Physiologically Based Pharmacokinetic (PBPK) Modeling : Methods and Applications in Toxicology and Risk Assessment

Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment presents foundational principles, advanced techniques and applications of PBPK modeling. Contributions from experts in PBPK modeling cover topics such as pharmacokinetic principles, classical physiological models, the application of physiological models for dose-response and risk assessment, the use of in vitro information, and in silico methods. With end-of-chapter exercises that allow readers to practice and learn the skills associated with PBPK modeling, dose-response, and its applications to safety and risk assessments, this book is a foundational resource that provides practical coverage of PBPK modeling for graduate students, academics, researchers, and more. Provides end-of-chapter exercises to teach hands-on computational tools used in toxicology Supplies computer code and explanations and includes examples of applied models used in regulatory toxicology and research Authored by expert editors and contributors who are among the best PBPK modelers in the world Cover Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment Copyright Contents List of contributors Foreword Preface 1 A history and recent efforts of selected physiologically based pharmacokinetic modeling topics 1.1 Introduction 1.2 A historical perspective 1.2.1 Early efforts on inhaled compounds 1.2.2 History and recent efforts in the pharmaceutical industry 1.2.3 History and recent efforts of physiologically based pharmacokinetic modeling in toxicology and risk assessment 1.2.4 History and recent efforts of physiologically based pharmacokinetic modeling in veterinary pharmacology and animal-de... 1.2.5 History and recent efforts of physiologically based pharmacokinetic modeling in nanomedicine and nanotoxicology 1.2.6 History and recent efforts of the physiologically based pharmacokinetic modeling software 1.2.7 History and recent efforts of physiologically based pharmacokinetic books 1.2.8 History of the Society of Toxicology Biological Modeling Specialty Section 1.3 Summary Acknowledgment Disclaimer References A.1 Review questions 2 Introduction to classical pharmacokinetics 2.1 Introduction 2.2 Chemical kinetics 2.2.1 First-order reaction rate 2.2.2 Zero-order reaction 2.2.3 Michaelis–Menten kinetics 2.3 Classical pharmacokinetics Models 2.3.1 Compartment pharmacokinetic models 2.3.1.1 One- or single-compartment pharmacokinetic models 2.3.1.2 Multiple-compartment pharmacokinetic models 2.3.2 Noncompartment pharmacokinetic models 2.3.3 Practice questions 2.4 Estimation of pharmacokinetic parameters using one-compartment open model or noncompartment model system 2.4.1 Elimination rate constant (kel) 2.4.2 Half-life (t1/2) 2.4.3 Apparent volume of distribution (Vd) 2.4.4 Clearance (Cl) 2.4.5 Area under the curve (AUC) 2.4.6 Practice questions 2.5 Factors affecting classical pharmacokinetics 2.5.1 Oral versus i.v. administration 2.5.1.1 Concept of fractional oral bioavailability 2.5.2 Multiple dosing 2.5.2.1 Concept of steady state 2.5.3 Nonlinear pharmacokinetics 2.5.4 Practice questions 2.6 Additional case examples/references 2.7 Conclusion 2.8 Glossary of key mathematical equations 2.9 Acknowledgement 2.10 Conflict of interest References A.1 Lab Exercises Solutions for A.1.1 Solutions for A.1.2 Solutions for A.1.3 Solutions for A.1.4 3 Fundamentals of physiologically based pharmacokinetic modeling* 3.1 Introduction to physiologically based pharmacokinetic modeling 3.2 Getting started in constructing a PBPK model 3.3 Components of a PBPK model 3.3.1 Physiology 3.3.2 Chemical-specific model parameters 3.3.3 Equations for a PBPK model 3.3.4 Inhalation 3.3.5 Intravenous dose 3.3.6 Oral dose Acknowledgment References A.1 Computer simulation exercise A.1.1 Writing code for the software Magnolia A.1.2 Setting up the software and using Magnolia A.1.3 Questions A.1.4 Using the slider function A.1.5 Running simulations with data A.1.6 Using the FIT command A.2 Key to questions for Chapter 3 4 Physiologically based pharmacokinetic modeling software 4.1 Introduction 4.1.1 Application driven 4.1.2 Closed and open platforms 4.1.3 Fixed versus flexible structures 4.1.4 The continuous systems simulation language (and derivative dialects) 4.1.5 Advanced features 4.2 Current tools 4.2.1 Survey of physiologically based pharmacokinetic modeling software use 4.2.2 Multiple routes of exposure and species 4.2.3 Currently available pharmacokinetic analysis tools 4.2.3.1 Berkeley Madonna 4.2.3.2 GastroPlus 4.2.3.3 INTEGRA 4.2.3.4 Magnolia 4.2.3.5 MEGen/RVis 4.2.3.6 Phoenix 4.2.3.7 PK-Sim/MoBi 4.2.3.8 Population Lifecourse Exposure to Health Effects Modeling 4.2.3.9 SimBiology 4.2.3.10 Simcyp 4.2.3.11 Simulink 4.2.3.12 WinSAAM 4.3 Conclusion 4.4 Example models References Further