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Sheila Annie Peters. Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulations
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODELING AND SIMULATIONS. Principles, Methods, and Applications in the Pharmaceutical Industry
PREFACE TO THE SECOND EDITION
PREFACE TO THE FIRST EDITION
ACKNOWLEDGMENTS
ABOUT THE COMPANION WEBSITE
1 A REVIEW OF PHARMACOKINETIC AND PHARMACODYNAMIC PRINCIPLES. CONTENTS
1.1 INTRODUCTION
1.2 PHARMACOKINETIC PRINCIPLES. 1.2.1 Routes of Drug Administration
1.2.2 Intravenous Bolus
1.2.2.1 Zero‐ and First‐Order Kinetics
1.2.2.2 Clearance, Volume of Distribution, Half‐life, and AUC
1.2.3 Plasma Protein Binding and Blood–Plasma Ratio
1.2.4 Hepatic, Renal, and Biliary Clearances
1.2.4.1 Hepatic Clearance
1.2.4.2 Hepatic Extraction
1.2.4.3 Renal Clearance
1.2.4.4 Biliary Clearance
1.2.5 Extravascular (Subcutaneous, Intramuscular, and Per Oral) Absorption
1.2.6 Absorption from Solid Dosage Forms
Box 1.1
1.2.7 Role of Transporters in ADME
1.2.8 Linear and Non‐Linear Pharmacokinetics
1.2.9 Intravenous Infusion, Repeated Dosing, Steady State Kinetics, and Accumulation
1.2.10 Active Metabolite and Prodrug Kinetics. 1.2.10.1 Active Metabolites
1.2.10.2 Prodrugs
1.2.10.3 Metabolite Kinetics
1.2.10.4 Limiting Conditions
1.2.10.5 Drug‐Metabolite Plasma Concentration Relationships After Single Drug Administration
1.2.10.6 Steady‐State Metabolite Kinetics
1.2.10.7 First‐Pass Metabolite Production
1.3 PHARMACOKINETIC VARIABILITY
1.4 PHARMACOKINETICS OPTIMIZATION IN DRUG DISCOVERY
1.5 PHARMACODYNAMIC PRINCIPLES
1.5.1 Pharmacological Targets and Drug Action
1.5.2 Functional Adaptation Processes
1.5.2.1 Desensitization, Tolerance, and Tachyphylaxis
1.5.3 Biomarkers, Surrogate Endpoints, and Clinical Endpoints
KEYWORDS
REFERENCES
2 A REVIEW OF DRUG–DRUG INTERACTIONS. CONTENTS
2.1 INTRODUCTION
2.2 DRUG INTERACTIONS MEDIATED BY ENZYMES AND TRANSPORTERS AT VARIOUS SITES
2.3 FACTORS AFFECTING DDI
2.4 IN VITRO METHODS TO EVALUATE DRUG–DRUG INTERACTIONS
2.4.1 Candidate Drug as a Potential Perpetrator
2.4.2 Candidate Drug as a Potential Victim of Inhibition
2.5 SOURCES OF UNCERTAINTY
2.6 THERAPEUTIC PROTEIN–DRUG INTERACTION
KEYWORDS
REFERENCES
3 MODELING PHARMACOKINETICS, PHARMACODYNAMICS, AND DRUG INTERACTIONS. CONTENTS
3.1 INTRODUCTION
3.2 MODELING PHARMACOKINETICS
3.2.1 Compartmental Modeling of Linear and Nonlinear Pharmacokinetics (Enzyme and/or Transporter Capacity Limitation as Well as Target‐Mediated Drug Disposition) 3.2.1.1 Linear PK
3.2.1.2 Nonlinear PK (Enzyme and/or Transporter Capacity Limitation)
3.2.1.3 Nonlinear PK (Target‐Mediated Drug Disposition, TMDD) (Gibiansky et al. 2008)
3.2.2 Population Pharmacokinetics
