Putting Pharmacokinetics and Pharmacodynamics to Work in Drug Discovery E-Book

Emile P. Chen

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Veröffentlichungsjahr: 2025

Beschreibung

Develop drugs with a greater understanding of their physiological effects

Pharmaceutical scientists in the fields of pharmacokinetics and pharmacodynamics study how drugs behave in the body and how they reach their site of action to exert their intended pharmacological activities. Drug discovery stands to benefit enormously from the timely application of pharmacokinetics and pharmacodynamics in order to make informed decisions and solve practical problems.

Putting Pharmacokinetics and Pharmacodynamics to Work in Drug Discovery bridges between scientific concepts and practical industrial practice by bringing these principles to bear on every stage of the drug discovery process. Beginning with target identification and moving through each subsequent decision point including high throughput screening, hit-to-lead, lead optimization and candidate selection. The book offers a comprehensive guide to using various analytical tools including modeling and AI/ML for minimizing attrition, reducing costs, and more. The result is an invaluable tool in developing smarter and more effective drug discovery processes.

Putting Pharmacokinetics and Pharmacodynamics to Work in Drug Discovery readers will also find:

• A work designed to make scientific principles accessible to pharmaceutical scientists in diverse areas, not just pharmacokinetists or DMPK scientists
• Industrial examples, both positive and negative, showing pharmacokinetic and pharmacodynamic principles at work
• Exercises throughout the book to encourage holistic and integrated thinking

Putting Pharmacokinetics and Pharmacodynamics to Work in Drug Discovery is ideal for any researchers or professionals involved in drug discovery and development, including medicinal chemists, biopharmaceutics scientists, clinicians, project leaders, and many others.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Part I: Optimizing Pharmacokinetics in Discovery

Chapter 1: The Importance of Pharmacokinetics in Early Drug Discovery

1.1 PK as a Surrogate for Efficacy

1.2 The Many Faces of Pharmacokineticists

1.3 The Criteria for Good PK of a Therapeutically Useful Drug

1.4 The Goals of Early Discovery

References

Chapter 2: In Search of “Good” Pharmacokinetics

2.1 Describing the Concentration-time Profile

2.2 Half-life (T

1/2

)

