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Physiologically-Based Pharmacokinetic (PBPK) Modeling and Simulations: Principles, Methods, and Applications in the Pharmaceutical Industry

ISBN: 978-0-470-48406-7
450 pages
March 2012
Physiologically-Based Pharmacokinetic (PBPK) Modeling and Simulations: Principles, Methods, and Applications in the Pharmaceutical Industry (0470484063) cover image

The only book dedicated to physiologically-based pharmacokinetic modeling in pharmaceutical science

Physiologically-based pharmacokinetic (PBPK) modeling has become increasingly widespread within the pharmaceutical industry over the last decade, but without one dedicated book that provides the information researchers need to learn these new techniques, its applications are severely limited. Describing the principles, methods, and applications of PBPK modeling as used in pharmaceutics, Physiologically-Based Pharmacokinetic (PBPK) Modeling and Simulations fills this void.

Connecting theory with practice, the book explores the incredible potential of PBPK modeling for improving drug discovery and development. Comprised of two parts, the book first provides a detailed and systematic treatment of the principles behind physiological modeling of pharmacokinetic processes, inter-individual variability, and drug interactions for small molecule drugs and biologics. The second part looks in greater detail at the powerful applications of PBPK to drug research.

Designed for a wide audience encompassing readers looking for a brief overview of the field as well as those who need more detail, the book includes a range of important learning aids. Featuring end-of-chapter keywords for easy reference—a valuable asset for general or novice readers without a PBPK background—along with an extensive bibliography for those looking for further information, Physiologically- Based Pharmacokinetic (PBPK) Modeling and Simulations is the essential single-volume text on one of the hottest topics in the pharmaceutical sciences today.