reading 5 Chemical absorption and writing code for portals of entry* 5.1 Introduction 5.2 The oral route 5.3 Generic rate equations for oral route of exposure to chemicals 5.3.1 Oral bolus gavage 5.3.2 Diet 5.3.3 Drinking water 5.3.4 Nursing pups or infants (lactational transfer) 5.4 Generic rate equations for inhaled chemicals 5.5 Dermal route References A.1 Exercise A.1.1 Drinking water ingestion (with simplifying assumptions) A.1.2 Dermal exposure 6 Physiologically based pharmacokinetic model: distribution processes 6.1 Introduction 6.2 The thermodynamics of chemical tissue distribution 6.3 Flow-limited and permeability-limited PBPK tissue compartments 6.4 Tissue binding 6.4.1 Plasma protein as a storage depot 6.4.2 Red blood cells as a storage depot 6.4.3 Fat as a storage depot 6.4.4 Bone as a storage depot 6.4.5 Liver as a storage depot 6.4.6 Lung as a storage depot 6.5 Species differences in chemical distribution 6.6 Predicting partition coefficients using mechanistic algorithms 6.7 Measurement of parameters representing rate and extent of tissue distribution 6.7.1 Partition coefficients 6.7.2 Permeability rate 6.8 Protein transporters 6.9 Conclusion Acknowledgment References Further reading A.1 Exercises A.2 Exercise answers 7 Metabolism and physiologically based pharmacokinetic models* 7.1 Introduction 7.2 In vitro methods to characterize metabolism 7.3 In vivo metabolism using gas uptake and solvents 7.4 In vitro metabolism using hepatic microsomes and plasma: deltamethrin and atrazine 7.5 In vitro metabolism using liver hepatocytes and high-throughput methods 7.6 In vivo metabolic suicide inhibition evaluation for two solvents 7.7 In vivo metabolic inhibition evaluation for a complex mixture, jet fuel vapor 7.8 Advanced exercise References 8 Physiologically based pharmacokinetic model: excretion via urine, feces, and breath 8.1 Excretion in physiologically based pharmacokinetic models 8.2 Excretion via urine 8.2.1 Mechanisms of urinary excretion 8.3 Physiologically based pharmacokinetic modeling of urinary excretion 8.3.1 Simple elimination descriptions 8.3.1.1 Elimination from blood or body compartment 8.3.1.1.1 Elimination from kidney compartment 8.3.1.1.2 Vesical reservoir 8.3.1.1.3 Description based on partitioning 8.3.1.2 More complex physiological models 8.4 Excretion via feces 8.4.1 Mechanisms of biliary excretion and enterohepatic recirculation 8.4.2 Physiologically based pharmacokinetic modeling of biliary excretion 8.4.2.1 Descriptions of biliary excretions 8.4.2.2 Fecal elimination and enterohepatic recycling 8.4.3 Mechanisms of direct intestinal excretion (exsorption) 8.4.4 Physiologically based pharmacokinetic modeling of intestinal excretion (exsorption) 8.4.4.1 Physiologically based pharmacokinetic model of exsorption of hexachlorobenzene 8.4.4.2 Generic physiologically based pharmacokinetic model exsorption of drugs (segregated flow model) 8.5 Excretion via breath 8.5.1 Mechanisms of respiratory excretion 8.5.2 Physiologically based pharmacokinetic modeling of respiratory excretion 8.6 Conclusion References A.1 Exercises 9 Sensitivity and Monte Carlo analysis techniques and their use in uncertainty, variability, and population analysis List of Abbreviations 9.1 Introduction 9.2 Sensitivity analysis 9.2.1 Constructing a sensitivity analysis 9.2.1.1 Model changes for tissue blood flows for mass balance 9.2.1.2 Model changes for tissue volumes for mass balance 9.2.1.3 Model changes for parameter correlations 9.2.1.4 Parameter settings for sensitivity analysis 9.2.2 Running the sensitivity analysis 9.2.3 Completing the sensitivity analysis 9.2.3.1 Output 9.2.3.2 Report of results 9.3 Monte Carlo analysis 9.3.1 Constructing a Monte Carlo analysis 9.3.1.1 Model selection 9.3.1.2 Software selection 9.3.1.3 Necessary model modifications 9.3.1.3.1 Model changes for mass balance 9.3.1.3.2 Model changes for parameter correlations 9.3.1.4 Parameter settings for Monte Carlo analysis 9.3.1.5 Defining parameter distributions 9.3.1.5.1 Shape of distribution 9.3.1.5.2 Means for distributions 9.3.1.5.3 Standard deviations for distributions 9.3.1.5.4 Transformations for means and standard deviations 9.3.1.5.5 Bounds for distributions 9.3.2 Running the Monte Carlo analysis 9.3.3 Completing the Monte Carlo analysis 9.3.3.1 Output 9.3.3.2 Report of results 9.4 Application of sensitivity and Monte Carlo analysis References A.1 Exercises 10 Physiologically based pharmacokinetic model calibration, evaluation, and performance assessment 10.1 Introduction 10.2 Physiologically based pharmacokinetic model calibration 10.2.1 Estimation of unknown parameter values 10.2.2 Starting values or initial values for parameter estimation 10.2.3 Relevant datasets for parameter estimation 10.2.4 Case Study 1: parameter estimation in Berkeley Madonna 10.2.5 Case Study 2: parameter estimation in R program 