2.3 Area Under the Curve (AUC)

2.4 Biphasic PK Parameters

2.5 Constant Intravenous Infusion PK

2.6 Extravascular PK

2.7 Repeat-dose PK

2.8 Using Secondary PK Parameters to Guide Compound Selection

2.9 Practical Considerations of Conducting In Vivo PK Studies

References

Chapter 3: Linking Descriptive Pharmacokinetics to Underlying ADME Processes

3.1 The Body as Compartments: Exponential Time Courses Explained

3.2 Relating Descriptive PK Parameters to the Underlying ADME Process

3.3 Using Primary PK Parameters to Identify ADME Liabilities

3.4 Volume of Distribution and Clearance for Compounds with Biphasic PK

3.5 Conversion Between Blood, Plasma, and Unbound PK Parameters

3.6 Ranges of PK Parameters

3.7 Using Primary PK Parameters to Guide Compound Selection

3.8 Linking Primary PK Parameters to Intrinsic Compound Properties

References

Chapter 4: Mechanistic Basis of Distribution

4.1 Overview of Distribution Process

4.2 Factors Affecting Extent of Distribution

4.3 Using Volume of Distribution to Guide Drug Design and Selection

4.4 Mechanistic Basis of Biphasic PK

4.5 Using Rate of Distribution to Guide Lead Optimization

4.6 Mechanistic Basis of Plasma Protein Binding

References

Chapter 5: Mechanistic Basis of Clearance

5.1 Liver Metabolism as the Primary Clearance Pathway

5.2 Mechanistic Basis of Hepatic Metabolic Clearance

5.3 Applications of Clearance Concept in Drug Discovery

5.4 Other Routes of Elimination

5.5 Identifying the Rate-limiting Clearance Mechanisms

5.6 Drug–Drug Interaction and Metabolism Considerations

References

Chapter 6: Mechanistic Basis of Absorption

6.1 Overview of the Absorption Process

6.2 Factors Affecting Fraction Absorbed

6.3 First Pass Extraction

6.4 Interplay Between Absorption, First-pass Elimination, and Transporters

6.5 Applications of Absorption Concepts in Drug Discovery

6.6 Effect of Food on Oral Bioavailability

6.7 Lymphatic Absorption of Lipophilic Compounds

References

Chapter 7: Integrated Pharmacokinetic Analysis in Discovery

7.1 Integration of PK Concepts: A Road Map

7.2 Is Plasma Protein Binding Important?

7.3 How Potent Is Enough to Elicit Efficacy In Vivo?

7.4 Compounds/Series Selection

7.5 Drug Design

7.6 Identify ADME Liabilities

7.7 Study Design

7.8 Physiologically Based Pharmacokinetic (PBPK) Modeling

References

Chapter 8: Pharmacokinetics of Therapeutic Antibodies and Derivatives

8.1 General PK Characteristics of mAbs

8.2 Absorption of Monoclonal Antibodies

8.3 Distribution of Monoclonal Antibodies

8.4 Clearance of Monoclonal Antibodies

8.5 PBPK Modeling for Monoclonal Antibodies

8.6 PK Screening and Optimization of Monoclonal Antibodies

References

Part II: From Pharmacokinetics to Efficacy

Chapter 9: The Importance of Pharmacodynamics in Early Drug Discovery

How Is Part 2 of the Book Organized?

9.1 The PK-PD Disconnect

9.2 The Three Pillars of PD

9.3 Experimental Approaches for Studying Pharmacological Effects

References

Chapter 10: Reaching the Site of Action

10.1 Free Drug Hypothesis

10.2 Asymmetry in Unbound Tissue and Systemic Concentration

10.3 Time-course of Unbound Concentration in Target Tissue

10.4 Tissue Disposition Considerations in Drug Discovery

10.5 Target Organs with Complex Structures

10.6 When Systemic PK Is Not the Main Driver for Tissue Exposure

10.7 Tissue Disposition for Therapeutic Antibodies

References

Chapter 11: Hitting a Moving Target

11.1 Concentration-response Relationships

11.2 Receptor Kinetics Theory

11.3 Agonism

11.4 Antagonism

11.5 Slow Association and Disassociation

11.6 Target Turnover

11.7 Irreversible Inactivation

11.8 Proteolysis Targeting Chimeras (PROTACs)

11.9 Bisubstrate Kinetics

11.10 Implications to Toxicological Effects

11.11 Monoclonal Antibodies Target Engagement

References

Chapter 12: The Tangled Web of Pharmacology

12.1 Fast Responding Processes

12.2 Slow Turnover Processes

12.3 Multistage Processes

12.4 Activation of Precursor

12.5 Tolerance and Resistance

12.6 Cell Growth and Death

12.7 Importance of Response Duration in Determining PK Endpoints

12.8 Monoclonal Antibodies

12.9 Complex and Novel Systems

12.10 Toxicodynamics

References

Chapter 13: Pharmacodynamics-informed Drug Discovery

13.1 Selection of Target, Modality, and Mode of Action

13.2 Compound Screening

13.3 Compound Optimization

13.4 Virtual Screening and Drug Design

13.5 Design and Interpretation of Pharmacology Studies

13.6 Model-informed Drug Discovery

References

Part III: Picking the Right Human Dose

Chapter 14: Human Dose Prediction: An Overview

References

Chapter 15: Predicting Human Systemic Pharmacokinetics

15.1 Predicting Human Volumes of Distribution and Clearance

15.2 Predicting Human Oral Bioavailability

15.3 Predicting the Concentration-time Profile

15.4 When Prediction of Human Systemic PK Is Unimportant

15.5 Predicting Human PK for Monoclonal Antibodies

References

Chapter 16: Predicting Human Pharmacodynamics

16.1 Species Difference in Tissue Disposition

16.2 Species Differences in Target Engagement

16.3 Species Differences in the Behaviors of Pharmacology

References

Chapter 17: Integrated PK/PD Approaches to Human Dose Prediction

17.1 A Simple Example of Starting First-in-human Dose Calculation

17.2 PK/PD Model-based MABEL Determination

Example #1: Mono-specific mAbs

Example #2: Bi-specific Antibodies

Example #3: Antibody-Drug Conjugates

17.3 Evaluating Data Quality

Reliability of Predicted Human Concentration-Time Profile

Healthy Volunteers Versus Patients

17.4 Quantifying the Impact of Uncertainty on Decisions

References

Appendix A: In Vitro ADME Assays

A.1 Permeability

A.2 Solubility and Dissolution

A.3 Plasma Protein Binding

A.4 Blood-to-plasma Ratio,

B : P

A.5 Tissue Partitioning

A.6 Transporters

A.7 Intrinsic Metabolic Clearance (Stability)