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Preface xv

Acknowledgments xvii

SECTION I. PRINCIPLES AND METHODS 1

1 MODELING IN THE PHARMACEUTICAL INDUSTRY 3

1.1 Introduction 3

1.2 Modeling Approaches 4

1.3 Steps Needed to Maximize Effective Integration of Models into R&D Workflow 7

1.4 Scope of the Book 8

Keywords 10

References 12

2 PHYSIOLOGICALLY-BASED MODELING 13

2.1 Introduction 13

2.2 Examples of Physiological Modeling 14

2.3 Need for Physiological Models in the Pharmaceutical Industry 15

2.4 Organs as Compartments 15

2.5 Bottom-Up vs. Top-Down Modeling in Pharmacokinetics 16

References 16

3 REVIEW OF PHARMACOKINETIC PRINCIPLES 17

3.1 Introduction 18

3.2 Routes of Administration 18

3.3 Drug Disposition 18

3.3.1 Absorption 18

3.3.2 Plasma Protein Binding, BloodPlasma Ratio 20

3.3.3 Distribution, Elimination, Half-Life, and Clearance 23

3.3.4 Role of Transporters in ADME 29

3.4 Linear and Nonlinear Pharmacokinetics 34

3.5 Steady-State Pharmacokinetics 34

3.6 Dose Estimations 37

3.7 Successful PK Optimization in Drug Discovery 40

Keywords 40

References 41

4 PHYSIOLOGICAL MODEL FOR ABSORPTION 43

4.1 Introduction 44

4.2 Drug Absorption and Gut Bioavailability 44

4.2.1 Solubility and Dissolution Rate 44

4.2.2 Permeability: Transcelluar, Paracellular, and Carrier-Mediated Pathways 51

4.2.3 Barriers to Membrane Transport—Luminal Degradation, Efflux, and Gut Metabolism 53

4.3 Factors Affecting Drug Absorption and Gut Bioavailability 56

4.3.1 Physiological Factors Affecting Oral Drug Absorption and Species Differences in Physiology 56

4.3.2 Compound-Dependent Factors 62

4.3.3 Formulation-Dependent Factors 63

4.4 In Silico Predictions of Passive Permeability and Solubility 66

4.4.1 In Silico Models for Permeability 66

4.4.2 In Silico Models for Solubility 67

4.5 Measurement of Permeability, Solubility, Luminal Stability, Efflux, and Intestinal Metabolism 67

4.5.1 In Vitro, in Situ and in Vivo Assays for Permeability 67

4.5.2 Measurement of Thermodynamic or Equilibrium Solubility 72

4.5.3 Luminal Stability 74

4.5.4 Efflux 74

4.5.5 In Vitro Models for Estimating Extent of Gut Metabolism 76

4.6 Absorption Modeling 76

Keywords 83

References 84

5 PHYSIOLOGICAL MODEL FOR DISTRIBUTION 89

5.1 Introduction 90

5.2 Factors Affecting Tissue Distribution of Xenobiotics 91

5.2.1 Physiological Factors and Species Differences in Physiology 91

5.2.2 Compound-Dependent Factors 98

5.3 In Silico Models of Tissue Partition Coefficients 98

5.4 Measurement of Parameters Representing Rate and Extent of Tissue Distribution 105

5.4.1 Assessment of Rate and Extent of Brain Penetration 105

5.5 Physiological Model for Drug Distribution 110

5.6 Drug Concentrations at Site of Action 111

Keywords 114

References 115

6 PHYSIOLOGICAL MODELS FOR DRUG METABOLISM AND EXCRETION 119

6.1 Introduction 119

6.2 Factors Affecting Drug Metabolism and Excretion of Xenobiotics 120

6.3 Models for Hepatobiliary Elimination and Renal Excretion 124

6.3.1 In Silico Models 124

6.3.2 In Vitro Models for Hepatic Metabolism 125

6.3.3 In Vitro Models for Transporters 127

6.4 Physiological Models 136

6.4.1 Hepatobiliary Elimination of Parent Drug and Metabolites 136

6.4.2 Renal Excretion 141

References 144

7 GENERIC WHOLE-BODY PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING 153

7.1 Introduction 153

7.2 Structure of a Generic Whole Body PBPK Model 154

7.3 Model Assumptions 157

7.4 Commercial PBPK Software 158

References 159

8 VARIABILITY, UNCERTAINTY, AND SENSITIVITY ANALYSIS 161

8.1 Introduction 161

8.2 Need for Uncertainty Analysis 162

8.3 Sources of Physiological, Anatomical, Enzymatic, and Transporter Variability 163

8.4 Modeling Uncertainty and Population Variability with Monte Carlo Simulations 169

8.5 Sensitivity Analysis 172

8.6 Conclusions 174

Keywords 174

References 175

9 EVALUATION OF DRUGDRUG INTERACTION RISK WITH PBPK MODELS 183

9.1 Introduction 184

9.2 Factors Affecting DrugDrug Interactions 186

9.3 In Vitro Methods to Evaluate DrugDrug Interactions 190

9.3.1 Candidate Drug as a Potential Inhibitor 190

9.3.2 Candidate Drug as a Potential Victim of Inhibition 192

9.4 Static Models to Evaluate DrugDrug Interactions 193

9.5 PBPK Models to Evaluate DrugDrug Interactions 195

9.5.1 Intrinsic Clearance of Victim (V) in the Absence of Inhibitor or Inducer 195

9.5.2 Intrinsic Clearance of Victim (V) in the Presence of Inhibitor 196

9.5.3 Time-Dependent Changes in the Abundance of an Enzyme Isoform Inhibited by an MBI 197

9.5.4 Intrinsic Clearance of Victim (V) in the Presence of Inducer 197

9.6 Comparison of PBPK Models and Static Models for the Evaluation of Drug Drug Interactions 198

Keywords 201

References 202

10 PHYSIOLOGICALLY-BASED PHARMACOKINETICS OF BIOTHERAPEUTICS 209

10.1 Introduction 210

10.2 Therapeutic Proteins 210

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10.2.1 Peptides and Proteins 210

10.2.2 Monoclonal Antibodies 212

10.3 Pharmacokinetics of Therapeutic Proteins 214

10.3.1 Peptides and Proteins 215

10.3.2 Monoclonal Antibodies 224

10.4 PBPK/PD Modeling for Therapeutic Proteins 230

10.4.1 Need for PBPK Modeling for Therapeutic Proteins 230

10.4.2 PBPK Modeling for Therapeutic Proteins 231

10.4.3 Pharmacokinetic Scaling 239

10.4.4 Applications of PBPK Models of Therapeutic Proteins 242

10.4.5 PBPK Integration with Pharmacodynamics 244

10.5 Antisense Oligonucletides and RNA Interferance 245

10.5.1 Antisense Oligonucletides (ASOs) 245

10.5.2 Ribonucleic Acid Interference (RNAi) 245

10.5.3 Pharmacokinetics of ASOs50 and Double-Stranded RNAs 247

10.5.4 Design and Modifications of ASOs to Improve Target Affinity and PD: the First, Second, and Third Generation ASOs 249

