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E-book
Author Chang, Mark.

Title Monte Carlo simulation for the pharmaceutical industry : concepts, algorithms, and case studies / Mark Chang
Published Boca Raton : CRC Press, ©2011

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Description 1 online resource (539 pages)
Series Chapman & Hall/CRC biostatistics series ; 36
Chapman & Hall/CRC biostatistics series ; 36
Contents 880-01 Front cover; Dedication; Contents; Preface; Chapter 1. Simulation, Simulation Everywhere; Chapter 2. Virtual Sampling Techniques; Chapter 3. Overview of Drug Development; Chapter 4. Meta-Simulation for the Pharmaceutical Industry; Chapter 5. Macro-Simulation for Pharmaceutical Research and Development; Chapter 6. Clinical Trial Simulation (CTS); Chapter 7. Clinical Trial Management and Execution; Chapter 8. Prescription Drug Commercialization; Chapter 9. Molecular Design and Simulation; Chapter 10. Disease Modeling and Biological Pathway Simulation; Chapter 11. Pharmacokinetic Simulation
880-01/(S Machine generated contents note: 1. Simulation, Simulation Everywhere -- 1.1. Modeling and Simulation -- 1.1.1. Art of Simulations -- 1.1.2. Genetic Programming in Art Simulation -- 1.1.3. Artificial Neural Network in Music Machinery -- 1.1.4. Bilingual Bootstrapping in Word Translation -- 1.2. Introductory Monte Carlo Examples -- 1.2.1. USA Territory -- 1.2.2. Π Simulation -- 1.2.3. Definite Integrals -- 1.2.4. Fastest Route -- 1.2.5. Economic Globalization -- 1.2.6. Percolation and Chaos -- 1.2.7. Fish Pond -- 1.2.8. Competing Risks -- 1.2.9. Pandemic Disease Modeling -- 1.2.10. Random Walk and Integral Equation -- 1.2.11. Financial Index and αStable Distribution -- 1.2.12. Nonlinear Equation System Solver -- 1.2.13. Stochastic Optimization -- 1.2.14. Symbolic Regression -- 1.3. Simulations in Drug Development -- 1.3.1. Challenges in the Pharmaceutical Industry -- 1.3.2. Classification of Simulations in Drug Development -- 1.4. Summary -- 1.5. Exercises -- 2. Virtual Sampling Techniques -- 2.1. Uniform Random Number Generation -- 2.2. General Sampling Methods -- 2.2.1. Inverse CDF Method -- 2.2.2. Acceptance-Rejection Method -- 2.2.3. Sampling of Order Statistics -- 2.2.4. Markov Chain Monte Carlo -- 2.2.5. Gibbs Sampling -- 2.2.6. Sampling from a Distribution in a Simplex -- 2.2.7. Sampling from a Distribution on a Hyperellipsoid -- 2.3. Efficiency Improvement in Virtual Sampling -- 2.3.1. Moments and Variable Transformation -- 2.3.2. Importance Sampling -- 2.3.3. Control Variables -- 2.3.4. Stratification -- 2.4. Sampling Algorithms for Specific Distributions -- 2.4.1. Uniform Distribution -- 2.4.2. Triangular Distribution -- 2.4.3. Normal Distribution -- 2.4.4. Gamma Distribution -- 2.4.5. Beta Distribution -- 2.4.6. Snedecor's F-Distribution -- 2.4.7. Chi-Square Distribution -- 2.4.8. Student Distribution -- 2.4.9. Exponential Distribution -- 2.4.10. Weibull Distribution -- 2.4.11. Inverse Gaussian Distribution -- 2.4.12. Laplace Distribution -- 2.4.13. Multivariate Normal Distribution -- 2.4.14. Equal Distribution -- 2.4.15. Binomial Distribution -- 2.4.16. Poisson Distribution -- 2.4.17. Negative Binomial -- 2.4.18. Geometric Distribution -- 2.4.19. Hypergeometric Distribution -- 2.4.20. Multinomial Distribution -- 2.5. Summary -- 2.6. Exercises -- 3. Overview of Drug Development -- 3.1. Introduction -- 3.2. Drug Discovery -- 3.2.1. Target Identification and Validation -- 3.2.2. Irrational Approach -- 3.2.3. Rational Approach -- 3.2.4. Biologics -- 3.2.5. Nanomedicine -- 3.3. Preclinical Development -- 3.3.1. Objectives of Preclinical