A.8 CYP450 Phenotyping

A.9 CYP450 Inhibition

A.10 CYP450 Time-dependent Inhibition

References

Appendix B: QSAR and QSPR Models

B.1 What Are QSAR and QSPR Models?

B.2 Model-building Process

B.3 Molecular Descriptors

B.4 Global Versus Local Models

B.5 Types of Models

B.6 Validation and Prediction

References

Appendix C: Methods for Monitoring Tissue Concentrations

Microdialysis

Quantitative Whole-Body Autoradiography (QWBA)

PET and Other Imaging Techniques

Matrix-assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI)

Appendix D: Anatomical and Physiological Parameters

D.1 Blood Flows

D.2 Volumes of Organs and Body Fluids

D.3 Intestinal Physiology

D.4 Miscellaneous Properties

References

Appendix E: Useful Equations

E.1 Calculating Secondary PK Parameters from Concentration-time Data

E.2 Calculating Primary PK Parameters from Secondary PK Parameters

E.3 Conversions Between Plasma, Blood, and Unbound PK Parameters

E.4 Calculating Primary PK Parameters from In Vitro or In Silico Data

E.5 Calculating the Concentration-time Profile from Primary PK Parameters

Symbols and Abbreviations

Index

End User License Agreement

List of Tables

Chapter 4

Table 4.1 Trends between physicochemical properties and volume of distribution.

Chapter 5

Table 5.1 Summary of metabolic reactions and enzymes in the liver.

Table 5.2 Trends between physicochemical properties and clearance.

Chapter 6

Table 6.1 Trends between physicochemical properties and oral bioavailability.

Chapter 10

Table 10.1

pH

in Various Body Compartments.

Table 10.2 Differential Effect of Tissue Partitioning on the Time Course of Unb...

Table 10.3 Calculations of Mean Tissue Residence Time in Human Spleen and Muscle.

Chapter 11

Table 11.1 Impact of Dissociation Half-Life and Target Turnover on Duration and...

Table 11.2 Impact of ATP and Protein Concentrations on Determination.

Chapter 13

Table 13.1 Discovery decisions and/or issues addressed by PK/PD analysis and mo...

Chapter 16

Table 16.1 Species difference in the turnover time of some biomolecules and cells.

Guide

Cover

Table of Contents

Title Page

Copyright

Dedication

Preface

Begin Reading

Appendix A: In Vitro ADME Assays

Appendix B: QSAR and QSPR Models

Appendix C: Methods for Monitoring Tissue Concentrations

Appendix D: Anatomical and Physiological Parameters

Appendix E: Useful Equations

Symbols and Abbreviations

Index

End User License Agreement

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Putting Pharmacokinetics and Pharmacodynamics to Work in Drug Discovery

A Practical Guide for Pharmaceutical Scientists

Emile P. Chen

Copyright © 2025 by John Wiley & Sons Inc. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at http://www.wiley.com/go/permission.

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Library of Congress Cataloging-in-Publication Data has been applied for:

Print ISBN: 9781119650201

ePDF ISBN: 9781119650249

ePub ISBN: 9781119650225

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Cover Design: Wiley

Cover Images: © lvcandy/Getty Images, © Rawpixel.com/Shutterstock

To my beloved wife, Sasa, who envisioned a project I believed to be impossible, and whose unwavering faith, encouragement, and steadfast support sustained me throughout the writing of this book. Your love and belief in me fueled my determination, and I dedicate these pages to you.

And to my cat, Snowball, who has quietly accompanied me during countless late nights at (or on top of) the keyboard. I hope you have learned as much as I have during this process to share with your feline friends at the Rainbow Bridge.

And to my parents, who have taught me what science and being a scientist are all about.

