Modern medicine is undergoing a paradigm shift from a "one-size-fits-all" strategy to a more precise patient-customized therapy and medication plan. While the success of precision medicine relies on the level of pharmacogenomic knowledge, dissecting the genetic mechanisms of drug response in a sufficient detail requires powerful computational tools. Quantitative Methods for Precision Medicine: Pharmacogenomics in Action presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. Beyond traditional reductionist-based statistical genetic approaches, statistical formulization in this book synthesizes elements of multiple disciplines to infer, visualize, and track how pharmacogenes interact together as an intricate but well-coordinated system to mediate patient-specific drug response.
Features
Functional and systems mapping models to characterize the genetic architecture of multiple medication processes
Statistical methods for analyzing informative missing data in pharmacogenetic association studies
Functional graph theory of inferring genetic interaction networks from association data
Leveraging the concept of epistasis to capture its bidirectional, signed and weighted properties
Modeling gene-induced cell-cell crosstalk and its impact on drug response
A graph model of drug-drug interactions in combination therapies
Critical methodological issues to improve pharmacogenomic research as the cornerstone of precision medicine
This book is suitable for graduate students and researchers in the fields of biology, medicine, bioinformatics and drug design and delivery who are interested in statistical and computational modelling of biological processes and systems. It may also serve as a major reference for applied mathematicians, computer scientists, and statisticians who attempt to develop algorithmic tools for genetic mapping, systems pharmacogenomics and systems biology. It can be used as both a textbook and research reference. Professionals in pharmaceutical sectors who design drugs and clinical doctors who deliver drugs will also find it useful.
Author(s): Rongling Wu
Series: Chapman & Hall/CRC Biostatistics Series
Publisher: CRC Press/Chapman & Hall
Year: 2022
Language: English
Pages: 306
City: Boca Raton
Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Biography
1. Methodological Foundation of Precision Medicine
1.1 Interpersonal Variability in Drug Response
1.2 Mechanistic Modeling of Drug Response
1.2.1 Genetic Architecture Modeling
1.2.2 Kinetic Models of Drug Reactions
1.2.3 Dynamic Models of Genetic Networks
1.2.4 Implementing Systems Approaches
1.3 Statistical Models for Mapping Drug Response
1.3.1 Study Design
1.3.2 Nonlinear Regression of Drug Response
1.3.3 Mechanistic Mapping
1.4 Network Mapping of Drug Response
1.5 Conclusions and Outlook
Part 1 Pharmacokinetic–Pharmacodynamic Pharmacogenetics
2. Pharmacogenetic Dissection by Functional Mapping
2.1 Introduction
2.2 Quantitative Genetics
2.2.1 One-Gene Model
2.2.2 Two-Gene Model
2.2.3 Haplotype Model
2.3 A General Framework for Functional Mapping
2.3.1 Why Functional Mapping
2.3.2 The Procedure of Functional Mapping
2.3.3 Biological Relevance of Functional Mapping
2.3.4 Inferring Casual QTLs
2.4 Pharmacogenetic Application of Functional Mapping
2.4.1 A Pharmacogenetic Association Study
2.4.2 Detection of Causal QTLs for Drug Response
2.4.3 Epistatic QTLs for Drug Response
2.5 Functional Mapping with High-Dimensional Predictors
2.6 Concluding Remarks
3. A Multiscale Model of Pharmacokinetic–Pharmacodynamic Mapping
3.1 Introduction
3.2 Heterochronopharmacodynamics and Chronotherapy
3.3 Heterochronopharmacodynamic Mapping
3.3.1 PD Model – The Hill Equation
3.3.2 Integrating Heterochrony into PD Modeling
3.3.3 Top-Down and Bottom-Up Mapping Approaches of HPD Genes
3.3.4 Identification of HPD QTLs
3.3.5 Heterochronopharmacodynamic Dissection
3.4 Mapping Multifaceted Drug Reactions
3.4.1 Multifaceted Features of Drug Response: A Blood Pressure Example
3.4.2 Composite Functional Mapping
3.4.3 Identification of Pleiotropic Pharmacogenes
3.5 Concluding Remarks
4. Pharmacogenetic Mapping of Missing Longitudinal Data
4.1 Introduction
4.2 Strategies for Modeling Non-Ignorable Dropout Data
4.2.1 The Factorization of Joint Distribution
4.2.2 The Selection Model
4.2.3 The Pattern-Mixture Model
4.3 Haplotyping Drug Response Using the Pattern-Mixture Model
4.3.1 Pharmacogenetic Dropout Example
4.3.2 Functional Mapping with Non-Ignorable Dropouts
4.3.3 Hypothesis Tests
4.3.4 The Identification of Significant Haplotypes
4.3.5 Comparison with Traditional Functional Mapping
4.4 Haplotyping Drug Response Using the Selection Model
4.4.1 Likelihood and Estimation
4.4.2 Asymptotic Sampling Variances
4.4.3 Computer Simulation
4.5 Concluding Remarks
5. Systems Mapping of Drug Response
5.1 Introduction
5.2 ODE Modeling of PK/PD Machineries
