................. 306, 308 Nanoparticle tracking analysis (NTA).......................... 403 Nanoprecipitation ...............................400, 402, 411, 427 Near infrared (NIR) ...................................................... 180 Neovascularization ........................................................ 384 NeuO selective labeling/imaging ................................ 190 Neurons ......................................................................... 190 Neurosensory retina........................................................ 16 New chemical entities (NCEs) ......................................... 4 Nikon Eclipse Ti inverted microscope......................... 151 Nikon NIS-Elements AR software............................... 153 NMR analysis fluorescence spectroscopy ....................................... 386 heparin competition assay....................................... 386 particle formation.................................................... 386 polymer suynthesis .................................................. 385 polyplex assembly .................................................... 386 Nonbiological models....................................................... 4 Noncancer cell lines ............................................. 241, 242 Nondisplaceable binding .............................................. 295 Nonlinear efflux kinetics ................................................. 58 Nonspecific binding ............................................. 282, 300 Nonspecific internalization ........................................... 307 Nontarget cells, ADC pharmacokinetics immune cell composition ......................366, 369–372 in vitro methods, conjugation-induced clearance auristatin drug linkers .............................. 366, 367 cell-based assay ......................................... 367, 368 Kupffer cells.............................................. 367, 368 MMAF drug linker............................................ 366 PEGylated drug linkers............................ 366, 367 surface hydrophobicity ..................................... 366 strains ....................................................................... 369 Normalization ............................................. 293, 294, 300 NSG-based xenograft models....................................... 369 Nuclear factor kB (NF-kB).................................. 156, 158 Nutritional factors........................................................... 20

O Objective response rates (ORRs) ................................. 351 Octreotide ..................................................................... 202 Ocular cell models........................................................... 16 Oligothioetheramides (oligoTEA) control crosslinker................................................... 313 FRET-based probe .................................................. 313 monofunctionalized thiol monomer...................... 313 RP-HPLC ................................................................ 314

QUANTITATIVE ANALYSIS

448 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Oligothioetheramides (oligoTEA) (cont.) sequence-defined polymers..................................... 312 synthetic antibacterial agents discovery ................. 313 terminal primary amine........................................... 314 Oncology ....................................................................... 357 One-bead-one-compound (OBOC) libraries.............. 222 Optical anisotropy......................................................... 129 Optical density ..................................................... 134, 137 Optical tissue phantom ................................................. 413 Oral absorption .................................................... 208, 209 Oral bioavailability ........................................................ 215 Organelle membranes ................................................... 164 Organelles.......................................................................... 7 Organic Quantum dots ................................................ 385

P P-53 signals degradation .............................................. 213 p65 ........................................................................ 156, 158 Paired-agent molecular imaging applications ............................................ 277, 287, 291 clinical trials ............................................................. 275 control imaging agent............................................. 280 data analysis ............................................................. 295 data collection ................................................ 290–292 data processing ............................................... 292–296 drug-target availability ............................................ 275 imaging-agent administration ................................ 290 imaging-agent dosing ............................................. 289 imaging-agent preparation ............................ 287, 289 imaging-agent retention ......................................... 276 imaging modality .................................................... 298 kinetic modeling (see Kinetic modeling) molecular imaging modality ................................... 287 molecular target ...................................................... 276 quantification protocols .......................................... 277 quantitative .............................................................. 276 realistic simulation................................................... 297 targeted imaging agent ........................................... 277 targeted imaging moiety......................................... 279 targeting reporter vector labelling (see Reportervectors) p-aminobenzyloxycarbonyl (PABC) ............................ 310 Pancreatic islets alpha and beta-cells ........................................ 184, 186 BD-105.................................................................... 185 BODIPY library ...................................................... 184 fluorescent probes ................................................... 184 GY ................................................................... 185, 186 PiY................................................................... 185, 186 TP ............................................................................ 186 Pan-macrophage marker ............................................... 140 Paracellular absorption.................................................. 216 Paracellular pathway..................................................5, 215 Paracellular permeability................................................. 23