Preface

A key mission of pharmaceutical scientists is to get safe and effective medicines to the right patients as quickly as possible while keeping the price affordable. Unfortunately, this goal is not easy to accomplish. Bringing a new medicine to the market could take over a decade, costing upward of one billion dollars with high failure rates. Over the past few decades, advancements in several areas have attempted to optimize this lengthy and costly process. This book focuses on how pharmacokinetics (PK) and pharmacodynamics (PD) can be leveraged to make informed decisions throughout each stage of drug discovery, aiming to minimize attrition and consequently reduce both the cost and duration of the process.

There are already many excellent books available covering PK and PD principles in a systematic and orderly fashion, written specifically for pharmacokineticists or PK/PD modelers or for the student studying to become one. However, relatively few were written from the point of view of the issues facing pharmaceutical scientists particularly during drug discovery: What are the key decisions I need to make today to design and progress drug molecules to the next milestone, and how do I apply PK and PD concepts practically to make these challenging decisions with limited data and time available? In my experience of teaching PK/PD workshops primarily in industry but to a lesser extend in academia, I have found that while many industrial scientists at the beginning of their career are familiar with PK and PD concepts, they do not always know when or how best to apply them to solve these everyday problems. Such expertise is not only needed just for pharmacokineticists or DMPK scientists, but also for pharmaceutical scientists in diverse areas (such as medicinal chemists, biologists, toxicologists, biopharmaceutics scientists, statisticians, computational scientists, clinicians, and project leaders) so that they can analyze situations and make decisions together as an effective team. This book is intended to help industrial scientists in all areas to bridge the gap between scientific concepts and practical industrial practice, one that provides an insider view of the practical applications of PK and PD principles in problem solving and decision making during the pharmaceutical R&D process.

The book is divided into three parts. Part One is devoted to PK principles and their applications to answer drug discovery questions. Part Two is devoted to PD principles and their applications, while Part Three focuses on how preclinical data are translated to humans to assist dose selection for first-in-human trials. In view of the growing significance of artificial intelligence and machine learning, their applications in various stages of early discovery are highlighted throughout the book. Furthermore, recognizing the reader’s busy work schedule, the book is structured with the assumption that the readers will not necessarily read the chapters in sequence. Each chapter is written so that it can be read alone, with appropriate redundancy and clear reference to materials presented in other chapters to provide context.

I would like to thank the many scientists who generously gave their time to provide inputs to various parts of the book. I would especially like to express my deepest gratitude to Dr. Svein Øie, whose nurturing mentorship laid the foundation for my exploration into the fascinating world of Pharmacokinetics, and Caroline Sychterz for her critical review, editing and valuable comments. I would not be able to complete this book without their help.

Emile P. Chen, 2024

Part IOptimizing Pharmacokinetics in Discovery

The Hunt for the “Right” Molecules

Part One of this book is devoted to the application of pharmacokinetics (PK) during various stages of drug discovery. In classical textbooks, PK is often astutely described as “what the body does to a drug.” It is the study of the process of a compound’s movement into (Absorption), through (Distribution), and out of the body (Metabolism and Excretion). Collectively, these ADME processes inform us on the concentration of a compound in the systemic circulation over time, which in turn dictates the compound exposure available in the site of action to elicit pharmacological effects. A central part of any discovery effort is devoted to optimize compounds’ ADME properties to attain and maintain certain unbound compound concentration for a suitable duration at the site of action. In Part One, we will learn how to use integrated PK reasonings to achieve this goal.

We must be forewarned, however, that the extent and duration required to elicit efficacy depend on many factors, including the mode and kinetics of compound-target interactions and pharmacology downstream from the target. These downstream processes are collectively referred to as pharmacodynamics (PD), often referred to in classical textbooks as “what a drug does to the body.” The downstream pharmacological activities triggered by the engagement between a compound and its target can be rather complex, with a multitude of compensatory or redundant pathways and feedback regulation. How much target engagement is needed and for how long in order to get the desired therapeutic effect at the end of a cascade depends on how this complex network responds to the engagement that triggered it. Some pharmacological processes require a short burst of intense target engagement (e.g., analgesics), while others may require a moderate level of prolonged engagement (e.g., antibiotics and antineoplastics). Unfortunately, criteria for an ideal target engagement and the PK required to achieve it (PK driver) are not always obvious nor fully elucidated in early discovery because in vivo evaluation of the pharmacological properties with repeat-dose pharmacological studies is too resource intensive, and is generally reserved only for the few compounds with promising PK properties and potency (loosely defined as the range of concentration over which a compound generates some measure of response) near candidate selection. Without guidance from a thorough understanding of downstream PD behavior, the effort to optimize PK properties of compounds could easily be misdirected. A discovery effort should therefore really begin with an assessment of the PD properties of the target/pathway of interest to determine what the optimal PK driver for efficacy is before initiating optimization efforts (“PD before PK”).

