Description
Book SynopsisPhysiologically Based Pharmacokinetic (PBPK) Modeling and Simulations The first book dedicated to the emerging field of physiologically based pharmacokinetic modeling (PBPK)
Now in its second edition, Physiologically Based Pharmacokinetic (PBPK) Modelling and Simulations: Principles, Methods, and Applications in the Pharma Industry remains the premier reference book throughout the rapidly growing PBPK user community. Using clear and concise language, author Sheila Annie Peters connects theory with practice as she explores the vast potential of PBPK modeling for improving drug discovery and development.
This fully updated new edition covers key developments in the field of PBPK modelling and simulations that have emerged in recent years. A brand-new section provides case studies in different application areas of PBPK modelling, including drug-drug interaction, genetic polymorphism, renal impairment, and pediatric extrapolation. Additional chapters address
Table of Contents
Preface xix
Acknowledgements xxi
About the companion xxiii
Section I. Principles, Methods, andBackground Information 1
1 A Review of Pharmacokinetic and Pharmacodynamic Principles 3
1.1 Introduction 4
1.2 Pharmacokinetic Principles 4
1.2.1 Routes of Drug Administration 4
1.2.2 Intravenous Bolus 4
1.2.3 Plasma Protein Binding and Blood–Plasma Ratio 9
1.2.4 Hepatic, Renal, and Biliary Clearances 12
1.2.5 Extravascular (Subcutaneous, Intramuscular, and Per Oral) Absorption 16
1.2.6 Absorption from Solid Dosage Forms 20
1.2.7 Role of Transporters in ADME 22
1.2.8 Linear and Non-Linear Pharmacokinetics 24
1.2.9 Intravenous Infusion, Repeated Dosing, Steady State Kinetics, and Accumulation 25
1.2.10 Active Metabolite and Prodrug Kinetics 28
1.3 Pharmacokinetic Variability 32
1.4 Pharmacokinetics Optimization in Drug Discovery 34
1.5 Pharmacodynamic Principles 34
1.5.1 Pharmacological Targets and Drug Action 35
1.5.2 Functional Adaptation Processes 39
1.5.3 Biomarkers, Surrogate Endpoints, and Clinical Endpoints 41
Keywords 47
References 48
2 A Review of Drug–Drug Interactions 51
2.1 Introduction 51
2.2 Drug Interactions Mediated by Enzymes and Transporters at Various Sites 54
2.3 Factors Affecting DDI 54
2.4 In Vitro Methods to Evaluate Drug–Drug Interactions 56
2.4.1 Candidate Drug as a Potential Perpetrator 57
2.4.2 Candidate Drug as a Potential Victim of Inhibition 58
2.5 Sources of Uncertainty 59
2.6 Therapeutic Protein–Drug Interaction 59
References 61
3 Modeling Pharmacokinetics, Pharmacodynamics, And Drug Interactions 65
3.1 Introduction 66
3.2 Modeling Pharmacokinetics 66
3.2.1 Compartmental Modeling of Linear and Nonlinear Pharmacokinetics (Enzyme and/or Transporter Capacity Limitation as Well as Target-Mediated Drug Disposition) 67
3.2.2 Population Pharmacokinetics 76
3.3 Pharmacokinetics/Pharmacodynamics and PK/Efficacy (Exposure/ Response) Modeling 80
3.3.1 PK/PD Models for Direct Effect: Sigmoid Emax Model 84
3.3.2 PK/PD Models for Direct Effect: Classical Receptor Theory 86
3.3.3 PK/PD Models Accommodating Delayed Pharmacological Response 89
3.3.4 PK/PD Models Accommodating Functional Adaptation Leading to Nonlinearity in Pharmacological Response with Respect to Time 96
3.3.5 PK/Efficacy Modeling 97
3.3.6 Translation of PK/PD and PK/Efficacy Modeling to Human 100
3.3.7 Average, Minimum, and Maximum Steady-State Concentrations 104
3.3.8 Estimation of Biologically Effective Dose in Human 107
