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
Inhaltsverzeichnis
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
5. 6. 3 Precision of Parameter Estimates (Based on $COV Step) 137
5. 7 Covariate Evaluation 138
5. 7. 1 Covariate Evaluation Methodologies 140
5. 7. 2 Statistical Basis for Covariate Selection 141
5. 7. 3 Diagnostic Plots to Illustrate Parameter-Covariate Relationships 143
5. 7. 4 Typical Functional Forms for Covariate-Parameter Relationships 148
5. 7. 5 Centering Covariate Effects 156
5. 7. 6 Forward Selection Process 160
5. 7. 7 Evaluation of the Full Multivariable Model 167
5. 7. 8 Backward Elimination Process 169
5. 7. 9 Other Covariate Evaluation Approaches 171
5. 8 Model Refinement 172
CHAPTER 6 Interpreting the NONMEM Output 178
6. 1 Introduction 178
6. 2 Description of the Output Files 178
6. 3 The NONMEM Report File 179
6. 3. 1 NONMEM-Related Output 179
6. 3. 2 PREDPP-Related Output 180
6. 3. 3 Output from Monitoring of the Search 180
6. 3. 4 Minimum Value of the Objective Function and Final Parameter Estimates 182
6. 3. 5 Covariance Step Output 186
6. 3. 6 Additional Output 187
6. 4 Error Messages: Interpretation and Resolution 188
6. 4. 1 NM-TRAN Errors 188
6. 4. 2 $ESTIMATION Step Failures 189
6. 4. 3 $COVARIANCE Step Failures 190
6. 4. 4 PREDPP Errors 191
6. 4. 5 Other Types of NONMEM Errors 192
6. 4. 6 FORTRAN Compiler or Other Run-Time Errors 193
6. 5 General Suggestions for Diagnosing Problems 193
CHAPTER 7 App lications Using Parameter Estimates from the Individual 198
7. 1 Introduction 198
7. 2 Bayes Theorem and Individual Parameter Estimates 200
7. 3 Obtaining Individual Parameter Estimates 202
7. 4 Applications of Individual Parameter Estimates 204
7. 4. 1 Generating Subject-Specific Exposure Estimates 204
7. 4. 2 Individual Exposure Estimates for Group Comparisons 210
CHAPTER 8 Introduction to Model Evaluation 212
8. 1 Introduction 212
8. 2 Internal Validation 212
8. 3 External Validation 213
8. 4 Predictive Performance Assessment 214
8. 5 Objective Function Mapping 217
8. 6 Leverage Analysis 220
8. 7 Bootstrap Procedures 222
8. 8 Visual and Numerical Predictive Check Procedures 223
8. 8. 1 The VPC Procedure 223
8. 8. 2 Presentation of VPC Results 225
8. 8. 3 The Numerical Predictive Check (NPC) Procedure 229
8. 9 Posterior Predictive Check Procedures 229
CHAPTER 9 User-Written Models 232
9. 1 Introduction 232
9. 2 $MODEL 235
9. 3 $SUBROUTINES 236
9. 3. 1 General Linear Models (ADVAN5 and ADVAN7) 236
9. 3. 2 General Nonlinear Models (ADVAN6, ADVAN8, ADVAN9, and ADVAN13) 238
9. 3. 3 $DES 238
9. 4 A Series of Examples 240
9. 4. 1 Defined Fractions Absorbed by Zero- and First-Order Processes 240
9. 4. 2 Sequential Absorption with First-Order Rates, without Defined Fractions 242
9. 4. 3 Parallel Zero-Order and First-Order Absorption, without Defined Fractions 243
9. 4. 4 Parallel First-Order Absorption Processes, without Defined Fractions 245
9. 4. 5 Zero-Order Input into the Depot Compartment 246
9. 4. 6 Parent and Metabolite Model: Differential Equations 247
CHAPTER 10 PK/PD Models 250
10. 1 Introduction 250
10. 2 Implementation of PD Models in NONMEM 251
10. 3 $PRED 252
10. 3. 1 Direct-Effect PK/PD Examples: PK Concentrations in the Dataset 253
10. 3. 2 Direct-Effect PK/PD Example: PK from Computed Concentrations 255
10. 4 $PK 256
10. 4. 1 Specific ADVANs (ADVAN1-ADVAN4 and ADVAN10-ADVAN12) 256
10. 4. 2 General ADVANs (ADVAN5-ADVAN9 and ADVAN13) 257
10. 4. 3 PREDPP: Effect Compartment Link Model Example (PD in $ERROR) 257
10. 4. 4 PREDPP: Indirect Response Model Example: PD in $DES 259
10. 5 Odd-Type Data: Analysis of Noncontinuous Data 261
10. 6 PD Model Complexity 262
10. 7 Communication of Results 263
CHAPTER 11 Simulation Basics 265
11. 1 Introduction 265
11. 2 The Simulation Plan 265
11. 2. 1 Simulation Components 266
11. 2. 2 The Input-Output Model 266
11. 2. 3 The Covariate Distribution Model 270
11. 2. 4 The Trial Execution Model 273
11. 2. 5 Replication of the Study 274
11. 2. 6 Analysis of the Simulated Data 275
11. 2. 7 Decision Making Using Simulations 275
11. 3 Miscellaneous Other Simulation-Related Considerations 276
11. 3. 1 The Seed Value 276
11. 3. 2 Consideration of Parameter Uncertainty 277
11. 3. 3 Constraining Random Effects or Responses 278
CHAPTER 12 Quality Control 285
12. 1 Introduction 285
12. 2 QC of the Data Analysis Plan 285
12. 3 Analysis Dataset Creation 286
12. 3. 1 Exploratory Data Analysis and Its Role in Dataset QC 287
12. 3. 2 QC in Data Collection 287
12. 4 QC of Model Development 288
12. 4. 1 QC of NM-TRAN Control Streams 289
12. 4. 2 Model Diagnostic Plots and Model Evaluation Steps as QC 290
12. 5 Documentation of QC Efforts 290
12. 6 Summary 291
References 292
Index 293