AND

DELIVERY

Paracellular transport marker ......................................... 21 Paraformaldehyde ......................................................... 151 Parallel artificial membrane permeability (PAMPA) assays ..................................................... 4, 7, 28 Partition coefficient........................................................... 6 Fu,cell .....................................................................89, 92 in vitro Kpuu .......................................................92, 94 protocol, fu,cell determination plated cells ........................................................... 91 suspension cells ................................................... 90 standard curve preparation .................................91, 92 Partitioning .........................................166, 167, 171, 172 Passive drug transport apical-to-basolateral/basolateral-to-apical concentration gradient .................................. 17 donor compartment............................................17, 18 epithelial cells ............................................................ 17 extracellular drug concentration gradient ............... 17 Fick’s first law ............................................................ 17 fluxes .......................................................................... 17 in vitro cell permeability ........................................... 18 membrane support.................................................... 20 receiver/sink compartment ...................................... 17 Pathogen-associated molecular patterns (PAMPs)....................................................... 154 Patient-derived TIC models ......................................... 185 Patient-derived xenograft (PDX) ........................ 187, 188 Payload molecules ......................................................... 332 Payloads ......................................................................... 332 PEG-PCL-PEI (PPP) ................................. 385, 387, 394 PEGylated ADCs .........................................360, 362–364 Peptide arrays (photolithography) ............................... 222 Peptide drug discovery distribution .............................................................. 207 exposure................................................................... 207 hydrogen bond donor ............................................ 208 intracellular metabolism.......................................... 214 molecules and biologics .......................................... 223 permeability and exposure ............................. 207–208 permeability mechanisms (see Peptide permeability mechanisms) pharmacological activity.......................................... 207 systemic exposure (see In vivo systemic exposure) Peptide drugs chemical structures.................................................. 203 clinical drugs............................................................ 205 conventional modalities .......................................... 201 cyclic......................................................................... 205 drug discovery ......................................................... 206 insulin ...................................................................... 202 motifs ....................................................................... 206 natural and unnatural amino acids ......................... 206 N-methylation ......................................................... 206 pharmaceutical industry.......................................... 201

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

proteolytic susceptibility ......................................... 202 receptor-targeted studies ........................................ 202 structure-based strategies ....................................... 205 therapeutic agents ................................................... 202 Peptide internalization.................................................. 245 Peptide mimetics uptake............................................... 216 Peptide permeability mechanisms cationic partitioning....................................... 211–212 chameleon-like passive partitioning .............. 210–211 lipophilic partitioning ............................................. 213 oral absorption ........................................................ 208 plasma membrane ................................................... 208 transcellular transport ............................................. 208 Peptide:N-glycosidase F (PNGase F) .......................... 315 Peptide-based targeting ligands ................................... 307 Peptide–target complex ................................................ 222 Peritoneal macrophages .............................. 131, 133, 138 Permeability..................................... 5, 6, 8, 9, 11, 12, 15, 16, 18, 21–23, 27–29 Permeabilization agents ................................................ 151 Pertuzumab .......................................................... 307, 308 P-glycoprotein (P-gp) confluent cell monolayer .................................... 42–46 cytosolic concentrations (see Cytosolic drug concentrations) drug partitioning................................................. 47–48 experiment extrapolation .................................... 60–63 IVIVE ........................................................................ 42 mass action kinetic equations ................................... 42 passive permeability (see Drug passive permeability) Transwell plate........................................................... 42 P-gp inhibitor.................................................................. 75 pH ...........................................6–8, 20, 21, 27, 30, 31, 34 Phagocytosis .................................................................. 148 Pharmaceutic properties ............................................... 206 Pharmaceutical/toxicological research ........................ 149 Pharmaceuticals ............................................................. 164 Pharmacodynamics........................................................ 148 Pharmacokinetic characteristics .................................... 428 Pharmacokinetic model ............. 102, 109–111, 115, 251 Pharmacokinetics ........................................................4, 29 cellular...................................................................... 128 CFZ.......................................................................... 141 drug ......................................................................... 128 measurements .......................................................... 143 parameters ............................................................... 127 and pharmacodynamics........................................... 148 Pharmacokinetics/pharmacodynamics (PK/PD) ............................................. 398, 399 Pharmacophore ............................................................. 216 Phenazine red dye ......................................................... 148 Phenomenological kinetic models ............................... 309 cell growth............................................................... 321 differential equation................................................ 321