3.3 PHARMACOKINETICS/PHARMACODYNAMICS AND PK/EFFICACY (EXPOSURE/RESPONSE) MODELING
3.3.1 PK/PD Models for Direct Effect: Sigmoid Emax Model
3.3.2 PK/PD Models for Direct Effect: Classical Receptor Theory
3.3.3 PK/PD Models Accommodating Delayed Pharmacological Response
3.3.4 PK/PD Models Accommodating Functional Adaptation Leading to Nonlinearity in Pharmacological Response with Respect to Time
3.3.5 PK/Efficacy Modeling
3.3.6 Translation of PK/PD and PK/Efficacy Modeling to Human
3.3.7 Average, Minimum, and Maximum Steady‐State Concentrations
Box 3.1 Decision Tree for Choice of PK Metric (Maximum/Minimum/Average Steady State Concentration) and Extent and Duration of PD Modulation for Estimation of Human Dose
3.3.8 Estimation of Biologically Effective Dose in Human
3.3.9 Therapeutic Window
3.3.10 Static Models for Drug Interactions
3.3.10.1 Reversible Inhibition
3.3.10.2 Time‐Dependent Inhibition
3.3.10.3 Induction
3.4 PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODELING AND ITS INTEGRATION WITH PHARMACODYNAMICS AND EFFICACY MODELS
3.4.1 PK Modeling Compartmental vs PBPK
3.4.2 PK Variability: Population PK (popPK) Modeling vs PBPK
3.4.3 Integration of PBPK with PD, Quantitative Systems Pharmacology (QSP) Models or Quantitative Systems Toxicology and Safety (QSTS)
3.4.4 PBPK Models to Evaluate Drug–Drug Interactions
3.4.4.1 Intrinsic Clearance of Victim (V) in the Absence of Inhibitor or Inducer
3.4.4.2 Intrinsic Clearance of Victim (V) in the Presence of Inhibitor
3.4.4.3 Time‐Dependent Changes in the Abundance of an Enzyme Isoform Inhibited by a MBI
3.4.5 DDI Risk Assessment with PBPK vs Static Models
Box 3.2 Evaluation of NME as Victim and Perpetrator of CYP Inhibition and Induction (USFDA 2020a). Adapted from USFDA, 2020b
Box 3.3 Evaluation of Metabolite as Victim of CYP Inhibition and Perpetrator of CYP and Transporter Inhibition (USFDA 2020a). Adapted from USFDA 2020b
Box 3.4 Evaluation of NME as Victim and Perpetrator of Transporter Inhibition (USFDA 2020a). Adapted from USFDA 2020b
NME as a Victim of Transporter Inhibition
NME as an Inhibitor of Transporters
KEYWORDS
REFERENCES
4 PHYSIOLOGICAL MODEL FOR ABSORPTION. CONTENTS
4.1 INTRODUCTION
4.2 DRUG ABSORPTION AND GUT BIOAVAILABILITY
4.2.1 Solubility and Dissolution Rate
4.2.2 Permeability: Transcellular, Paracellular, and Carrier‐Mediated Pathways
4.2.3 Barriers to Membrane Transport – Luminal Degradation, Efflux, and Gut Metabolism
4.3 FACTORS AFFECTING DRUG ABSORPTION AND GUT BIOAVAILABILITY
4.3.1 Physiological Factors Affecting Oral Drug Absorption and Species Differences in Physiology
4.3.2 Compound‐Dependent Factors
4.3.3 Formulation‐Dependent Factors
