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E-raamat: Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies

(AMAG Pharmaceuticals, Inc, Lexington, Massachusetts, USA)
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Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case StudiesSuurem pilt
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"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 manypractical 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.

Arvustused

"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

Preface xi
1 Simulation, Simulation Everywhere
1(38)
1.1 Modeling and Simulation
1(5)
1.1.1 The Art of Simulations
1(1)
1.1.2 Genetic Programming in Art Simulation
2(1)
1.1.3 Artificial Neural Network in Music Machinery
3(2)
1.1.4 Bilingual Bootstrapping in Word Translation
5(1)
1.2 Introductory Monte Carlo Examples
6(25)
1.2.1 USA Territory
6(1)
1.2.2 Π Simulation
7(2)
1.2.3 Definite Integrals
9(2)
1.2.4 Fastest Route
11(2)
1.2.5 Economic Globalization
13(1)
1.2.6 Percolation and Chaos
14(2)
1.2.7 Fish Pond
16(2)
1.2.8 Competing Risks
18(1)
1.2.9 Pandemic Disease Modeling
19(1)
1.2.10 Random Walk and Integral Equation
20(3)
1.2.11 Financial Index and αStable Distribution
23(2)
1.2.12 Nonlinear Equation System Solver
25(1)
1.2.13 Stochastic Optimization
26(2)
1.2.14 Symbolic Regression
28(3)
1.3 Simulations in Drug Development
31(2)
1.3.1 Challenges in the Pharmaceutical Industry
31(1)
1.3.2 Classification of Simulations in Drug Development
32(1)
1.4 Summary
33(3)
1.5 Exercises
36(3)
2 Virtual Sampling Techniques
39(42)
2.1 Uniform Random Number Generation
39(1)
2.2 General Sampling Methods
40(8)
2.2.1 Inverse CDF Method
40(1)
2.2.2 Acceptance-Rejection Method
41(2)
2.2.3 Sampling of Order Statistics
43(1)
2.2.4 Markov Chain Monte Carlo
44(2)
2.2.5 Gibbs Sampling
46(1)
2.2.6 Sampling from a Distribution in a Simplex
47(1)
2.2.7 Sampling from a Distribution on a Hyperellipsoid
48(1)
2.3 Efficiency Improvement in Virtual Sampling
48(5)
2.3.1 Moments and Variable Transformation
48(1)
2.3.2 Importance Sampling
49(1)
2.3.3 Control Variables
50(1)
2.3.4 Stratification
51(2)
2.4 Sampling Algorithms for Specific Distributions
53(21)
2.4.1 Uniform Distribution
53(1)
2.4.2 Triangular Distribution
54(1)
2.4.3 Normal Distribution
55(1)
2.4.4 Gamma Distribution
56(2)
2.4.5 Beta Distribution
58(3)
2.4.6 Snedecor's F-Distribution
61(1)
2.4.7 Chi-Square Distribution
62(1)
2.4.8 Student Distribution
62(1)
2.4.9 Exponential Distribution
63(1)
2.4.10 Weibull Distribution
64(1)
2.4.11 Inverse Gaussian Distribution
65(1)
2.4.12 Laplace Distribution
66(1)
2.4.13 Multivariate Normal Distribution
67(1)
2.4.14 Equal Distribution
67(1)
2.4.15 Binomial Distribution
68(1)
2.4.16 Poisson Distribution
69(1)
2.4.17 Negative Binomial
70(1)
2.4.18 Geometric Distribution
71(1)
2.4.19 Hypergeometric Distribution
72(1)
2.4.20 Multinomial Distribution
73(1)
2.5 Summary
74(3)
2.6 Exercises
77(4)
3 Overview of Drug Development
81(28)
3.1 Introduction
81(2)
3.2 Drug Discovery
83(8)
3.2.1 Target Identification and Validation
83(2)
3.2.2 Irrational Approach
85(1)
3.2.3 Rational Approach
86(1)
3.2.4 Biologics
87(3)
3.2.5 Nanomedicine
90(1)
3.3 Preclinical Development
91(7)
3.3.1 Objectives of Preclinical Development
91(1)
3.3.2 Pharmacokinetics
92(4)
3.3.3 Pharmacodynamics
