Monte Carlo Simulation for the Pharmaceutical Industry : Concepts, Algorithms, and Case Studies book cover
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Monte Carlo Simulation for the Pharmaceutical Industry
Concepts, Algorithms, and Case Studies





ISBN 9781138374386
Published October 25, 2018 by CRC Press
564 Pages - 116 B/W Illustrations

 
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Book Description

Helping you become a creative, logical thinker and skillful "simulator," Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies provides broad coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementation of the simulation methods, and illustrates real-world problems through case studies.

The text first emphasizes the importance of analogy and simulation using examples from a variety of areas, before introducing general sampling methods and the different stages of drug development. It then focuses on simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and various challenges in clinical trial management and execution. The author goes on to cover prescription drug marketing strategies and brand planning, molecular design and simulation, computational systems biology and biological pathway simulation with Petri nets, and physiologically based pharmacokinetic modeling and pharmacodynamic models. The final chapter explores Monte Carlo computing techniques for statistical inference.

This book offers a systematic treatment of computer simulation in drug development. It not only deals with the principles and methods of Monte Carlo simulation, but also the applications in drug development, such as statistical trial monitoring, prescription drug marketing, and molecular docking.

Table of Contents

Simulation, Simulation Everywhere
Modeling and Simulation
Introductory Monte Carlo Examples
Simulations in Drug Development

Virtual Sampling Techniques
Uniform Random Number Generation
General Sampling Methods
Efficiency Improvement in Virtual Sampling
Sampling Algorithms for Specific Distributions

Overview of Drug Development
Introduction
Drug Discovery
Preclinical Development
Clinical Development

Meta-Simulation for Pharmaceutical Industry
Introduction
Game Theory Basics
Pharmaceutical Games
Prescription Drug Global Pricing

Macro-Simulation for Pharmaceutical R & D
Sequential Decision-Making
Markov Decision Process
Pharmaceutical Decision Process
Extension of Markov Decision Process

Clinical Trial Simulation (CTS)
Classical Trial Simulation
Adaptive Trial Simulation

Clinical Trial Management and Execution
Introduction
Clinical Trial Management
Patient Recruitment and Projection
Randomization
Dynamic and Adaptive Drug Supply
Statistical Trial Monitoring

Prescription Drug Commercialization
Dynamics of Prescription Drug Marketing
Stock-Flow Dynamic Model for Brand Planning
Competitive Drug Marketing Strategy
Compulsory Licensing and Parallel Importation

Molecular Design and Simulation
Why Molecular Design and Simulation
Molecular Similarity Search
Overview of Molecular Docking
Small Molecule Confirmation Analysis
Ligand-Receptor Interaction
Docking Algorithms
Scoring Functions

Disease Modeling and Biological Pathway Simulation
Computational System Biology
Petri Nets
Biological Pathway Simulation

Pharmacokinetic Simulation
Overview of ADME
Absorption Modeling
Distribution
Metabolism Modeling
Excretion Modeling
Physiologically Based PK Model

Pharmacodynamic Simulation
Way to Pharmacodynamics
Enzyme Kinetics
Pharmacodynamic Models
Drug-Drug Interaction
Application of Pharmacodynamic Modeling

Monte Carlo for Inference and Beyond
Sorting Algorithm
Resampling Methods
Genetic Programming

Appendix A: JavaScript Programs
Appendix B: K-Stage Adaptive Design Stopping Boundaries

Afterword

Bibliography

A Summary and Exercises appear at the end of each chapter.

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Author(s)

Biography

Mark Chang is the executive director of biostatistics and data management at AMAG Pharmaceuticals in Lexington, Massachusetts. Dr. Chang is an elected fellow of the American Statistical Association. He is the author of the best-selling Adaptive Design Theory and Implementation Using SAS and R and co-author of the best-selling Adaptive Design Methods in Clinical Trials.

Reviews

"Overall, the book does not only cover a very broad range of different topics but manages to explain these coherently. … this book is not only of interest for scientists in the pharmaceutical industry but also for academia due to its thorough presentation."
—Frank Emmert-Streib, Statistical Methods in Medical Research, 21(6), 2012

"… well written and easy to read. … this book is worthwhile reading as a long introduction to Monte Carlo simulation and its eventual application in pharmaceutical industry. It can convince people to consider this methodology …"
—Sophie Donnet, International Statistical Review, 2012

"This is an ambitious book covering a very wide array of topics … the theoretical presentation is reliable and sophisticated … the ability of the author to condense such a broad array of topics, and to present them in a cohesive manner, is quite impressive, and means that the book will contain information of relevance to a wide audience. … Many statisticians working in the pharmaceutical industry will benefit from having access to a copy of this book. Some statisticians working outside the industry may also benefit from having access to a copy, particularly those working in areas overlapping with the pharmaceutical industry, such as clinical science and health economics."
—Ian C. Marschner, Australian & New Zealand Journal of Statistics, 2011

"For industry statisticians, scientists, and software engineers and programmers, Chang, who works for a pharmaceutical company, details concepts, theories, algorithms, and case studies for carrying out computer simulations in the drug development process, from drug discovery to clinical trial aspects to commercialization. He covers analogy and simulation using examples from different areas, general sampling methods and the different stages of drug development, simulation approaches based on game theory and the Markov decision process, simulations in classical and adaptive trials, and challenges in clinical trial management and execution. He then addresses prescription drug marketing strategies and brand planning, molecular design and simulation, computational systems biology and biological pathway simulation with Petri nets, and physiologically based pharmacokinetic modeling and pharmacodynamic models, ending with Monte Carlo computing techniques for statistical inference."
SciTech Book News, February 2011