4.4 IN SILICO PREDICTIONS OF PASSIVE PERMEABILITY AND SOLUBILITY. 4.4.1 In Silico Models for Permeability
4.4.2 In Silico Models for Solubility
4.5 MEASUREMENT OF PERMEABILITY, SOLUBILITY, LUMINAL STABILITY, EFFLUX, INTESTINAL METABOLISM
4.5.1 In Vitro, In Situ, and In Vivo Models for Effective Permeability
4.5.2 Measurement of Thermodynamic or Equilibrium Solubility
4.5.3 Luminal Stability
4.5.4 Efflux
4.5.5 In Vitro Models for Gut Metabolism and Estimation of Fraction Escaping Gut Metabolism
4.6 ABSORPTION MODELING
KEYWORDS
REFERENCES
5 PHYSIOLOGICAL MODEL FOR DISTRIBUTION. CONTENTS
5.1 INTRODUCTION
5.2 FACTORS AFFECTING TISSUE DISTRIBUTION OF XENOBIOTICS
5.2.1 Physiological Factors and Species Differences in Physiology
5.2.1.1 Blood Flow Rate
5.2.1.2 Membrane Permeability
5.2.1.3 Transporters
5.2.1.4 Tissue Compositions
5.2.1.5 Binding to Plasma Proteins and Tissue Components
5.2.2 Compound‐Dependent Factors
5.3 IN SILICO MODELS OF TISSUE PARTITION COEFFICIENTS
5.4 MEASUREMENT OF PARAMETERS REPRESENTING THE RATE AND EXTENT OF TISSUE DISTRIBUTION
5.4.1 Assessment of Rate and Extent of Brain Penetration
5.5 PHYSIOLOGICAL MODEL FOR DRUG DISTRIBUTION
5.6 DRUG CONCENTRATIONS AT THE SITE OF ACTION
KEYWORDS
REFERENCES
6 PHYSIOLOGICAL MODELS FOR DRUG METABOLISM AND EXCRETION. CONTENTS
6.1 INTRODUCTION
6.2 FACTORS AFFECTING DRUG METABOLISM AND EXCRETION OF XENOBIOTICS
6.3 MODELS FOR HEPATOBILIARY AND RENAL EXCRETION. 6.3.1 In Silico Models
6.3.2 In Vitro Models for Hepatic Metabolism
6.3.3 In Vitro Models for Transporters
6.3.3.1 Efflux Transporters
6.3.3.2 Uptake Transporters
6.4 PHYSIOLOGICAL MODELS
6.4.1 Hepato‐Biliary Elimination of Parent Drug and Metabolites
6.4.1.1 Parent Drug. 6.4.1.1.1 INTERSTITIAL (I) LIVER (LI) COMPARTMENT
6.4.1.1.2 INTRACELLULAR (IC) LIVER COMPARTMENT
6.4.1.2 Metabolite. 6.4.1.2.1 INTRACELLULAR LIVER COMPARTMENT
6.4.1.2.2 SYSTEMIC (SYS) CIRCULATION
6.4.2 Renal Excretion
6.4.2.1 Kidney. 6.4.2.1.1 VASCULAR (V)
6.4.2.1.2 INTERSTITIAL (I)
6.4.2.1.3 CHANGES IN VOLUME OF WATER ALONG THE TUBULE LUMEN
6.4.2.1.4 TUBULE LUMEN
6.4.2.1.5 INTRACELLULAR
REFERENCES
7 GENERIC WHOLE‐BODY PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING. CONTENTS
7.1 INTRODUCTION
7.2 STRUCTURE OF A GENERIC PHYSIOLOGICALLY‐BASED PHARMACOKINETIC (PBPK) MODEL
7.3 SOMATIC COMPARTMENTS. 7.3.1 Lungs (LU)
7.3.2 Arterial Blood (ART)
7.3.3 Venous Blood (VEN)
7.3.4 Stomach (ST)
7.3.5 Gut (GU)
7.4 MODEL ASSUMPTIONS
7.5 PBPK SOFTWARE
REFERENCES
8 PBPK MODELING OF BIOTHERAPEUTICS. CONTENTS
8.1 INTRODUCTION
8.2 THERAPEUTIC PROTEINS. 8.2.1 Peptides and Proteins
8.2.2 Antibodies and Antibody‐Based Therapies
8.3 PHARMACOKINETICS OF THERAPEUTIC PROTEINS
8.3.1 Absorption
8.3.2 Renal Elimination
8.3.3 Immunogenicity
8.3.4 PEGylation