That being said, it is still more convenient to present PK concepts first, while leaving the discussion on how to use PD concepts to determine the optimal PK driver for efficacy until Part Two of this book. The PK discussions in Part One would assume that the ideal PK profile needed to drive efficacy is known.

Chapter 1

begins with an overview of the drug discovery process and how PK can be used to answer key questions at various phases of discovery.

Chapter 2

introduces the PK parameters used to quantify the concentration of compounds in systemic circulation over time.

Chapter 3

discusses the link between the outward appearance of concentration-time profiles and the underlying PK (ADME) processes.

Chapters 4

to

6

discuss how these PK processes are linked to compound properties and physiology, and can be predicted from in vitro determined or in silico predicted compound properties commonly available in discovery.

Chapter 7

discusses the integration of these concepts to address specific discovery problems.

Chapter 8

discusses the PK of monoclonal antibodies.

Chapter 1The Importance of Pharmacokinetics in Early Drug Discovery

Learning Objectives

Upon completion of this chapter, you should be able to answer the following questions:

What is the process of drug discovery?

What is PK and what are the various roles it plays in drug discovery?

Why is PK often used as a surrogate for efficacy?

How is “good” PK defined for the purpose of compound screening and optimization?

Imagine! You have just been asked to participate in a brand-new drug discovery effort for an important yet unmet medical need. Many patients are depending on your team’s effort to get a safe and effective drug to them as soon as possible and at an affordable price. Early basic research has already demonstrated with experimental data that intervention of a certain part of a specific pharmacological pathway could result in the desired therapeutic effect. Several potential “hits,” molecules that can engage the target, have been identified. Now it is up to your team, consisting of computational and medicinal chemists, biologists, toxicologists, drug metabolism and pharmacokinetic (PK) scientists, among others, to decide how to further optimize the hits and to find the best one to take into human trial. There is excitement in the air.

1.1 PK as a Surrogate for Efficacy

Because of the huge number of compounds typically evaluated in the discovery stages, the screening process employs mostly high-throughput in vitro or in-silico assays amenable to automation, which require less (or no) synthesized material. But how does one know if the collection of compound properties measured in vitro would result in clinical efficacy in vivo? Short of testing the compounds directly in humans, the only way to demonstrate efficacy in vivo is to test it in a suitable animal model. Unfortunately, animal models are not always predictive of human effectiveness (Henderson et al., 2013; McGonigle and Ruggeri, 2014). Even for a validated animal model with demonstrated translatability to human efficacy, such in vivo studies require long durations for the pharmacological effect to manifest. The observed effect can often be highly variable and requires averaging data across many study animals in order to ascertain a reliable readout. All of these mean long study duration, hundreds of milligrams of chemical substances and involvement of many animals and personnel, not something practical to execute for the large number of compounds screened during early discovery. How else can the potential efficacy of a large number of compounds be evaluated without testing them in such labor-intensive and time-consuming animal pharmacology studies?

This is why human PK predicted from in vivo animal PK or in vitro ADME (absorption, distribution, metabolism, and excretion) data is often used as a surrogate for efficacy. Basic principles of pharmacology tell us that effect is elicited by the compounds that are not bound to any proteins or tissue components at the site of action. In most cases, the compound is carried to the target organ by the arterial blood that perfuses it. So, if a compound’s systemic concentration in human in vivo can be predicted from in vivo or in vitro screening data, then it should be possible to use it as a gauge to determine if the compound can reach a high enough concentration and with sufficient persistence at the site of action to achieve efficacy.

Let’s begin by considering the fate of a compound as it moves through the body. The focus is placed only on oral administration here, which is the most frequently used route of administration for most drugs on the market. Other dose routes will be discussed in Chapter 6.