AND

DELIVERY Index 449

fluorescence signal................................................... 309 FRET-based readouts ............................................. 311 HER2 receptor........................................................ 320 intracellular payload ................................................ 309 mass-action kinetics................................................. 321 nonspecific proteolytic degradation .............. 311, 321 probe–receptor complex ......................................... 320 Phenotypic high-throughput screening....................... 192 Phosphatidylcholine transporter MDR2 (ABC B4) ..................................................... 223 Phosphine-catalyzed thiol-Michael addition ............... 313 Phospholipid bilayer .................................................6, 163 Phospholipid headgroups ............................................... 47 Phospholipid molecules ..............................................6, 47 Physicochemical processes ................................... 166–167 PiY.................................................................................. 185 PK parameters ............................................................... 350 Plasma clearance ................................................... 363, 364 Plasma cytosolic monolayer............................................ 47 Plasma drug concentrations ........................................... 97 Plasma half-life/pharmacokinetics ............................... 250 Plasma input function ................................................... 287 Plasma kinetics .............................................................. 281 Plasma membrane ......................163, 167, 168, 171, 172 Plasma PK model ................................................. 347, 350 Plasma protein binding........................................ 216, 282 Plasmid pVITRO-Trastuzumab-IgG1/k .................... 315 PLGA polymer–dye conjugate ..................................... 400 PLGA-BODIPY630...................................................... 400 PLGA-PEG PLGA-BODIPY630....................... 415, 418, 419, 424, 426 Polarization ............................................. 6, 9, 15, 17, 129 Polarized light microscopy (PLM)............................... 129 Poly D-lysine (PDL) ..................................................... 429 Poly(lactic-co-glycolic acid)–poly(ethylene glycol) (PLGA-PEG) ............ 400–402, 405, 407, 411 Polyanionic glycosaminoglycan .................................... 392 Polyanionic species........................................................ 392 Polycationic peptides .................................................... 212 Polyethylenimine (PEI) ................................................ 385 Polymeric conjugates .................................................... 306 Polymers ........................................................................ 165 Polymer–siRNA interaction.......................................... 391 Polyplex assembly........................................ 385, 389, 395 Polyplexes ...................................................................... 384 Polyplexes (Px) ..................................................... 389, 394 Porphyrin PDT drugs ................................................... 164 Positron emission tomography (PET) ......................... 112 Post–agent-administration images ............................... 293 Post–agent-administration timepoints......................... 299 Powdered Lab Diet 5001 ............................................. 150 PPP polyplexes .............................................................. 392 Pre–agent-administration image/measurement.......... 292 Precision/variance errors.............................................. 298

QUANTITATIVE ANALYSIS

450 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Preclinical-to-clinical translation, ADCs.......................................... 347, 350, 351 Pre-saturation method .................................................... 87 Primary antibodies ............................................... 150, 152 Primary hepatocytes ...................................................... 103 Probe discovery ............................................................. 180 Probe–receptor complex............................................... 320 Progression-free survival (PFS) .................................... 351 Prokaryotic cells ................................................... 164, 167 Protein binding ........................................... 119, 167, 171 Protein–protein interactions................................ 213, 384 Proteolytic metabolism ................................................. 219 Proteolytic stability ....................................................... 214 Proteomic/transcriptome analysis ............................... 182 Protocol factor .............................................................. 168 Proton-dependent transport mechanisms ..................... 27 Pt(IV)-BODIPY ........................ 400, 401, 406–408, 413, 418, 419, 424, 430 Pt(IV) cisplatin prodrug ............................................... 400 Pt(IV) prodrug.............................................................. 403 Pulmonary hypertension ................................................ 16 Pulse labeling................................................................. 168

