Advances in Pharmacokinetics and Pharmacodynamics

Preface
Contents
Part I: Pharmacokinetics
Chapter 1: Current Status in PBPK Modeling
1.1 Introduction
1.1.1 Background of Model-Informed Drug Discovery and Development
1.1.2 Methods for PBPK Model Construction
1.1.2.1 Bottom-Up Approach
1.1.2.2 Local Middle-Out Approach (LMO)
1.1.2.3 Global Middle-Out Approach (GMO)
1.1.3 Summary
1.2 Predictive Accuracy of IVIVE for Pharmacokinetic Parameters
1.2.1 Fraction of Dose Absorbed from Gut Lumen (Fa)
1.2.2 Fraction Available after Intestinal Metabolism (Fg)
1.2.3 Hepatic Clearance (CLh)
1.2.4 Blood-to-Plasma Concentration Ratio (RB)
1.3 PK Predictions for Clinical Situations by PBPK Models
1.3.1 Drug-Drug Interaction (DDI)
1.3.1.1 Inhibition
Modeling
Direct Inhibition
Mechanism-Based Inhibition
Predictive Performance of PBPK Models
1.3.1.2 Induction
Modeling
Predictive Performance of PBPK Models
1.3.2 Food Effect
1.3.3 Hepatic Impairment
1.3.4 Renal Impairment
1.3.5 Children
1.4 Summary and Opinion on PBPK Models
1.4.1 Transparency of Modeling Processes
1.4.2 Literacy about Middle-out Approach
1.4.3 Evidence Level of Local Middle-Out Approach
References
Chapter 2: Physiologically Based Pharmacokinetic (PBPK) Modeling Application on Food Effect Assessment
2.1 Introduction
2.2 Food-Drug Interactions
2.2.1 Specific Food-Drug Interactions
2.2.1.1 Milk
2.2.1.2 Grapefruit Juice
2.2.1.3 Alcohol (Ethanol)
2.2.2 Non-specific Food-Drug Interactions
2.2.2.1 Description of Fasted and Fed Conditions in Clinical Studies by the Agency
2.2.2.2 Gastrointestinal Transit Time
2.2.2.3 Physicochemical Properties of Luminal Fluid
2.2.2.4 Intestinal Barrier: Transporters and Blood Flow
2.2.2.5 In Vitro, In Vivo Testing of Food Effects
2.3 Tutorial of PBPK Modeling for Food Effect Assessment
2.3.1 Compound Tab: Physiochemical Property
2.3.2 Physiology Tab: Meal Type and Physiological Changes
2.3.3 PK Tab: Compartmental or Whole-Body Models
2.3.4 Model Optimization and Strategy
2.3.5 PBBM/PBPK Model Validation and Acceptance Criteria
2.4 Case Studies of PBPK Models to Assess Food Effects
2.4.1 Positive Food Effect: A PBPK Model of the BCS Class II Drug Alpelisib
2.4.2 Negative Food Effect: A PBPK Model of the BCS Class III Drug Trospium-Cl
2.5 Utilization of PBPK to Streamline Food Effect Assessment in Clinical Development
2.6 Current Gaps and Future Directions
References
Chapter 3: Physiologically Based Finite Time Pharmacokinetic (PBFTPK) Models: Inception and Development
3.1 Introduction
3.1.1 Background
3.2 Coupling Biopharmaceutic Classification System (BCS) with Pharmacokinetics Using the Finite Absorption Time (FAT) Concept
3.3 Mathematical Modeling
3.4 Simulations
3.5 Sample Data Fitting
3.6 Towards a Biophamraceutic-Pharmacokinetic Classification System
References
Chapter 4: Physiologically Based Finite Time Pharmacokinetic (PBFTPK) Models: Applications
4.1 The Finite Absorption Time (FAT) Concept as Columbus´ Egg
4.2 Bioavailability/Bioequivalence Implications
4.3 Theoretical Background for Bioavailability and Bioequivalence
4.4 Model Implementations
4.5 PBPK Modeling and Pharmacometrics with Finite-Absorption Time
References
Part II: Pharmacodynamics
Chapter 5: Pharmacokinetic-Pharmacodynamic Modeling and Simulation in Clinical Practice and Studies