8.3.5 Transport by Convective and Transcytotic Extravasation
8.3.6 Catabolic Elimination (Proteolysis)
8.3.7 FcRn‐Mediated Protection of IgGs Against Catabolism in FcRn‐Rich Cells
8.3.8 Distribution and lymphatic elimination
8.3.9 Target‐Mediated Drug Disposition and Receptor‐Mediated Endocytosis
8.4 PBPK MODELING OF MONOCLONAL ANTIBODIES. 8.4.1 Full PBPK Model for Monoclonal Antibodies
8.4.1.1 Plasma (PL)
8.4.1.2 Lung (LU) 8.4.1.2.1 VASCULAR SPACE
8.4.1.2.2 ENDOSOMAL SPACE
8.4.1.3 Interstitial Space
8.4.1.4 Other Organs (OT) and Tumor (TU) 8.4.1.4.1 VASCULAR SPACE
8.4.1.4.2 ENDOSOMAL SPACE
8.4.1.4.3 INTERSTITIAL SPACE
8.4.2 Minimal PBPK Model for Monoclonal Antibodies
8.5 APPLICATIONS OF PBPK MODELING OF MONOCLONAL ANTIBODIES. 8.5.1 Pharmacokinetic Scaling
8.5.2 PBPK Integration with Pharmacodynamics of Monoclonal Antibodies
KEYWORDS
REFERENCES
9 UNCERTAINTY AND POPULATION VARIABILITY. CONTENTS
9.1 INTRODUCTION
9.2 DISTINGUISHING UNCERTAINTY AND VARIABILITY
9.3 SOURCES OF UNCERTAINTY IN DRUG‐RELATED PARAMETERS
9.4 SOURCES OF VARIABILITY IN SYSTEM PARAMETERS
9.5 HANDLING POPULATION VARIABILITY. 9.5.1 A POSTERIORI and A PRIORI Approaches to Handling Population Variability
9.5.2 Correlations Between Parameters
9.6 UNCERTAINTY AND SENSITIVITY ANALYSIS
9.6.1 Local Sensitivity Analysis (One‐at‐a‐time (OAT) and Derivative‐based Methods)
9.6.2 Parameter Interactions and Global Sensitivity Analysis (GSA)
9.6.3 Global Sensitivity Analysis for Correlated Parameters (cGSA)
9.6.4 Applications of Sensitivity Analysis for PBPK Models
9.6.5 Limitations of Global Sensitivity Analysis
9.7 UNCERTAINTY AND POPULATION VARIABILITY IN CLINICAL EFFICACY AND SAFETY
KEYWORDS
REFERENCES
10 NONCLINICAL, CLINICAL, AND MODEL‐INFORMED DRUG DEVELOPMENT. CONTENTS
10.1 INTRODUCTION: AN OVERVIEW OF DIFFERENT PHASES OF DRUG DEVELOPMENT
10.2 NONCLINICAL DEVELOPMENT
10.2.1 Preclinical Pharmacology, PK/PD Modeling, and Human Dose Prediction
10.2.2 Safety and Toxicology Studies. 10.2.2.1 Safety Pharmacology Studies
10.2.2.2 Genotoxicity
10.2.2.3 Toxicology Studies
10.2.3 Studies with Radiolabeled Compound. 10.2.3.1 Metabolite Identification and Quantification (Isin et al., 2012)
10.2.3.2 Quantitative Whole‐Body Autoradiography (QWBA) (Solon, 2015)
10.2.3.3 Metabolite profiling
10.2.3.4 Preclinical ADME/Mass Balance (ADME/MB) Studies
10.2.3.5 Safety Testing of Drug Metabolites
10.2.3.6 Drug Interaction Potential of Metabolites of Significant Exposure or Pharmacological or Toxicological Activity
10.3 CLINICAL PHARMACOLOGY STUDIES
10.3.1 First‐in‐Human, Single, and Multiple Ascending Dose Studies
10.3.2 Biopharmaceutics – Absolute Oral Bioavailability and Bioequivalence Study
10.3.3 Food Effect Study
10.3.4 Organ (Hepatic and Renal) Impairment Study
