Introduction to Population Pharmacokinetic / Pharmacodynamic Analysis with Nonlinear Mixed Effects Models

This book provides a user-friendly, hands-on introduction to the
Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful
tool for pharmacokinetic / pharmacodynamic analysis.

• Introduces requisite background to using Nonlinear
  Mixed Effects Modeling (NONMEM), covering data requirements, model
  building and evaluation, and quality control aspects

• Provides examples of nonlinear modeling concepts and
  estimation basics with discussion on the model building
  process and applications of empirical Bayesian estimates in the
  drug development environment

• Includes detailed chapters on data set structure,
  developing control streams for modeling and simulation, model
  applications, interpretation of NONMEM output and results, and
  quality control

• Has datasets, programming code, and practice exercises
  with solutions, available on a supplementary website

Autorentext

Joel S. Owen is Professor of Pharmaceutics at Union University, Jackson, Tennessee and President and Principal Scientist of Joel S. Owen, LLC. He has led workshops on NONMEM and PK/PD modeling concepts and applications and served as Director PK/PD at Cognigen Corporation in Buffalo, New York. He has published 16 articles in research publications.

Jill Fiedler-Kelly is Vice President and Chief Scientific Officer of Cognigen Corporation and Adjunct Associate Professor of Pharmaceutical Sciences at the University at Buffalo. She has been teaching workshops and graduate courses on population modeling for over 10 years and has published more than 20 articles and book chapters on pharmacokinetics and pharmacodynamics.

Zusammenfassung
This book provides a user-friendly, hands-on introduction to the Nonlinear Mixed Effects Modeling (NONMEM) system, the most powerful tool for pharmacokinetic / pharmacodynamic analysis.
• Introduces requisite background to using Nonlinear Mixed Effects Modeling (NONMEM), covering data requirements, model building and evaluation, and quality control aspects
• Provides examples of nonlinear modeling concepts and estimation basics with discussion on the model building process and applications of empirical Bayesian estimates in the drug development environment
• Includes detailed chapters on data set structure, developing control streams for modeling and simulation, model applications, interpretation of NONMEM output and results, and quality control
• Has datasets, programming code, and practice exercises with solutions, available on a supplementary website

Inhalt

Preface xiii

CHAPTER 1 The Practice of Pharmacometrics 1

1.1 Introduction 1

1.2 Applications of Sparse Data Analysis 2

1.3 Impact of Pharmacometrics 4

1.4 Clinical Example 5

CHAPTER 2 Population Model Concepts and Terminology 9

2.1 Introduction 9

2.2 Model Elements 10

2.3 Individual Subject Models 11

2.4 Population Models 12

2.4.1 Fixed-Effect Parameters 13

2.4.2 Random-Effect Parameters 14

2.5 Models of Random Between-Subject Variability (L1) 17

2.5.1 Additive Variation 17

2.5.2 Constant Coefficient of Variation 18

2.5.3 Exponential Variation 18

2.5.4 Modeling Sources of Between-Subject Variation 19

2.6 Models of Random Variability in Observations (L2) 19

2.6.1 Additive Variation 20

2.6.2 Constant Coefficient of Variation 21

2.6.3 Additive Plus CCV Model 22

2.6.4 Log-Error Model 24

2.6.5 Relationship Between RV Expressions and Predicted Concentrations 24

2.6.6 Significance of the Magnitude of RV 25

2.7 Estimation Methods 26

2.8 Objective Function 26

2.9 Bayesian Estimation 27

CHAPTER 3 NONMEM Overview and Writing an NM-TRAN Control Stream 28

3.1 Introduction 28

3.2 Components of the NONMEM System 28

3.3 General Rules 30

3.4 Required Control Stream Components 31

3.4.1 $PROBLEM Record 31

3.4.2 The $DATA Record 32

3.4.3 The $INPUT Record 35

3.5 Specifying the Model in NM-TRAN 35

3.5.1 Calling PREDPP Subroutines for Specific PK Models 35

3.5.2 Specifying the Model in the $PK Block 38

3.5.3 Specifying Residual Variability in the $ERROR Block 45

3.5.4 Specifying Models Using the $PRED Block 49

3.6 Specifying Initial Estimates with $THETA, $OMEGA, and $SIGMA 50

3.7 Requesting Estimation and Related Options 56

3.8 Requesting Estimates of the Precision of Parameter Estimates 62

3.9 Controlling the Output 63

CHAPTER 4 Datasets 66

4.1 Introduction 66

4.2 Arrangement of the Dataset 68

4.3 Variables of the Dataset 71

4.3.1 TIME 71

4.3.2 DATE 71

4.3.3 ID 72

4.3.4 DV 74

4.3.5 MDV 74

4.3.6 CMT 74

4.3.7 EVID 75

4.3.8 AMT 76

4.3.9 RATE 77

4.3.10 ADDL 78

4.3.11 II 79

4.3.12 SS 80

4.4 Constructing Datasets with Flexibility to Apply Alternate Models 80

4.5 Examples of Event Records 81

4.5.1 Alternatives for Specifying Time 81

4.5.2 Infusions and Zero-Order Input 81

4.5.3 Using ADDL 82

4.5.4 Steady-State Approach 83

4.5.5 Samples Before and After Achieving Steady State 83

4.5.6 Unscheduled Doses in a Steady-State Regimen 84

4.5.7 Steady-State Dosing with an Irregular Dosing Interval 84

4.5.8 Multiple Routes of Administration 85

4.5.9 Modeling Multiple Dependent Variable Data Types 86

4.5.10 Dataset for $PRED 86

4.6 Beyond Doses and Observations 87

4.6.1 Other Data Items 87

4.6.2 Covariate Changes over Time 88

4.6.3 Inclusion of a Header Row 89

CHAPTER 5 Model Building: Typical Process 90

5.1 Introduction 90

5.2 Analysis Planning 90

5.3 Analysis Dataset Creation 92

5.4 Dataset Quality Control 93

5.5 Exploratory Data Analysis 94

5.5.1 EDA: Population Description 95

5.5.2 EDA: Dose-Related Data 99

5.5.3 EDA: Concentration-Related Data 99

5.5.4 EDA: Considerations with Large Datasets 111

5.5.5 EDA: Summary 115

5.6 Base Model Development 116

5.6.1 Standard Model Diagnostic Plots and Interpretation 116

5.6.2 Estimation of Random Effects 130 &...