Development -- 3.3.2. Pharmacokinetics -- 3.3.3. Pharmacodynamics -- 3.3.4. Toxicology -- 3.4. Clinical Development -- 3.4.1. Overview of Clinical Development -- 3.4.2. Classical Clinical Trial Paradigm -- 3.4.3. Adaptive Trial Design -- 3.4.4. Clinical Trial Protocol -- 3.5. Summary -- 3.6. Exercises -- 4. Meta-Simulation for the Pharmaceutical Industry -- 4.1. Introduction -- 4.1.1. Characteristics of Meta-Simulation -- 4.1.2. Macroeconomics -- 4.1.3. Microeconomics -- 4.1.4. Health Economics and Pharmacoeconomics -- 4.1.5. Profitability of the Pharmaceutical Industry -- 4.2. Game Theory Basics -- 4.2.1. Prisoners' Dilemma -- 4.2.2. Extensive Form -- 4.2.3. Nash Equilibrium -- 4.2.4. Mixed Strategy -- 4.2.5. Game with Multiple Options -- 4.2.6. Oligopoly Model -- 4.2.7. Games with Multiple Equilibria -- 4.2.8. Cooperative Games -- 4.2.9. Pareto Optimum -- 4.2.10. Multiple-Player and Queuing Games -- 4.3. Pharmaceutical Games -- 4.3.1. Two-Player Pharmaceutical Game -- 4.3.2. Mixed n-player Pharmaceutical Game -- 4.3.3. Bayesian Adaptive Gaming Strategy -- 4.3.4. Pharmaceutical Partnerships -- 4.4. Prescription Drug Global Pricing -- 4.4.1. Prescription Drug Price Policies -- 4.4.2. Drug Pricing Strategy -- 4.4.3. Cost Projection of Drug Development -- 4.5. Summary -- 4.6. Exercises -- 5. Macro-Simulation for Pharmaceutical Research and Development -- 5.1. Sequential Decision Making -- 5.1.1. Descriptive and Normative Decisions -- 5.1.2. Sequential Decision Problem -- 5.1.3. Backwards Induction -- 5.2. Markov Decision Process -- 5.2.1. Markov Chain -- 5.2.2. Markov Decision Process -- 5.2.3. Dynamic Programming -- 5.3. Pharmaceutial Decision Process -- 5.3.1. MDP for a Clinical Development Program -- 5.3.2. Markov Decision Tree and Out-Licensing -- 5.3.3. Research and Development Portfolio Optimization -- 5.4. Extension of the Markov Decision Process -- 5.4.1. Q-Learning -- 5.4.2. Bayesian Learning Process -- 5.4.3. Bayesian Decision Theory -- 5.4.4. Bayesian Stochastic Decision Process -- 5.4.5. One-Step Forward Approach -- 5.4.6. Partially Observable Markov Decision Processes -- 5.5. Summary -- 5.6. Exercises -- 6. Clinical Trial Simulation (CTS) -- 6.1. Classical Trial Simulation -- 6.1.1. Types of Trial Designs -- 6.1.2. Clinical Trial Endpoint -- 6.1.3. Superiority and Noninferiority Designs -- 6.1.4. Two-Group Equivalence Trial -- 6.2. Adaptive Trial Simulation -- 6.2.1. Adaptive Trial Design -- 6.2.2. Hypothesis-Based Adaptive Design Method -- 6.2.3. Method Based on the Sum of p-values -- 6.2.4. Method with Product of p-values -- 6.2.5. Method with Inverse-Normal p-values -- 6.2.6. Method Based on Brownian Motion -- 6.2.7. Design Evaluation --- Operating Characteristics -- 6.2.8. Sample Size Re-Estimation -- 6.2.9. Pick-Winner Design -- 6.2.10. Adaptive Design Case Studies -- 6.3. Summary -- 6.4. Exercises -- 7. Clinical Trial Management and Execution -- 7.1. Introduction -- 7.2. Clinical Trial Management -- 7.2.1. Critical Path Analysis -- 7.2.2. Logic-Operations Research (OR) Networks---Shortest Path -- 7.2.3. Logic-AND Networks---Longest Path -- 7.2.4. Algorithms for Critical Path Analysis -- 7.3. Patient Recruitment and Projection -- 7.3.1. Clinical Trial Globalization -- 7.3.2. Target Population and Site Selection -- 7.3.3. Time-to-Event Projection -- 7.4. Randomization -- 7.4.1. Simple Randomization -- 7.4.2. Stratified Randomization -- 7.4.3. Adaptive Randomization -- 7.5. Dynamic and Adaptive Drug Supply -- 7.5.1. Conventional Drug Supply -- 7.5.2. Dynamic and Adaptive Drug Supply -- 7.5.3. Adaptive Drug Supply -- 7.6. Statistical Trial Monitoring -- 