3.3.9 Therapeutic Window 109
3.3.10 Static Models for Drug Interactions 109
3.4 Physiologically Based Pharmacokinetic (PBPK) Modeling and Its Integration with Pharmacodynamics and Efficacy Models 112
3.4.1 PK Modeling Compartmental vs PBPK 112
3.4.2 PK Variability: Population PK (popPK) Modeling vs PBPK 114
3.4.3 Integration of PBPK with PD, Quantitative Systems Pharmacology (QSP) Models or Quantitative Systems Toxicologyand Safety (QSTS) 114
3.4.4 PBPK Models to Evaluate Drug–Drug Interactions 115
3.4.5 DDI Risk Assessment with PBPK vs Static Models 118
Keywords 123
References 125
4 Physiological Model For Absorption 129
4.1 Introduction 130
4.2 Drug Absorption and Gut Bioavailability 130
4.2.1 Solubility and Dissolution Rate 130
4.2.2 Permeability: Transcellular, Paracellular, and Carrier-Mediated Pathways 136
4.2.3 Barriers to Membrane Transport – Luminal Degradation, Efflux, and Gut Metabolism 138
4.3 Factors Affecting Drug Absorption and Gut Bioavailability 140
4.3.1 Physiological Factors Affecting Oral Drug Absorption and Species Differences in Physiology 140
4.3.2 Compound-Dependent Factors 144
4.3.3 Formulation-Dependent Factors 144
4.4 In Silico Predictions of Passive Permeability and Solubility 147
4.4.1 In Silico Models for Permeability 147
4.4.2 In Silico Models for Solubility 147
4.5 Measurement of Permeability, Solubility, Luminal Stability, Efflux, Intestinal Metabolism 148
4.5.1 In Vitro, In Situ, and In Vivo Models for Effective Permeability 148
4.5.2 Measurement of Thermodynamic or Equilibrium Solubility 153
4.5.3 Luminal Stability 154
4.5.4 Efflux 154
4.5.5 In Vitro Models for Gut Metabolism and Estimation of Fraction Escaping Gut Metabolism 155
4.6 Absorption Modeling 156
Keywords 162
References 163
5 Physiological Model For Distribution 169
5.1 Introduction 170
5.2 Factors Affecting Tissue Distribution of Xenobiotics 170
5.2.1 Physiological Factors and Species Differences in Physiology 171
5.2.2 Compound-Dependent Factors 176
5.3 In Silico Models of Tissue Partition Coefficients 176
5.4 Measurement of Parameters Representing the Rate and Extent of Tissue Distribution 181
5.4.1 Assessment of Rate and Extent of Brain Penetration 181
5.5 Physiological Model for Drug Distribution 186
5.6 Drug Concentrations at the Site of Action 187
Keywords 189
References 189
6 Physiological Models For Drug Metabolism And Excretion 193
6.1 Introduction 193
6.2 Factors Affecting Drug Metabolism and Excretion of Xenobiotics 194
6.3 Models for Hepatobiliary and Renal Excretion 197
6.3.1 In Silico Models 197
6.3.2 In Vitro Models for Hepatic Metabolism 197
6.3.3 In Vitro Models for Transporters 200
6.4 Physiological Models 203
6.4.1 Hepato-Biliary Elimination of Parent Drug and Metabolites 205
6.4.2 Renal Excretion 208
References 211
7 Generic Whole-Body Physiologically Based Pharmacokinetic Modeling 217
7.1 Introduction 217
7.2 Structure of a Generic Physiologically-Based Pharmacokinetic (PBPK) Model 218
7.3 Somatic Compartments 220
7.3.1 Lungs (LU) 220
7.3.2 Arterial Blood (ART) 220
7.3.3 Venous Blood (VEN) 220
7.3.4 Stomach (ST) 220
7.3.5 Gut (GU) 220
7.4 Model Assumptions 221
7.5 PBPK Software 221
References 223
8 Pbpk Modeling Of Biotherapeutics 225
8.1 Introduction 226
8.2 Therapeutic Proteins 226
8.2.1 Peptides and Proteins 226
8.2.2 Antibodies and Antibody-Based Therapies 227
8.3 Pharmacokinetics of Therapeutic Proteins 234
8.3.1 Absorption 234