Figure 1.1 highlights the journey a compound takes from where it is administered to reach its intended target. For example, an orally administered compound is emptied from the stomach into the small intestine, where most compounds are absorbed. As the molecules permeate across the enterocytes lining the gut wall, they could be metabolized by enzymes residing inside. The surviving molecules will then enter the portal vein, which takes them to the liver, where they may be subject to further metabolism or excretion into the bile. The molecules that escape the liver finally enter the general circulation and are carried to various organs and could be subject to further metabolism and/or excretion in the kidney or other tissues.

Figure 1.1 The fate of an orally administered compound as it journeys around the body and distributes into the target tissue. Only the unbound and unionized form of the compound can move between capillary blood and tissue mass, as well as between interstitial and intracellular spaces. Once inside the target tissue, unbound compound engages its intended target to trigger a cascade of pharmacological activities leading to clinical effect. Only selected organs are represented in the diagram. Red arrows represent arterial blood flow, except for portal vein blood flow that carries the absorbed compound to the liver, purple arrows represent venous blood flow, green arrows represent metabolism and excretion, and blue arrows represent pharmacological actions.

This journey is commonly summarized into four distinct processes: Absorption, distribution, metabolism, and excretion, abbreviated as ADME. These four processes collectively influence and determine the systemic and tissue level of all compounds.

1.1.1 Absorption

An orally administered compound must first be dissolved in the digestive tract and move across the intestinal mucous membrane into the portal vein before reaching the bloodstream via the liver.

1.1.2 Distribution

While in the systemic circulation, the compound could be bound to plasma proteins or be taken up into blood cells. The compound would also quickly settle into an equilibrium between the ionized and unionized form according to its acid/base characteristics and the pH in blood. The bloodstream carries the compound to various organs in the body. On reaching individual organs, only the unbound and unionized form of the compound can cross the blood vessel walls to get inside. This transit can occur either passively according to the concentration gradient on both sides of the membrane, or via active transport. Once inside the tissue, the compound could further interact nonspecifically with tissue components (i.e., tissue proteins, lipoproteins, etc.). Only the unbound and unionized portion of the compound can leave the tissue through the membranes separating the capillary blood and tissue mass.

The disposition of the compound at its site of action is governed by the same distribution process. In most cases, it must reach systemic circulation from the site of administration first, before being carried to the target tissue. Exceptions include local administration, such as inhaled drug targeting the lung, or topical applications targeting the skin, or even orally dosed drugs targeting the intestinal wall. In these cases, the administered compound reaches its intended target organ before it reaches general circulation (see section 10.6). Once reaching the target tissue, it is usually assumed that only the unbound compound in the target tissue is free to engage the intended target.

1.1.3 Metabolism

Compounds inevitably begin to break down once inside the body. Most compounds are metabolized in the liver by reduction and oxidation enzymes, called cytochrome P450 enzymes, to form metabolites that are usually pharmacologically inactive and more readily excreted. However, some metabolites may also be pharmacologically active, sometimes more so than the parent compound, and can also be toxic (Obach, 2013).

1.1.4 Excretion

Compounds and their metabolites are removed from the body via excretion. The three main routes of excretions are (1) the kidney, which is the main route, where compounds and their metabolites are excreted through urine, (2) biliary excretion initiated in the liver into the gut and excreted in the feces, and to a lesser extent (3) through the lung.

Because a compound is carried to the tissue by the arterial blood that perfuses it, its concentration and persistence in the tissue is reflected by its concentration and persistence in the systemic circulation. To be sure, a compound’s concentration in the target tissue would be somewhat different from that in the systemic circulation due to various physiological and compound-related properties (discussed in greater detail in Chapter 10), but concentration in the tissue is unlikely to be high and long-lasting without the same in the systemic circulation. In other words, the time course of a compound’s concentration in the systemic circulation (either blood or plasma) can serve as a reasonable surrogate for efficacy.