7.6.1. Necessities of Trial Monitoring -- 7.6.2. Data Monitor Committee Charter -- 7.6.3. Statistical Monitoring Tool -- 7.7. Summary -- 7.8. Exercises -- 8. Prescription Drug Commercialization -- 8.1. Dynamics of Prescription Drug Marketing -- 8.1.1. Challenges in Innovative Drug Marketing -- 8.1.2. Structure of the Pharmaceutical Market -- 8.1.3. Common Marketing Strategies -- 8.2. Stock-Flow Dynamic Model for Brand Planning -- 8.2.1. Traditional Approach -- 8.2.2. Concept of the Stock-Flow Model -- 8.2.3. Patient Flow -- 8.2.4. Doctor Adoption---Prescription -- 8.2.5. Treatment Attractions -- 8.2.6. Diffusion Model for Drug Adoption -- 8.2.7. Strategy Framework for NCE Introductions -- 8.2.8. Data Source for Simulation -- 8.3. Competitive Drug Marketing Strategy -- 8.3.1. Pricing and Payer Strategies -- 8.3.2. Marketing Strategies after Patent Expiration -- 8.3.3. Stochastic Market Game -- 8.4. Compulsory Licensing and Parallel Importation -- 8.4.1. Legal Complications of Drug Marketing -- 8.4.2. Grossman-Lai's Game Model -- 8.4.3. Sequential Game of Drug Marketing -- 8.5. Summary -- 8.6. Exercises -- 9. Molecular Design and Simulation -- 9.1. Why Molecular Design and Simulation -- 9.1.1. Landscape of Molecular Design -- 9.1.2. Innovative Drug Discovery Approach -- 9.1.3. Drug-Likeness Concept
Summary "Preface Drug development, aiming at improving people's health, becomes more costly every year. The pharmaceutical industry must join its efforts with government and health professions to seek new, innovative, and cost- effective approaches in the development process. During this evolutionary process in the next decades, computer simulations will no doubt play a critical role. Computer simulation or Monte Carlo is the technique of simulating a dynamic system or process using a computer program. Computer simulations, as an efficient and effective research tool, have been used virtually in every concern of engineering, science, mathematics, etc. In this book, I am going to present the concept, theory, algorithm, and cases studies of Monte Carlo simulation in the pharmaceutical and health industries. The concepts refer not only to simulation in general, but also to various types of simulations in drug development. The theory will include virtual data sampling, game theory, deterministic and stochastic decision theories, adaptive design methods, Petrinet, genetic programming, resampling methods, and other strategies. These theories and methods either are necessary to carry out the simulations or make the simulations more efficient, even though there are many practical problems that can be simulated directly in ad hoc fashion without any theory of their efficiency or convergence considerations. The algorithms, which can be descriptive, computer pseudocode, or a combination of both, provide the basis for implementation of simulation methods. The case studies or applications are the simplified versions of the real world problems. These simplifications are necessary because a single case could otherwise occupy the whole book, preventing readers from exploring broad issues"--Provided by publisher
Bibliography Includes bibliographical references (pages 487-501) and index
Notes Print version record
Subject Drug development -- Computer simulation
Monte Carlo method.
Drugs -- Design.
Drug Industry -- methods
Computer Simulation
Drug Design
Monte Carlo Method
Technology, Pharmaceutical -- methods
simulation.
MEDICAL -- Drug Guides.
MEDICAL -- Pharmacology.
MEDICAL -- Pharmacy.
MEDICAL -- Nursing -- Pharmacology.
Drugs -- Design
Monte Carlo method
Form Electronic book
ISBN 9781439835937
1439835934