8.3.2 Renal Elimination 235
8.3.3 Immunogenicity 235
8.3.4 PEGylation 239
8.3.5 Transport by Convective and Transcytotic Extravasation 239
8.3.6 Catabolic Elimination (Proteolysis) 239
8.3.7 FcRn-Mediated Protection of IgGs Against Catabolism in FcRn-Rich Cells 241
8.3.8 Distribution and lymphatic elimination 242
8.3.9 Target-Mediated Drug Disposition and Receptor-Mediated Endocytosis 243
8.4 PBPK Modeling of Monoclonal Antibodies 244
8.4.1 Full PBPK Model for Monoclonal Antibodies 244
8.4.2 Minimal PBPK Model for Monoclonal Antibodies 253
8.5 Applications of PBPK Modeling of Monoclonal Antibodies 253
8.5.1 Pharmacokinetic Scaling 253
8.5.2 PBPK Integration with Pharmacodynamics of Monoclonal Antibodies 255
Keywords 156
References 258
9 Uncertainty And Population Variability 263
9.1 Introduction 264
9.2 Distinguishing Uncertainty and Variability 264
9.3 Sources of Uncertainty in Drug-related Parameters 264
9.4 Sources of Variability in System Parameters 266
9.5 Handling Population Variability 269
9.5.1 A POSTERIORI and A PRIORI Approaches to Handling Population Variability 269
9.5.2 Correlations Between Parameters 271
9.6 Uncertainty and Sensitivity Analysis 272
9.6.1 Local Sensitivity Analysis (One-at-a-time (OAT) and Derivative-based Methods) 272
9.6.2 Parameter Interactions and Global Sensitivity Analysis (GSA) 275
9.6.3 Global Sensitivity Analysis for Correlated Parameters (cGSA) 278
9.6.4 Applications of Sensitivity Analysis for PBPK Models 280
9.6.5 Limitations of Global Sensitivity Analysis 281
9.7 Uncertainty and Population Variability in Clinical Efficacy and Safety 282
Keywords 285
References 285
10 Nonclinical, Clinical, and Model-Informed Drug Development 293
10.1 Introduction: An Overview of Different Phases of Drug Development 294
10.2 Nonclinical Development 295
10.2.1 Preclinical Pharmacology, PK/PD Modeling, and Human Dose Prediction 297
10.2.2 Safety and Toxicology Studies 297
10.2.3 Studies with Radiolabeled Compound 298
10.3 Clinical Pharmacology Studies 302
10.3.1 First-in-Human, Single, and Multiple Ascending Dose Studies 302
10.3.2 Biopharmaceutics – Absolute Oral Bioavailability and Bioequivalence Study 304
10.3.3 Food Effect Study 304
10.3.4 Organ (Hepatic and Renal) Impairment Study 305
10.3.5 Pediatric Assessment 306
10.3.6 Mass Balance Study 307
10.3.7 Drug Interaction Study 307
10.3.8 Pharmacogenomics Study 308
10.3.9 Thorough QT (TQT) and Concentration QT (C-QT) Study 308
10.3.10 Immunogenicity Assays and Comparability Study for Biologics 309
10.3.11 Drug Labelling 309
10.4 Clinical Development in Oncology 310
10.5 Fast Track Routes to Address Unmet Medical Need in the Treatment of Serious Conditions 311
10.6 Model-Informed Drug Development 312
10.7 Physiologically Based Pharmacokinetic Models Complementing Clinical Pharmacology Studies 314
10.8 PBPK in Oncology 315
Regulatory Guidelines 316
References 319
Section II. Applications In The Pharmaceutical Industry 323
11 Overview Of Pbpk Applications 325
11.1 Introduction 325
11.2 PBPK Applications for Internal Decisions 326
11.3 PBPK Applications for Regulatory Filing 328
11.4 PBPK Modeling and Simulations Along the Value Chain 332
References 335
12 Applications Of Hypothesis Generation And Testing With Pbpk Models 337
12.1 Introduction 338
12.2 Hypothesis Generation and Testing with PBPK Models 338
12.2.1 Parameter Estimation from Intravenous Pharmacokinetic Profiles 338