This is a good thing, because the concentration in systemic circulation is much easier to measure than at the target site, not to mention the pharmacological effect it elicits in most cases. Instead of screening for efficacious compounds in lengthy and costly pharmacological studies in animal models, it would be far more efficient to look for compounds with reasonable concentration in the systemic circulation. The in vivo studies to characterize the compound’s systemic concentration-time profile is far less resource intensive than most pharmacology studies; they involve only a few animals, small amounts of drug substance, and quick turnaround of results, typically within a few days rather than a few weeks. As we will learn in later chapters, the concentration-time profile of a compound can even be inferred with some accuracy using data from in vitro experiments or in-silico predictions, further reducing the resource requirement and making this approach very amenable as part of the screening strategy. By the same logic, PK can also serve as a surrogate for safety evaluation, as toxicology is just the expression of undesirable pharmacological effects.

1.2 The Many Faces of Pharmacokineticists

From these discussions, it is easy to see why systemic PK can play a significant role in all stages of discovery to improve drug discovery efficiency (Peck et al., 1993). Over the past few decades, the pharmaceutical industry has become increasingly adept at optimizing the PK of small molecules resulting in fewer failures at Phase I clinical trials. In 1991, PK and bioavailability were cited as the most significant cause of attrition (Kola and Landis, 2004). But by 2000, these factors had ceased to be a significant cause of attrition (Arrowsmith, 2011). Instead, by 2000, the greatest reason for drug failure was lack of efficacy and toxicity, together accounting for approximately 50% of attrition.

Five roles PK play in discovery decision making are illustrated in Figure 1.2 and also outlined in text. Applications of PK principles under each role are detailed throughout the subsequent chapters.

Figure 1.2 A roadmap outlining the many roles PK play in discovery decision making. The central box depicts the connections between a compound’s systemic concentration-time profile (right), the underlying primary PK processes (middle), and the compound’s ADME properties (left). Only representative compound properties are depicted. Block arrows depict the direction of logic flow in decision making. Secondary PK parameters are those describing the observed concentration-time profiles, while primary PK parameters characterize the underlying PK processes (see Chapter 2).

1.2.1 Pharmacokineticists as Observers

In vivo PK data from preclinical studies become available during the later phases of discovery, especially in lead optimization. In this role, PK is used to describe the time course of compound concentration in systemic circulation observed from in vivo preclinical animal studies to answer questions such as: how high do compound concentrations reach, how long does it take to reach the peak, how quickly or slowly does it disappear from the blood, how much does compound accumulate when it is administered repeatedly? From this information, one can infer the compound concentration and persistence in the target site. The observed concentration-time profile can then be compared to potency of the compound to determine at what dosage and dosing frequency drug concentrations would be high enough relative to the potency to elicit the desired response. This topic is covered in Chapter 2. The potency used here would either be measured in in vitro assays or be deduced from in vivo animal pharmacology studies. If potency is measured in vitro, such as using biochemical assays quantifying binding affinity or cell-based assays that measure a biological signal to more closely approximate the in vivo situation, caution must be exercised in the utilization of these in vitro data as a surrogate for in vivo potency (see Chapter 11 for more details). If potency is deduced from in vivo animal pharmacology studies, then species-differences in potency must be considered (discussed in section 16.2).

Either way, both the PK and potency estimates would need to be suitably translated to human before comparisons are made (detailed in Part Three of this book). Another prerequisite is knowing which part of the concentration-time profile should be compared to potency. As mentioned earlier in this chapter, the PK endpoint (defined in section 2.1) driving efficacy (a.k.a. PK driver) is not generic, but highly dependent on the pharmacodynamics (PD) modulated by the interaction of a compound with its target. How to identify the PK drivers of efficacy is detailed in Part Two of the book, specifically in Chapter 12. In Chapter 2, the discussion would presume PK and potency have been suitably translated and the PK endpoints most important to drive efficacy have been determined.

1.2.2 Pharmacokineticists as Predictors

At an earlier stage of discovery when only in vitro data are available, then PK principles could be used to translate the in vitro data to predict in vivo PK. The time course of a compound’s concentration in the systemic circulation is the consequence of how fast it is absorbed from the site of administration, how much and how quickly it is distributed into the various body compartments, and how quickly and through which routes the compound is cleared from the body. All these rate processes are mechanistically linked to, and can be predicted from, compound properties that can be measured in vitro. Once predicted, the concentration-time profile can be compared to measures of potency to decide if the criteria for efficacy can be met when administered at a reasonable dosing regimen or not. Several steps are required to understand how such predictions are made. First, the PK profile is deconvolved into its underlying PK processes in Chapter 3. Each of the PK processes, namely distribution, clearance, and absorption, are subsequently linked mechanistically to the in vitro measured compound properties in Chapters 4 to 6. Compound selection based on the action of these ADME properties acting in concert is discussed in Chapter 7 where all the PK concepts discussed in Part One of the book are integrated.