10.5.5 Integration of PK/PBPK and PD Modeling 253

Keywords 254

References 256

SECTION II. APPLICATIONS IN THE PHARMACEUTICAL INDUSTRY 261

11 DATA INTEGRATION AND SENSITIVITY ANALYSIS 263

11.1 Introduction 263

11.2 Examples of Data Integration with PBPK Modeling 264

11.3 Examples of Sensitivity Analysis with PBPK Modeling 267

References 271

12 HYPOTHESIS GENERATION AND PHARMACOKINETIC PREDICTIONS 273

12.1 Introduction 274

12.2 PBPK Simulations of Pharmacokinetic Profiles for Hypothesis Generation and Testing 274

12.2.1 Methodology 274

12.2.2 In Vivo Solubility 278

12.2.3 Delayed Gastric Emptying 280

12.2.4 Regional Variation in Intestinal Loss: Gut Wall Metabolism, Intestinal Efflux, and Luminal Degradation 282

12.2.5 Enterohepatic Recirculation 284

12.2.6 Inhibition of Drug-Metabolizing Enzymes 286

12.2.7 Inhibition of Hepatic Uptake 286

12.2.8 Inhibition of Hepatobiliary Efflux 290

12.3 Pharmacokinetic Predictions 293

12.3.1 Human Predictions from Preclinical Data 293

12.3.2 Pharmacokinetic Predictions in Clinical Development 293

References 294

13 INTEGRATION OF PBPK AND PHARMACODYNAMICS 299

13.1 Introduction 300

13.2 Pharmacodynamic Principles 300

13.2.1 Pharmacological Targets and Drug Action 300

13.2.2 Functional Adaptation Processes: Tolerance, Sensitization, and Rebound (Fig 13.2) 301

13.3 Pharmacodynamic Modeling 307

13.3.1 ConcentrationEffect, DoseResponse Curves, and Sigmoid Emax Models 307

13.3.2 Mechanism-Based PD Modeling 315

13.3.3 Simple Direct Effects 315

13.3.4 Models Accommodating Delayed Pharmacological Response 321

13.3.5 Models Accommodating Nonlinearity in Pharmacological Response with Respect to Time 332

13.4 Pharmacokinetic Modeling: Compartmental PK and PBPK 335

13.5 Integration of PK or PBPK with PD Modeling 335

13.6 Reasons for Poor PK/PD Correlation 339

13.7 Applications of PK or PBPK/PD Modeling in Drug Discovery and Development 340

13.7.1 Need for a Mechanistic PBPK/PD Integration 341

13.7.2 Applications of PK or PBPK/PD in Drug Discovery 342

13.7.3 Applications of PK or PBPK/PD in Drug Development 360

13.8 Regulatory Perspective 370

13.9 Conclusions 371

Keywords 372

References 376

14 PHYSIOLOGICALLY-BASED PHARMACOKINETIC MODELING OF POPULATIONS 383

14.1 Introduction 383

14.2 Population Modeling with PBPK 384

14.3 Healthy to Target Patient Population: Impact of Disease on Pharmacokinetics 386

14.4 Modeling Subpopulations: Impact of Age, Gender, Co-morbidities, and Genetics on Pharmacokinetics 389

14.5 Personalized Medicine with PBPK/PD 392

Keyword 395

References 396

15 PBPK MODELS ALONG THE DRUG DISCOVERY AND DEVELOPMENT VALUE CHAIN 401

15.1 Summary of Applications of PBPK Models along Value Chain 401

15.2 Obstacles and Future Directions for PBPK Modeling 403

Keyword 405

References 405

Appendices 407

Index 423

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Sheila Annie Peters, PhD, is an Associate Principal Scientist for R&I DMPK at AstraZeneca. Previously, she was Principal Scientist at Cyprotex Discovery and lectured at the National Institute of Technology (Trichy, India), University of Madras, and Pondicherry University.

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“There is little to fault the book. ... [it] should be on the shelf and a companion to anyone seriously interested in mechanistic model-based drug development.”  (Nature CPT, 10 July 2013)

“Peters does a credible job of bringing this huge area of complex physiology and mathematics together.”  (Pharmaceutical Journal, 21 July 2012)

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