Q Quantitative structure–activity relationship (QSAR) ........................................................ 211 amphiphiles.............................................................. 171 amphiphilicity ........................................ 169, 172, 173 application ............................................................... 167 artifactual membrane binding ....................... 173, 174 decision-rule ................................................... 165, 169 decision-tree flowchart .................................. 169–171 development ............................................................ 167 eukaryotic cells ........................................................ 167 fluorescent probe............................................ 174, 175 functional cell factors .............................................. 168 Hansch and Leo procedure .................................... 169 integrated uptake model......................................... 171 lipophilicity .............................................................. 169 localization............................................................... 166 membrane-binding molecules ................................ 171 membrane-localizing compounds .......................... 172 structural cell factors ...................................... 167, 168 structure parameters ............................................... 169 xenobiotics...................................................... 169, 171 Quantum dots (Qdots)................................................. 385 Quantum™ Simply Cellular® ........................................ 257 Quartz cuvette .............................................................. 391 Quenching efficiency (QE).................................. 258, 260

R Rapid equilibrium dialysis device (RED)....................... 85 Reactive oxygen species (ROS) .................................... 173 Receptor binding .......................................................... 166

AND

DELIVERY

Receptor internalization ............................................... 307 Receptor/target occupancy.......................................... 276 Recombinant Trastuzumab production/purification.............................. 311 Recycling endosomes ........................................... 263, 264 Region of interest (ROI) ....................285–287, 290, 412 Reporter-vectors binding affinity ........................................................ 281 charge....................................................................... 280 chemical and pharmacokinetic properties.............. 279 diffusion coefficient................................................. 281 extravasation ............................................................ 281 imaging strategies.................................................... 279 intensity ................................................................... 280 intracellularization................................................... 282 lipophilicity .............................................................. 280 metabolites .............................................................. 282 molecular imaging................................................... 279 nonspecific binding ................................................. 282 plasma kinetics................................................ 281, 282 size ........................................................................... 280 SPECT ..................................................................... 279 Respiratory/cardiac gating imaging protocols............ 299 Reversible binding kinetic models ............................... 295 RNA-sequencing analysis ............................................. 188

S Sacituzumab govitecan ................................................. 353 Sandwich-cultured hepatocytes (SCH) advantages................................................................ 103 applications ..................................................... 105, 106 Basal media .............................................................. 103 bile canalicular structures........................................ 103 BioIVT..................................................................... 104 canalicular networks ................................................ 104 differential centrifugation .............105, 106, 112, 113 drug metabolism and transport.............................. 103 extracellular matrices............................................... 103 hormones................................................................. 103 insulin ...................................................................... 104 limitation ................................................................. 103 materials.......................................................... 107, 108 Matrigel™ ............................................................... 103 primary hepatocytes ................................................ 103 serum ....................................................................... 103 species ...................................................................... 104 supplements............................................................. 103 TCA ......................................................................... 104 technical factors....................................................... 103 transport proteins.................................................... 103 Scaffolds......................................................................... 206 SDS-PAGE fluorescence scanning ............................... 187 Secondary antibody.............................................. 150, 152 Self-assembled nanoparticles ........................................ 306 Self-associating polymers .............................................. 306