96(1)
3.3.4 Toxicology
97(1)
3.4 Clinical Development
98(8)
3.4.1 Overview of Clinical Development
98(2)
3.4.2 Classical Clinical Trial Paradigm
100(4)
3.4.3 Adaptive Trial Design
104(1)
3.4.4 Clinical Trial Protocol
105(1)
3.5 Summary
106(2)
3.6 Exercises
108(1)
4 Meta-Simulation for the Pharmaceutical Industry
109(40)
4.1 Introduction
109(6)
4.1.1 Characteristics of Meta-Simulation
109(1)
4.1.2 Macroeconomics
109(2)
4.1.3 Microeconomics
111(1)
4.1.4 Health Economics and Pharmacoeconomics
112(1)
4.1.5 Profitability of the Pharmaceutical Industry
113(2)
4.2 Game Theory Basics
115(14)
4.2.1 Prisoners' Dilemma
116(1)
4.2.2 Extensive Form
117(1)
4.2.3 Nash Equilibrium
118(2)
4.2.4 Mixed Strategy
120(1)
4.2.5 Game with Multiple Options
121(3)
4.2.6 Oligopoly Model
124(1)
4.2.7 Games with Multiple Equilibria
125(1)
4.2.8 Cooperative Games
126(1)
4.2.9 Pareto Optimum
126(1)
4.2.10 Multiple-Player and Queuing Games
127(2)
4.3 Pharmaceutical Games
129(8)
4.3.1 Two-Player Pharmaceutical Game
129(1)
4.3.2 Mixed n-player Pharmaceutical Game
130(2)
4.3.3 Bayesian Adaptive Gaming Strategy
132(2)
4.3.4 Pharmaceutical Partnerships
134(3)
4.4 Prescription Drug Global Pricing
137(6)
4.4.1 Prescription Drug Price Policies
137(2)
4.4.2 Drug Pricing Strategy
139(2)
4.4.3 Cost Projection of Drug Development
141(2)
4.5 Summary
143(4)
4.6 Exercises
147(2)
5 Macro-Simulation for Pharmaceutical Research and Development
149(38)
5.1 Sequential Decision Making
149(3)
5.1.1 Descriptive and Normative Decisions
149(1)
5.1.2 Sequential Decision Problem
150(1)
5.1.3 Backwards Induction
151(1)
5.2 Markov Decision Process
152(8)
5.2.1 Markov Chain
152(3)
5.2.2 Markov Decision Process
155(2)
5.2.3 Dynamic Programming
157(3)
5.3 Pharmaceutial Decision Process
160(14)
5.3.1 MDP for a Clinical Development Program
160(9)
5.3.2 Markov Decision Tree and Out-Licensing
169(2)
5.3.3 Research and Development Portfolio Optimization
171(3)
5.4 Extension of the Markov Decision Process
174(8)
5.4.1 Q-Learning
174(1)
5.4.2 Bayesian Learning Process
175(2)
5.4.3 Bayesian Decision Theory
177(1)
5.4.4 Bayesian Stochastic Decision Process
178(2)
5.4.5 One-Step Forward Approach
180(1)
5.4.6 Partially Observable Markov Decision Processes
180(2)
5.5 Summary
182(3)
5.6 Exercises
185(2)
6 Clinical Trial Simulation (CTS)
187(42)
6.1 Classical Trial Simulation
187(9)
6.1.1 Types of Trial Designs
187(3)
6.1.2 Clinical Trial Endpoint
190(1)
6.1.3 Superiority and Noninferiority Designs
190(5)
6.1.4 Two-Group Equivalence Trial
195(1)
6.2 Adaptive Trial Simulation
196(28)
6.2.1 Adaptive Trial Design
196(1)
6.2.2 Hypothesis-Based Adaptive Design Method
197(4)
6.2.3 Method Based on the Sum of p-values
201(3)
6.2.4 Method with Product of p-values
204(2)
6.2.5 Method with Inverse-Normal p-values
206(2)
6.2.6 Method Based on Brownian Motion
208(3)
6.2.7 Design Evaluation --- Operating Characteristics
211(3)
6.2.8 Sample Size Re-Estimation
214(4)
6.2.9 Pick-Winner Design
218(2)
6.2.10 Adaptive Design Case Studies
220(4)
6.3 Summary
224(2)
6.4 Exercises
226(3)
7 Clinical Trial Management and Execution
229(36)
7.1 Introduction
229(1)
7.2 Clinical Trial Management
230(6)
7.2.1 Critical Path Analysis
230(1)
7.2.2 Logic-Operations Research (OR) Networks---Shortest Path
231(3)
7.2.3 Logic-AND Networks---Longest Path
234(1)
7.2.4 Algorithms for Critical Path Analysis