5.1 Introduction
5.2 Use of Pharmacokinetic-Pharmacodynamic Models in Clinical Practice
5.2.1 Personalized Medicine
5.2.2 Model-Informed Precision Dosing
5.2.2.1 From Therapeutic Drug Monitoring to Model-Informed Precision Dosing
5.2.2.2 Model-Informed Precision Dosing Software Tools
5.2.2.3 Issues in Model-Informed Precision Dosing
5.2.2.4 Pharmacodynamic Model-Informed Precision Dosing
International Normalized Ratio for Warfarin Dose Individualization
Neutrophil-Guided Dose Individualization of Chemotherapy
sVEGFR-3 for Tyrosine Kinase Inhibitor Sunitinib Dose Individualization
5.2.3 Optimizing Pediatric Dosing
5.2.3.1 Updating Pediatric Dosing Guidelines for Tuberculosis in Children
5.2.3.2 Optimized Dose Regimens for Children with the Neglected Tropical Disease Visceral Leishmaniasis
5.3 Use of Pharmacokinetic-Pharmacodynamic Models in Design of Clinical Studies
5.3.1 Stochastic Simulation and Estimation
5.3.2 Monte-Carlo Mapped Power
5.3.3 Aiding the Study Design of Clinical Studies Focusing on Dose-Exposure-Response
References
Chapter 6: On the Verge of Impossibility: Accounting for Variability Arising from Permutations of Comorbidities that Affect th...
6.1 Introduction
6.2 Accounting for Sources of Interindividual Variability in Pharmacokinetics
6.3 The Growing Role of Physiologically Based Pharmacokinetics (PBPK)
6.4 Predicting Pharmacokinetics in Subgroups of Patients Versus Predictions in an Individual
6.5 Modelling the Impact of Permutations of Various Comorbidities
6.6 Conclusions and Future Use of PBPK for Model-Informed Precision Dosing
References
Chapter 7: Impact of Clinical Pharmacology on the Modernization of Drug Development and Regulation
7.1 Introduction
7.2 A Historical Account of the Clinical Pharmacology Emergence in Drug Regulation
7.2.1 The Evolving Landscape for Drug Regulation for Licensing Drug Products in FDA
7.2.2 The Intertwined Emergence of Bioequivalence (BE) and Clinical Pharmacology in Regulatory Science
7.2.2.1 The Emerging Need and Evolving Selection of Criteria for BE Assessment
7.2.2.2 The Co-emergence of Quantitative Clinical Pharmacology (QCP) and Modern BE Assessment and Uptake by the FDA
7.2.2.3 Uptake of QCP in New Drug Development and Regulation
7.3 The Evolving Role of Clinical Pharmacology in Drug Development
7.3.1 Clinical Pharmacology in the Twentieth Century
7.3.2 Clinical Pharmacology in the Twenty-First Century
7.3.3 Pharmacometrics: The Combination of Pharmacology Models and Statistics for Decision-Making
7.3.3.1 Model-Informed Drug Development (MIDD)
7.3.3.2 Generic Drugs
7.3.3.3 Real-World Data
7.3.4 Role of Academia in the Science of Drug Development and Regulation
7.4 Training in FDA
7.4.1 Tools for Drug Development and Evaluation that Leverage Advances in Basic, Biomedical, and Clinical Science
7.4.2 Generic Drug Development and Research
7.4.3 Coupling Real-World Data on Generic Drugs with Clinical Pharmacology
7.4.4 Research on Artificial Intelligence (AI) and Machine Learning (ML) Models
7.5 The Evolving Role of Clinical Pharmacology in Drug Regulation and Guidance
7.5.1 Statutory Recognition and Regulatory Initiatives on Quantitative Clinical Pharmacology (QCP)
7.6 Perspectives for the Future
References
Index