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Self–nonself immune recognition pathways ................ 148 Semaglutide .......................................................... 202, 216 Semipolar organic solvent............................................. 400 Sensitive disulfide bond ................................................ 309 Sequence homologies ................................................... 206 Sequestering vs. non-sequestering cell populations imaging setup .......................................................... 134 LC-PolScope ........................................................... 134 xenobiotic-sequestering cells......................... 135–139 Serum............................................................................. 103 Severe combined immunodeficiency (SCID) ....................................... 344, 366, 369 Short interfering RNA (siRNA)................................... 188 Signaling pathways ........................................................ 223 Simpler models approximation ......................................................... 280 binding rate-constant.............................................. 287 compartments.......................................................... 287 drug-target concentration ...................................... 288 equations ................................................................. 286 fitting function ........................................................ 287 in vitro affinity ......................................................... 288 plasma input function ............................................. 287 Simply cellular® beads................................................... 270 Single photon emission computed tomography (SPECT) ............................................. 279, 290 Single-cell measurements.............................................. 133 Size exclusion chromatography.................................... 253 SKBR3 cells .......................................................... 309, 311 Slow Ketoconazole.......................................................... 52 Small interfering RNA (siRNA) AF647-labeled ......................................................... 389 fluorescence labeling ...................................... 385–387 fluorescing molecule ............................................... 391 labeled...................................................................... 388 N/P ratio calculation.............................................. 388 quantification........................................................... 389 quantitative analysis ....................................... 394, 395 RNase contamination ............................................. 386 unlabeled ................................................................. 389 Small molecule therapeutics carriers ............................ 306 Small molecules ............................................................. 279 Small-molecule drugs ................................................... 216 Sodium caprate.............................................................. 216 Sodium caprylate........................................................... 216 Solid cancers .................................................................. 398 Solid core nanoparticles................................................ 306 Somatostatin......................................................... 202, 205 Spectrophotometer ....................................................... 132 Spin-labeled/radiolabeled imaging probes ................. 164 Standard equilibrium dialysis method .....................86, 87 Steady-state conditions ................................................. 207 Sterile petri dish ............................................................ 137 Sterile scalpel blade ....................................................... 132

AND

DELIVERY Index 451

Sterile technique............................................................ 132 Stimuli-responsive delivery systems cleavage bonds (see Intracellular cleavage bonds) components ............................................................. 305 conjugate-based carriers ......................................... 306 internalization ......................................................... 305 nanoparticle-based carriers ..................................... 306 pathways ......................................................... 307–308 phenomenological models............................ 309, 311, (see also Phenomenologicalkinetic models) Stimuli-responsive drug carriers ................................... 311 Stirring method............................................................... 27 Structural cell factors ........................................... 167, 168 Structural mass action kinetic model ................ 42, 59, 63 Structure–activity relationship (SAR) .......................... 195 Structure-based design/diverse library-driven strategies....................................................... 205 Subcellular fractionation............................. 106, 119, 213 Subcellular markers ....................................................... 119 Subcellular sequestration .............................................. 120 Submicromolar concentrations .................................... 213 Substantial cellular processes .......................................... 60 Superamphiphilicity ...................................................... 166 Superlipophilic/superamphiphilic xenobiotics............ 167 Surface enhanced Raman scattering (SERS) ............... 300 Surface hydrophobicity ................................................. 366 Surface sampling micro liquid chromatography tandem mass spectrometry (SSμLC-MS/MS) ........ 120 SYBR Gold assay ........................................................... 391 SYBR Gold dye ............................................................. 392 Systemic bioavailability ................................................. 216 Systemic circulation ...................................................... 216 Systemic unbound fraction........................................... 216

T Tame probes azide/cyclooctyne functional groups..................... 194 computational approach ......................................... 194 definition ................................................................. 192 high-quality fluorescent signal ............................... 189 multicolor labelling ........................................ 193, 194 nonspecific binding background ............................ 192 predictive model............................................. 192, 194 structures ................................................................. 194 water solubility ........................................................ 193 Target mediated drug disposition (TMDD) ............... 254 Targeted biomolecule .......................................... 277, 295 Targeted-control agent binding ................................... 299 Target-oriented synthesis (TOS).................................. 180 Tau proteins................................................................... 190 Taurocholic acid (TCA)................................................ 104 TEER (transepithelial resistance) measurement............ 22 Temperature .................................................................. 270 Tetradecylamide (TM) .................................................... 47