235(1)
7.3 Patient Recruitment and Projection
236(7)
7.3.1 Clinical Trial Globalization
236(2)
7.3.2 Target Population and Site Selection
238(2)
7.3.3 Time-to-Event Projection
240(3)
7.4 Randomization
243(5)
7.4.1 Simple Randomization
243(1)
7.4.2 Stratified Randomization
244(1)
7.4.3 Adaptive Randomization
245(3)
7.5 Dynamic and Adaptive Drug Supply
248(4)
7.5.1 Conventional Drug Supply
248(1)
7.5.2 Dynamic and Adaptive Drug Supply
249(1)
7.5.3 Adaptive Drug Supply
250(2)
7.6 Statistical Trial Monitoring
252(8)
7.6.1 Necessities of Trial Monitoring
252(2)
7.6.2 Data Monitor Committee Charter
254(2)
7.6.3 Statistical Monitoring Tool
256(4)
7.7 Summary
260(3)
7.8 Exercises
263(2)
8 Prescription Drug Commercialization
265(38)
8.1 Dynamics of Prescription Drug Marketing
265(6)
8.1.1 Challenges in Innovative Drug Marketing
265(2)
8.1.2 Structure of the Pharmaceutical Market
267(1)
8.1.3 Common Marketing Strategies
268(3)
8.2 Stock-Flow Dynamic Model for Brand Planning
271(12)
8.2.1 Traditional Approach
271(1)
8.2.2 Concept of the Stock-Flow Model
272(2)
8.2.3 Patient Flow
274(1)
8.2.4 Doctor Adoption---Prescription
275(2)
8.2.5 Treatment Attractions
277(1)
8.2.6 Diffusion Model for Drug Adoption
277(3)
8.2.7 Strategy Framework for NCE Introductions
280(1)
8.2.8 Data Source for Simulation
281(2)
8.3 Competitive Drug Marketing Strategy
283(8)
8.3.1 Pricing and Payer Strategies
284(2)
8.3.2 Marketing Strategies after Patent Expiration
286(2)
8.3.3 Stochastic Market Game
288(3)
8.4 Compulsory Licensing and Parallel Importation
291(6)
8.4.1 Legal Complications of Drug Marketing
291(2)
8.4.2 Grossman-Lai's Game Model
293(2)
8.4.3 Sequential Game of Drug Marketing
295(2)
8.5 Summary
297(3)
8.6 Exercises
300(3)
9 Molecular Design and Simulation
303(36)
9.1 Why Molecular Design and Simulation
303(6)
9.1.1 The Landscape of Molecular Design
303(1)
9.1.2 The Innovative Drug Discovery Approach
304(2)
9.1.3 The Drug-Likeness Concept
306(1)
9.1.4 Structure-Activity Relationship (SAR)
307(2)
9.2 Molecular Similarity Search
309(7)
9.2.1 Molecular Representation
309(1)
9.2.2 Tauimoto Similarity Index
310(2)
9.2.3 SimScore
312(1)
9.2.4 Bayesian Network for Similarity Search
313(3)
9.3 Overview of Molecular Docking
316(3)
9.3.1 Concept of Molecular Docking
316(1)
9.3.2 Database for Virtual Screening
317(1)
9.3.3 Docking Approaches
318(1)
9.4 Small Molecule Confirmation Analysis
319(4)
9.4.1 Quantum Mechanics
319(2)
9.4.2 Molecular Mechanics
321(2)
9.4.3 Geometry Optimization
323(1)
9.5 Ligand-Receptor Interaction
323(4)
9.5.1 Concept of Energy Minimization
323(1)
9.5.2 Hard Sphere-Fitting Method
324(1)
9.5.3 Method of Moments
325(1)
9.5.4 Ligand and Protein Flexibility
326(1)
9.6 Docking Algorithms
327(2)
9.6.1 Incremental Construction Methods
327(1)
9.6.2 Genetic Algorithms
327(1)
9.6.3 Monte Carlo Simulated Annealing
328(1)
9.7 Scoring Functions
329(6)
9.7.1 Empirical Scoring Functions
330(1)
9.7.2 Force-Field-Based Scoring Functions
331(1)
9.7.3 Iterative Knowledge-Based Scoring Function
331(3)
9.7.4 Virtual Screening of 5-Lipoxygenase Inhibitors
334(1)
9.8 Summary
335(2)
9.9 Exercises
337(2)
10 Disease Modeling and Biological Pathway Simulation
339(38)
10.1 Computational Systems Biology
339(6)
10.1.1 Cell, Pathway, and Systems Biology
339(2)
10.1.2 Monte Carlo with Differential Equations