10.3.5 Pediatric Assessment
10.3.6 Mass Balance Study
10.3.7 Drug Interaction Study
10.3.8 Pharmacogenomics Study
10.3.9 Thorough QT (TQT) and Concentration QT (C‐QT) Study
10.3.10 Immunogenicity Assays and Comparability Study for Biologics
10.3.11 Drug Labelling
10.4 CLINICAL DEVELOPMENT IN ONCOLOGY
10.5 FAST TRACK ROUTES TO ADDRESS UNMET MEDICAL NEED IN THE TREATMENT OF SERIOUS CONDITIONS
10.6 MODEL‐INFORMED DRUG DEVELOPMENT
10.7 PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELS COMPLEMENTING CLINICAL PHARMACOLOGY STUDIES
10.8 PBPK IN ONCOLOGY
REGULATORY GUIDELINES
REFERENCES
11 OVERVIEW OF PBPK APPLICATIONS. CONTENTS
11.1 INTRODUCTION
11.2 PBPK APPLICATIONS FOR INTERNAL DECISIONS
11.3 PBPK APPLICATIONS FOR REGULATORY FILING
11.4 PBPK MODELING AND SIMULATIONS ALONG THE VALUE CHAIN
REFERENCES
12 APPLICATIONS OF HYPOTHESIS GENERATION AND TESTING WITH PBPK MODELS. CONTENTS
12.1 INTRODUCTION
12.2 HYPOTHESIS GENERATION AND TESTING WITH PBPK MODELS
12.2.1 Parameter Estimation from Intravenous Pharmacokinetic Profiles
12.2.2 Simulation of Oral PK Profile
12.2.3 Sensitivity Analysis
12.2.4 Verification of Hypotheses
12.2.5 Auto‐inhibition of Drug‐Metabolizing Enzymes, Uptake and Efflux Transporters
12.3 HYPOTHESIS GENERATION AND TESTING ALONG THE VALUE CHAIN
12.4 CONCLUSIONS
REFERENCES
13 APPLICATIONS OF PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELS INTEGRATED WITH DRUG EFFECT MODELS (PBPK/PD) CONTENTS
13.1 INTRODUCTION: INTEGRATION OF PBPK WITH DRUG EFFECT MODELS
13.2 DOSING IN SPECIFIC POPULATIONS
13.3 PBPK/PD FOR BOTTOM‐UP PREDICTION OF INTER‐PATIENT VARIABILITY IN DRUG RESPONSE
13.4 PBPK/PD FOR PREDICTING THE INTER‐PATIENT VARIABILITY IN RESPONSE TO PRODRUGS AND ACTIVE METABOLITES
13.5 PBPK/PDWHEN SYSTEMIC CONCENTRATIONS ARE NOT THE DRIVER FOR DRUG RESPONSE
13.5.1 Pre‐Systemic Drug Target
13.5.2 Effect‐Site Drug Concentration Different from Systemic Concentration
13.6 PBPK/PD FOR MONOCLONAL ANTIBODIES
13.7 PBPK MODELS LINKED TO QUANTITATIVE SYSTEMS PHARMACOLOGY AND TOXICOLOGY MODELS
13.7.1 PBPK–QST Models to Predict Drug‐Induced Liver Injury
13.7.2 PBPK–QST Models to Predict Drug‐Induced Cardiotoxicity
13.8 CONCLUSIONS
REFERENCES
14 PBPK MODELING AND SIMULATIONS TO EVALUATE CLINICAL DRUG‐DRUG INTERACTIONS. CONTENTS
14.1 INTRODUCTION
14.2 CLINICAL DDI STUDIES AND MODELING APPROACHES TO ADDRESS KEY QUESTIONS RELATED TO DRUG–DRUG INTERACTIONS
14.2.1 Dedicated Clinical DDI Studies
14.2.2 Investigation of Phenotypic Effects for NMEs Predominantly Cleared by Polymorphic Enzyme or Transporter
14.2.3 Prospective Nested DDI Study
14.2.4 Cocktail DDI Study
14.2.5 PBPK Modeling and Simulations
14.2.6 Claims Relating to Results of DDI Studies