QUANTITATIVE ANALYSIS

452 Index

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Therapeutic agent ................................................ 127, 143 Therapeutic antibodies ........................................ 358, 359 Therapeutic cargo ......................................................... 306 Therapeutic nanoparticles (TNP) animal models................................................. 404, 405 cell lines .......................................................... 404, 405 cellular drug uptake ................................................ 401 chemotherapeutic agents ........................................ 397 clinical trials ............................................................. 397 DNA damage........................................................... 398 EPR effect................................................................ 398 fluorescence microscopy ......................................... 398 fluorescent drug conjugates .......................... 399, 400 image-based reporters drug action ............................................... 403, 404 subcellular compartments........................ 403, 404 immunogenic therapies........................................... 399 in vitro (see In vitro, TNP) in vivo (see In vivo, TNP) microscopy platforms .............................................. 405 payload redistribution ............................................. 401 PK/PD .................................................................... 398 Pt drug payload ....................................................... 398 solid cancers............................................................. 398 synthesis and characterization (see TNP synthesis and characterization) Thermodynamic phase transition..................................... 7 Thiol-Michael additions ............................................... 313 Tight junctions.................................................................. 6 Timepoint image/measure........................................... 292 TiNIR ............................................................................ 188 Tissue cryosections............................................... 137–141 Tissue damage/infection .............................................. 148 Tissue homogenate ......................................................... 85 Tissue-specific targeting................................................ 306 TiY ........................................................................ 185, 188 TLR2 .................................................................... 154–156 TLR4 .................................................................... 154–156 TNP components................................................. 412, 413 TNP synthesis and characterization biodegradable polymeric nanoparticles ................. 400 charge....................................................................... 403 hydrophobic prodrugs ............................................ 400 loading amount ....................................................... 403 nanoprecipitation ........................................... 400, 402 payload release......................................................... 403 PLGA-PEG.............................................................. 400 size ........................................................................... 401 stability..................................................................... 401 Toll-like receptors (TLRs) ................................... 154, 155 Tolypocladium inflatum ................................................ 202 Topical-agent-delivery applications.............................. 297 TP-α ............................................................................... 186 TP-β ............................................................................... 186

AND

DELIVERY

Tracer-kinetics ............................................................... 297 Traditional GFP-based genetic markers ...................... 183 Trafficking pathways ..................................................... 250 Transcellular drug permeability...................................... 20 Transcellular pathway................................................5, 215 Transcellular permeability.........................................26, 27 Transcellular transport .................................................... 51 Transcription factor EB (TFEB) .................148, 155–157 Transepithelial electrical resistance (TEER) measurements ................................... 21, 23–25 Transglutaminase-based bioconjugation ............ 315, 324 Translational PK-PD model ......................................... 351 Transmission electron microscopy (TEM) .................. 401 Transmitted light .......................................................... 129 Transporter inhibition efflux transporters data analysis ......................................................... 75 experimental procedures..................................... 75 inhibitors ....................................................... 75–76 test drug ........................................................76, 77 uptake transporters data analysis ......................................................... 77 EMA and FDA .................................................... 79 experimental procedures..................................... 77 hepatic.................................................................. 78 test drug .............................................................. 78 transporter-transfected cells................................ 78 Transporter substrate efflux transporters data analysis ......................................................... 71 ER ........................................................................ 72 experimental procedures..................................... 71 test drugs .......................................................72, 73 uptake transporters data analysis ......................................................... 73 experimental procedures..................................... 73 FDA guidance ..................................................... 74 influx rate ratios................................................... 74 test drug .............................................................. 74 Transporter-/enzyme-mediated DDIs processes.......... 70 Transporter-mediated/enzyme-mediated drug interactions..................................................... 69 Transporters .................................................................. 128 Transporter-transfected MDCK cell systems................. 72 Transwell cells............................................................51, 61 Transwell inserts..................................5, 9, 15, 20–26, 30 Transwell plastic insert.................................................... 49 Transwell plates ............................................................... 71 Transwell system ............................................................. 43 Trastuzumab................................................ 307, 310, 339 Trastuzumab emtansine (T-DM1)..................... 343, 347, 350, 351 Trastuzumab site-specific modification........................ 311