12.2.2 Simulation of Oral PK Profile 341
12.2.3 Sensitivity Analysis 342
12.2.4 Verification of Hypotheses 346
12.2.5 Auto-inhibition of Drug-Metabolizing Enzymes, Uptake and Efflux Transporters 347
12.3 Hypothesis Generation and Testing Along the Value Chain 348
12.4 Conclusions 351
References 351
13 Applications of Physiologically Based Pharmacokinetic Models Integrated With Drug Effect Models (Pbpk/Pd) 353
13.1 Introduction: Integration of PBPK with Drug Effect Models 354
13.2 Dosing in Specific Populations 355
13.3 PBPK/PD for Bottom-Up Prediction of Inter-Patient Variability in Drug Response 357
13.4 PBPK/PD for Predicting the Inter-Patient Variability in Response to Prodrugs and Active Metabolites 358
13.5 PBPK/PD When Systemic Concentrations are not the Driver forDrug Response 359
13.5.1 Pre-Systemic Drug Target 359
13.5.2 Effect-Site Drug Concentration Different from Systemic Concentration 360
13.6 PBPK/PD for Monoclonal Antibodies 362
13.7 PBPK Models Linked to Quantitative Systems Pharmacology and Toxicology Models 363
13.7.1 PBPK–QST Models to Predict Drug-Induced Liver Injury 363
13.7.2 PBPK–QST Models to Predict Drug-Induced Cardiotoxicity 367
13.8 Conclusions 371
References 371
14 Pbpk Modeling and Simulations to Evaluate Clinical Drug-Drug Interactions 375
14.1 Introduction 376
14.2 Clinical DDI Studies and Modeling Approaches to Address Key Questions Related to Drug–Drug Interactions 376
14.2.1 Dedicated Clinical DDI Studies 378
14.2.2 Investigation of Phenotypic Effects for NMEs Predominantly Cleared by Polymorphic Enzyme or Transporter 379
14.2.3 Prospective Nested DDI Study 380
14.2.4 Cocktail DDI Study 381
14.2.5 PBPK Modeling and Simulations 381
14.2.6 Claims Relating to Results of DDI Studies 381
14.2.7 Impact on Label 382
14.3 PBPK Modeling of Different Types of Drug Interactions 382
14.3.1 PBPK Modeling Strategy: New Molecular Entity as Victim of CYP-Based Drug Interactions 382
14.3.2 PBPK Modeling Strategy: New Molecular Entity as Perpetrator of CYP-Based Drug Interactions 383
14.3.3 Non-CYP Based Drug Interactions 384
14.3.4 Transporter-Mediated Drug Interactions 385
14.4 DDI Predictions with PBPK Modeling and Simulations in Clinical Drug Development and Regulatory Submissions 387
14.4.1 DDI Predictions Along the Value Chain (Figure 14.5) 387
14.4.2 Possible Regulatory Outcomes, Based on the Predictions from a Verified and Validated PBPK Model 389
14.4.3 Regulatory Acceptance of PBPK Analyses Included in Regulatory Submissions 390
14.4.4 Predictive Performance of PBPK Models 391
14.5 Comparison of DDI Prediction Using Static and Dynamic Models 392
14.6 Conclusions 393
References 394
15 Dose Extrapolation Across Populations (Healthy Adult Caucasian To Pediatric, Pregnant Women, Different Ethnicities, Geriatric, Smokers And Obese Populations) 397
15.1 Introduction 398
15.2 PBPK Modeling Strategy for Dose Extrapolation to Specific Populations 398
15.3 Potential Benefits of PBPK Modeling for Dose Extrapolations to Specific Populations 399
15.4 Dose Extrapolations to Specific populations 404
15.4.1 Pediatric Starting Dose Selection 404
15.4.2 Pregnancy 406
15.4.3 Ethnicity – Japanese Population 407
15.4.4 Geriatric Population 408
15.4.5 Obese 409
15.4.6 Smokers 410
15.5 Conclusions 410
References 411
16 Dose Extrapolation Across Populations: Healthy Adult To Hepatic And Renal Impairment Populations 417
16.1 Introduction 418
16.2 Pathophysiological Changes in Organ Impairment 419