341(1)
10.1.3 Cellular Automata Method
342(1)
10.1.4 Agent-Based Models
343(1)
10.1.5 Network Models
344(1)
10.2 Petri Nets
345(13)
10.2.1 Basic Concept of Petri Nets
345(2)
10.2.2 Why a Petri Net
347(1)
10.2.3 Petri Net Dynamics
348(6)
10.2.4 Petri Net Static Properties
354(4)
10.3 Biological Pathway Simulation
358(14)
10.3.1 Introduction
358(2)
10.3.2 Modeling of Metabolic Networks
360(3)
10.3.3 PN for a Signal Transduction Pathway
363(3)
10.3.4 Stochastic PN for Regulatory Pathways
366(2)
10.3.5 Hybrid PN for Regulatory Pathways
368(2)
10.3.6 General Stochastic PN and Algorithm
370(2)
10.4 Summary
372(3)
10.5 Exercises
375(2)
11 Pharmacokinetic Simulation
377(36)
11.1 Overview of ADME
377(1)
11.2 Absorption Modeling
378(6)
11.2.1 Formulations and Delivery Systems
379(1)
11.2.2 Drug Dissolution
380(4)
11.3 Distribution Modeling
384(6)
11.3.1 Darcy's Law for Perfusion
384(2)
11.3.2 Fick's Law for Diffusion
386(4)
11.4 Metabolism Modeling
390(2)
11.4.1 Metabolic Process
390(1)
11.4.2 Enzyme Dynamics
391(1)
11.5 Excretion Modeling
392(2)
11.6 Physiologically-Based PK Model
394(14)
11.6.1 Classic Compartment Model
394(3)
11.6.2 Description of the PBPK Model
397(5)
11.6.3 Probabilistic PBPK Model
402(2)
11.6.4 Relationship to MCMC
404(1)
11.6.5 Monte Carlo Implementation
405(3)
11.7 Summary
408(3)
11.8 Exercises
411(2)
12 Pharmacodynamic Simulation
413(30)
12.1 Way to Pharmacodynamics
413(5)
12.1.1 Objectives of Pharmacodynamics
413(2)
12.1.2 ADME Review
415(2)
12.1.3 Intraspecies and Interspecies Scaling
417(1)
12.2 Enzyme Kinetics
418(4)
12.2.1 Enzyme Inducer and Inhibitor
418(2)
12.2.2 Occupancy Theory
420(2)
12.2.3 Feedback Mechanism
422(1)
12.3 Pharmacodynamic Models
422(11)
12.3.1 Pharmacodynamics-Pharmacokinetic Relationship
422(2)
12.3.2 Maximum Effect (Emax) Model
424(1)
12.3.3 Logistic Regression
424(1)
12.3.4 Artificial Neural Network
425(7)
12.3.5 Genetic Programming for Pharmacodynamics
432(1)
12.4 Drug-Drug Interaction
433(2)
12.4.1 Drug-Drug Interaction Mechanisms
433(1)
12.4.2 Pharmacokinetic Drug Interactions
433(1)
12.4.3 Pharmacodynamic Drug Interactions
434(1)
12.5 Application of Pharmacodynamic Modeling
435(3)
12.6 Summary
438(3)
12.7 Exercises
441(2)
13 Monte Carlo for Inference and Beyond
443(32)
13.1 Sorting Algorithm
443(2)
13.1.1 Quicksorting Algorithms
443(1)
13.1.2 Indexing and Ranking
444(1)
13.2 Resampling Methods
445(10)
13.2.1 Bootstrap: The Plug-in Principle
445(3)
13.2.2 Asymptotic Theory of Bootstrap
448(3)
13.2.3 Bayesian Bootstrap
451(1)
13.2.4 Jackknife
452(1)
13.2.5 Permutation Tests
453(2)
13.3 Genetic Programming
455(16)
13.3.1 Genetics and Inheritance
455(2)
13.3.2 Natural Selection
457(1)
13.3.3 Genetic Algorithm and Price's Theorem
458(2)
13.3.4 Concept of Genetic Programming
460(2)
13.3.5 Adaptive Genetic Programming
462(2)
13.3.6 GP Algorithm
464(4)
13.3.7 GP Schema Theory
468(3)
13.4 Summary
471(2)
13.5 Exercises
473(2)
Appendix A Java Script Programs
475(8)
A.1 Pi Simulation
475(1)
A.2 Adaptive Trial Simulation
476(1)
A.3 Genetic Programming
477(6)
Appendix B K-Stage Adaptive Design Stopping Boundaries
483(4)
B.1 Stopping Boundaries with MSP
483(2)
B.2 Stopping Boundaries with MPP
485(2)
References 487(16)
Index 503
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.
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