QUANTITATIVE ANALYSIS

OF

CELLULAR DRUG TRANSPORT, DISPOSITION,

Trastuzumab–valine–citrulline–monomethyl auristatin E (T-vc-MMAE) .......... 339–341, 343, 345, 347 Triton X-100 ................................................................. 151 Troglitazone .................................................................... 99 Trypan blue ..................................................................... 23 Trypsin–EDTA ................................................................ 22 Tubulin occupancy........................................................ 350 Tubulin-binding antimitotic drug ............................... 307 Tubulin-bound drug molecules ................................... 338 Tumor antigen expression ............................................ 369 Tumor-associated antigens (TAAs) .................... 332, 333, 339, 343, 352 Tumor-associated macrophages (TAM) ADC activity ............................................................ 377 ADC-mediated drug delivery ................................. 377 ADCs .............................................................. 373, 379 antibody therapeutics.............................................. 373 anti-PD-1 antibodies .............................................. 373 antitumor activities.................................................. 378 BR620...................................................................... 378 CD22 and CD79b expression................................ 379 conjugation-induced clearance............................... 378 DNA damage......................................... 424, 425, 427 drug depots .................................................... 398, 399 Fcγ interactions ....................................................... 378 HT1080 cancer cells ............................................... 419 IHC.......................................................................... 377 KM-H2 .................................................................... 378 L-428 tumors ................................................. 377, 378 nonbinding hIgG1ADC ......................................... 373 TNP ................................................................ 398, 423 TNP Pt payload redistribution ...................... 422, 424 TNP vehicle and payload uptake............................ 422 xenograft models............................................ 373, 377 Tumor growth inhibition (TGI) .................................. 344 Tumor initiating cells (TICs) biomarkers ............................................................... 185 CSCs ........................................................................ 185 selective probes............................................... 188, 189 TiNIR ...................................................................... 188 TiY ......................................................... 185, 187, 188 Tumor microenvironment (TME) ...................... 373, 398 Tumor xenografts ......................................................... 223 Tunable resistive pulse sensing (TRPS) ....................... 403 Tween-20 ...................................................................... 151 Two-photon (TP) ......................................................... 186

U Ultracentrifugation ......................................................... 89 Unbound partition coefficient (Kpuu) ...... 82, 83, 92, 93 Unidirectional uptake processes................................... 222 Unilamellar liposomes .................................................... 48

AND

DELIVERY Index 453

University of Michigan’s Unit for Laboratory Animal Medicine (ULAM) ...................................... 150 Unmasked polyarginine ................................................ 223 Unmethylated amide bonds ......................................... 210 Unmethylated macrocyclic hexapeptides peptides ...... 210 Unstirred water layer ...................................................... 28 UPLC ............................................................................ 132 Uptake transporter inhibitors......................................... 77 Urine/feces ................................................................... 127 US Food and Drug Administration (FDA)................... 70

V Vacuolar-type H+-ATPase (V-ATPase)............... 158–160 Valine–citrulline–monomethyl auristatin E (vcMMAE) 360 Valine–citrulline–PABC linker ............................. 310, 324 van der Waals surface area ............................................ 193 Vascular permeability .................................................... 286 Vasculature..................................................................... 215 V-ATPase ....................................................................... 160 Visualization .................................................................. 179

W Weakly basic drugs .......................................................... 34 WinNonlin..................................................................... 111 Wound healing .............................................................. 148

X Xenobiotic agent ........................................................... 202 Xenobiotic sequestration tissue structure/function........................................ 148 Xenobiotic sequestration cells biomarkers ...................................................... 153–160 immune effector cytokines ..................................... 148 and physical markers ............................................... 149 and supramolecular aggregate formation .............. 149 Xenobiotics .....................................................98, 105, 171 accumulation ........................................................... 166 membrane binding.................................................. 170 QSAR (see Quantitative structure–activity relations (QSAR)) superlipophilic/superamphiphilic .......................... 167 types ................................................................ 164–166 Xenobiotic-sequestering cells .............................. 133, 140 image analysis and quantification .................. 136–139 multiparameter imaging ......................................... 135 polarization imaging ............................................... 135 Xylene ............................................................................ 133

Z Zetasizer ........................................................................ 401