5.3 Systems Mapping: Model and Algorithm
5.3.1 Clinical Design
5.3.2 Likelihood and Estimation
5.3.3 Hypothesis Tests
5.3.4 Computer Simulation
5.4 Stochastic Systems Mapping
5.4.1 Why Stochastic Modeling in Drug Response
5.4.2 Stochastic Modeling of Dynamic Systems
5.4.3 Parameter Estimation of SDEs
5.4.4 Extended Kalman Filter (EKF)
5.4.5 Differentiation of EKF Equations
5.4.6 Likelihood Function of Stochastic Systems Mapping
5.4.7 Computer Simulation
5.4.8 Application of Stochastic Systems Mapping
5.5 Concluding Remarks
Part 2 Network Pharmacogenetics
6. Network Mapping of Drug Response
6.1 Introduction
6.2 Functional Graph Theory
6.2.1 Functional Mapping
6.2.2 Social Decomposition of the Net Genetic Effect
6.2.3 LV Modeling of Evolutionary Game Theory
6.2.4 Reconstructing Causal, Sparse, and Stable Networks
6.2.5 Bidirectional, Signed, and Weighted Formulation of Epistasis
6.2.6 Pharmacokinetically and Pharmacodynamically Varying Networks
6.3 Functional Pharmacogenetic Interaction Networks: An Example
6.3.1 Estimation of Pharmacogenetic Effect Curves by Functional Mapping
6.3.2 Reconstructing Pharmacogenetic Interaction Networks
6.4 Fine-Grained Dissection of Pharmacogenetic Networks
6.4.1 Model
6.4.2 Effect-Based Pharmacogenetic Networks
6.5 Modularity Theory and Dunbar's Law
6.5.1 Genome-Wide Network Validation of Omnigenic Theory
6.5.2 Developmental Modularity Theory Meets Dunbar's Law
6.5.3 Functional Clustering Algorithms
6.6 Concluding Remarks
7. Learning Individualized Pharmacogenetic Networks
7.1 Introduction
7.2 A Framework for Network Inference
7.2.1 Pharmacogenetic Mapping
7.2.2 LV-Based ODE Modeling of Epistatic Networks
7.2.3 Bidirectional, Signed, and Weighted Epistasis
7.2.4 Example for Individualized Networks Mediating Drug Response
7.3 Coalescing Individualized Networks into Stratification-Specific Networks
7.3.1 Functional Clustering
7.3.2 Example for Stratification-Specific Networks
7.4 Computer Simulation
7.5 Reconstructing Multilayer Genetic Networks
7.6 Concluding Remarks
8. A Game-Theoretic Model of Cell Crosstalk in Drug Response
8.1 Introduction
8.2 GameTalker: A Crosstalk Model of Tumor-Microenvironment Interactions
8.2.1 Modeling Inter-, Intra-, and Extratumoral Heterogeneities
8.2.2 Methodological Foundation of GameTalker
8.2.3 Transcriptomic Atlas of Tumor-Microenvironment Interactions
8.2.4 Mapping Tumor-TME Interactions for Liver Cancer
8.3 Modeling Personalized Cell-Cell Interaction Networks
8.3.1 A Generic Formulism of Intratumoral and Extratumoral Internal Workings
8.3.2 Reconstructing Bidirectional, Signed, and Weighted Cell-Cell Interaction Networks
8.3.3 Detection of Personalized Intra- and Extratumoral Cell-Cell Interactions
8.4 Reconstructing Multilayer Gene Regulatory Networks of Tumor-TME Interactions
8.4.1 Allometric Scaling Law of Intra- and Extratumoral Heterogeneities
8.4.2 Identifying Network Communities
8.4.3 Reconstructing Multilayer GRNs
8.4.4 Compartment-Specific GRNs
8.5 Predictive Network Model for Cancer Growth
8.5.1 Normalized Allometric Scaling Laws
8.5.2 Causal Inference of Tumor Growth from Intra- and Extratumoral Transcription
8.5.3 Causal Inference of Tumor Growth from Tumor-TME Crosstalk
8.5.4 An Atlas Charting the Impact of Tumor-TME Crosstalk on Liver Size: An Example
8.6 Concluding Remarks
9. A Graph Model of Personalized Drug–Drug Interactions
9.1 Introduction
9.2 Inferring DDI Networks
9.2.1 Combination of Ecosystem Theory and Evolutionary Game Theory
9.2.2 A Procedure of DDI Network Reconstruction
9.2.3 Reconstructing Personalized DDI Networks
9.2.4 Unveiling the Mechanisms behind Drug Interactions
9.3 Inferring Dynamic DDI Networks from Static Data
9.3.1 Theoretical Foundation
9.3.2 Procedure for Network Inference from Static or Semi-Static Data
9.3.3 A Proof of Concept: Analyzing a Real Data Set
9.4 Coalescing High-Order DDIs into Hypernetworks
9.5 Learning Large-Scale DDI Networks
9.6 Concluding Remarks
10. Pharmacogenomics as a Cornerstone of Precision Medicine: Methodological Leveraging
10.1 Introduction
10.2 How Drug Works
10.2.1 Drug–Receptor Interaction
10.2.2 Drug Metabolism
10.2.3 Targeted Drug Development
10.3 Correcting for Relatedness in Pharmacogenomics Genome-wide Association Studies (GWAS)
10.3.1 Population Structure and Kinship
10.3.2 A General LMM Framework
10.3.3 Dynamic Issue in PGx GWAS
10.4 Family-Based Designs for PGx Studies
10.4.1 Advantages of Family-Based Designs
10.4.2 Genetic Modeling
10.4.3 Statistical Modeling
10.5 Intertwined Epistatic and Epistatic Networks
10.5.1 Pleiotropic Networks
10.5.2 Epistatic Networks of Pleiotropic Networks
10.6 Pharmacosystems Biology: From Pharmacogenomics to Pharmaco-Omics
10.6.1 Integrating Pharmaco-Omics into Pharmacogenomics
10.6.2 TWAS as a Gene-Based Association Approach
10.6.3 A Network Consideration
10.7 Concluding Remarks
Bibliography
Index