14.2.7 Impact on Label
14.3 PBPK MODELING OF DIFFERENT TYPES OF DRUG INTERACTIONS
14.3.1 PBPK Modeling Strategy: New Molecular Entity as Victim of CYP‐Based Drug Interactions
14.3.2 PBPK Modeling Strategy: New Molecular Entity as Perpetrator of CYP‐Based Drug Interactions
14.3.3 Non‐CYP Based Drug Interactions
14.3.4 Transporter‐Mediated Drug Interactions
14.4 DDI PREDICTIONS WITH PBPK MODELING AND SIMULATIONS IN CLINICAL DRUG DEVELOPMENT AND REGULATORY SUBMISSIONS. 14.4.1 DDI Predictions Along the Value Chain (Figure 14.5)
14.4.2 Possible Regulatory Outcomes, Based on the Predictions from a Verified and Validated PBPK Model
14.4.3 Regulatory Acceptance of PBPK Analyses Included in Regulatory Submissions
14.4.4 Predictive Performance of PBPK Models
14.5 COMPARISON OF DDI PREDICTION USING STATIC AND DYNAMIC MODELS
14.6 CONCLUSIONS
REFERENCES
15 DOSE EXTRAPOLATION ACROSS POPULATIONS (HEALTHY ADULT CAUCASIAN TO PEDIATRIC, PREGNANT WOMEN, DIFFERENT ETHNICITIES, GERIATRIC, SMOKERS AND OBESE POPULATIONS) CONTENTS
15.1 INTRODUCTION
15.2 PBPK MODELING STRATEGY FOR DOSE EXTRAPOLATION TO SPECIFIC POPULATIONS
15.3 POTENTIAL BENEFITS OF PBPK MODELING FOR DOSE EXTRAPOLATIONS TO SPECIFIC POPULATIONS
15.4 DOSE EXTRAPOLATIONS TO SPECIFIC POPULATIONS
15.4.1 Pediatric Starting Dose Selection
15.4.2 Pregnancy
15.4.3 Ethnicity – Japanese Population
15.4.4 Geriatric Population
15.4.5 Obese
15.4.6 Smokers
15.5 CONCLUSIONS
REFERENCES
16 DOSE EXTRAPOLATION ACROSS POPULATIONS: HEALTHY ADULT TO HEPATIC AND RENAL IMPAIRMENT POPULATIONS. CONTENTS
16.1 INTRODUCTION
16.2 PATHOPHYSIOLOGICAL CHANGES IN ORGAN IMPAIRMENT. 16.2.1 Hepatic Impairment
16.2.2 Renal Impairment
16.3 PBPK MODELING STRATEGY: MODEL DEVELOPMENT, VERIFICATION, VALIDATION, AND APPLICATION
16.4 BENEFITSOFAPPLYINGVALIDATEDPBPKMODELSTOORGAN‐IMPAIRED POPULATIONS
16.4.1 Enhancing Regulatory Confidence in the Application of PBPK Modeling for the Prediction of Exposure in the Organ‐Impaired Population
16.4.2 Contribution of PBPK to the Totality of Evidence in Evaluating the Effect of Renal Impairment on Drug Exposure to Inform Labelling
16.5 CONCLUSIONS
REFERENCES
17 ABSORPTION‐RELATED APPLICATIONS OF PBPK MODELING. CONTENTS
17.1 INTRODUCTION
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
17.3 NON‐REGULATORY INTERNAL APPLICATIONS OF PBPK MODELING AND SIMULATIONS. 17.3.1 Prediction of Fraction Absorbed
17.3.2 Oral Formulation Development
17.4 REGULATORY APPLICATIONS OF PBPK MODELING AND SIMULATIONS
17.4.1 Food–Drug Interactions
17.4.1.1 Food Effect Predictions
17.4.1.2 PBPK Modeling Strategy for Food Effect Prediction
17.4.1.3 Evaluation of Food Effect Predictions
17.4.1.4 Benefits of Food Effect Predictions and Challenges