At an even earlier discovery stage, when decisions are being made on which compounds to synthesize and to be taken into in vitro assays, the same type of analysis can still be performed by predicting the PK profiles from molecular properties using in silico quantitative-structure-property-relationship (QSPR) models (see Appendix B for more details). This topic is also covered in Chapter 7 and in various chapters for each of the ADME properties.

1.2.3 Pharmacokineticists as Detectives

PK is useful not only to describe or predict the systemic concentration-time profile to aid compound selection and prioritization. If all the compounds fail the selection criteria for PK related issues (i.e., rather than for potency or safety concerns), then the above-mentioned PK concepts can be applied in reverse to find out which compound properties should be improved in future compounds. PK can be used as a detective tool to reveal which PK process is responsible for inadequate peak systemic compound concentration, delay in reaching the peak, or disappearing from the systemic circulation too quickly to elicit the pharmacological effect (e.g., may need to be administered at an unreasonably high dose or frequency). Through such a diagnostic process, PK can help inform computational and medicinal chemists in designing future molecules to mitigate the root causes of suboptimal PK.

The approach to identify the causes of suboptimal PK is presented in Chapter 3. Chapters 4 to 6 subsequently discuss how various compound properties contribute to the primary PK processes being the root causes of the PK endpoint not being fulfilled. Integration of these concepts, with examples of their applications in discovery scenarios, are presented in Chapter 7.

1.2.4 Pharmacokineticists as Designers

Even though PK can be an effective tool in diagnosing the causes of suboptimal PK, it would be more efficient to design a compound a priori with optimal properties rather than optimizing the properties of a compound after it has been synthesized. Beginning with an identified ideal PK driver for efficacy (Part Two of this book), the logic flow from compound properties to primary PK processes to concentration-time profile, described previously, can be applied in reverse to identify the optimal compound space required for an efficacious drug. Because compound properties work in concert to define its concentration-time profile, they can trade off for one another in complex manners. One approach is to build a physiologically based PK (PBPK) model detailed in section 7.8 that mathematically links compound properties to the concentration-time profile, and to use Monte-Carlo simulation to virtually explore the compound property space to identify the optimal space (Chen et al., 2021).

To effectively inform medicinal chemistry strategy, however, it would be necessary to map the optimal compound property space to molecular properties. For example, should we make compounds that are more lipophilic or less lipophilic, what are the optimal numbers of hydrogen bond donors and acceptors, including or excluding which functional group or molecular fragments? One can leverage QSPR models that predict compound properties from molecular descriptors to determine the optimal molecular properties to synthesize. However, because the relationship between compound properties and their molecular features are not one-to-one, this becomes a complex multidimensional optimization problem. One solution is to use virtually enumerated compounds, with compound properties predicted using QSPR models, to virtually probe PBPK models in order to identify the molecular features or functional groups most important to drive efficacy (Chen et al., 2022). These model-based virtual exploration approaches are described in more detail in Chapters 7 and 13.

PK principles can also be used to aid the design of studies, such as picking the dose levels, frequency of dosing, duration of study, number of animals, types, and timing of measurements to make. Chapter 2 illustrates how one uses PK data from existing in vivo studies to design new studies. However, the approach can be extended to design first-time in vivo PK studies using PK profiles predicted from in vitro measured or in silico predicted compound properties as well.

1.2.5 Pharmacokineticists as Interpreters

At the earliest stages of discovery, in the absence of indicative mechanistic data, PK prediction and diagnostics are usually conducted without assuming the involvement of more complex PK processes. However, as soon as in vivo PK data from preclinical species become available, one could use these data to check the validity of such simplifying assumptions. Misprediction of in vivo PK from in vitro ADME properties could reveal the involvement of more complex mechanisms. This topic is covered in each of Chapters 4 to 6