10.2.6 Case Study 3: parameter estimation in GastroPlus, Simcyp, and PK-Sim 10.2.6.1 Physiological parameters 10.2.6.2 Chemical-specific parameters 10.2.6.3 Case study of 2,4-dichlorophenoxyacetic acid with GastroPlus 10.3 Physiologically based pharmacokinetic model evaluation and validation 10.3.1 Case Study 1: performance assessment of the physiologically based pharmacokinetic model for gold nanoparticles in Be... 10.3.2 Case Study 2: performance assessment for the perfluorooctane sulfonate physiologically based pharmacokinetic model i... 10.3.3 Case Study 3: physiologically based pharmacokinetic model performance assessment in GastroPlus 10.4 Lab exercises: demo of calibrating a physiologically based pharmacokinetic model in Berkeley Madonna and R program 10.4.1 Lab Exercise 1: parameter estimation in Berkeley Madonna 10.4.1.1 Applying “Batch Runs” to help visually fit to biodistribution data 10.4.1.2 Applying “Sliders” to help fit to the biodistribution data 10.4.1.3 Applying “Curve Fit” module to optimize parameter values 10.4.2 Lab Exercise 2: parameter estimation in R program 10.4.2.1 The perfluorooctane sulfonate example model 10.4.2.2 Observed data 10.4.2.3 Implementation in R 10.4.2.4 Define the cost function 10.4.2.5 Model fitting 10.4.2.6 Model performance 10.5 Acknowledgment References 11 Examples of physiologically based pharmacokinetic modeling applied to risk assessment List of abbreviations 11.1 Introduction 11.1.1 Overview 11.1.2 Relationship to other chapters 11.2 Background 11.2.1 Toxicological risk assessment basics 11.2.2 Why use physiologically based pharmacokinetic models in risk assessment? 11.2.2.1 Uncertainty in extrapolation 11.2.2.2 Data aggregation 11.2.2.3 Basis for pharmacodynamic modeling 11.3 Deciding when a physiologically based pharmacokinetic model is suitable to use in risk assessment 11.3.1 Disclaimer 11.3.2 Overview of the risk assessment suitability evaluation process 11.3.3 Literature searches and organization 11.3.4 Model evaluation 11.3.4.1 Goal 11.3.4.2 Evaluate the biological plausibility and suitability of the model structure 11.3.4.3 Evaluate model relevance to the problem statement and toxicity database 11.3.4.4 Determine the verifiability of the mathematical description, computational implementation, and previous simulation... 11.3.4.5 Evaluate the model parameter values 11.3.4.6 Evaluate the model’s performance 11.3.4.7 Drawing conclusions on model applicability and confidence 11.3.4.8 Additional considerations for model selection 11.4 Examples of how to use physiologically based pharmacokinetic models in risk assessment 11.4.1 Selection of examples 11.4.2 Chronic oral reference dose for chromium (Thompson et al., 2018) 11.4.3 Physiologically based pharmacokinetic models for environmental/ecological risk assessment (Grech et al., 2017, 2019) 11.4.4 Prioritization/in vitro to in vivo extrapolation screening assessment (Wetmore et al., 2012; Gannon et al., 2019) 11.4.5 Thresholds of toxicological concern for occupational inhalation exposure (Chebekoue and Krishnan, 2019) References A.1 Exercises 12 Physiologically based pharmacokinetic models to support modernized chemical safety assessment 12.1 Introduction 12.2 Emergence of rapid physiologically based pharmacokinetic modeling 12.2.1 Generic physiologically based pharmacokinetic modeling 12.2.2 Rapid model parameterization 12.2.2.1 In vitro to in vivo extrapolation as modern parameterization tool 12.3 Applications to modern risk assessment 12.3.1 Quantitative in vitro to in vivo extrapolation for supporting high-throughput testing 12.3.2 Quantitative in vitro to in vivo extrapolation for supporting context-dependent risk assessment beyond prioritization 12.3.3 Quantitative in vitro to in vivo extrapolation-physiologically based pharmacokinetic for supporting compound-specifi... 12.4 Conclusion 12.5 Hands-on exercises 12.5.1 High-throughput in vitro to in vivo extrapolation for margin of exposure analysis 12.5.2 Rapid parameterization to develop an IVIVE-PBPK model for early life risk assessment References Index Back Cover __Physiologically Based Pharmacokinetic (PBPK) Modeling: Methods and Applications in Toxicology and Risk Assessment__ presents foundational principles, advanced techniques and applications of PBPK modeling. Contributions from experts in PBPK modeling cover topics such as pharmacokinetic principles, classical physiological models, the application of physiological models for dose-response and risk assessment, the use of in vitro information, and in silico methods. With end-of-chapter exercises that allow readers to practice and learn the skills associated with PBPK modeling, dose-response, and its applications to safety and risk assessments, this book is a foundational resource that provides practical coverage of PBPK modeling for graduate students, academics, researchers, and more.