17.4.1.5 PBPK Predictions of Food Effect in the Future
17.4.2 Interactions of a Poorly Soluble Weak Base with Acid Reducing Agents (ARAs)
17.4.3 In Vitro – In Vivo Correlations (IVIVCs) to Serve as Surrogate for Bioequivalence Testing (Case Study 12)
17.4.4 Biowaivers Based on Virtual Bioequivalence
17.4.5 Virtual Bioequivalence of Locally Acting Products (LAPs)
17.5 CONCLUSIONS
REFERENCES
18 REGULATORY GUIDELINES ON THE REPORTING OF PHYSIOLOGICALLY BASED PHARMACOKINETIC (PBPK) MODELING ANALYSIS. CONTENTS
18.1 INTRODUCTION
18.2 FOOD AND DRUG ADMINISTRATION (FDA) GUIDELINES
18.3 EUROPEAN MEDICINES AGENCY (EMA) GUIDELINES
18.4 COMPARISON OF FDA AND EMA GUIDELINES
18.5 RISK‐INFORMED EVIDENTIARY FRAMEWORK TO ASSESS PBPK MODEL CREDIBILITY
18.6 DRUG MODEL VERIFICATION OF LOCALLY ACTING PRODUCTS (LAPs)
REFERENCES
19 RESOLVING THE CHALLENGES TO ESTABLISHING CONFIDENCE IN PBPK MODELS. CONTENTS
19.1 INTRODUCTION
19.2 REQUIREMENTS FOR DEVELOPING MECHANISTICALLY CREDIBLE PBPK MODELS FOR THE THREE BROAD CATEGORIES OF APPLICATIONS
19.3 CHALLENGES TO DEVELOPING MECHANISTICALLY CREDIBLE PBPK MODELS AND CONSEQUENCES
19.3.1 Model Building. 19.3.1.1 Identifying Key Mechanisms Impacting An Application
19.3.1.2 Model Parameterization with In Vitro Data in a Bottom‐Up Approach
19.3.1.3 Parameter Estimation from Clinical Data in Top‐Down or Middle‐Out Approaches
19.3.1.4 Parameter Non‐Identifiability as a Barrier to Deconvolute Mechanisms Contributing to Gut Bioavailability
19.3.2 Model Verification of Predicted Exposure and Validation of Predictive Performance
19.4 RESOLVING THE CHALLENGES TO DEVELOPING MECHANISTICALLY CREDIBLE PBPK MODELS
19.5 TOTALITY OF EVIDENCE
19.6 CONCLUSIONS
REFERENCES
20 EPILOGUE. CONTENTS
20.1 PBPK MODELING SUCCESSES
20.2 CHALLENGES
20.2.1 Drug Model Parameterization
20.2.2 Knowledge Gaps in Physiological Parameters
20.2.3 Prospective Validation of Prediction Performance
20.3 MEETING THE CHALLENGES
20.4 FUTURE DIRECTIONS FOR PBPK MODELING
REFERENCES
SECTION III. CASE STUDIES OF PBPK APPLICATIONS IN THE PHARMACEUTICAL INDUSTRY
CASE STUDY 1 HYPOTHESIS TESTING (SOLUBILITY) S1.1 IDENTIFICATION OF HIGHER IN VIVO SOLUBILITY THAN MEASURED IN VITRO. S1.1.1 Key Question
S1.1.2 Background
S1.1.3 Objective
S1.1.4 Preclinical Data Leading to Hypothesis Generation
S1.1.5 Data Needed for Initial Model Building
S1.1.6 Hypothesis Testing
S1.1.7 Further Verification of Hypothesis
S1.1.8 Learnings
REFERENCES
CASE STUDY 2 HYPOTHESIS TESTING (GASTRIC EMPTYING) S2.1 IDENTIFICATION OF GASTRIC EMPTYING-LIMITED ORAL DRUG ABSORPTION. S2.1.1 Key Question
S2.1.2 Background
S2.1.3 Objective
S2.1.4 Data Needed for Initial Model Building
S2.1.5 Hypothesis Testing
S2.1.6 Learnings
REFERENCES