16.2.1 Hepatic Impairment 419
16.2.2 Renal Impairment 420
16.3 PBPK Modeling Strategy: Model Development, Verification, Validation, and Application 420
16.4 Benefits of Applying Validated PBPK Models to Organ-Impaired Populations 421
16.4.1 Enhancing Regulatory Confidence in the Application of PBPK Modeling for the Prediction of Exposure in the Organ-Impaired Population 421
16.4.2 Contribution of PBPK to the Totality of Evidence in Evaluating the Effect of Renal Impairment on Drug Exposure to Inform Labelling 424
16.5 Conclusions 425
References 426
17 Absorption-Related Applications Of Pbpk Modeling 429
17.1 Introduction 429
17.2 In Vitro – In Vivo Disconnect, Parameter Non-Identifiability and the Importance of Identifying Factors Limiting Absorption Through a Deconvolution of the Mechanisms Contributing to Gut Bioavailability 431
17.3 Non-Regulatory Internal Applications of PBPK Modeling and Simulations 433
17.3.1 Prediction of Fraction Absorbed 433
17.3.2 Oral Formulation Development 433
17.4 Regulatory Applications of PBPK Modeling and Simulations 438
17.4.1 Food–Drug Interactions 438
17.4.2 Interactions of a Poorly Soluble Weak Base with Acid Reducing Agents (ARAs) 444
17.4.3 In Vitro – In Vivo Correlations (IVIVCs) to Serve as Surrogate for Bioequivalence Testing (Case Study 12) 445
17.4.4 Biowaivers Based on Virtual Bioequivalence 449
17.4.5 Virtual Bioequivalence of Locally Acting Products (LAPs) 450
17.5 Conclusions 450
References 452
18 Regulatory Guidelines On The Reporting Of Physiologically Based Pharmacokinetic (Pbpk) Modeling Analysis 457
18.1 Introduction 457
18.2 Food and Drug Administration (FDA) Guidelines 458
18.3 European Medicines Agency (EMA) Guidelines 459
18.4 Comparison of FDA and EMA Guidelines 461
18.5 Risk-Informed Evidentiary Framework to Assess PBPK Model Credibility 463
18.6 Drug Model Verification of Locally Acting Products (LAPs) 464 References 466
19 Resolving The Challenges To Establishing Confidence In Pbpk Models 469
19.1 Introduction 470
19.2 Requirements for Developing Mechanistically Credible PBPK Models for the Three Broad Categories of Applications 470
19.3 Challenges to Developing Mechanistically Credible PBPK Models and Consequences 473
19.3.1 Model Building 473
19.3.2 Model Verification of Predicted Exposure and Validation of Predictive Performance 476
19.4 Resolving the Challenges to Developing Mechanistically Credible PBPK Models 476
19.5 Totality of Evidence 478
19.6 Conclusions 480
References 481
20 Epilogue 483
20.1 PBPK Modeling Successes 483
20.2 Challenges 484
20.2.1 Drug Model Parameterization 484
20.2.2 Knowledge Gaps in Physiological Parameters 485
20.2.3 Prospective Validation of Prediction Performance 485
20.3 Meeting the Challenges 485
20.4 Future Directions for PBPK Modeling 486
References 488
Section III. Case Studies Of Pbpk Applications In The Pharmaceutical Industry 491
Case Study 1 Hypothesis Testing (Solubility) 493
Case Study 2 Hypothesis Testing (Gastric Emptying) 499
Case Study 3 Hypothesis Testing (Intestinal Loss) 505
Case Study 4 Pbpk/Pd 509
Case Study 5 Drug–Drug Interaction (Inhibition) 515
Case Study 6 Drug–Drug Interaction (Induction) 521
Case Study 7 Genetic Polymorphism 527
Case Study 8 Pediatric Extrapolation 535
Case Study 9 Pregnancy 541
Case Study 10 Hepatic Impairment 547
Case Study 11 Renal Impairment 555
Case Study 12 Absorption – Ivivc 561
Appendices 567
Index 579