CASE STUDY 3 HYPOTHESIS TESTING (INTESTINAL LOSS) S3.1 IDENTIFICATION OF INTESTINAL LOSS. S3.1.1 Key Question
S3.1.2 Background
S3.1.3 Objective
S3.1.4 Data Needed for Initial Model Building
S3.1.5 Hypothesis Testing
S3.1.6 Further Verification of Hypothesis
S3.1.7 Learnings
REFERENCES
CASE STUDY 4 PBPK/PD. Use of PBPK/PD for the Dose Optimization of the Antimicrobial Agent, Ciprofloxacin. S4.1 KEY QUESTION
S4.2 BACKGROUND
S4.3 OBJECTIVES
S4.4 DATA
S4.5 MODELING STRATEGY
S4.6 SENSITIVITY ANALYSIS
S4.7 CONCLUSION
REFERENCES
CASE STUDY 5 DRUG–DRUG INTERACTION (INHIBITION) Drug–Drug Interaction Risk Assessment for Midazolam Due to CYP3A Inhibition by Itraconazole. S5.1 KEY QUESTION
S5.2 BACKGROUND
S5.3 OBJECTIVES
S5.4 DATA
S5.5 MODELING STRATEGY
S5.6 SENSITIVITY ANALYSIS
S5.7 LEARNINGS
Additional questions that can be addressed with PBPK modelling
REFERENCES
CASE STUDY 6 DRUG–DRUG INTERACTION (INDUCTION) S6.1 DRUG–DRUG INTERACTION RISK ASSESSMENT FOR MIDAZOLAM DUE TO CYP3A INDUCTION BY RIFAMPICIN. S6.1.1 Key Question
S6.1.2 Background
S6.1.3 Objectives
S6.1.4 Data
S6.1.5 Modeling Strategy
S6.1.6 Sensitivity Analysis
REFERENCES
CASE STUDY 7 GENETIC POLYMORPHISM. S7.1 IMPACT OF GENETIC POLYMORPHISM ON THE PHARMACOKINETICS OF RISPERIDONE. S7.1.1 Key Question
S7.1.2 Background
S7.1.3 Objectives
S7.1.4 Modeling Strategy
S7.2 Results. S7.2.1 Sensitivity Analysis
REFERENCES
CASE STUDY 8 PEDIATRIC EXTRAPOLATION. S8.1 IMPACT OF UGT2B7 MATURATION ON THE PHARMACOKINETICS OF MORPHINE. S8.1.1 Key Question
S8.1.2 Background
S8.1.3 Objectives
S8.1.4 Modeling Strategy
S8.1.5 Morphine: Compound Data
S8.1.6 Morphine: Clinically Observed Data
S8.1.7 Sensitivity Analysis
S8.2 CONCLUSION
REFERENCES
CASE STUDY 9 PREGNANCY. S9.1 IMPACT OF PREGNANCY ON THE PHARMACOKINETICS OF METRONIDAZOLE. S9.1.1 Key Question
S9.1.2 Background
S9.1.3 Objectives
S9.1.4 Modeling Strategy
S9.2 RESULTS
REFERENCES
CASE STUDY 10 HEPATIC IMPAIRMENT. S10.1 IMPACT OF HEPATIC IMPAIRMENT ON THE PHARMACOKINETICS OF MIDAZOLAM AND LIDOCAINE. S10.1.1 Key Question
S10.1.2 Background
S10.1.3 Objectives
S10.2 MODELING STRATEGY
S10.3 RESULTS
REFERENCES
CASE STUDY 11 RENAL IMPAIRMENT. S11.1 IMPACT OF RENAL IMPAIRMENT ON THE PHARMACOKINETICS OF GENTAMICIN. S11.1.1 Key Question
S11.1.2 Background
S11.1.3 Objectives
S11.1.4 Modeling Strategy
S11.2 RESULTS
REFERENCES
CASE STUDY 12 ABSORPTION – IVIVC. S12.1 KEY QUESTION
S12.2 BACKGROUND
S12.3 OBJECTIVES
S12.4 DATA
S12.5 MODELING STRATEGY
S12.6 MODELING WORKFLOW
S12.7 SENSITIVITY ANALYSIS
S12.8 CONCLUSION
REFERENCES
APPENDICES. APPENDIX A: PHYSIOLOGICAL PARAMETERS IN PRECLINICAL SPECIES
Appendix B: Human
References
INDEX. A
B
C
D
E
F
G
H
I
K
L
M
N
O
P
Q
R
S
T
U
V
W
Y
Z
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